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Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation

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  • Mohseni, Soheil
  • Brent, Alan C.
  • Kelly, Scott
  • Browne, Will N.
  • Burmester, Daniel

Abstract

While industrial demand response programmes have long been valued to support the power grid, recent advances in information and communications technology have enabled new opportunities to leverage the potential of responsive loads in less energy-dense end-use sectors. This brings to light the importance of accurately projecting flexible demand-side resources in the long-term investment planning process of micro-grids. This paper introduces a customer comfort-aware, demand response-integrated long-term micro-grid planning optimisation model. The model (1) draws on non-cooperative game theory and the Stackelberg leadership principles to understand and reflect the strategic behaviour of energy utilities, demand response aggregators, and end-consumers, (2) produces optimal trade-offs between power imported from the main grid and available demand response resources, (3) determines the cost-optimal resource allocation for energy infrastructure, including multiple energy storage systems, and (4) provides a level playing field for emerging technologies, such as power-to-gas and vehicle-to-grid interventions. The multi-energy-storage-technology test-case was effectively applied to achieve 100%-renewable energy generation for the town of Ohakune, New Zealand. Numerical simulation results suggest that the proposed incentive-compatible demand-side management market-clearing mechanism is able to estimate the cost-optimal solution for the provision of renewable energy during the planning phase. The cost-optimal system saves ~21% (equating to around US$5.5 m) compared to a business-as-usual approach, where the participation of end-users in demand response programmes is projected by running uniform price demand response auctions. The most salient distinction of the proposed two-stage (wholesale and retail) demand-side management market model is the continual process of trading, with incentive prices unique to each transaction.

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  • Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N. & Burmester, Daniel, 2021. "Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation," Applied Energy, Elsevier, vol. 287(C).
    • Handle: RePEc:eee:appene:v:287:y:2021:i:c:s0306261921001100
    DOI: 10.1016/j.apenergy.2021.116563
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    2. Jann Michael Weinand & Maximilian Hoffmann & Jan Gopfert & Tom Terlouw & Julian Schonau & Patrick Kuckertz & Russell McKenna & Leander Kotzur & Jochen Lin{ss}en & Detlef Stolten, 2022. "Global LCOEs of decentralized off-grid renewable energy systems," Papers 2212.12742, arXiv.org, revised Mar 2023.
    3. Li, Ke & Ye, Ning & Li, Shuzhen & Wang, Haiyang & Zhang, Chenghui, 2023. "Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory," Energy, Elsevier, vol. 273(C).
    4. Khojasteh, Meysam & Faria, Pedro & Lezama, Fernando & Vale, Zita, 2022. "Optimal strategy of electricity and natural gas aggregators in the energy and balance markets," Energy, Elsevier, vol. 257(C).
    5. Yan, Zhongzhen & Zhu, Xinyuan & Chang, Yiming & Wang, Xianglong & Ye, Zhiwei & Xu, Zhigang & Fars, Ashk, 2023. "Renewable energy effects on energy management based on demand response in microgrids environment," Renewable Energy, Elsevier, vol. 213(C), pages 205-217.
    6. Wang, Yudong & Hu, Junjie, 2023. "Two-stage energy management method of integrated energy system considering pre-transaction behavior of energy service provider and users," Energy, Elsevier, vol. 271(C).

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