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
OPSD	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 70%/ 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
EIA	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 50% - F2 50% - F3 100% - F4 50% / A1.1 100% - A1.2 100% - A2 50%/ I1 100% - I2 100% - I3 50% / R1.1 100% - R1.2 50% - R1.3 100%	What FAIR/O target was decided? Findable: Improve F1,F2,F4, e.g needs unique identifier Accessibility: Improve A2, e.g. no information regarding if metadata are accessible even when the data are no longer available Interoperability: Improve I2 to include qualified references to other (meta)data Interoperability: improve R1.2. (meta)data are associated with detailed provenance.
OpenEI	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.
PSE	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 80%.	What FAIR/O target was decided? Improve F2 data are described with rich metadata
NREL	NREL develops data and tools for the analysis of grid technologies and strategies, including renewable resource data sets and models of the electric power system..	It is an extensive dataset that meets FAIR criteria.	F1 100% - F2 100% - F3 80% - F4 100% - A1.1 100% - A1.2 100% - A2 100% - I1 80% - I2 80% - I3 100% - R1.1 100% - R1.2 100% - R1.3 80%	What FAIR/O target was decided? Accessibility: A1.2 the protocol allows for an authentication and authorization
ENTSO-E Transparency Platform	Central collection and publication of electricity generation, transportation and consumption data and information for the pan-European market. Main data categories are: Load, generation, transmission, balancing, outages, congestion management, system operations. It covers data from the starting year 2014 in hourly resolution.	One of the main data sources for this use case in respect of transmission capacities. Widely used by researchers working on different aspects of electricity market in Europe.	F1 50 % - F2 50% - F3 20% - F4 100% A1.1 100% - A1.2 100% - A2 50% I1 100% - I2 80% - I3 100% R1.1 50% - R1.2 50% - R1.3 90%	F1: Assign data set a globally unique and persistent identified (e.g. DOI). Improve F2: data should be better described with rich metadata Improve F3: downloaded data should include explicitly the identifier of the data (e.g. source) Improve A2: not sure, no information regarding if metadata are accessible even when the data are no longer available Improve I3: data should include qualified references to other data (which TSO or national authority are the main reference?) R1.1 and R1.2: it should contain more clear and accessible data usage licence and associated provenance. regarding licence, there is only information in pdf. under “terms and conditions” it states not sub-licensable or transferrable. I2: There is a long list of databases under sitemap, this can be linked also explanations.
SMARD (Strommarktdaten)	Electricity market information platform of German Federal Network Agency Bundesnetzagentur (BnetzA). It presents the most important electricity market data for Germany containing electricity market data such as electricity generation, consumption, import and export, market balancing and power plants in different periods of time (i.e. power plants data between 2015-2025 in hourly resolution, generation in 15 min.).	Germany is one of the pioneer countries in terms of energy data transparency. Energy researchers addresses databases from German Federal Network Agencies as a good example for openness and transparency.	F1 100% - F2 100% - F3 100% - F4 100% A1.1 100% - A1.2 100% - A2 100% I1 100% - I2 80% - I3 100% R1.1 100% - R1.2 100% - R1.3 90%	Improve I2 for using vocabularies that follow FAIR principles
Example	Example	Example	Example	Example
Example	Example	Example	Example	Example
Example	Example	Example	Example	Example

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
OPSD	[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
EIA	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
OpenEI	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
PSE	Administrative 80%, descriptive 100%, structural 100%	Rich	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 80%	What framework is used, e.g., controlled vocabulary, taxonomy, thesaurus, ontology? taxonomy	How are metadata implemented? As xls, csv, pdf, etc.
NREL	Administrative 100%, descriptive 100%, structural 80%	Rich	F1 100% - F2 100% - F3 80% - F4 100% - A1.1 100% - A1.2 100% - A2 100% - I1 80% - I2 80% - I3 100% - R1.1 100% - R1.2 100% - R1.3 80%	What framework is used, e.g., controlled vocabulary, taxonomy, thesaurus, ontology? taxonomy	How are metadata implemented? As ??????xls, jpg, pdf, graphs, charts, ect.
ENTSO-E Transparency Platform	Administrative 50%, descriptive 50%, structural 50%	Metadata is partly described, some of basic metadata are missing or not clear to identify	F1 50 % - F2 50% - F3 20% - F4 100% A1.1 100% - A1.2 100% - A2 50% I1 100% - I2 80% - I3 100% R1.1 50% - R1.2 50% - R1.3 90%	Taxonomy	Csv, xlsx, xml, xml zip, graphs and charts
SMARD (Strommarktdaten)	Administrative 80%, descriptive 100%, structural 100%	Rich and basic metadata is available	F1 100% - F2 100% - F3 100% - F4 100% A1.1 100% - A1.2 100% - A2 100% I1 100% - I2 80% - I3 100% R1.1 100% - R1.2 100% - R1.3 90%	Taxonomy	csv, xls, xml
Example	Example	Example	Example	Example	Example
Example	Example	Example	Example	Example	Example
Example	Example	Example	Example	Example	Example