CAREER: Managing uncertainties in renewable powered grids

这个NSF CAREER项目旨在开发算法来管理可再生能源发电的固有不确定性，以提供可靠和最低成本的电力系统运营。该项目将通过将可再生能源的不确定性充分纳入不稳定风险的在线评估和资源调度决策，为公用事业控制室带来变革性变化。这将通过开发一种新的计算框架来实现，该框架将基于物理的电网建模，实时传感器测量和来自数值天气预测的伪测量与先进的机器学习和数据分析相结合，以实现管理电网运营中可再生不确定性所需的更高计算效率和准确性。该项目的智力优势包括新的方法，以提高可再生能源发电的情况意识，评估瞬态不稳定风险，并协调资源调度，以减轻可再生能源的不确定性的影响。该项目的更广泛影响包括对可再生能源电网不确定性管理基本理论的创新，以及对劳动力管道的改进，使其能够顺利过渡到100%脱碳电力系统。现有技术水平，即，缺乏对海上风力发电的态势感知、通过在线应用的离线研究来确定传输稳定性裕度以及静态/动态（容量）储备，不能充分捕捉具有高不确定性的可再生电网的新特征。该项目将通过以下方式弥合这些差距：1）通过提供一个新的框架来融合基于物理的天气预测和深度学习方法，以改善海上风力发电预测，从而提高系统运营商的态势感知能力; 2）通过开发计算数据分析算法来近似系统动态，从而实现实时瞬态不稳定风险评估;以及3）通过动态能量储备技术重新设计操作储备，以纳入空间和时间相关的可再生不确定性。这些基础理论和技术将公用事业控制室的风险管理科学从具有确定性实践的离线研究转移到数据驱动的风险感知在线解决方案，从而做出明智的决定-该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估，被认为值得支持。影响审查标准。

This NSF CAREER project aims to develop algorithms to manage the inherent uncertainties of renewable generation in order to provide reliable and least cost operations of electric power systems. The project will bring transformative change to utility control rooms by fully incorporating renewable uncertainties into online assessment of instability risks and decision-making of resource dispatch. This will be achieved by developing a novel computational framework that combines physics-based power grid modeling, real-time sensor measurement, and pseudo measurement from numerical weather predictions with advanced machine learning and data analytics to achieve higher computation efficiency and accuracy required to manage renewable uncertainties in grid operations. The intellectual merits of the project include novel methodologies to enhance situational awareness of renewable generation, assess transient instability risks, and coordinate resource dispatch to mitigate the impact of renewable uncertainties. The broader impacts of the project include innovations to fundamental theories of uncertainty management in renewable energy powered grids and improvements to the workforce pipeline enabling the smooth transition to 100% decarbonized electricity systems. Current state of the art technologies, i.e., lack of situational awareness of offshore wind generation, determining transmission stability margin via off-line studies for online applications, and static/dynamic (capacity) reserve, do not adequately capture the new features of a renewable powered grid with high uncertainties. This project will bridge these gaps by 1) enhancing system operators' situational awareness through providing a new framework to fuse physics-based weather prediction and deep-learning methodologies for improved offshore wind generation forecasting; 2) enabling real-time transient instability risk assessment by developing a computational data analytic algorithm to approximate system dynamics; and 3) redesigning the operating reserve via the dynamic energy reserve technology to incorporate spatially and temporally correlated renewable uncertainties. These fundamental theories and technologies move the science of risk management in the utility control room from offline study with deterministic practices to data-driven, risk-aware online solutions, leading to informed decision-making and prompt mitigation actions by system operators to prevent cascading events.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.