Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices

Di Cao, Weihao Hu, Xiao Xu, Qiuwei Wu, Qi Huang, Zhe Chen, Frede Blaabjerg

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Abstract

This study proposes a deep reinforcement learning (DRL) based approach to analyze the optimal power flow (OPF) of distribution networks (DNs) embedded with renewable energy and storage devices. First, the OPF of the DN is formulated as a stochastic nonlinear programming problem. Then, the multi-period nonlinear programming decision problem is formulated as a Markov decision process (MDP), which is composed of multiple single-time-step sub-problems. Subsequently, the state-of-the-art DRL algorithm, i.e., proximal policy optimization (PPO), is used to solve the MDP sequentially considering the impact on the future. Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN. The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results. The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones. Comparative results demonstrate the effectiveness of the proposed approach.
Original languageEnglish
JournalJournal of Modern Power Systems and Clean Energy
Volume9
Issue number5
Pages (from-to)1101-1110
ISSN2196-5625
DOIs
Publication statusPublished - 2021

Keywords

  • Deep reinforcement learning (DRL)
  • Distribution network
  • Optimal power flow (OPF)
  • Wind turbine

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