Optimization of PV Battery Systems Using Genetic Algorithms

被引：40

作者：

Magnor, Dirk ^{[1
,2
,3
]}

Sauer, Dirk Uwe ^{[1
,2
,3
]}

机构：

[1] Rhein Westfal TH Aachen, Inst Power Elect & Elect Drives, Electrochem Energy Convers & Storage Syst Grp, Aachen, Germany

[2] JARA Energy, Julich Aachen Res Alliance, Julich, Germany

[3] Rhein Westfal TH Aachen, E ON ERC, Inst Power Generat & Storage Syst, Aachen, Germany

来源：

10TH INTERNATIONAL RENEWABLE ENERGY STORAGE CONFERENCE, IRES 2016 | 2016年 / 99卷

关键词：

PV; battery system; grid-connected; optimization; genetic algorithm; levelized cost of electricity;

D O I：

10.1016/j.egypro.2016.10.123

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

A modular simulation model of a PV battery system has been developed and integrated into a genetic algorithm framework in order to evaluate optimal sizing of such systems under various boundary conditions. The presented paper describes the simulation assumptions and presents optimization results for a PV battery system having a DC topology, comparing current economic scenarios with and without KfW funding. Sensitivity analyses provide information on critical boundaries to reach economic operation. The fitness of a system is evaluated based on the levelized cost of electricity (LCOE) defining the average cost - including all investment and operation cost over the system lifetime - per kWh supplied to the load. (C) 2016 Published by Elsevier Ltd.

引用

页码：332 / 340

页数：9

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