CAREER: Holistic Distributed Resource Management and Discovery via Augmented Learning and Robust Optimization

这个NSF职业项目旨在开发能源工程解决方案，以体现不同住宅电力消费者的偏好和需求。这项研究将实现公用事业公司拥有的资产和客户拥有的资产的共同管理，并为分布式能源(例如屋顶太阳能)和配电系统的运行方式带来革命性的变化。这一目标将通过利用人工智能算法来分析能源系统的人在回路中的组成部分，同时考虑到偏好和能源需求的多样性来实现。该项目的智力优势包括描述最终用户的消费行为，并允许在不对行为本身建模的情况下设计行为感知智能电网解决方案。该项目提出了一丝不苟的方法，以推进网格边缘资源管理，同时考虑关键因素。该项目的更广泛影响包括提高所有人，特别是低收入社区的能源复原力。将设计体验式学习模块，教育公众了解智能电网技术以及先进的公用事业和消费者所有资产共同管理的好处。它还将启动一项跨学科研究和教育计划的设计和开发，重点是具有社会意识和公平的清洁能源工程解决方案，重点是代表不足的少数民族的机会。该项目将开发自下而上的方法，以克服积极管理电网边缘资源和配电系统的多重障碍。将开发创新的方法，从智能电表数据中识别设备使用情况，以实现非侵入性负载发现。新的人工智能算法(例如，因果条件隐式半马尔可夫模型)将被开发用于行为感知的非侵入式负荷预测。开发了一种基于组合深浅神经网络的方法，以有效地聚集具有互补控制能力的分布式能源的混合和匹配。一个新的不平衡交流最优潮流将得到增强，以促进基于逆变器的分布式能源调度，同时识别协调的最优逆变器控制模式及其设置。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。

This NSF CAREER project aims to develop energy engineering solutions that embody the preferences and needs of diverse residential electricity consumers. The research will enable co-management of utility-owned and customer-owned assets, and bring transformative changes to how distributed energy resources (e.g., rooftop solar) and power distribution systems are operated. This goal will be achieved by leveraging artificial intelligence algorithms to analyze the human-in-the-loop component of energy systems along with consideration of diversity in preferences and energy needs. The intellectual merits of the project include characterizing end users’ consumption behavior and enabling design of behavior-aware smart grid solutions without modeling the behavior itself. The project proposes meticulous methodologies to advance grid-edge resource management while accounting for critical factors. The broader impacts of the project include enhancing energy resilience for all specifically for low-income communities. Experiential learning modules will be designed to educate the public on smart grid technologies and benefits of advanced co-management of utility and consumer -owned assets. It will also initiate the design and development of an interdisciplinary research and educational program focused on socially-aware and equitable clean energy engineering solutions, with emphasis on opportunities for under-represented minorities.The project will develop ground-up approaches to overcome multiple hurdles for active management of grid-edge resources and power distribution systems. Innovative methodologies will be developed to identify appliance utilization from smart meter data to enable non-intrusive load discovery. Novel artificial intelligence algorithms (for example, causal conditional hidden semi-Markov model) will be developed for behavior-aware non-intrusive load forecasting. An approach based on combined deep-shallow neural networks will be developed for efficient aggregation of mix and match of distributed energy resources with complementary control capabilities. A novel unbalanced AC optimal power flow will be enhanced to facilitate inverter-based distributed energy resources scheduling while identifying coordinated optimal inverter control modes and their settings.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.