论文标题
关于瓦斯尔斯坦的仿射政策，分布稳健的单位承诺
On Affine Policies for Wasserstein Distributionally Robust Unit Commitment
论文作者
论文摘要
本文提出了基于数据驱动的Wasserstein的单位承诺模型（UC）模型，该模型在可再生生成的不确定性及其可处理的精确重新印度的情况下，针对功率系统的分布强劲优化（WDRO）。提出的模型被提出为WDRO问题，该问题依赖于仿射政策，该政策嵌套了无限的最差期望值问题并满足了非及格性约束。为了降低保守性，我们开发了一种新型技术，该技术定义了具有概率保证的不确定性集的子集。随后，提出的模型被重塑为半官方编程问题，可以使用现有算法有效地解决。值得注意的是，这种重新制定的规模与样本量不变。结果，在不使用复杂分解算法的情况下很容易地合并许多样品。 6和24总线测试系统上的数值模拟证明了该模型的经济和计算效率。
This paper proposes a unit commitment (UC) model based on data-driven Wasserstein distributionally robust optimization (WDRO) for power systems under uncertainty of renewable generation as well as its tractable exact reformulation. The proposed model is formulated as a WDRO problem relying on an affine policy, which nests an infinite-dimensional worst-case expectation problem and satisfies the non-anticipativity constraint. To reduce conservativeness, we develop a novel technique that defines a subset of the uncertainty set with a probabilistic guarantee. Subsequently, the proposed model is recast as a semi-infinite programming problem that can be efficiently solved using existing algorithms. Notably, the scale of this reformulation is invariant with the sample size. As a result, a number of samples are easily incorporated without using sophisticated decomposition algorithms. Numerical simulations on 6- and 24-bus test systems demonstrate the economic and computational efficiency of the proposed model.
