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Prediction and Decision Integrated Scheduling of Energy Storage System in Wind Farm Based on Deep Reinforcement Learning

1.Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, China;2.Electric Power Research Institute of State Grid Shaanxi Electric Power Corporation, Xi’an 710054, China


The optimal control of the energy storage system (ESS) in the wind farm can improve the competitiveness of the wind farm, which is a power producer in the electricity market. In this paper, a prediction and decision integrated scheduling method of ESS of the wind farm is proposed based on deep reinforcement learning, in order to make the original high-dimensional measurement data of the wind farm directly drive the ESS. Compared with the traditional scheduling model that separates prediction and decision, the prediction and decision integrated scheduling model combines the wind power prediction and ESS operation decision together, to avoid the loss of effective decision-making information in the prediction period. Stochastic laws of wind power do not need to be artificially portrayed through mathematical assumptions and modeling errors are avoided. Then, Rainbow algorithm, a deep reinforcement learning algorithm, is introduced to optimize the end-to-end control strategy between the wind farm measurement data and the ESS operation instruction, which can dynamically coordinate multi-period system profits. Finally, by analyzing case studies based on the wind farm historical data, the superiority of the proposed integrated scheduling mode and the effectiveness of deep reinforcement learning in dealing with the uncertainty problems are verified.



This work is supported by State Grid Corporation of China (No. 52060018000X).

Get Citation
[1]YU Yixiao, YANG Jiajun, YANG Ming, et al. Prediction and Decision Integrated Scheduling of Energy Storage System in Wind Farm Based on Deep Reinforcement Learning[J]. Automation of Electric Power Systems,2021,45(1):132-140. DOI:10.7500/AEPS20200226003
  • Received:February 26,2020
  • Revised:June 01,2020
  • Adopted:
  • Online: January 05,2021
  • Published: