电力建设2024，Vol.45Issue(5)：59-70,12.DOI:10.12204/j.issn.1000-7229.2024.05.007
多智能体深度强化学习驱动的跨园区能源交互优化调度
Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling
摘要
Abstract
In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions,and achieve overall optimization and scheduling of the comprehensive energy system,this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge.In this model,the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm,which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems.The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them.This leads to a reduction in wind curtailment rate from 16.3%to 0%and lowers the overall operating cost by 5445.6 Yuan,demonstrating significant economic and environmental benefits.关键词
多智能体深度强化学习/综合能源系统/优化调度/可再生能源消纳/负荷特征学习/多园区能量交互Key words
multi-agent deep reinforcement learning/integrated energy system/optimal scheduling/renewable energy consumption/load characteristic learning/energy interaction among communities分类
信息技术与安全科学引用本文复制引用
李扬,马文捷,卜凡金,杨震,王彬,韩猛..多智能体深度强化学习驱动的跨园区能源交互优化调度[J].电力建设,2024,45(5):59-70,12.基金项目
This work is supported by Natural Science Foundation of Jilin Province(No.YDZJ202101ZYTS149). 吉林省自然科学基金项目(YDZJ202101ZYTS149) （No.YDZJ202101ZYTS149）
