Optimization of normal operation mode of an electric system with renewable energy sources in Mongolia

Abstract

This article is aimed at developing an algorithm for optimizing the operation modes of the electric power system of Mongolia, particularly the central power system that include not only conventional thermal power plants, but also renewable sources (wind and solar power plants). This power system accounts for a large share of electricity consumption and generation in Mongolia. The method of linear programming was chosen to minimize financial costs and active power losses during power generation at thermal power plants, while Newton’s method was used to minimize power losses. In addition, the article uses load schedules of each node of the studied power system for its modeling based on the ranking model. Load graphs are predicted using ensemble machine learning algorithms. After the optimization by the criterion of power loss minimization in the grid, power losses were found to be 3.05% of the total power consumption (with power losses in the basic variant of 3.12% and the average selling price of thermal power plants of 0.51 units). Thus, the reduction in losses amounted to 0.07 percentage points, or 2.24%. In terms of the cost minimization criterion, the average selling price of electricity was 0.49 units, i.e., decreased by 3.92%. Average losses of electric power in the grid decreased by 0.6%. According to empirical data, the suggested algorithms can be applied to the optimization of power distribution between thermal power plants by given criteria. The suggested algorithms are implemented using pandapower, a Python-based tool for power system analysis, thus creating a unified system of predictive analytics of power system operation modes