CAREER: A Hybrid Physics-Based/Data-Driven Modeling and Mitigation Approach for Interdependencies Between the Electric Power, ICT, and Transportation Critical Infrastructures

职业：基于物理/数据驱动的混合建模和缓解方法，用于电力、ICT 和交通关键基础设施之间的相互依赖关系

• 批准号: 1846940
• 负责人: Ahmed Mohamed
• 金额: $ 50万
• 依托单位: CUNY City College
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846940&HistoricalAwards=false
• 关键词: CAREER; Hybrid; Physics; Based; Data

Improving the protection and resilience of critical infrastructures (CIs) against natural disasters and manmade threats is an imperative short-term goal. Three highly interdependent CIs are the focus of this research: electric power systems, information and communication technologies (ICT), and electricity- based vehicles/transportation (e-transportation). Network modeling of failure modes and propagation is needed by managers to devise strategies that mitigate the impact of failures across interdependent CIs. Although much progress has been made in network modeling of CIs to date, these efforts fail to capture the complexity of CIs, are based on inadequate or inaccurate assumptions, neglect interdependencies, and are mostly empirical and statistically driven rather than based on principles of physics. The objective of this research is to improve failure and resilience modeling of interdependent CIs. This work will then be expanded using reinforcement learning to aid in decision-making. The outcome will be a framework to account for CI interdependencies that are critical for failure and disaster planning and recovery.Intellectual Merit. The influence graph-based model and reinforcement learning-based interdependency mitigation framework eloquently address a key problem that has long challenged city/infrastructure planners and operators: how to model, forecast, and prevent cascaded failures that propagate through multiple critical infrastructures. Current approaches lack accuracy and the ability to handle complexities and interdependencies. This work applies detailed physics-based bottom-up modeling coupled with the innovative application of reinforcement learning to provide a transformative advancement to the field, resulting in a much more flexible and accurate analysis methodology for use in CI design, management, disaster planning, and recovery. Broader Impact. This project will positively impact society by reducing the operational cost of infrastructures, decreasing the likelihood and impact of blackouts, and improving cities' responses to major disturbances. The educational components will benefit hundreds of students from CUNY and neighboring high schools that enroll significant numbers of students who are underrepresented in STEM fields. This will increase the number of highly trained engineers and scientists with multidisciplinary backgrounds who will have a major role in defining the future of society.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.

提高关键基础设施对自然灾害和人为威胁的保护和恢复能力是当务之急的短期目标。三个高度相互依存的ci是本研究的重点：电力系统、信息和通信技术（ICT）和基于电力的车辆/运输（e-transportation）。管理人员需要故障模式和传播的网络建模来设计策略，以减轻相互依赖的ci之间故障的影响。尽管迄今为止在ci的网络建模方面取得了很大进展，但这些努力未能捕捉到ci的复杂性，基于不充分或不准确的假设，忽略了相互依赖性，并且主要是经验和统计驱动的，而不是基于物理原理。本研究的目的是改进相互依赖ci的失败和弹性建模。然后，这项工作将扩展使用强化学习来帮助决策。其结果将是一个框架，用于解释CI的相互依赖性，这对于失败和灾难规划和恢复至关重要。知识价值。基于影响图的模型和基于强化学习的相互依赖缓解框架有力地解决了一个长期困扰城市/基础设施规划者和运营商的关键问题：如何建模、预测和防止在多个关键基础设施中传播的级联故障。目前的方法缺乏准确性和处理复杂性和相互依赖性的能力。这项工作将详细的基于物理的自下而上建模与强化学习的创新应用相结合，为该领域提供了变革性的进步，从而为CI设计、管理、灾难规划和恢复提供了更加灵活和准确的分析方法。更广泛的影响。该项目将通过降低基础设施的运营成本，减少停电的可能性和影响，以及改善城市对重大骚乱的反应，对社会产生积极影响。教育部分将使来自纽约市立大学和邻近高中的数百名学生受益，这些高中招收了大量在STEM领域代表性不足的学生。这将增加训练有素的工程师和具有多学科背景的科学家的数量，他们将在定义社会的未来方面发挥重要作用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Impact of smart photovoltaic inverter control modes on medium‐voltage grid voltage and inverter lifetime: An experimental approach

智能光伏逆变器控制模式对中压电网电压和逆变器寿命的影响：实验方法

• DOI: 10.1049/stg2.12105
• 发表时间: 2023
• 期刊: IET Smart Grid
• 影响因子: 2.3
• 作者: Mohamed, Ahmed;Saadeh, George;Kaviani, Ali Kashefi
• 通讯作者: Kaviani, Ali Kashefi

Reinforcement Learning-Based Active Distribution Management for Reducing the Risk of Cascading Failure

基于强化学习的主动分配管理，降低级联故障风险

• DOI: 10.1109/td39804.2020.9299924
• 发表时间: 2020
• 期刊: 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D
• 作者: Dutta, Oindrilla;Chan, Chin;Saleh, Mahmoud;Mohamed, Ahmed
• 通讯作者: Mohamed, Ahmed

Common direct current (DC) bus integration of DC fast chargers, grid‐scale energy storage, and solar photovoltaic: New York City case study

直流快速充电器、电网规模储能和太阳能光伏的通用直流 (DC) 总线集成：纽约市案例研究

• DOI: 10.1049/stg2.12154
• 发表时间: 2024
• 期刊: IET Smart Grid
• 影响因子: 2.3
• 作者: Kamaludeen, Mohamed K.;Zafar, Kirn;Esa, Yusef;Mohamed, Ahmed Ali A.;Nyemah, Elihu;Salmeron, Lizzette;Odie, Simon
• 通讯作者: Odie, Simon

Modeling and Simulation of DC Electric Rail Transit Systems With Wayside Energy Storage

• DOI: 10.1109/tvt.2019.2895026
• 发表时间: 2019-01
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Mahdiyeh Khodaparastan;Oindrilla Dutta;Mahmoud Saleh;A. Mohamed

Communication-Based Control for DC Microgrids

• DOI: 10.1109/tsg.2018.2791361
• 发表时间: 2018-01
• 期刊: IEEE Transactions on Smart Grid
• 影响因子: 9.6
• 作者: Mahmoud Saleh;Yusef Esa;A. Mohamed