PNNL

Abstract

With the increasing electricity consumption and lack of transmission investment, today’s power systems are operated much closer to their limits, raising concerns of inter- area oscillations that deteriorate the system stability. This paper presents a novel energy storage placement and control approach for enhanced damping of inter-area oscillations. Combining the residual analysis and dominant mode analysis, we are able to identify the advantageous locations for placing energy storage that achieve improved damping performance. To overcome the challenges such as fixed control parameters and insufficient damping, we propose to use a deep reinforcement learning- based approach for energy storage control. A state-of-the-art guided surrogate-gradient-based evolutionary strategy is used to train a learning agent in a robust, efficient, and reproducible manner. Parallel computing is also adopted to speed up the training process. The proposed methods were tested using both medium and large-scale test systems. The proposed methods have demonstrated their effectiveness in mitigating various inter- area oscillations within a timeframe of 20 seconds, thereby averting system collapse and enhancing power grid stability more effectively than conventional damping approaches.