Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method and achieve superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningMeta Reinforcement LearningModel Predictive Controlreinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Efficient MPC for Emergency Evasive Maneuvers, Part I: Hybridization of the Nonlinear Problem
Despite the extensive application of nonlinear Model Predictive Control (MPC) in automated driving, balancing its computational efficiency with respect to the control performance and constraint satisfaction remains a cha…
Computational EfficiencyModel Predictive ControlAdaptive Power System Emergency Control using Deep Reinforcement Learning
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case sc…
BenchmarkingDeep Reinforcement Learningreinforcement-learningReinforcement Learning+1Proactive Posturing of Large Power Grid for Mitigating Hurricane Impacts
In the past decade, natural disasters such as hurricanes have challenged the operation and control of U.S. power grid. It is crucial to develop proactive strategies to assist grid operators for better emergency response …
Distributed Frequency Emergency Control with Coordinated Edge Intelligence
Developing effective strategies to rapidly support grid frequency while minimizing loss in case of severe contingencies is an important requirement in power systems. While distributed responsive load demands are commonly…
Safe Reinforcement Learning for Grid Voltage Control
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usually trips a massive amount of load ineffi…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement Learning