the Energeo Factsheets

Transcription

Earth Observation for monitoring and assessment of the environmental impact of energy use
Platform of
Integrated
Assessment (PIA)
LOSS OF LIFE EXPECTANCY (LLE)
Rationale
The Platform of Integrated Assessment (PIA) aims at
assessing the environmental and health impacts by
computing and gathering key information on human
activity using different energy scenarios for the next 50
years.
To get a wider view of environmental impacts, the PIA
focuses on different environmental and health aspects:
- Annual direct assessment of impacts related to
key pollutants: Particulate Matter (PM2.5), Ozone
and Greenhouse Gases (GHG)
- Global impact by Life Cycle Assessment approach
(LCA)
Calculated for the European population; time-span: 30 years
(starting in 2005); key-pollutant: PM2.5 (from GAINS baseline).
METHODOLOGY
The PIA functions are the following:
1. Hosting the outcomes of EnerGEO pilots (wind and fossil fuels) through a depository action
2. Implementing damage functions relating pollution indicators to impacts
3. Enabling free access to these impact indicators via Web Services and Web GIS Clients
4. Assessing the environmental impacts of each EnerGEO scenario
KEY RESULTS
INNOVATIVE IMPACT
The PIA gathers and computes European maps of impact
indicators for four EnerGEO energy scenarios:
The EnerGEO project proposes a new way to assess
environmental impacts of energy scenarios through
indicators issued from different methodologies:
- Baseline: continuation of current European policies
with regard to limitation of CO2
- Maximum Renewable Power: highest feasible
renewable energy share
- GAINS and LOTOS EUROS as direct models that
provide air pollutants concentrations and produce a
series of direct impact indicators
- Open Europe: high renewable energy share with
large imports of solar energy from North Africa
- A global systemic approach through life cycle
assessment (LCA)
- Island Europe: high renewable energy share with
no energy exchange planned outside Europe
The Platform of Integrated Assessment gathers a
wide range of sources, and gives a spatial overview of
environmental impacts across four possible energy
scenarios.
Web Services and WebGIS Clients, which are major
outcomes of the PIA, are enabling free access to this data:
http://viewer.webservice-energy.org/energeo_pia/index.htm
Results can be used to anticipate which scenarios could
reduce environmental impacts.
Platform of
Integrated
Assessment (PIA)
APPLICATION FIELD AND SCALE
The Platform of Integrated Assessment (PIA) is currently applied to Europe and focuses on climate change and human health.
PIA could be applied worldwide and extended to handle more environmental issues according to emissions and concentrations
pollutants availability.
DATA AND MODELS
Annual direct assessment:
Concentration of pollutants from GAINS which integrates
renewable energy potentials from REMix and TASES energy
models over Europe for different energy pathways (wind
power and fossil fuels) are combined with population density
(SEDAC) and forecasted cohorts survivability for the next 50
years for European population (UN). Damage function models
are applied to compute the Loss of Life Expectancy (LLE).
LLE CALCULATION
(DIRECT ASSESSMENT)
Global Life Cycle Assessment (LCA):
The LCA approach is applied to the energy pathways
corresponding to each scenario for each European country.
Loss of Life Expectancy
REFERENCES
Drebszok, K. M., Wyrwa, A. and Blanc, I. (2012) ‚ Estimating the loss of life
Blanc, I., Gschwind, B., Lefevre, M., Beloin-Saint-Pierre, D., Ranchin, T., Ménard, L.,
expectancy attributable to PM2.5 emissions in Europe with the use of high
Cofala, J., Fuss, S., Wyrwa, A., Drebszok, K., Stetter, D., Schaap, M., (2013), « The
special resolution modelling`, 6th SETAC World Congress / SETAC Europe
EnerGEO Platform of Integrated Assessment (PIA) : environmental assessment
22nd Annual Meeting, [Online], Available: http://berlin.setac.eu/embed/
of scenarios as a web service», Proceedings of the 27th International
Berlin/Abstractbook3_Part1.pdf
Conference on Informatics for Environmental Protection, Hamburg, Germany.
