Deep Reinforcement Learning for Load Shedding Against Short-Term Voltage Instability in Large Power Systems

We introduce an innovative solution approach to the challenging dynamic load-shedding problem which directly affects the stability of large power grid. Our proposed deep Q-network for load-shedding (DQN-LS) determines optimal load-shedding strategy to maintain power system stability by taking into account both spatial and temporal information of a dynamically operating power system, using a convolutional long-short-term memory (ConvLSTM) network to automatically capture dynamic features that are translation-invariant in short-term voltage instability, and by introducing a new design of the reward function. The overall goal for the proposed DQN-LS is to provide real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery. To demonstrate the efficacy of our proposed approach and its scalability to large-scale, complex dynamic problems, we utilize the China Southern Grid (CSG) to obtain our test results, which clearly show superior voltage recovery performance by employing the proposed DQN-LS under different and uncertain power system fault conditions. What we have developed and demonstrated in this study, in terms of the scale of the problem, the load-shedding performance obtained, and the DQN-LS approach, have not been demonstrated previously.

Automated summary

In this study, an innovative solution approach to the challenging dynamic load-shedding problem in large power grids is introduced. The proposed approach, called DQN-LS, takes into account spatial and temporal information of the power system and uses a ConvLSTM network to capture dynamic features. It provides real-time, fast, and accurate load-shedding decisions to increase the quality and probability of voltage recovery.