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On representation of energy storage in electricity planning models

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  • James H. Merrick
  • John E. T. Bistline
  • Geoffrey J. Blanford

Abstract

This paper considers the representation of energy storage in electricity sector capacity planning models. The incorporation of storage in long-term systems models of this type is increasingly relevant as the cost of storage technologies, particularly batteries, and of complementary variable renewable technologies, decline. To value storage technologies appropriately, a representation of linkages between time periods is required, breaking classical temporal aggregation strategies that greatly improve computation time. We appraise approaches to address this problem, highlighting a common underlying structure, conditions for lossless aggregation, and challenges of aggregation at relevant geographical scales. We then investigate solutions to the modeling problem including a decomposition scheme to avoid temporal aggregation at a parallelizable computational cost. These examples frame aspects of the problem ripe for contributions from the operational research community.

Suggested Citation

  • James H. Merrick & John E. T. Bistline & Geoffrey J. Blanford, 2021. "On representation of energy storage in electricity planning models," Papers 2105.03707, arXiv.org, revised May 2021.
    • Handle: RePEc:arx:papers:2105.03707
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    1. Bistline, John E.T. & Blanford, Geoffrey J., 2020. "Value of technology in the U.S. electric power sector: Impacts of full portfolios and technological change on the costs of meeting decarbonization goals," Energy Economics, Elsevier, vol. 86(C).
    2. Lion Hirth, 2013. "The Market Value of Variable Renewables. The Effect of Solar and Wind Power Variability on their Relative Price," RSCAS Working Papers 2013/36, European University Institute.
    3. Bistline, John E., 2017. "Economic and technical challenges of flexible operations under large-scale variable renewable deployment," Energy Economics, Elsevier, vol. 64(C), pages 363-372.
    4. Álvaro García-Cerezo & Luis Baringo & Raquel García-Bertrand, 2020. "Representative Days for Expansion Decisions in Power Systems," Energies, MDPI, vol. 13(2), pages 1-18, January.
    5. Denholm, Paul & Mai, Trieu, 2019. "Timescales of energy storage needed for reducing renewable energy curtailment," Renewable Energy, Elsevier, vol. 130(C), pages 388-399.
    6. Nahmmacher, Paul & Schmid, Eva & Hirth, Lion & Knopf, Brigitte, 2016. "Carpe diem: A novel approach to select representative days for long-term power system modeling," Energy, Elsevier, vol. 112(C), pages 430-442.
    7. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    8. Collins, Seán & Deane, John Paul & Poncelet, Kris & Panos, Evangelos & Pietzcker, Robert C. & Delarue, Erik & Ó Gallachóir, Brian Pádraig, 2017. "Integrating short term variations of the power system into integrated energy system models: A methodological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 839-856.
    9. William A. Braff & Joshua M. Mueller & Jessika E. Trancik, 2016. "Value of storage technologies for wind and solar energy," Nature Climate Change, Nature, vol. 6(10), pages 964-969, October.
    10. Hirth, Lion, 2013. "The market value of variable renewables," Energy Economics, Elsevier, vol. 38(C), pages 218-236.
    11. P. Massé & R. Gibrat, 1957. "Application of Linear Programming to Investments in the Electric Power Industry," Management Science, INFORMS, vol. 3(2), pages 149-166, January.
    12. Gautam Gowrisankaran & Stanley S. Reynolds & Mario Samano, 2016. "Intermittency and the Value of Renewable Energy," Journal of Political Economy, University of Chicago Press, vol. 124(4), pages 1187-1234.
    13. Diaz, Gabriel & Inzunza, Andrés & Moreno, Rodrigo, 2019. "The importance of time resolution, operational flexibility and risk aversion in quantifying the value of energy storage in long-term energy planning studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 797-812.
    14. Merrick, James H., 2016. "On representation of temporal variability in electricity capacity planning models," Energy Economics, Elsevier, vol. 59(C), pages 261-274.
