IMAGE - User contributions [en]

Energy conversion/Description

2022-11-03T09:37:17Z

Boerdhs: Added hydrogen sector improvements

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">

[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
In TIMER, electricity can be generated by 32 technologies. These include the VRE sources solar utility scale photovoltaic (PV), residential photovoltaics (RPV), concentrated solar power (CSP), ocean wave power and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. A recent addition is the use of hydrogen for electricity and heat generation. ([[De Boer and Van Vuuren, 2017]])

As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical lifetime. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs and carbon emissions. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]), Koberle et al., ([[Köberle et al., 2015|2015]]) and Gernaat et al., (<nowiki>[[2018]]</nowiki>)
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are 17 supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* grid electrolysis:
#** a technology producing hydrogen at a constant rate resulting in a baseload electricity demand
#** a technology producing hydrogen just from cheap VRE curtailments, reducing curtailment levels in the electricity sector
#*direct renewable electrolysis:
#** combining solar PV, CSP, onshore wind and offshore wind technologies directly with an electrolyser
#** avoids electricity grid costs
#** shared technological learning with electricity production technologies
#* small scale technologies at low hydrogen demand levels:
#** small methane reform plant
#** small scale electrolyser
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region. The direct electrolysis technologies and the curtailment electrolyser are exceptions: here the load factor is limited by the supply technology
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

Energy conversion/Description

2022-11-01T14:19:57Z

Boerdhs: Added hydrogen as a power generation option

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">

[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
In TIMER, electricity can be generated by 32 technologies. These include the VRE sources solar utility scale photovoltaic (PV), residential photovoltaics (RPV), concentrated solar power (CSP), ocean wave power and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. A recent addition is the use of hydrogen for electricity and heat generation. ([[De Boer and Van Vuuren, 2017]])

As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical lifetime. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs and carbon emissions. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]), Koberle et al., ([[Köberle et al., 2015|2015]]) and Gernaat et al., (<nowiki>[[2018]]</nowiki>)
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

Energy supply/Data uncertainties limitations

2021-10-21T09:44:23Z

Boerdhs: Updated references

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; WEC, 2010;
}}
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==Data, uncertainty and limitations==
===Data===
Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy supply module</div><table class="pbltable">

<tr><th>Data input
</th><th>Sources
</th></tr>
<tr><td>Fossil-fuel resources and costs
</td><td>IEA, [[IEA, 2017|2017]]; USGS, [[USGS, 2013|2013]]; BGR, [[BGR, 2015|2015]]; [[EDGAR database]]; Abundant Gas Project. Costs mainly based on Rogner et al. (in prep.)
</td></tr>
<tr><td>Nuclear fuel data (uranium and thorium)
</td><td>[[WEC-Uranium]] ([[WEC, 2010]])
</td></tr>
<tr><td>Bio-energy potential and costs
</td><td>PBL calculations ([[Van Vuuren et al., 2009]]; [[Van Vuuren et al., 2010]])
</td></tr>
<tr><td>Solar, wind, and hydropower potential
</td><td>PBL calculations ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>CCS potential
</td><td>Based on ([[Hendriks et al., 2004b]]; [[IPCC, 2005]])
</td></tr>
</table>

===Uncertainties===
One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.

Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios ([[Van Vuuren et al., 2008]]) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO<sub>2</sub> emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bio-energy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.

===Limitations===
The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in [[Region classification map|26 global regions]]. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models.

Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.
</div>

Energy supply/Description

2021-10-21T09:40:53Z

Boerdhs: Updated references

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">

===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; IEA, [[IEA, 2017|2017]]; USGS, [[USGS, 2013|2013]]; BGR, [[BGR, 2015|2015]]; [[EDGAR database]]; Abundant Gas Project)</div><table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2015 production
</td><td>6.5
</td><td>3.4
</td><td>2.4
</td><td>1.5
</td></tr>
<tr><td>Reserves
</td><td>9.4
</td><td>7.3
</td><td>17
</td><td>3.6
</td></tr>
<tr><td>Other conventional resources
</td><td>33
</td><td>17
</td><td>481
</td><td>56
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.0
</td><td>0.30
</td><td></td><td></td></tr>
<tr><td>Other unconventional resources
</td><td>54
</td><td>2023
</td><td></td><td></td></tr>
<tr><td>Total
</td><td>105
</td><td>2051
</td><td>501
</td><td>61
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Renewable energy===
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]; [[Gernaat et al., 2017]]; [[Köberle et al., 2015]]; [[Gernaat et al., 2014]]; [[Daioglou et al., 2019]]; [[Gernaat]]):
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]

The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.

The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.

Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Köberle et al., 2015]]). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.

Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.

Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Gernaat et al., 2014]]). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.

Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Daioglou et al., 2019]]). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data .This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables ([[Daioglou et al., 2019]]) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see [[Gernaat et al., 2017]] for a full description of the site selection process).

[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]

The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map.

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</div>

BGR, 2015

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USGS, 2013

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IEA, 2017

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|Title=World Energy Balances (Edition 2017)
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Energy supply/Data uncertainties limitations

2021-10-21T08:54:36Z

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{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; WEC, 2010;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy supply module</div><table class="pbltable">

<tr><th>Data input
</th><th>Sources
</th></tr>
<tr><td>Fossil-fuel resources and costs
</td><td><nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>. Costs mainly based on Rogner et al. <nowiki>[[(2018, in prep.)]]</nowiki>
</td></tr>
<tr><td>Nuclear fuel data (uranium and thorium)
</td><td>[[WEC-Uranium]] ([[WEC, 2010]])
</td></tr>
<tr><td>Bio-energy potential and costs
</td><td>PBL calculations ([[Van Vuuren et al., 2009]]; [[Van Vuuren et al., 2010]])
</td></tr>
<tr><td>Solar, wind, and hydropower potential
</td><td>PBL calculations ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>CCS potential
</td><td>Based on ([[Hendriks et al., 2004b]]; [[IPCC, 2005]])
</td></tr>
</table>

===Uncertainties===
One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.

Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios ([[Van Vuuren et al., 2008]]) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO<sub>2</sub> emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bio-energy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.

===Limitations===
The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in [[Region classification map|26 global regions]]. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models.

Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.
</div>

Energy supply/Description

2021-10-21T08:44:40Z

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{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">

===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term cost-supple curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate, and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2015 production
</td><td>6.5
</td><td>3.4
</td><td>2.4
</td><td>1.5
</td></tr>
<tr><td>Reserves
</td><td>9.4
</td><td>7.3
</td><td>17
</td><td>3.6
</td></tr>
<tr><td>Other conventional resources
</td><td>33
</td><td>17
</td><td>481
</td><td>56
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.0
</td><td>0.30
</td><td></td><td></td></tr>
<tr><td>Other unconventional resources
</td><td>54
</td><td>2023
</td><td></td><td></td></tr>
<tr><td>Total
</td><td>105
</td><td>2051
</td><td>501
</td><td>61
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Renewable energy===
IMAGE model the supply of eight renewable energy options: utility-scale photovoltaic (PV), rooftop PV, concentrated solar power (CSP), onshore wind energy, offshore wind energy, first-generation bio-energy, lignocellulosic bio-energy, and hydropower is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]; [[Gernaat et al., 2017]]; [[Köberle et al., 2015]]; [[Gernaat et al., 2014]]; [[Daioglou et al., 2019]]; [[Gernaat]]):
Firstly, physical and geographical data are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital databases. [[File:Physical climate data renewables.png|thumb|621x621px|'''Model mean (GFLD-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC5) historical 30-year (1970–2000) average climate data used as input to calculate energy potentials as available in the ISIMIP2b database.''' '''a''', Solar irradiance (kWh m<sup>-2</sup> day<sup>-1</sup>). '''b''', Temperature (°C). '''c''', Wind speeds (m s<sup>-1</sup>). '''d''', Run-off (kg km<sup>-2</sup> s<sup>-1</sup>). '''e''', Sugar and maize yields (crop selected with highest yield per cell) (%). '''f''', Lignocellulosic crop yields (switchgrass and Miscanthus) (%).]]

The methodology assumes that part of the grid cell can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production, for example, is estimated using suitability factors taking considering competing land-use options and the harvested rain-fed yield of energy crop. Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential. The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.

The calculation of each renewable energy potential is explained in detail in separate published articles. Here, a short explanation is given introducing each.

Utility-scale PV and CSP starts with the theoretical potential based on a global solar irradiation map (kWh m<sup>-2</sup> day<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Köberle et al., 2015]]). This is subsequently restricted by excluding unsuitable areas (e.g. areas with snow cover or steep mountainous terrain) to calculate the geographical potential. The area that remains is further restricted by suitability factors. The idea behind suitability factors is that only part of the land is physically available for solar applications to ensure that it may keep the land-use function that it has, such as agricultural crop production. To calculate the technical potential, conversion efficiencies are assumed that are explained in method section ‘Climate impacts on renewable energy’.

Rooftop PV builds on the method of utility-scale PV, using the theoretical and technical aspects, but differentiates on the geographical potential (). For rooftop PV, the geographical potential is determined according to roof area. This area is estimated by dividing the living area per household by the number of floors per household, both of which are based on census data. The estimates distinguish between urban areas and rural areas, and are combined with an urban/rural population map to scale down the estimated roof areas to grid level. The technical calculations are similar as the ones used to calculate utility-scale PV and explained in method section ‘Climate impacts on renewable energy’.

