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Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy

Author

Listed:
  • Shen, Rendong
  • Zhong, Shengyuan
  • Wen, Xin
  • An, Qingsong
  • Zheng, Ruifan
  • Li, Yang
  • Zhao, Jun

Abstract

Under the background of high global building energy consumption, meeting the ever-growing energy consumption demand of building energy system (BES) through renewable energy is one of the effective ways to promote the clean transformation of global energy structure and achieve “carbon neutrality”. However, with the introduction of renewable energy, BES control becomes more complicated. The mismatch between supply and demand sides limits the further growth of renewable energy consumption, which is caused by fluctuation of renewable energy and randomness of load. Therefore, it is challenging to develop an efficient framework to realize the cooperative control of various controlled objects in supply and demand sides. To address this challenge, a multi-agent deep reinforcement learning framework was proposed to optimize the energy management of the building. In this paper, a dueling double deep Q-network was used for optimization of single agent, and value-decomposition network was put forward to solve the cooperation optimization of multiple agents. Also, considering the controlled characteristics of BES, prioritized experience replay and feasible action screening mechanism were introduced to accelerate the convergence and maintain stability of the algorithm applied to BES. Simulation results show that, the multi-agent cooperation algorithm can realize the control of variously different devices at the same time and achieve multi-objective cooperation optimization of BES. Moreover, the proposed approach reduced the uncomfortable duration by 84%, the unconsumed amount of renewable energy by 43%, and the energy cost by 8% compared with the conventional rule-based control approach.

Suggested Citation

  • Shen, Rendong & Zhong, Shengyuan & Wen, Xin & An, Qingsong & Zheng, Ruifan & Li, Yang & Zhao, Jun, 2022. "Multi-agent deep reinforcement learning optimization framework for building energy system with renewable energy," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001829
    DOI: 10.1016/j.apenergy.2022.118724
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    3. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    4. 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).
    5. Duan, Pengfei & Zhao, Bingxu & Zhang, Xinghui & Fen, Mengdan, 2023. "A day-ahead optimal operation strategy for integrated energy systems in multi-public buildings based on cooperative game," Energy, Elsevier, vol. 275(C).
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    8. Jiang, Yuliang & Zhu, Shanying & Xu, Qimin & Yang, Bo & Guan, Xinping, 2023. "Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system," Applied Energy, Elsevier, vol. 334(C).
    9. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    10. Xu, Wenjie & Svetozarevic, Bratislav & Di Natale, Loris & Heer, Philipp & Jones, Colin N., 2024. "Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach," Applied Energy, Elsevier, vol. 358(C).
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