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To meet the growing load demand, power industries throughout the world
are undergoing a restructuring process. The Independent Power Producers (IPP)
must respond quickly to those load changes with respect to time. This paper describes
the solution for Optimal Power Flow (OPF) in a deregulated environment using
Differential Evolution (DE) technique. The proposed approach is capable of obtaining
minimum solution irrespective of the nature of the objective function. The effectiveness of
the approach is compared with the performance of Particle Swarm Optimization
(PSO) technique. The algorithm has been demonstrated on IEEE-14, 30 systems.
A decade earlier, the power system structure was almost a vertical one. A single
utility controlled the power generation, transmission and distribution in an area. The rate
of electricity was regulated and the market was a monopoly. Today, most of the
power transfer in the electric power industry is carried out through the wheeling
transactions. Retail wheeling will create an open market to encourage vigorous and fair competition
in electric supply. Retail wheeling allows customers to choose power supplies. The
utilities have to provide better services and cheaper power to attract customers. Thus
wheeling is a hybrid concept resulting from integrating two inherently different economic
concepts: an ideal world of regulated utilities and an ideal deregulated competitive market
place (Yog et al., 2002). The Optimal Power Flow (OPF) is the tool used to minimize
the objective function of the IPP subjected to the power balance and inequality
constraints, imposed on it. OPF has been used widely for system planning and operation,
energy management, etc. Use of the OPF is becoming more important in the deregulated
power industry to deploy the resources optimally.
The objective functions of the OPF problem are generally non-linear and
non-convex in nature (Ongsakul and Tantimapron, 2006). The traditional optimization
approaches, such as non-linear programming, quadratic programming, linear programming, mixed
integer programming and interior point method, are used to solve the OPF problem. The
literature on those approaches was reviewed by Mamoh et al. (1999a and 199b). But the convergence of those methods depends upon the nature of the objective function.
To overcome these difficulties, many heuristic search algorithms such as
Evolutionary Programming (EP) (Yuryevich and Wong, 1999; and Venkatesh et al., 2003), Genetic Algorithms (GA) (Bakirtzis et al., 2002), Tabu Search (TS) (Abido, 2002a;
and Kulworawanichpong and Sujitorn, 2002), and Simulated Annealing (SA)
(Roa-Sepulveda and Power-Lazo, 2003), have been proposed by many researchers to solve the
OPF problem. An approach to solve the optimal power dispatch problem with
bilateral and multilateral transactions has been proposed (Yog et al., 2001; and
Gnanadass, 2005). |
