中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 人工智能与机器学习
Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics

文献类型：期刊论文

作者Chen, Yingfeng5; Wang, Rui2,7; Ming, Mengjun2,7; Cheng, Shi3; Bao, Yiping1; Zhang, Wensheng4; Zhang, Chi6
刊名COMPLEX & INTELLIGENT SYSTEMS
出版日期2021-04-19
页码15
关键词Constraint optimization Multi-objective optimization Hybrid renewable energy system Evolutionary algorithms
ISSN号2199-4536
DOI10.1007/s40747-021-00363-4
通讯作者Wang, Rui(ruiwangnudt@gmail.com)
英文摘要Finding the optimal size of a hybrid renewable energy system is certainly important. The problem is often modelled as an multi-objective optimization problem (MOP) in which objectives such as annualized system cost, loss of power supply probability etc. are minimized. However, the MOP model rarely takes the load characteristics into account. We argue that ignoring load characteristics may be inappropriate when designing HRES for a place with intermittent high load demand. For example, in a training base the load demand is high when there are training tasks while the demand decreases to a low level when there is no training task. This results in an interesting issue, that is, when the loss of power supply probability is determined at a specific value, say 15%, then it is very likely that most of loss of power supply would occur right in the training period which is unexpected. Therefore, this study proposes a constraint multi-objective model to deal with this issue-in addition to the general multi-objective optimization model, the loss of power supply probability over a critical period is set as a constraint. Correspondingly, the non-dominated sorting genetic algorithm II with a relaxed epsilon constraint handling strategy is proposed to address the constraint MOP. Experimental results on a real world application demonstrate that the proposed model and algorithm are both effective and efficient.
资助项目National Natural Science Foundation of China[61773390] ; National Natural Science Foundation of China[61627808] ; HunanYouth elite program[2018RS3081] ; scientific key research project of National University of Defense Technology[ZZKY-ZX-11-04] ; [193-A11-101-03-01]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000641221600001
出版者SPRINGER HEIDELBERG
资助机构National Natural Science Foundation of China ; HunanYouth elite program ; scientific key research project of National University of Defense Technology
源URL[http://ir.ia.ac.cn/handle/173211/44346]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Wang, Rui
作者单位1.Hunan Inst Traff Engn, Hengyang 421000, Peoples R China
2.Natl Univ Def Technol, Coll Syst Engn, Changsha 410073, Peoples R China
3.Shaanxi Normal Univ, Sch Comp Sci, Xian 710119, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100084, Peoples R China
5.Jiangsu Univ, Sch Management, Zhenjiang 212013, Jiangsu, Peoples R China
6.Hunan Datang Xianyi Technol Co Ltd, Changsha 430103, Peoples R China
7.Hunan Key Lab Multienergy Syst Intelligent Interc, Changsha 410073, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Yingfeng,Wang, Rui,Ming, Mengjun,et al. Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics[J]. COMPLEX & INTELLIGENT SYSTEMS,2021:15.
APA Chen, Yingfeng.,Wang, Rui.,Ming, Mengjun.,Cheng, Shi.,Bao, Yiping.,...&Zhang, Chi.(2021).Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics.COMPLEX & INTELLIGENT SYSTEMS,15.
MLA Chen, Yingfeng,et al."Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics".COMPLEX & INTELLIGENT SYSTEMS (2021):15.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。