CRISP Type 2/Collaborative Research: Understanding the Benefits and Mitigating the Risks of Interdependence in Critical Infrastructure Systems

CRISP 类型 2/协作研究：了解关键基础设施系统相互依赖的好处并减轻风险

• 批准号: 1735513
• 负责人: Mads Almassalkhi
• 金额: $ 97.95万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735513&HistoricalAwards=false
• 关键词: CRISP; Type; Collaborative; Research; Understanding

This Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) project will identify new strategies to increase resilience in interdependent electric power, communication and natural gas networks. These three critical systems increasingly depend on one another to keep our energy and communication systems running. In some ways connections between these systems can make them work better, but in other ways connections can increase the chance of disastrous failures that could leave millions of people without heat, electricity or the ability to communicate. For example, a severe winter storm in the Northeastern United States could lead to both power grid failures and natural gas failures, leading to failures in telephone and Internet services, making it even more difficult to restore these critical services. Such "cascading failures" make it even harder for these systems to recover from natural disasters and intentional attacks. This project will identify strategies to make interdependent infrastructure systems more resilient to these cascading failures. Four Research Directions will combine to address this problem. Research Direction 1 will adapt new computational algorithms, such as Influence Graphs that can identify non-obvious critical connections and the Random Chemistry algorithm that can rapidly find critical triggering events, to the particular problems of cascading failures in interdependent infrastructure systems. Research Direction 2 will create new models of interdependence among natural gas, electric power and communication networks, which will form a testbed for computational algorithms. The resulting models will balance computational complexity and engineering detail by using detailed dynamical models of each system when necessary and simplified mathematical models when abstractions can be validated from real data. Research Direction 3 will develop and evaluate engineering solutions and coordination strategies that can mitigate harmful interdependencies and leverage beneficial interconnections. These will leverage insights from the application of new computational algorithms to the interdependence testbed, such as the identification of critical failure paths, to develop both real-time dynamic rescheduling algorithms and cost-effective long-term planning strategies. Research Direction 4 will use stakeholder interviews to evaluate the diverse ways that the electricity, natural gas, and communications industries understand risk, and facilitate discussion among key industry participants regarding interdependencies among these systems. The results will reveal the most effective paths to integrating new control and planning strategies to increase resilience in these diverse systems.This project will create significant societal benefits by uncovering new ways to reduce the risk of catastrophic failures among critical infrastructure systems. Because of interdependence among infrastructures, low probability, high cost cascading failures, which can have billions of dollars of economic and societal impacts, can contribute more to overall risk, relative to more frequent, small events. Reducing this risk can have enormous benefits to society. To ensure that results from this project have practical impacts the team will be guided by a Research Advisory Board that includes a large power grid operator (ISO New England), a software vendor for the electricity industry (GE/Alstom), a natural gas company (Vermont Gas), and the MITRE corporation. Furthermore, the project will integrate education and research through new curriculum and outreach to high school students. Public data that result from this project will be released through the github repository at: https://github.com/phines/infrastructure-risk, as well as through the project web site at http://www.uvm.edu/~tesla/project/nsf-crisp/. All research data associated with this project, including public and non-public data, will be preserved for at least 5 years after the end of the project.

