c limate l ocal i nformation in the m editerranean - r esponding to u ser n eeds
DESCRIPTION
C limate L ocal I nformation in the M editerranean - R esponding to U ser N eeds. Melanie Davis, Climate Forecasting Unit (CFU). Presentation Contents. 1. Energy status (European Union) 2. Introduction CLIM-RUN 3. Climate predictions 4. Climate variables for renewable energy - PowerPoint PPT PresentationTRANSCRIPT
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Climate Local Information in the Mediterranean - Responding to User Needs
Melanie Davis, Climate Forecasting Unit (CFU)
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Presentation Contents
1. Energy status (European Union)2. Introduction CLIM-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask
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2011130 €c/litre
2009100 €c/litre
€40
€55
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Instituto para la Diversificación y el Ahorro Energético (IDAE):
1. No other country receives so much oil from Libya as Spain
2. No country is so dependent on importation of fossil fuels (80% importations)
3. No country uses so much energy per unit of GDP (energy intensity)
In Europe: For every $10 rise in the barrel price = one tenth less GDP
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EU Energy GenerationEnergy Consumption of EU27
20% by 2020
10.3% in 2008
EU Renewable Energy (RE) Target
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Renewable Challenge
''The amount of usable solar and wind energy far exceeds the world's total energy demand, with current technology feasibility considered''
2009 American Institute of Physics
Energy Demand
00.00 10.00 16.00 22.00Time: One Day
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Presentation Contents
1. Energy status (European Union)2. Introduction CLIM-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask
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CLIM-RUN Research Project
Improve the provision of adequate climate information, that is relevant to and usable by different sectors of society
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CLIM-RUN: Work Package 7
Illustrate how climate information can play an important role in future
changes and developments in the energy sector
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A Renewable EuropePower Grid System
Power Stations
Wind Farms
Solar Farms
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A Renewable SpainPower Grid System
Power Stations
Wind Farms
Solar FarmsExport to Africa
Export to France
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Climate Data and RE
1. Site selection
2. Predicted annual energy yield
3. Long-term energy yield performance
4. Frequency when energy yield below a defined threshold
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Presentation Contents
1. Energy status (European Union)2. Introduction CLIM-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask
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Climate Predictions – Current Status
Implications: Results…??? Many…
Assumed consistency in RE climatic resources Considerable multiplication of RE costs
Timeline (years)
season and annual variation with
decades
0 1 2 3 4 5 10 20 30 40
CLIMATE PREDICTIONS – CLIM-RUN PROJECT
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Climate Prediction SensitivitiesInvestment influence using inter-annual climate resource variability
Example: planning of a solar power plant in Spain
•Typical size: 50 MW, cost €300 million •Guaranteed price per unit of electricity generated: 0.20 €/kWh •This provides a annual yield of €31 millionAssumptions: small solar irradiance variation
Uncertainty of 1% leads to:- Annual increase or decrease of total revenue = €310000-Across the investment return period = €8 million
or ~ 15% investment
´´Components of uncertainty are commonly based on subjective
estimations rather than on statistical sound data analysis´´
Heinz-Theo Mengelkamp et al. 2010, Risk analysis for a mixed wind farm and solar power plant portfolio.
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Climate Prediction Sensitivities
´´The fact that a trend has existed in the recent past is no certain guarantee of its continuation in to the future e.g. rainfall may readily reverse or disappear over a period of a few decades´´ Climate Impact on Energy Systems, World Bank Study, 2011
CLIM-RUN activities
1. Characterising the climate using statistical analyses
2. Improving the reliability of databases and techniques
3. Collaboration with energy stakeholders
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Climate Prediction – Current StatusAims to provide climate predictions from days to decades into the future.
Climate predictions are produced with numerical models of the climate system.
Used alongside observed climate patterns in order to project to future timescales.
Improves understanding of how the climate works and helps predict how it will act and react in the future.
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Climate Prediction with RE
Better understanding of :
- Confidence in energy yield forecasts- Assist decision making- Understand mechanism to accelerate
RE generation efficiently
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Guidance for:
- Site selection and system planning- Offsetting yield variability- Infrastructure adjustments- Future energy demand/requirement
Climate Prediction with RE
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Protect against:
- Excess costs for renewable energy operation and maintenance
- Vulnerability of industry and society
Climate Prediction with RE
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Contribute to:
- Climate change adaptation policy- Energy security policy- Building codes and other regulations- Investment opportunities
Climate Prediction with RE
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CLIM-RUN Questions
? How representative is current climate data for estimating the performance of a RE plant over its lifetime (e.g. 30 years)?
