论文标题
数据驱动的辅助偶然限制的能源和储备计划，并减少限制
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
论文作者
论文摘要
偶然受限的优化（CCO）已被广泛用于电力系统操作中的不确定性管理。随着风能的流行，可以将风减少视为CCO中的调度变量。但是，削减会导致不确定性分布的冲动，这对机会限制建模产生了挑战。为了解决这个问题，开发了一个数据驱动的框架。通过对风力输出强制执行的限制进行建模，提议的框架构建了高斯工艺（GP）代理，以描述风力减少与机会限制之间的关系。这使我们能够通过降低风力重新将CCO重新制定为混合量的二阶编程（MI-SOCP）问题。通过求解凸线性编程（LP）来提高建模精度，可以开发出误差校正策略。对PJM 5-BUS和IEEE 118-BUS系统进行的案例研究表明，所提出的方法能够准确地计算CCO中风力减少的影响。
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.
