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

用户积极参与大型基础设施系统，虽然带来了前所未有的机遇，但也给运营商带来了重大挑战。一个这样的例子是电力分配系统，其中分布式能源资源(DER)和灵活负载的大规模集成通过用户参与的需求响应激励新的决策模式。本项目介绍了一种新的配电系统智能决策方法，以在高度不确定和随机的环境中有效地利用灵活的需求承诺。该项目的目标是(1)开发所需的分析，以充分反映几个小用户对用电量的限制以及他们与系统和能源供应商的互动，从而实现可行的需求侧灵活性；(2)使用配电系统管理的开放源码试验台开发需求侧协调的原型，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适应性和智能基础设施系统提供解决方案，在这些系统中，被动用户变成主动参与者。对于这里的需求响应重点，该项目将通过电网运营从负荷跟随转变为供应跟随，经济地实现灵活负荷和DER的高水平渗透。该项目的结果将为政策制定者和电力公用事业公司在管理聚合器驱动的市场方面提供宝贵的指导。这项提议的中心目标是使许多小客户能够在需求侧参与配电网，并解决客户和能源供应商之间的接口问题。建议的体系结构遵循两级结构：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。研究将沿着两个方向进行：(1)基于学习的控制，在学习并将客户约束和偏好纳入决策过程时，实现家庭级别的灵活性；(2)博弈论构造，在具有未知客户效用函数的受限环境中，聚合和协调家庭级别的灵活性。HEMS-客户界面的技术创新将包括HEMS使用的基于自动机学习的算法来学习客户随时间演变的能源使用限制，以及强化学习算法以满足时间限制，同时优化用电成本。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，允许提供商仅与几个客户类别进行交互，以及Stackelberg博弈公式，明确纳入网络拥塞约束。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.