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Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling

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Listed:
  • Xiaohan Fang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Jinkuan Wang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Guanru Song

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Yinghua Han

    (School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Qiang Zhao

    (School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Zhiao Cao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Residential microgrid is widely considered as a new paradigm of the home energy management system. The complexity of Microgrid Energy Scheduling (MES) is increasing with the integration of Electric Vehicles (EVs) and Renewable Generations (RGs). Moreover, it is challenging to determine optimal scheduling strategies to guarantee the efficiency of the microgrid market and to balance all market participants’ benefits. In this paper, a Multi-Agent Reinforcement Learning (MARL) approach for residential MES is proposed to promote the autonomy and fairness of microgrid market operation. First, a multi-agent based residential microgrid model including Vehicle-to-Grid (V2G) and RGs is constructed and an auction-based microgrid market is built. Then, distinguish from Single-Agent Reinforcement Learning (SARL), MARL can achieve distributed autonomous learning for each agent and realize the equilibrium of all agents’ benefits, therefore, we formulate an equilibrium-based MARL framework according to each participant’ market orientation. Finally, to guarantee the fairness and privacy of the MARL process, we proposed an improved optimal Equilibrium Selection-MARL (ES-MARL) algorithm based on two mechanisms, private negotiation and maximum average reward. Simulation results demonstrate the overall performance and efficiency of proposed MARL are superior to that of SARL. Besides, it is verified that the improved ES-MARL can get higher average profit to balance all agents.

Suggested Citation

  • Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:123-:d:301988
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    References listed on IDEAS

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    Cited by:

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    2. Kapil Deshpande & Philipp Möhl & Alexander Hämmerle & Georg Weichhart & Helmut Zörrer & Andreas Pichler, 2022. "Energy Management Simulation with Multi-Agent Reinforcement Learning: An Approach to Achieve Reliability and Resilience," Energies, MDPI, vol. 15(19), pages 1-35, October.
    3. Eleonora Achiluzzi & Kirushaanth Kobikrishna & Abenayan Sivabalan & Carlos Sabillon & Bala Venkatesh, 2020. "Optimal Asset Planning for Prosumers Considering Energy Storage and Photovoltaic (PV) Units: A Stochastic Approach," Energies, MDPI, vol. 13(7), pages 1-20, April.
    4. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.

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