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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | University of Maryland, College Park |
| Country | United States |
| Start Date | Sep 01, 2021 |
| End Date | Aug 31, 2025 |
| Duration | 1,460 days |
| Number of Grantees | 3 |
| Roles | Principal Investigator; Co-Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2125775 |
This NSF project aims to examine empirically the heterogeneous electricity consumption and technology-using behavioral changes due to the co-adoption of distributed solar panels (PVs), battery storage, and electric vehicles (EVs) of residential consumers. Understanding the heterogeneous co-adopter behaviors is important for utilities and policymakers to adopt better-targeted interventions to induce behavioral changes needed for demand-side management.
The project will bring transformative change to the understanding and simulation of the power systems with increasing penetration of consumers that co-adopt these technologies. This will be achieved by using a large scale, multi-year, high frequency individual-consumer-level smart meter data, combined with an interdisciplinary approach encompassing advanced data analytics, econometrics, machine learning, and simulation methods.
The intellectual merits of the project include 1) developing a causal framework, through which we provide the empirical estimates of the impact of co-adoption on consumer behaviors, as well as examine how changes are affected by tariffs and observable characteristics of technologies, homes, and households; 2) uncovering the behavioral heterogeneity and categorizing each individual’s behavior changes into rational, irrational, misinformed, or environmental-driven behaviors; 3) developing the first empirically-validated buildings-to-grid integration framework that enables evaluation of the impacts of new energy technology co-adoption. The broader impacts of the project include facilitating the adoption of PVs, EVs, and battery storage technologies, transforming relevant engineering modeling by combining engineering with empirical behavioral analysis, and engaging industry practitioners, whose decision-making will be assisted by the project’s products.
Existing models to understand the electricity consumption behaviors of co-adopters of these technologies are largely engineering-based and do not account for actual consumer behaviors and the related heterogeneity. However, consumers’ actual behaviors can deviate from those predicted by engineering models and such deviations can be heterogeneous. Also, co-adopters’ behavior changes are not just a simple summation of the changes due to individual technology adoption, because the usage of one technology can change due to co-adopting another technology.
This project fills these major gaps by providing a first-of-a-kind empirical assessment of the heterogeneous impacts of energy technology co-adoption as well as by advancing existing buildings-to-grid modeling through incorporating empirical co-adopters’ behaviors.
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.
University of Maryland, College Park
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