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: 2300355
• 负责人: Vijay Gupta
• 金额: $ 33万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300355&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.

用户积极参与大型基础设施系统在带来机遇的同时，也给运营商带来了重大挑战，其中一个例子就是配电系统，其中分布式能源（DER）和灵活负载的大规模集成激发了新的决策。该项目引入了一种在配电系统中进行智能决策的新方法，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）开发实现可操作需求所需的分析。 -来自几个小消费者的侧面灵活性通过充分表示他们在电力使用方面的限制以及他们与系统和能源供应商的互动；（2）使用配电系统管理的开源测试平台开发需求方协调的原型，并根据现实世界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，该项目将通过转型经济地实现灵活负载和分布式能源的高水平渗透。电网运行从负载跟踪到电源跟踪的结果。该项目将为政策制定者和电力公司管理聚合驱动市场提供有价值的指导。该提案的中心目标是使许多小客户能够参与配电网，并解决客户和能源提供商之间的接口问题。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。将提出两项主旨： (1) 基于学习的控制，通过学习实现家庭级灵活性，并将客户约束和偏好纳入决策过程；(2) 博弈论结构，在网络层面聚合和协调家庭级灵活性；在具有未知客户效用函数的受限环境中，HEMS-客户界面的技术创新将包括 HEMS 使用基于自动机学习的技术来了解客户随时间变化的能源使用限制，以及在优化成本的同时满足时间算法限制的强化学习算法。电力的在提供商-HEMS 界面，技术创新将包括基于新的平均场的客户模型，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 的贡献。法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Pricing Demand-Side Flexibility With Noisy Consumers: Mean-Variance Trade-Offs

与吵闹的消费者一起定价需求方灵活性：均值方差权衡

• DOI: 10.1109/tpwrs.2022.3169409
• 发表时间: 2023-03
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Aguiar, Nayara;Dubey, Anamika;Gupta, Vijay
• 通讯作者: Gupta, Vijay

Maximization of nonsubmodular functions under multiple constraints with applications

应用程序多重约束下非子模函数的最大化

• DOI: 10.1016/j.automatica.2023.111126
• 发表时间: 2023-09
• 期刊: Automatica
• 影响因子: 6.4
• 作者: Ye, Lintao;Liu, Zhi;Chi, Ming;Gupta, Vijay
• 通讯作者: Gupta, Vijay