论文标题
在微电网中单位承诺的分布强大的优化方法
A Distributionally Robust Optimization Approach for Unit Commitment in Microgrids
论文作者
论文摘要
本文提出了在净负载和电力市场价格不确定性下针对微电网的分配强大的单位承诺方法。拟议方法的关键目的是利用kullback-leibler的差异来构建一组概率分布的歧义集，并制定优化问题，以最大程度地减少歧义集中最差的分布带来的预期成本。所提出的方法有效利用历史数据并利用K-Means聚类算法，并结合软动态时间循环评分，形成了名义概率分布及其相关的支持。开发了两级分解方法，以实现设计问题的有效解决方案。我们进行代表性研究，并量化在不同的差异公差值下相对于基于随机优化的模型的建议方法的相对优点。
This paper proposes a distributionally robust unit commitment approach for microgrids under net load and electricity market price uncertainty. The key thrust of the proposed approach is to leverage the Kullback-Leibler divergence to construct an ambiguity set of probability distributions and formulate an optimization problem that minimizes the expected costs brought about by the worst-case distribution in the ambiguity set. The proposed approach effectively exploits historical data and capitalizes on the k-means clustering algorithm, in conjunction with the soft dynamic time warping score, to form the nominal probability distribution and its associated support. A two-level decomposition method is developed to enable the efficient solution of the devised problem. We carry out representative studies and quantify the relative merits of the proposed approach vis-à-vis a stochastic optimization-based model under different divergence tolerance values.
