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Coalitional Demand Response Management in Community Energy Management Systems

Author

Listed:
  • Nicholas Kemp

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131-0001, USA)

  • Md Sadman Siraj

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131-0001, USA)

  • Eirini Eleni Tsiropoulou

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131-0001, USA)

Abstract

With the advent of the Distributed Energy Resources within smart grid systems, traditional demand response management (DRM) models need to be redesigned to capture prosumers’ energy consumption requests and dynamic behavior within the energy market. In this paper, a coalitional DRM model is introduced based on the principles of Game Theory and reinforcement learning to dynamically determine prosumers’ formation in local energy trading communities and their optimal energy consumption. A hedonic game-theoretic model is introduced to enable prosumers to autonomously and dynamically select an energy trading community considering the partially available information regarding prosumers’ energy generation and consumption characteristics and utility companies’ provided rewards per community. Then, a log-linear reinforcement learning model is proposed to enable each prosumer to distributedly determine their optimal energy consumption. A detailed evaluation of the proposed coalitional DRM model is performed based on real data collected from the southwest area of the USA.

Suggested Citation

  • Nicholas Kemp & Md Sadman Siraj & Eirini Eleni Tsiropoulou, 2023. "Coalitional Demand Response Management in Community Energy Management Systems," Energies, MDPI, vol. 16(17), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6363-:d:1231459
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