Risk Assessment of Power Systems to Extreme Events using Polynomial-Chaos-based Methods

使用基于多项式混沌的方法对电力系统进行极端事件风险评估

• 批准号: 1917308
• 负责人: Lamine Mili
• 金额: $ 47.05万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1917308&HistoricalAwards=false
• 关键词: Risk; Assessment; Power; Systems; Extreme

Modern power systems involve numerous uncertainties induced by random load variations, renewable energy variations, and random outages of generating units, lines, and transformers. As a result, they have been subject to an increasing risk of cascading failures leading to large-scale blackouts. The computational burden current methods is prohibitively large for large-scale systems, restricting their practicability when assessing the risk of cascading failures under topology and power injection uncertainties. To address these challenges, this proposal will resort to polynomial-chaos methods and uncertainty quantification theory to develop a new risk assessment framework for power systems subject to extreme events. The developed framework will enhance power system planning, operations, online monitoring and local and global controls. Besides, the uncertainty quantification methods can be generalized to provide great improvement in the control and design procedures of many other engineering fields, such as aircraft design and control, vehicle and train control, among others. The developed framework will be validated on two real power systems, namely the Southern Brazil power system and the Dominion Virginia Power 500-KV transmission system. The project also contains an integrated educational agenda for K-12 students, undergraduates and graduate students who are interested in the STEM (Science Technology Engineering and Mathematics) area.Currently, Monte-Carlo-based methods are widely used in power system planning and operation. The computational burden of these methods for the problem of interest in this proposal is very high. In this project, a new risk assessment framework for power systems subject to extreme events and large uncertainties will be developed based on polynomial-chaos methods and uncertainty quantification theory. Specifically, our proposal will aim to 1) designing a new polynomial chaos-based algorithm that can handle arbitrary probability distributions assumed for the random inputs while providing accurate results for both short-time and long-time simulations, and 2) providing fast calculation speed when the developed algorithm is applied to a highly nonlinear system subject to high-dimensional uncertain inputs. This is achieved by initiating a hybrid approach to conduct dimension reduction for very high-dimension data, which consists of two steps. In the first step, a kernel-based principle component analysis (PCA) is applied to obtain intermediate dimension reduction results. In the second step, we use an analysis of variance (ANOVA) or sliced-inverse-regression to obtain a more active central subspace. Furthermore, scenario-based multi-element polynomial chaos expansion to handle system topology changes will be initiated.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.

现代电力系统涉及由随机负载变化，可再生能量变化以及生成单元，线条和变压器的随机中断引起的许多不确定性。结果，他们遭受级联失败的风险增加，导致大规模停电。对于大规模系统，计算负担当前方法非常大，在评估拓扑和功率注入不确定性下级联故障的风险时，限制了它们的实用性。为了应对这些挑战，该提案将诉诸多项式 - chaos方法和不确定性量化理论，以开发针对受到极端事件的电力系统的新风险评估框架。开发的框架将增强电力系统计划，操作，在线监控以及本地和全球控制。此外，可以将不确定性量化方法推广到许多其他工程领域的控制和设计程序，例如飞机设计和控制，车辆和火车控制等。 开发的框架将在两个真实的电力系统上进行验证，即南部巴西电力系统和Dominion Virginia Power 500-KV传输系统。该项目还包含针对对STEM（科学技术工程和数学）领域感兴趣的K-12学生，本科生和研究生的综合教育议程。目前，基于蒙特 - 卡洛的方法广泛用于电力系统计划和运营中。这些方法在此提案中的兴趣问题的计算负担很高。 在该项目中，将基于多项式 - chaos方法和不确定性量化理论开发针对极端事件的电力系统的新风险评估框架。具体而言，我们的建议将旨在1）设计一种新的基于多项式混乱的算法，该算法可以处理为随机输入假定的任意概率分布，同时为短时和长时间模拟提供准确的结果，以及2）在开发的算法中提供快速计算速度，将其应用于高度非线性系统对高度非线系统的高度不高度不相关的无关无关紧要的含量。这是通过启动混合方法来降低尺寸的非常高维数据来实现的，该数据由两个步骤组成。 第一步，应用基于内核的原理分析（PCA）来获得中间维度的减小结果。在第二步中，我们使用方差分析（ANOVA）或切成不表示的分析来获得更活跃的中央子空间。 此外，将启动基于方案的多项式混乱扩展以处理系统拓扑变更。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估审查标准的评估来支持的。

项目成果

A Surrogate-Enhanced Scheme in Decision Making under Uncertainty in Power Systems

• DOI: 10.1109/pesgm46819.2021.9637922
• 发表时间: 2021-07
• 期刊: 2021 IEEE Power & Energy Society General Meeting (PESGM)
• 作者: Yijun Xu;L. Mili;M. Korkali;Xiao Chen;J. Valinejad;Long Peng

Planning-Oriented resilience assessment and enhancement of integrated electricity-gas system considering multi-type natural disasters

• DOI: 10.1016/j.apenergy.2022.118824
• 发表时间: 2022-06
• 期刊: Applied Energy
• 影响因子: 11.2
• 作者: Han Wang;K. Hou;Junbo Zhao;Xiaodan Yu;H. Jia;Yunfei Mu

A Bayesian Approach for Estimating Uncertainty in Stochastic Economic Dispatch Considering Wind Power Penetration

• DOI: 10.1109/tste.2020.3015353
• 发表时间: 2021-01-01
• 期刊: IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
• 影响因子: 8.8
• 作者: Hu, Zhixiong;Xu, Yijun;Valinejad, Jaber
• 通讯作者: Valinejad, Jaber

A Data-Driven Global Sensitivity Analysis Framework for Three-Phase Distribution System With PVs

数据驱动的光伏三相配电系统全局敏感性分析框架

• DOI: 10.1109/tpwrs.2021.3069009
• 发表时间: 2021
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Ye, Ketian;Zhao, Junbo;Huang, Can;Duan, Nan;Zhang, Yingchen;Field, Thomas E.
• 通讯作者: Field, Thomas E.

An Efficient Multifidelity Model for Assessing Risk Probabilities in Power Systems under Rare Events

• DOI: 10.24251/hicss.2020.381
• 发表时间: 2020
• 作者: Yijun Xu;M. Korkali;L. Mili;Xiao Chen