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An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm

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  • Meng, Fanyi
  • Bai, Yang
  • Jin, Jingliang

Abstract

A desirable dispatching strategy is essentially important for securely and economically operating of wind-thermal hybrid distribution systems. Existing dispatch strategies usually assume that wind power has priority of injection. For real-time control, such strategies are simple and easy to realize, but they lack flexibility and incur higher operation and maintenance (O&M) costs. This study analyzed the power dispatching process as a dynamic sequential control problem and established a Markov decision process model to explore the optimal coordinated dispatch strategy for coping with wind and demand disturbance. As a salient feature, the improved dispatch strategy minimizes the long-run expected operation and maintenance costs. To evaluate the model efficiently, a Monte Carlo method and the Q-learning algorithm were employed to the growing computational cost over the state space. Through a specified numerical case, we demonstrated the properties of the coordinated dispatch strategy and used it to address a 24-h real-time dispatching problem. The proposed algorithm shows high efficiency in solving real-time dispatching problems.

Suggested Citation

  • Meng, Fanyi & Bai, Yang & Jin, Jingliang, 2021. "An advanced real-time dispatching strategy for a distributed energy system based on the reinforcement learning algorithm," Renewable Energy, Elsevier, vol. 178(C), pages 13-24.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:13-24
    DOI: 10.1016/j.renene.2021.06.032
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    Cited by:

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    3. Jin, Jingliang & Wen, Qinglan & Cheng, Siqi & Qiu, Yaru & Zhang, Xianyue & Guo, Xiaojun, 2022. "Optimization of carbon emission reduction paths in the low-carbon power dispatching process," Renewable Energy, Elsevier, vol. 188(C), pages 425-436.
    4. Fang, Jianhao & Hu, Weifei & Liu, Zhenyu & Chen, Weiyi & Tan, Jianrong & Jiang, Zhiyu & Verma, Amrit Shankar, 2022. "Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    5. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.

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