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Research Spending & Results

Award Detail

Awardee:UNIVERSITY OF TEXAS AT EL PASO
Doing Business As Name:University of Texas at El Paso
PD/PI:
  • Paras Mandal
  • (915) 747-8653
  • pmandal@utep.edu
Award Date:08/13/2020
Estimated Total Award Amount: $ 120,000
Funds Obligated to Date: $ 120,000
  • FY 2020=$120,000
Start Date:10/01/2020
End Date:09/30/2023
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.076
Primary Program Source:040106 NSF Education & Human Resource
Award Title or Description:Improving Student Learning in Power Engineering
Federal Award ID Number:2021470
DUNS ID:132051285
Parent DUNS ID:042000273
Program:IUSE
Program Officer:
  • John Jackman
  • (703) 292-4816
  • jjackman@nsf.gov

Awardee Location

Street:ADMIN BLDG RM 209
City:El Paso
State:TX
ZIP:79968-0001
County:El Paso
Country:US
Awardee Cong. District:16

Primary Place of Performance

Organization Name:University of Texas at El Paso
Street:500 W University Ave
City:El Paso
State:TX
ZIP:79902-5816
County:El Paso
Country:US
Cong. District:16

Abstract at Time of Award

This project aims to serve the national interest by improving student learning in power engineering to include the new technologies being used in the power industry. Power engineering is a field of electrical engineering that focuses on the structures and processes needed to generate, transmit, and use electric power. Today’s power engineers require multi-disciplinary engineering knowledge to manage an increasingly complex power grid. For example, the power grid includes a growing amount of renewable energy resources, as well as more digital devices, information technology tools, and sensors. To address the need to align power engineering education with the power industry’s needs, the project team will develop and assess a set of active learning modules. These modules will be integrated into existing power engineering courses and focus on Smart Energy Management Systems. The modules will include hands-on laboratory experiences, case studies, and interactive simulations. The modules will help students learn how to model power systems, solve forecasting problems for power systems, and use data analytics to characterize power systems. The electric power industry is a critical part of the nation’s infrastructure and touches the lives of everyone. Improving students’ understanding of modern electric power systems will help ensure the integrity and performance of the nation’s electric power grid. The goal of this project is to improve power engineering education by (i) developing active learning experiences that incorporate real world problems in modern power systems, and (ii) integrating issues, solutions, and emerging trends in the area of Smart Energy Management Systems, specifically targeting power distribution systems, renewable energy sources, and intelligent energy forecasting. The design of the active learning modules will be guided by a situated learning framework, using case-based evaluations of simulated data from real-world power system problems to engage students in authentic forecasting and data analytics. This project will address the following research questions: 1) Do students exposed to situated learning develop a more comprehensive understanding of energy management, integrated power system analysis, and data analytics that is relevant for emerging challenges in power systems? and 2) Do they take greater account of context and community? To answer these questions, student learning will be measured using pre- and post-tests for the modules. Formative assessment using a situated learning survey and student interviews will be used to improve the modules. A workshop will be held to disseminate project results to the power engineering education community. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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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