Optimal Distribution System Restoration with Microgrids and Distributed Generators

TL;DR

This paper presents an optimal distribution system restoration method using MILP that efficiently forms islands, coordinates multiple DGs, and models voltage regulators, enhancing reliability and resilience in power distribution networks.

Contribution

It introduces a novel MILP-based restoration scheme that optimally forms islands without heuristic pre-identification, supports multiple DGs, and models voltage regulators exactly, improving restoration speed and scalability.

Findings

Restoration scheme solves in less than four seconds for 1-5 line outages.

Supports multiple DGs within the same island.

Models voltage regulators exactly in the optimization.

Abstract

Increasing emphasis on reliability and resiliency call for advanced distribution system restoration (DSR). The integration of grid sensors, remote controls, and distributed generators (DG) brings about exciting opportunities in DSR. In this context, this work considers the task of single-step restoration of a single phase power distribution system. Different from existing works, the devised restoration scheme achieves optimal formation of islands without heuristically pre-identifying reference buses. It further facilitates multiple DGs running within the same island, and establishes a coordination hierarchy in terms of their PV/PQ operation modes. Generators without black-start capability are guaranteed to remain connected to a black-start DG or a substation. The proposed scheme models remotely-controlled voltage regulators exactly, and integrates them in the restoration process. Numerical tests on a modified IEEE 37-bus feeder demonstrate that the proposed mixed-integer linear program (MILP) takes less than four seconds to handle random outages of 1-5 lines. The scalability of this novel MILP formulation can be attributed to the unique use of cycles and paths on the grid infrastructure graph; the McCormick linearization technique; and an approximate power flow model.