Research On Collaborative Optimization Scheduling Strategy Of PV-EV In Urban Power Grid

In the context of low carbonization of power industry,photovoltaic(PV),as a representative of new energy,has received more and more attention.On the other hand,electric vehicle(EV)has attracted extensive attention because of their advantages of energy conservation,environmental protection,green and low carbon.However,due to the intermittence and fluctuation of PV and the strong randomness of EV charging load in charging time and location,the connection of large-scale PV and EV charging load will increase the peak-valley difference and network loss of the power system.Therefore,the schedulability of EV should be flexibly utilized to study the effective optimal scheduling strategy for PV-EV to participate in urban power grid cooperatively,so as to ensure the stable,economic and lowcarbon operation of the system.The thesis mainly includes the following research contents:1)An EV charging load forecasting model is established considering meteorological factors.Firstly,the relationship between meteorological factors and EV power consumption is measured from two aspects of on-board air conditioning power consumption and on-board battery capacity,and the influence of meteorological factors on EV charging demand is analyzed.Secondly,according to the characteristics of EV charging behavior in each functional area,the spatio-temporal prediction model framework of EV charging load under suitable meteorological conditions is established.Then,based on this framework,combined with the correlation model between meteorological factors and EV charging demand,the EV charging load prediction model in different regions is established considering meteorological factors.Finally,the simulation verifies the effectiveness of the model for predicting EV charging loads in different regions under different meteorological conditions,and EV charging load prediction data can be provided for the subsequent formulation of collaborative optimization scheduling strategy of PV-EV in urban power grid.2)A collaborative optimization scheduling strategy of PV-EV taking into account meteorological factors is presented.Firstly,based on the forecast results of EV charging load and PV output considering meteorological factors,the optimization objectives of minimum system operating cost,carbon transaction cost,mean square error of system load and charging cost of EV owners are established.Secondly,the optimal charging strategy of EV is formulated,and PV output is tracked by optimizing EV charging load distribution to carry out collaborative optimization scheduling of PV-EV.Particle swarm optimization is used to solve the model.Finally,the simulation verifies that the proposed scheduling strategy can effectively absorb PV,realize peak cutting and valley filling,reduce system operating cost,carbon transaction cost and EV owners charging cost,and provide a model basis for the subsequent formulation of the coordinated hierarchical partition optimization scheduling strategy of PV-EV in urban power grid.3)An optimization scheduling strategy of PV-EV cooperative hierarchical partition in urban power grid is presented,and the studied power system is divided into system layer and regional layer.Firstly,in the optimal scheduling model of the system layer,the scheduling center of the system layer formulates the optimal operation strategy by region according to the operation information and demand information of each unit and the feedback information uploaded by the scheduling center of each regional layer,and then delivers to the scheduling center of each regional layer.Secondly,in the regional layer optimization scheduling model,the regional layer scheduling center balances the system scheduling instructions with the current charging demand of EV in the region,formulates the optimal charging strategy of each EV,and delivers it to every EV.At the same time,the regional actual scheduling strategy is fed back to the scheduling center of the system layer.Particle swarm optimization is used to solve the model.Finally,the simulation results show that the proposed scheduling strategy can effectively balance EV charging load distribution between regions and reduce the scheduling pressure of the scheduling center.