<![CDATA[Techno-economic evaluation of ACCC reconductoring and dynamic line rating on the 150 kV Java-Bali transmission network requires a validated multi-objective optimization framework capable of quantifying thermal, economic, and environmental benefits under real meteorological conditions. This study develops such a framework using a calibrated IEEE 118-Bus benchmark representing Indonesian grid characteristics, applying the IEEE Standard 738 thermal model to 52,608 hours of NASA POWER meteorological data across five Java-Bali cities from 2020 to 2025. A surrogate machine learning model based on Histogram-based Gradient Boosting (HistGradientBoosting) trained on 121,764 valid load flow solutions achieved R² = 0.9995 (on a held-out test set of 30,441 samples) for power loss prediction with a mean absolute error (MAE) of 4.49 MW (0.20% of the observed loss range), and 98.0% accuracy within ±1 line for thermal violation prediction, enabling Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multi-objective optimization with 210× computational speedup. All Pareto-optimal solutions were re-validated using full Newton-Raphson load flow, confirming the validated nature of the optimal configurations. The IEEE 738 model achieved less than 0.07% deviation from CTC Global manufacturer data at all tested temperatures. The dynamic ampacity of existing conductors ranged from 730 A to 1,458 A, revealing 43% underutilization of median capacity under static rating. Thermal violations begin in 2028–2029 under RUPTL 2025–2034 load growth and reach 52,775 line-hours by 2034; full reconductoring eliminates over 99% of violations. Financial evaluation over 40 years at 9.5% WACC yields NPV of IDR 2,218 billion, IRR of 17.3%, BCR of 2.09, and discounted payback of 11.79 years. Selective reconductoring of 26 critical lines delivers IRR up to 109.6% and BCR up to 11.23. Sensitivity analysis confirms NPV positivity across all ±20% parameter variations and combined adverse scenarios.]]>
