Application and comparison of metaheuristic techniques to generation expansion planning in the partially deregulated environment

Abstract

The deregulation of the electric utility industry is an on-going process in many developing countries. A novel generation expansion planning (GEP) model is proposed that is suitable for developing countries such as India in a partially deregulated environment. In a partially deregulated/restructured environment, both utilities and independent power producers (IPPs) participate in the generation market. In this model, the utility purchases electric power from the IPPs and sells it to the consumer. The utility maximises its profit and ensures profits for all the participating IPPs. In addition, the utility checks under/over investment and considers system security, national security (fuel-mix ratio), social welfare and reliability simultaneously. The budget constraints of the utility are to be taken into consideration during the expansion plan. Metaheuristic techniques, such as genetic algorithms, differential evolution, evolutionary programming, evolutionary strategy, particle swarm optimisation, tabu search, simulated annealing, and the hybrid approach are used to solve the restructured GEP problem, and their performances are evaluated and validated against the dynamic programming (DP) method for a synthetic test system having five types of candidate plant for the utility and three types of candidate plant for IPP, with a 6 year planning horizon. The effectiveness of the proposed modifications and techniques is also addressed.