Modelling the optimal switching scheme of the Ukrainian power grid during blackout (Volyn region)

Abstract

The relevance of the study, in particular, in the Volyn region, is due to the need to ensure the reliability and efficiency of the energy infrastructure in the face of growing challenges associated with the war between Russia and Ukraine, technological development and ensuring the country’s energy security. The purpose of the study is to develop a model of the optimal switching scheme for the Volyn region’s power grids during blackout to minimize the negative consequences and ensure priority power supply to critical facilities. The methods used include mathematical modelling, simulation, optimization, sensitivity analysis, and others. The study optimized the power grid switching scheme under the risk of war and other crisis situations, including a thorough analysis of various options in the event of a blackout. Taking into account additional aspects of grid safety and reliability, the optimal routes for power transmission, placement of backup power sources were identified and effective algorithms for grid management were developed. By optimizing the grid switching scheme, the damage caused by blackouts can be minimized, and power supply can be restored quickly. The results confirmed that an optimal switching scheme can significantly reduce blackout time and its impact on the economy and the lives of the population. The development of optimal switching schemes for power grids is an important step towards increasing the resilience of Ukraine’s electricity system to crisis situations. The study has highlighted the importance of developing such models to ensure energy security and resilience of the power grid under the threat of blackouts, which affects the practical aspects of power supply management and economic development. The practical significance of the study is to improve strategies for managing the power grid in blackout conditions, contributing to the resilience of energy systems and economic development