Generation Expansion Planning Optimized by Genetic Algorithm Considering Seasonal Impact and Fuel Price

Abstract

Generation Expansion Planning (GEP) is determining the type, location and number of new generating stations (GSs). In this paper, a GEP problem is formed by considering three types of GSs and then their possible combinations are sorted. Infeasible combinations are screened out based on the capacity limit and maximum allowable budget. The best solution with minimum cost is recognized by optimizing the feasible combinations using Genetic Algorithm (GA). Share of fuel mix (gas and oil) for winter and other seasons are considered as the constraints. In simulation, 14 out of 75 combinations came out feasible. GA was used to find the best combination which had an optimized amount of gas and oil usage. The results display the superiority of proposed methodology in contrast with other studies in finding the best solution of the GEP problem with minimum iteration.