Constraint multi-objective optimal design of hybrid renewable energy system considering load characteristics

作     者：Chen, Yingfeng Wang, Rui Ming, Mengjun Cheng, Shi Bao, Yiping Zhang, Wensheng Zhang, Chi 

作者机构：Jiangsu Univ Sch Management Zhenjiang 212013 Jiangsu Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Hunan Inst Traff Engn Hengyang 421000 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Chinese Acad Sci Inst Automat Beijing 100084 Peoples R China Hunan Datang Xianyi Technol Co Ltd Changsha 430103 Peoples R China Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China 

出 版 物：《COMPLEX & INTELLIGENT SYSTEMS》 (Complex Intell. Syst.)

年 卷 期：2022年第8卷第2期

页      面：803-817页

核心收录：

基　　金：National Natural Science Foundation of China [61773390, 61627808] HunanYouth elite program [2018RS3081] scientific key research project of National University of Defense Technology [ZZKY-ZX-11-04] 193-A11-101-03-01 

主　　题：Constraint optimization Multi-objective optimization Hybrid renewable energy system Evolutionary algorithms 

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.