Multi-objective comprehensive optimization based on probabilistic power flow calculation of distribution network

Abstract

In recent years, distributed generation technology develops rapidly due to its’ flexible and environment-friendly nature, and the wide application of electric vehicles poses a challenge to the safety of the system. In order to better analyze the impact of DG on the economics and safety of distribution networks, a probability model for random load, micro gas turbines and photovoltaic power generation system is formulated. With the objective of minimizing the network loss, lowest static insecurity probability, and the lowest cost of purchasing electricity, the optimization of distribution network with distributed generation is carried out by adjusting the distribution network topology and the output of controllable DG. The stochastic power flow is combined with the particle swarm optimization algorithm to obtain the Pareto non-inferior solution set, and then the subject is selected to obtain the optimal solution. Finally, simulations are carried out on the IEEE 33-bus test system and it is shown that the proposed method can effectively reduce the network loss and static insecurity probability on the basis of low cost of purchasing electricity.