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A game-theoretic approach for price-based coordination of flexible devices operating in integrated energy-reserve markets

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  • Gong, Xuan
  • De Paola, Antonio
  • Angeli, David
  • Strbac, Goran

Abstract

This paper presents a novel distributed control strategy for large scale deployment of demand response. In the considered framework, large populations of storage devices and electric vehicles (EVs) participate to an integrated energy-reserve market. They react to prices and autonomously schedule their operation in order to optimize their own objective functions. The price signals are obtained through the resolution of an optimal power flow problem that explicitly takes into account the impact of demand response on the optimal power dispatch and reserve procurement of committed generation. Differently from previous approaches, the adopted game-theoretic framework provides rigorous theoretical guarantees of convergence and optimality of the proposed control scheme in a multi-price setup that includes ancillary services. The performance of the coordination scheme is also evaluated in simulation on the PJM 5-bus system, demonstrating its capability to flatten demand profiles and reduce the costs of generators and flexible devices.

Suggested Citation

  • Gong, Xuan & De Paola, Antonio & Angeli, David & Strbac, Goran, 2019. "A game-theoretic approach for price-based coordination of flexible devices operating in integrated energy-reserve markets," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219318481
    DOI: 10.1016/j.energy.2019.116153
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    References listed on IDEAS

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    Cited by:

    1. Dong, Zihang & Zhang, Xi & Strbac, Goran, 2021. "Evaluation of benefits through coordinated control of numerous thermal energy storage in highly electrified heat systems," Energy, Elsevier, vol. 237(C).
    2. Luciana Marques & Wadaed Uturbey & Miguel Heleno, 2021. "An Integer Non-Cooperative Game Approach for the Transactive Control of Thermal Appliances in Energy Communities," Energies, MDPI, vol. 14(21), pages 1-22, October.
    3. Löschenbrand, Markus, 2021. "Modeling competition of virtual power plants via deep learning," Energy, Elsevier, vol. 214(C).

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