电力系统保护与控制2025，Vol.53Issue(3)：95-107,13.DOI:10.19783/j.cnki.pspc.240303
基于行为克隆TD3强化学习的低碳园区柔性资源优化策略
Flexible resource optimization strategy for low-carbon parks based on behavioral cloning TD3 reinforcement learning
摘要
Abstract
Industrial parks in China are significant contributors to the country's carbon dioxide emissions.Prioritizing the achievement of carbon neutrality in parks is a crucial in helping China reach its'dual-carbon'goal.This paper presents the construction of a low-carbon park integrated energy system.The system incorporates electrolyzers and hydrogen-blended gas turbines with carbon capture technology into the energy supply side,and considers various flexible resources on the storage,supply,and consumption sides.To efficiently optimize the low-carbon economic dispatch of various flexible resources in this integrated energy system,a TD3 reinforcement learning algorithm considering behavioral cloning is proposed for offline training and online optimization.Finally,the superiority of the proposed optimization strategy is verified through simulation examples.关键词
园区综合能源系统/多类型柔性资源/强化学习/行为克隆/低碳经济调度Key words
park integrated energy system/multiple flexible resources/reinforcement learning/behavioral cloning/low-carbon economic dispatch引用本文复制引用
舒展,孙旻,吴越,万子镜,段伟男,彭春华..基于行为克隆TD3强化学习的低碳园区柔性资源优化策略[J].电力系统保护与控制,2025,53(3):95-107,13.基金项目
This work is supported by the Science and Technology Project of the Headquarters of State Grid Corporation of China(No.5400-202325227A-1-1-ZN). 国家电网公司总部科技项目资助(5400-202325227A-1-1-ZN) （No.5400-202325227A-1-1-ZN）