? How confident can we be about the energy yield forecasts?
??
What are the likely lowest level of energy yield from a RE project in a season/year? (known as ´´climate droughts´´)
How can solar and wind climatic resources co-vary to supply a more consistent stream of energy?
Worst case scenarios:Worst case scenarios:
Can we characterise the frequency, amplitude and duration of high energy demand (extreme heat/cold periods) and low RE yield climatic resources?
?
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Presentation Contents
1. Energy status (European Union)2. Introduction CLIM-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask
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Climate VariablesBoth wind & solar:
Air temperature (oC) : stabilityAir density (ρ) : environment
Solar radiation (W/m2) with wind speed (m/s): stability
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Climate Variables - RegionWind only:•Wind speed (m/s)
•Direction (degrees)
•Consistency/Direction frequency (degrees, %)
•Power density (W/m2)
•Vertical wind shear (m/s)
•Turbulence profile/Turbulence Intensity (TI)
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Challenges: WindWind resource highly variable (spatially) compared to solar and is complicated by complex land orography
Wind speed and direction must be taken into account but can complicate the statistical procedures
Large-scale land use change has an unknown impact on regional climate
!!!!
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Solar only:•Surface Direct Natural Irradiance, DNI (W/m2)
•Surface Global Horizontal Irradiance, GHI (W/m2)
Both affected by:- Cloud cover and type- Concentration of aerosols (anthropogenic and natural)
Absorb and/or scatter solar radiation
Climate Variables - Region
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Challenges: Solar
Solar surface irradiance varies dramatically with cloud cover and aerosols
GHI is most often the only available solar radiation data so conversion models are used to derive estimates of DNI (Perez et al, 1987)
!
!!
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Climate Variables - Continent
Climate PhenomenaSeasonal:- Tropical Pacific: El Niño Southern Oscillation (ENSO) - North Atlantic Oscillation (NAO)
Inter-annual:- Pacific Decadal Oscillation (PDO)- Atlantic Multi-decadal Oscillation (AMO)
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Climate Variables - Others
Anthropogenic : land use, industry etc..
Natural Events: volcanoes etc..Anthropogenic? Natural?Anthropogenic? Natural?
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Presentation Contents
1. Energy status (European Union)2. Introduction CLIM-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask
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Renné et al, 2008, Solar Resource Assessment, NREL
Climate Prediction - Results
Map background: average global radiation data from 1998-2005 (kWh/m2/day)
Points: difference annual average between 1961-1990 and 1998-2005 (kWh/m2/day)
1998-2005 > 1961-1990Up to 10% higher
1998-2005 < 1961-1990Up to 10% lower
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Climate Predictions - Results
Difference in annual mean value of global irradiance between 2003 and 1998-2005 (Watt-hours/m2/day)
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Climate Predictions - Results
Winter Spring
Summer Autumn
Difference in seasonal mean value of global irradiance between 2003 and 1998-2005 (Watt-hours/m2/day)
Awareness of the differences between short-term (monthly/annual) datasets and longer-term means.
By using more years of data for the analysis, there is less chance that anomalous climate events or patterns
could influence the results.
By using more years of data for the analysis, there is less chance that anomalous climate events or patterns
could influence the results.
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Presentation Contents
1. Energy status (European Union)2. Introduction CLIM-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask
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Questions: Wind
Are there dominant climate patterns associated with high winds?
Is there an interplay between i) large scale & local scale, ii) multi-annual & decadal, climate patterns?
What is the frequency and intensity of such predictions over annual or decadal timescales?
????
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Questions: Solar
How well can we estimate inter-annual & intra-annual variations of surface solar irradiance using observed datasets?
What is the long-term climate effect of changes in atmospheric aerosols on solar radiation?
?
??
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ConclusionFor the RE sector as a whole, simple and reliable climate predictions are needed.
Higher-quality RE climate resource assessment can accelerate technology deployment by making a positive impact on decision making and reducing uncertainty of financial investments.