A GPU-Based Resilience Enhanced Voltage Optimization Model for Distribution Networks

Improving the survivability of critical loads after extreme events is essential to enhance the resilience of power systems, especially for distribution networks. A distribution network with various operational resources can be separated into several sub-distribution networks without electrical connections. Maintaining the power supply with acceptable power quality to critical loads in such separated distribution networks is a challenging task for the operators of power systems. In this paper, an optimization model is proposed to maximize the ability to supply power to critical loads in distribution networks. Moreover, a GPU was employed to accelerate the proposed model using genetic algorithm. With the acceleration of the GPU platform, the solving time was reduced and the population size can be enlarged to enhance the convergence rate and convergence quality of the algorithm. Finally, case studies were carried out in IEEE 33-bus and 118-bus systems, and the effectiveness of the method was validated by comparing the solution results on GPU and CPU platforms.