DocumentCode :
2755350
Title :
PSO-Based Multi-Criteria Optimum Design of A Grid-Connected Hybrid Power System With Multiple Renewable Sources of Energy
Author :
Wang, Lingfeng ; Singh, Chanan
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX
fYear :
2007
fDate :
1-5 April 2007
Firstpage :
250
Lastpage :
257
Abstract :
With the stricter environmental regulation and diminishing fossil-fuel reserve, various renewable sources of energy are being exploited. These alternative sources of energy are usually environmentally friendly and emit no pollutants. However, the capital investments for those renewable sources of energy are normally high and there are also maintenance cost differences to be considered. Furthermore, due to the variability of these power sources, reliability issues should be addressed when integrating different power sources. In this paper, a grid-connected hybrid generating system comprising wind turbine generators, photovoltaic panels, and storage batteries is designed. In this multi-source generation system design, three design objectives are considered, that is, costs, reliability, and pollutant emissions. Considering the complexity of this problem, we have developed a multi-objective particle swarm optimization (MOPSO) algorithm to derive a set of non-dominated solutions, each of which represents a candidate system design. A numerical example is discussed to illustrate the design procedure and the simulation results are analyzed
Keywords :
design; hybrid power systems; particle swarm optimisation; photovoltaic power systems; power generation reliability; secondary cells; wind turbines; grid-connected hybrid power system; multicriteria optimum design; multiobjective particle swarm optimization; multisource generation system design; photovoltaic panels; renewable energy; storage batteries; wind turbine generators; Costs; Hybrid power systems; Investments; Maintenance; Mesh generation; Photovoltaic systems; Pollution; Power system reliability; Wind energy generation; Wind turbines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Swarm Intelligence Symposium, 2007. SIS 2007. IEEE
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0708-7
Type :
conf
DOI :
10.1109/SIS.2007.367945
Filename :
4223182
Link To Document :
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