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A moving window method for time series optimisation, with applications to energy storage and hydrogen production

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  • Green, Dylan S.S.
  • Klimenko, Alexander Y.

Abstract

Temporal decomposition methods aim to solve optimisation problems by converting one problem over a large time series into a series of subproblems over shorter time series. This paper introduces one such method where subproblems are defined over a window that moves back and forth repeatedly over the length of the large time series, creating a convergent sequence of solutions and mitigating some of the boundary considerations prevalent in other temporal decomposition methods. To illustrate this moving window method, it is applied to two models: an energy storage facility trading electricity in a market; and a hydrogen electrolyser powered by renewable electricity produced and potentially stored onsite. The method is simple to implement and it is found that for large optimisation problems, it consistently requires less computation time than the base optimisation algorithm used in this study (by factors up to 100 times). In addition, it is analytically demonstrated that decomposition methods in which a minimum is attained for each subproblem need not attain a minimum for the overall problem.

Suggested Citation

  • Green, Dylan S.S. & Klimenko, Alexander Y., 2025. "A moving window method for time series optimisation, with applications to energy storage and hydrogen production," Energy, Elsevier, vol. 318(C).
    • Handle: RePEc:eee:energy:v:318:y:2025:i:c:s0360544225002580
    DOI: 10.1016/j.energy.2025.134616
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    1. Brayshaw, David James & Troccoli, Alberto & Fordham, Rachael & Methven, John, 2011. "The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK," Renewable Energy, Elsevier, vol. 36(8), pages 2087-2096.
    2. Moradi, Mohammad Hossein & Widmer, Fabio & Turin, Raymond C. & Onder, Christopher H., 2024. "Optimization of charging infrastructure and strategy for an electrified public transportation system," Energy, Elsevier, vol. 313(C).
    3. Swisher, Philip & Murcia Leon, Juan Pablo & Gea-Bermúdez, Juan & Koivisto, Matti & Madsen, Helge Aagaard & Münster, Marie, 2022. "Competitiveness of a low specific power, low cut-out wind speed wind turbine in North and Central Europe towards 2050," Applied Energy, Elsevier, vol. 306(PB).
    4. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2017. "Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany," Energy Economics, Elsevier, vol. 62(C), pages 270-282.
    5. Dan Tong & David J. Farnham & Lei Duan & Qiang Zhang & Nathan S. Lewis & Ken Caldeira & Steven J. Davis, 2021. "Geophysical constraints on the reliability of solar and wind power worldwide," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    6. Martinez Lopez, V.A. & Ziar, H. & Haverkort, J.W. & Zeman, M. & Isabella, O., 2023. "Dynamic operation of water electrolyzers: A review for applications in photovoltaic systems integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    7. Macedo, Daniela Pereira & Marques, António Cardoso & Damette, Olivier, 2022. "The role of electricity flows and renewable electricity production in the behaviour of electricity prices in Spain," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 885-900.
    8. Qiyuan Peng & Jun Zhao & Chao Wen, 2013. "A rolling horizon-based decomposition algorithm for the railway network train timetabling problem," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 1(3), pages 129-160, August.
    9. Zhang, Zihang & Li, Jiayi & Lei, Zhenyu & Zhu, Qianyu & Cheng, Jiujun & Gao, Shangce, 2024. "Reinforcement learning-based particle swarm optimization for wind farm layout problems," Energy, Elsevier, vol. 313(C).
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