Design of Distributed Power Planning Decision Algorithm for Transmission and Distribution Networks Based on Neural Network Model

DG (Distributed Generation) planning is a key technology in the construction of intelligent distribution networks. The large-scale integration of DGs into the distribution network will cause changes in its power flow and affect the normal operation of the network. The location and capacity are two important factors that determine the degree of impact of DG integration on the power system. In the continuous adjustment of the power system, voltage regulation, reactive power balance, and relay protection devices all affect the operational stability of the power system. This article proposes a decision-making algorithm for DG planning in transmission and distribution networks based on the NN (Neural Network) model. The research results show that when training the fitting model, the training time of GA (Genetic Algorithm) and ACA (Ant Colony Algorithm) is longer than that of the algorithm in this paper, which is 22.81S and 18.31S, respectively. Comparing the results, it can be seen that the regression fitting speed of the algorithm in this paper is better than that of GA and ACA., Taking the economic indicators, power quality indicators, and environmental protection indicators of DG as the objective functions, and considering the temporal characteristics of DG in the planning process, this algorithm is used to obtain a non inferior solution set and the ideal point method is used to obtain typical planning schemes.