Generation scheduling of interconnected power systems using fuzzified particle swarm optimization

Abstract

The main objective of this research is to develop an improved stochastic technique namely Fuzzified Particle Swarm Optimization (FPSO) for solving various power system optimization problems. The popularity of Particle Swarm Optimization (PSO) is due to its significant property of dealing with the optimization problems without any restrictions on the structure or type of the function to be optimized and due to the ease of computation. The various problems investigated with the proposed FPSO algorithm include multi-constrained dynamic Economic Dispatch (ED), Emission Constrained Economic Dispatch (ECED), Multi-Area Economic Dispatch (MAED), Optimal Power Flow (OPF) and Security Constrained Optimal Power Flow (SCOPF) with the incorporation of Flexible AC Transmission System (FACTS) controllers. The purpose of Emission Constrained Economic Dispatch (ECED) is to obtain the optimal generation schedule by minimizing the fuel cost and emission level simultaneously, while satisfying load demand and operational constraints. The primary objective of Optimal Power Flow (OPF) is to provide the electric utility with optimal set points of operation with respect to various objectives, such as minimization of the total generation cost, minimization of the total active power losses and maximization of the degree of security. The optimal solutions of multi-constrained dynamic ED, ECED, multi-area OPF and multi-area SCOPF with multiple FACTS controller problems obtained using Evolutionary Programming (EP), Tabu Search (TS) and PSO based algorithms are compared with FPSO. The analysis reveals that the FPSO algorithm has superior (faster) convergence than EP, TS and PSO techniques for multi-constrained dynamic ED, ECED, multi-area OPF and multi-area SCOPF with multiple FACTS controller problems. Therefore the proposed algorithm is relatively simple, efficient, reliable and applicable to other power engineering optimization problems. newline