Calculations of onshore and offshore wind energy potential start with wind speeds (m s<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Gernaat et al., 2014]]). Then, similar as for solar power, areas are excluded and further restricted according to suitability factors. For the remaining geographical area, based on wind data, the electricity output is calculated using a Weibull distribution function and power curve of the turbine. For details on offshore wind methodology see Supplementary Text 7-S2.

Bio-energy potential calculations start with primary biomass production, represented through yields (t ha<sup>-1</sup> y<sup>-1</sup>) ([[Hoogwijk, 2004]]; [[Daioglou et al., 2019]]). Potential primary biomass sources include maize, sugar, and lignocellulosic crops (trees, switchgrass, and Miscanthus). Land availability for bio-energy production is limited by agricultural production following a ‘food-first’ principle where agricultural lands are determined first and are off-limits for biomass production. The technical potential is further limited by excluding forests, nature reserves and water stressed areas. In principle, bio-energy can be produced on remaining unprotected lands but also on abandoned agricultural lands. Besides energy crops, residues from agricultural and forestry can also be used as a feedstock. The costs of primary bio-energy crops are calculated with a Cobb-Douglas economic growth model using labour , land rent and capital costs as inputs <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref>. The land costs are based on average regional income levels per km2, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data .This technical potential is converted to several secondary energy carriers (solids, liquids, electricity, hydrogen) that compete in the energy system with other secondary energy carriers, such as fossil fuels or renewables ([[Daioglou et al., 2019]]) for a full description of biomass supply and demand in IMAGE) <ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref> <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

Calculations of hydropower potential start with run-off (kg km<sup>-2</sup> s<sup>-1</sup>) that flows from high elevation to low elevation (representing discharge). On the basis of these discharge maps, >3.8 million site-specific hydropower installations were evaluated, at a 25km interval for every river between 56° S and 60° N (the excluded area is due to unavailable topographic data). At each site, high-resolution topographic data (3” × 3”) were used to calculate the cost-optimal dam dimensions and associated production potential. In this way, 60,000 suitable sites were identified, which together represent the remaining technical potential (see [[Gernaat et al., 2017]] for a full description of the site selection process).

[[File:Technical potential maps renewables.png|thumb|617x617px|'''Global maps showing technical potential of renewable energy sources for 2010.''' Calculated with climate data from HadGEM2-ES (30y-average 1970-2000) and the suitability factors of Table 5-1. One cell has an area of 0.5°×0.5°. '''a,''' Solar PV (utility-scale PV) (based on Chapter 3 and Hoogwijk (2004), Köberle et al. (2015)). '''b''', CSP (based on Köberle et al. (2015)). '''c''', Wind (onshore and offshore) (based on Chapter 2 and Hoogwijk (2004)). '''d''', Hydropower (defined as: remaining technical potential, explained and based on Chapter 4). '''e''', 1<sup>st</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). '''f''', 2<sup>nd</sup> generation bio-energy (based on Daioglou et al. (2019), Hoogwijk (2004)). Note that the scales are different.]]

The maps on technical potential for all renewables are combined with economic information to generate cost-supply curves. Assumptions on cost can be found in the separate articles but the general methodology is as follows. Each technology requires an investment before it can produce energy. This investment (in USD) is divided by the annual production (kWh) to calculate the production cost (USD kWh<sup>-1</sup>). This yields two global maps, a technical potential map (kWh) and a production cost map (USD kWh<sup>-1</sup>). Together they are used to generate a cost-supply curve, by sorting (in ascending order) the cells in the production cost map while simultaneously adding the same cells from the technical potential map.

<div class="version newv31"></div><references />
</div>

Energy supply

2021-10-21T08:19:16Z

Boerdhs: Text edits

{{ComponentTemplate2
|Application=Roads from Rio+20 (2012) project; ADVANCE project;
|IMAGEComponent=Drivers; Land cover and land use; Crops and grass; Climate policy; Atmospheric composition and climate;
|KeyReference=De Vries et al., 2007; Van Vuuren et al., 2008; Van Vuuren et al., 2009;
|InputVar=Technology development of energy supply; Energy resources; Trade restriction; Demand for primary energy; Potential bioenergy yield - grid; Land supply for bioenergy - grid; Learning rate;
|Parameter=Initial production costs;
|OutputVar=Primary energy price; Carbon storage price; Energy security indicators; Total primary energy supply; Marginal abatement cost; Energy and industry activity level; Bioenergy production;
|ComponentCode=ES
|AggregatedComponent=Energy supply and demand
|FrameworkElementType=pressure component
}}
<div class="page_standard">

The energy supply model simulates long-term trends in energy supply. This model describes the investments in, and the use of, different types of energy carriers by technology development and resource depletion. Technological development is implemented in form of learning curves for most fuels and renewable energy options. Costs decrease endogenously as a function of the cumulative energy capacity. On the other hand, resource costs increase as they get depleted which is based on cost-supply curves.

Energy supply is assumed to always meet energy demand. In order to do so, not only domestic resources can be used, but energy carriers, such as coal, oil and gas, can also be traded. The impact of depletion and technology development lead to changes in primary fuel prices, which influence investment decisions in the end-use and energy-conversion modules. Linkages to other parts of IMAGE framework include available land for bio-energy production and emissions of greenhouse gases and air pollutants.

{{InputOutputParameterTemplate}}
</div>

Energy supply

2021-10-21T08:12:35Z

Boerdhs: Removed tags

{{ComponentTemplate2
|Application=Roads from Rio+20 (2012) project; ADVANCE project;
|IMAGEComponent=Drivers; Land cover and land use; Crops and grass; Climate policy; Atmospheric composition and climate;
|KeyReference=De Vries et al., 2007; Van Vuuren et al., 2008; Van Vuuren et al., 2009;
|InputVar=Technology development of energy supply; Energy resources; Trade restriction; Demand for primary energy; Potential bioenergy yield - grid; Land supply for bioenergy - grid; Learning rate;
|Parameter=Initial production costs;
|OutputVar=Primary energy price; Carbon storage price; Energy security indicators; Total primary energy supply; Marginal abatement cost; Energy and industry activity level; Bioenergy production;
|ComponentCode=ES
|AggregatedComponent=Energy supply and demand
|FrameworkElementType=pressure component
}}
<div class="page_standard">

The energy supply model simulates long-term trends in energy supply. This model describes the investments in, and the use of, different types of energy carriers by technology development and resource depletion. Technological development is implemented in form of learning curves for most fuels and renewable energy options. Costs decrease endogenously as a function of the cumulative energy capacity. On the other hand, resource costs increase as they get depleted which is based on cost-supply curves.

Energy demand is assumed to always meet energy supply and, because regions are sometimes unable to meet their own demand, energy carriers, such as coal, oil and gas, are traded. The impact of depletion and technology development lead to changes in primary fuel prices, which influence investment decisions in the end-use and energy-conversion modules. Linkages to other parts of IMAGE framework include available land for bio-energy production and emissions of greenhouse gases and air pollutants.

{{InputOutputParameterTemplate}}
</div>

Energy conversion/Data uncertainties limitations

2021-10-15T14:49:20Z

Boerdhs: Added references

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b; Van Ruijven et al., 2007; WEC, 2010; MIT, 2003; IRENA, 2016; De Boer and Van Vuuren, 2017; Pietzcker et al., 2017; Luderer et al., 2017;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The data for the model come from a variety of sources, the main of which are:

<div class="thumbcaption dark">Table: Main data sources for the TIMER energy conversion module<table class="pbltable">

<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>Gernaat et al. ([[Gernaat et al., 2017|2017]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]), residential rooftop PV ([[Gernaat|Gernaat et al., 2020]]), offshore wind ([[Gernaat et al., 2014]]), concentrated solar power ([[Köberle et al., 2015|Koberle et al., 2015]]), onshore wind and central solar PV ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>

===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, 2017]]; [[Pietzcker et al., 2017]]; [[Luderer et al., 2017]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed. A more detailed discussion on the model limitations can be found in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]).

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.

Energy conversion/Data uncertainties limitations

2021-10-15T14:42:16Z

Boerdhs: Added reference

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b; Van Ruijven et al., 2007; WEC, 2010; MIT, 2003; IRENA, 2016; De Boer and Van Vuuren, 2017; Pietzcker et al., 2017; Luderer et al., 2017;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
The data for the model come from a variety of sources, the main of which are:

<div class="thumbcaption dark">Table: Main data sources for the TIMER energy conversion module<table class="pbltable">

<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>Gernaat et al. ([[Gernaat et al., 2017|2017]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>

===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, 2017]]; [[Pietzcker et al., 2017]]; [[Luderer et al., 2017]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed. A more detailed discussion on the model limitations can be found in De Boer and Van Vuuren ([[De Boer and Van Vuuren, 2017]]).

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.

