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Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert CLIM-RUN GA, July 8-10 th 2013

Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert CLIM-RUN GA, July 8-10 th 2013. IC3. CLIMRUN WP7: Renewable Energy Second Round Workshops. EEWRC. DHMZ. ENEA, PIK. Product 1: Mediterranean Seasonal Wind Forecast. Product 2: Europe Long-term Wind Speed Scenarios.

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Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert CLIM-RUN GA, July 8-10 th 2013

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  1. Melanie Davis, Francisco Doblas-Reyes, Fabian Lienert CLIM-RUN GA, July 8-10th 2013 IC3 CLIMRUN WP7: Renewable Energy Second Round Workshops EEWRC DHMZ ENEA, PIK

  2. Product 1: Mediterranean Seasonal Wind Forecast

  3. Product 2: Europe Long-term Wind Speed Scenarios

  4. Product 3: Rabat Long-term Wind Speed Scenarios

  5. 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)

  6. Second round workshops

  7. Seasonal Wind Forecasts Demonstrating the value obs. forecast + 1.0 forecast - 1.0 forecast - 1.0 Wind Forecast Skill Assessment 1St validation of the climate forecast system: forecast example 1 example 2 wind speed Can the wind forecast mean tell us about the wind resource variability at a specific time? example 3 Spring 10m wind resource ensemble mean correlation (ECMWF S4, 1 month forecast lead time, once a year from 1981-2010) time Perfect Forecast Same as Climatology Worse than Clima-tology

  8. Seasonal Wind Forecasts Demonstrating the value obs. forecast + 1.0 forecast - 1.0 forecast - 1.0 Wind Forecast Skill Assessment 2nd validation of the climate forecast system: forecast example 1 wind speed Can the wind forecast distribution tell us about the magnitude of the wind resource variability, and its uncertainty at a specific time? example 2 example 3 Spring 10m wind resource CR probability skill score (ECMWF S4, 1 month forecast lead time, once a year from 1981-2010) time Perfect Forecast Same as Climatology Worse than Clima-tology

  9. Seasonal Wind Forecasts Demonstrating the value 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 forecast 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

  10. Seasonal Wind Forecasts Demonstrating the potential 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 Europe Mexico/ S.Canada N.Spain/ S.E Europe S.America Australia E.Brasil N.Chile W. Australia Africa Asia Kenya Somalia Indonesia/ W.India %

  11. 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 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. Run seasonal wind forecasts with wind energy models to get power outputs. 4. Explore the potential of decadal wind forecasts for wind energy sector.

  12. Conclusions: Climate information for wind energy 1. Climate related wind information can help to minimise risk of future wind variability on operational, planning and investment decisions: BUT every application is different. 2. The value of such information needs to be demonstrated in the decision making process i.e. forecast quality → forecast value. 3. Past climate assessment is advanced in the energy sector, but climate forecasting is not. Reason: Forecast skill is a concern for all, especially for predicting forecast magnitude. 4. Key regions where operational climatic wind information demonstrate the greatest value could be explored further to evaluate forecast value in DMP. 5. Users want to see the best possible forecasts to benchmark its potential and limitations. 6. An index of operational forecast skill (in practice) over space and time is requested by many users.

  13. Next Steps 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

  14. Next Steps Advancing Renewable Energy with Climate Services (ARECS)

  15. Next Steps Advancing Renewable Energy with Climate Services (ARECS)

  16. Croatia Workshop Energy Roadmap to 2050

  17. Croatia Workshop Number of hours working on nominal power for wind power plants in Croatia (2011) Zagreb, HEP - OIE, d.o.o 17

  18. Croatia Workshop Climate change 2011-2040, A2 scenarioWind at 10m, summer

  19. Croatia Workshop Hydro in Croatian power system - Half of electricity production in Croatia from 2000 – 2007 came from hydro power plants - 50% of installed Croatian power capacities are in hydro - Heat wave in 2003: electricity production in hydro`s down 25%, similar appeared in 2007 19

  20. Croatia Workshop Precipitation change2030-2040 Results from 18 simulations by 13 regional climate models (RCMs) which participatedin the ENSEMBLES project Crossesindicate that 66% of the RCMs agree in thesign of change.

  21. Croatia Workshop Precipitation change2030-2040 Bars denote number of RCMs!

  22. Croatia Workshop From estimation to modeling To propose the methodology how to model climate change impact on RES CLIMRUN 22

  23. Croatia Workshop Current work

  24. Croatia Workshop Conclusions By the midcentury in Croatia expected climate change impact on renewables is: - neutral impact on solar energy, - positive impact on wind generation, - negative impact on generation from hydropower (especially in the summer) Currently, stakeholders poorly use climate information (not only decadal projections and seasonal predictions, but also weather forecasts!) Obstacle for stakeholders: poor understanding of probabilistic approach of forecasts (instated of span of possibilities, they would like to have deterministic approach) During the workshop, there was a clear progress achieved in both way communication and understanding between two circles (meteorologists and energy experts)

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