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Receding horizon dispatch of multi-period look-ahead market for energy storage integration

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  • Huang, Yi
  • Gordon, Dan
  • Scott, Paul

Abstract

Although noteworthy efforts towards energy storage participation in electricity markets have been witnessed worldwide during the last decades, market mechanisms for optimal storage integration are yet to be comprehensively studied. This paper presents a novel approach wherein a multi-period look-ahead market is proposed to progressively solve the optimisation problem over a receding horizon, incorporating intertemporal constraints to enable forward planning. The focus is specifically on modelling the bidding behaviour of energy storage, and to address this, we develop two distinct models for the participation of energy storage in the market. The integrated model aims to schedule the future dispatch of the storage to minimise total operational cost, taking into account the fixed costs of the storage, while the strategic model pursues the dual ambition to maximise storage profit by formulating a bilevel optimisation problem. We validate the viability of our proposed market mechanisms through case studies on the Australian National Electricity Market. We conduct case studies which provide evidence that both the strategic and integrated bidding models effectively address the requirement for forward planning, resulting in reduced market costs and volatility. Notably, the strategic model outperforms in terms of generating higher storage revenue, while the integrated model exhibits a slight advantage in terms of lower market costs compared to the strategic model.

Suggested Citation

  • Huang, Yi & Gordon, Dan & Scott, Paul, 2023. "Receding horizon dispatch of multi-period look-ahead market for energy storage integration," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923012205
    DOI: 10.1016/j.apenergy.2023.121856
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    References listed on IDEAS

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    5. Wang, Jingxing & Chung, Seokhyun & AlShelahi, Abdullah & Kontar, Raed & Byon, Eunshin & Saigal, Romesh, 2021. "Look-ahead decision making for renewable energy: A dynamic “predict and store” approach," Applied Energy, Elsevier, vol. 296(C).
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    1. Liu, Jiantao & Wang, Lei & Jiao, Ticao & Yang, Wenjing & Xu, Ruilong & Wu, Ke & Ha, Hengxu, 2024. "Multi-period voltage stability-constrained optimal power flow with uncertainties," Applied Energy, Elsevier, vol. 369(C).

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