Talk:Energy conversion/Policy issues

2021-10-15T14:11:26Z

Boerdhs: Created page with "Maybe nice to update the figures here. I guess they need PBL formatting right? I don't think we have these available for the latest SSP results"

Maybe nice to update the figures here. I guess they need PBL formatting right? I don't think we have these available for the latest SSP results

Energy conversion/Description

2021-10-15T13:45:07Z

Boerdhs: Small changes and removed change tags

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">

[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
In TIMER, electricity can be generated by 30 technologies. These include the VRE sources solar utility scale photovoltaics (PV), residential photocoltaics (RPV), concentrated solar power (CSP), ocean wave power and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, 2017]])

As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs and carbon emissions. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]), Koberle et al., ([[Köberle et al., 2015|2015]]) and Gernaat et al., (<nowiki>[[2018]]</nowiki>)
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. [!CHANGE] Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

Energy supply/Data uncertainties limitations

2019-03-04T10:12:03Z

Boerdhs:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; WEC, 2010;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy supply module</div><table class="pbltable">

<tr><th>Data input
</th><th>Sources
</th></tr>
<tr><td>Fossil-fuel resources and costs
</td><td>[!CHANGE] <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>. Costs mainly based on Rogner et al. <nowiki>[[(2018, in prep.)]]</nowiki>
</td></tr>
<tr><td>Nuclear fuel data (uranium and thorium)
</td><td>[[WEC-Uranium]] ([[WEC, 2010]])
</td></tr>
<tr><td>Bioenergy potential and costs
</td><td>PBL calculations ([[Van Vuuren et al., 2009]]; [[Van Vuuren et al., 2010]])
</td></tr>
<tr><td>Solar and wind potential
</td><td>PBL calculations ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>CCS potential
</td><td>Based on ([[Hendriks et al., 2004b]]; [[IPCC, 2005]])
</td></tr>
</table>

===Uncertainties===
One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.

Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios ([[Van Vuuren et al., 2008]]) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO<sub>2</sub> emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bioenergy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.

===Limitations===
The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in [[Region classification map|26 global regions]]. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models.

Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.
</div>

Energy supply/Data uncertainties limitations

2019-03-04T10:11:23Z

Boerdhs:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; WEC, 2010;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy supply module<table class="pbltable"></div>

<tr><th>Data input
</th><th>Sources
</th></tr>
<tr><td>Fossil-fuel resources and costs
</td><td>[!CHANGE] <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>. Costs mainly based on Rogner et al. <nowiki>[[(2018, in prep.)]]</nowiki>
</td></tr>
<tr><td>Nuclear fuel data (uranium and thorium)
</td><td>[[WEC-Uranium]] ([[WEC, 2010]])
</td></tr>
<tr><td>Bioenergy potential and costs
</td><td>PBL calculations ([[Van Vuuren et al., 2009]]; [[Van Vuuren et al., 2010]])
</td></tr>
<tr><td>Solar and wind potential
</td><td>PBL calculations ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>CCS potential
</td><td>Based on ([[Hendriks et al., 2004b]]; [[IPCC, 2005]])
</td></tr>
</table>

===Uncertainties===
One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.

Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios ([[Van Vuuren et al., 2008]]) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO<sub>2</sub> emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bioenergy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.

===Limitations===
The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in [[Region classification map|26 global regions]]. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models.

Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.
</div>

Energy supply/Data uncertainties limitations

2019-03-04T10:09:45Z

Boerdhs: Update fossil fuel references

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; WEC, 2010;
}}
<div class="page_standard">
==Data, uncertainty and limitations==
===Data===
Main data for the supply side of TIMER are the size of the resources available at different production costs (the table below).
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy supply module<table class="pbltable">

<tr><th>Data input
</th><th>Sources
</th></tr>
<tr><td>Fossil-fuel resources and costs
</td><td>[!CHANGE] <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>. Costs mainly based on Rogner et al. <nowiki>[[(2018, in prep.)]]</nowiki>
</td></tr>
<tr><td>Nuclear fuel data (uranium and thorium)
</td><td>[[WEC-Uranium]] ([[WEC, 2010]])
</td></tr>
<tr><td>Bioenergy potential and costs
</td><td>PBL calculations ([[Van Vuuren et al., 2009]]; [[Van Vuuren et al., 2010]])
</td></tr>
<tr><td>Solar and wind potential
</td><td>PBL calculations ([[Hoogwijk, 2004]])
</td></tr>
<tr><td>CCS potential
</td><td>Based on ([[Hendriks et al., 2004b]]; [[IPCC, 2005]])
</td></tr>
</table>

===Uncertainties===
One of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly, especially non-conventional resource estimates for oil and natural gas. Equally important uncertainties are the nature and rate of technological advances, and the design and implementation of energy policies in different regions.

Various PBL publications have analysed the sensitivity of the model to supply uncertainties. The Monte Carlo uncertainty analysis of various scenarios ([[Van Vuuren et al., 2008]]) identified model parameters as important determinants of the future supply such as oil and natural gas resources and renewable energy learning rates. Some of these factors were only important for a subset of scenario output. For instance, size of oil resources was found to directly influence future oil production, but had limited impact on future CO<sub>2</sub> emissions. The main reason is that oil production in the medium-term is constrained by competition from other fossil fuels and bioenergy. The results were also shown to be scenario dependent. Fossil fuel related uncertainties were more important in a scenario that resulted in a high rather than low fossil-fuel demand.

===Limitations===
The general limitations of TIMER also apply to energy supply modules with a few specific limitations. As a global model, TIMER specifies resource availability in [[Region classification map|26 global regions]]. However, to some degree this does not take into account the underlying geographical dimensions of individual countries and specific areas. For fossil fuels, this issue leads to heterogeneity within a region (e.g., due to different tax systems), but is more important for renewable energy. A key factor can be transport from one area to another, and calculations require the use of other models.

Another main limitation concerns the focus on production costs in describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs over the last few decades, issues related to capacity constraints and market formation over longer time periods have lead to fossil fuels prices that differ from production costs.
</div>

Energy supply/Description

2019-03-04T10:07:39Z

Boerdhs: Updated references fossil resource

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2015 production
</td><td>6.5
</td><td>3.4
</td><td>2.4
</td><td>1.5
</td></tr>
<tr><td>Reserves
</td><td>9.4
</td><td>7.3
</td><td>17
</td><td>3.6
</td></tr>
<tr><td>Other conventional resources
</td><td>33
</td><td>17
</td><td>481
</td><td>56
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.0
</td><td>0.30
</td><td></td><td></td></tr>
<tr><td>Other unconventional resources
</td><td>54
</td><td>2023
</td><td></td><td></td></tr>
<tr><td>Total
</td><td>105
</td><td>2051
</td><td>501
</td><td>61
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Bioenergy===
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]):
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).
* The model describes the conversion of biomass (including residues, in addition to wood crops, grassy crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.

The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

<div class="version newv31">
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>.
</div>

===Other renewable energy===
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]):
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]).
# The model assesses the part of the grid cell that can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
# Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
# The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.
<references />
</div>

Energy supply/Description

2019-03-04T09:34:51Z

Boerdhs: Added short description on EROI.

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. [!CHANGE] Upstream energy use is endogenously determined based energy carrier, region in which the energy carrier is produced, production rate and resource category. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; [[Rogner, 1997]]; [[Mulders et al., 2006]]; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2015 production
</td><td>6.5
</td><td>3.4
</td><td>2.4
</td><td>1.5
</td></tr>
<tr><td>Reserves
</td><td>9.4
</td><td>7.3
</td><td>17
</td><td>3.6
</td></tr>
<tr><td>Other conventional resources
</td><td>33
</td><td>17
</td><td>481
</td><td>56
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.0
</td><td>0.30
</td><td></td><td></td></tr>
<tr><td>Other unconventional resources
</td><td>54
</td><td>2023
</td><td></td><td></td></tr>
<tr><td>Total
</td><td>105
</td><td>2051
</td><td>501
</td><td>61
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Bioenergy===
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]):
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).
* The model describes the conversion of biomass (including residues, in addition to wood crops, grassy crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.

The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

<div class="version newv31">
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>.
</div>

===Other renewable energy===
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]):
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]).
# The model assesses the part of the grid cell that can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
# Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
# The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.
<references />
</div>

Energy supply/Description

2019-03-01T14:13:47Z

Boerdhs: Updated fossil resource estimates

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: [!CHANGE]Main assumptions on fossil fuel resources (in ZJ; coal does not have a distinction between conventional and unconventional; [[Rogner, 1997]]; [[Mulders et al., 2006]]; <nowiki>[[IEA, 2017]]</nowiki>, <nowiki>[[USGS, 2012]]</nowiki>; <nowiki>[[BGR, 2016]]</nowiki>; [[EDGAR database]]; <nowiki>[[Abundant Gas Project]]</nowiki>)</div><table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2015 production
</td><td>6.5
</td><td>3.4
</td><td>2.4
</td><td>1.5
</td></tr>
<tr><td>Reserves
</td><td>9.4
</td><td>7.3
</td><td>17
</td><td>3.6
</td></tr>
<tr><td>Other conventional resources
</td><td>33
</td><td>17
</td><td>481
</td><td>56
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.0
</td><td>0.30
</td><td></td><td></td></tr>
<tr><td>Other unconventional resources
</td><td>54
</td><td>2023
</td><td></td><td></td></tr>
<tr><td>Total
</td><td>105
</td><td>2051
</td><td>501
</td><td>61
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only [!CHANGE] about 7 times the 1970–2015 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Bioenergy===
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]):
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).
* The model describes the conversion of biomass (including residues, in addition to wood crops, grassy crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.

