Random Forest-assisted Dynamic TRM Estimation for Improved ATC in Renewable-rich Power Systems
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Edeh, Uchenna Emmanuel
Lie, Tek Tjing
Mahmud, Md Apel
Zhang, Pei
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IEEE
Abstract
Accurate Available Transfer Capability (ATC) assessment is critical for reliable operation of renewable-rich power systems. Conventional dynamic Transmission Reliability Margin (TRM) methods based on rolling-window statistics mainly react to recent volatility, limiting responsiveness during rapid system changes. This paper proposes a Random Forest (RF)-assisted dynamic TRM framework where a lightweight RF regression model predicts short-term volatility using historical forecast errors and operating-condition features. The predicted volatility is incorporated into an adaptive confidence factor, K(t), enabling anticipatory TRM adjustment while preserving the original probabilistic framework. Validation on the IEEE 24-bus Reliability Test System with integrated wind generation demonstrates improved performance, achieving 97.2% coverage during ramp events compared with 91.7% for the rolling-window approach and 69.4% for the static method. The RF model achieved RMSE = 3.60 MW and R2 = 0.825 for 3-step-ahead volatility prediction, showing strong potential for renewable-integrated Australasian power systems.
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Keywords
40 Engineering, 4008 Electrical Engineering, 7 Affordable and Clean Energy, 13 Climate Action, Available transfer capability, transmission reliability margin, machine learning, random forest, renewable energy integration, power system reliability
Source
Proceedings of the 2026 IEEE 3rd International Conference on Electrical Power Systems and Intelligent Control (EPSIC), 22-24 May 2026, Tianjin, China.
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This is the Author's Accepted Manuscript of a conference paper presented at IEEE 3rd International Conference on Electrical Power Systems and Intelligent Control (EPSIC). The final, published version is available at (see Publisher's version).