Design of data-driven optimization method and multidimensional benefit evaluation system for power grid project investment decision-making

Guozhen Ma1, Xiangming Wu2, Po Hu1, Hangtian Li1
1Economic and Technology Research Institute, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, 050000, China
2State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, Hebei, 050000, China

Abstract

According to the decision-making process of power grid investment, this paper sets the objective function and constraints, realizes the construction of optimization model, and selects genetic algorithm as the solution algorithm of optimization model. Under the requirement of evaluation index principle, 16 secondary indexes and 4 primary indexes are screened, thus forming the evaluation index system of power grid project investment efficiency. The experimental conditions are set to evaluate and analyze the optimization of investment decision and multidimensional benefits of power grid project respectively. Along with the reduction of voltage data, the diversity of optimal solutions for grid project benefits begins to materialize, and the diversity of optimal solutions of GA algorithm is higher than that of PSO algorithm, indicating that the use of genetic algorithm to calculate optimal solutions for grid investment benefits is more effective. In addition, the closeness of the seven projects to the optimal solution is 0.4613, 0.5044, 0.4681, 0.5398, 0.6342, 0.5759, 0.4116, respectively, of which project 5 has the best investment benefit.

Keywords: Genetic algorithm, constraints, objective function, investment decision optimization model