An Improved Brain Storm Optimization for a Hybrid Renewable Energy System

被引:10
作者
Chen, Xing-Rui [1 ]
Li, Jun-Qing [1 ,2 ,3 ]
Han, Yuyan [2 ]
Niu, Ben [1 ]
Liu, Lili [2 ]
Zhang, Biao [2 ]
机构
[1] Shandong Normal Univ, Sch Informat Sci & Engn, Jinan 250014, Shandong, Peoples R China
[2] Liaocheng Univ, Sch Comp, Liaocheng 252059, Shandong, Peoples R China
[3] Southeast Univ, Key Lab Comp Network & Informat Integrat, Nanjing 211189, Jiangsu, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
基金
美国国家科学基金会;
关键词
Brain storm optimization; city block distance; K-Means method; hybrid renewable energy system; ARTIFICIAL BEE COLONY; MIGRATING BIRDS OPTIMIZATION; VEHICLE-ROUTING PROBLEM; MULTIOBJECTIVE OPTIMIZATION; CONTROL STRATEGIES; POWER-GENERATION; OPTIMAL-DESIGN; ALGORITHM; SOLAR; MANAGEMENT;
D O I
10.1109/ACCESS.2019.2908227
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, an improved brain storm optimization (BSO) algorithm is proposed to solve the optimization problem in a hybrid renewable energy system. The objective of the proposed algorithm is the minimization of the annualized costs of the system (ACS), the loss of power supply probability (LPSP), and the total fuel emissions. In the proposed algorithm, first, the K-Means clustering method is embedded to make the same clusters have similar solutions. Then, the distance of a city block is taken as the distance measure, which makes the solution feasible. Then, to measure the merits and demerits of each individual, the composite index is utilized as the fitness value. In addition, to improve the efficiency of the algorithm, a pair of crossover and mutation strategies are designed in detail. Finally, a set of realistic instances are used to test the performance of the proposed algorithm, and after detailed experimental comparisons, the competitive performance of the proposed algorithm is verified.
引用
收藏
页码:46513 / 46526
页数:14
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