Simulation of Power Grid Investment Decision Evaluation Model Based on Improved Genetic Algorithm

Power grid is an important infrastructure industry and public utility to ensure national economic development, social harmony and stability, and improve people’s quality of life. Its development has always been paid attention to. As an important facility to directly transmit electric energy to users, the investment in distribution network is increasing. However, the difference of regional distribution network development and the imbalance of economic development have caused some difficulties in the investment decision of distribution network. Due to the wide business scope, many participating departments and large amount of participating data, there are many problems in the current power grid investment(PGI) evaluation activities, such as inaccurate evaluation results, no unified evaluation model, complicated evaluation calculation process, a large amount of manpower and material resources, etc. Based on the traditional genetic algorithm, the constraint conditions of combination forecasting are optimized and the crossover operator and mutation operator in genetic algorithm are improved, so that the algorithm has stronger global search ability and convergence ability. This topic has summarized and studied the characteristics of PGI. On the basis of fully investigating the current investment evaluation scheme of power grid enterprises, the results of using the proposed combination prediction model to predict the PGI in a certain region show that, compared with the single prediction model and the other two combination prediction models, the proposed combination prediction model can make full use of the information of the original data and has higher prediction accuracy.