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Work Package 2 UPDATE

Work Package 2 UPDATE. Karlsruhe Transnational Steering Group Meeting 18 th March 2013. UPDATE. Progress – Action 7 Requirements analysis PESTLE and Case Studies/ User cases. Progress - Action 8 -Writing elements of DSS. Martina – GIS modeling and biomass potentials

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Work Package 2 UPDATE

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  1. Work Package 2 UPDATE Karlsruhe Transnational Steering Group Meeting 18th March 2013

  2. UPDATE • Progress – Action 7 Requirements analysis • PESTLE and Case Studies/ User cases. • Progress - Action 8 -Writing elements of DSS. • Martina – GIS modeling and biomass potentials • Monika – GIS based application • Dan/Jim – System overview/development of user interfaces

  3. Evaluate and Expand PESTLE analysis Develop Case studies within regions • Critical Pathways (Oct2012) • Working with Partners (particularly BSCs) • Iterative process: • Scoping (what’s required) • Info gathering (case studies/PESTLE) • Designing , demonstrating and validating models and end user interfaces. • Identifying gaps. • 1 full Cycle. Design user friendly data collection proforma Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  4. Evaluate and Expand PESTLE analysis Develop Case studies within regions Design user friendly data collection proforma • PURPOSE: • Regionally specific and End User Specific information/data • Elements Critical to Decision Support • Consistency of data collaction • Sufficient level of granularity – Tailored support • Capture resources for signposting purposes • Clearer picture of when, where and how data is capture over time. Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  5. PESTLE Matrix – October 2012 • 1-2-1 Meetings • SRE Eindhoven • HAUC • KIT-ITAS • Followed up with more detailed interviews to develop specific user case studies. • Thank you for input so far. Work with BSCs to further develop this.

  6. Action 7 • Developed process logic. • Framework - capture real data/ models. • Where real data is missing or not yet available –Determine assumptions/use pre-determined/typical data • We have determined where in the DSS signposting will be necessary (policy documents, legislation, standards etc)

  7. Evaluate and Expand PESTLE analysis Develop Case studies within regions Design user friendly data collection proforma • Collecting and developing case studies from different regions • Real data and information about the site, technology, feedstocks etc. • Key stages of project development/ associated decisions that were made. • Enabled us to further develop the DSS process logic Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  8. Evaluate and Expand PESTLE analysis Develop Case studies within regions Design user friendly data collection proforma Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  9. Developing DST Logic and Structure -Process mapping - considerations across supply chain (PESTLE) -High level process flow and logic for the user cases -Detailed DST logic flow (identify key questions/ data required to support decisions)

  10. Evaluate and Expand PESTLE analysis Develop Case studies within regions Design user friendly data collection proforma Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  11. Development of Storyboards for User Interface GO

  12. Evaluate and Expand PESTLE analysis Develop Case studies within regions Design user friendly data collection proforma Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  13. DST structure • 5 levels (tiers) within DST • Recommended project depends on the user motivation • Current known motivations: • Waste removal • Energy independence • Energy price security • Return on investment • Reduction in net carbon emissions from site • Any others?

  14. Technology selection example • Technology selection user case (1 of 7) • High level DST Structure ?

  15. System overview • Feedstock Data • Feedstock assessment – quantity and type • Logistics • Site data • Neighbouring heat demand & grid infrastructure • Nitrate vulnerable zones • Access etc Region selection User case selection 1 Locational information capture Feedstock information capture 2 3 Solver (technology selection) [HIDDEN] 4 Project motivation questions User moving through system Output reports Data moving through system 0

  16. DST structure and data flow H&P data Feedstock data

  17. Financial outputs • Discounted cash flow or similar (5yr,15yr • Sensitivity • Different variables and unit of measurement: levelised cost, NPV, ROI etc • [potential for more sophisticated probabilistic analysis]

  18. Risk and opportunities register outputs • What might go wrong? • What might go right? • What to do next • How long development might take? DEMO

  19. Evaluate and Expand PESTLE analysis Develop Case studies within regions • Regionally specific information – Biomass resources, infrastructure, heat and power and logistics, protected zones (NVZ etc) • Site identification and suitability DSS • Collection and Modeling of GIS data (KIT-ITAS) • Integration into DSS (BCU) Design user friendly data collection proforma Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  20. GIS-based modeling of biomass potentials – Model structure and preliminary results for agricultural residues Martina Haase

  21. Content • Overview on model regions • Model structure for agricultural residues estimation • Preliminaryresults: Residuesfromcerealscultivation • Data requirements • Next steps

  22. Overview on modelregions • Initial model development for the German model region • Implementation of the model for all model regions • Île-de-France • Nordrhein-Westfalen • Wallonie • West Midlands • Zuid-Nederland • Consideration of different administrative units with respective statistical data, e.g. • Federal state Nordrhein-Westfalen (NUTS 1) • Administrative region Köln (NUTS 2) • Rural district Düren (NUTS 3) • Fig. 1: Overview on model regions

