Multi-Objective Planning Of Distributed Generation In Distribution Network

With the development of new technologies,the penetration rate of intermittent distributed generation(DG)and electric vehicles in the distribution network continues to increase.The uncertainty of intermittent DG output and load demand,as well as the disordered charging and discharging of electric vehicles,makes the operation status of the distribution network complex and changeable,and has a profound impact on the operation and planning of the distribution network.Therefore,in the new environment with complex uncertain factors,reasonable planning of DG in the distribution network has clear practical significance.The research work of this thesis mainly includes the following aspects:First,research on the location of DG through the method of active power loss sensitivity analysis.Secondly,aiming at the uncertainty and correlation between wind speed,light intensity,and load demand,this thesis adopts a multi-scene analysis that considers correlation,and through Latin hypercube sampling and Cholesky decomposition combined with Spearman rank correlation coefficient to obtain a relevant set of DG output and load demand scenarios.The scene set is reduced by a fast search and peak density clustering algorithm(CFSFDP)to obtain typical scenes.On this basis,a multi-objective DG planning model that considers the minimum DG investment and operation cost and the power purchase cost of the superior grid is established.When solving the model,the analytic hierarchy process is used to transform the multi-objective optimization model into a single-objective optimization model,the equations of Dist Flow are introduced into the planning model,and the planning model is converted into a mixed integer second-order cone programming problem through second-order cone relaxation and the Cplex solver is called to solve it.Then,aiming at the uncertain factors caused by random charging of electric vehicles,Monte Carlo is used to simulate the charging load of electric vehicles.In order to reduce the workload of planning,the CFSFDP algorithm is used to effectively reduce the scene set containing the charging load of electric vehicles to obtain typical scenes.On this basis,a multi-objective DG planning model is established that considers the minimum DG investment and operating costs,active power loss costs,and environmental costs.The programming model is transformed into a mixed-integer second-order cone programming problem through second-order cone relaxation,and the Cplex solver is used to solve it.Finally,the models and methods proposed in this thesis are simulated and verified by the IEEE 33-node calculation example.The simulation results show that the above models and methods can obtain the optimal DG planning scheme under the constraint conditions.Compared with the particle swarm algorithm,the second-order cone programming method used in this thesis has the advantages of high convergence accuracy and fast calculation speed.