气候变化领域集成服务门户(CCPortal): Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events

CCPortal

DOI	10.3233/JCM-247322
	Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events
	Chen, Hongyin; Dou, Zhenlan; Li, Jianfeng; Wang, Songcen; Zhang, Chunyan; Li, Dezhi; Liu, Yang; Pang, Jingshuai; Zhang, Baihan
发表日期	2024
ISSN	1472-7978
EISSN	1875-8983
起始页码	24
结束页码	2
卷号	24 期号:2
英文摘要	Because the global climate change intensifies as well as the natural disasters frequently occur, extreme events have caused serious impacts on the energy system in urban areas, and at the same time, they have brought great challenges to the supply and scheduling of urban energy systems. Therefore, in order to better integrate and manage various energy resources in urban areas, a Deep Q-Leaning Network-Quasi Upper Confidence Bound model is innovatively constructed using deep reinforcement learning technology to learn the state and behavior mapping relationship of energy system. Use deep learning to fit complex nonlinear models to optimize the entire energy system. Compare and verify the experiment with the real energy system. The improved Deep reinforcement learning algorithm is compared with Q-learning model, PDWoLF PHC algorithm model, Quasi Upper Confidence Bound algorithm model and deep Q-Leaning Network algorithm model. The results show that the research algorithm has the smallest instantaneous error value and absolute value of frequency deviation for area control, and the average value of the research algorithm in the absolute value of the frequency deviation is reduced by 45%-73% compared to other algorithms; over time, the unit output power of the research algorithm is able to flexibly track the stochastic square wave loads. Therefore, the proposed system strategies can provide feasible solutions to meet the challenges of extreme events and promote the sustainable development and safe operation of urban energy systems.
英文关键词	Integrated energy systems; multi-intelligence; deep reinforcement learning; sampling mechanisms; extreme events
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Multidisciplinary
WOS记录号	WOS:001228373000035
来源期刊	JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298872
作者单位	China Southern Power Grid; Nanchang University
推荐引用方式 GB/T 7714	Chen, Hongyin,Dou, Zhenlan,Li, Jianfeng,et al. Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events[J],2024,24(2).
APA	Chen, Hongyin.,Dou, Zhenlan.,Li, Jianfeng.,Wang, Songcen.,Zhang, Chunyan.,...&Zhang, Baihan.(2024).Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events.JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING,24(2).
MLA	Chen, Hongyin,et al."Multi-agent cooperative optimal scheduling strategy of integrated energy system in urban area under extreme events".JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 24.2(2024).