    15. Kavinesh J. Singh & Andy B. Philpott & R. Kevin Wood, 2009. "Dantzig-Wolfe Decomposition for Solving Multistage Stochastic Capacity-Planning Problems," Operations Research, INFORMS, vol. 57(5), pages 1271-1286, October.
    16. Yangfang (Helen) Zhou & Alan Scheller-Wolf & Nicola Secomandi & Stephen Smith, 2016. "Electricity Trading and Negative Prices: Storage vs. Disposal," Management Science, INFORMS, vol. 62(3), pages 880-898, March.
    17. Frederic H. Murphy & Yves Smeers, 2005. "Generation Capacity Expansion in Imperfectly Competitive Restructured Electricity Markets," Operations Research, INFORMS, vol. 53(4), pages 646-661, August.
    18. de Sisternes, Fernando J. & Jenkins, Jesse D. & Botterud, Audun, 2016. "The value of energy storage in decarbonizing the electricity sector," Applied Energy, Elsevier, vol. 175(C), pages 368-379.
    19. James Cruise & Lisa Flatley & Richard Gibbens & Stan Zachary, 2019. "Control of Energy Storage with Market Impact: Lagrangian Approach and Horizons," Operations Research, INFORMS, vol. 67(1), pages 1-9, January.
    20. Nelson, James & Johnston, Josiah & Mileva, Ana & Fripp, Matthias & Hoffman, Ian & Petros-Good, Autumn & Blanco, Christian & Kammen, Daniel M., 2012. "High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures," Energy Policy, Elsevier, vol. 43(C), pages 436-447.
    21. Ruoyu Sun & Zhi-Quan Luo & Yinyu Ye, 2020. "On the Efficiency of Random Permutation for ADMM and Coordinate Descent," Mathematics of Operations Research, INFORMS, vol. 45(1), pages 233-271, February.
    22. Després, Jacques & Mima, Silvana & Kitous, Alban & Criqui, Patrick & Hadjsaid, Nouredine & Noirot, Isabelle, 2017. "Storage as a flexibility option in power systems with high shares of variable renewable energy sources: a POLES-based analysis," Energy Economics, Elsevier, vol. 64(C), pages 638-650.
    23. Paul H. Zipkin, 1980. "Bounds for Row-Aggregation in Linear Programming," Operations Research, INFORMS, vol. 28(4), pages 903-916, August.
    24. David F. Rogers & Robert D. Plante & Richard T. Wong & James R. Evans, 1991. "Aggregation and Disaggregation Techniques and Methodology in Optimization," Operations Research, INFORMS, vol. 39(4), pages 553-582, August.
    25. Brown, T. & Schlachtberger, D. & Kies, A. & Schramm, S. & Greiner, M., 2018. "Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system," Energy, Elsevier, vol. 160(C), pages 720-739.
    26. Hanspeter Höschle & Hélène le Cadre, & Yves Smeers & Anthony Papavasiliou & Ronnie Belmans, 2018. "An ADMM-based method for computing risk-averse equilibrium in capacity markets," LIDAM Reprints CORE 3020, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    27. Jayadev, Gopika & Leibowicz, Benjamin D. & Kutanoglu, Erhan, 2020. "U.S. electricity infrastructure of the future: Generation and transmission pathways through 2050," Applied Energy, Elsevier, vol. 260(C).
    28. Frew, Bethany A. & Jacobson, Mark Z., 2016. "Temporal and spatial tradeoffs in power system modeling with assumptions about storage: An application of the POWER model," Energy, Elsevier, vol. 117(P1), pages 198-213.
    29. Geoffrey J. Blanford, James H. Merrick, and David Young, 2014. "A Clean Energy Standard Analysis with the US-REGEN Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    30. Mallapragada, Dharik S. & Papageorgiou, Dimitri J. & Venkatesh, Aranya & Lara, Cristiana L. & Grossmann, Ignacio E., 2018. "Impact of model resolution on scenario outcomes for electricity sector system expansion," Energy, Elsevier, vol. 163(C), pages 1231-1244.
    31. Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
    32. Teichgraeber, Holger & Brandt, Adam R., 2019. "Clustering methods to find representative periods for the optimization of energy systems: An initial framework and comparison," Applied Energy, Elsevier, vol. 239(C), pages 1283-1293.
    33. Bistline, John E.T. & Young, David T., 2020. "Emissions impacts of future battery storage deployment on regional power systems," Applied Energy, Elsevier, vol. 264(C).
    34. Merrick, James H. & Weyant, John P., 2019. "On choosing the resolution of normative models," European Journal of Operational Research, Elsevier, vol. 279(2), pages 511-523.
    35. Geoffrey J. Blanford, James H. Merrick, John E.T. Bistline, and David T. Young, 2018. "Simulating Annual Variation in Load, Wind, and Solar by Representative Hour Selection," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
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