Search for: All records

Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to frequency control in power systems with significant time-varying net load. We leverage the advances in short-term load forecasting, where the net load in the system can be accurately predicted using weather and other features. We integrate these predictions into the design of adaptive controllers, which can be seamlessly combined with most existing controllers including conventional droop control and emerging neural network-based controllers. We prove that the overall control architecture achieves frequency restoration decentralizedly. Case studies verify that the proposed method improves both transient and frequency-restoration performances compared to existing approaches. 

Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, the standard reinforcement learning (RL) methods lack formal stabilization guarantees, which limits their applicability for the control of real-world dynamical systems. We propose a novel policy optimization method that adopts Krasovskii's family of Lyapunov functions as a stability constraint. We show that solving this stability-constrained optimization problem using a primal-dual approach recovers a stabilizing policy for the underlying system even under modeling error. Combining this method with model learning, we propose a model-based RL framework with formal stability guarantees, Krasovskii-Constrained Reinforcement Learning (KCRL). We theoretically study KCRL with kernel-based feature representation in model learning and provide a sample complexity guarantee to learn a stabilizing controller for the underlying system. Further, we empirically demonstrate the effectiveness of KCRL in learning stabilizing policies in online voltage control of a distributed power system. We show that KCRL stabilizes the system under various real-world solar and electricity demand profiles, whereas standard RL methods often fail to stabilize. 

Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their real-world deployment. This letter constructs a decentralized RL-based controller for inverter-based real-time voltage control in distribution systems. It features two components: a transient control policy and a steady-state performance optimizer. The transient policy is parameterized as a neural network, and the steady-state optimizer represents the gradient of the long-term operating cost function. The two parts are synthesized through a safe gradient flow framework, which prevents the violation of reactive power capacity constraints. We prove that if the output of the transient controller is bounded and monotonically decreasing with respect to its input, then the closed-loop system is asymptotically stable and converges to the optimal steady-state solution. We demonstrate the effectiveness of our method by conducting experiments with IEEE 13-bus and 123-bus distribution system test feeders. 

Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee the satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe Voltage Regulator, which is composed of an RL learner and a specifically designed, computationally efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages are within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm.