电测与仪表2024，Vol.61Issue(4)：9-14,6.DOI:10.19753/j.issn1001-1390.2024.04.002
基于深度强化学习的微电网在线优化
On-line optimization of micro-grid based on deep reinforcement learning
摘要
Abstract
In view of the micro-grid random optimization scheduling problem,this paper proposes an online optimization algorithm of micro-grid based on deep reinforcement learning.The deep neural network is used to approximate the state-action value function,and the action of the battery is discretized as the output of the neural network.And then,the non-linear programming is used to solve the remaining decision variables and calculate the immediate return,and obtain the optimal strategy through the Q-learning algorithm.In order to make the neural network adapt to the randomness of wind,photovoltaic and load power,according to the wind,photovoltaic and load power prediction curves and their prediction er-rors,Monte Carlo sampling is used to generate multiple sets of training curves to train the neural network.After the train-ing is completed,the weights are saved.According to the real-time input status of the micro-grid,the neural network can output the actions of the battery in real time so as to realize the online optimal dispatching of the micro-grid.Compared with day-ahead optimization results under different fluctuations of wind power,photovoltaic and load power,the effective-ness and superiority of this algorithm in online optimization of micro-grid are verified.关键词
微电网调度/Q学习/在线优化/蒙特卡洛/深度强化学习Key words
micro-grid dispatching/Q-learning/online optimization/Monte Carlo/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
余宏晖,林声宏,朱建全,陈浩悟..基于深度强化学习的微电网在线优化[J].电测与仪表,2024,61(4):9-14,6.基金项目
广东省自然科学基金资助项目(2018A0303131001) （2018A0303131001）
国家自然科学基金资助项目(51977081) （51977081）
