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

职业：通过增强学习和鲁棒优化进行整体​​分布式资源管理和发现

• 批准号: 2339243
• 负责人: Mojdeh Hedman
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-08-01 至 2029-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339243&HistoricalAwards=false
• 关键词: CAREER; Holistic; Distributed; Resource; Management

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.

该 NSF 职业项目旨在开发体现不同住宅用电消费者的偏好和需求的能源工程解决方案，该研究将实现公用事业拥有和客户拥有的资产的共同管理，并为分布式能源（例如，能源）的方式带来变革。这一目标将通过利用人工智能算法来分析能源系统的人机交互部分，并考虑偏好和能源需求的多样性来实现。该项目包括表征结束该项目提出了细致的方法来推进电网边缘资源管理，同时考虑到关键因素，包括增强所有人的能源弹性。专门针对低收入社区的体验式学习模块旨在教育公众了解智能电网技术以及公用事业和消费者拥有的资产的先进共同管理的好处，它还将设计和开发跨学科的研究和教育计划。专注于具有社会意识和公平的清洁能源工程该项目将开发基础方法来克服电网边缘资源和配电系统主动管理的多重障碍，以根据智能电表数据确定设备利用率。将开发新的人工智能算法（例如因果条件隐藏半马尔可夫模型），以进行行为感知的非侵入式负载预测。为了高效A 体现了新型不平衡交流最优功率流的聚合，将增强分布式能源的混合和匹配能力，以促进基于逆变器的分布式能源调度，同时确定最佳逆变器控制模式及其设置。这授予了 NSF 的法定使命。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，并被认为值得支持。