A Submodular Approach for Electricity Distribution Network Reconfiguration

TL;DR

This paper introduces a novel submodular optimization approach for distribution network reconfiguration to minimize losses, providing theoretical guarantees and performance bounds for the first time.

Contribution

It formulates the DNR problem as a supermodular function minimization under matroid constraints and proposes a polynomial-time local search algorithm with performance bounds.

Findings

The algorithm matches the branch exchange method in performance.

The problem is proven strongly NP-hard.

Experimental results on a 33-bus network demonstrate effectiveness.

Abstract

Distribution network reconfiguration (DNR) is a tool used by operators to balance line load flows and mitigate losses. As distributed generation and flexible load adoption increases, the impact of DNR on the security, efficiency, and reliability of the grid will increase as well. Today, heuristic-based actions like branch exchange are routinely taken, with no theoretical guarantee of their optimality. This paper considers loss minimization via DNR, which changes the on/off status of switches in the network. The goal is to ensure a radial final configuration (called a spanning tree in the algorithms literature) that spans all network buses and connects them to the substation (called the root of the tree) through a single path. We prove that the associated combinatorial optimization problem is strongly NP-hard and thus likely cannot be solved efficiently. We formulate the loss minimization problem as a supermodular function minimization under a single matroid basis constraint, and use existing algorithms to propose a polynomial time local search algorithm for the DNR problem at hand and derive performance bounds. We show that our algorithm is equivalent to the extensively used branch exchange algorithm, for which, to the best of our knowledge, we pioneer in proposing a theoretical performance bound. Finally, we use a 33-bus network to compare our algorithm's performance to several algorithms published in the literature.