EAGER: Real-Time: Learning, Selection, and Control in Residential Demand Response for Grid Reliability

随着可再生能源的增加和常规发电机的退役，需求响应(DR)已被用来解决电网实时供需平衡的可靠性问题。然而，作为电力需求中最大份额的住宅DR的潜力在实践中还没有得到充分的开发。现有的试点揭示了许多问题，例如：i)小额金钱奖励在用户参与中发挥的作用有限；ii)当公用事业公司广泛开发DR资源时用户不满意；iii)由于用户行为的不可预测性而缺乏可靠性。与ThinkEco Inc.合作，这项提案将为住宅DR开发新的适用方法，并提供可证明的担保。该方法将学习DR行为，选择正确的住宅用户，并自动控制住宅电器-所有这些都是为了提高系统可靠性。这项研究将在使用ThinkEco平台的真实住宅灾难恢复项目上进行测试和验证。研究成果将推进人在环社会系统的实时学习，应用范围从交通到电网，再到未来的人工智能系统。该团队坚定地致力于为K-12、妇女和代表性不足的少数族裔提供STEM机会。此外，学术界和产业界的密切合作保证了学术成果快速有效地转化为行业实践。具体地说，通过从历史和实时测量中了解用户的能耗行为，并实时调整用户选择和控制策略，本研究将发明灾难恢复学习和控制机制，以满足不同的电网运行需求。这项建议的一个主要主题是在人在环的社会系统中关闭学习(探索)和控制(利用)之间的循环：如何在同时采取良好的控制行动(利用)的同时学习(探索)用户行为。在探索和开发之间有一个基本的权衡，拟议的研究旨在揭示权衡并设计实时决策规则，以实现住宅博士的近乎最佳的性能。与传统的计算机科学或统计学学习方法不同，该建议旨在解决人类用户和工程系统之间相互交织的挑战。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。

As renewable energy sources increase and conventional generators retire, demand response (DR) has been utilized to address the reliability issue on balancing real-time demand and supply in power grids. However, the potential of residential DR, which is the largest share of electricity demands, has not been fully exploited in practice. Existing pilots reveal many issues, such as i) small monetary rewards which play a limited role in user participation, ii) user dissatisfaction when utility companies exploit DR resources extensively, and iii) the lack of reliability due to the unpredictability of user behavior. In collaboration with ThinkEco Inc, this proposal will develop novel and applicable approaches for residential DR with provable guarantees. The method will learn DR behavior, select the correct residential users, and automatically control residential appliances -- all in the service of enhancing system reliability. The research will be tested and validated on real-world residential DR programs using ThinkEco platforms. The research results will advance real-time learning for human-in-the-loop societal systems with applications ranging from transportation to power grids to AI-enabled systems of the future. The team is strongly committed to providing opportunities in STEM to K-12, women, and under-represented minorities. Moreover, the close collaboration between academia and industry promises a fast and effective transition of academic results to industry practice. Specifically, by understanding users' energy consumption behavior from both historical and real-time measurements, and adjusting user selection and control strategies in real-time, this proposed research will invent DR learning and control mechanisms to satisfy various power grid operation requirements. A major theme in this proposal is to close the loop between learning (exploration) and control (exploitation) in human-in-the-loop societal systems: how to learn (explore) user behavior while taking good control actions (exploitation) at the same time. There is a fundamental tradeoff between exploration and exploitation, and the proposed research aims to uncover the tradeoff and design real-time decision-making rules to achieve near-optimal performance for residential DR. Different from the conventional approaches to learning in computer science or statistics, this proposal aims to tackle the challenge of intertwined interactions between human users and the engineered systems.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.