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arXiv · 2606.11366

Probabilistic Repair Logistics Modeling for Utility-Scale PV Inverter Fleets Using Event-Driven Simulation

Jinlei Wei·Yongxin Zhang·Guanyu Tian
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Abstract

As renewable energy systems expand, inverter availability becomes increasingly important for grid reliability and economics, yet photovoltaic inverter repair logistics remain under-modeled. This paper presents an event-driven Monte Carlo framework for a centralized repair facility with parallel production lines, capturing the full repair cycle from administrative pre-wait and transport to health-driven repair and return-to-inventory. The model incorporates opportunistic scheduling that uses mandatory hold periods to insert additional units onto temporarily idle lines, improving throughput without added capacity. Stage durations are represented by a two-component VaR-style mixture distribution for routine and heavy-tailed delays, while a continuous health score determines repair completion. Calibrated by minimizing the one-dimensional Wasserstein distance between simulated and empirical repair-duration distributions, the model is applied to 43 field-observed repairs, reproducing the empirical bimodal structure with a Wasserstein distance of 53.3 days. Results show that 51.2% of units are accommodated through opportunistic insertion, indicating that hold periods provide a significant recoverable scheduling resource.

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Jinlei Wei, Yongxin Zhang, Guanyu Tian. 2026-06-09. Probabilistic Repair Logistics Modeling for Utility-Scale PV Inverter Fleets Using Event-Driven Simulation. https://arxiv.org/abs/2606.11366

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