CONTACT
Isabelle Blanc / Benoit Gschwind
MINES ParisTech / ARMINES
60, Boulevard Saint-Michel
F-75272 Paris Cedex 06
[email protected]
Artur Wyrwa
AGH
Al.Mickiewicza 30
P-30-059 Krakow
[email protected]
MORE INFORMATION AT:
www.energeo-project.eu
The research leading to these results has receved funding
from European Community’s Seventh Framework
Programme (FP7, 2007-2013) under Grant Agreement
Number 226364.
EnerGEO
Geoportal
Rationale
KNOWLEDGE GEOPORTAL
In many applications of the energy sector, the availability
of current and comprehensive spatial information is
crucial for decision making. This is true for emergency
management, monitoring and predictions as well as for
analyses.
Recently, Geoportals have been established as main
spatial data infrastructure (SDI) gateways for finding,
evaluating and using spatial information.
Within EnerGEO a Knowledge Geoportal was created
providing information from the energy domain. It
promotes technical and semantic interoperability as well
as efficient discovery mechanisms connecting expert and
casual users across all businesses.
Web-interface to the EnerGEO Knowledge Geoportal:
http://energeo.researchstudio.at/energeo/catalog/main/home.page
METHODOLOGY
The EnerGEO Knowledge Geoportal uses international standards that form the foundation for information exchange based on
metadata of spatial and non-spatial resources. Several improvements to facilitate the discovery of huge data amounts were
made.
One of the major enhancements is the integration of recommender systems in the EnerGEO Knowledge Geoportal.
Recommender systems facilitate the process of discovery, giving users meaningful recommendations on what might interest
them. They are based on previous users’ behaviour (items previously viewed, bought or rated) and the search of other users.
KEY RESULTS
INNOVATIVE IMPACT
New methods and modules were developed for the
EnerGEO Knowledge Geoportal that contribute to an
enhanced discovery experience of energy information
and usability:
The concept of the Knowledge Geoportal website is
geared to the task-oriented design philosophy based on
User Experience (UX) design concepts for smartphones.
- Enhanced App-based user experience
- Distributed metadata discovery
- Tag cloud-based search
- Integration in the GEOSS portal
- Auto-suggestion lists
- Recommender-enhanced discovery
- Semantic matching of spatial and non-spatial
content
The focused presentation approach enhances the clarity
of the website content and draws the attention of the
users directly to the available functionalities e.g. the
project web-apps.
The EnerGEO Knowledge Geoportal is the first Geoportal
providing meaningful discovery results in form of
recommendations.
Recommendations are based on user interactions with
the content of the portal as well as automatically derived
information of semantic matching of spatial and nonspatial content.
EnerGEO
Geoportal
The EnerGEO Knowledge Geoportal fulfils the purpose of providing information on existing resources from the energy domain to
a broad audience using standards for technical interoperability as well as efficient discovery mechanisms. The portal contributes
its content to the Group on Earth Observations (GEO) Portal. Resources being registered comprise world-wide spatial and
non-spatial data that are discoverable in form of standards-based and structured metadata.
The EnerGEO Knowledge Geoportal serves the fundamental basis for information exchange between providers and users and
interlinks with other catalogues leveraging harvesting principles. It is built for a wide range of users, both expert and casual users
of the energy domain. Users can either discover information or register datasets, services or applications.
DATA AND MODELS
RECOMMENDATION WORKFLOW
The discovery component of the EnerGEO Knowledge Geoportal
uses a recommendation engine offering the possibility to
show additional resources that might be of interest to a user
based on interactions with search results of other users.
View actions within the EnerGEO Knowledge Geoportal can be
considered as clicks on a specific search result, whereas a click
on the details or preview page or invocation of a web service
could be thought of a buy action.
For calculation of recommendations, a shopping cart analyser
called “Association Rule Miner (ARM)” based on the Apriori
algorithm “R” and “SlopeOne” is used. For creating additional
recommendations, semantic text matching methods such as
Latent Semantic Analysis (LSA) are applied.
