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Control of Energy Storage with Market Impact: Lagrangian Approach and Horizons

Author

Listed:
  • James Cruise

    (Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom)

  • Lisa Flatley

    (Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom)

  • Richard Gibbens

    (Computer Laboratory, University of Cambridge, Cambridge CB3 0FD, United Kingdom)

  • Stan Zachary

    (Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom)

Abstract

We study the control of large-scale energy storage operating in a market. Reoptimization of deterministic models is a common pragmatic approach when prices are stochastic. We apply Lagrangian theory to develop such a model and to establish decision and forecast horizons when storage trading affects these prices, an important aspect of some energy markets. The determination of these horizons also provides a simple and efficient algorithm for the determination of the optimal control. The forecast horizons vary between one and 15 days in realistic electricity storage examples. These examples suggest that modeling price impact is important.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:oropre:v:67:y:2019:i:1:p:1-9
    DOI: 10.1287/opre.2018.1761
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    References listed on IDEAS

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    Cited by:

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    2. Qiong Jia & Liyuan Wei & Xiaotong Li, 2019. "Visualizing Sustainability Research in Business and Management (1990–2019) and Emerging Topics: A Large-Scale Bibliometric Analysis," Sustainability, MDPI, vol. 11(20), pages 1-37, October.
    3. Finnah, Benedikt & Gönsch, Jochen, 2021. "Optimizing trading decisions of wind power plants with hybrid energy storage systems using backwards approximate dynamic programming," International Journal of Production Economics, Elsevier, vol. 238(C).
    4. 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.
    5. Christian Mandl & Selvaprabu Nadarajah & Stefan Minner & Srinagesh Gavirneni, 2022. "Data‐driven storage operations: Cross‐commodity backtest and structured policies," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2438-2456, June.

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    Keywords

    control; storage; arbitrage; energy;
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