AMPS: Scalable Methods for Real-time Estimation of Power Systems under Uncertainty

AMPS：不确定性下电力系统实时估计的可扩展方法

• 批准号: 2229495
• 负责人: Noemi Petra
• 金额: $ 28万
• 依托单位: University of California - Merced
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229495&HistoricalAwards=false
• 关键词: AMPS; Scalable; Methods; Real; time

The United States power grid, which is arguably the most complex civil engineering system, is facing unprecedented challenges stemming from the advent of new sensing technologies, adoption of large amounts of renewable energy, and emergence of smart grids. Power system operators rely on estimating parameters for monitoring power systems in real-time, detecting risks, verifying technical compliance, and decision-making. However, this estimation problem is particularly challenging due to the large size and complexity of the power grid, as well as the need to perform such tasks in real-time. Decisions about the best and safe power grid operations depend critically on knowing the current parameters and states of the grid. This research will address these challenges by developing computational methods that are scalable, exploit problem structures, and are robust with respect to uncertainties in the power system models. In particular, the project will address identifying the most influential parameters in the power system models and developing mathematical tools to efficiently estimate power system model parameters from data with quantified uncertainties. The project will provide training opportunities for students from underrepresented groups in STEM.Bayesian inversion facilitates the integration of data with complex physics-based models, such as power systems, to quantify the uncertainties in model predictions. The algorithmic developments for Bayesian inversion in the context of power grid, face a number of fundamental challenges. Among those are high-dimensionality of the inversion parameters (stemming from the size of the power grid), expensive and real-time evaluations of the parameter-to-observable maps, and model uncertainty additional to the uncertainty in inversion parameters. The project will develop mathematically rigorous, computationally efficient, and robust methods that overcome mathematical and computational barriers in solving large-scale estimation problems governed by uncertain power system models. In particular, the investigators will build on the existing state-of-the-art for Bayesian inversion algorithms and extend these by using (i) sensitivity analysis to classify the uncertain parameters based on their importance, (ii) the Bayesian approximation error approach to incorporate additional uncertainty into the Bayesian inverse problem governed by power grid models, (iii) surrogate modeling for power systems (via machine learning and dimension reduction techniques) and (iv) second-order methods and approximations of second derivative information to reduce the computational cost when solving the Bayesian inverse problem. The algorithms, mathematical findings, and open-source codes will be disseminated through peer-reviewed journal papers and presentations at conferences and workshops.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.

美国电网可以说是最复杂的土木工程系统，但由于新的传感技术的出现、大量可再生能源的采用以及智能电网的出现，美国电网正面临前所未有的挑战。电力系统运营商依靠估计参数来实时监控电力系统、检测风险、验证技术合规性和决策。然而，由于电网的规模和复杂性，以及需要实时执行此类任务，这一估计问题尤其具有挑战性。关于最佳和安全电网运行的决策关键取决于对电网当前参数和状态的了解。这项研究将通过开发可扩展的、利用问题结构并且对于电力系统模型中的不确定性具有健壮性的计算方法来解决这些挑战。特别是，该项目将致力于识别电力系统模型中最具影响力的参数，并开发数学工具，从具有量化不确定性的数据中有效地估计电力系统模型参数。该项目将为STEM中代表性不足群体的学生提供培训机会。贝叶斯反演促进了数据与复杂的基于物理的模型(如电力系统)的集成，以量化模型预测中的不确定性。在电网背景下，贝叶斯逆算法的发展面临着许多根本性的挑战。其中包括反演参数的高维性(源于电网的大小)、昂贵的实时参数可观测图的评估以及除了反演参数的不确定性之外的模型不确定性。该项目将开发数学严谨、计算高效和健壮的方法，以克服解决由不确定电力系统模型支配的大规模估计问题的数学和计算障碍。特别是，研究人员将建立在现有最先进的贝叶斯逆算法的基础上，并通过使用(I)敏感度分析来根据不确定参数的重要性对其进行分类，(Ii)贝叶斯近似误差方法将额外的不确定性加入到由电网模型控制的贝叶斯逆问题中，(Iii)电力系统的代理建模(通过机器学习和降维技术)和(Iv)二阶方法和二阶导数信息的近似来降低求解贝叶斯逆问题的计算成本。算法、数学发现和开源代码将通过同行评议的期刊论文和在会议和研讨会上的演示文稿进行传播。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。