AI 中文总结
研究针对机组组合问题在大规模场景下的计算限制，定义不同细节水平模型，基于凸包给出公式化，证明相关公式紧凑性，可减轻大规模问题计算负担。
AI 中文摘要
机组组合（UC）问题对于电力系统的优化运行至关重要，但由于包含二元变量，在大规模场景中面临计算限制，尤其是在投资或随机模型中。许多研究试图通过减小模型规模（导致较低的保真度和准确性）或提高公式的紧凑性来改善UC模型的计算性能。紧凑性和模型规模是UC模型计算性能的最佳先验指标，但对于不同发电机的最佳公式尚无清晰概述。本研究定义了不同细节水平的模型，并基于凸包为每个水平给出了一种公式化。我们给出了关于爬坡、启动和关闭成本及能力的著名公式紧凑性的新证明。如第二部分所示，这些具有不同细节水平的模型可纳入大规模问题以减轻计算负担。
英文摘要
The unit commitment (UC) problem is paramount for optimal operation of power systems, but it faces computational limitations in large-scale settings, especially in investment or stochastic models, because of the binary variables that it contains. A lot of research has attempted to improve the computational performance of UC models, either by reducing model size, resulting in lower fidelity and accuracy, or by improving the tightness of the formulation. Tightness and model size are the best a priori indicators of the computational performance of UC models, but there is no clear overview of what the best formulation is for different generators. In this research, we define models with different levels of detail, and present a formulation for each level that is based on the convex hull. We show new proofs on the tightness of well-known formulations for ramping, for start-up and shut-down costs and capabilities, and for UC with investment. These models, with a different level of detail, can be incorporated into large-scale problems to reduce the computational burden, as demonstrated in Part II.
CommentsIncludes additional lemma on tightness of UC formulations with investment
