水力发电学报2024，Vol.43Issue(8)：76-88,13.DOI:10.11660/slfdxb.20240808
基于深度强化学习的三峡电站机组负荷分配实时决策方法
Real-time decision-making method for unit commitment of Three Gorges hydropower station based on deep reinforcement learning
摘要
Abstract
This paper focuses on the key issue of the Three Gorges hydropower station's in-plant economic operation,which is aimed at achieving a real-time load allocation of large-scale units for minimizing water consumption.Dynamic programming usually encounters the curse of dimensionality when dealing with a large-scale hydropower unit cluster,and therefore,it cannot meet the requirement of real-time dispatching decision for the station.For training a multi-period unit load distribution model and its decision-making,we develop a deep reinforcement learning-based framework to train the deep neural network and generates unit load distribution plans through a pre-trained network model.We apply a group theory idea to processing the state and action features of the learning,so as to compress the state and action space significantly and improve model training efficiency.The results indicate that compared to dynamic programming,our new method shortens the decision-making time by two orders of magnitude at a cost of less than 1%benefit loss.Thus,it offers a rapid and efficient solution for the unit load allocations in large-scale hydropower stations.关键词
厂内经济运行/机组负荷分配/深度强化学习/实时决策Key words
economic operation/unit commitment/deep reinforcement learning/real-time decision分类
水利科学引用本文复制引用
徐弘玮,徐刚,吴碧琼,任玉峰..基于深度强化学习的三峡电站机组负荷分配实时决策方法[J].水力发电学报,2024,43(8):76-88,13.基金项目
国家自然科学基金重大研究计划项目(91647207) （91647207）
湖北省自然科学基金创新群体项目(2019CFA032) （2019CFA032）
