Specific Planning For Distribution Systems Based On EV Wireless Power Transfer Of Road Network

With the development of science and technology and the continuous improvement of the urban infrastructure, the original distribution network planning method unable to adapt to the day growing emerging load.In order to better deal with various new types of load with different characteristics, we need to segment the load, formulating different distribution network planning criteria for different types of load.According to the features of road network wireless charging, based on the operating reliability and security of distribution network, established the distribution network planning model with the objective of construction cost and operate cost minimum, it can provide technical information for road network wireless charging load integration into power grid in the future.In this paper, the road network load refers to the load produced by dynamic charging of a large number of electric vehicles with the road which has installed the dynamic wireless charging system of electric vehicles.Highway network load forecasting is based on power grid planning and scheduling control. The dynamic wireless charging load of EVs is fairly random on time and space scale. On the basis of large amounts of literature, according to the working principle and different scenes of the highway network dynamic wireless charging system, charging load calculation formula is acquired when a large number of electric vehicles access to power grid and parameters of model are found. By analyzing the traveling characteristics of EVs with different functions, Monte Carlo sampling method is used to generalize the probability of starting charging on different periods and the logarithmic normal probability distribution function and the three parameter Weibull function are used to fit the probabilistic model. Meanwhile, Greenshields-LN model is proposed to acquire the correlation between traffic flow and average speed to simulate traffic flow and average speed model. The penetration of electric vehicles, weather conditions and lane sharing rate are calibrated. Finally by using Monte Carlo sampling, the mean and variance of daily charging load per road unit are calculated.By comparing the urban subway distribution measures and ordinary residential distribution scheme, combined with the State Grid Corporation of the Urban Distribution Network Technical Guidelines, and according to the features that road network load cannot one-time complete access to the existing distribution network and the load was zonal distribution and other characteristics, this paper suggests:"road network load priority access to the existing grid, and fine-tuning according to the actual situation, and finally the remaining load access to the new substation".In order to make the road load in the optimal way to access the existing distribution network, we proposed the new ideas of "Grid find the load, through the point connect a line", using the grid node as the source to find the shortest path connecting the road network, choosing the hybrid Floyd and maze algorithm with the introduction of competition mechanism, this method can solve the problem of selecting the road load access point and optimal path when constructing the actual grid. By using IEEE33 node network system, this paper had simulated when the road load access to the distribution network the randomness of the traffic flow impact on the power quality, and analysis the influence of daily road load on the daily distribution network load curve.In this paper, we use the multi-source and continuous selecting location model to analysis the problem of new constructing substation selecting location and confirming capacity. A novel intelligent optimization algorithm called Crisscross optimization algorithm (CSO) is proposed to solve this problem. Also, according to the actual situation of load, this paper carries out the division of the road load unit, extracted the size of load unit and location parameters, and then input these information into the multi-objective transformer substation model. Basing the constraints and objective function and associated message to set the parameters of CSO algorithm to solve this problem, the simulation results ware compared with the results obtained by PSO and GA. The results show that CSO not only has stronger global search ability, faster convergence rate, but also enriches the diversity of the population, greatly improve the shortcomings of low convergence rate at the late period and easy fall into local optimal solution of ordinary intelligent algorithm. The optimization process can quickly make substation cost, network loss cost and other economic investment spending converge to the optimal solution, obtained results has good engineering application value.