UC2

Power transmission & distribution networks

General description of use case

The rapid growth of renewable energies and greater emphasis on improving grid sustainability and resilience will require changes in the ways that power transmission and distribution networks are operating. This grid of the future requires advances in transmission and distribution system management with algorithms to control and optimize how power is transmitted and distributed through the grid. However, the development of these algorithms is challenging due to the lack of high-fidelity, public, and large-scale data that realistically represent current and evolving grid properties. The proposed use case for an open-access repository of power systems models and data will enable more productive research collaborations. On a higher level, it improves the integration of renewable electricity onto the grid, which would help reduce reliance on carbon-emitting, fossil fuel generation. This use case goal is mimicking the characteristics of the actual electric grid without disclosing sensitive information. Existing FAIRification and opening activities EERAdata builds on are a) Creating a library of publicly available test data from prominent power system related organizations such as IEEE, the United States Department of Energy, and the International Smart Grid Action Network (ISGAN). The Repository pulls together grid models and related test data from across the utility industry to improve community access. Relevant data could also be coming from the European Network of Transmission System Operators (ENTSO-E) as well as from the Open Power System Data Platform. b) Validate use cases and the associated data toward the proposed EERAdata FAIR/O ecosystem especially on the impact of renewable energy on the low carbon energy system transformation. Multiple scenarios for data validation will be taken into account.

List of selected databases

During the first workshop (see notes from Day 2), the following databases were selected to analyze and improve their compliance with FAIR and Open data principles:

Name of database	Short description	Reasoning of choice	Current state of FAIR/O principles	Target of FAIR/O to achieve within EERAdata
[[1]]	The database is an outcome of the Open Energy Modelling Initiative that dates back to 2014. The aim is to construct an open platform for energy systems modelling data.	It is an extensive dataset that almost meets FAIR criteria.	F1 100% - F2 100% - F3 80% - F4 100% / A1.1 100% - A1.2 100% - A2 100%/ I1 100% - I2 80% - I3 100% / R1.1 100% - R1.2 50% - R1.3 90%	What FAIR/O target was decided? Improve I2 for using vocabularies that follow FAIR principles and
[[2]]	The U.S. Energy Information Administration (EIA) collects, analyzes, and disseminates independent and impartial energy information to promote sound policymaking, efficient markets, and public understanding of energy and its interaction with the economy and the environment.	It is an extensive dataset that almost meets FAIR criteria.	F1 100% - F2 100% - F3 100% - F4 70% / A1.1 100% - A1.2 100% - A2 100%/ I1 100% - I2 100% - I3 70% / R1.1 100% - R1.2 50% - R1.3 100%	What FAIR/O target was decided? Improve I2 for include qualified references to other (meta)data and improve R1.2. (meta)data are associated with detailed provenance.
[[3]]	OpenEI is developed and maintained by the National Renewable Energy Laboratory with funding and support from the U.S. Department of Energy since 2017. The platform is wiki based. Users can view, edit, and add data – and download data for free.	It is not an extensive dataset. It seems it is in Beta version. So, it doesn't meets FAIR criteria.	F1 50% - F2 70% - F3 70% - F4 50% / A1.1 100% - A1.2 50% - A2 50%/ I1 50% - I2 50% - I3 100% / R1.1 100% - R1.2 50% - R1.3 100%	What FAIR/O target was decided? It need major improvement in Accessibility and Interoperability.
[[4]]	Energy sector database for Poland. The database.The scope and information presented in the database includes: Polish Power system operation; balancing market operation and reports map.	It is an extensive dataset that almost meets FAIR criteria.	F1 80% - F2 100% - F3 100% - F4 100% A1 100% - A1.1 100% - A1.2 50% - A2 100%- I1 100% - I2 100% - I3 80% - R1.1 100% - R1.2 100% - R1.3 100%.	What FAIR/O target was decided? Improve F2 data are described with rich metadata
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Metadata assessments

Databases above were assessed with respect to their current meta practices. The table below summarizes the current state and issues identified during WS 1:

Name of database	Type of metadata provided	Extend of metadata provided	Level of implementation of FAIR/O principles	Frameworks for metadata used	Technical implementation of metadata
[[5]]	[refer to DC table in word file] Administrative 70%, descriptive 100%, structural 100%	Rich	F1 100% - F2 100% - F3 80% - F4 100% / A1.1 100% - A1.2 100% - A2 100%/ I1 100% - I2 80% - I3 100% / R1.1 100% - R1.2 50% - R1.3 90%	What framework is used, e.g., controlled vocabulary, taxonomy, thesaurus, ontology? taxonomy	How are metadata implemented? As xml, plain text, RDF, etc. xml, csv, json, jupyter
[[6]]	Administrative 80%, descriptive 100%, structural 100%	Rich	F1 100% - F2 100% - F3 100% - F4 70% / A1.1 100% - A1.2 100% - A2 100%/ I1 100% - I2 100% - I3 70% / R1.1 100% - R1.2 50% - R1.3 100%	What framework is used, e.g., controlled vocabulary, taxonomy, thesaurus, ontology? taxonomy	How are metadata implemented? As xml, plain text, RDF, etc. xml, csv, text, PDF
[[7]]	Administrative 80%, descriptive 100%, structural 100%	Medium	F1 50% - F2 70% - F3 70% - F4 50% / A1.1 100% - A1.2 50% - A2 50%/ I1 50% - I2 50% - I3 100% / R1.1 100% - R1.2 50% - R1.3 100%	What framework is used, e.g., controlled vocabulary, taxonomy, thesaurus, ontology? taxonomy	How are metadata implemented? As xml, plain text, RDF, etc. xml, csv, text, PDF
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