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Generating Realistic Information for the Development of Distribution and Transmission Algorithms

The Generating Realistic Information for the Development of Distribution and Transmission Algorithms (GRID DATA) program will fund the development of large-scale, realistic, validated, and open-access power system network models. These models will have the detail required to allow the successful development and testing of transformational power system optimization and control algorithms, including new Optimal Power Flow (OPF) algorithms. Project teams will take one of two tracks to develop models. The first option is to partner with a utility to collect and then anonymize real data as the basis for a model that can be released publically. The second approach is to construct purely synthetic power system models. The program will also fund the creation of an open-access, self-sustaining repository for the storage, annotation, and curation of these power systems models, as well as others generated by the community.

For a detailed technical overview about this program, please click here.

GridBright, Inc.

A Standards-Based Intelligent Repositorty for Collaborative Grid Model Management

GridBright, Inc. and Utility Integration Solutions, LLC. (UISOL, a GE Company) will develop a power systems model repository based on state-of-the-art open-source software. The models in this repository will be used to facilitate testing and adoption of new grid optimization and control algorithms. The repository will use field-proven open-source software and will be made publicly available in the first year of the project. Key features of the repository include an advanced search capability to support search and extraction of models based on key research characteristics, faster model upload and download times, and the ability to support thousands of users. The team will establish a long-term strategy for managing the repository that will allow its operation to continue after its project term with ARPA-E ends.

National Renewable Energy Laboratory

SMARtDaTa: Standardized multi-scale Models of Anonymized Realistic Distribution and Transmission data

The National Renewable Energy Laboratory (NREL), with partner MIT-Comillas-IIT, will develop combined distribution-transmission power grid models. The team will create distribution models using a version of Comillas' Reference Network Model (RNM) that will be adapted to U.S. utilities and based on real data from a broad range of utility partners. The models will be complemented by the development of customizable scenarios that can be used for accurate algorithm comparisons. These scenarios will take into account unknown factors that affect the grid, such as future power generation technologies, increasing distributed energy resources, varying electrical load, disruptions due to weather events, and repeatable contingency sequences. These enhanced datasets and associated data building tools are intended to provide large-scale test cases that realistically describe potential future grid systems and enable the nation's research community to more accurately test advanced algorithms and control architectures. MIT-Comillas-IIT will assist NREL with the distribution model creation. Alstom Grid will assist in validating the distribution models.

Pacific Northwest National Laboratory

Data Repository for Power system Open models With Evolving Resources (DR POWER)

The Pacific Northwest National Laboratory (PNNL) has partnered with the National Rural Electric Cooperative Association (NRECA) to build a power system model repository, which will maintain and develop open-access power grid models and data sets. The DR POWER approach will review, annotate, and verify submitted datasets while establishing a repository and a web portal to distribute open-access models and scenarios. Through the portal, users can explore the curated data, create suitable datasets (which may include time variation), review and critique models, and download datasets in a specified format. Key features include the ability to collaboratively build, refine, and review a range of large-scale realistic power system models. For researchers, this represents a significant improvement over the current open availability of only small-scale, static models that do not properly represent the challenging environments encountered by present and future power grids. The repository and the web portal will be hosted in PNNL's Electricity Infrastructure Operations Center with access to petabytes of computing storage and load-balancing across multiple computing resources.

Pacific Northwest National Laboratory

Sustainable Data Evolution Technology for Power Grid Optimization 

The Pacific Northwest National Laboratory (PNNL), along with the National Rural Electric Cooperative Association, PJM, Avista, and CAISO, will develop a sustainable data evolution technology (SDET) to create open-access transmission and distribution power grid datasets as well as data creation tools that the grid community can use to create new datasets based on user requirements and changing grid complexity. The SDET approach will derive features and metrics from many private datasets provided by PNNL's industry partners. For transmission systems, PNNL will develop advanced, graph-theory based techniques and statistical approaches to reproduce the derived features and metrics in synthetic power systems models. For distribution systems, the team will use anonymization and obfuscation techniques and apply them to datasets from utility partners.

University of Illinois, Urbana Champaign

Synthetic Data for Power Grid R&D

The University of Illinois at Urbana-Champaign, with partners from Cornell University, Virginia Commonwealth University, and Arizona State University will develop a set of entirely synthetic electric transmission system models. Their 10 open-source system models and associated scenarios will match the complexity of the actual power grid. By utilizing statistics derived from real data, the team's models will have coordinates based on North American geography with network structure, characteristics, and consumer demand that mimics real grid profiles. Smaller models will be based on smaller areas, such as part of a U.S. state, while the large models will cover much of the continent. All models and their scenarios will be validated using security-constrained optimal power flows, with parameters tuned to emulate the statistical characteristics of actual transmission system models.

University of Michigan

High Fidelity, Year Long Power Network Data Sets for Replicable Power System Research

The University of Michigan, with partners from Los Alamos National Laboratory, the California Institute of Technology, and Columbia University, will develop a transmission system data set with greater reliability, size, and scope compared to current models. The project combines existing power systems data with advanced obfuscation techniques to anonymize the data while still creating realistic models. In addition, the project delivers year-long test cases that capture grid network behavior over time, enabling the analysis of optimization algorithms over different time scales. These realistic datasets will be used to develop synthetic test cases to examine the scalability and robustness of optimization algorithms. The team is also developing a new format for capturing power system model data using JavaScript Object Notation and will provide open-source tools for data quality control and validation, format translation, synthetic test case generation, and obfuscation. Finally, the project aims at developing an infrastructure for ensuring replicable research and easing experimentation, using the concept of virtual machines to enable comparison of algorithms as hardware and software evolve over time.

University of Wisconsin

EPIGRIDS:Electric Power Infrastructure & Grid Representation in Interoperable Data Sets

The University of Wisconsin-Madison and its partners will develop realistic transmission system models and scenarios that will serve as test cases to reduce barriers to the development and adoption of new technologies in grid optimization and control. The EPIGRIDS project aims to construct realistic grid models by using software to emulate the transmission and generation expansion decision processes used by utility planners. This synthetic model development will utilize Geographic Information Systems (GIS) data on population density, industrial and commercial energy consumption patterns, and land use, over sizes ranging from the city-level to continental-scale. In order to test the robustness of the system's solutions, it will allow users to tailor specific data sets and scenarios to challenge particular aspects of optimization and control algorithm development. Flexible methodologies for data set construction and connecting features of these data sets to geographically described energy use and land use constraints will enable collaborative development of new models, far beyond those directly delivered by this project.

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