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A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning

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  • Deng, Xiangtian
  • Zhang, Yi
  • Jiang, Yi
  • Zhang, Yi
  • Qi, He

Abstract

Reducing carbon emissions has been a focus problem with the rapidly increasing building energy consumption. One solution is adopting more Renewable Energy Resources (RESs) for building energy supply. To overcome the intermittence of RESs, researchers paid efforts in flexible demand response based on centralized operation and model-based control, however, which get challenges for scalability and uncertain dynamic building systems. Moreover, few works have considered user willingness as an important part of human–machine interaction and user satisfaction. Thus, we propose a novel operation method called DC-RL for renewable building energy systems. DC-RL designs a distributed DC energy system, which is scalable, control-friendly, and provides users the willingness option for flexible operation. For energy control, DC-RL adopts a model-free deep reinforcement learning (DRL) algorithm Soft-Actor-Critic (SAC) to adjust demand to matching renewable supply with maintaining user satisfaction. We evaluate DC-RL on two real-life datasets. Compared to baselines, DC-RL improves energy saving and PV self-consumption by 38% and user satisfaction by 9%. DC-RL achieves near-zero-carbon buildings with 93% self-sufficiency rate and reduces up to 33% of battery dependency.

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  • 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).
  • Handle: RePEc:eee:appene:v:353:y:2024:i:pb:s0306261923015520
    DOI: 10.1016/j.apenergy.2023.122188
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    References listed on IDEAS

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    1. 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).
    2. Ferahtia, Seydali & Djeroui, Ali & Rezk, Hegazy & Houari, Azeddine & Zeghlache, Samir & Machmoum, Mohamed, 2022. "Optimal control and implementation of energy management strategy for a DC microgrid," Energy, Elsevier, vol. 238(PB).
    3. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    4. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    5. Ahmad, Ashfaq & Khan, Jamil Yusuf, 2020. "Real-Time Load Scheduling, Energy Storage Control and Comfort Management for Grid-Connected Solar Integrated Smart Buildings," Applied Energy, Elsevier, vol. 259(C).
    6. Costa, Andrea & Keane, Marcus M. & Torrens, J. Ignacio & Corry, Edward, 2013. "Building operation and energy performance: Monitoring, analysis and optimisation toolkit," Applied Energy, Elsevier, vol. 101(C), pages 310-316.
    7. Yan, Rujing & Wang, Jiangjiang & Huo, Shuojie & Qin, Yanbo & Zhang, Jing & Tang, Saiqiu & Wang, Yuwei & Liu, Yan & Zhou, Lin, 2023. "Flexibility improvement and stochastic multi-scenario hybrid optimization for an integrated energy system with high-proportion renewable energy," Energy, Elsevier, vol. 263(PB).
    8. Ferahtia, Seydali & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2022. "Optimal techno-economic energy management strategy for building’s microgrids based bald eagle search optimization algorithm," Applied Energy, Elsevier, vol. 306(PB).
    9. Boccalatte, A. & Fossa, M. & Ménézo, C., 2020. "Best arrangement of BIPV surfaces for future NZEB districts while considering urban heat island effects and the reduction of reflected radiation from solar façades," Renewable Energy, Elsevier, vol. 160(C), pages 686-697.
    10. Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(C).
    11. Çimen, Halil & Bazmohammadi, Najmeh & Lashab, Abderezak & Terriche, Yacine & Vasquez, Juan C. & Guerrero, Josep M., 2022. "An online energy management system for AC/DC residential microgrids supported by non-intrusive load monitoring," Applied Energy, Elsevier, vol. 307(C).
    12. Jin, Yuhui & Wu, Xiao & Shen, Jiong, 2022. "Power-heat coordinated control of multiple energy system for off-grid energy supply using multi-timescale distributed predictive control," Energy, Elsevier, vol. 254(PB).
    13. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
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