CRISP项目将确定新的战略，以提高相互依赖的电力、通信和天然气网络的弹性。这三个关键系统越来越相互依赖，以保持我们的能源和通信系统的运行。在某些方面，这些系统之间的连接可以使它们更好地工作，但在其他方面，连接可能会增加灾难性故障的可能性，这可能会使数百万人失去热量，电力或通信能力。例如，美国东北部的一场严重冬季风暴可能导致电网和天然气故障，导致电话和互联网服务中断，使恢复这些关键服务变得更加困难。这种“连锁故障”使这些系统更难从自然灾害和故意攻击中恢复。该项目将确定战略，使相互依赖的基础设施系统对这些级联故障更具弹性。四个研究方向将结合联合收割机来解决这个问题。研究方向1将适应新的计算算法，如影响图，可以识别非明显的关键连接和随机化学算法，可以快速找到关键的触发事件，在相互依赖的基础设施系统级联故障的特定问题。研究方向2将创建天然气、电力和通信网络之间相互依赖的新模型，这将形成计算算法的测试平台。所产生的模型将在必要时使用每个系统的详细动态模型，并在可以从真实的数据中验证抽象时使用简化的数学模型，从而平衡计算复杂性和工程细节。研究方向3将开发和评估工程解决方案和协调战略，可以减轻有害的相互依赖性和利用有益的相互联系。这些将利用从应用新的计算算法到相互依存试验平台（如确定关键故障路径）的洞察力，开发实时动态重新安排算法和具有成本效益的长期规划战略。研究方向4将使用利益相关者访谈来评估电力，天然气和通信行业理解风险的不同方式，并促进关键行业参与者就这些系统之间的相互依赖性进行讨论。研究结果将揭示整合新的控制和规划策略的最有效途径，以提高这些不同系统的弹性。该项目将通过发现减少关键基础设施系统灾难性故障风险的新方法，创造重大的社会效益。 由于基础设施之间的相互依赖性，低概率，高成本的连锁故障，可以有数十亿美元的经济和社会影响，可以贡献更多的整体风险，相对于更频繁，小事件。减少这种风险可以给社会带来巨大的好处。为确保该项目的结果产生实际影响，该团队将接受研究咨询委员会的指导，该委员会包括一家大型电网运营商（ISO新英格兰）、一家电力行业软件供应商（GE/Alstom）、一家天然气公司（Vermont Gas）和MITRE公司。此外，该项目将通过新课程和对高中生的宣传，将教育和研究结合起来。该项目产生的公共数据将通过github存储库发布：https：//github.com/phines/infrastructure-risk，以及通过项目网站http://www.uvm.edu/~tesla/project/nsf-crisp/发布。与本项目相关的所有研究数据，包括公开和非公开数据，将在项目结束后至少保存5年。

项目成果

Joint Frequency Regulation and Economic Dispatch Using Limited Communication

使用有限通信的联合频率调节和经济调度

• 发表时间: 2018
• 期刊: IEEE SmartGridComm
• 作者: Jianan Zhang, Eytan Modiano

Data-driven Localization and Estimation of Disturbance in the Interconnected Power System

• DOI: 10.1109/smartgridcomm.2018.8587509
• 发表时间: 2018-06
• 期刊: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
• 作者: Hyang-Won Lee;Jianan Zhang;E. Modiano

Real-Time Grid and DER Co-Simulation Platform for Testing Large-Scale DER Coordination Schemes

用于测试大规模分布式能源协调方案的实时电网和分布式能源联合仿真平台

• DOI: 10.1109/tsg.2022.3184491
• 发表时间: 2022
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Khurram, Adil;Amini, Mahraz;Espinosa, Luis A. Duffaut;Hines, Paul D. H.;Almassalkhi, Mads R.
• 通讯作者: Almassalkhi, Mads R.

Using historical utility outage data to compute overall transmission grid resilience

使用历史公用事业停电数据来计算整体输电网的恢复能力

• DOI: 10.1109/meps46793.2019.9395039
• 发表时间: 2019
• 期刊: 2019 Modern Electric Power Systems (MEPS
• 作者: Kelly-Gorham, Molly Rose;Hines, Paul;Dobson, Ian
• 通讯作者: Dobson, Ian

Mitigating the Risk of Voltage Collapse Using Statistical Measures From PMU Data

使用 PMU 数据的统计测量来降低电压崩溃的风险

• DOI: 10.1109/tpwrs.2018.2866484
• 发表时间: 2019
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Chevalier, Samuel C.;Hines, Paul D.
• 通讯作者: Hines, Paul D.