CyberSEES: Type 1: Collaborative Research: Sustainability-aware Management of Interdependent Power and Water Systems

Cyber​​SEES：类型 1：协作研究：相互依赖的电力和水系统的可持续性意识管理

• 批准号: 1539462
• 负责人: Andrew Liu
• 金额: $ 16.43万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539462&HistoricalAwards=false
• 关键词: CyberSEES; Type; Collaborative; Research; Sustainability

While extensive attention has been given to sustainability in the energy systems, including the subsystems of electricity, petroleum, and natural gas, an oft-overlooked aspect is the interdependence between energy and other infrastructure systems, such as water and transportation systems, and the potential adverse impacts to economics, reliability, and sustainability caused by such interdependence. For example, regulations in the water sector to preserve freshwater may restrict water usage in the power sector, likely causing reduced available generation capacities and hence jeopardizing the reliability of power systems. On the other hand, environmental policies only focused on the power sector, such as those encouraging retrofitting or installing carbon dioxide capture and sequestration capabilities to existing and new coal plants would further constrain the water system as coal plants with carbon sequestration are among the heaviest users of water. Thus, there is a clear need to better understand and manage the interdependence of critical infrastructure systems to promote sustainability across all systems, while not undermining economic and reliability considerations. This proposed work aims to address this need through the theory, modeling and computation of large-scale, interdependent complex systems by way of distributed, highly scalable computing. The results will be widely disseminated through publications and seminars. Further, the project team will leverage established institutional outreach programs to the general public, especially to high-school students and teachers, such as through the Engineering Projects In Community Service program and Purdue?s Energy Academy. The grand vision of this project is to promote sustainability across interdependent systems, as well as to achieve economic efficiency and to maintain reliability through decentralized yet coordinated management of individual systems by establishing a complete modeling, analytical, and computational framework based upon the general class of augmented Lagrangian methods originating from convex optimization. While the augmented Lagrangian method is not a new algorithm, the current implementation of such algorithms has not taken advantage of its distributed feature, which would be particularly suitable to deal with large-scale, interlinked systems. One of the major goals of this work is to establish the theoretical foundations of distributed Lagrangian methods and to implement the algorithms on supercomputer clusters to demonstrate the benefits of distributed computing. This research aims to pave the way for cloud computing such that the algorithms can be used by decision-makers even without access to supercomputers. Another contribution is that the augmented Lagrangian method algorithms will be extended to incorporate stochastic data, both in terms of theoretical issues such as algorithm convergence as well as practical implementation. The computational methods will be tested and validated through real-world models of interdependent power and water systems.

尽管已经广泛关注能源系统的可持续性，包括电力，石油和天然气的子系统，但经常被忽视的方面是能源与其他基础设施系统（例如水和运输系统）之间的相互依存关系，例如水和运输系统，以及对经济性，可靠性和可持续性的潜在不利影响。例如，水部门保存淡水的法规可能会限制电力部门的用水量，从而导致可用发电能力降低，从而危害电力系统的可靠性。另一方面，环境政策仅关注电力部门，例如那些鼓励改造或安装二氧化碳捕获碳和对现有燃煤电厂的固存能力的人，将进一步限制水系统，因为含碳固执的燃煤电厂是最重的水。因此，显然需要更好地理解和管理关键基础设施系统的相互依存，以促进所有系统的可持续性，同时并没有破坏经济和可靠性考虑。这项提出的工作旨在通过通过分布式，高度可扩展的计算方式对大规模，相互依存的复杂系统的理论，建模和计算来满足这一需求。结果将通过出版物和研讨会广泛传播。此外，项目团队将向公众提供既定的机构外展计划，尤其是向高中生和老师提供的，例如通过社区服务计划中的工程项目和普渡大学的能源学院。该项目的宏伟愿景是通过分散的，分析性和计算框架的完整建模，分析性和计算框架来促进相互依存系统的可持续性，以及实现经济效率，并通过对单个系统的分散管理管理的管理来维持可靠性，并基于增强的Lagrangian方法的一般类别，这些框架源自convex优化。虽然增强的拉格朗日方法不是一种新算法，但此类算法的当前实现尚未利用其分布式功能，该功能特别适合处理大型，相互关联的系统。这项工作的主要目标之一是建立分布式拉格朗日方法的理论基础，并在超级计算机簇上实施算法，以证明分布式计算的好处。这项研究旨在为云计算铺平道路，以便决策者也可以使用算法也可以使用超级计算机。另一个贡献是，将扩展的Lagrangian方法算法将扩展到合并随机数据，这是在理论问题（例如算法收敛又是实际实现的理论问题。计算方法将通过相互依存的功率和水系统的现实世界模型进行测试和验证。