Collaborative Research: Learning-Assisted Estimation and Management of Flexible Energy Resources in Active Distribution Networks

合作研究：主动配电网络中灵活能源的学习辅助估计和管理

• 批准号: 2313767
• 负责人: Hanif Livani
• 金额: $ 25万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313767&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Assisted; Estimation

This NSF project aims to develop novel learning-based approaches for estimating the flexibility amount of grid edge resources (GERs), such as solar, solar and storage, or smart thermostat, and then design equitable resource coordination and management methods based on multi-agent and distributed optimization approaches. The project will bring transformative changes to the area of GER management in distribution electricity networks by combining machine learning (ML) and artificial intelligence (AI) with the physics-based models of such resources for estimating geo-spatial flexibility at the grid level according, and also by developing a multi-time scale distributed optimization method for GERs coordination to provide grid services. The outcome of this project is expected to have significant impacts on grid reliability and resilience, while providing customers with new financial and monetary opportunities. The intellectual merits of the project include new hybrid physics-based/data-driven flexibility estimation methods for GERs along with their uncertainties, and creation of configurable, multi-time scale, distributed optimization for providing fast and slow grid services according to the customers’ computation and communication capabilities. The broader impacts of the project include integrating educating the public through print media, broadcast news, and the Internet, and providing educational and research opportunities for underrepresented students.This project will advance management of flexible energy resources of distribution grids in the following four directions. The first direction will be in utilizing generative ML techniques and leveraging spatial, temporal, and channel-wise information from nearby observable behind-the-meter (BTM) solar and storage assets to address data gaps. This approach enhances the estimation of availability and flexibility of these BTM units. The second direction will be in developing a geo-spatial flexibility estimation method that improves the characterization of smart thermostat loads. This method combines physics-based and data-driven models to obtain expected power and energy adjustments and associated uncertainties. The third direction will be in building a configurable multi-time scale distributed coordination framework to package BTM flexibilities as fast and slow grid services. Enabling end-use customers to provide multi-time scale grid services increases power system resilience and boosts customer revenue. The final direction will be in facilitating participation of underserved customers by accounting for their computation and communication limitations in multi-agent coordination procedure. This advancement will better distribute societal welfare and unlock potentials of underutilized BTM assets.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项目旨在开发基于新颖的学习方法，以估计网格边缘资源（GERS）的灵活性量（例如太阳能，太阳能和存储）或智能恒温器，然后设计基于多主体和分布式优化方法的公平资源协调和管理方法。该项目将通过将机器学习（ML）和人工智能（AI）与基于物理的基于物理的资源模型相结合，从而为分销电网中的GER管理区域带来变革性的变化，以根据网格级别的地理空间灵活性，以及​​通过开发多时间尺度分布方法来估算网格级别的地理灵活性，以提供GERS的配置，以提供网格服务。预计该项目的结果将对网格可靠性和韧性产生重大影响，同时为客户提供新的财务和货币机会。该项目的智力优点包括GERS的新型混合物理/数据驱动的灵活性估计方法及其不确定性以及可配置的可配置，多时间量表的创建，分布式优化，以根据客户的计算和通信功能提供快速和缓慢的网格服务。该项目的广播公司的影响包括通过印刷媒体，广播新闻和互联网对公众进行教育，并为代表性不足的学生提供教育和研究机会。该项目将在以下四个方向上促进分销网格的灵活能源的管理。第一个方向将是利用通用ML技术，并利用来自接近可观察的落后（BTM）太阳能和存储资产的空间，临时和渠道信息来解决数据差距。这种方法增强了这些BTM单元的可用性和灵活性的估计。第二个方向将是开发一种地理空间灵活性估计方法，该方法改善了智能恒温器负载的表征。该方法结合了基于物理和数据驱动的模型，以获得预期的功率和能量调整以及相关的不确定性。第三个方向将是构建可配置的多个时间比例分布协调框架，以将BTM灵活性作为快速和缓慢的网格服务打包。使最终用途的客户能够提供多个时间比例网格服务可以提高电源系统的弹性并提高客户收入。最终方向将是通过考虑其在多代理协调程序中的计算和沟通限制来促进服务欠缺的客户的参与。这一进步将更好地分配社会福利，并解锁未充分利用的BTM资产的潜力。该奖项反映了NSF的法定使命，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。