电力系统保护与控制2025，Vol.53Issue(13)：93-104,12.DOI:10.19783/j.cnki.pspc.241244
融入SAC算法的光储微网混合储能自驱优级联自抗扰控制
Self-driven optimal cascade active disturbance rejection control for PV-storage microgrid with hybrid energy storage integrated with the SAC algorithm
摘要
Abstract
Bus voltage stability is a critical prerequisite for achieving high-level integration of new energy.To address the problem of bus voltage fluctuations in PV-storage DC microgrid with hybrid energy storage systems caused by source-load uncertainties,a self-driven optimal cascade active disturbance rejection control strategy integrated with the soft actor-critic(SAC)deep reinforcement learning algorithm is proposed.First,a cascaded extended state observer is designed to estimate and compensate for system uncertainties in real time,improving the accuracy of disturbance estimation.Then,a Markov decision model is established for the system,and a SAC agent,designed with a comprehensive evaluation of state rewards and information entropy,is integrated into the controller parameter optimization.By leveraging online learning and experience replay,the control parameters are autonomously and optimally tuned,further enhancing the system's disturbance rejection capability and robustness.Finally,the performance of three control strategies under typical working conditions is compared by simulation experiment,validating the effectiveness and superiority of the proposed approach.关键词
光储直流微电网/混合储能/自抗扰控制/深度强化学习/SAC算法Key words
PV-storage DC microgrid/hybrid energy storage/active disturbance rejection control/deep reinforcement learning/SAC algorithm引用本文复制引用
周雪松,张宇轩,马幼捷,王馨悦,陶珑,问虎龙..融入SAC算法的光储微网混合储能自驱优级联自抗扰控制[J].电力系统保护与控制,2025,53(13):93-104,12.基金项目
This work is supported by the Major Program of National Natural Science Foundation of China(No.U24B6011). 国家自然科学基金重大项目资助(U24B6011) （No.U24B6011）
国家自然科学基金重点项目资助(U23B20142) （U23B20142）
