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State-of-the-Art Climate Forecasting for Wind Energy

State-of-the-Art Climate Forecasting for Wind Energy. Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert CLIMRUN General Assembly, ENEA, Rome, July 2013. Presentation Outline: Climate forecasting for wind energy. Problem: How can climate variability be a risk in wind energy decisions?

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State-of-the-Art Climate Forecasting for Wind Energy

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  1. State-of-the-Art Climate Forecasting for Wind Energy Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert CLIMRUN General Assembly, ENEA, Rome, July 2013

  2. Presentation Outline: Climate forecasting for wind energy Problem: How can climate variability be a risk in wind energy decisions? Solution: How can climate forecasting minimise this risk? Methodology: Climate forecasting of wind speed, a seasonal example. Caveats/Further research: What are the limitations and potential for wind energy forecasting? Conclusions: State-of-the-art climate forecasting for wind energy, current status.

  3. Seasonal Variability in Wind Resource at Site X 15 10 Observations High ForecastUncertainty Low 5 High High Climatology Uncertainty Low High 0 1980 1990 2000 2010 2020 JJA '13 Time in yrs(lines represent 1st May./yr) Problem: Climate variability risk in wind decisions Problem: How can climate variability be a risk in wind energy decisions? Mean Wind Speed (m/s) - Reduced uncertainty of future wind variability - Identify likelihood of extreme events

  4. Problem: Climate variability risk in wind decisions Climate Forecasts Weather Forecasts Hindcasts Seasonal Annual-Decadal Climate Change -30 years Hours/days/weeks Months to seasons (1month-1year) Inter/multi-annual (1-30years) Multi-decadal (30+years) PRESENT FUTUREPredictions PAST Observations Operational decisions (Wind farm/grid operator, trader) Planning decisions (Policy maker, energy planning, grid development) Energy generation – balancing resources, energy trading, extremes, insurance? Maintenance – offshore most vulnerable Market strategies – incentives, energy mix Spatial planning – balancing resources, reinforce/redesign distribution network Investment decisions Site selection – robust resource assessments, portfolio design Revenue – robust projections, volatility over time, insurance? (debt financing, throughout project)

  5. Solution: Climate forecasting of wind resources GUIDANCE/RISK MANAGEMENT ACTION/RISK MINIMISATION Operational decisions Planning decisions Investment decisions - Robust assessments - Contingency plans - Early-warning systems - Monitoring - Mobilise resources - Prepare measures - Instruction - Action

  6. Methodology: Climate forecasting of wind speed Stage A: Wind Resource Assessment Wind energy potential: Where does the highest wind occur? Wind energy volatility: Where does the wind vary the greatest? Stage B: Wind Forecast Skill Assessment Validation of the climate forecasts: How well can it reproduce the wind resources and its variability over past timescales Stage C: Operational Wind Forecasts Probabilistic forecast of future wind resource information

  7. Methodology: Wind Forecasts Stages Stage A: Wind Resource Assessment Wind energy potential: Where is it the windiest? Spring 10m wind speed from 1981-2011 (ERA-Interim) in m/s

  8. Methodology: Wind Forecasts Stages Stage A: Wind Resource Assessment Wind energy volatility: Where does the wind vary the greatest? Spring 10m wind inter-annual variability from 1981-2011 (ERA-Interim) in m/s

  9. Methodology: Wind Forecasts Stages Stage A: Wind Resource Assessment Where is wind resource potential and variability the highest? Spring 10m wind resource availability Spring 10m wind inter-annual variability Europe N.America S.America Africa Australia Asia Areas of interest: Patagonia/E.Brasil Central Sahara, Sahel W. Australia/ Tasmania UK/ Baltic Sea N.Mexico/ N.Canada China/ Mongolia/ N. Russia

  10. Methodology: Wind Forecasts Stages Stage B: Wind Forecast Skill Assessment 1St validation of the climate forecast system: Can the wind forecast mean tell us about the future wind resource variability at a specific time? Spring 10m wind resource ensemble mean correlation (ECMWF S4, 1 month forecast lead time, once a year from 1981-2010) Perfect Forecast Same as Climatology Worse than Clima-tology

  11. Methodology: Wind Forecasts Stages Stage B: Wind Forecast Skill Assessment 2nd validation of the climate forecast system: Can the wind forecast distribution tell us about both the magnitude of the wind resource variability, and its uncertainty at a specific time? Spring 10m wind speed continuous ranked probability skill score (ECMWF S4, 1 month forecast lead time, once a year from 1981-2010, no calibration) Perfect Forecast Same as Climatology Worse than Clima-tology