REFERENCES
Blaschke, T., Mittlboeck, M., Biberacher, M., Gadocha, S., Vockner, B.,
Hochwimmer, B., and Lang, S. (2010)‘The GEOSS - ENERGEO portal:
towards an interactive platform to calculate, forecast and monitor the
environmental impact of energy carriers’, in Greve, K. and Cremers,
A.B. (ed.) (2010) EnviroInfo 2010 - Proceedings of the 24th International
Conference on Informatics for Environmental Protection, Aachen:
Shaker Verlag.
Vockner, B., Belgiu, M. and Mittlboeck, M. (2012) ‘Recommender-based
enhancement of discovery in Geoportals, IJSDIR vol. 7, [Electronic]
Available:
http://ijsdir.jrc.ec.europa.eu/index.php/ijsdir/article/
viewFile/292/333 [Feb 2012]
Scholz, J. and Mittlboeck, M. (2012) ‘Spatio-temporal Visualization
of Simulation Results using a task-oriented Tile-based DesignMetaphor’. Conference Proceedings, International Symposium on
Service Oriented Mapping Vienna.
CONTACT
Manfred Mittelböck
Research Studios Austria Forschungsgesellschaft mbH
Schillerstr. 25,
A- 5020 Salzburg
[email protected]
Number 226364.
Biomass Pilot
Rationale
The EnerGEO Biomass Pilot implements observational and
modeling capacity for using biomass as a current and future
energy resource. Biomass energy potentials are delivered
and validated at global, continental and regional scale.
One focus is to use historical and actual time series of EOdata as input for the biophysical carbon model BETHY/DLR
to derive bioenergy maps with a resolution of 1 km².
The second focus is to forecast biomass distribution of
forestry and agricultural areas on a global scale using the
models EPIC and G4M.
Forest biomass maps from BETHY/DLR are validated using
forest inventory data. G4M and EPIC are cross-validated
with BETHY/DLR. Energy maps are used as input layers for
energy models (REMix, TASES, BeWhere).
Energy potentials (terra joules per year and 1 km² grid for Europe)
computed with G4M. High values are presented in dark green and
low values in grey colour. White areas represent areas which were not
considered as forests.
METHODOLOGY
1. The Biosphere Energy Transfer Hydrology Model (BETHY/DLR) delivers Net Primary Productivity (NPP) maps
2. The Environmental Policy Including Climate Model (EPIC) is used to assess and forecast agricultural side products (straw)
and the associated bioenergy potential on a European and global scale
3. The Global Forest Model (G4M) is used to calculate and forecast theoretical energy potentials for forests on European and global scale. For Europe the G4M is driven by NPP maps from BETHY/DLR to estimate future biomass potentials of forests.
4. Biomass and bioenergy maps are computed from NNP maps and are used as input layers for energy models as e.g. REMix,
TASES and BeWhere
KEY RESULTS
INNOVATIVE IMPACT
Time series of the biomass energy potentials for wood
and agricultural crops are delivered by applying the
biomass energy models BETHY/DLR, EPIC or G4M at
European scale (1 km ²) and global scale (10 km²).
EnerGEO delivered for the first time a continuous 10
years time series of bioenergy potentials for Europe
and the globe starting with the year 2000 and ending in
2050 with 10 years step.
The agricultural biomass energy models are sensitive
to input data (mainly meteorology for both models and
land cover map for BETHY/DLR). The annual variability
of NPP reaches 36% for BETHY/DLR and 39% for EPIC
when changing these major input datasets. This is a
consequence of biological sensitivity to factors, such
as weather, soil, species and cultivation that determine
growth.
The post-processing of NPP (or dry matter) to
bioenergy potential for forestry and agricultural areas is
a new tool based on literature data that was published
during the course of the project.
Intensive effort was put on validation activities for all
three models as well as a model intercomparison. For
agricultural and forest areas all models show a significant
linear relationship with reference data (R2 up to 0.95).
G4M estimates for Europe seem to be acceptable as
they are close to forest inventory observations.
All results will be used finally in the Platform of
Integrated Assessment (PIA).
Bioenergy potentials for Europe (1 km2 resolution) and
the globe (10 km2 resolution) derived from BETHY/DLR,
EPIC and G4M are available via the EnerGEO Portal.
