论文标题
通过贝叶斯优化的概率托管能力分析
Probabilistic Hosting Capacity Analysis via Bayesian Optimization
论文作者
论文摘要
本文研究了分布网络中考虑分布式能源（DER）和住宅负载的不确定性的概率托管能力分析（PHCA）问题。 PHCA的目的是计算托管能力，该托管能力定义为DER的最大水平，可以将其牢固地集成到配电网络中，同时以高概率满足操作约束。我们将PHCA作为偶然受限的优化问题，并使用历史数据对DER和负载的不确定性进行建模。由于使用了非凸度和大量的历史场景，PHCA通常被表达为大规模的非线性优化问题，因此在计算上可以解决解决方案。为了应对核心计算挑战，我们提出了一个基于贝叶斯优化（Bayesopt）的快速且可扩展的框架来解决PHCA。与最先进的算法（例如内部点和活动集合）相比，数值结果表明，所提出的贝诺诺特方法能够找到更好的解决方案（托管能力提高25％），平均节省了70％的计算时间。
This paper studies the probabilistic hosting capacity analysis (PHCA) problem in distribution networks considering uncertainties from distributed energy resources (DERs) and residential loads. PHCA aims to compute the hosting capacity, which is defined as the maximal level of DERs that can be securely integrated into a distribution network while satisfying operational constraints with high probability. We formulate PHCA as a chance-constrained optimization problem, and model the uncertainties from DERs and loads using historical data. Due to non-convexities and a substantial number of historical scenarios being used, PHCA is often formulated as large-scale nonlinear optimization problem, thus computationally intractable to solve. To address the core computational challenges, we propose a fast and extensible framework to solve PHCA based on Bayesian Optimization (BayesOpt). Comparing with state-of-the-art algorithms such as interior point and active set, numerical results show that the proposed BayesOpt approach is able to find better solutions (25% higher hosting capacity) with 70% savings in computation time on average.
