Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments

TL;DR

This paper introduces a fast, black-box optimization method for power distribution feeder reconfiguration using Mesh Adaptive Direct Search, which efficiently finds near-optimal solutions without explicit models.

Contribution

It develops a novel MADS-based framework that optimizes feeder reconfiguration solely through simulation-based performance evaluations, suitable for proprietary utility environments.

Findings

Achieves near-optimal configurations with fewer evaluations.

Outperforms heuristic methods in efficiency.

Effectively balances power loss and operational constraints.

Abstract

Feeder reconfiguration is a critical operational strategy in power distribution systems. However, existing optimization approaches typically rely on explicit mathematical formulations and analytical models, which are often infeasible in practical utility environments characterized by heterogeneous, proprietary, and black-box simulation modules. To address this challenge, this paper proposes a fast feeder reconfiguration framework based on Mesh Adaptive Direct Search (MADS). The proposed approach requires only performance metric evaluations through simulation modules used for power flow, protection, and voltage regulation analysis. A bi-objective formulation is adopted to jointly minimize active power loss and operational constraint violations. A Pareto-based frontier filter is integrated into the MADS algorithm to efficiently guide the search toward high-quality configurations while systematically pruning dominated solutions. The approach adaptively refines the search space around promising candidates using local polling strategies and convergence aware updates. Case studies on the IEEE-123 node test feeder demonstrate that the proposed approach achieves near-optimal configurations with significantly fewer evaluations compared to heuristic methods.