标题： 将预测模型整合到稳态分析和优化中 显示英文标题

标题： Integrating Forecasting Models Within Steady-State Analysis and Optimization

摘要： 极端天气变化和负荷行为日益增加的不可预测性使得确定对不确定性具有鲁棒性的电力调度变得困难。 虽然机器学习（ML）方法提高了建模由负荷和可再生能源引起的不确定性的能力，但将这些预测及其敏感性准确地整合到稳态分析和决策策略中仍然是一个开放性挑战。 为了实现这一目标，我们提出了一种通用的方法，该方法能够无缝地将基于机器学习的预测引擎嵌入基于物理的潮流计算和电网优化工具中。 通过将基于物理的电网建模与黑箱机器学习方法相结合，我们通过直接将训练好的机器学习预测模型的输入和输出集成到潮流计算和电网优化的数值方法中，准确捕捉负荷和天气事件的行为和敏感性。 在不拟合代理负荷模型的情况下，我们的方法直接从数据中获取敏感性，以准确预测预测设备对电网变化的响应。 我们的方法结合了通过反向传播获得的预测设备的敏感性和基于物理定义的电网设备的敏感性。 我们通过展示敏感性计算的改进，并利用这些改进设计一种稳健的电力调度，从而证明了我们方法的有效性，该调度在随机天气事件下提高了电网可靠性。 我们的方法使系统对外部因素的敏感性计算成为可能，这支持了更广泛的分析，在负荷波动和极端天气条件下提高了电网的可靠性。 显示英文摘要

摘要： Extreme weather variations and the increasing unpredictability of load behavior make it difficult to determine power grid dispatches that are robust to uncertainties. While machine learning (ML) methods have improved the ability to model uncertainty caused by loads and renewables, accurately integrating these forecasts and their sensitivities into steady-state analyses and decision-making strategies remains an open challenge. Toward this goal, we present a generalized methodology that seamlessly embeds ML-based forecasting engines within physics-based power flow and grid optimization tools. By coupling physics-based grid modeling with black-box ML methods, we accurately capture the behavior and sensitivity of loads and weather events by directly integrating the inputs and outputs of trained ML forecasting models into the numerical methods of power flow and grid optimization. Without fitting surrogate load models, our approach obtains the sensitivities directly from data to accurately predict the response of forecasted devices to changes in the grid. Our approach combines the sensitivities of forecasted devices attained via backpropagation and the sensitivities of physics-defined grid devices. We demonstrate the efficacy of our method by showcasing improvements in sensitivity calculations and leveraging them to design a robust power dispatch that improves grid reliability under stochastic weather events. Our approach enables the computation of system sensitivities to exogenous factors which supports broader analyses that improve grid reliability in the presence of load variability and extreme weather conditions.