Wspólnotowy Serwis Informacyjny Badan i Rozwoju - CORDIS

FP6

SRS NET AND EEE Streszczenie raportu

Project ID: 6631
Źródło dofinansowania: FP6-POLICIES
Kraj: Greece

Final Report Summary - SRS NET AND EEE (Scientific Reference System on new energy technologies, energy end-use efficiency and energy RTD)

The project SRS NET AND EEE aimed at enhancing the timely availability, validation and traceable quality of data concerning renewable energy and energy end-use efficiency. This means primarily technology data and data describing the Research and technological development (RTD) status, e.g. learning curve parameters, future development potential, etc.

The purpose of the current project was to collect data from existing statistical offices, international and national energy agencies and also from the most competent research teams in the field, who had a clear idea about industrial state of the art. Principal aim was not to simply duplicate data or strip it of its original authorship.

The difficulty and complexity of achieving green energy targets in the enlarged European Union (EU) would require strengthened measures to promote implementation of renewable energy and other energy technologies and energy end-use efficiency, as well as measures to support the related RTD. In this framework, the project aimed operationally at the following goals:
- wide category harmonising, data-quality oriented validation and summary generation and consensus building of the existing data, including their RTD status and technology dependent socio-economic indicators;
- wide determination of the RTD expenditures for all energy technologies. Data collection is primarily based on data from the cooperating Eurostat, IEA, national and regional ministries and energy agencies etc.;
- the consortium uses its intrinsic multidisciplinary of statisticians, energy researchers and experts in energy socio-economics, in order to define an optimised set of methodological best practice to operate with;
- publishing results as a 'one-stop-shop' all elaborated synopses and data validations, trend-charts, etc. in order to prepare a European consensus building process by inviting stakeholders to a final conference.

The project structure consisted of the following work packages (WPs):
1) project management
2) methodology for validation and data quality
3) technology data validation synopsis
4) energy RTD expenditure data gathering (public and private)
5) consensus building and diffusion for decision support.

The project objectives necessitated a coherent work methodology. In particular, the project management (WP1), was accompanying the whole project length, in order to manage all activities, from the method and categories defining phase of the project (WP2) over two intensive data validation and gathering years (WP3, 4), to finally the consensus building process (WP5). The methodology (WP2) was of high functional importance within this project, in order to underpin the overarching objectives of correct data collection, data representation and comparisons, as well as fair benchmarking in later use of the validated results.

The work performed in each one of the project WPs was the following:
- As concerns the project management activities (WP1), the coordinator supervised the overall timeliness and quality of the WPs in collaboration with the WP leaders, so as to ensure that the WPs remained in line with the expected outcomes. In addition, the coordinator performed the timely communication with and the transfer of contractually promised deliverables to the European Commission. Moreover, the project website was set up at the end of the first reporting period, in close collaboration with the WP5.
- Special emphasis was given to the methodology (WP2) activities, which were related to the development of the methodology for the data collection. These activities were of high functional importance, in order to underpin the overarching objectives of correct data collection, data representation and comparisons, as well as fair benchmarking in later use of the validated results.
- The consortium efforts set the bases for the effective start-up phase of the large extent data validating and data gathering procedures of the WPs 3 and 4. In particular, the preparation for the data gathering procedures included the identification of the final categories for data collection with their definitions, the format for data collection and the work allocation among the contractors.
- Based on the proactive project strategy for consensus building and dissemination (WP5) all preparation's steps were initiated. For the establishment of communication channels with all interested actors, targeted users-stakeholders were identified and their potential to use the SRS-material was analysed.

More analytically, the objectives of the WP2 are described as follows:
- to better harmonise existing methodological approaches for sustainable energy data gathering and comparisons;
- to define best practice and, where appropriate;
- to suggest improvements in data aggregation and statistical interpretation, based on a common understanding between statisticians, energy technology experts and energy socio-economists. This regards methods for reporting on green energy technology- and 'system performance' - data as well as for related environmental and socio-economic data sets. True methodology development shall be limited to problematic issues of typically decentralised renewable energies and energy end-use efficiency implementations in the enlarged EU;
- to create a working basis for, and interacting directly with, WP3;
- to review and develop existing approaches and to suggest a harmonised set of data-gathering categories, aggregation levels and best practice definitions about historical energy RTD expenditures, of both, private and public nature. Thus creating a working basis for, and interacting with, WP4;
- to define workable rules for a quality-, actuality-, completeness- and cross-check system for all SRS-cited and gathered data;
- to produce a description of this internal data quality system sufficiently early for creating a working basis for more reliable results in WP3 and WP4;
- to increase traceability and objectivity of project data results and thus underpin the SRS output quality and credibility for later consensus building;
- to seek consensus and beyond consortium commentary on the achieved WP2 results.

