@article{Lei2026, 
author = {Xingyu Lei and Zhifang Yang and Yao Zhou and Juan Yu and Linze Yang and Wenyuan Li},
title = {Data-driven Approach to Chance-constrained AC Optimal Power Flow with Optimization of AGC Participation Factors},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {3},
pages = {1277-1287},
keywords = {AC optimal power flow, chance constraints, data-driven approach, Monte Carlo simulations, power system operation, renewable energy, uncertainty},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2024.06270},
doi = {10.17775/CSEEJPES.2024.06270},
abstract = {The AGC participation factors’ optimization, coupled with the renewable energy uncertainties, changes the distribution of status variables. Consequently, it poses both modeling and computational challenges for existing chance-constrained alternating current optimal power flow (CC-ACOPF) methods. This paper proposes a data-driven method to reformulate and solve the CC-ACOPF, optimizing participation factors for automatic generation control (AGC) without prior assumptions about uncertainties. Based on historical data, a surrogate model is constructed to describe the relationship between uncertainties and chance constraints. A linearized power flow model, including reactive power and voltage magnitude, is introduced to reduce the dimensionality of the surrogate model. By embedding the surrogate model into the chance-constrained optimization, a tractable data-driven CC-ACOPF model is developed. A two-stage, data-driven Monte Carlo-based method is established to correct errors in power flow linearization and surrogate learning. Numerical results are presented for the PJM 5-bus and IEEE 118-bus systems to validate the effectiveness and robustness of the proposed method.}
}