The use of Internet-of-Things (IoT) technology in electric power distribution is growing, specially with smart meters, automated controls, demand response (DR), and renewable distributed energy resource (DER) integration. These advancements have made it easier to monitor, control, and optimize DER-rich cyber-physical distribution systems (CPDS). However, as IoT devices become more widespread, they also increase the risk of cyber-attacks, which makes ensuring system resiliency critical. This work presents a novel resiliency-driven load restoration technique leveraging demand response through IoT devices to enhance the resiliency of a three-phase unbalanced power distribution systems. A hierarchical optimization framework is proposed, featuring a continuous linear program for primary optimization and a binary linear program for secondary optimization, aimed at achieving load-source energy balance and connection of secondary-level house/building loads based on their criticality. To address the limitations of traditional theoretical methods in analyzing distribution system losses across multiple devices and configurations, this paper proposes a novel deep neural network-based approach for system loss calculation. Validation of the proposed methodology is conducted using the IEEE 123-node system modeled in the HELICS co-simulation testbed, to demonstrate optimally configuring switch status, supplying critical loads, balancing load generation, and maintaining feeder voltages.

The electric grid operation is constantly threatened with natural disasters and cyber intrusions. The introduction of Internet of Things (IoTs) based distributed energy resources (DERs) in the distribution system provides opportunities for flexible services to enable efficient, reliable and resilient operation. At the same time, IoT based DERs comes with cyber vulnerabilities and requires cyber-power resiliency analysis of the IoT-integrated distribution system. This work focuses on developing metrics for monitoring resiliency of cyber-power distribution system, while maintaining consumers’ privacy. Here, resiliency refers to the system’s ability to keep providing energy to the critical load even with adverse events. In the developed cyber-power Distribution System Resiliency (DSR) metric, the IoT Trustability Score (ITS) considers the effects of IoTs using a neural network with federated learning. ITS and other factors impacting resiliency are integrated into a single metric using Fuzzy Multiple-Criteria Decision Making (F-MCDM) to compute Primary level Node Resiliency (PNR). Finally, DSR is computed by aggregating PNR of all primary nodes and attributes of distribution level network topology and vulnerabilities utilizing game-theoretic Data Envelopment Analysis (DEA) based optimization. The developed metrics will be valuable for i) monitoring the distribution system resiliency considering a holistic cyber-power model; ii) enabling data privacy by not utilizing the raw user data; and iii) enabling better decision-making to select the best possible mitigation strategies towards resilient distribution system. The developed ITS, PNR, and DSR metrics have been validated using multiple case studies for the IoTs-integrated IEEE 123 node distribution system with satisfactory results.