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-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask

2011130 €c/litre

2009100 €c/litre

€40

€55

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

EU Energy GenerationEnergy Consumption of EU27

20% by 2020

10.3% in 2008

EU Renewable Energy (RE) Target

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

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

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 EuropePower Grid System

Power Stations

Wind Farms

Solar Farms

A Renewable SpainPower Grid System

Power Stations

Wind Farms

Solar FarmsExport to Africa

Export to France

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-RUN3. Climate predictions4. Climate variables for renewable energy5. Examples of research results6. Questions to ask

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

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.

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

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.

Climate Prediction with RE

Better understanding of :

- Confidence in energy yield forecasts- Assist decision making- Understand mechanism to accelerate

RE generation efficiently

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

Climate Prediction with RE

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?

?

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

Climate VariablesBoth wind & solar:

Air temperature (oC) : stabilityAir density (ρ) : environment

Solar radiation (W/m2) with wind speed (m/s): stability

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)

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

!!!!

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

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 PhenomenaSeasonal:- 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: volcanoes etc..Anthropogenic? Natural?Anthropogenic? Natural?

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

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

Climate Predictions - Results

Difference in annual mean value of global irradiance between 2003 and 1998-2005 (Watt-hours/m2/day)

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.

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

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?

?

??

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.