High Performance Adaptive Deep-Reinforcement-Learning-based Real-time Emergency Control (HADREC) to Enhance Power Grid Resilience in Stochastic Environment
Pacific Northwest National Laboratory (PNNL) will construct an intelligent, real-time emergency control system to help safeguard the U.S. electric grid by providing effective and fast control actions to system operators in response to large contingencies or extreme events. PNNL’s scalable platform will utilize advanced machine learning techniques (deep-meta-reinforcement learning) as well as high-performance computing to automatically provide effective emergency control strategies seconds after disturbances or attacks. Platform development will focus on the determination, timing, coordination, and automation of control actions, including adaptation under uncertainty. The technology will diminish the need for costly preventive security measures as well as reduce action time sixtyfold and system recovery time by at least 10%, enabling more efficient and resilient grid operation.
If successful, PNNL's HADREC will displace existing technologies for grid centralized emergency control by solving the key technical hurdles for determining and coordinating emergency control actions based on real-time system states.