The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

<div class="version newv31">
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>.
</div>

===Other renewable energy===
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]):
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]).
# The model assesses the part of the grid cell that can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
# Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
# The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.
<references />
</div>

Energy conversion/Description

2019-03-01T13:48:30Z

Boerdhs: Added reference

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
[!CHANGE] In TIMER, electricity can be generated by 30 technologies. These include the VRE sources solar utility scale photovoltaics (PV), residential photocoltaics (RPV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, 2017]])

[!CHANGE] As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 10 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. [!CHANGE]VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* [!CHANGE] The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* [!CHANGE]Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. [!CHANGE]This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* [!CHANGE]Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]), Koberle et al., ([[Köberle et al., 2015|2015]]) and Gernaat et al., (<nowiki>[[2018]]</nowiki>)
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

[!CHANGE]The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. [!CHANGE] Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

Energy supply/Description

2019-03-01T13:22:33Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])</div><table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2005 production
</td><td>4.4
</td><td>2.1
</td><td>1.6
</td><td>1.1
</td></tr>
<tr><td>Reserves
</td><td>4.8
</td><td>4.6
</td><td>23.0
</td><td>2.2
</td></tr>
<tr><td>Other conventional resources
</td><td>6.6
</td><td>6.9
</td><td>117.7
</td><td>10.0
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.9
</td><td>6.9
</td><td>25.0
</td><td>233.5
</td></tr>
<tr><td>Other unconventional resources
</td><td>46.2
</td><td>498.6
</td><td>1.3
</td><td>23.0
</td></tr>
<tr><td>Total
</td><td>65.0
</td><td>519.2
</td><td>168.6
</td><td>270.0
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only two to eight times the 1970–2005 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions (a threshold of 60% is used) are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Bioenergy===
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]):
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).
* The model describes the conversion of biomass (including residues, in addition to wood crops, grassy crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.

The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

<div class="version newv31">
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>.
</div>

===Other renewable energy===
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]):
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]).
# The model assesses the part of the grid cell that can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
# Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
# The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.
<references />
</div>

Energy supply/Description

2019-03-01T13:21:05Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])<table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2005 production
</td><td>4.4
</td><td>2.1
</td><td>1.6
</td><td>1.1
</td></tr>
<tr><td>Reserves
</td><td>4.8
</td><td>4.6
</td><td>23.0
</td><td>2.2
</td></tr>
<tr><td>Other conventional resources
</td><td>6.6
</td><td>6.9
</td><td>117.7
</td><td>10.0
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.9
</td><td>6.9
</td><td>25.0
</td><td>233.5
</td></tr>
<tr><td>Other unconventional resources
</td><td>46.2
</td><td>498.6
</td><td>1.3
</td><td>23.0
</td></tr>
<tr><td>Total
</td><td>65.0
</td><td>519.2
</td><td>168.6
</td><td>270.0
</td></tr>
</table>

Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only two to eight times the 1970–2005 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions (a threshold of 60% is used) are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Bioenergy===
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]):
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).
* The model describes the conversion of biomass (including residues, in addition to wood crops, grassy crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.

The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

<div class="version newv31">
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>.
</div>

===Other renewable energy===
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]):
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]).
# The model assesses the part of the grid cell that can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
# Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
# The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.
<references />
</div>

Energy supply/Description

2019-03-01T13:20:30Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; De Vries et al., 2007; New et al., 1997; Rogner, 1997; Mulders et al., 2006;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
===Fossil fuels and uranium===
Depletion of fossil fuels (coal, oil and natural gas) and uranium is simulated on the assumption that resources can be represented by a long-term supply cost curve, consisting of different resource categories with increasing costs levels. The model assumes that the cheapest deposits will be exploited first. For each region, there are 12 resource categories for oil, gas and nuclear fuels, and 14 categories for coal.

A key input for each of the fossil fuel and uranium supply submodules is fuel demand (fuel used in final energy and conversion processes). Additional input includes conversion losses in refining, liquefaction, conversion, and energy use in the energy system. These submodules indicate how demand can be met by supply in a region and other regions through interregional trade.

<div class="thumbcaption dark">Table: Main assumptions on fossil fuel resources ([[Rogner, 1997]]; [[Mulders et al., 2006]])<table class="pbltable">

<tr><th></th><th>Oil
</th><th>Natural gas
</th><th>Underground coal
</th><th>Surface coal
</th></tr>
<tr><td>Cum. 1970-2005 production
</td><td>4.4
</td><td>2.1
</td><td>1.6
</td><td>1.1
</td></tr>
<tr><td>Reserves
</td><td>4.8
</td><td>4.6
</td><td>23.0
</td><td>2.2
</td></tr>
<tr><td>Other conventional resources
</td><td>6.6
</td><td>6.9
</td><td>117.7
</td><td>10.0
</td></tr>
<tr><td>Unconventional resources (reserves)
</td><td>2.9
</td><td>6.9
</td><td>25.0
</td><td>233.5
</td></tr>
<tr><td>Other unconventional resources
</td><td>46.2
</td><td>498.6
</td><td>1.3
</td><td>23.0
</td></tr>
<tr><td>Total
</td><td>65.0
</td><td>519.2
</td><td>168.6
</td><td>270.0
</td></tr>
</table>Fossil fuel resources are aggregated to five resource categories for each fuel (the table above). Each category has typical production costs. The resource estimates for oil and natural gas supply imply that for conventional resources supply is limited to only two to eight times the 1970–2005 production level. Production estimates for unconventional resources are much larger, albeit very speculative. Recently, some of the occurrences of these unconventional resources have become competitive such as shale gas and tar sands. For coal, even current reserves amount to almost ten times the production level of the last three decades. For all fuels, the model assumes that, if prices increase, or if there is further technology development, the energy could be produced in the higher cost resource categories. The values presented in the table above represent medium estimates in the model, which can also use higher or lower estimates in the scenarios. The final production costs in each region are determined by the combined effect of resource depletion and learning-by-doing.

===Trade===
Trade is dealt with in a generic way for oil, natural gas and coal. In the fuel trade model, each region imports fuels from other regions. The amount of fuel imported from each region depends on the relative production costs and those in other regions, augmented with transport costs, using multinomial logit equations. Transport costs are calculated from representative interregional transport distances and time- and fuel-dependent estimates of the costs per GJ per kilometre.

To reflect geographical, political and other constraints in the interregional fuel trade, an additional 'cost' is added to simulate trade barriers between regions (this costs factor is determined by calibration). Natural gas is transported by pipeline or liquid-natural gas ({{abbrTemplate|LNG}}) tanker, depending on distance, with pipeline more attractive for short distances. In order to account for cartel behaviour, the model compares production costs with and without unrestricted trade. Regions that can supply at lower costs than the average production costs in importing regions (a threshold of 60% is used) are assumed to supply oil at a price only slightly below the production costs of the importing regions. Although also this rule is implemented in a generic form for all energy carriers, it is only effective for oil, where the behaviour of the OPEC cartel is simulated to some extent.

===Bioenergy===
The structure of the biomass submodule is similar to that for fossil fuel supply, but with the following differences ([[Hoogwijk, 2004]]):
* Depletion of bioenergy is not governed by cumulative production but by the degree to which available land is used for commercial energy crops.
* The total amount of potentially available bioenergy is derived from bioenergy crop yields calculated on a 0.5x0.5 degree grid with the IMAGE [[Crops and grass|crop model]] for various land-use scenarios for the 21st century. Potential supply is restricted on the basis of a set of criteria, the most important of which is that bioenergy crops can only be on abandoned agricultural land and on part of the natural grassland. The costs of primary bioenergy crops (woody, grassy, maize and sugar cane) are calculated with a Cobb-Douglas economic growth model <ref><div style="clear:both float:right">The Cobb–Douglas production function is a particular functional form of the production function widely used to represent the technological relationship between the amounts of two or more inputs, particularly physical capital and labor, and the amount of output that can be produced by those inputs.</div></ref> using labour , land rent and capital costs as inputs. The land costs are based on average regional income levels per km<sup>2</sup>, which was found to be a reasonable proxy for regional differences in land rent costs. The production functions are calibrated to empirical data ([[Hoogwijk, 2004]]).
* The model describes the conversion of biomass (including residues, in addition to wood crops, grassy crops, maize and sugar cane) to two generic secondary fuel types: bio-solid fuels used in the industry and power sectors; and liquid fuel used mostly in the transport sector.
* The trade and allocation of biofuel production to regions is determined by optimisation. An optimal mix of bio-solid and bio-liquid fuel supply across regions is calculated, using the prices of the previous time step to calculate the demand.

The production costs for bioenergy are represented by the costs of feedstock and conversion. Feedstock costs increase with actual production as a result of depletion, while conversion costs decrease with cumulative production as a result of ‘learning by doing’. Feedstock costs include the costs of land, labour and capital, while conversion costs include capital, {{abbrTemplate|O&M}} and energy use in this process. For both steps, the associated greenhouse gas emissions (related to spatially explicit land use change, N<sub>2</sub>O from fertilisers, energy) are estimated (see Component [[Emissions]]), and are subject to carbon tax, where relevant <ref>Daioglou, V., Doelman, J.C., Stehfest, E., Müller, C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2017. Greenhouse gas emission curves for advanced biofuel supply chains. ''Nature Climate Change'', ''7''(12), p.920.</ref>.