Biomass Pilot
BIOMASS GEO-WIKI
The Biomass Geo-Wiki has collected a comprehensive set of recent
biomass data around the globe, and makes it freely available for visualization. This allows users an instant gap analysis of global data.
With a critical mass of data it would be possible to produce a global
mosaic of terrestrial biomass. Also additional data like in-situ measurements could be uploaded.
Web-interface to the biomass GEO-WIKI
www.biomass.geo-wiki.org
As these datasets are crucial in the determination of global bioenergy supplies, such a portal allowing users to visualize and compare
datasets is highly relevant.
DATA AND MODELS
The use of data derived from satellite imagery from GEOSS
(e.g. fAPAR, LAI, Land Cover) as input for vegetation models with
relatively high spatial resolution (1 km2) is very applicable for
deriving NPP or dry matter on a continental scale.
In a post-processing step bioenergy potentials can be estimated
and used as input for energy models in order to investigate e.g.
energy mix scenarios when solar, wind and bioenergy sources
are available.
These results are then linked to the global assessment models
GAINS and GLOBIOM, which then model the effects on transboundary air pollution (GAINS) and global land use (GLOBIOM).
REFERENCES
Tum, M. and Guenther, K.P. (2011) ‘Validating modeled NPP using statistical data’,
Biomass and Bioenergy, vol. 35, pp. 4665-4674.
Tum, M., Buchhorn, M., Guenther, K.P. and Haller, B.C. (2011) ‘Validation of modeled
forest biomass in Germany using BETHY/DLR’, Geoscientific Model Development,
vol. 4, pp. 1019-1034.
Tum, M., Strauss, F., McCallum, I., Guenther, K.P. and Schmid, E. (2012) ‘How sensitive
are estimates of carbon fixation in agricultural models to input data’, Carbon
Balance and Management, vol7, pp.1-13.
Vegetation models used in the biomass pilot (green).
Output of vegetation models is used as input for energy models
(yellow) and assessment models (blue).
Wetterlund, E., Leduc, S., Dotzauer, E. and Kindermann, G. (2012) ‘Optimal use of
forest residues in Europe under different policies-second generation biofuels
versus combined heat and power`, Biomass Conversion Biorefinery, vol.3, pp.116.
Leduc, S., Wetterlund, E., Dotzauer, E. and Kindermann, G. (2012) ‘CHP or biofuel
production in Europe?’, Energy Procedia, vol. 20, pp. 40-49.
Tiede, D., Hoffmann, C. and Willhauck, G. (2012) ‘Fully integrated workflow for
combining object-based image analysis and LiDAR point cloud metrics for
feature extraction and classification improvement’, Conference Proceedings,
International LiDAR Mapping Forum (ILFM 2012), Denver.
CONTACT
Kurt P. Günther
DLR-DFD
Münchnerstr. 20
D-82234 Wessling
[email protected]
Ian McCallum
IIASA
Schlossplatz 1
A-2361 Laxenburg
[email protected]
Number 226364.
Fossil Fuel Pilot
Rationale
Combustion of fossil fuels is the main driver for major
environmental problems such as climate change and
air pollution.
To minimize the impact of fossil fuels, a better
understanding of the relations between emissions, air
quality and the impact on climate, human health and
vegetation is needed.
To optimize the choice of which energy sources to apply
where and when, in-depth knowledge of the relations
between fossil fuel source and impact, as well as the
effects of energy transitions is required.
Furthermore, a monitoring system is necessary to
oversee the implementation of improvements.
Relative increase in SOMO35 (ozone indicator) when 5% of the
agricultural land is converted into poplar plantations.
METHODOLOGY
Within the EnerGEO Fossil Fuel Pilot a dedicated source apportionment methodology has been developed, which is used to
understand the contribution of fossil fuel combustion to atmospheric concentrations and the resulting impacts.
1.
A mercury modeling system has been set up to model the impact of mercury on the environment
2.
The LOTOS-EUROS model is used to model some of the impacts of the energy transition on the environment
3.
Satellite data are used to quantify changes in concentrations, emissions and land degradation/subsidence
KEY RESULTS
INNOVATIVE IMPACT
State-of-the-art emission modeling links the pollutant
concentrations to its sources, which can be validated by
Positive Matrix Factorisation (PMF) modelling.
