Fast Feeder Reconfiguration via Mesh Adaptive Direct Search in Black-Box Distribution System Environments
Junyuan Zheng, Wenlong Shi, Zhaoyu Wang

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…
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Taxonomy
TopicsPower Systems and Technologies · Optimal Power Flow Distribution · Power Systems and Renewable Energy
