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With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. This paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

This article proposes a novel energy management algorithm that controls the battery energy storage system (BESS) and on-grid supply. It employs the deep-Q-network agent with prioritized experience replay, and its efficacy is validated and verified by comparison to a benchmark method for mixed integer linear programming. The grid and energy storage systems are governed by switching operations initiated by BESS controllers via the automatic transfer switch. The primary objective is to accomplish optimal scheduling of batteries one day in advance to reduce electricity costs while maintaining battery health and primary power supply reliability. The methods proposed in this work provide practicable grid and battery operation patterns that test all conceivable planning scenarios for energy storage operation. Finally, a comparative analysis is performed to evaluate the efficacy of the proposed BESS operation scheduling methods.