INSPIRE: Optimization Algorithms for Regional Thermoelectric Power Generation with Nonlinear Interference

INSPIRE：非线性干扰下区域热电发电的优化算法

• 批准号: 1547205
• 负责人: Matthew Johnson
• 金额: $ 31.6万
• 依托单位: Research Foundation Of The City University Of New York (Lehman)
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547205&HistoricalAwards=false
• 关键词: INSPIRE; Optimization; Algorithms; Regional; Thermoelectric

This INSPIRE project is jointly funded by the Algorithmic Foundations program in the Computing and Communications Foundations Division in the Directorate for Computer and Information Science and Engineering, the Environmental Sustainability program in the Chemical, Bioengineering, Environmental, and Transport Systems Division in the Directorate for Engineering, and the Office of Integrative Activities (OIA) INSPIRE program.A thermoelectric power plant's operations can affect its surrounding environment, for example by raising water temperatures, which can be harmful to aquatic life, and so must comply with government regulation such as the Clean Water Act (CWA). It has recently been observed that the effects of power plants' operations can be much longer-reaching and subtler than this, however: the use of this warmer water by a second power plant located downstream can degrade that plant's efficiency, causing it in turn to heat the river water more than it otherwise would have, or even forcing it to shut down in order to comply with the CWA. Such complex dynamics characterizing the joint effects of a region's power plants suggest possible gains from managing plants jointly rather than individually. This analytical perspective prompts consideration of a huge variety of potential benefits to seek and costs to avoid in optimizing regional plant operations, and the interference phenomenon prompts (re)consideration of a number of classical algorithmic problems in the field of combinatorial optimization. This research offers many potential societal benefits in terms of environmental protection, economic savings, energy security, protection from blackouts, public health, and so on. Insights provided by the algorithmic solutions this project develops will be conveyed to decision makers and, if successful, will ultimately lead to improvements in management practices in existing plants and in long-range strategic planning. The project will provide research training for graduate students and will expose undergraduates at Lehman College and CCNY (both Minority-Serving Institutions) to interdisciplinary scientific research.This project will inaugurate the study of a novel class of combinatorial optimization problems. More specifically, it will investigate new variations on classical problems such as knapsack and job scheduling, modified to incorporate a distinctive feature of the motivating application setting, i.e. the *nonlinear interference* that can occur between active power plants. The PI and his team will design efficient (near-)optimal algorithms for these problems in the sense of guaranteed approximation, and, leveraging existing analytical models, they will perform algorithmic engineering studies assessing their algorithms' real-world viability. Finally, using tools from algorithmic game theory, they will quantify and provide a rigorous foundation for the perceived benefits of solving the motivating plant management problems jointly rather than plant-by-plant.

该启发项目由计算机和通信基础算法基础计划共同资助。计算机和信息科学与工程局，化学，生物工程，环境和运输系统部门的环境可持续性计划，工程局的环境可持续性计划，以及综合活动（OIA）的范围范围的范围范围范围的范围，可以影响境地。可能对水生生物有害，因此必须遵守政府法规，例如《清洁水法》（CWA）。最近已经观察到，电厂运营的影响可能比这更长和更微妙，但是：在下游的第二个发电厂使用这种温暖的水可以降低该植物效率的降低，从而使其又导致其加热河水比否则会更大，甚至迫使它迫使它遵守CWA。这种复杂的动力学表征了一个地区发电厂的关节作用，这表明共同管理植物而不是单独管理植物可能的收益。这种分析观点促使人们考虑了各种潜在的收益，以寻求和成本以避免优化区域植物运营，以及干扰现象提示（RE）考虑组合优化领域的许多经典算法问题。这项研究在环境保护，经济储蓄，能源安全，免受停电，公共卫生等方面提供了许多潜在的社会利益。该项目开发的算法解决方案提供的见解将被传达给决策者，如果成功的话，最终将导致现有工厂和远程战略规划中的管理实践的改善。该项目将为研究生提供研究培训，并将在雷曼学院（Lehman College）和CCNY（两个少数职业服务机构）中揭露本科生，以实现跨学科的科学研究。该项目将启动研究一系列新型组合优化问题的研究。更具体地说，它将调查有关经典问题（例如背包和工作计划）的新变化，并修改为纳入激励应用程序设置的独特特征，即可能发生的 *非线性干扰 *可能发生在主动的动力工厂之间。 PI和他的团队将在保证近似的意义上为这些问题设计有效的（近）最佳算法，并且利用现有的分析模型，他们将执行评估其算法的现实世界可行性的算法工程研究。最后，使用算法游戏理论中的工具，他们将量化并为共同解决动机的植物管理问题而不是逐植物的益处提供严格的基础。