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: 2313768
• 负责人: Javad Mohammadi
• 金额: $ 25万
• 依托单位: University of Texas at Austin
• 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=2313768&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 项目旨在开发新颖的基于学习的方法来估计电网边缘资源（GER）的灵活性量，例如太阳能、太阳能和存储或智能恒温器，然后设计基于多智能体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) 与此类资源的基于物理的模型相结合来估计地理空间，从而为配电网的 GER 管理领域带来变革。网格级别的灵活性根据，并通过多时间尺度分布式优化方法进行 GER 协调以提供电网服务，预计该项目的成果将对电网可靠性和弹性产生重大影响，同时为客户提供新的财务和货币机会。该项目的内容包括针对 GER 及其不确定性的新的基于物理/数据驱动的混合灵活性估计方法，以及创建可配置的、多时间尺度的分布式优化，以便根据客户的计算和通信提供快速和慢速的网格服务项目的更广泛影响。包括通过印刷媒体、广播新闻和互联网整合教育公众，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推进配电网灵活能源的管理。第一个方向是。利用生成式机器学习技术并利用附近可观测的表后 (BTM) 太阳能和存储资产的空间、时间和通道信息来解决数据缺口问题。方向将是发展一种地理空间灵活性估计方法，可改善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。规模分布式协调框架将 BTM 灵活性打包为快速和慢速电网服务，使最终用户能够提供多时间规模的电网服务，从而提高电力系统的弹性并增加客户收入。为了这一进步将更好地分配社会福利并释放未充分利用的 BTM 资产的潜力。该奖项反映了奖项的法定使命，并通过使用基金会的智力价值和更广泛的影响进行评估，被认为值得支持。审查标准。