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: 1735354
• 负责人: Ian Dobson
• 金额: $ 40万
• 依托单位: Iowa State University
• 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=1735354&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.

这一关键的弹性相互依赖的基础设施系统和流程(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依赖于彼此，以保持我们的能源和通信系统运行。在某些方面，这些系统之间的连接可以让它们更好地工作，但在其他方面，连接可能会增加灾难性故障的可能性，可能会导致数百万人失去供暖、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故障和天然气故障，导致电话和互联网服务中断，使这些关键服务的恢复变得更加困难。这种“连锁故障”使这些系统更难从自然灾害和蓄意攻击中恢复过来。该项目将确定使相互依赖的基础设施系统对这些连锁故障更具弹性的策略。四个研究方向将联合起来解决这个问题。研究方向1将采用新的计算算法，例如可以识别非明显关键连接的影响图和可以快速找到关键触发事件的随机化学算法，以适应相互依赖的基础设施系统中的连锁故障这一特殊问题。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，这将成为计算算法的试验台。所得到的模型将平衡计算复杂性和工程细节，在必要时使用每个系统的详细动力学模型，当可以从真实数据验证抽象时使用简化的数学模型。研究方向3将开发和评估工程解决方案和协调战略，以减轻有害的相互依存关系并利用有益的相互联系。这些将利用将新的计算算法应用于相互依赖试验台的洞察力，例如识别关键故障路径，以开发实时动态重新调度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、天然气和通信行业了解风险的不同方式，并促进关键行业参与者就这些系统之间的相互依赖关系进行讨论。研究结果将揭示整合新的控制和规划策略以提高这些不同系统的弹性的最有效途径。该项目将通过发现新的方法来降低关键基础设施系统中灾难性故障的风险，从而创造显著的社会效益。由于基础设施之间的相互依赖，与更频繁的小事件相比，可能会产生数十亿美元经济和社会影响的低概率、高成本的连锁故障可能对总体风险有更大的贡献。降低这种风险可能会给社会带来巨大的好处。为了确保该项目的结果具有实际影响，该团队将由一个研究咨询委员会指导，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/阿尔斯通)、一家天然气公司(佛蒙特州天然气公司)和MITRE公司。此外，该项目将通过新课程和对高中生的推广将教育和研究结合起来。该项目产生的公共数据将通过GitHub储存库发布：https://github.com/phines/infrastructure-risk，，以及通过项目网站http://www.uvm.edu/~tesla/project/nsf-crisp/.发布与该项目相关的所有研究数据，包括公共和非公共数据，将在项目结束后至少保存5年。

项目成果

Resilience of electric utilities during the COVID-19 pandemic in the framework of the CIGRE definition of Power System Resilience

在CIGRE定义的电力系统弹性框架中，在COVID-19大流行期间电力公司的弹性

• DOI: 10.1016/j.ijepes.2021.107703
• 发表时间: 2021-10-20
• 期刊: International Journal of Electrical Power & Energy Systems
• 影响因子: 5.2
• 作者: Skarvelis-Kazakos S;Van Harte M;Panteli M;Ciapessoni E;Cirio D;Pitto A;Moreno R;Kumar C;Mak C;Dobson I;Challen C;Papic M;Rieger C
• 通讯作者: Rieger C

Can the Markovian influence graph simulate cascading resilience from historical outage data?

马尔可夫影响图可以模拟历史中断数据的级联弹性吗？

• DOI: 10.1109/pmaps47429.2020.9183492
• 发表时间: 2020
• 期刊: Probability Methods in Power Systems
• 作者: Zhou, Kai;Dobson, Ian;Wang, Zhaoyu
• 通讯作者: Wang, Zhaoyu

Applying Bayesian estimates of individual transmission line outage rates

应用贝叶斯估计单个输电线路停电率

• DOI: 10.1109/pmaps47429.2020.9183429
• 发表时间: 2020
• 期刊: Probability Methods Applied to Power Systems
• 作者: Zhou, Kai;Cruise, James R.;kill, Chris J.;Dobson, Ian;Wehenkel, Louis;Wang, Zhaoyu;Wilson, Amy L.
• 通讯作者: Wilson, Amy L.

Can an influence graph driven by outage data determine transmission line upgrades that mitigate cascading blackouts?

由停电数据驱动的影响图能否确定缓解级联停电的输电线路升级？

• DOI: 10.1109/pmaps.2018.8440497
• 发表时间: 2018
• 期刊: 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS
• 作者: Zhou, Kai;Dobson, Ian;Hines, Paul D.H.;Wang, Zhaoyu
• 通讯作者: Wang, Zhaoyu

Risk Assessment and Mitigation of Cascading Failures Using Critical Line Sensitivities

使用临界线路敏感性进行级联故障的风险评估和缓解

• DOI: 10.1109/tpwrs.2023.3305093
• 发表时间: 2024
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Dai, Yitian;Noebels, Matthias;Preece, Robin;Panteli, Mathaios;Dobson, Ian
• 通讯作者: Dobson, Ian