Robust Partitioning and Operation for Maximal Uncertain-Load Delivery in Distribution Grids
Abstract
To mitigate the vulnerability of distribution grids to severe weather events, some electric utilities use preemptive de-energization as the primary line of defense, causing significant power outages. In such instances, networked microgrids could improve resiliency and maximize load delivery, though the modeling of three-phase unbalanced network physics and computational complexity pose challenges. These challenges are further exacerbated by an increased penetration of uncertain loads. In this paper, we present a two-stage mixed-integer robust optimization problem that configures and operates networked microgrids, and is guaranteed to be robust and feasible to all realizations of loads within a specified uncertainty set, while maximizing load delivery. To solve this problem, we propose a cutting-plane algorithm, with convergence guarantees, which approximates a convex recourse function with sub-gradient cuts. Finally, we provide a detailed case study on the IEEE 37-bus test system to demonstrate the economic benefits of networking microgrids to maximize uncertain-load delivery.
Cite
@article{arxiv.2404.03137,
title = {Robust Partitioning and Operation for Maximal Uncertain-Load Delivery in Distribution Grids},
author = {Hannah Moring and Harsha Nagarajan and Kshitij Girigoudar and David M. Fobes and Johanna L. Mathieu},
journal= {arXiv preprint arXiv:2404.03137},
year = {2024}
}