<div class="version newv31">
Besides the energy crops mentioned above, agricultural and forestry residues can also be used as a primary feedstock for modern bioenergy. The availability of residues is linked to the productivity of agriculture and forestry, taking into account the effect of changing yields (see [[Agricultural economy/Description|Agricultural economy]] description) or [[Forest management]] techniques. The available potential is limited by environmental constraints as well as competing uses (use of agricultural residues as feed for livestock, see [[Agricultural economy/Description|Agricultural economy]]). As with bioenergy crops, availability and costs of residues are calculated on a 0.5x0.5 degree grid<ref>Daioglou, V., Doelman, J.C., Wicke, B., Faaij, A. and van Vuuren, D.P., 2019. Integrated assessment of biomass supply and demand in climate change mitigation scenarios. ''Global Environmental Change'', ''54'', pp.88-101.</ref><ref>Daioglou, V., Stehfest, E., Wicke, B., Faaij, A. and Van Vuuren, D.P., 2016. Projections of the availability and cost of residues from agriculture and forestry. ''Gcb Bioenergy'', ''8''(2), pp.456-470.</ref>.
</div>

===Other renewable energy===
Potential supply of renewable energy (wind, solar and bioenergy) is estimated generically as follows ([[Hoogwijk, 2004]]; [[De Vries et al., 2007]]):
# Physical and geographical data for the regions considered are collected on a 0.5x0.5 degree grid. The characteristics of wind speed, insulation and monthly variation are taken from the digital database constructed by the Climate Research Unit ([[New et al., 1997]]).
# The model assesses the part of the grid cell that can be used for energy production, given its physical–geographic (terrain, habitation) and socio-geographical (location, acceptability) characteristics. This leads to an estimate of the geographical potential. Several of these factors are scenario-dependent. The geographical potential for biomass production from energy crops is estimated using suitability/availability factors taking account of competing land-use options and the harvested rain-fed yield of energy crops.
# Next, we assume that only part of the geographical potential can be used due to limited conversion efficiency and maximum power density, This result of accounting for these conversion efficiencies is referred to as the technical potential.
# The final step is to relate the technical potential to on-site production costs. Information at grid level is sorted and used as supply cost curves to reflect the assumption that the lowest cost locations are exploited first. Supply cost curves are used dynamically and change over time as a result of the learning effect.
<references />
</div>

Energy conversion/Description

2019-03-01T13:14:42Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
[!CHANGE] In TIMER, electricity can be generated by 30 technologies. These include the VRE sources solar utility scale photovoltaics (PV), residential photocoltaics (RPV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, 2017]])

[!CHANGE] As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 10 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. [!CHANGE]VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* [!CHANGE] The capital costs of power generating technologies can be exogenously described, but they can also develop as a result of endogenous learning mechanisms explained [[Technical learning|here]]. For the endogenous method, technologies are split up in different cost components. These components have individual learning characteristics, like learning rate, floor costs and start costs. However, spillovers are possible between technologies and regions. Technology spillovers occur when technologies share a component.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* [!CHANGE]Fixed and variable operation and maintenance costs develop according to the same principles as the capital costs
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. [!CHANGE]This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* [!CHANGE]Load factor reduction results from the utilisation of resource sites with less favourable environmental conditions, such as lower wind speeds, lower water discharge or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Köberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

[!CHANGE]The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. [!CHANGE] Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

Energy conversion/Description

2019-03-01T11:58:02Z

Boerdhs: Update of EPG sector

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Köberle et al., 2015; De Boer and Van Vuuren, 2017;
}}
<div class="page_standard">
==Model description of {{ROOTPAGENAME}}==
[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
[!CHANGE] In TIMER, electricity can be generated by 30 technologies. These include the VRE sources solar utility scale photovoltaics (PV), residential photocoltaics (RPV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, 2017]])

[!CHANGE] As shown in the [[Flowchart Energy conversion|flowchart]], two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, 2017]]).

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% on peak demand plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs. ([[De Boer and Van Vuuren, 2017]])
</div>

====Decisions to invest in specific options ====
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the expected amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 10 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. [!CHANGE]VRE load factors per load band are derived from the marginal load band contributions resulting from the RLDC. A system with more VRE sources will result in lower residual load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, 2017]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are usually higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology. [!CHANGE]This backup capacity is installed together with regular investments in load bands
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Köberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

[!CHANGE]The exceptions are ''other renewables'' and CHP. ''Other renewables'' are exogenously prescribed, because of a lack of available data. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, 2017]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see [[De Boer and Van Vuuren, 2017]]).

====Operational strategy====

The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant. [!CHANGE] Hydropower has a monthly dispatch potential. This limited availability of hydropower is distributed so that so that it creates most system benefits. Generally, this has a peak shaving effect on residual demand for electricity.

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
</div>

ADVANCE project

2019-03-01T11:14:42Z

Boerdhs: Emphasised that ADVANCE project has ended

{{ApplicationTemplate
|Website=http://themasites.pbl.nl/models/advance
|ApplicationType=5. Scientific research
|IMAGERoleDescription=[!CHANGE]The IMAGE team led the ADVANCE work package which improved the representation of energy demand in IAMs. In addition, IMAGE participated in all other work packages, covering topics like: model documentation, heterogeneity, subsidies, taxes, uncertainty, technological learning, renewable integration, life cycle assessment, water energy nexus, infrastructure and policy relevance.
|Summary=[!CHANGE] The ADVANCE project improved the representations of complex system interactions and thoroughly validated model behavior in order to increase confidence in climate policy assessments.
|Partners=PIK; IIASA; FEEM; JRC; UCL; SMASH; UEA; ICCS/E3MLab; UPMF-EDDEN; NTNU; DLR; UU; Enerdata;
|KeyReference=ADVANCE publications; Edelenbosch et al., 2016;
}}<div class="page_standard">__NOEDITSECTION__
==ADVANCE project==
Integrated Assessment Models (IAMs) have become central tools used in forming long-term global and regional climate mitigation strategies. However, sound policy advice requires improved representations of complex system interactions and thorough validation of model behaviour, in order to increase confidence in climate policy assessments.
[!CHANGE]To respond to this demand, the ADVANCE project formulated the following objectives:
* Development of a new generation of IAMs for the analysis of climate change mitigation policies;
* Improving the level of confidence that politicians have in the results of IAMs by increasing transparency;
* Model validation with the aim of evaluating their strengths and limitations;
* Improvement of the representation of energy demand: especially energy services, technologies, and consumer behaviour;
* Enhanced representation of technological innovation, uncertainty, and system integration;
* Evaluation of the impacts of mitigation policies on economic sectors in the EU and beyond;
* Creation of a platform for sharing methodologies and input data sets in the modelling community.
Based on improved IAMs, the ADVANCE project answered to the following key questions:
* What is the role of energy efficiency improvements for climate change mitigation?
* What are the bottlenecks for the development of a low-carbon energy supply system?
* What are broader sustainability implications of alternative mitigation pathways?
* How does uncertainty about technological innovation affect optimal innovation policies?
* How can climate change mitigation targets and energy access objectives be reconciled?

[!CHANGE]Model Documentation
As part of the ADVANCE project, harmonised model documentation has been elaborated for all energy-economic and Integrated Assessment Models (IAMs) included in the project. The documentation enhanced the understanding of models, as well as the comparability and interpretability of their results. To achieve comparability, model-specific reference cards have been made for all models. The [https://www.iamcdocumentation.eu/index.php/Reference_card_-_IMAGE Reference card] for IMAGE 3.0 is also presented on this website. If you are interested in model comparison, visit the [http://themasites.pbl.nl/models/advance/index.php/Special:RunQuery/Models-AttributesForm Model comparison] page of the ADVANCE project.
</div>

Energy conversion/Description

2017-04-12T14:26:53Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the reliability of a plant type to supply power during the peak hours) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs. ([[De Boer and Van Vuuren, under review]])
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, under review]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology.
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, under review]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren ([[De Boer and Van Vuuren, under review|under review]]).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

De Boer and Van Vuuren, 2017

2017-04-12T14:19:58Z

Boerdhs:

{{ReferenceTemplate
|Author=H.S. de Boer and D.P. van Vuuren
|Year=2016
|Title=Representation of variable renewable energy source in TIMER, an aggregated energy system simulation model
|PBL-link=http://www.pbl.nl/en/publications/representation-of-variable-renewable-energy-sources-in-timer-an-aggregated-energy-system-simulation-model
|DOI=http://doi.org/10.1016/j.eneco.2016.12.006
|PublicationType=Journal article
|Volume5=
|Publisher=
|City=
|ISBN=
|BookTitle=
|Editor=
|Publisher2=
|City2=
|Volume=
|Pages=
|ISBN2=
|Editor2=
|SeriesTitle=
|Volume4=
|Publisher3=
|City3=
|ISBN3=
|Editor3=
|Institution=
|ReportNumber=
|SeriesTitle2=
|Publisher5=
|City5=
|Journal=Energy Economics
|SecondaryTitle=
|SecondaryAuthor=
|Publisher4=
|City4=
|Volume3=
|Pages3=
|Date=
}}

Energy conversion/Description

2016-11-18T14:02:39Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the ability of capacity to supply peak load) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power capacity is assumed to be replaced at the end of its lifetime, which varies from 25 to 80 years, depending on the technology.

