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

该NSF项目旨在开发基于学习的新方法来估计网格边缘资源（GERs）的灵活性，如太阳能、太阳能和存储或智能恒温器，然后基于多智能体和分布式优化方法设计公平的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，以估计电网层面的地理空间灵活性，并通过开发用于GERs协调以提供电网服务的多时间尺度分布式优化方法，为配电网络中的GER管理领域带来革命性变化。该项目的结果预计将对电网的可靠性和弹性产生重大影响，同时为客户提供新的金融和货币机会。该项目的智力优势包括新的基于混合物理/数据驱动的ger灵活性估计方法及其不确定性，以及根据客户的计算和通信能力创建可配置的、多时间尺度的分布式优化，以提供快速和缓慢的电网服务。该项目的更广泛影响包括通过印刷媒体、广播新闻和互联网对公众进行综合教育，并为代表性不足的学生提供教育和研究机会。本项目将从以下四个方面推进配电网柔性能源管理。第一个方向将是利用生成式机器学习技术，并利用来自附近可观察到的仪表后（BTM）太阳能和存储资产的空间、时间和渠道信息来解决数据缺口。这种方法增强了对这些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，以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。第三个方向将是构建一个可配置的多时间尺度分布式协调框架，将BTM的灵活性打包为快速和慢速网格服务。使终端用户能够提供多时间尺度的电网服务，增加了电力系统的弹性并提高了客户收入。最后一个方向将是通过考虑到在多代理协调程序中的计算和通信限制，促进服务不足的客户的参与。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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.