Dependable Parallel Multi-Swarm C-DEEPSO with Migration for Voltage and Reactive Power Control

摘要

This paper proposes dependable parallel multi-swarm canonical differential evolutionary particle swarm optimization with migration (DPMS-CDEEPSOw/M) for voltage and reactive power control (Volt/Var Control: VVC). The proposed DPMS-CDEEPSOw/M is a general evolutionary computation technique for dependable and fast optimization applications. So far, applications of evolutionary computation methods such as Genetic Algorithm (GA), advanced PSOs, and Differential Evolution (DE) have been studied to VVC because VVC is a mixed integer nonlinear optimization programming (MINLP) problem. Considering recent progress of deregulated environment and large renewable energy penetration in power systems, Fast VVC is strongly needed. Utilization of parallel and distributed computing may solve the challenge. However, since power system is a social infrastructure, both fast computation and sustainable (dependable) control are eagerly awaited for VVC. A multi-swarm evolutionary computation technique is verified to improve quality of solution. Therefore, its application to VVC has a possibility to increase dependability. The simulation results indicate that the proposed DPMS-CDEEPSOw/M based method can speed up computation and improve dependability by comparison with the conventional dependable parallel C-DEEPSO based method.