Capacity can also be decommissioned before the end of the technical life time. This so-called early retirement can occur if the operation of the capacity has become relatively expensive compared to the operation and construction of new capacity. The operational costs include fixed O&M, variable O&M, fuel and CCS costs. Capacity will not be retired early if the capacity has a backup role, characterized by a low load factor resulting in low operational costs. ([[De Boer and Van Vuuren, under review]])
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies. ([[De Boer and Van Vuuren, under review]])

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology.
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven. ([[De Boer and Van Vuuren, under review]])

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren ([[De Boer and Van Vuuren, under review|under review]]).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

Energy conversion/Description

2016-11-18T13:55:21Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and technology ramp rates. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the ability of capacity to supply peak load) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power plants are assumed to be replaced at the end of their lifetime, which varies from 25 to 80 years, depending on the technology.
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies.

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology.
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven.

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren ([[De Boer and Van Vuuren, under review|under review]]).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

Energy conversion/Description

2016-11-18T13:52:51Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and the time required to switch on technologies. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the ability of capacity to supply peak load) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power plants are assumed to be replaced at the end of their lifetime, which varies from 25 to 80 years, depending on the technology.
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies.

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology.
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven.

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren ([[De Boer and Van Vuuren, under review|under review]]).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

Energy conversion/Data uncertainties limitations

2016-11-11T11:34:55Z

Boerdhs:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b; Van Ruijven et al., 2007; WEC, 2010; MIT, 2003; IRENA, 2016; De Boer and Van Vuuren, under review; Pietzcker et al., under review; Luderer et al., in preparation;
|Description=<h2>Data, uncertainties and limitations</h2>
===Data===
The data for the model come from a variety of sources, the main of which are:

<div class="version changev31">
<table class="pbltable">
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy conversion module</div>
<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>World Energy Council ([[WEC, 2010]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>
</div>

===Uncertainties===
<div class="version changev31">
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, under review]]; [[Pietzcker et al., under review]]; [[Luderer et al., in preparation]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.
</div>

===Limitations===
<div class="version changev31">
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed. A more detailed discussion on the model limitations can be found in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]]).
</div>

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
}}

Energy conversion/Data uncertainties limitations

2016-11-11T11:32:21Z

Boerdhs:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Hendriks et al., 2004b; Van Ruijven et al., 2007; WEC, 2010; MIT, 2003; IRENA, 2016; De Boer and Van Vuuren, under review; Pietzcker et al., under review; Luderer et al., in preparation;
|Description=<h2>Data, uncertainties and limitations</h2>
===Data===
The data for the model come from a variety of sources, the main of which are:

<div class="version changev31">
<table class="pbltable">
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy conversion module</div>
<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>World Energy Council ([[WEC, 2010]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>
</div>

<div class="version changev31">
===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy ([[De Boer and Van Vuuren, under review]]; [[Pietzcker et al., under review]]; [[Luderer et al., in preparation]]). The model was shown to reproduce the behaviour of more detailed models that describe electricity system developments.
</div>

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed.

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
}}

Pietzcker et al., 2017

2016-11-11T11:21:44Z

Boerdhs: Created page with "{{ReferenceTemplate |Author=R. Pietzcker, F. Ueckerdt, S. Carrara, H.S. de Boer, J. Despres, S. Fujimori, N. Johnson, A. Kitous, Y. Scholz, P. Sullivan, G. Luderer |Year=under..."

{{ReferenceTemplate
|Author=R. Pietzcker, F. Ueckerdt, S. Carrara, H.S. de Boer, J. Despres, S. Fujimori, N. Johnson, A. Kitous, Y. Scholz, P. Sullivan, G. Luderer
|Year=under review
|Title=Evaluating the capacity of Integrated Assessment Models to represent system integration challenges of wind and solar power
|PublicationType=Journal article
|Volume5=
|Publisher=
|City=
|ISBN=
|BookTitle=
|Editor=
|Publisher2=
|City2=
|Volume=
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Luderer et al., 2017

2016-11-11T11:15:07Z

Boerdhs: Created page with "{{ReferenceTemplate |Author=G. Luderer, R. Pietzcker, S. Carrara, H.S. de Boer, S. Fujimori, N. Johnson, S. Mima, D. Arent |Year=in preparation |Title=Renewable Energy Futures..."

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Energy conversion/Data uncertainties limitations

2016-11-11T11:05:43Z

Boerdhs:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Van Vliet et al., 2013; Hoogwijk et al., 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; WEC, 2010; MIT, 2003; IRENA, 2016; De Boer and Van Vuuren, under review;
|Description=<h2>Data, uncertainties and limitations</h2>
===Data===
The data for the model come from a variety of sources, the main of which are:

<div class="version changev31">
<table class="pbltable">
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy conversion module</div>
<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]]), various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>World Energy Council ([[WEC, 2010]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>
</div>
===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy in the United States and western Europe ([[Hoogwijk et al., 2007]]). The model was shown to reproduce the behaviour of more detailed models that describe system integration costs. More recent studies seem to suggest that some of the limitations in renewable energy penetration can be overcome at reasonable costs, implying the current description is rather conservative. Integration costs for renewable energy are very uncertain because large shares of market penetration still need to be achieved, except in a few countries. In experiments run by The power system was exposed to all types of technology limitations in experiments run by Van Vliet et al. ([[Van Vliet et al., 2013|2013]]). These experiments showed that to achieve low stabilisation targets, a large portfolio of mitigation options should be available.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed.

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
}}

Energy conversion/Data uncertainties limitations

2016-11-11T11:04:51Z

Boerdhs:

{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Van Vliet et al., 2013; Hoogwijk et al., 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; WEC, 2010; MIT, 2003; IRENA, 2016; De Boer and Van Vuuren, under review;
|Description=<h2>Data, uncertainties and limitations</h2>
===Data===
The data for the model come from a variety of sources, the main of which are:

<div class="version changev31">
<table class="pbltable">
<div class="thumbcaption dark">Table: Main data sources for the TIMER energy conversion module</div>
<tr>
<th>Input
</th>
<th>Data source
</th></tr>
<tr><td>Electricity production and primary inputs
</td>
<td> [[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Capacity of different plant types per region
</td>
<td>Energy Statistics and Data ([[Enerdata Global Energy & CO2 Data]]; [[IEA database|IEA Statistics and Data]]), IRENA REsource database ([[IRENA, 2016|2016]])
</td></tr>
<tr><td>Performance of fossil fuel and bio-energy fired plants
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004a|2004a]])
</td></tr>
<tr><td>{{abbrTemplate|CCS}} plants and storage
</td>
<td>Hendriks et al. ([[Hendriks et al., 2004b|2004b]])
</td></tr>
<tr><td>Prices
</td>
<td>[[IEA database|IEA Statistics and Data]]
</td></tr>
<tr><td>Hydropower potential
</td>
<td>World Energy Council ([[WEC, 2010]])
</td></tr>
<tr><td>Solar and wind costs
</td>
<td>Various sources described in De Boer and Van Vuuren ([[De Boer and Van Vuuren, under review|under review]])
</td></tr>
<tr><td>Nuclear power - technology and resources
</td>
<td>[[WEC-Uranium]] ([[WEC, 2010]]; [[MIT, 2003]])
</td></tr>
<tr><td>Hydrogen technologies
</td>
<td>[[Van Ruijven et al., 2007]]
</td>
</tr>
</table>
</div>
===Uncertainties===
The two main uncertainties are calculation of future energy conversion relating to development rates of the conversion technologies, and the consequences for the electricity system of a high level of market penetration of renewable energy.
TIMER electric power generation submodule has been tested for different levels of market penetration of renewable energy in the United States and western Europe ([[Hoogwijk et al., 2007]]). The model was shown to reproduce the behaviour of more detailed models that describe system integration costs. More recent studies seem to suggest that some of the limitations in renewable energy penetration can be overcome at reasonable costs, implying the current description is rather conservative. Integration costs for renewable energy are very uncertain because large shares of market penetration still need to be achieved, except in a few countries. In experiments run by The power system was exposed to all types of technology limitations in experiments run by Van Vliet et al. ([[Van Vliet et al., 2013|2013]]). These experiments showed that to achieve low stabilisation targets, a large portfolio of mitigation options should be available.

===Limitations===
The model describes long-term trends in the energy system, which implies that the focus is on aggregated factors that may determine future energy demand and supply. However in energy conversion, many short-term dynamics can be critical for the system, such as system reliability and ability to respond to demand fluctuations. These processes can only be represented in an aggregated global model in terms of meta-formulations, which implies that some of the integration issues regarding renewable energy are still not addressed.

