Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment

TL;DR

This paper introduces a data-driven framework for incorporating wind curtailment into chance-constrained energy scheduling, using Gaussian process surrogates and mixed-integer programming to improve accuracy in power system operation under uncertainty.

Contribution

It develops a novel Gaussian process-based surrogate model to handle wind curtailment effects in chance-constrained optimization for power systems.

Findings

Accurately models wind curtailment impact on chance constraints.

Reformulates the problem as a mixed-integer second-order cone program.

Demonstrates effectiveness on PJM 5-bus and IEEE 118-bus systems.

Abstract

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.