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

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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.