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，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（DER）和灵活负载的大规模集成通过用户参与的需求响应来激励新的决策模式。该项目介绍了一种新的方法，在配电系统中的智能决策，有效地利用灵活的需求承诺在高度不确定和随机的环境。该项目的目标是：（1）开发所需的分析，通过充分代表他们对电力使用的限制以及他们与系统和能源供应商的互动，从几个小消费者那里实现可操作的需求侧灵活性;（2）使用开源测试平台开发需求侧协调原型，用于配电系统管理，并使用真实世界的公用事业数据评估拟议的算法。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。对于这里的需求响应重点，该项目将通过将电网运行从负荷跟踪转变为供应跟踪，经济地实现灵活负荷和DER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驱动的市场提供宝贵的指导。该提案的中心目标是使许多小客户能够在配电网中进行需求方参与，并解决客户与能源供应商之间的接口问题。建议的架构遵循两个层次的结构：家庭能源管理系统（HEMS）提供家庭级的消费者和HEMS之间的互动，和馈线级的HEMS和需求响应提供商之间的互动。研究沿着两个推力将被提出：（1）基于学习的控制，以实现家庭层面的灵活性学习和客户的约束和偏好纳入决策过程中;（2）博弈论的结构，以聚合和协调家庭层面的灵活性，在网络层面的约束环境与未知的客户效用函数。HEMS客户界面的技术创新将包括HEMS用于学习客户随时间变化的能源使用约束的基于自动机学习的算法，以及满足时间约束同时优化电力消耗成本的强化学习算法。在供应商HEMS接口，技术创新将包括一个新的平均场为基础的客户模型，使供应商只与少数客户类，和一个Stackelberg游戏公式，明确纳入网络拥塞的约束条件。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.