Collaborative Research: CPS: Medium: Adaptive, Human-centric Demand-side Flexibility Coordination At-scale in Electric Power Networks

合作研究：CPS：中：电力网络中大规模的自适应、以人为中心的需求方灵活性协调

• 批准号: 2208794
• 负责人: Vijay Gupta
• 金额: $ 33万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2208794&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Medium; Adaptive

Active user participation in large-scale infrastructure systems, while presenting unprecedented opportunities, also poses significant challenges for the operator. One such example is electric power distribution systems, where the massive integration of distributed energy resources (DERs) and flexible loads motivates new decision-making paradigms via demand response through user engagement. This project introduces a novel approach for intelligent decision making in power distribution systems to efficiently leverage flexible demand commitments in highly uncertain and stochastic environments. The project goals are to (1) develop analytics required to enable actionable demand-side flexibility from several small consumers by adequately representing their constraints regarding electricity usage and their interactions with the system and the energy provider; and (2) develop a prototype for demand-side coordination using an open-source testbed for distribution systems management and evaluate the proposed algorithms with real-world utility data. Successful completion of this project will provide solutions to adaptive and smart infrastructure systems in which passive users turn into active participants. For the demand response focus here, this project will enable high levels of penetration of flexible loads and DERs economically through the transformation of grid operation from load following to supply following. The results from this project will provide valuable guidance to policymakers and electric utilities in managing aggregator-driven markets. The central aim of this proposal is to enable the demand-side participation of many small customers in a distribution grid and solve for an interface between customers and an energy provider. The proposed architecture follows a two-level structure: a home energy management system (HEMS) providing a home-level interaction between the consumer and the HEMS, and a feeder-level interaction between the HEMS and the demand-response provider. Research along two thrusts will be proposed: (1) learning-based control to achieve home-level flexibility upon learning and incorporating customer constraints and preferences into the decision-making process; and (2) game-theoretic constructs to aggregate and coordinate the home-level flexibility at the network-level in a constrained environment with unknown customer utility functions. Technical innovations at the HEMS-customer interface will include automata learning-based algorithms used by HEMS to learn customers’ temporally evolving energy usage constraints, and reinforcement learning algorithms to satisfy temporal constraints while optimizing the cost of electricity consumption. At the provider-HEMS interface, technical innovations will include a new mean field based model of customers that allows the provider to interact with only a few customer classes, and a Stackelberg game formulation that explicitly incorporates network congestion constraints.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.

用户积极参与大规模基础设施系统，在带来前所未有的机遇的同时，也给运营商带来了重大挑战。配电系统就是这样的一个例子，其中分布式能源（DER）和灵活负载的大规模集成通过用户参与的需求响应激发了新的决策范例。该项目引入了一种在配电系统中进行智能决策的新颖方法，以在高度不确定和随机的环境中有效利用灵活的需求承诺。该项目的目标是 (1) 开发所需的分析，通过充分代表多个小型消费者在用电方面的限制以及他们与系统和能源提供商的互动，实现可操作的需求方灵活性； (2) 使用用于配电系统管理的开源测试平台开发需求侧协调原型，并使用真实世界的公用事业数据评估所提出的算法。该项目的成功完成将为自适应和智能基础设施系统提供解决方案，使被动用户转变为主动参与者。对于此处的需求响应重点，该项目将通过将电网运行从负荷跟踪转变为供应跟踪，经济地实现灵活负载和分布式能源的高水平渗透。该项目的结果将为政策制定者和电力公司管理聚合驱动市场提供宝贵的指导。该提案的中心目标是使许多小客户的需求方参与配电网，并解决客户和能源供应商之间的接口问题。所提出的架构遵循两级结构：家庭能源管理系统（HEMS）提供消费者和 HEMS 之间的家庭级交互，以及 HEMS 和需求响应提供商之间的馈线级交互。我们将提出以下两个重点的研究：（1）基于学习的控制，通过学习并将客户约束和偏好纳入决策过程来实现家庭级别的灵活性； (2) 博弈论构造，用于在客户效用函数未知的受限环境中聚合和协调网络级别的家庭级别灵活性。 HEMS-客户界面的技术创新将包括 HEMS 使用基于自动机学习的算法来了解客户随时间变化的能源使用限制，以及强化学习算法来满足时间限制，同时优化电力消耗成本。在提供商-HEMS 界面，技术创新将包括一个新的基于平均场的客户模型，该模型允许提供商仅与少数客户类别进行交互，以及明确纳入网络拥塞约束的 Stackelberg 博弈公式。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。