  23. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data exportfor web application Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  24. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data exportfor web application Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  25. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data export Select Restriction • e.g. • Düren (NUTS 3) • Köln (NUTS 2) • Nordrhein-Westfalen (NUTS 1) Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  26. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data exportfor web application Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  27. Identificationofareasforresidueextraction Model structure - Agriculturalresidues • e.g. • City of Jülich • City of Düren Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data export Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  28. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data exportfor web application Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  29. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data export • e.g. • - Exclusionofareasforresidueextraction due toecologicalrestrictions, e.g. • Areas susceptible for soil erosion • Protectedareas • - Limitation ofresidueextractiondue totechnicalorecologicalrestrictions, e.g. • Residuesformaintenanceoforganiccarbonbalance • Harvestlosses • Strawdemandforcattlebreeding Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  30. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data exportfor web application Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  31. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data export • e.g. District Düren: • Arableland: 43,849 ha1) • Cerealsshare: 54 %1) • WeightedCerealsyield : 8.72 t/ha1) • Residue-to-Product-ratio: 0.8 – 1.42) Select Restriction Select Region 1)RegionalstatistikDeutschland, base year 2010 2) LWK NRW, 2013, Esteban and Carrasco, 2011, RegionalstatistikDeutschland ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  32. Identificationofareasforresidueextraction Model structure - Agriculturalresidues Select plant site • Generateregionspecificmapsdata • Calculationofresiduesamount • Statistical dataofselectedregion • Spatialdistributionofresidues • Data exportfor web application Select Restriction Select Region ArcGISmodules Input data, modeloutput Parameter selection Optional selection

  33. Preliminaryresults- Residuesfromcerealscultivation (1) • Cereals area and residue yield rural district Düren

  34. Preliminaryresults- Residuesfromcerealscultivation (2) • Cereals area and residue yield within 5 km around the city of Jülich

  35. Preliminaryresults - Residuesfromcerealscultivation(3) • Calculation of straw potentials for each 1 km x 1 km grid cell of the EEA reference grid Fig. 9: Straw amounts per grid cell(district Düren), Base Scenario Fig. 10: Straw amounts per grid cell (administrative region Köln), Base Scenario

  36. Preliminaryresults - Residuesfromcerealscultivation(4) • Calculation of straw potentials for each 1 km x 1 km grid cell of the EEA reference grid Fig. 11: Straw amounts per grid cell(district Düren), Scenario Restrict_3 Fig. 12: Straw amounts per grid cell (administrative region Köln), Scenario Restrict_3

  37. Data requirements • Estimation of residues from cereals cultivation for France, UK, Netherlands, Belgium • Region-specific data on cereals cultivation (base year 2010) • Regions area (ha) • Arable area (ha) • Sum of cereals area (ha) • Yield (t/ha) and area (ha) of most common cereals • Residue- to-Product ratios for most common cereals • Excel file for data collection for each country (different administrative units) • Region-specific data on cattle breeding (base year 2010) • Excel file for data collection for each country (different administrative units) • Data on possible plant sites and plant sizes for transport distance calculations

  38. Next steps • First model calculations for other model regions (region specific statistical data is required) • Estimation of residue amounts from other crops on arable land • Estimation of residue amounts from other agricultural areas, e.g. pastures • Potential for energy crop production, e.g. short rotation coppice • Identification of suitable areas including • Data on terrain elevation • Climate data (rainfall, temperature) • Data on soil quality • Estimation of biomass yields

  39. Contact Dr. Martina Haase Karlsruhe Institute of Technology (KIT)Institute for Technology Assessment and Systems Analysis (ITAS) Phone: +49 721 608-26094 E-Mail: martina.haase@kit.edu

  40. Interdependencies • WP1/A1-Recruitment of members/ stakeholder database. • WP1/A2 – Collate existing data on biomass (GIS database) • WP1/A2 – programme of education (stakeholder demo of DSS (future) • WP1/A3 – identify 25 candidate sites – parameters used to determine these ‘opportunities’ (PESTLE and case studies) • WP1/A5 – Collation of locally relevant information (GIS/policy and regulation?)

  41. WP3/A10 – Installation/operation of pyroformers – PESTLE analysis, document repository, glossary of terms (signposting). • WP3/A11 – Effects of pyrolysis liquids on biogas (Technology integration model/project design model). • WP3/A11 – Screen digestates and test (technology integration model/project design model). • WP3/A13 – Stakeholder workshops (demo and feedback on DSS).

  42. WP 4/A14 - development of primary sites – Case studies and PESTLE for informing standard proforma and project design model (PDP)

  43. Evaluate and Expand PESTLE analysis Develop Case studies within regions Design user friendly data collection proforma Review/ conduct Gap analysis Determine interrelationships/Logic models Data collection by BSCs Review/ conduct Gap analysis Develop user interfaces for DSS Based on case studies and data collected develop ‘use cases’ Identify and evaluate other case studies in different regions Gain input/ feedback on existing logic model and DSS structure. Develop further user cases (6 more) - test and validate existing DSS Develop standard proforma for ongoing collection of data from BSCs Stakeholder Engagement to Demonstrate/ gain feedback Site ID and assessment DST Outputs Project development plan

  44. Next steps • Upload Action 7 report – Feedback • 1-2-1 with individual partners to discuss interdependencies- Data requirements/work schedule. • Work through existing case studies with BSCs • Scenario planning workshop to further develop other user cases

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