Optimization of the Electric Power Leveling System Using a Superconducting Magnetic Energy Storage with Genetic Algorithm

Abstract

A new optimization method of the electric power leveling system using an SMES is proposed. The SMES is parallelly connected with rolling mills in steel works. The leveling control is based on fuzzy reasoning. The SMES capacity and the scaling factors of the fuzzy controller will be optimized so that the power leveling control can be achieved and then the total cost of the added SMES cost and reduced contract electricity rate becomes lower. The optimization is carried out using the genetic algorithm and the cost reduction of 7.76 billion yen can be achieved. It is confirmed by the power leveling simulation that the proposed optimization method is very effective for designing the power leveling system.