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Competitive Energy Storage and the Duck Curve

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  • Richard Schmalensee

Abstract

Power systems with high penetrations of solar generation need to replace solar output when it falls rapidly in the late afternoonthe duck curve problem. Storage is a carbon-free solution to this problem. This essay considers investment in generation and storage to minimize expected cost in a Boiteux-Turvey-style model of an electric power system with alternating daytime time periods, with solar generation, and nighttime periods, without it. In the most interesting cases, if energy market prices are uncapped, all expected cost minima are long-run competitive equilibria, and the long-run equilibrium value of storage capacity minimizes expected system cost conditional on generation capacities.

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  • Richard Schmalensee, 2022. "Competitive Energy Storage and the Duck Curve," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    • Handle: RePEc:aen:journl:ej43-2-schmalensee
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    References listed on IDEAS

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    1. Joachim Geske and Richard Green, 2020. "Optimal Storage, Investment and Management under Uncertainty: It is Costly to Avoid Outages!," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-28.
    2. Gerard Llobet and Jorge Padilla, 2018. "Conventional Power Plants in Liberalized Electricity Markets with Renewable Entry," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    3. Joskow, Paul L., 2008. "Capacity payments in imperfect electricity markets: Need and design," Utilities Policy, Elsevier, vol. 16(3), pages 159-170, September.
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    Cited by:

    1. Chaves, J. P. & Cossent, R. & Gómez San Román, T. & Linares, P. & Rivier, M., 2023. "An assessment of the European electricity market reform options and a pragmatic proposal," Cambridge Working Papers in Economics 2325, Faculty of Economics, University of Cambridge.
    2. Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
    3. Moshe Kelner & Zinoviy Landsman & Udi E. Makov, 2022. "Probabilistic Peak Demand Estimation Using Members of the Clayton Generalized Gamma Copula Family," Energies, MDPI, vol. 15(16), pages 1-15, August.
    4. Isogai, Hirotaka & Nakagaki, Takao, 2024. "Power-to-heat amine-based post-combustion CO2 capture system with solvent storage utilizing fluctuating electricity prices," Applied Energy, Elsevier, vol. 368(C).
    5. Zhang, Tuo & Tang, Maogang, 2024. "Solar surge and cost shifts: Heterogenous effects of redistribution in the electricity bills in Japan," Energy Economics, Elsevier, vol. 137(C).
    6. Alberto J. Lamadrid & Hao Lu & Timothy D. Mount, 2024. "A simple way to integrate distributed storage into a wholesale electricity market," Journal of Regulatory Economics, Springer, vol. 65(1), pages 27-63, June.
    7. J.P. Chaves & R. Cossent & T. Gómez San Román & P. Linares & M. Rivier, 2023. "An assessment of the European electricity market reform options and a pragmatic proposal," Working Papers EPRG2305, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    8. Mays, Jacob, 2024. "Sequential pricing of electricity," Energy Economics, Elsevier, vol. 137(C).

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