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Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system

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  • Gao, Yuan
  • Matsunami, Yuki
  • Miyata, Shohei
  • Akashi, Yasunori

Abstract

With the application of renewable energy in building energy systems (BES), an increasing number of power grids require building energy systems coupled to realize off-grid operation which is one type of energy flexible and grid responsive operations. In this case, deep reinforcement learning (DRL) algorithms have gained more and more attention in the operation control of BES due to their strong fitting ability and model-free utilization characteristics. However, mainstream DRL algorithms cannot solve the reinforcement learning problem of hybrid action spaces, which also restricts its further application in BES including variety of energy sources. In this paper, we firstly use a multi-agent deep reinforcement learning algorithm (MADRL) to solve the RL problem of hybrid action spaces in the building controls domain. The proposed algorithm is validated on a measured dataset of a real office building in Japan. The results show that compared to the currently used baseline control logic, MADRL can achieve a 60% improvement in off-grid operation tasks. For battery safety, MADRL can reduce unsafe battery runtime by at least 80%. Furthermore, through experiments we find that the single-agent DRL algorithm cannot solve the reinforcement learning problem with hybrid action spaces. The MADRL framework achieves stable training and optimization by layering problems and agents.

Suggested Citation

  • Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012788
    DOI: 10.1016/j.apenergy.2022.120021
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    Cited by:

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    2. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty," Applied Energy, Elsevier, vol. 371(C).
    3. Li, Yutong & Hou, Jian & Yan, Gangfeng, 2024. "Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data," Applied Energy, Elsevier, vol. 359(C).
    4. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "Energy saving and indoor temperature control for an office building using tube-based robust model predictive control," Applied Energy, Elsevier, vol. 341(C).
    5. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
    6. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    7. Dong, Lei & Lin, Hao & Qiao, Ji & Zhang, Tao & Zhang, Shiming & Pu, Tianjiao, 2024. "A coordinated active and reactive power optimization approach for multi-microgrids connected to distribution networks with multi-actor-attention-critic deep reinforcement learning," Applied Energy, Elsevier, vol. 373(C).
    8. Essayeh, Chaimaa & Morstyn, Thomas, 2024. "OPLEM: Open Platform for Local Energy Markets," Applied Energy, Elsevier, vol. 373(C).

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