A state-of-the-art modeling system, which quantifies
the contribution of fossil fuels to air pollution levels
has been demonstrated.
In some areas up to 90% of the HgII and HgP concentrations
are due to coal-fired power plants.
The use of fossil-based energy as back-up and the
associated change in the timing of emissions implies
that the expected concentration reductions due to
reductions in fuel combustion are smaller.
Accounting for intermittent fossil fuel use it is important
to assess co-benefits between climate change and air
quality mitigation strategies.
A trend in NOx emissions has been estimated for Europe,
by combining satellite NO2 observations with air quality
modelling.
Coal mining activities can cause ground deformations
of a few centimetres per month.
Changing land use for biomass plantations may increase
biogenic emissions of Volatile Organic Compounds
(VOCs) and therefore also increase tropospheric ozone
concentrations significantly.
It has been shown that it is possible to estimate trends
in NOx emissions based on NO2 observations from
satellite and a dedicated modeling system.
Fossil Fuel Pilot
Application
This research has resulted in recommendations for policy makers, regarding the development of new energy policies. Questions
that emerged during the project will be addressed in future research studies.
Scale
The pilot results are mainly applicable to the European scale, although also some global issues have been addressed. However,
in principle the models could be extended to global scale if the necessary input data is available.
Coal mining and land subsidence have been addressed at local scale for specific pilot study sites.
DATA AND MODELS
This EnerGEO Fossil Fuel Pilot is about assessing the impact
of fossil fuels on the environment. Modeling emissions, from
fossil fuel combustion as well as other sources, and air quality
dispersion are the key modeling tools in this assessment.
The modeling tools used in EnerGEO are :
- LOTOS-EUROS model (www.lotos-euros.nl) used by TNO
- POLYPHEMUS model used by AGH
In the framework of this study, POLYPHEMUS is mainly used
for modeling mercury dispersion.
REFERENCES
Quass, U., Vercauteren, J. Schaap, M., Hendriks, C. Kuhlbusch, T. et al. (in prep.),
‘Multi-site/multi period PM10 source apportionment by Positive Matrix
Factorisation for north-west Europe’, in preparation.
Kranenburg, R., Segers, A.J., Hendriks, C., Schaap, M. (2013) ‘Source apportionment
using LOTOS-EUROS: module description and evaluation’, Geoscientific
Model Development, vol.6, pp.721-733.
Schaap, M., Kranenburg, R., Curier, L., Jozwicka, M., Dammers, E., Timmermans R.,
(submitted),’ On the sensitivity of OMI-NO2 product to emission changes
The percentage rate of emissions from power sector to overall
concentration of mercury of Hgp in the surface level [%].
across Europe using a chemistry transport model’, Remote Sensing.
Hendriks, C., Kranenburg, R., Kuenen, J., van Gijlswijk, R., Wichink Kruit, R.,
Segers, A., Denier van der Gon, H., Schaap, M. (2013) ‘The origin of ambient
particulate matter concentrations in the Netherlands’, Atmospheric
Environment , vol.69, pp.289-303.
Hendriks, C., Kuenen, J., Kranenburg, R., Scholz, Y., Schaap M., et al. (in prep.),
‘Changing emission time profiles because of energy transitions impact
source receptor matrices’.
Beltman, J., Hendriks, C., Tum, M., Schaap, M. (2013), ‘The impact of large scale
biomass production on ozone air pollution in Europe’, Atmospheric
Environment, vol.71, pp. 352-363.
CONTACT
Jeroen Kuenen
TNO
P.O. Box 80015
NL- 3508 TA Utrecht
[email protected]
Number 226364.
Solar Pilot
Rationale
DIRECT NORMAL IRRADIATION
The need to decarbonize the energy system for environmental and economic reasons will lead to larger shares of
renewable energies in the energy system.
Solar energy is one of the most established renewable
energy resources, and is gaining more and more importance.
The following aspects are addressed by the EnerGEO Solar
Pilot:
– Life Cycle Assessment (LCA) of Photovoltaic Systems
(PV)
– Optimal location for solar power plants
– PV integration into existing electric grids
Annual sum of direct normal irradiation [kWh/m²]
for the Mediterranean basin for the year 2002.
