论文标题
数据驱动的优化方法在不确定性下进行电力系统计划
Data-driven Optimization Approaches to the Power System Planning under Uncertainty
论文作者
论文摘要
为了保护环境并解决化石燃料稀缺性，可再生能源越来越多地用于发电。但是，由于它给电力生产带来的不确定性，确定性优化不再足以满足运营需求。因此，已经提出了大量的优化技术，这些技术提供了解决不确定性的好方法。本文在不确定性下选择了三种更重要的优化技术来介绍：随机编程（SP），健壮优化（RO），以及基于前两个的新型方法，名为“分布鲁棒性优化”（DRO）。我们使用特定示例来解释每种方法的基本框架和一般过程。重点是每种方法如何解决不确定性。此外，我们还比较了他们的优势和劣势，并讨论了未来的研究方向。
In order to protect the environment and address fossil fuel scarcity, renewable energy is increasingly used for power generation. However, due to the uncertainties it brings to electricity production, deterministic optimization is no longer sufficient for operational needs. Therefore, a large number of optimization techniques under uncertainty have been proposed, which provide good ways to address uncertainties. This paper selects three of the more important optimization techniques under uncertainty to introduce: stochastic programming (SP), robust optimization (RO), and a novel approach named distributionally robust optimization (DRO) based on the first two. We explain the basic framework and general process of each approach using specific examples. The focus is on how each method addresses the uncertainties. In addition, we also compare their strengths and weaknesses and discuss future research directions.
