Dynamic TRM Estimation with Load–Wind Uncertainty Using Rolling Window Statistical Analysis for Improved ATC
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Edeh, Uchenna Emmanuel
Lie, Tek Tjing
Mahmud, Md Apel
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Journal Article
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MDPI AG
Abstract
The rapid integration of renewable energy sources (RES), particularly wind, together with fluctuating demand, has introduced significant uncertainty into power system operation, challenging traditional approaches for estimating Transmission Reliability Margin (TRM) and Available Transfer Capability (ATC). This paper proposes a fully adaptive TRM estimation framework that leverages rolling-window statistical analysis of net-load forecast errors to capture real-time uncertainty fluctuations. By continuously updating both the confidence factor and window length based on evolving forecast-error statistics, the method adapts to changing grid conditions. The framework is validated on the IEEE 30-bus system with 80 MW wind (42.3% penetration) and assessed for scalability on the IEEE 118-bus system (40.1% wind penetration). Comparative analysis against static TRM, fixed-confidence rolling-window, and Monte Carlo Simulation (MCS)-based methods shows that the proposed approach achieves 88.0% reliability coverage (vs. 81.8% for static TRM) while providing enhanced transfer capability for 31.5% of the operational day (7.5 h). Relative to MCS, it yields a 20.1% lower mean TRM and a 2.5% higher mean ATC, with an adaptation ratio of 18.8:1. Scalability assessment confirms preserved adaptation (12.4:1) with sub-linear computational scaling (1.82 ms to 3.61 ms for a 3.93× network size increase), enabling 1 min updates interval.Description
Keywords
02 Physical Sciences, 09 Engineering, 33 Built environment and design, 40 Engineering, 51 Physical sciences, available transfer capability (ATC), transmission reliability margin (TRM), Latin hypercube sampling (LHS), load forecasting, rolling window statistical analysis, uncertainty quantification, wind power forecasting
Source
Energies, ISSN: 1996-1073 (Online), MDPI AG, 19(3), 844-844. doi: 10.3390/en19030844
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.