Dependable Parallel Canonical Differential Evolutionary Particle Swarm Optimization for Voltage and Reactive Power Control

This paper proposes dependable parallel canonical differential evolutionary particle swarm optimization (C-DEEPSO) for voltage and reactive power control (Volt/Var Control: VVC). Since the problem can be formulated as a mixed integer nonlinear optimization problem (MINLP), various evolutionary computation techniques have been applied to the problem including PSO, differential evolution (DE), and DEEPSO. Considering large penetration of renewable energies and deregulated environments of power systems, VVC requires fast computation for larger-scale VVC problems. One of the solutions to speed-up the computation is to utilize 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 VVC. The simulation results with with IEEE 14, 30, 57, and 118 bus systems indicate that parallel C-DEEPSO is superior to the conventional parallel DEEPSO from the dependability point of view. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.