Optimization of the Electric Power Leveling System Using Distributed Genetic Algorithms

Abstract

Electric power demand has an increasing tendency year by year. The load power fluctuation becomes larger with the incleasing demand，thus electric power quality is getting worse. To improve the electric power quality，electric power leveling systems (EPLS's) using energy storage technology are desired to be developed. This paper proposes a new optimization method of the EPLS with superconducting magnetic energy storages (SMES's) using distributed genetic algorithms. The evaluation function in the optimization is composed of the initial costs of EPLS and the reduction cost of the electricity rate. The efficiency of the power converter in the ELPS and the effect of the reactive power compensation on the electricity rate are also taken into account. From the optimization results，installing the ELPS can achieve the cost reduction by sixteen billion yen in the SMES life. The effective optimization can be realized compared with the simple genetic algorithms. Thus，the proposed method is clarified to be valid for the optimization of the EPLS.