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

用户积极参与大规模基础设施系统，在带来前所未有机遇的同时，也给运营商带来了重大挑战。其中一个例子是电力分配系统，其中分布式能源（DERs）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式。该项目介绍了一种新颖的配电系统智能决策方法，以有效地利用高度不确定和随机环境中的灵活需求承诺。该项目的目标是：(1)通过充分代表几个小消费者对电力使用的限制以及他们与系统和能源供应商的互动，开发必要的分析，以实现可操作的需求侧灵活性；(2)使用开源配电系统管理测试平台开发需求侧协调原型，并使用实际效用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，在这些系统中，被动用户将转变为主动参与者。对于这里的需求响应焦点，该项目将通过将电网运行从负荷跟随转变为供应跟随，从而在经济上实现灵活负荷和需求需求的高水平渗透。该项目的结果将为决策者和电力公司管理聚合器驱动的市场提供有价值的指导。该方案的核心目标是使许多小客户能够参与配电网的需求侧，并解决客户和能源供应商之间的接口问题。提出的体系结构遵循两层结构：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。研究将围绕两个重点提出：(1)基于学习的控制，通过学习和将客户约束和偏好纳入决策过程，实现家庭水平的灵活性；(2)构建了在客户效用函数未知的约束环境下，对网络层面家庭级灵活性进行聚合和协调的博弈论结构。HEMS-客户界面的技术创新将包括HEMS使用的基于自动学习的算法来学习客户的时间演变的能源使用约束，以及强化学习算法来满足时间约束，同时优化电力消耗成本。在供应商- hems接口上，技术创新将包括一个新的基于平均字段的客户模型，该模型允许供应商仅与少数客户类进行交互，以及一个明确包含网络拥塞约束的Stackelberg游戏公式。该奖项反映了美国国家科学基金会的法定使命，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。

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.