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An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

By T. Nikman, M. Mayeripour, J. Olamaei and A. Arefi

Abstract

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder

Topics: Ant colony optimization (ACO), Distributed generators, Particle Swarm optimization (PSO), Voltage and reactive power control
Publisher: Praise Worthy Prize
Year: 2008
OAI identifier: oai:eprints.qut.edu.au:68735
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