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Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system

Author

Listed:
  • Li, Jiawen
  • Yu, Tao
  • Zhang, Xiaoshun
  • Li, Fusheng
  • Lin, Dan
  • Zhu, Hanxin

Abstract

To balance the stochastic power disturbance in integrated energy system (IES), a novel automatic generation control (AGC) dispatch is proposed by taking account of the regulation rule that applies to a performance-based frequency regulation market, with the aim to reduce area control deviation and regulation mileage payment while complying with constraints of various regulation units. Thus, a multiple experience pool replay twin delayed deep deterministic policy gradient (MEPR-TD3) is put forward to improve the training efficiency and the action quality via four improvements including the multiple experience pool probability replay strategy. Finally, the performance of the proposed algorithm is verified on an extended two-area load frequency control (LFC) model and Hainan province IES with different demand of multiple energy.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261920317608
    DOI: 10.1016/j.apenergy.2020.116386
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    References listed on IDEAS

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