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.