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Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning

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  • Li, Jiawen
  • Yu, Tao
  • Zhang, Xiaoshun

Abstract

To dynamically balance multiple energy fluctuations in a multi-area integrated energy system (IES), a coordinated power control framework, named distributed intelligent coordinated automatic generation control (DIC-AGC), is constructed among different areas during load frequency control (LFC). Furthermore, an evolutionary imitation curriculum multi-agent deep deterministic policy gradient (EIC-MADDPG) algorithm is proposed as a novel deep reinforcement learning algorithm to realize coordinated control and improve the performance of DIC-AGC in the performance-based frequency regulation market. EIC-MADDPG, which combines imitation learning and curriculum learning, can adaptively derive the optimal coordinated control strategies for multiple areas of LFC controllers through centralized learning and decentralized implementation. The simulation of a four-area LFC-IES model on the China Southern Grid (CSG) demonstrates the effectiveness of the proposed method in maximizing control performance while minimizing regulation mileage payment in every area against stochastic load and renewable power fluctuations.

Suggested Citation

  • Li, Jiawen & Yu, Tao & Zhang, Xiaoshun, 2022. "Coordinated load frequency control of multi-area integrated energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012137
    DOI: 10.1016/j.apenergy.2021.117900
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    References listed on IDEAS

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    1. Li, Jiawen & Yu, Tao & Zhang, Xiaoshun & Li, Fusheng & Lin, Dan & Zhu, Hanxin, 2021. "Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    2. Chi Zhang, 2017. "Population in China," Europe-Asia Studies, Taylor & Francis Journals, vol. 69(8), pages 1333-1334, September.
    3. Li, Jiawen & Yu, Tao & Yang, Bo, 2021. "A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    4. Ji, Haoran & Wang, Chengshan & Li, Peng & Zhao, Jinli & Song, Guanyu & Ding, Fei & Wu, Jianzhong, 2018. "A centralized-based method to determine the local voltage control strategies of distributed generator operation in active distribution networks," Applied Energy, Elsevier, vol. 228(C), pages 2024-2036.
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