基于贝叶斯在线学习的数据驱动随机配电系统加固
Data-Driven Stochastic Distribution System Hardening Based on Bayesian Online Learning
AI总结：
针对极端天气下配电系统停电及现有加固决策未充分利用实际数据的问题，提出结合DNN预测、决策依赖分布鲁棒优化与贝叶斯在线学习的加固策略，经实际系统24年数据验证有效。
摘要：
极端天气频繁导致配电系统（DSs）大面积停电，凸显了加固策略对提升韧性的重要性。然而，如何充分利用带有相关气象条件的实际停电数据来制定明智的配电系统加固决策，仍是一个待解决的问题。为填补这一研究空白，本文提出了一种数据驱动的随机配电线路（DL）加固策略。首先，本文构建了一个深度神经网络（DNN）回归模型，用于预测不同加固决策下停电场景的概率演化。基于DNN的预测结果，该问题被建模为一个决策依赖的分布鲁棒优化（DRO）模型，通过数据驱动的模糊集来刻画停电场景分布的不确定性。为解决决策依赖的不确定性问题，本文提出了一种贝叶斯在线学习算法。该算法将原问题分解为内层问题和外层问题，随后通过依次纳入停电数据，并利用贝叶斯定理和贝叶斯推理动态更新针对特定决策的模糊集，迭代优化加固决策。此外，本文还通过动态悔度分析证明了该算法的收敛性。最后，本文在美国爱荷华州雷德菲尔德的一个实际配电系统上开展了案例研究，基于公用事业公司的停电记录构建了一个跨度24年（2001-2024年）的数据集。仿真结果验证了所提策略的有效性。
英文摘要：
Extreme weather frequently cause widespread outages in distribution systems (DSs), demonstrating the importance of hardening strategies for resilience enhancement. However, the well-utilization of real-world outage data with associated weather conditions to make informed hardening decisions in DSs is still an open issue. To bridge this research gap, this paper proposes a data-driven stochastic distribution line (DL) hardening strategy. First, a deep neural network (DNN) regression model is developed to predict the probabilistic evolution of outage scenarios under various hardening decisions. Based on the DNN predictions, the problem is formulated as a decision-dependent distributionally robust optimization (DRO) model, accounting for uncertainties in outage scenario distributions using a data-driven ambiguity set. To address decision-dependent uncertainty, a Bayesian online learning algorithm is proposed. This algorithm decomposes the original problem into inner and outer problems. Then, it iteratively refines hardening decisions by sequentially incorporating outage data and dynamically updating decision-specific ambiguity sets by using Bayes' theorem and Bayesian Inference. Also, the convergence of the algorithm is proven through dynamic regret analysis. Finally, case studies are implemented on a real-world DS in Redfield, Iowa, USA. A dataset spanning 24 years (2001-2024) is constructed based on the utility outage records. The simulation results validates the effectiveness of the proposed strategy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
