摘要
为应对可再生能源并网给电网侧带来的不确定性与复杂性,解决电网侧储能系统在容量分配、运行成本及能源消纳等方面的问题,提升电网稳定性与能源消纳效率,本文融合改进的深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法与自适应分布式模型预测控制(distributed model predictive control,DMPC)方法,提出电网侧储能系统自适应协同优化策略。改进DDPG引入偏好体验回放和噪声调整机制以增强学习效率与探索能力,自适应DMPC通过分解大规模问题实现并行计算与局部优化。测试表明,相较于传统DDPG算法,该策略在电网侧储能容量分配优化、降低系统运行成本及提高可再生能源消纳率等方面效果显著。该策略为电网侧可再生能源储能系统的优化分配提供了创新解决方案,对保障电网稳定运行具有重要意义。
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.
作者
谭金龙
陈军
赵启
崔大林
刘永强
张路
TAN Jinlong;CHEN Jun;ZHAO Qi;CUI Dalin;LIU Yongqiang;ZHANG Lu(State Key Laboratory of Power Transmission and Distribution Equipment and System Safety and New Technology,Chongqing University,Chongqing 400044,China;Electric Power Research Institute,State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830011,Xinjiang,China;State Grid Xinjiang Electric Power Co.,Ltd.,Urumqi 830002,Xinjiang,China)
出处
《储能科学与技术》
北大核心
2025年第11期4289-4299,共11页
Energy Storage Science and Technology
基金
国网新疆电力有限公司科技项目(5230DK230013)。
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
energy storage system
collaborative optimization
renewable energy integration
deep reinforcement learning
model predictive control
adaptive control