  12. Methodology: Wind Forecasts Stages Stage B: Wind Forecast Skill Assessment Where is wind forecast skill highest? Spring 10m wind resource magnitude and its uncertainty forecast skill Spring 10m wind resource variability forecast skill Both wind resource magnitude and its uncertainty skill Wind resource variability forecast skill only Europe N.America S.America Africa Australia Asia Areas of interest: Kenya Somalia E.Brasil N.Chile Indonesia/ W.India W. Australia N.Spain/ S.E Europe Mexico/ S.Canada

  13. Spring 10m wind speed availability Stage A: Wind Resource Assessment Where is wind resource potential and volatility highest? Stage B: Wind Forecast Skill Assessment Where is wind forecast skill highest? Methodology Conclusion: Global Wind Forecasts in Spring Climate Forecasting Unit m/s m/s Wind resource availability Wind resource inter-annual variability S.America Africa Australia Europe N.America Asia Areas of Interest: (Resources) Patagonia/ E.Brazil UK/ Baltic Sea N.Mexico/ N.Canada C.Sahara, Sahel China/ Mongolia/ N.Russia W.Australia/ Tasmania N.Mexico/ W.Australia E.Brasil Magnitude + uncertainty forecast skill Variability forecast skill Europe N.America S.America Africa Australia Areas of Interest: (Forecast skill) Asia Kenya Somalia N.Spain/ S.E Europe Mexico/ S.Canada E.Brazil N.Chile Indonesia/ W.India W. Mexico Mexico E.Brasil W.Australia

  14. Methodology: Wind Forecasts Stages Stage C: Operational Wind Forecasts Probabilistic forecast of (future) spring 2011,10m wind resource most likely tercile (ECMWF S4, 1 month forecast lead time) Areas of Interest Identified: (Resources and Forecast Skill) N.America Mexico Mexico S.America S.America E.Brasil E.Brasil Australia W. W.Australia

  15. Methodology: Wind Forecasts Stages Stage C: Operational Wind Forecasts Probabilistic forecast of spring 2011,10m wind resource most likely tercile (ECMWF S4, 1 month forecast lead time) Areas of Interest Identified: (Resources and Forecast Skill) N.America Mexico Mexico S.America S.America E.Brasil E.Brasil Australia W. W.Australia

  16. Caveats and further research: Climate forecasting for wind energy Caveats 1. 10m wind not representative of wind turbine hub height. 2. Lack of relevant, observational wind data for robust validations of forecast skill: reanalysis data used instead. 3. Seasonal wind forecasts assessed with a single climate model with 15 ensemble members: a multi-model, calibrated approach is needed with more ensemble members. Further research 1. Multi-model approach needed for a more robust forecast skill assessment. 2. Seasonal wind forecasts to be made down to site-specific scales. 3. Collaborations undertaken to formulate seasonal wind power forecasts with simple wind energy models to issue theoretical power predictions. 4. Explore the potential of decadal wind forecasts for wind energy sector.

  17. Conclusions: Climate forecasting for wind energy 1. Wind forecasting over seasonal to decadal timescales can help to minimise risk of future wind variability on operational, planning and investment decisions 2. Seasonal wind forecasting is an emerging climate service within the renewable energy sector, whilst decadal wind forecasts are yet to be explored. 3. Some global regions are more vulnerable to wind resource variability over seasonal timescales than others 4. Although wind forecast skill is limited in some regions, there are others that show good potential (more so for predicting the resource variability than magnitude) 5. Based on points 3, 4, regions where operational Spring wind forecasts demonstrate the greatest value from research to date includes: Mexico, E.Brasil, W.Australia. 6. Seasonal and decadal wind forecast research to date includes several caveats, and there is scope for significant improvement with further research and better observational datasets.

  18. Advancing Renewable Energy with Climate Services (ARECS) Join the initiative at: www.arecs.org • Seasonal and decadal, wind and solar forecast information • Provide feedback, register your needs • Receive a quarterly seasonal wind forecast newsletter

  19. THANK YOU melanie.davis@ic3.cat The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under the following projects: CLIM-RUN, www.climrun.eu (GA n° 265192)EUPORIAS, www.euporias.eu (GA n° 308291)SPECS, www.specs-fp7.eu (GA n° 308378)

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