| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | August 6, 2020 |
| Latest Amendment Date: | August 6, 2020 |
| Award Number: | 1951813 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Sandip Roy
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2020 |
| End Date: | September 30, 2021 (Estimated) |
| Total Intended Award Amount: | $150,000.00 |
| Total Awarded Amount to Date: | $150,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3124 TAMU COLLEGE STATION TX US 77843-3124 (979)862-6777 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
3128 TAMU College Station TX US 77843-3128 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | S&CC: Smart & Connected Commun |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The energy supply is the backbone of Smart and Connected Communities (S&CC). It ties together various energy stakeholders (providers, consumers, and services) across different social, economic and cultural layers, and among groups corresponding to the residential, commercial, or industrial settings. Recent statistics indicate that despite all the measures taken by the utility industry to maintain the reliability and security of the energy supply, the number of major blackouts throughout the world is increasing. Loss of electricity affects residents, students, private and public sector employees, small and large businesses, and critical city services, such as, police, firefighters, and first responders. The fundamental question is whether the data sciences and engineering, combined with social sciences and technology can help reduce the losses and related societal impacts. This project postulates that the combination of the Big Data spatiotemporal analytics and physical models will allow us to achieve predictive outage risk capabilities to address the mitigation options not presently available.
The Advance Learning for Energy Risk Tracking (ALERT) approach will focus on predicting the outages and asset failure risks using historical data from utilities (outage, smart meter data, etc.), along with additional data from weather-related government and private sources (radar, satellite, ASOS, NLDN and NDFD, Vaisala), as well as topology and vegetation data. Such risk predictions will be shared with participating utility companies, and with their customers to mobilize mitigation measures, which needs a strong social study aspect to better understand customer behavior. Those measures may include: a) equipment repair/replacement and feeder switching actions, b) relocation of the volatile population, and local power back up for schools, businesses and essential city services, c) scheduling firefighter and emergency services for evacuation of at-risk populations, and d) dispatching police forces to prevent looting of vacated houses and businesses. The goal of the communities to minimize the risk of electricity supply failure and undesirable environmental impacts will be achieved by engaging community stakeholders in creating and sharing outage prediction risk maps to allow for individual and collaborative mitigation actions. The main objective is to build research capability to develop a methodology for predicting the risk of electricity outages, which emphasizes the S&CC aspects. To strengthen the community engagement, and to consolidate thinking in the research team and among the various stakeholder groups in metropolitan areas in Texas, different types of meetings will be held.. These multidisciplinary meetings will focus on defining ALERT goals: the need for data collection and behavioral pilots, the requirements for the integration platform, the logistics of the user portal implementations for the dissemination of ALERT messages and handling of false positive and false negative scenarios, as well as collection of panel polling data in future steps. The project will identify the research gaps and means of addressing them, the data requirements, and will develop a comprehensive network of contacts for each partner organization, and each prospective pilot participant.
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.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The Advance Learning for Energy Risk Tracking (ALERT) planning grant was focused on the key hypothesis that advanced data analytics may be used to predict the risk of outages in the electricity supply, which will then help the community to manage and mitigate the negative impacts for the benefits of all the consumers and stakeholders. This led to defining the research activities, which resulted in several planning grant outcomes: a) the community of San Antonio, Texas was selected as the pilot site due to a close collaboration between the city government and utility company that is owned by the city, b) number of consumers within the city representing residential, commercial and public service facilities (water supply utility, schools, health center, nursing homes) customers were recruited to support the pilot implementation, and c) community information needed for the research was gathered through qualitative interviews with the key stakeholder representatives that included large and small businesses, a hospital, and a nursing home. Information from the qualitative interviews informed the quantitative survey. Our research benefitted from having interviews before and then after the extreme outages caused by the February, 2021 winter storm in Texas. A quantitative survey was developed and tested in Philadelphia by the team at Temple University using BeHeardPhilly, a survey panel of Philadelphia City residents, in the PECO service territory. Survey results confirmed that residents are interested in predictive alerts, that they would trust their utility company to provide them, and that there is some resilience in the face of prediction errors. We also identified the collaborators for the education outreach including the University of Texas San Antonio, Texas A&M University in San Antonio and College Station, the University of Houston, in Houston, and Temple University in Philadelphia.
The main objective of our planning grant was achieved by building research capacity to develop a methodology for predicting the risk of electricity outages, which emphasizes the smart and connected community (S&CC) aspects: strengthening the community engagement, and consolidating thinking in the research team and among the various stakeholder groups in San Antonio. Stakeholder meetings, which were held on-line due to the COVID restrictions, were held to develop strategic research questions and to summarize expected outcomes. As a result, we were able to also identify the research infrastructure needed to perform the project tasks, and recruit potential members of the various oversight groups: The Advisory Board, the Pilot Advisory Committee, and the Focus Groups. We also discussed the research gaps and means of addressing them, explained the data requirements, and developed a comprehensive network of contacts for each partner organization, and each prospective pilot participant. This eventually resulted in the proposal that was submitted to the NSF solicitation for S&CC in early 2021
The intellectual merit is in creating a multidisciplinary research team with engineering and social/data science background, and networking the team members with diverse stakeholder groups representing ALERT users in different community segments. The contribution of the team members from social and behavioral sciences was in identifying scientific questions to be used for the survey and lab experiments aimed at learning behavioral aspect of the ALERT users. The contribution of the team members from data science and engineering was in recognizing the physic- and data-based methods needed to collect data and defining technical means how to engage the utility and community to adopt the ALERT solution. We engaged graduate students in the planning grant activities to expose them to the S&CC goals and created a pool of potential Research Assistants that can be recruited for the full grant.
The broader impacts were bolstered by the realities of the February, 2021 extreme power and water outages in Texas. Through the qualitative interviews we were able to increase buy-in for the project as leadership in large and small businesses and health care settings began to understand that loss of electricity affects resiliency of other critical infrastructures such water, transportation fuel, or food supply. In addition, they realized that their “emergency plans” were not good enough. We also envisioned how the ALERT demos may be used to promote data science approaches as an addition to the STEM curricula in San Antonio ISDs and across the participating universities in San Antonio, College Station, Houston and Philadelphia. We recruited museums in San Antonio and Philadelphia to reach out to students with uniquely diverse social, cultural, age and economic background reflecting the mentioned communities. This education outreach model will motivate other communities to collaborate, which will reinforce the core mission of the S&CC program.
Last Modified: 11/02/2021
Modified by: Mladen Kezunovic
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