综合智慧能源2025，Vol.47Issue(1)：1-9,9.DOI:10.3969/j.issn.2097-0706.2025.01.001
基于可解释强化学习的智能虚拟电厂最优调度
Optimal scheduling of intelligent virtual power plants based on explainable reinforcement learning
摘要
Abstract
With the increasing popularity of electric vehicles(EVs),energy systems are becoming more complex.Virtual power plants(VPPs)can aggregate and optimize distributed energy resources such as distributed generation,energy storage systems,controllable loads,and EVs through internet of things(IoT)and artificial intelligence(AI)technologies,enhancing energy efficiency and facilitating the consumption of non-renewable energy while reinforcing grid stability.However,current AI technologies lack reliability and transparency in high-safety applications like power systems,potentially making it challenging for users and operators to understand how algorithms make specific energy allocation decisions.To address the balance between achieving optimal scheduling of VPPs utilizing AI and explaining the decision-making processes,this study proposed an interactive framework based on explainable reinforcement learning.This framework employed the proximal policy optimization(PPO)algorithm for optimal scheduling of VPPs and constructed an explainable reinforcement learning framework using decision trees to provide transparent decision support that enabled non-expert users to understand AI's decision-making processes in regulating energy systems.The results indicated that compared to traditional reinforcement learning optimization methods,this approach not only improved energy allocation efficiency but also strengthened user trust in intelligent VPP management systems by enhancing model interpretability.关键词
虚拟电厂/电动汽车/近端策略优化算法/强化学习/决策树/可解释性框架/分布式电源/人工智能Key words
virtual power plant/electric vehicle/proximal policy optimization algorithm/reinforcement learning/decision tree/explainable framework/distributed energy/artificial intelligence分类
能源科技引用本文复制引用
袁孝科,沈石兰,张茂松,石晨旭,杨凌霄..基于可解释强化学习的智能虚拟电厂最优调度[J].综合智慧能源,2025,47(1):1-9,9.基金项目
国家自然科学基金项目(62303006)National Natural Science Foundation of China(62303006) （62303006）
