Expert energy management of a micro-grid considering wind energy uncertainty

Recently, the use of wind generation has rapidly increased in micro-grids. Due to the fluctuation of wind power, it is difficult to schedule wind turbines (WTs) with other distributed energy resources (DERs). In this paper, we propose an expert energy management system (EEMS) for optimal operation of WTs and other DERs in an interconnected micro-grid. The main purpose of the proposed EEMS is to find the optimal set points of DERs and storage devices, in such a way that the total operation cost and the net emission are simultaneously minimized. The EEMS consists of wind power forecasting module, smart energy storage system (ESS) module and optimization module. For optimal scheduling of WTs, the power forecasting module determines the possible available capacity of wind generation in the micro-grid. To do this, first, an artificial neural network (ANN) is used to forecast wind speed. Then, the obtaining results are used considering forecasting uncertainty by the probabilistic concept of confidence interval. To reduce the fluctuations of wind power generation and improve the micro-grid performances, a smart energy storage system (ESS) module is used. For optimal management of the ESS, the comprehensive mathematical model with practical constraints is extracted. Finally, an efficient modified Bacterial Foraging Optimization (MBFO) module is proposed to solve the multi-objective problem. An interactive fuzzy satisfying method is also used to simulate the trade-off between the conflicting objectives (cost and emission). To evaluate the proposed algorithm, the EEMS is applied to a typical micro-grid which consists of various DERs, smart ESS and electrical loads. The results show that the EEMS can effectively coordinate the power generation of DERs and ESS with respect to economic and environmental considerations.