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Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning

Author

Listed:
  • Zeyue Sun

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia)

  • Mohsen Eskandari

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia)

  • Chaoran Zheng

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia)

  • Ming Li

    (School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia)

Abstract

With the development of microgrids (MGs), an energy management system (EMS) is required to ensure the stable and economically efficient operation of the MG system. In this paper, an intelligent EMS is proposed by exploiting the deep reinforcement learning (DRL) technique. DRL is employed as the effective method for handling the computation hardness of optimal scheduling of the charge/discharge of battery energy storage in the MG EMS. Since the optimal decision for charge/discharge of the battery depends on its state of charge given from the consecutive time steps, it demands a full-time horizon scheduling to obtain the optimum solution. This, however, increases the time complexity of the EMS and turns it into an NP-hard problem. By considering the energy storage system’s charging/discharging power as the control variable, the DRL agent is trained to investigate the best energy storage control method for both deterministic and stochastic weather scenarios. The efficiency of the strategy suggested in this study in minimizing the cost of purchasing energy is also shown from a quantitative perspective through programming verification and comparison with the results of mixed integer programming and the heuristic genetic algorithm (GA).

Suggested Citation

  • Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
  • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:90-:d:1010650
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    References listed on IDEAS

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    1. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    2. Yousef Asadi & Mohsen Eskandari & Milad Mansouri & Andrey V. Savkin & Erum Pathan, 2022. "Frequency and Voltage Control Techniques through Inverter-Interfaced Distributed Energy Resources in Microgrids: A Review," Energies, MDPI, vol. 15(22), pages 1-29, November.
    3. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    4. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    5. Muhammad Uzair & Mohsen Eskandari & Li Li & Jianguo Zhu, 2022. "Machine Learning Based Protection Scheme for Low Voltage AC Microgrids," Energies, MDPI, vol. 15(24), pages 1-19, December.
    6. Tabar, Vahid Sohrabi & Jirdehi, Mehdi Ahmadi & Hemmati, Reza, 2017. "Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option," Energy, Elsevier, vol. 118(C), pages 827-839.
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    Cited by:

    1. Alireza Gorjian & Mohsen Eskandari & Mohammad H. Moradi, 2023. "Conservation Voltage Reduction in Modern Power Systems: Applications, Implementation, Quantification, and AI-Assisted Techniques," Energies, MDPI, vol. 16(5), pages 1-36, March.
    2. Pakeeza Bano & Kashif Imran & Abdul Kashif Janjua & Abdullah Abusorrah & Kinza Fida & Hesham Alhumade, 2023. "System and Market-Wide Impact Analysis of Coordinated Demand Response and Battery Storage Operation by a Load-Serving Entity," Energies, MDPI, vol. 16(4), pages 1-22, February.

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