Optimal Wind Energy Integrated in Electrical Energy Generation Expansion Planning Considering Variation and Uncertainty

Wind Power generation integrated in electrical power system can cause of variation and uncertainty which must be considered in process of generation expansion planning (GEP). The goal of this study is to model the GEP problem integrated with wind power generation and introduce the fuzzy-probability model to consider variation and uncertainty of wind power generation. To verify and optimize the GEP problem, genetic algorithm, gravitational search algorithm, and improved gravitational search algorithm (IGSA) are applied for a test system which IGSA achieved the best answer. For wind power generation modeling, fuzzy-probability model is introduced and it is proposed to use k-means clustering algorithm to find the best number of clusters based on Bayesian information criterion and then fuzzy numbers are assigned for each cluster. Based on the simulation results, it is concluded that considering the variation and uncertainty of wind power generation in the GEP model will change the optimal combination of generation units corresponding to optimal total cost (TC) while with decreasing in share percentage of installed wind units, because of decreasing in wind power generation, TC value will be increased.