Enabling Resiliency Through Outage Management and Data-Driven Real-Time Aggregated DERs

Abstract

The integration of distributed energy resources (DERs) has revolutionized the power distribution grids, transitioning them into a decentralized mode. While DERs bring numerous advantages, they also introduce the complexity and uncertainties to the distribution system operation, necessitating adaptable operational strategies to enhance system resilience. This paper presents a pioneering approach that exploits the load and DERs in outage managements to enhance the distribution grid restoration. The proposed network equivalent model aggregates DERs and loads, preserving controllability information for effective real-time system outage management and restoration. A data-driven load model that combines an artificial neural network-based short-term load forecasting model and a delay exponential cold load pickup (CLPU) model is developed to compute the reliable safety margin for restoration and pick up the maximum number of loads during the restoration. Furthermore, an optimal restoration strategy is presented, encompassing restored network topology, controllable utility loads, and DERs set-point determination.