AI 中文总结
本文提出PowerModels-ACOPF-AI，一种两阶段贝叶斯神经网络代理模型，结合即时学习机制动态重训练，以快速求解含可再生能源的交流最优潮流问题，并在多规模基准网络上验证了其泛化能力与不确定性预测性能。
AI 中文摘要
现代电力系统因高比例可再生能源渗透、负荷变化和运行不确定性而日益复杂，这要求对交流最优潮流问题（AC-OPF）提供快速且可靠的解决方案。传统优化方法虽然精确，但往往面临可扩展性和高计算负担的挑战，使其难以在大规模网络中实时应用。本文介绍了PowerModels-ACOPF-AI，一种两阶段贝叶斯神经网络（BNN）代理模型，用于预测发电机设定点、母线电压和相角，并给出不确定性估计。该框架集成了对性能敏感的即时学习机制，能够识别预测精度下降的区域，并利用由this http URL生成的额外AC-OPF解动态重新训练。这种自适应循环确保了鲁棒性能，使模型在新型或高度可变的运行条件下保持准确性。在不同规模的基准测试系统（即30母线、200母线和500母线网络）上的验证表明，该方法具有强大的泛化能力，能有效处理随机可再生能源注入，并提供快速、具有不确定性意识的预测。除了预测精度外，所提方法作为系统运营商的实时咨询工具和传统求解器的快速初始化器具有实用价值，从而支持未来电力系统中弹性高效的电网运行。
英文摘要
The increasing complexity of modern power systems, driven by high renewable penetration, load variability, and operational uncertainty, demands fast and reliable solutions to the AC optimal power flow problem (AC-OPF). Traditional optimization methods, though accurate, often struggle with scalability and high computational burdens, making them impractical for real-time use in large networks. This paper introduces PowerModels-ACOPF-AI, a two-stage Bayesian Neural Network (BNN) surrogate designed to predict generator set points, bus voltages, and phase angles with uncertainty. The framework integrates a performance-sensitive on-the-fly learning mechanism that identifies regions of degraded prediction accuracy and dynamically retrains with additional AC-OPF solutions generated by PowerModels.jl. This self-adaptive loop ensures robust performance, enabling the model to maintain accuracy under novel or highly variable operating conditions. Validation on benchmark test systems of different sizes, namely the 30-bus, 200-bus, and 500-bus networks, demonstrates strong generalization capability, efficient handling of stochastic renewable injections, and the ability to provide rapid, uncertainty-aware predictions. Beyond predictive accuracy, the proposed approach offers practical value as both a real-time advisory tool for system operators and a fast initializer for conventional solvers, thus supporting resilient and efficient grid operation in future power systems.
