Research On Energy Management And Optimal Scheduling Strategy Of New Energy Vehicle Charging Station

As an important infrastructure to promote the development of new energy vehicles,new energy vehicle charging stations provide convenient and safe charging services.At present,there are many problems in charging stations of electric vehicles which are put into market and widely used: long charging time,harmful influence and heavy stress on power grid and the demand of fuel cell vehicles,especially hydrogen-fueled vehicles are not considered.Systematic modeling and collaborative optimal dispatching of new energy vehicle charging stations with wind,solar,hydrogen and storage can satisfy the charging demand of hydrogenfueled vehicles,promote the development of hydrogen-fueled vehicles,achieve non-pollution and high efficiency of charging,absorb renewable energy through hydrogen production and storage module,reduce the waste of wind and solar.The scheme can alleviate the burden of the power grid,reduce the operating cost of charging station,promote the safe,stable and economic operation of power grid and charging station.Therefore,the research on energy management and optimal dispatching scheme of new energy vehicle charging station has a foresighted significance.Firstly,the concept of new energy vehicle charging station with wind,solar,hydrogen and energy storage is put forward,and its overall structure and working principle are analyzed.Mathematical models of each unit in charging station are established,including electric vehicle charging system,hydrogen-fueled vehicle hydrogen charging and storage system,wind power generation system,photo voltaic power generation system and energy storage system.Secondly,considering the randomness and fluctuation of wind power and solar power,a short-term power forecasting method based on cloud model is proposed.The paper analyses the influencing factors of photo voltaic power generation and wind power generation,uses cloud peak transformation to transform the influencing factors into qualitative concepts in the universe,combines the qualitative concepts through cloud merging theory,and builds membership function to divide the data,searches the association relationship between various concepts combination through data mining and uses cloud rule generator to predict.Prediction is carried out to obtain uncertainty prediction result set.The case study shows that short-term power forecasting method based on cloud model can realize the uncertain forecasting of wind and solar output and provide accurate decision information for dispatching plan.Thirdly,the driving laws and charging characteristics of electric vehicles and hydrogenfueled vehicles are analyzed.Based on Monte Carlo simulation,the charging decision-variables of electric vehicles and hydrogen-fueled vehicles are generated.The relationship between hydrogen and electric energy conversion is excavated and analyzed,and the optimal charging strategy of new energy vehicles is formulated.The objective function is to minimize the comprehensive operating cost and the circulating electricity.The multi-objective optimization is carried out through NSGA-? algorithm.Scheduling method is used to solve the problem.The case study shows that the optimal dispatching model has obvious advantages over the conventional operation mode,which reduces the comprehensive operation cost and circulating electricity of the charging station in theory.Finally,based on the multi-objective optimal dispatching method,the uncertainty of windsolar power output and new energy vehicles is further analyzed,the concept of extreme scenarios is proposed and the extreme scenarios under uncertain conditions are constructed.Then the NSGA-? algorithm is used for robust optimal dispatching of new energy vehicle charging stations.Through case comparison and analysis,the uncertainties of wind power output,photo voltaic output and vehicle disturbance are given.The corresponding measures to deal with uncertainties in the charging station are pointed out,which can provide a reference for the capacity allocation and equipment selection of components in the new energy vehicle charging station.