Optimal Configuration Of Distributed Generation Based On Point Estimation Method

With the rapid development of science and technology,the demand for energy increases gradually,as a new generation of distributed generation,especially good prospects for the development of distributed generation with new energy such as wind and solar energy.However,due to the obvious uncertainty and randomness of wind energy and solar energy,it is necessary to discuss the influence of uncertainty on the system when planning the location and sizing of distributed power sources.Under the background of developing new energy and distributed energy,this paper establishes a scheduling model based on chance constrained programming with the minimum annual comprehensive cost of distribution network as objective function.Considering the uncertain factors in the system planning,the probabilistic flow is introduced and the probability distribution is modeled according to the Weibull distribution of wind speed and solar intensity.According to Monte Carlo traditional processing method of uncertain factors,this paper uses a more efficient point estimate method of the wind turbine and photovoltaic battery output are calculated,including wind power and solar energy uncertainty model is transformed into a deterministic model.In the scheduling model,the chance constrained programming of the line power and the node voltage is adopted to make the planning more suitable to the actual demand.In order to verify the superiority of the point estimation method in IEEE33 system,as an example,consider a variety of weight coefficient combination scheme,the genetic algorithm based on Monte Carlo(GA-MCS)and genetic algorithm based on point estimate method(GA-PEM)for comparative analysis,verify that the proposed method is reasonable and superiority.