Optimal load sharing strategy for a wind/diesel/battery hybrid power system based on imperialist competitive neural network algorithm

In this study, optimal load sharing strategy for a stand-alone hybrid power generation system that consists of wind turbine, diesel generator and battery banks is presented. The diesel generator is used to complement the intermittent output of the wind source whereas the battery is used to compensate for part of the temporary peak demand, which the wind and diesel generator cannot meet thus avoiding oversizing of the diesel generator. To optimise the performance of the system, imperialist competitive algorithm (ICA), ant colony optimisation (ACO) and particle swarm optimisation (PSO) are used to optimal load sharing. These algorithms are used to select the best available energy source so that the system has the best performance. To verify the system performance simulation studies have been carried out using forecasted data (load demand and wind speed). Accordingly, ICA, ACO and PSO are used to train a three-layer feed forward neural network. This trained artificial neural network is applied to short-term wind speed and load demand forecasting on a specific day in the Qazvin. The results show that the proposed control methods can reduce fuel consumption and increase the battery lifetime and battery ability to respond to real-time load turbulences simultaneously.