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

这个INSPIRE项目是由计算机和信息科学与工程局计算和通信基础部门的算法基础计划、工程局化学、生物工程、环境和运输系统部门的环境可持续发展计划和综合活动办公室(OIA)INSPIRE计划共同资助的。热电厂的运营可能会影响周围环境，例如提高水温，这可能对水生生物有害，因此必须遵守政府法规，如清洁水法(CWA)。然而，最近观察到，发电厂运营的影响可能比这更深远、更微妙：位于下游的第二座发电厂使用这种较热的水可能会降低该电厂的效率，导致它反过来对河水进行更多的加热，甚至迫使它关闭，以遵守CWA。一个地区发电厂的联合效应表现出如此复杂的动态特征，这表明联合管理发电厂而不是单独管理发电厂可能会带来好处。这种分析观点促使人们考虑在优化区域工厂运营中需要寻求的大量潜在收益和需要避免的成本，而干扰现象促使(重新)考虑组合优化领域的一些经典算法问题。这项研究在环境保护、经济节约、能源安全、防止停电、公共健康等方面提供了许多潜在的社会效益。该项目开发的算法解决方案提供的见解将传达给决策者，如果成功，最终将改善现有工厂的管理实践和长期战略规划。该项目将为研究生提供研究培训，并将使雷曼学院和CCNY(都是为少数民族服务的机构)的本科生接触跨学科的科学研究。该项目将开启一类新的组合优化问题的研究。更具体地说，它将研究背包和作业调度等经典问题的新变体，对其进行修改，以纳入激励性应用环境的一个独特特征，即在活跃的发电厂之间可能发生的*非线性干扰*。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(接近)最优的算法，并利用现有的分析模型，他们将进行算法工程研究，评估他们的算法在现实世界中的可行性。最后，使用算法博弈论的工具，他们将量化并为共同解决激励性工厂管理问题而不是逐个工厂解决所感知的好处提供严格的基础。

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.