METHODOLOGY
1. Life Cycle Assessment (LCA) of PV systems assesses various environmental impacts of a given PV system over its life
cycle, also accounting for the manufacture of the modules and cells.
2. The solar site ranking service is a Spatial Decision Support System (SDSS) that calculates optimal location for solar power
plants expressed by a ranking value.
3. Detailed resource quality information is provided by the high resolution models REMix and TASES to better represent
the large scale generation of electricity from intermittent renewable energy resources in GAINS and MESSAGE.
KEY RESULTS
INNOVATIVE IMPACT
The Life Cycle Assessment for 3 kWp PV systems as well as
the site ranking service for solar power plants are available
as Web Services: http://stbgis.dlr.de/geoserver/
LCA models for PV systems can be used for worldwide
PV installations over a large spectrum of technologies
(multi Si, mono Si, CdTe, etc.) and configurations.
The site ranking service is also accessible in a WebGIS
client with additional tools for analysis and integration of
further data.
The solar site ranking service is a state-of-the-art webbased decision support tool for locating suitable
regions for solar power plants. The previously offline
analysis is now freely accessible on the internet and
allows users to understand the impacts of certain criteria
based on their user-defined preferences.
Full-load-hour potential curves are available, enabling
the spatial variability assessment of regional renewable
energy.
Time series of the potential renewable power generation
are provided to generate emissions profiles of pollutants
of different energy pathways.
Renewable energies are relatively new resources
used in the power supply system at large scale. Their
representation in integrated assessment models
needs to take into account the varying spatial and
temporal availability.
Solar Pilot
The Life Cycle Assessment covers 3 kWp PV systems for several environmental impacts and configurations and has a worldwide
coverage.
The selection of optimal locations for solar power plants systems is a multi-criteria based approach that heavily depends on
the availability of data in equal and good resolution. The site ranking service therefore covers the area of Europe, where those
requirements are met. However, the methods applied are generally transferable to other regions or global scale.
Renewable energy resource data are provided for Europe and parts of North Africa. Global data can be provided in the future
but might partly have a lower spatial and temporal resolution.
DATA AND MODELS
The solar LCA web service has been developed specifically for the EnerGEO
project, taking advantage of the AIP3 scenario created within GEOSS.
http://viewer.webservice-energy.org/energeo_aip3/index.htm
The siting support for solar power plants is based on a spatial multi-criteria
analysis considering different optimization objectives and exclusion
constraints like radiation data, geophysical conditions and anthropogenic
infrastructure. To account for a user-specific focus, the analysis allows
individual criteria weightings.
The full-load-hour potential curves and the time series of potential power
generation from renewable energy resources are based on meteorological,
geographical and statistical data, assumptions about the available area types
and shares and characteristic power plant models.
REFERENCES
Ménard, L., Blanc, I., Beloin-Saint-Pierre, D., Gschwind, B., Wald, L., Blanc, P., Ranchin,
T., Hischier, R., Gianfranceschi, S., Smolder, S., Gilles, M. and Grassin, C. (2012)
‘Benefit of GEOSS Interoperability in Assessment of Environmental Impacts
Illustrated by the Case of Photovoltaic Systems’, IEEE Journal of Selected Topics
in Applied Earth Observations and Remote Sensing, vol. 5, pp. 1722 – 1728.
The Solar Site Ranking WebGIS client with a
resulting suitability map and criteria analysis tool.
Scholz, Y. (2012) ‘Renewable energy based electricity supply at low costs – Development of the REMix model and application for Europe‘ Dissertation,, University of
Stuttgart [Online], Available:
http://elib.uni-stuttgart.de/opus/volltexte/2012/7635/
Wanderer, T., Herle, S., (2013) ‘Using a web-baed SDSS for siting solar power
plants’, Proceedings of the 27th International Conference on Informatics for
Environmental Protection, Hamburg, Germany.
CONTACT
Thomas Wanderer
DLR
Pfaffenwaldring 38-40
D-70569 Stuttgart
[email protected]
Isabelle Blanc
MINES Paris Tech / ARMINES
[email protected]
Number 226364.