Another limitation is the formulation of primary fossil-fuel conversions in secondary fuels. TIMER currently does not include a module that explicitly describes these processes.
}}

IRENA, 2016

2016-11-11T11:01:57Z

Boerdhs: Created page with "{{ReferenceTemplate |Author=IRENA |Year=2016 |Title=REsource |DOI=http://resourceirena.irena.org/ |PublicationType=Other |Volume5= |Publisher= |City= |ISBN= |BookTitle= |Edito..."

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|Author=IRENA
|Year=2016
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Energy conversion

2016-11-11T09:35:21Z

Boerdhs:

{{ComponentTemplate2
|Application=ADVANCE project; Roads from Rio+20 (2012) project; Energy Modelling Forum - EMF;
|IMAGEComponent=Energy supply and demand; Energy demand; Energy supply; Land-use allocation; Climate policy; Drivers;
|Model-Database=Enerdata Global Energy & CO2 Data; IEA database; WEC-Uranium;
|KeyReference=Hoogwijk et al., 2007; Hendriks et al., 2004a; De Boer and Van Vuuren, under review;
|InputVar=Energy policy; Air pollution policy; Demand for electricity, heat and hydrogen; Primary energy price; Carbon storage price; Carbon price; Technology development of energy conversion;
|Parameter=Initial technology cost; Rules on use of technology;
|OutputVar=Electricity price; Demand for primary energy; CO2 stored; Energy and industry activity level;
|Description=Energy from primary sources often has to be converted into secondary energy carriers that are more easily accessible for final consumption, for example the production of electricity and hydrogen, oil products from crude oil in refineries, and fuels from biomass. Studies on transitions to more sustainable energy systems also show the importance of these conversions for the future.

<div class="version changev31">

The energy conversion module of TIMER simulates the choices of input energy carriers in two steps. In the first step, investment decisions are made on the future generation mix in terms of newly added capital. In the second step, the actual use of the capacity in place depends on a set of model rules that determine how frequently the different types of power plants are used. The discussion focuses on the production of electricity and hydrogen. Other conversion processes have only been implemented in the model by simple multipliers, as they mostly convert energy from a single primary source to one secondary energy carrier. These processes are discussed in [[Energy supply]].
</div>
|ComponentCode=EC
|AggregatedComponent=Energy supply and demand
|FrameworkElementType=pressure component
}}

Energy conversion/Policy issues

2016-11-09T15:32:42Z

Boerdhs:

{{ComponentPolicyIssueTemplate
|Reference=Kruyt et al., 2009; PBL, 2012;
|Description=The energy conversion module may be used to generate scenarios with and without climate policy. The results for a typical baseline scenario are shown in the figure below. At present, coal is the main feedstock for power generation globally. In high-income regions, coal faces competition from natural gas, but in emerging economies, such as China and India, coal is still by far the largest resource used. The baseline scenario projects coal use to expand. The underlying reasons for this expansion are the rapid increase in electricity use in emerging economies, and the stronger price increases for natural gas than for coal. The latter, clearly, also depends on the uncertainty in future natural gas supply. On a global scale, wind power and biomass-fired power plants are rapidly expanding in total capacity.
|Example=IMAGE model simulations include several types of policy interventions that may influence electricity and hydrogen production:
* Carbon tax: this measure is usually implemented on an economy-wide scale and has strong influence on investment and operational strategies in the power system. Because prices are relatively low, there are several competitive power alternatives, and power system choices are usually rather objectively.
* An imposed minimum or maximum share per energy source - renewable energy, CCS technology, nuclear power and other forms of power generation. This would directly influence the capacity installed for each option.
* Promoting the use of electricity and hydrogen on end-user level. With the high flexibility in the choice of feedstock in these systems, large proportions of electricity and hydrogen use in final energy would increase the ability of the total system to reduce greenhouse gas emissions.
* The exclusion of certain power-generation options for environmental and/or security reasons ([[Kruyt et al., 2009]]).

Model analyses show that a high proportion of emission reductions would be achieved through supply side changes. The capacity for different supply-side options under the baseline scenario and various pathways consistent with the 2 °C climate change target are presented in the figure below.

<div class="version changev31">
The proportion of unabated fossil fuel use is still 80% of total primary energy under the baseline scenario (see above) but by 2050, this would need to be around 15 to 20% according to the 2 °C scenarios. The results show that pathways can be identified in which the remaining energy comes from bioenergy, other renewable energy, nuclear energy, and from fossil-fuel energy combined with {{abbrTemplate|CCS}}. There is flexibility in the choice of these options, as illustrated in the Decentralised Solutions and Global Technology pathways with different patterns for nuclear power and renewable energy. In the IMAGE framework under nearly all mitigation scenarios, the combination of bioenergy and CCS plays a critical role in achieving the 2 °C target.
</div>
}}

Energy conversion/Policy issues

2016-11-09T15:30:44Z

Boerdhs:

{{ComponentPolicyIssueTemplate
|Reference=Kruyt et al., 2009; PBL, 2012;
|Description=The energy conversion module may be used to generate scenarios with and without climate policy. The results for a typical baseline scenario are shown in the figure below. At present, coal is the main feedstock for power generation globally. In high-income regions, coal faces competition from natural gas, but in emerging economies, such as China and India, coal is still by far the largest resource used. The baseline scenario projects coal use to expand. The underlying reasons for this expansion are the rapid increase in electricity use in emerging economies, and the stronger price increases for natural gas than for coal. The latter, clearly, also depends on the uncertainty in future natural gas supply. On a global scale, wind power and biomass-fired power plants are rapidly expanding in total capacity.
|Example=IMAGE model simulations include several types of policy interventions that may influence electricity and hydrogen production:
* Carbon tax: this measure is usually implemented on an economy-wide scale and has strong influence on investment and operational strategies in the power system. Because prices are relatively low, there are several competitive power alternatives, and power system choices are usually rather objectively.
* An imposed minimum or maximum share per energy source - renewable energy, CCS technology, nuclear power and other forms of power generation. This would directly influence the capacity installed for each option.
* Promoting the use of electricity and hydrogen on end-user level. With the high flexibility in the choice of feedstock in these systems, large proportions of electricity and hydrogen use in final energy would increase the ability of the total system to reduce greenhouse gas emissions.
* The exclusion of certain power-generation options for environmental and/or security reasons ([[Kruyt et al., 2009]]).

Model analyses show that a high proportion of emission reductions would be achieved through supply side changes. The capacity for different supply-side options under the baseline scenario and various pathways consistent with the 2 °C climate change target are presented in the figure below.

The proportion of unabated fossil fuel use is still 80% of total primary energy under the baseline scenario (see above) but by 2050, this would need to be around 15 to 20% according to the 2 °C scenarios. The results show that pathways can be identified in which the remaining energy comes from bioenergy, other renewable energy, nuclear energy, and from fossil-fuel energy combined with {{abbrTemplate|CCS}}. There is flexibility in the choice of these options, as illustrated in the Decentralised Solutions and Global Technology pathways with different patterns for nuclear power and renewable energy. In the IMAGE framework under nearly all scenarios, the combination of bioenergy and CCS, and CCS in general, plays a critical role in achieving the 2 °C target.
}}

Energy conversion

2016-11-09T15:26:10Z

Boerdhs:

Energy conversion/Description

2016-11-09T15:24:29Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and the time required to switch on technologies. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the ability of capacity to supply peak load) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power plants are assumed to be replaced at the end of their lifetime, which varies from 25 to 80 years, depending on the technology.
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies.

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology.
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven.

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren (n.d.).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

Energy conversion/Description

2016-11-06T13:29:45Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and the time required to switch on technologies. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the ability of capacity to supply peak demand) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power plants are assumed to be replaced at the end of their lifetime, which varies from 25 to 80 years, depending on the technology.
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies.

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined.
* Fuel cost result from the supply modules described [[Energy supply|here]].
* Fixed and variable operation and maintenance costs are exogenously prescribed.
* Construction costs result from interest paid during construction. Construction times vary among the technologies.
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]].
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology.
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated.
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.).
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres.

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven.

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren (n.d.).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

Energy conversion/Description

2016-11-06T13:24:52Z

Boerdhs:

{{ComponentDescriptionTemplate
|Reference=Hoogwijk, 2004; Van Vuuren, 2007; Hendriks et al., 2004b; Van Ruijven et al., 2007; Ueckerdt et al., 2016; Gernaat et al., 2014; Koberle et al., 2015; De Boer and Van Vuuren, under review;
|Description=[[TIMER model|TIMER]] includes two main energy conversion modules: Electric power generation and hydrogen generation. Below, electric power generation is described in detail. In addition, the key characteristics of the hydrogen generation model, which follows a similar structure, are presented.

