储能科学与技术2025，Vol.14Issue(11)：4289-4299,11.DOI:10.19799/j.cnki.2095-4239.2025.0473
基于改进型双重深度确定性策略梯度与自适应分布式模型预测控制融合的电网侧储能系统协同优化方法
A grid-side energy storage system optimization method based on improved twin deep deterministic policy gradient and adaptive distributed model predictive control
摘要
Abstract
To address the uncertainties and complexities brought to the grid-side by the grid connection of renewable energy,to solve problems such as capacity allocation,operation cost,and energy accommodation of the grid-side energy storage system,and to improve grid stability and energy accommodation efficiency.Through the integration of the enhanced Deep Deterministic Policy Gradient(DDPG)algorithm and the Adaptive Distributed Model Predictive Control(DMPC)approach,an adaptive collaborative optimization strategy for the grid-side energy storage system is proposed.The enhanced DDPG incorporates a preference experience replay and a noise adjustment mechanism,thereby enhancing learning efficiency and exploration ability.The adaptive DMPC performs parallel computing and local optimization by decomposing large-scale problems.Compared with the traditional DDPG algorithm,this strategy has been shown to have remarkable effects in optimizing the capacity allocation of grid-side energy storage,reducing the system operation cost,and improving the renewable energy consumption rate.This strategy provides an innovative solution for the optimal allocation of the grid-side renewable energy storage system and is of great significance for ensuring the stable operation of the power grid.关键词
储能系统/协同优化/可再生能源集成/深度强化学习/模型预测控制/自适应控制Key words
energy storage system/collaborative optimization/renewable energy integration/deep reinforcement learning/model predictive control/adaptive control分类
信息技术与安全科学引用本文复制引用
谭金龙,陈军,赵启,崔大林,刘永强,张路..基于改进型双重深度确定性策略梯度与自适应分布式模型预测控制融合的电网侧储能系统协同优化方法[J].储能科学与技术,2025,14(11):4289-4299,11.基金项目
国网新疆电力有限公司科技项目(5230DK230013). （5230DK230013）
