Variable Renewable Energy integration module

This module provides both data and a method to represent key integration challenges of wind and solar power in large-scale energy-economy models.

Cite: Ueckerdt, Pietzcker et al. (n.d.) when using any RLDC data or the described method to implement wind or solar variability.

Files required to use the module

This module contains the RLDCs for the eight world regions Sub-Saharan Africa, China, Europe, India, Japan, Latin America, Middle East and North Africa, and the USA.

Data Description:

• The full description of the RLDC data and its calculation can be found in (Ueckerdt, Pietzcker et al., in this issue), a preprint of which can be accessed here [please insert link to preprint on the ADVANCE page. The pdf to upload on the ADVANCE page can be downloaded at https://www.pik-potsdam.de/members/robertp/ueckerdt-et-al-decarbonizing-global-power-supply.pdf ] 
• Each row contains the information for one assumed gross share of wind and PV, both of which are varied in steps of 10%-points. “Gross” in “gross VRE/wind/PV share” (columns C-E) means that curtailment and storage losses have not been deducted. “Net” in “net VRE share” (column H) means that curtailment and storage losses were deducted
• The RLDCs were calculated by taking the load time series, subtracting the rescaled wind and PV time series from it, and reordering from highest to lowest. The values are rescaled so that 100% is the peak load in a system without wind and PV
• Column F shows if in this scenario the DIMES model was allowed to invest into short-term storage to reduce total system costs, or not.

Overview of key system parameters for each RLDC:
ADVANCE_VREintegration_RLDC-data.xlsx → worksheet: “overview_results”

• “Total Storage Investment costs in a system with 100kW peak load”: as DIMES sees increasing short-term storage costs with increasing storage deployment, we report also the total investment cost of the installed storage capacity and reservoir.
• “Residual peak demand after VRE+ storage”: even at 0% wind and solar contribution (row 6), it would be cost-optimal for the model to invest in short-term storage to reduce peak demand slightly”

Residual load duration curves:
ADVANCE_VREintegration_RLDC-data.xlsx → worksheet: “detailed RLDCs”

• Columns L to AHC give the residual load in % of peak demand in 10h-bins to reduce data size
• In the “monthly” file, the reordering from highest to lowest residual demand was done for each month, not for the full year. 

How to use the module

Data can be used to calibrate integration challenges for wind and solar, including

• peak residual demand after wind and solar feed-in (w/ and w/o short-term storage)
• curtailment (w/ and w/o short-term storage),
• short-term storage requirements,
• long-term storage,
• and full-load hour reduction of dispatchable power plants

An example for how this information can be implemented in an IAM is described below. Other examples such as the representation of RLDCs in a linear IAM (Johnson et al, n.d.) are compared in (Pietzcker et al., n.d.).

Residual load duration curves – implementation example in a non-linear model (REMIND)

Data

1. The third-order polynomial fit of the values of the following key system parameters for eight world regions Sub-Saharan Africa, China, Europe, India, Japan, Latin America, Middle East and North Africa, and USA:
   • peak residual demand after wind and solar feed-in
   • curtailment
   • short-term storage requirements
   • and full-load hour reduction of dispatchable power plants
2. Figures of the above-mentioned polynomial fits compared to the raw data from the RLDCs:
   ADVANCE_VREintegration_RLDC-polynomial_fit_figures.zip

Description:

• The full description of the non-linear implementation of RLDCs via load boxes and storage and curtailment equations can be found in (Ueckerdt, Pietzcker et al., n.d.), a preprint of which can be accessed here [please insert link to preprint on the ADVANCE page. The pdf to upload on the ADVANCE page can be downloaded at https://www.pik-potsdam.de/members/robertp/ueckerdt-et-al-decarbonizing-global-power-supply.pdf ] 
• The dependence of key electricity system parameters on the gross share of wind and solar in electricity generation can be represented by third-order polynomials
• The parameters are
  
  • Curtailment (“curt” = curtailment as share of demand; “curtShVRE” = curtailment as share of gross VRE production)
  • short-term storage (“shtStor” = storage capacity, “STSRes2Cap” = storage reservoir per capacity in hours, “STScost” = total storage investment costs in $/W peak demand of the electricity system)
  • 5 load band heights (peak residual demand plus 4 load boxes of 700, 2200, 4400 and 8760 FLh). For implementation reasons, the load band height parameters in the Excel file are normalized such that the sum of the four RLDC boxes is the share of total load that needs to be covered by non-VRE generation (i.e. 1 for a system with no VRE). Accordingly, the average capacity factor of load is 1/residual peak load at 0% VRE.
• The plots visualize the polynomial fits, with coloured surfaces representing the polynomial fits, compared to the raw data from the RLDCs which is displayed with blue +
• The fits were calculated with higher weights on low VRE shares, and lower weights on high VRE shares. This was done to a) balance the higher influence of high VRE shares that arises from combinatorial reasons, and b) represent the larger uncertainty about system configurations at high VRE shares.

How to use the module

Any or all of the polynomial equations can be implemented in an energy-economy-model to improve the dynamic representation of the effects of increasing wind and solar share on

• peak residual demand after wind and solar feed-in
• curtailment
• short-term storage requirements,
• and full-load hour reduction of dispatchable power plants

References

• Johnson, N., Strubegger, M., McPherson, M., Parkinson, S.C., Krey, V., Sullivan, P., n.d. A reduced-form approach for representing the impacts of wind and solar PV deployment on the structure and operation of the electricity system. Energy Economics. doi:10.1016/j.eneco.2016.07.010
• Pietzcker, R.C., Ueckerdt, F., Carrara, S., Sytze de Boer, H., Després, J., Fujimori, S., Johnson, N., Kitous, A., Scholz, Y., Sullivan, P., Luderer, G., n.d. System integration of wind and solar power in Integrated Assessment Models: A cross-model evaluation of new approaches. Energy Economics. doi:10.1016/j.eneco.2016.11.018
• Ueckerdt, F., Brecha, R., Luderer, G., Sullivan, P., Schmid, E., Bauer, N., Böttger, D., Pietzcker, R., 2015. Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves. Energy 90, Part 2, 1799–1814. doi:10.1016/j.energy.2015.07.006
• Ueckerdt, F., Pietzcker, R., Scholz, Y., Stetter, D., Giannousakis, A., Luderer, G., n.d. Decarbonizing global power supply under region-specific consideration of challenges and options of integrating variable renewables in the REMIND model. Energy Economics. doi:10.1016/j.eneco.2016.05.012