Vulnerability analysis on random matrix theory for power grid with flexible impact loads

Abstract

The stochastic volatility of the rail transit load brings greater uncertainty to the vulnerability of the power grid. To solve the problem of the inaccurate results caused by the incomplete time-domain simulation model of the power system with rail transit load integration, this paper proposes a vulnerability analysis method for the power system with rail transit load integration based on the random matrix theory. In this paper, we first constructed a rail transit load model based on Deep Convolutional Generative Adversarial Networks (DCGAN) to simulate the situation that massive rail transit load merged into the Grid Scenario. Then, we generate a high-dimensional random matrix based on the power flow of the grid-connected system under different rail transit loads. Then, we construct a vulnerability analysis model combining the random matrix theory and the real-time separation window. Finally, we take the IEEE-39 bus system and a regional power grid in China as examples to evaluate the vulnerability of the grid-connected system. The results show that our method quantifies not only the impact of the rail transit load volatility on the system vulnerability, but the system endurance under different capacities of the rail transit load connected to grid. Moreover, it also provides a new way for system planning and safety monitoring in the power system with rail transit load integration.