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Concept of Data Collection

Concept of Data Collection. Vincent Scherrer Junior Adviser, System Development. ENTSOG’s Transparency Workshop. Brussels – 11 September 2012. Main goals of the project Current Situation Expected PDWS improvements Conclusion.

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Concept of Data Collection

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  1. Concept of Data Collection Vincent Scherrer Junior Adviser, System Development ENTSOG’s Transparency Workshop Brussels – 11 September 2012

  2. Main goals of the projectCurrent SituationExpected PDWS improvementsConclusion

  3. Main goals of the projectCurrent SituationExpected PDWS improvementsConclusion

  4. Improve scope and quality New Transparency Guidelines • Binding publication • More relevant points (distribution systems, storages, production facilities...) • More data (physical flows, auction requests...) More detailed ENTSOG deliverables • Demand breakdown (CCGT, final customers, industry) • More scenarios and cases • More accurate modelling Upcoming tasks • Cost-benefit analysis • Gas quality

  5. Streamline the data collection processes Data collection among members • Automation • Web-based interfaces • Eliminated redundancy • Warning system ENTSOG internal data collection • Single repository of data • Versioning • User-friendly manual import mechanisms • More time for analysis and better deliverables

  6. Availability and openness DataWarehouse Transparency Platform Operational data Future projects Interruptions Auctionresults TYNDP data Information should be meaningful, exploitable, and consistent

  7. Main goals of the projectCurrent SituationExpected PDWS improvementsConclusion

  8. Various data sources TSOs TSOs TSOs Promoters Third parties(GSE, GLE, …) Transparency TYNDP Forecast data Outlook&Review Historical data Infrastructure projects Other data Transparency Platform/DB System Development files and databases Scatteredenvironment, intensive treatment, insulated data

  9. Complexaggregationprocedures Supply Demand Technicalcapacities Flows (Re)Nominations

  10. Historical data analysis Detailed analysis of historical data and a comparison of multiple periods is a regular part of data processing

  11. Data quality issues Understanding the data and changes to themis crucial • Different calculation assumptions ? • Uploaded at different points in time ? • Unmatched system enhancements ?

  12. Forecast vs Historical • Data questionnaire corrupted ? • Non-Standard Gross Calorific Value ? • What to do ?

  13. Varying data fromdifferent data sources Spread ?

  14. Main goals of the projectCurrent SituationExpected PDWS improvementsConclusion

  15. Manual data collection : Web-based

  16. Automated imports : Monitoring Monitor the flow of messages • TSOs : direct access to the system, warning per mail ? • Detect an inform about odd values • Detectmissing values • …

  17. Data consistency and data views Quality control and good datawarehouse practices will enable us to look at the data from many different angles • Entry flows in a country ? • Exit flows out of a country ? • Aggregated EU-27 demand • For a particular relevant point, what infrastructure projects are planned ? • Analyze cross-border flows and loads • What are the most congested cross-border connections ? • What happened during the 2012 cold snap ? • Maximal and minimal demand over the last 52 weeks for each country ? At the EU level ? • What is the net demand per country on a daily basis ? • What are the planned infrastructure projects per country ? Per infrastructure type ?

  18. Data consistency : Infrastructure projects Available as a Transparency Module • Different views but same data • Advanced search capabilities

  19. Data consistency : Different time scales Fromday… To year To month…

  20. Odd values Detect odd values with… • Fixed constraints • Comparison with the past • Check if missing • Comparison with previous versions of the same data Treat odd values with… • Quarantinedubious values • Each value has a state • Associatewithworkflows (manual or automatic)

  21. Data availability Conform to best practices • Push and pull mechanisms • Most common formats • Flexible data sets • Quantitative and qualitative analysis

  22. Export flexibility

  23. Analysis

  24. Aggregation LNG imports Hub-to-Hub

  25. Main goals of the projectCurrent SituationExpected PDWS improvementsConclusion

  26. Benefits The new system willfacilitate data collection and processing and shouldsignificantlyimproveENTSOG’sability to deliver reports such as TYNDP, Outlooks and reviews • Comprehensive quality control... • Drastically expanded analysis possibilitiesfor ENTSOG, members, stakeholders, 3rd parties… • Flexible data extracts and reports… • Better deliverables from ENTSOG... Sound technical basis to face current and future Transparency Requirements

  27. Do you have any questions ?

  28. Vincent ScherrerJunior Adviser Vincent.scherrer@entsog.eu

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