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Representing Cross-border Trade in Long-term Power System Planning Models with Limited Geographical Scope

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Representing Cross-border Trade in Long-term Power System Planning Models with Limited Geographical Scope

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Representing Cross-border Trade in Long-term Power System Planning Models with Limited Geographical Scope

  1. 1. Representing Cross-border Trade in Long-term Power System Planning Models with Limited Geographical Scope Tim Mertens, Kris Poncelet, Jan Duerinck & Erik Delarue ETSAP workshop, Paris 7 June 2019 1 / 17
  2. 2. Planning models with a limited geographical scope Recent studies regarding long-term planning have focused on the impact of: • the temporal/spatial resolution • the level of technical detail However, less attention has been drawn to the impact of the model’s geographical scope. 2 / 17
  3. 3. Planning models with a limited geographical scope Recent studies regarding long-term planning have focused on the impact of: • the temporal/spatial resolution • the level of technical detail However, less attention has been drawn to the impact of the model’s geographical scope. Often long-term planning models are designed for a specific country or region, e.g., TIMES-Belgium, which requires a proper representation of cross-border trade with neighbouring regions. • Extend the geographical scope beyond the focus region, e.g. NREL’s RPM • Define exogenous import and export functions/processes 2 / 17
  4. 4. Planning models with a limited geographical scope Recent studies regarding long-term planning have focused on the impact of: • the temporal/spatial resolution • the level of technical detail However, less attention has been drawn to the impact of the model’s geographical scope. Often long-term planning models are designed for a specific country or region, e.g., TIMES-Belgium, which requires a proper representation of cross-border trade with neighbouring regions. • Extend the geographical scope beyond the focus region, e.g. NREL’s RPM • Define exogenous import and export functions/processes How to properly design and use these import and export functions? 2 / 17
  5. 5. Content • Introduction • Methodology • Illustration • Wrap-up 3 / 17
  6. 6. Content • Introduction • Methodology • Illustration • Wrap-up 3 / 17
  7. 7. Methodology Make assumptions regarding the capacity mix and electricity demand in the neigbouring countries. • Existing studies/scenario ananlyses • Communicated policy targets • ... The methodology adopted for representing cross-border trade can be summarized in the following three steps. 1 Construct import/export functions. 2 Include the obtained functions from 1 in the optimization model (and solve the optimization model). 3 Perform an ex-post cost reallocation. 4 / 17
  8. 8. 1. Construct import/export functions Construct import and export functions that: • Reflects the potential of other countries to facilitate electricity imports (import curve) • Reflects the willingness-to-pay of other countries for electricity exports (export curve) 5 / 17
  9. 9. 1. Construct import/export functions Construct import and export functions that: • Reflects the potential of other countries to facilitate electricity imports (import curve) • Reflects the willingness-to-pay of other countries for electricity exports (export curve) Due to varying demand and intermittent RES, the process is repeated for every time step. D ImportExport p q 5 / 17
  10. 10. 2. Planning model formulation Min Investment cost+Generation cost + Import cost - Export revenue Subject to: • System constraints (e.g., supply-demand balance) • Policy constraints (e.g., RES targets, emission prices) • Technical constraints (e.g., generation limits) • Cross-border trade constraints Import cost = i∈I t∈T Pimp i · importi,t Export revenue = e∈E t∈T Pexp e · exporte,t D ImportExport p q 6 / 17
  11. 11. Export revenue in objective function DAp q DBp q A B 7 / 17
  12. 12. Export revenue in objective function DAp q f DBp q f A Bf 7 / 17
  13. 13. Export revenue in objective function DAp q f DBp q f A Bf 7 / 17
  14. 14. Export revenue in objective function DAp q f DBp q f A Bf 7 / 17
  15. 15. Import cost in objective function DAp q f DBp q f A Bf 8 / 17
  16. 16. 3. Ex-post cost reallocation DAp q f DBp q f A Bf 9 / 17
  17. 17. 3. Ex-post cost reallocation DA λA p q f DB λB p q f A Bf 9 / 17
  18. 18. 3. Ex-post cost reallocation DA λA p q f DB λB p q f A Bf 9 / 17
  19. 19. 3. Ex-post cost reallocation DA λA p q f DB λB p q f A Bf 9 / 17
  20. 20. 3. Ex-post cost reallocation DA λA p q f DB λB p q f A Bf 9 / 17