Wind Pilot
Rationale
Wind energy is one of the primary sources of renewable
energy which is currently exploited. However, what is the
real impact on our environment of a wind farm over its life
cycle?
IMPACT ON CLIMATE CHANGE
[g CO2eq/kWh]
In the EnerGEO Wind Pilot we evaluate the impact of a
wind farm throughout its lifetime, including the materials
and energy used to build, install and maintain it.
By visualizing the impact and energy production of wind
turbines on a map, decision makers can easily compare
the benefits and impacts of wind energy with other forms
of energy production.
Configuration selected:
20 years life time; 40 turbines; high maintenance; high failure rate;
fixed and floating foundations
METHODOLOGY
The EnerGEo Wind Pilot performs a Life Cycle Assessment (LCA) for a wind turbine, based on geo-dependent environmental
performance expressed as the ratio of the impacts issued from a geo-localized Life Cycle Assessment algorithm over the life
time electricity production.
The generated electricity windspotentials have been assessed from the selected turbine power curve (5 MW) and the wind
speed distribution at hub height. Wind Speed distribution is issued from satellite observations and numerical models.
KEY RESULTS
INNOVATIVE IMPACT
The main results derived from the wind farm life cycle
assessment workflow are:
The EnerGEO Wind Pilot allows decision makers
to compare not only the financial cost of energy
production, but also the impact on our environment.
– LCA environmental performance maps for several
scenarios
– Scenarios based on variable failure rates, operation
maintenance schemes, turbine lifetime, potential
losses, and technical choices
– Maps published into a WebGIS client tool to ease
their dissemination
The results can be explored at:
http://viewer.webservice-energy.org/energeo_wind_pilot/index.htm
All aspects such as materials, maintenance and efficiency
are taken into account to assess the wind farm life cycle
environmental impacts:
Wind Pilot
Application
The EnerGEO Wind Pilot has potential applications in policy making as well as for the planning and the development of wind
farms.
Scale
The EnerGEO Wind Pilot was demonstrated for North-West Europe, with a focus on offshore wind turbine installations of the
North Sea region. The methodology can directly be applied to worldwide locations of offshore installations and could be
translated for onshore wind turbine installations.
DATA AND MODELS
Components considered for the offshore
modular LCA model
Maps of wind speed were generated with the Weather Research and
Forecasting (WRF) regional atmosphere model, which is based on
a 12 years historical simulation.
Wind energy production was computed based on a standard 5 MW
wind turbine power curve.
For the geo-dependent LCA model, site-sensitive components of the
farm have been considered: the length of sub-marine cabling, which
is influenced by the distance of the farm to the coast, the marine
transport scheme, which depends on the distance to the nearest
relevant harbor and the foundation choice (floating vs. fixed), which
depends on the water depth.
REFERENCES
Blanc, I., Guermont, C., Gschwindt, B., Menard, L., Calkoen, C. and Zelle, H. (2012)
‘Web tool for energy policy decision-making through geo-localized LCA
models: A focus on offshore wind farms in Northern Europe`, in Arndt, H.K.,
Knetsch, G. and Pillmann, W. (ed.) EnviroInfo 2012 – Proceedings of the 26th
International Conference on Informatics for Environmental Protection: Part 2:
Open Data and Industrial Ecological Management Aachen: Shaker Verlag, p.
499-506.
Zelle, H., Mika, A., Calkoen, C., Santbergen, P., Blanc, I., Guermont, C., Me,nard, L.,
Gschwind, B. (2013) `Environmental data for the planning of off-shore wind
parks from the EnerGEO Platform of Integrated Assessment (PIA)´, Proceedings
of the 27th International Conference on Informatics for Environmental
Protection, Hamburg, Germany.
CONTACT
Isabelle Blanc
MINES ParisTech / ARMINES
[email protected] Hein Zelle
BMT ARGOSS
Voorsterweg 28
NL- Marknesse 8316 PT
[email protected]
Number 226364.

the Energeo Factsheets

Transcription

Similar documents

Kurzanleitung MORECO