全球能源互联网（英文）2024，Vol.7Issue(2)：117-129,13.DOI:10.1016/j.gloei.2024.04.001
集成GRU的约束柔性动作-评价器住宅虚拟电厂全分布式调度策略
GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant
摘要
Abstract
In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.关键词
住宅虚拟电厂/住宅分布式资源/约束柔性动作-评价器/完全分布式调度策略Key words
Residential virtual power plant/Residential distributed energy resource/Constrained soft actor-critic/Fully distributed scheduling strategy引用本文复制引用
邓孝云,陈永东,范东川,刘友波,马超..集成GRU的约束柔性动作-评价器住宅虚拟电厂全分布式调度策略[J].全球能源互联网（英文）,2024,7(2):117-129,13.基金项目
This study was supported by the Sichuan Science and Technology Program(grant number 2022YFG0123). （grant number 2022YFG0123）
