Optimization of electric power system modes under conditions of the probabilistic nature of the initial information

Abstract

Relevance: In problems of optimal planning of short-term states of electric power systems, the initial probabilistic quantities are node loads and the active resistances of network elements. The probabilistic nature of network resistances is related to the ambient temperature. Therefore, based on the weather forecast for the planning period, they are calculated by making appropriate adjustments to the resistances of the elements. The probabilistic nature of active node loads is associated with many factors, which creates certain difficulties. Existing methods for forecasting node loads do not allow for the determination of their load schedules for the planning period, taking into account all influencing factors. As a result, optimization results obtained using such load schedules typically contain significant errors, reducing the effectiveness of optimization. Therefore, improving methods and algorithms for optimal planning of short-term states of power system operating modes, taking into account current operating conditions, is a pressing issue.

Aim: Improving the algorithm for optimizing the short-term mode of the power system under conditions of probabilistic initial information.

Methods: Comparative analysis methods and optimization algorithms are applied taking into account international experience and requirements.

Results: Optimal planning of short-term EPS situations in Uzbekistan under probabilistic initial data conditions, testing the effectiveness of the proposed algorithm on test circuits.