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Award Detail

Doing Business As Name:Purdue University
  • Xiaojun Lin
  • (765) 494-0626
  • Andrew L Liu
Award Date:06/15/2021
Estimated Total Award Amount: $ 219,993
Funds Obligated to Date: $ 219,993
  • FY 2021=$219,993
Start Date:07/01/2021
End Date:06/30/2023
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:EAGER: Design of Distribution-Level Electricity Markets: Demarginalization and Decentralized Learning
Federal Award ID Number:2129631
DUNS ID:072051394
Parent DUNS ID:072051394
Program Officer:
  • Aranya Chakrabortty
  • (703) 292-8113

Awardee Location

Street:Young Hall
City:West Lafayette
County:West Lafayette
Awardee Cong. District:04

Primary Place of Performance

Organization Name:Purdue University, School of ECE
Street:465 Northwestern Ave.
City:West Lafayette
County:West Lafayette
Cong. District:04

Abstract at Time of Award

This NSF EAGER project aims to design new electricity markets for distribution-level power systems, which can enable small prosumers with renewable generation to directly trade energy at the distribution level. Such distribution-level markets face significant new challenges due to low marginal cost and high uncertainty of renewable generation, as well as decentralized decision making of a large number of prosumers. This project will bring transformative changes by developing new market structures that are radically different from the existing transmission-level markets, which greatly reduce price-volatility, eliminate price-fixing, encourage long-term investment, and improve grid reliability. This will be achieved by considering new energy products that are tailored to resources with low marginal costs and significant generation uncertainty. The intellectual merits of the project include (i) novel distribution-level market designs that will produce much more stable market prices and encourage investment despite uncertain long-run outlook of future operating conditions; and (ii) learning algorithms that enable small and inexperienced prosumers to learn how to bid in the market. The broader impacts of the project include (i) contributing to the increasing adoption of renewable energy, which benefits our society as a whole and is essential for the evolution towards a sustainable future; and (ii) training of students interested in future energy systems and dissemination of results to both academic and industry. While electricity markets at the transmission level are well-established, markets at the distribution level are still at their infancy. Replicating the market principles from the transmission-level market to the distribution level will likely produce highly volatile market prices, increase the financial risks of both consumers and suppliers, and may even incentivize price-manipulation. To address these challenges, this project will design novel plan-ahead usage-right markets for both energy and ancillary services that naturally lower price-volatility and greatly eliminate the potential for market-power abuse and price-fixing. Further, in view of the limited expertise and resources of prosumers, the project will design bidding-by-learning strategies to automate the bidding processes for rationality-bounded bidders who may not even know their own valuation of electricity consumption. 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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