Intelligent Energy Management In A Micro-gird With Storage System

Photovoltaics,wind turbines and other renewable energy source coupled with energy storage system to supply power to the local consumers in the micro-grid is a method to solve the energy crisis and the environment pollution problem.However,the wind and the solar energy cannot be utilized completely by the local consumers due to their intrinsic intermittence.Therefore,the energy management becomes the key research to achieve the effective utilization of the energy sources locally,avoiding the excess power flowing into the main grid and shaving the peak load demand.Presently,most of the existing energy managements depend on the precise predicted data or need short-term predictive correction to reduce the reliance on predicted data.But the acquisition of the precise data and the calculation of the predictive correction are limited by the prediction technology and the computing ability of the software.In addition,the increasing electric vehicle charging load with high randomness of charging characteristic,large instantaneous charging power and difficult to be accurately predicted may weaken the control effect of the energy management relying on the precise data.Focusing on above problem,this paper put forwards a cooperation energy management of the combined cooling heating and power(CCHP)of the micro-turbine and the energy storage system in a community micro-grid with the photovoltaic,the wind turbine and the micro-turbine as the distributed generations.This energy management can achieve the locally effective utilization of the energy sources and classified reduction of the cooling/thermal load demand and the electric load demand.Also,it can improve the condition of the reversed distribution of the energy supply and load demand.In addition,this paper proposes a predicted data real-time modification algorithm considering the increasing electric vehicle charging load.This algorithm does not rely on the precise data while it uses the predicted data.It does not need the repeated predictive correction calculation as well.It directly corrects the dispatch power of the storage system in real-time to achieve the maximum reduction of the large unpredicted fluctuant load(e.g.the electric vehicle charging power).Tested by the on-site measured data and simulated data,the proposed energy management strategy in this paper for the combined operation of the CCHP and the energy storage system is programmed.Compared with the fixed threshold algorithm and the adaptive intelligence technique algorithm,the proposed energy management method in this paper can achieve the locally effective utilization of the energy sources and the maximum impact load reduction on the basis of the optimal operation economy.