Dependable parallel multi-population different evolutionary particle swarm optimization for voltage and reactive power control in electric power systems

This paper presents dependable parallel multipopulation differential evolutionary particle swam optimization (DEEPSO) for voltage and reactive power control (VRPC) in electric power systems. Considering large penetration of renewable energies and deregulated environment of power systems, VRPC requires fast computation even for larger-scale problems. One solutions to increase the computation speed is to use parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for VRPC. A multi-population model is known to be one of the techniques to improve solution quality. The simulation results with IEEE 118 bus systems indicate that dependable parallel multi-population DEEPSO is superior to parallel DEEPSO using a master-slave model especially for dependability on VRPC.