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

该NSF项目旨在开发新的基于学习的方法来估计网格边缘资源（GERs）的灵活性，例如太阳能，太阳能和存储，或智能恒温器，然后设计基于多代理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）与这些资源的基于物理的模型相结合，以估计网格级别的地理空间灵活性，并通过开发多时间尺度的分布式优化方法来协调GERs以提供网格服务，从而为配电网络中的格尔管理领域带来变革。该项目的成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财务和货币机会。该项目的智力优势包括新的混合基于物理/数据驱动的灵活性估计方法的GERs沿着其不确定性，并创建可配置的，多时间尺度，分布式优化提供快速和慢速网格服务，根据客户的计算和通信能力。通过印刷媒体、广播新闻、互联网等对公众进行综合教育，为代表性不足的学生提供教育和研究机会等，在更大范围内发挥影响。本项目将从以下四个方面推进配电网灵活能源的管理。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。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特性。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。第三个方向是构建一个可配置的多时间尺度分布式协调框架，将BTM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，提高电力系统的弹性，并提高客户的收入。最后的方向将是在促进参与服务不足的客户占他们的计算和通信的限制，在多智能体协调过程。该奖项反映了NSF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.