Active Power Distribution Network Planning Based On Probabilistic Characteristic

The purpose of active power distribution network planning including distributed generation is to ensure that the distribution task of distribution network is safe, reliable and economical in the planning period. At the same time, consuming distributed energy reasonable and achieving optimal power flow operations and schedule of distribution network.This paper studied the probability characteristics of load requirements and typical distributed power outputs, such as wind power and photovoltaic energy storage systems and establish the corresponding mathematical model. Considering the volatility of the load demand and distributed power outputs, this paper uses Monte Carlo to calculate the probabilistic load flow during the planning, which avoid the limitations of a single load flow scenario and make planning more practical significance.This paper focuses on the method of locating and sizing of DG (distributed generation) and the method of collaborative optimizing DG capacity and network structure of active distribution network. During the DG planning, the paper takes the minimization of the investment and operation cost as objective function and builds normalized objective function to solve the issue of sitting and sizing of DG by combining probabilistic load flow calculation and improved particle swarm optimization. According to fitness variance and the current optimal solution in improved particle swarm optimization, determine the current portion of particles mutation rate, get rid of the shackles of local optima and improve the non-linear accuracy by introducing mutation operator and Migration operator during search process in the algorithm. At the same time we optimize convergence properties by improving the update methods of speed and position and termination criteria in the PSO. The paper verifies the feasibility and efficiency of the algorithm by simulation examples, and the statistics show that probabilistic load flow calculation can guide the planning better.In order to solve the problem of the distribution network expansion planning, this paper proposes a hybrid algorithm based on particle swarm optimization and Kruskal algorithm, collaborative optimizes DG capacity and network structure of active distribution network, simplifies the chromosome structure of the algorithm which avoids the "dimension disaster" problem of traditional genetic algorithm. Meanwhile, the algorithm avoids the infeasible solution in traditional network programming, simplifies the planning process. The paper verifies the feasibility and efficiency of the algorithm by simulation examples, and the statistics get from probabilistic load flow calculation show the weaknesses in the planning scheme.