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

NSF CAREER项目旨在开发能源工程解决方案，体现不同住宅电力消费者的偏好和需求。该研究将使公用事业拥有的和客户拥有的资产的共同管理，并带来如何分布式能源的变革（例如，屋顶太阳能）和配电系统。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组件沿着考虑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。该项目提出了细致的方法，以推进网格边缘资源管理，同时考虑到关键因素。该项目更广泛的影响包括提高所有人的能源复原力，特别是低收入社区。将设计体验式学习模块，教育公众了解智能电网技术和先进的公用事业和消费者拥有的资产共同管理的好处。该项目还将启动跨学科研究和教育计划的设计和开发，重点关注社会意识和公平的清洁能源工程解决方案，重点关注代表性不足的少数群体的机会。该项目将开发自下而上的方法，以克服电网边缘资源和配电系统积极管理的多重障碍。将开发创新的方法来从智能电表数据中识别设备利用率，以实现非侵入式负载发现。新型人工智能算法（例如，因果条件隐半马尔可夫模型）将被开发用于行为感知的非侵入式负荷预测。将开发基于组合的深-浅神经网络的方法，用于具有互补控制能力的分布式能源的混合和匹配的有效聚合。一个新的不平衡交流最优潮流将得到加强，以促进基于逆变器的分布式能源调度，同时确定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，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.