In accordance with the EU-Directive 2001/77/EC the following technologies are defined as Renewable energy sources (RES):
- hydropower (large (>10 MW) and small (<10 MW));
- photovoltaic electricity;
- solar thermal electricity;
- solar thermal heat;
- biogas (including landfill gas, sewage gas and gas from animal slurries);
- solid biomass (including forestry products, forestry residues, agricultural products, agricultural residuals);
- biodegradable fraction of municipal waste;
- liquid bio-fuels;
- wind energy (onshore, offshore);
- geothermal energy (electricity and heat);
- tidal and wave energy.

RES data categories considered in the current project were the following:
- RES-electricity (E) capacity and production data: hydropower (large (>10 MW) and small (<10 MW)) (excluding pumping), photovoltaics, solar thermal electricity, wind energy (onshore, offshore), biogas (including landfill gas, sewage gas and gas from animal slurries), solid biomass, biodegradable fraction of municipal waste, geothermal electricity, tidal and wave electricity;
- RES-heat (H) capacity and production data: grid and non-grid connected biomass (wood, agricultural products and residues), renewable municipal solid waste, biogas, solar collectors (grid and non-grid), geothermal (including ground coupled heat pumps);
- RES-transport (T): liquid bio-fuels.

To examine existing approaches for collecting data on RES and other systems important for sustainable development, it was reasonable to compare definitions, methods and assumptions used by outstanding international data providers. At European level, data consolidation and harmonisation of data collected by the respective Member States in the main was put into practice by Eurostat (Luxembourg), the statistical body of the EU, by the Statistical Division of the Economic Commission for Europe of the United Nations (UNECE, Geneva), and by the OECD / International Energy Agency (IEA, Paris).

As a result of close cooperation and coordination of statistical activities in the field of renewable energy sources a joint questionnaire between UNECE, IEA and Eurostat was implemented in August 2000 (Annual renewables and waste questionnaire). The national administrations / energy agencies were requested to complete the questionnaire in order to provide reliable statistics for electricity and heat production, primary supply, transformation sector, end-use consumption and installed capacity for electricity from renewable energy sources. Improved data collection methods were needed especially for off-grid renewable and waste production, e.g. small wind turbines and solar panels.

Energy efficiency indicators are ratios, which are frequently used to describe trends in the use of energy at the level of a country as a whole, of a sector or an end-use, and to make cross-country comparisons. They are basically intended to provide information on changes in energy input or emissions from particular applications and to shed light on the causes of certain developments and in particular, improvements in energy efficiency. Beyond that, further objectives of energy efficiency indicators are the definition and monitoring of energy consumption targets, the evaluation of energy efficiency programmes, the planning of future actions, and for feeding energy demand forecasting models. The meaningfulness and ability of such indicators to be interpreted depend not only on the solution of a number of methodology problems but to a considerable extend also on the availability and quality of the data base used for computations.

The main data categories for fuel cells and hydrogen technologies and carbon capture and sequestration are cost, production and efficiency data. With this data all technologies which are technically and commercially mature can be adequately described. However, for technologies which are at a very early stage of development and do therefore have a potential for further cost reduction and technological improvements it is also important to model the progress that such technologies might have over time and progress data are therefore important. Yet, progress data is probably the data category which imposes the highest difficulty in finding data.

The most relevant and striking data inconsistencies are listed for the general groups (RES, Energy end-use efficiency (EEE) and Other energy technologies (OET)).

RES: In the main strategies given to overcome data gaps and data inconsistencies seemed to be sufficient to repair a multitude of weak data. RES techno-physical data representation was on a good way due to a boosted national and international advancement. Data gathering on RES potentials and RES R&D demonstrated progress due to studies launched in recent years. Step by step data supply improvements could contribute to confirm the SRS data demand framework for the most part and not to skip some facets.

EEE: The list of inconsistencies for the EEE data group clearly was more comprehensive than for RES data. For techno-physical data most problems were seen in the sectoral data gaps. Private R&D data sources were identified as a fundamental problem. In some cases, it may emerged that collecting data would be extremely time-consuming. Then a new consensus on the future priorities could be necessary within the SRS team.

OET: Data gathering for the selected technologies mainly was concentrated on study results given by research institutes or by technology-driven initiatives all of them showing inconsistencies. On the one hand, the early stage of technology development implicates that in some cases data from studies were not fully open for the public. On the other hand, nationally and internationally sponsored studies already provided fundamental information. For a more harmonised data gathering via reduction of inconsistencies, an example data sheet was designed for this methodology paper.

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