Collaborative Research Modeling Strategic Regulators in Network Infrastructure Planning

大型基础设施系统（如天然气、电力和供水网络）通常跨越地方、区域和国家管辖范围。在这种情况下，每个管辖区监督整个基础设施系统的某一部分，并可以选择使用不同的政策工具来实现自己的社会经济目标。这样的管辖区划分自然为监管竞争创造了肥沃的环境。该项目将开发数学模型，研究这一竞争对最佳基础设施设计的影响。例如，它将重点关注目前正在向智能电网快速过渡的电力网络。如果成功，该项目将为基础设施运营者和规划者（如电力公司和州/联邦监管机构）以及公民提供工具，以评估不同监管结构对实现其政策目标的影响。 项目成果将通过直接外联以及专业讲习班、会议和同行评审出版物的方式传达。此外，更广泛的影响包括通过项目指导，独立研究学习和对现有课程的研究改进，在研究生和本科生水平上培训未来的STEM研究人员，以及为来自STEM代表性不足群体的当地高中生提供早期研究机会（通过纽约大学科学与工程应用研究创新计划）该项目将开发用于优化大规模、分层、网络受限基础设施系统的决策的正式数学模型。该模型将内生地优化目前被认为是基础设施规划任务中的外生参数的政策决策，并允许研究多个管辖区之间不同形式的监管竞争。为了代表这种竞争，并考虑基础设施运营和规划任务的各个方面，该项目将利用多领导者-共同追随者博弈理论框架。 众所周知，该框架的计算要求很高，因此，该项目将基于渐进式对冲算法开发可扩展的算法解决方案，以实现实际大型网络的框架。该项目的算法部分将调查解决方案的质量，例如其最优性和唯一性。更广泛的科学影响，预计在模型和算法，以解决多阶段优化问题的不确定性。该项目将直接受益于运筹学和智能电网/城市领域的研究人员和开发人员，并扩展到公共政策和网络经济社区。该奖项反映了NSF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。

Large-scale infrastructure systems (e.g. gas, power, and water networks) often cross local, regional, and national jurisdiction boundaries. When this is the case, each jurisdiction oversees a certain part of the entire infrastructure system and can elect to pursue its own socio-economic objectives using different policy instruments. Such a jurisdictional divide naturally creates a fertile environment for regulatory competition. This project will develop mathematical models that study implications of this competition on the optimal infrastructure design. As an example, it will focus on electric power networks that are currently undergoing a rapid transition toward smart grids. If successful, this project will provide tools for infrastructure operators and planners (e.g. power utilities and state/federal regulators), as well as for citizens, to evaluate the effects of different regulatory structures on achieving their policy objectives. Project outcomes will be conveyed by means of direct outreach and via professional workshops, conferences, and peer-reviewed publications. Furthermore, broader impacts include training of future STEM researchers at graduate and undergraduate levels via project mentorship, independent research studies, and research-informed improvements to existing courses, as well as early-stage research opportunities for local high-school students from underrepresented groups in STEM (via NYU's Applied Research Innovations in Science and Engineering program).This project will develop formal mathematical models for optimizing policy decisions for large-scale, hierarchical, network-constrained infrastructure systems. This model will endogenously optimize policy decisions that are currently considered as exogenous parameters in infrastructure planning tasks and allow for studying different forms of regulatory competition between multiple jurisdictions. To represent this competition and account for all aspects of infrastructure operating and planning tasks, the project will leverage the multi-leader-common-follower game-theoretic framework. This framework is notoriously known to be computationally demanding and, therefore, this project will develop scalable algorithmic solutions, based on the Progressive Hedging algorithm, to implement the framework for realistically large networks. The algorithmic component of this project will investigate solution quality, e.g. its optimality and uniqueness properties. Broader scientific impacts are anticipated in models and algorithms to solve multi-stage optimization problems under uncertainty. This project will directly benefit researchers and developers working in the area of operations research and smart grids/cities, with extensions relevant to public policy and network economics communities.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.