Transcript Climate Local Information in the Mediterranean - Responding to User Needs Melanie Davis, Climate Forecasting Unit (CFU)

Climate Local Information in the Mediterranean - Responding to User Needs
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
5. Examples of research results
6. Questions to ask
2009
100 €c/litre
€40
€55
2011
130 €c/litre
In Europe: For every $10 rise in the barrel price = one tenth less GDP
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)
EU Energy Generation
Energy Consumption of EU27
EU Renewable Energy (RE) Target
20% by 2020
10.3% in 2008
Renewable Challenge
''The amount of usable solar and wind
energy
far
exceeds
the
world's
total
Energy
Demand
energy demand, with current
technology feasibility considered''
00.00
10.00
16.00
2009 American Institute of Physics
Time: One Day
22.00
Presentation Contents
1. Energy status (European Union)
2. Introduction CLIM-RUN
3. Climate predictions
4. Climate variables for renewable energy
5. Examples of research results
6. Questions to ask
CLIM-RUN Research Project
Improve the provision of adequate climate information, that is
relevant to and usable by different sectors of society
CLIM-RUN: Work Package 7
Illustrate how climate information can
play an important role in future
changes and developments in the
energy sector
A Renewable Europe
Power Grid
System
Power Stations
Wind Farms
Solar Farms
A Renewable Spain
Export to France
Power Grid
System
Power Stations
Wind Farms
Solar Farms
Export to Africa
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
Presentation Contents
1. Energy status (European Union)
2. Introduction CLIM-RUN
3. Climate predictions
4. Climate variables for renewable energy
5. Examples of research results
6. Questions to ask
Climate Predictions – Current Status
Timeline (years)
2
3
10 20 30 40
season and annual
variation with
CLIMATE PREDICTIONS – CLIM-RUN PROJECT
decades
Implications:
4
5
season
variation with
multiple years
1
seasons - year
mins - hours
weather
days - months
predictions
0
Results…???
Many…
Assumed consistency in RE climatic resources Considerable multiplication of RE costs
Climate Prediction Sensitivities
Investment influence using inter-annual climate resource variability
´´Components of uncertainty are
•Typical size: 50 MW, cost €300 million
commonly
based
on subjective
•Guaranteed
price per unit
of electricity
generated: 0.20 €/kWh
•This
provides a annual
yield of than
€31 million
estimations
rather
on statistical
Assumptions: small solar irradiance variation
sound data analysis´´
Example: planning of a solar power plant in Spain
Uncertainty of 1% leads to:
- Annual Mengelkamp
increase or et
decrease
Heinz-Theo
al. 2010, of total revenue = €310000
-Across
theforinvestment
period
€8 million
Risk
analysis
a mixed windreturn
farm and
solar =
power
plant portfolio.
or ~ 15% investment
Climate Prediction Sensitivities
CLIM-RUN
´´The
fact that a activities
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´´
1. Characterising
the climate using statistical analyses
Climate Impact on Energy Systems, World Bank Study, 2011
2. Improving the reliability of databases and techniques
3. Collaboration with energy stakeholders
Climate Prediction – Current Status
Aims 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.
Climate Prediction with RE
Better understanding of :
- Confidence in energy yield forecasts
- Assist decision making
- Understand mechanism to accelerate
RE generation efficiently
Climate Prediction with RE
Guidance for:
- Site selection and system planning
- Offsetting yield variability
- Infrastructure adjustments
- Future energy demand/requirement
Climate Prediction with RE
Protect against:
- Excess costs for renewable energy
operation and maintenance
- Vulnerability of industry and society
Climate Prediction with RE
Contribute to:
- Climate change adaptation policy
- Energy security policy
- Building codes and other regulations
- Investment opportunities
CLIM-RUN Questions
?
Can we characterise the frequency,
?amplitude and duration of high energy
(extreme heat/cold periods)
?demand
and low RE yield climatic resources?
?
How representative is current climate data for estimating the
performance of a RE plant over its lifetime (e.g. 30 years)?
Worst
Worst case
case scenarios:
scenarios:
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?
Presentation Contents
1. Energy status (European Union)
2. Introduction CLIM-RUN
3. Climate predictions
4. Climate variables for renewable energy
5. Examples of research results
6. Questions to ask
Climate Variables
Both wind & solar:
Air temperature (oC) : stability
Air density (ρ) : environment
Solar radiation (W/m2) with
wind speed (m/s): stability
Climate Variables - Region
Wind 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)
Challenges: Wind
!
!
!
Wind 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
Climate Variables - Region
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
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)
Climate Variables - Continent
Climate Phenomena
Seasonal:
- Tropical Pacific: El Niño Southern Oscillation (ENSO)
- North Atlantic Oscillation (NAO)
Inter-annual:
- Pacific Decadal Oscillation (PDO)
- Atlantic Multi-decadal Oscillation (AMO)
Climate Variables - Others
Anthropogenic :
land use, industry etc..
Natural Events:
Anthropogenic?
volcanoes
etc..
Natural?
Presentation Contents
1. Energy status (European Union)
2. Introduction CLIM-RUN
3. Climate predictions
4. Climate variables for renewable energy
5. Examples of research results
6. Questions to ask
Climate Prediction - Results
Renné et al, 2008,
Solar Resource
Assessment, NREL
1998-2005 > 1961-1990
Up to 10% higher
1998-2005 < 1961-1990
Up to 10% lower
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)
Climate Predictions - Results
Difference in annual mean value of global irradiance
between 2003 and 1998-2005 (Watt-hours/m2/day)
Climate Predictions - Results
By
using more years of data forSpring
the analysis, there is less
Winter
Awareness
of
the differences
between
short-term
chance
that
anomalous
events
patterns
Difference
in seasonal
meanclimate
value
of
globalor
irradiance
(monthly/annual)
datasets and
means.
could
influence
the longer-term
results.
between
2003 and
1998-2005
(Watt-hours/m2/day)
Summer
Autumn
Presentation Contents
1. Energy status (European Union)
2. Introduction CLIM-RUN
3. Climate predictions
4. Climate variables for renewable energy
5. Examples of research results
6. Questions to ask
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?
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?
Conclusion
For 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.