Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach

Jingxian Yang; Jichun Liu; Yue Xiang; Shuai Zhang; Junyong Liu

doi:10.17775/CSEEJPES.2021.07210

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Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach

Special Section Papers | 更新时间：2026-10-15

- Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach
- Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach
- 中国电机工程学会电力与能源系统学报(英文) 2023年9卷第3期页码：846-858
- 作者机构：
  
  1. College of Electrical Engineering, Sichuan University,Chengdu,China
  2. State Grid Sichuan Electric Power Company,Chengdu,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.07210
  中图分类号：
- 纸质出版：2023-05-01
- 稿件说明：
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Jingxian Yang, Jichun Liu, Yue Xiang, 等. Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(3):846-858.

Jingxian Yang, Jichun Liu, Yue Xiang, et al. Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach[J]. CSEE Journal of Power and Energy Systems, 2023, 9(3): 846-858.
Jingxian Yang, Jichun Liu, Yue Xiang, 等. Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach[J]. 中国电机工程学会电力与能源系统学报(英文), 2023,9(3):846-858. DOI： 10.17775/CSEEJPES.2021.07210.

Jingxian Yang, Jichun Liu, Yue Xiang, et al. Data-driven Optimal Dynamic Dispatch for Hydro-PV-PHS Integrated Power Systems Using Deep Reinforcement Learning Approach[J]. CSEE Journal of Power and Energy Systems, 2023, 9(3): 846-858. DOI： 10.17775/CSEEJPES.2021.07210.

摘要

Abstract

To utilize electricity in a clean and integrated manner

a zero-carbon hydro-photovoltaic (PV)-pumped hydro storage (PHS) integrated power system is studied

considering the uncertainties of PV and load demand. It is a challenge for operators to develop a dynamic dispatch mechanism for such a system

and traditional dispatch methods are difficult to adapt to random changes in the actual environment. Therefore

this study proposes a real-time dynamic dispatch strategy considering economic operation and complementary regulatory ability. First

the dynamic dispatch of a hydro-PV-PHS integrated power system is presented as a multi-objective optimization problem and the weight factor between different goals is effectively calculated using information entropy. Afterwards

the dispatch model is converted into the Markov decision process

where the dynamic dispatch decision is formulated as a reinforcement learning framework. Then

a deep deterministic policy gradient (DDPG) is deployed towards the online decision for dispatch in continuous action spaces. Finally

a case study is applied to evaluate the performance of the proposed method based on a real hydro-PV-PHS integrated power system in China. Simulations show that the system agent reduces the power volatility of supply by 26.7% after hydropower regulating and further relieves power fluctuation at the point of common coupling (PCC) to the upper-level grid by 3.28% after PHS participation. The comparison results verify the effectiveness of the proposed method.

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