储能科学与技术2025，Vol.14Issue(6)：2439-2441,3.DOI:10.19799/j.cnki.2095-4239.2025.0504
基于深度强化学习的储能系统能量管理与优化调度策略
Energy management and optimal scheduling strategies for energy storage systems based on deep reinforcement learning
摘要
Abstract
With the proposal of the"3060 Plan"and the introduction of a series of reform schemes for the power system,the development of new energy grid connection technology has been vigorously promoted.However,due to the randomness of photovoltaic power generation,accurately predicting photovoltaic power generation has become quite challenging.The large-scale connection of photovoltaic power plants to the power system poses severe challenges to the power system's power flow distribution and scheduling operations.This paper proposes an optimization scheduling method based on deep reinforcement learning,emphasizing its intelligence,self-regulation,and dynamic adjustment characteristics.It also attempts to explore multi-objective optimization and multi-level scheduling strategies,providing theoretical support and guidance for the efficient and sustainable development of energy storage systems.关键词
深度强化学习/储能系统/能量管理/优化调度/多目标优化Key words
deep reinforcement learning/energy storage system/energy management/optimization scheduling/multi-objective optimization分类
能源科技引用本文复制引用
陈勋..基于深度强化学习的储能系统能量管理与优化调度策略[J].储能科学与技术,2025,14(6):2439-2441,3.基金项目
江西省教育厅科学技术研究项目(GJJ2206007). （GJJ2206007）
