Collaborative Research: AMPS: Multi-Fidelity Modeling via Machine Learning for Real-time Prediction of Power System Behavior

当前电力系统的运行依赖于确定性和静态模型，由于电网和传感器收集的数据量越来越大，以及需要集成间歇性可再生资源和动态负荷组成，这些模型不适合分析智能电网。模型预测中的巨大不确定性是有问题的，因为它现在确实允许进行仔细的规划，如果不能识别巨大的波动和可能的不稳定，可能会危及电网的可靠运行。因此，将机器学习和预测多速率建模等新工具实现的新监测能力融入智能电网建模中是至关重要的。处理不确定性的经典方法由于收敛速度太慢而导致解的效率低下，因此不能有效地用于电网的实时控制。这一困难源于对非常复杂的电网进行数千次采样以得出合理准确的解决方案的要求。该项目的目标是在电力系统的机器学习和实时预测建模方面的研究和教育方面取得重大进展，特别是在智能电网方面。近年来，机器学习和实时预测建模受到越来越多的关注。人们对这些问题进行了广泛的研究，并开发了新的数值方法来有效地处理传感器数据和复杂的工程系统。机器学习和实时预测建模都使我们能够更好地从可用的传感器数据中提取有用的信息，并在存在不确定性的情况下实时做出关键决策。例如，太阳能和风能将取决于天气条件。因此，机器学习和实时预测建模对于电力系统稳定分析和社会网络预测等许多重要的实际问题至关重要。对于大规模电力系统，确定性仿真可能非常耗时，而进行预测仿真则进一步增加了仿真成本，而且成本可能高得令人望而却步。机器学习和实时预测建模的最大挑战之一是如何开发分层降维模型，以及如何从这种分层降维模型中融合信息。该项目旨在应对这些关键挑战。基于深度学习的多保真算法(深度高斯过程)将被开发用于电力系统的实时预测。本研究项目中正在开发的方法是基于可扩展的算法来建立基于深度学习的降阶模型，以实现有效的电力系统降维。新的算法将基于通过深度学习建立电力系统的多保真模型，它们将显著提高深度学习和实时预测建模的技术水平。该项目还将整合教育机会，并将扩大使用机器学习和预测建模工具解决网络问题的建模者群体。该项目将使一群不同的本科生和少数族裔学生接触到机器学习和预测建模。

The operation of current power systems depend on deterministic and static models, which are not suitable for analyzing smart power grids due to the increasing large-volume of data collected by the grids and sensors and the need to integrate intermittent renewable resources and dynamic load compositions. Large uncertainty in the model prediction is problematic as it does now allow careful planning, and failure to identify large fluctuations and possible instabilities could endanger the reliable operation of the power grid. Hence, it is crucial to incorporate new monitoring capabilities realized by new tools such as machine learning and predictive multi-rate modeling in modeling the smart grid. Classical methods that deal with uncertainty lead to inefficient solutions as they are too slow to converge to a solution and hence they cannot be used effectively for real-time control of power grids. This difficulty stems from the requirement of sampling the very complex power grid thousands of times in order to arrive to a reasonably accurate solution. The goal of this project is to establish significant advances in research and education in the development of machine learning and real-time predictive modeling of power systems, with particular focus on the smart grid.Machine learning and real-time predictive modeling have received increasing attention in recent years. Extensive research effort has been devoted to these topics, and novel numerical methods have been developed to efficiently deal with sensor data and complex engineering systems. Both machine learning and real-time predictive modeling enable us to better extract the useful information from available sensor data and make critical decision in real time with the presence of uncertainties. For example, solar and wind energy will depend on the weather condition. Machine learning and real-time predictive modeling are thus critical to many important practical problems such as power system stability analysis and social cyber-network prediction, etc. For large-scale power systems, deterministic simulations can be very time-consuming, and conducting predictive simulations further increases the simulation cost and can be prohibitively expensive. One of the biggest challenges in machine learning and real-time predictive modeling is how to develop hierarchical reduced-order models and how to fuse information from such hierarchical reduced-order models. This project aims to address these critical challenges. A novel set of deep-learning based multi-fidelity algorithms (deep Gaussian processes) will be developed for real-time prediction of power systems. The approach under development in this research project is based on scalable algorithms for building deep-learning based reduced-order models for efficient power system dimension reduction. The new algorithms will be based on building multi-fidelity models via deep learning for power systems, and they will significantly advance the current state of the art of deep learning and real-time predictive modeling. The project will also integrate educational opportunities and will expand the population of modelers who use machine learning and predictive modeling tools to solve network problems. The project will expose a diverse group of undergraduates and minority students to machine learning and predictive modeling.