Research On Microgrid Optimization Operation Considering Energy Storage Power Control

With the rapid development of our national economy, the demand for energy is increasing constantly. However, at the same time, the traditional fossil fuels such as coal, oil and other reserves are decreasing. So, the question of how to develop and utilize the clean and green energy efficiently and reasonably is more and more important. As an effective access of clean green energy such as solar energy and wind energy, the microgrid attracts people'attention all over the world. The optimal operation of microgrid not only can improve the utilization of resources effectively, but also can reduce the cost of distributed generation and the emission of NOx, SO2, CO2 and other pollutants, which is meaningful for system's economy, environmental protection and reliable operation. Therefore, the optimal operation of microgrid makes the great practical and theoretical significance.Nowadays, the daily ahead economic dispatch of microgrid is used mostly and widely,which is based on short-term load forecasting and output forecast, such as photovoltaic generation, wind power generation and other uncontrolled distributed power supply. However,because of the error of load forecasting and output power prediction of uncontrolled distributed power supply, there are some deviations from the actual value of the power generation plan.The hybrid energy storage microgrid is composed of photovoltaic, wind turbine, micro turbine, fuel cell,diesel generator, storage battery and super capacitor. Then this paper establishes the operational math mode, and utilize the particle swarm optimization to solve it.Aiming at the shortcoming of particle swarm optimization, the paper, based on the cloud mutation operator formed of one-dimensional normal cloud, improve the particle swarm optimization algorithm.Considering the influence of forecast error on economic dispatch nowadays, this paper puts forward the application of t location-scale distribution function, which can makes a quantitative estimation of the prediction error. In addition, the t location-scale can introduce the prediction error into the net load power and make a revise. This paper uses the hybrid storage battery and super capacitor device to balance the power shortage caused by the forecast errors. In order to avoid shorten the utility time caused by overcharging and deep discharging energy, which will make an extra cost, in this paper, the cloud model is applied to the energy management of energy storage devices. The charging and discharging power of the storage battery and super capacitor is controlled by constructing a three-dimensional cloud model controller. For comparing and studying, the paper conducts a two dimensional cloud model controller for battery energy storage at the same time. Finally, the simulation results show that the hybrid energy storage can effectively reduce the number of cycles of battery charging and discharging, and protect the life of energy storage devices.