Chance-constrained optimization (CCO) has been widely used for uncertainty
management in power system operation. With the prevalence of wind energy, it
becomes possible to consider the wind curtailment as a dispatch variable in
CCO. However, the wind curtailment will cause impulse for the uncertainty
distribution, yielding challenges for the chance constraints modeling. To deal
with that, a data-driven framework is developed. By modeling the wind
curtailment as a cap enforced on the wind power output, the proposed framework
constructs a Gaussian process (GP) surrogate to describe the relationship
between wind curtailment and the chance constraints. This allows us to
reformulate the CCO with wind curtailment as a mixed-integer second-order cone
programming (MI-SOCP) problem. An error correction strategy is developed by
solving a convex linear programming (LP) to improve the modeling accuracy. Case
studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that
the proposed method is capable of accurately accounting the influence of wind
curtailment dispatch in CCO