AI 中文总结
该研究提出自适应随机谱嵌入（ASSE）方法，用于高效准确评估概率AC-OPF，经IEEE测试系统验证，其性能优于多种基准方法，可为电力系统决策提供实用边界。
AI 中文摘要
本文提出一种自适应随机谱嵌入（ASSE）方法，以解决概率交流最优潮流（AC-OPF）问题，该问题是电力系统运行的关键环节。所提方法可高效且准确地估计AC-OPF解的概率特性（如均值、方差、中位数及基于分位数的指标），同时最小化功率损耗。基于估计的AC-OPF决策（即发电机出力），可确定基于置信区间（CI）的生产成本指标。特别地，采用自适应域划分策略以指导细化域的选择与划分；集成基于贝叶斯压缩感知的系数计算算法以提升性能。对改进后的IEEE 9节点和IEEE 118节点系统的数值研究表明，与蒙特卡洛模拟相比，所提ASSE方法能实现准确且快速的评估；与稀疏多项式混沌展开、高斯过程回归及深度神经网络的对比进一步说明，其在准确评估具有强局部行为和非对称分布的响应方面具有有效性，为不确定性下的发电机出力和运行成本提供了实用的决策边界。
英文摘要
This paper presents an adaptive stochastic spectral embedding (ASSE) method to solve the probabilistic AC optimal power flow (AC-OPF), a critical aspect of power system operation. The proposed method can efficiently and accurately estimate the probabilistic characteristics (e.g., mean, variance, median, and quantile-based metrics) of AC-OPF solutions while minimizing power losses. Based on estimated AC-OPF decisions (i.e., generator outputs), the confidence interval (CI)-based production cost index can be determined. Specially, an adaptive domain partition strategy is adopted to guide refinement domain selection and partition. The Bayesian compressive sensing-based coefficient calculation algorithm is integrated to enhance its performance. Numerical studies on modified IEEE 9-bus and IEEE 118-bus systems demonstrate that the proposed ASSE method offers accurate and fast evaluations compared to Monte Carlo simulations. Comparisons with a sparse polynomial chaos expansion, Gaussian process regression, and deep neural networks, further illustrate its efficacy in accurately assessing the responses with strongly localized behavior and non-symmetric distributions, providing practical decision-making bounds for generator outputs and operating costs under uncertainty.
Comments18 pages, 15 figures. To appear in IEEE Transactions on Power Systems
DOI：10.1109/TPWRS.2026.3727456
