Research On Optimal Siting And Sizing Of Distributed Generators Considering Electric Vehicles

With the increasing environmental friendliness and energy scarcity,new energy technologies have been developed vigorously.A large number of distributed generation(DG)sources are integrated into the grid,making the power system structure more complex and more difficult to plan.The conventional load is also affected by human habits,and has a timeseries and random nature.The supply of wind distributed power and photovoltaic distributed power are also affected by wind speed and light intensity,having strong uncertainty and seasonality,which will seriously affect the accuracy of distribution network planning if their characteristics are not considered.In recent years,the penetration rate of electric vehicles(EVs)in the load has been increasing year by year due to the government’s promotion and people’s awareness of energy saving,and the impact on the distribution network cannot be ignored.The charging load of EVs is also influenced by human habits and has time-series characteristics.The uncertainty of wind distributed power(WTG)and photovoltaic distributed power(PVG)output affected by natural environment and the fluctuation of demand load are considered,and the wind speed,light intensity and load demand are modeled with timing and correlation.Based on Monte Carlo stochastic simulation,a timing model of EVs charging load is established and combined with the obtained wind-PV-load model multi-dimensional distributed power planning scenarios are generated.In order to enhance the computational rate,the K-means clustering algorithm is used to cluster scenarios of complex multidimensional scenarios to obtain representative typical operating scenarios.The Whale Optimization Algorithm(WOA)has the advantages of unique search mechanism and simple principle,and has a relatively excellent performance in dealing with engineering problems.This paper describes the basic principle of the whale optimization algorithm and the process steps of the enhanced whale optimization algorithm,and verifies the superiority of the improved whale optimization algorithm for solving this model by comparing it with the whale optimization algorithm and the particle swarm optimization algorithm through the classical test function.Using the chance constraint method,the distributed power supply siting and capacity planning model is established with the system operation reliability as the constraint and the annual operation cost as the minimum objective function,and the enhanced whale optimization algorithm is used to solve the resulting distributed power supply siting and capacity planning model.The accuracy of the above model and the efficiency and effectiveness of the selected algorithm are verified by comparing the planning schemes and voltage magnitudes under different scenarios of whether the distributed power supply is connected to the distribution network and the installation of different types of distributed power supplies.