Abstract
To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.
| Original language | English |
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| Title of host publication | 2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 155-159 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350336962 |
| DOIs | |
| State | Published - 2023 |
| Event | 2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023 - San Juan, United States Duration: Nov 6 2023 → Nov 9 2023 |
Publication series
| Name | 2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023 |
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Conference
| Conference | 2023 IEEE PES Innovative Smart Grid Technologies Latin America, ISGT-LA 2023 |
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| Country/Territory | United States |
| City | San Juan |
| Period | 11/6/23 → 11/9/23 |
Funding
This material is based upon work supported by the U.S. Department of Energy s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office. This manuscript has been authored by UTBattelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan). Relevant research is summarized in Section II. Section III describes the use-case of Puerto Rico, where the authors have an ongoing collaboration funded by the U.S. Department of Energy’s Solar Energy Technologies Office. Leveraging the lessons learned from this use-case, Section IV introduces the framework while Section V discusses open research questions. Section VI offers conclusion and future work. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan).
Keywords
- data privacy
- distributed energy resources
- extreme weather
- federated learning
- privacy-aware
- resilience