Exploiting network topology optimization and demand side management to improve bulk power system resilience under windstorms

Abstract

Natural hazards are great threats to the safe and reliable operation of electric power systems. Some high-impact low-probability (HILP) events usually cause severe load losses to the power grid. Therefore, the enhancement of power system resilience appears to be particularly important. Nowadays, novel smart grid technologies provide more flexible ways for the power system operation. However, these technologies are not comprehensively incorporated in the resilience assessment framework to quantify their contribution for resilience enhancement. In this paper, fragility models for transmission lines and towers and sequential wind data are applied to simulate the operational states of power system under windstorm events. Then, a demand side management (DSM) program termed weather condition based real time pricing (WCRTP) framework is proposed to regulate the customer's electricity consumption behavior according to the extreme weather conditions. A network topology optimization (NTO) operation strategy is applied to mitigate the transmission congestion and realize the potential of transmission capacity by optimizing the network topology. The sequential Monte Carlo Simulation (MCS) method based resilience enhancement evaluation framework is developed to incorporate WCRTP and NTO. Numerical case studies are performed on modified RTS-79 systems. Both methods are proved to be effective self-adaptive measures for power systems in both customer behavior regulation aspect and transmission strategy aspect to boost the system resilience, which could eventually help power system operators to deal with the natural hazards in a more flexible, resourceful, and reliable manner.