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Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning

Author

Listed:
  • Yushen Miao

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Tianyi Chen

    (James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK)

  • Shengrong Bu

    (Department of Engineering, Brock University, St. Catharines, ON L2S 3A1, Canada)

  • Hao Liang

    (Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Zhu Han

    (Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, USA)

Abstract

Battery energy storage systems (BESSs) play a critical role in eliminating uncertainties associated with renewable energy generation, to maintain stability and improve flexibility of power networks. In this paper, a BESS is used to provide energy arbitrage (EA) and frequency regulation (FR) services simultaneously to maximize its total revenue within the physical constraints. The EA and FR actions are taken at different timescales. The multitimescale problem is formulated as two nested Markov decision process (MDP) submodels. The problem is a complex decision-making problem with enormous high-dimensional data and uncertainty (e.g., the price of the electricity). Therefore, a novel co-optimization scheme is proposed to handle the multitimescale problem, and also coordinate EA and FR services. A triplet deep deterministic policy gradient with exploration noise decay (TDD–ND) approach is used to obtain the optimal policy at each timescale. Simulations are conducted with real-time electricity prices and regulation signals data from the American PJM regulation market. The simulation results show that the proposed approach performs better than other studied policies in literature.

Suggested Citation

  • Yushen Miao & Tianyi Chen & Shengrong Bu & Hao Liang & Zhu Han, 2021. "Co-Optimizing Battery Storage for Energy Arbitrage and Frequency Regulation in Real-Time Markets Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8365-:d:700404
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    References listed on IDEAS

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    1. Walawalkar, Rahul & Apt, Jay & Mancini, Rick, 2007. "Economics of electric energy storage for energy arbitrage and regulation in New York," Energy Policy, Elsevier, vol. 35(4), pages 2558-2568, April.
    2. Sioshansi, Ramteen & Denholm, Paul & Jenkin, Thomas & Weiss, Jurgen, 2009. "Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects," Energy Economics, Elsevier, vol. 31(2), pages 269-277, March.
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    Cited by:

    1. Lyu, Chenghao & Wang, Weiquan & Wang, Junyue & Bai, Yilin & Song, Zhengxiang & Wang, Wei & Meng, Jinhao, 2024. "The role of co-optimization in trading off cost and frequency regulation service for industrial microgrids," Applied Energy, Elsevier, vol. 375(C).
    2. Alba Leduchowicz-Municio & Miguel Edgar Morales Udaeta & André Luiz Veiga Gimenes & Tuo Ji & Victor Baiochi Riboldi, 2022. "Socio-Environmental Evaluation of MV Commercial Time-Shift Application Based on Battery Energy Storage Systems," Energies, MDPI, vol. 15(14), pages 1-21, July.
    3. 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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