NSF Org:	ECCS Division of Electrical, Communications and Cyber Systems
Recipient:	BOARD OF REGENTS OF THE NEVADA SYSTEM OF HIGHER ED
Initial Amendment Date:	July 21, 2023
Latest Amendment Date:	June 10, 2024
Award Number:	2313767
Award Instrument:	Standard Grant
Program Manager:	Eyad Abed eabed@nsf.gov  (703)292-2303 ECCS  Division of Electrical, Communications and Cyber Systems ENG  Directorate for Engineering
Start Date:	September 1, 2023
End Date:	August 31, 2026 (Estimated)
Total Intended Award Amount:	$250,000.00
Total Awarded Amount to Date:	$305,000.00
Funds Obligated to Date:	FY 2023 = $250,000.00 FY 2024 = $55,000.00
History of Investigator:	Hanif Livani (Principal Investigator) hlivani@unr.edu
Recipient Sponsored Research Office:	Board of Regents, NSHE, obo University of Nevada, Reno 1664 N VIRGINIA ST # 285 RENO NV  US  89557-0001 (775)784-4040
Sponsor Congressional District:	02
Primary Place of Performance:	Board of Regents, NSHE, obo University of Nevada, Reno 1664 N VIRGINIA ST RENO NV  US  89557-0001
Primary Place of Performance Congressional District:	02
Unique Entity Identifier (UEI):	WLDGTNCFFJZ3
Parent UEI:	WLDGTNCFFJZ3
NSF Program(s):	GOALI-Grnt Opp Acad Lia wIndus, EPCN-Energy-Power-Ctrl-Netwrks
Primary Program Source:	01002425DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	7607, 019Z, 1504
Program Element Code(s):	150400, 760700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.041

ABSTRACT

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 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.

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