IMPACT OF WIND FARM INTEGRATION ON ELECTRICITY GENERATION COST USING ANN AND PSO

Abstract

This paper presents an efficient approach using particle swarm optimization to solve optimal power flow problem including wind power generation. In this study, opportunistic costs of wind power surplus and shortage are taken into consideration. Artificial neural network is used to predict the wind speed where the data to train ANN were collected from Radhapuram, southern part of TamilNadu in India. The wind power cost is estimated for the predicted power of wind farm. The proposed method is tested on a modified IEEE 30 bus system by forming a virtual wind farm and integrating it to different load buses and the suitable bus for the integration of wind farm is chosen based on minimum generation cost using PSO. The simulation results demonstrate the influence of wind power variation in the overall generation cost of the system when it is integrated to a power system.