===Electric power generation===
<div class="version newv31">
In TIMER, electricity can be generated by 28 technologies. These include the VRE sources solar photovoltaics (PV), concentrated solar power (CSP), and onshore and offshore wind power. Other technology types are natural gas-, coal-, biomass- and oil-fired power plants. These power plants come in multiple variations: conventional, combined cycle, carbon capture and storage (CCS) and combined heat and power (CHP). The electricity sector in TIMER also describes the use of nuclear, other renewables (mainly geothermal power) and hydroelectric power. ([[De Boer and Van Vuuren, under review]])
</div>

<div class="version changev31">
As shown in the flowchart, two key elements of the electric power generation are the investment strategy and the operational strategy in the sector. A challenge in simulating electricity production in an aggregated model is that in reality electricity production depends on a range of complex factors, related to costs, reliance, and the time required to switch on technologies. Modelling these factors requires a high level of detail and thus IAMs, such as TIMER, concentrate on introducing a set of simplified, meta relationships ([[Hoogwijk, 2004]]; [[Van Vuuren, 2007]]; [[De Boer and Van Vuuren, under review]]).
</div>

====Total demand for new capacity====
<div class="version changev31">
The electricity generation capacity required to meet the demand per region is based on a forecast of the maximum annual electricity demand plus a reserve margin. The reserve margin consists of a general reserve margin of 10-20% plus a compensation for imperfect capacity credits (the ability of capacity to supply peak demand) of existing capacity. The maximum annual demand is calculated on the basis of an assumed shape of the load duration curve (LDC) and the gross electricity demand. The latter comprises the net electricity demand from the end-use sectors plus electricity trade and transmission losses. An LDC shows the distribution of load over a certain timespan in a downward form. The peak load is plotted to the left of the LDC and the lowest load is plotted to the right. The shape of the LDC is based on work by Ueckerdt et al. ([[Ueckerdt et al., 2016|2016]]), who derived regional normalized residual LDCs (RLDC) for different solar and wind shares, including the application of optimized electricity storage.

The final demand for new generation capacity is equal to the difference between the required and existing capacity. Power plants are assumed to be replaced at the end of their lifetime, which varies from 25 to 80 years, depending on the technology.
</div>

====Decisions to invest in specific options ====
<div class="version changev31">
In the model, the decision to invest in generation technologies is based on the levelized cost of electricity (LCOE; in USD/kWhe) produced per technology, using a multinomial logit equation that assigns larger market shares to the lower cost options.

An important variable used in determining the LCOE is the amount of electricity generated. Often, the LCOEs of technologies are compared at maximum full load hours. However, only a limited share of the installed capacity will actually generate electricity at full load. This effect is captured in a heuristic: 20 different load bands have been introduced to link the investment decision to expected dispatch. The different load bands are distributed among the LDC, resulting in a load factor for each load band. The inclusion of different load factors for each load band means that less capital-intensive technologies are attractive to use for lower load factor load bands. These are likely to be gas-fired peaker plants. For load bands with higher load factors, the electricity submodule chooses technologies with lower operational costs. These are likely to be base load plants, such as coal-fired or nuclear power plants. A system with more VRE sources will result in lower load factors and therefore in a higher demand for peak or mid load technologies.

The standard costs of each option can be broken down into several categories: investment or capital cost; fuel cost; fixed and variable operational and maintenance costs; construction costs; and carbon capture and storage costs.
* The capital costs of VRE and nuclear power develop as a result of endogenous learning mechanisms explained [[Energy supply and demand/Technical learning|here]]. The capital cost development of other technologies is exogenously determined
* Fuel cost result from the supply modules described [[Energy supply|here]]
* Fixed and variable operation and maintenance costs are exogenously prescribed
* Construction costs result from interest paid during construction. Construction times vary among the technologies
* More information on carbon capture and storage cost can be found [[Carbon capture and storage|here]]
Also, additional costs are distinguished: backup costs; curtailment costs; VRE load factor decline; storage costs; and transmission and distribution costs.
* Backup costs have been added to represent the additional costs required in order to meet the capacity and energy production requirements of a load band. Backup costs are higher for technologies with low capacity credits. Backup costs include all standard cost components for the chosen backup technology
* Curtailment costs are only relevant for VRE technologies and CHP. Curtailments occur when the supply exceeds the demand. The degree to which curtailment occurs depends on VRE share, storage use and the regional correlation between electricity demand and VRE or CHP supply. Curtailment influences the LCOE by reducing the potential amount of electricity that could be generated
* Load factor reduction results from the utilisation of VRE sites with less favourable environmental conditions, such as lower wind speeds or less solar irradiation. This results in a lower potential load influencing the LCOE by reducing the potential electricity generation. The development of load factor reduction is captured in cost supply curves. For more information on the TIMER cost supply curves see: Hoogwijk ([[Hoogwijk, 2004|2004]]), Gernaat et al., ([[Gernaat et al., 2014|2014]]) and Koberle et al., ([[Koberle et al., 2015|2015]]).
* Storage use has been optimised in the RLDC data set. For more information on storage use, see Ueckerdt et al. (n.d.)
* Transmission and distribution costs are simulated by adding a fixed relationship between the amount of capacity and the required amount of transmission and distribution capital. VRE cost supply curves contain additional transmission costs resulting from distance between VRE potential and demand centres

The exceptions are hydropower, other renewables and CHP. Hydropower and other renewables are exogenously prescribed, because of a lack of available data or because technologies like large hydropower plants often have additional functions such as water supply and flood control. The demand for CHP capacity is heat demand driven.

Finally, in the equations, some constraints are added to account for limitations in supply, for example restrictions on biomass availability. For a more detailed description on electricity sector investments in TIMER, see De Boer & van Vuuren (n.d.).
</div>

====Operational strategy====
<div class="version newv31">
The demand for electricity is met by the installed capacity of power plants. The available capacity is used according to the merit order of the different types of plants; technologies with the lowest variable costs are dispatched first, followed by other technologies based on an ascending order of variable costs. This results in a cost-optimal dispatch of technologies. The dispatch of VRE is described by the RLDC dataset. CHP dispatch is distributed based on monthly heating degree days. Within each month, the CHP load stays constant.
</div>

===Hydrogen generation===
The structure of the hydrogen generation submodule is similar to that for electric power generation ([[Van Ruijven et al., 2007]]) but with following differences:
#There are only eleven supply options for hydrogen production:
#* coal, oil, natural gas and bioenergy, with and without carbon capture and storage (8 plants);
#* hydrogen production from electrolysis, direct hydrogen production from solar thermal processes;
#* small methane reform plants.
#No description of preferences for different power plants is taken into account in the operational strategy. The load factor for each option equals the total production divided by the capacity for each region.
#Intermittence does not play an important role because hydrogen can be stored to some degree. Thus, there are no equations simulating system integration.
#Hydrogen can be traded. A trade model is added, similar to those for fossil fuels described in [[Energy supply]].

See the additional info on [[Grid and infrastructure]].
}}

De Boer and Van Vuuren, 2017

2016-11-06T12:53:18Z

Boerdhs: Boerdhs moved page De Boer and Van Vuuren, in review to De Boer and Van Vuuren, under review

{{ReferenceTemplate
|Author=H.S. de Boer and D.P. van Vuuren
|Year=under review
|Title=Representation of variable renewable energy source in TIMER, an aggregated energy system simulation model
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Energy conversion

2016-11-06T12:50:07Z

Boerdhs:

{{ComponentTemplate2
|Application=ADVANCE project; Roads from Rio+20 (2012) project; Energy Modelling Forum - EMF;
|IMAGEComponent=Energy supply and demand; Energy demand; Energy supply; Land-use allocation; Climate policy; Drivers;
|Model-Database=Enerdata Global Energy & CO2 Data; IEA database; WEC-Uranium;
|KeyReference=Hoogwijk et al., 2007; Hendriks et al., 2004a;
|InputVar=Energy policy; Air pollution policy; Demand for electricity, heat and hydrogen; Primary energy price; Carbon storage price; Carbon price; Technology development of energy conversion;
|Parameter=Initial technology cost; Rules on use of technology;
|OutputVar=Electricity price; Demand for primary energy; CO2 stored; Energy and industry activity level;
|Description=Energy from primary sources often has to be converted into secondary energy carriers that are more easily accessible for final consumption, for example the production of electricity and hydrogen, oil products from crude oil in refineries, and fuels from biomass. Studies on transitions to more sustainable energy systems also show the importance of these conversions for the future.

<div class="version changev31">

The energy conversion module of TIMER simulates the choices of input energy carriers in two steps. In the first step, investment decisions are made on the future generation mix in terms of newly added capital. In the second step, the actual use of the capacity in place depends on a set of model rules that determine how frequently the different types of power plants are used. The discussion focuses on the production of electricity and hydrogen. Other conversion processes have only been implemented in the model by simple multipliers, as they mostly convert energy from a single primary source to one secondary energy carrier. These processes are discussed in [[Energy supply]].
</div>
|ComponentCode=EC
|AggregatedComponent=Energy supply and demand
|FrameworkElementType=pressure component
}}

Ueckerdt et al., 2016

2016-11-04T13:52:09Z

Boerdhs: Created page with "{{ReferenceTemplate |Author=F. Ueckerdt, R. Pietzcker, Y. Scholz, D. Stetter, A. Giannousakis, G. Luderer |Year=2016 |Title=Decarbonizing global power supply under region-spec..."

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|Author=F. Ueckerdt, R. Pietzcker, Y. Scholz, D. Stetter, A. Giannousakis, G. Luderer
|Year=2016
|Title=Decarbonizing global power supply under region-specific consideration of challenges and options of integrating variable renewables in the REMIND model
|DOI=http://dx.doi.org/10.1016/j.eneco.2016.05.012
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