  21. 21. 3. Ex-post cost reallocation The Import/export curves trigger the correct investment decisions, however the objective function does not represent the true cost for the modeled country. • Objective function • Import cost is underestimated while the export revenue is overestimated • Total welfare increase due to cross-border trade is allocated to the modeled country. • Ex-post cost reallocation • Traded electricity is valued at the locational electricity price (pay-as-cleared/marginal pricing) • Total welfare1 due to cross-border trade are split up in (i) profits for exporting country, (ii) avoided costs for importing country and (iii) a congestion rent. 1 not including cost reductions due to more efficient investment decisions 10 / 17
  22. 22. Content • Introduction • Methodology • Illustration • Wrap-up 11 / 17
  23. 23. 2-country example A (BE) B (NL) Static greenfield optimization for different RES shares Three cases • A+B (co-optimized case) • A (isolated) • A + import/export curves 12 / 17
  24. 24. Performance of methodology RES 50 A + B A (isolated) A + Import/Export curves Cost for country A (no congestion rents included) [billion EUR] 5.854 5.917 5.854 Error [%] - + 1.06 + 0.0 Computation time [s] 22.23 2.25 4.31 If the import/export curves are constructed based on the optimal capacity mix for country B. • We get the same solution for country A as would be obtained in the multi-country optimization. • The computation time can be reduced substantially This method reduces the cross-border trade issue to making accurate exogenous assumptions about the power system in the neigbouring countries (without the need for co-optimization). 13 / 17
  25. 25. Ex-post cost reallocation The objective function overestimates the cost reductions due to cross-border trade. We need to compensate the objective value for: 1 The congestion rent in case of congested transmission line. 2 Profits and avoided costs that are actually contributing to welfare increases in neigbouring countries. 0 10 20 30 40 50 60 70 RES share [%] 0 1 2 3 4 5 6 7 8 Totalcostreduction[%] Objective value Market-based cost allocation 14 / 17
  26. 26. Wrap-up Limiting the geographical scope of long-term planning models requires correctly representing cross-border trade. The proposed methodology has the benefit of: 1 Reducing the cross-border trade issue to making (accurate) assumptions about the surrounding power systems. 2 Correctly exogenizing the countries excluded from the scope of the model, hereby improving computational tractability. There is a need to reallocate country-specific costs. Future work: • Perform a proper case study focusing on CWE-system • Perform sensitivity analyses with Belgian TIMES model. • Include stochasticity in current approach 15 / 17
  27. 27. References [1] Devogelaer, D., Duerinck, J., Gusbin, D., Marenne, Y., Nijs, W., Orsini, M., & Pairon, M. (2012). Towards 100% renewable energy in Belgium by 2050. Belgium: FPB, ICEDD, VITO, 156. [2] Balyk, O., Andersen, K. S., Dockweiler, S., Gargiulo, M., Karlsson, K., Næraa, R., Petrovic, S., Tattini, J., Termansen, L. B., & Venturini, G. (2019). TIMES-DK: technology-rich multi-sectoral optimisation model of the Danish energy system. Energy Strategy Reviews, 23, 13-22. [3] Poncelet, K., Delarue, E., Six, D., Duerinck, J., & D’haeseleer, W. (2016). Impact of the level of temporal and operational detail in energy-system planning models. Applied Energy, 162, 631-643. [4] Krishnan, V., & Cole, W. (2016). Evaluating the value of high spatial resolution in national capacity expansion models using ReEDS. In 2016 IEEE Power and Energy Society General Meeting (PESGM) (pp. 1-5). IEEE Power and Energy Society General Meeting (PESGM). [5] Mai, T., Drury, E., Eurek, K., Bodington, N., Lopez, A., & Perry, A. (2013). Resource planning model: an integrated resource planning and dispatch tool for regional electric systems (No. NREL/TP-6A20-56723). National Renewable Energy Lab.(NREL), Golden, CO (United States). [6] Georgiou, P. N. (2016). A bottom-up optimization model for the long-term energy planning of the Greek power supply sector integrating mainland and insular electric systems. Computers & Operations Research, 66, 292-312. [7] Biggar, D. R., & Hesamzadeh, M. R. (2014). The economics of electricity markets. John Wiley & Sons. 16 / 17
  28. 28. Total welfare increase due to cross-border trade Welfare gains due to CBT increase for increasing RES shares: • More efficient RES investments - RES potential varies geographically • Smoothing of variability - correlation effect of generation profiles and demand profiles Total welfare gains are split between: • Welfare increase for country A • Welfare increase for country B • Congestion rent 0 10 20 30 40 50 60 70 RES share [%] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Totalwelfareincrease[%] Total Welfare increase allocated to A Welfare increase allocated to B Congestion rent 17 / 17

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