Affiliated with RCfalse
Status已發表Published
Evolutionary computing assisted deep reinforcement learning for multi-objective integrated energy system management
Huang, Chao1,2,3; Wang, Long1,2; Luo, Xiong1,2; Zhang, Hongcai3; Song, Yonghua3
2021
Conference NameInternational Conference on Tools with Artificial Intelligence, ICTAI
Source PublicationProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2021-November
Pages506-511
Conference Date01-03 November 2021
Conference PlaceWashington
Abstract

This paper investigates the multi-objective optimal operation problem of an integrated energy system (IES) which integrates grid-connected photovoltaic (PV) generator, gas boiler, battery energy storage system, and thermal storage to satisfy energy demand in forms of electricity and heat. To handle the changes from the system uncertainty (e.g., PV generation, electrical loads, thermal loads, etc.) and unknown thermal dynamic model for temperature control, deep reinforcement learning-based model-free optimization method is proposed to solve the multi-objective optimization problem in which the multi-objective optimization problem is firstly formulated as a multi-objective Markov decision process (MDP) problem. The multi-objective MDP problem is converted to many single-objective MDP problems by the sum technique which are solved by multi-agent deep deterministic policy gradient (DDPG) algorithm. To improve the performance of multi-agent DDPG algorithm, evolutionary computing-based parameter-tuning method is further proposed to fine-tune the policy parameters in DDPG algorithm. The proposed methods are verified on real data. Experiments results illustrate that the multi-agent DDPG algorithm can efficiently solve the multi-objective optimal operation problem of the IES while the evolutionary computing-based policy parameter-tuning method can further improve the approximation of Pareto frontier.

KeywordDeep Reinforcement Learning Evolutionary Computing Integrated Energy System Multi-objective Optimization
DOI10.1109/ICTAI52525.2021.00082
URLView the original
Language英語English
Scopus ID2-s2.0-85123951064
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Affiliation1.University of Science And Technology Beijing, School of Computer And Communication Engineering, Beijing, China
2.Shunde Graduate School, University of Science And Technology Beijing, Guangdong, FoShan, China
3.University of Macau, State Key Laboratory of Internet of Things For Smart City, Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Huang, Chao,Wang, Long,Luo, Xiong,et al. Evolutionary computing assisted deep reinforcement learning for multi-objective integrated energy system management[C],2021:506-511.
APA Huang, Chao,Wang, Long,Luo, Xiong,Zhang, Hongcai,&Song, Yonghua.(2021).Evolutionary computing assisted deep reinforcement learning for multi-objective integrated energy system management.Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI,2021-November,506-511.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Huang, Chao]'s Articles
[Wang, Long]'s Articles
[Luo, Xiong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Huang, Chao]'s Articles
[Wang, Long]'s Articles
[Luo, Xiong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Huang, Chao]'s Articles
[Wang, Long]'s Articles
[Luo, Xiong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.