Research On Optimal Scheduling Strategy Of Electric Vehicle Charging And Discharging Based On V2G

With the rapid development of the global economy, energy and environmental issues have become the most concern.Therefore, electric vehicle, as a new means of transportation, will become the leading direction of the future automobile industry’s development because of the characteristics of energy conservation and environmental protection. But the out-of-order charging of large scale electric vehicles(EVs)provides such challenges as increasing the system losses, the operation costs, and the harmonic pollution to a safe and economic operation of the power grid.Therefore, it is necessary to study the effect of the random charging of electric vehicles on the grid.In this thesis, only private EVs are considered, and the impacts of random charging and optimal scheduling charging/discharging of EVs on the grid are analyzed.The major achievements are as follows:Firstly, this dissertation starts with the factors that affect the EVs charging load,and studies the effect of the EVs charging characteristics on the safe and economic operation of the power grid. The charging load model is established on the basis of considering the scale and type of EVs, the characteristics of the battery, the way of charging and the driving characteristics of EVs, and the charging load of EVs is obtained by Monte Carlo simulation. A case results show that EVs charging load increases the peak load of power grid and the load valley-to-peak deference ratio, and as the number of electric vehicles increases, the effects of EVs on power system will be obvious.Secondly, based on the the-of-use price mechanism, taking the storage capacity of EVs, the charging/discharging power into consideration, a multi-objective optimal model is built to decrease the valley-to-peak deference ratio and reduce the charging costs of EVs users, the proposed model is solved by particle swarm optimization algorithm based on improved learning factor and inertia weight.Simulation results show optimizing the charging and discharging power of EVs can reduce the peak load of power grid and play the role of peak load shifting. Especially under time-of-use price, the valley-to-peak deference ratio and charging costs of EVs users are further reduced.Finally, with the development of China’s photovoltaic power station in the renewable energy model cities and demonstration counties, taking the storagecapacity of EVs, the charging/discharging power, the distributed power flow, and the driving characteristics of EVs into consideration, a multi-objective optimization model is proposed in this dissertation to mitigate the peak-to-valley deference of equivalent load and reduce the active power losses of the distributed grid for a regional electrical power system. Defining each objective membership function,multi-objective optimization problem is reformulated into a nonlinear single-objective programming problem by means of fuzzy satisfaction- maximizing method, and this nonlinear single-objective programming problem is solved by using modified particle swarm optimization algorithm based on hybrid mechanism.Simulation results indicate that the proposed model and algorithm can flat the curve of equivalent load, reduce the reserved capacity in adjusting the peak, optimize the active power losses and provide the voltage support for the system.