Abstract
Operational scenarios of the power grids grow multifold to accommodate the diverse needs of both the utilities and end consumers, and the various other stakeholders in-between. To comprehensively model and apply analytics to support objectives and business functions of grid sectors, a reliable approach to characterize and design data flows is crucial. The flows bridge business functions with communications protocols, stakeholders such as the grid actors, and data interfaces comprising different data objects. Additionally, constraints applied to the flow such as cybersecurity, trust, privacy, and ownership among others intersect these entities, requiring the delineation of their interactions under different scenarios. This paper aims to not only highlight relevant research in the space of grid data flows, but also proposes, for the transmission-distribution sector, a novel modeling approach that marries the aforementioned entities: objectives, business functions, data interfaces, communication protocols, data stakeholders, and flow constraints. It elaborates on the design philosophy and the significance of each entity within the model and applies it to an example function of fault location, isolation and service restoration (FLISR). Finally, the next steps to extend the application of this data flow model for other practical operational scenarios are discussed.
| Original language | English |
|---|---|
| Title of host publication | 2024 Resilience Week, RWS 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350388985 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 2024 Resilience Week, RWS 2024 - Austin, United States Duration: Dec 3 2024 → Dec 5 2024 |
Publication series
| Name | 2024 Resilience Week, RWS 2024 |
|---|
Conference
| Conference | 2024 Resilience Week, RWS 2024 |
|---|---|
| Country/Territory | United States |
| City | Austin |
| Period | 12/3/24 → 12/5/24 |
Funding
This research work 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 Award Number (CID or WBS): DEEE 37771. 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/doepublic- access-plan).
Keywords
- data privacy
- data strategy
- governance
- grid data flows
- modeling approach
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