Solar and wind power forecasting
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This presentation was used in Euro Arab Training Course “SMART GRID AND INTEGRATION OF RENEWABLE ENERGY”. The course took place 25 – 29 April 2016
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Solar and wind power forecasting
- 1. Jan Schmelter Euro Arab Training Course: SMART GRID AND INTEGRATION OF RENEWABLE ENERGY Granada, Spain 28th April 2016 >>>Solar and wind power forecasting
- 2. >>> Company profile Integration of renewables into grids and markets Service provider for energy meteorology since 2004 Areas of business – Wind and solar power predictions worldwide – Predictions for grid operators and traders – Software for Virtual power plants and demand side management – Development • Industry projects • National and international research projects 70 people
- 3. >>> Choice of international customers PJM Interconnection BPA Bonneville Power Administration Tennessee Valley Authority
- 4. >>> Areas with operational forecasting experience about 80GW wind power 48GW solar power
- 5. >>> Central Questions Why are power predictions necessary?
- 6. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions?
- 7. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts?
- 8. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work?
- 9. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work? Why is a power prediction difficult?
- 10. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work? Why is a power prediction difficult?
- 11. >>>load[%max.load] time [d] 0 0 100 1 2 3 4 5 6 7 Grid load without wind power Why are power predictions necessary? wind and solar power depends on meteorological conditions
- 12. >>>load[%max.load] time [d] 0 0 100 1 2 3 4 5 6 7 Residual load due to renewable production Grid load without wind power Why are power predictions necessary? wind and solar power depends on meteorological conditions
- 13. >>>load[%max.load] time [d] 0 0 100 1 2 3 4 5 6 7 Residual load due to renewable production Grid load without wind power Why are power predictions necessary? wind and solar power depends on meteorological conditions contribution of wind and solar must be known in advance power forecasts provide schedule of expected generation
- 14. >>> How does a wind power prediction look like? Schedule containing 24 or 96 values per day Single plants or aggregate Optional confidence bands
- 15. >>> How does a solar power prediction look like? Schedule containing 24 or 96 values per day Single plants or aggregate Optional confidence bands
- 16. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work? Why is a power prediction difficult?
- 17. >>> Who needs wind and solar power forecasts? grid operators (TSOs, DSOs) – balancing – dispatch / re-dispatch – load flow calculations – congestion management
- 18. >>> Who needs wind and solar power forecasts? grid operators (TSOs, DSOs) – balancing – dispatch / re-dispatch – load flow calculations – congestion management energy traders – trading wind and solar power on energy markets – influence of renewable energy on spot market price
- 19. >>> Who needs wind and solar power forecasts? grid operators (TSOs, DSOs) – balancing – dispatch / re-dispatch – load flow calculations – congestion management energy traders – trading wind and solar power on energy markets – influence of renewable energy on spot market price wind farm / solar plant operators – schedule maintenance – send schedule to grid operator
- 20. >>> Who needs wind and solar power forecasts? Time scale of forecast Stakeholder Area of application Shortest-term (0 – 6 h) Traders Trading on intraday energy market Control of curtailment due to negative market price Grid operators load dispatch centers system operators Balancing Unit re-dispatch Curtailment of power plants Speculators Influence of renewable production on market price Short-term (6 – 48 h) Traders Trading on day-ahead energy market Participation in regulation market Influence of reneables on market price Grid operators load dispatch centers system operators Unit dispatch Load flow calculations DA congestion forecast Plant operators Day-ahead planning of maintenance Medium-term (2 – 10 days) Traders Trading on long-term markets Grid operators load dispatch centers system operators 2DA congestions forecast Week-ahead planning Plant operators Medium-term planning of maintenance
- 21. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work? Why is a power prediction difficult?
- 22. >>> Integration of forecasting right from the beginning
- 23. >>> Integration of forecasting right from the beginning
- 24. >>> National register of wind and solar plants Register should contain following standing data of new installations for every unit: unique identifier for each unit technology of the generator (i.e. wind, pv, csp) installed capacity of each generator geographical location of each generator according to the World Geodetic System 1984 as a degree in decimals associated grid connection point for wind turbines: hub height and rotor diameter for PV modules: inclination angle and orientation date of initial operation and date of decommissioning if the generated power is used on location or only fed in to the grid
- 25. >>> The plant register for renewables in Germany Forecaster plant register
- 26. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency
- 27. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO
- 28. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1
- 29. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 ~ 20 direct marketers
- 30. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 ~ 20 direct marketers
- 31. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 ~ 20 direct marketers DSOs ~ 880
- 32. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 ~ 20 direct marketers DSOs ~ 880
- 33. >>> ~ 20 direct marketers The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 DSOs ~ 880
- 34. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 DSOs Wind farm owners ~ 20.000 ~ 880 ~ 20 direct marketers
- 35. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 DSOs Wind farm owners ~ 20.000 ~ 880 ~ 20 direct marketers Solar plant owners ~ 3.000.000
- 36. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 DSOs Wind farm owners ~ 20.000 ~ 880 ~ 20 direct marketers Solar plant owners ~ 3.000.000 some other companies
- 37. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 DSOs Wind farm owners ~ 20.000 ~ 880 ~ 20 direct marketers Solar plant owners ~ 3.000.000 some other companies
- 38. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO No 4 TSO No 3 TSO No 2 TSO No 1 DSOs Wind farm owners ~ 20.000 ~ 880 ~ 20 direct marketers Solar plant owners ~ 3.000.000 some other companies TRANSPARENCY?
- 39. >>> The plant register for renewables: the transparent way Forecaster plant register Federal Network Agency / TSO Wind farm owners Solar plant owners
- 40. >>> Integration of forecasting right from the beginning
- 41. >>> Integration of forecasting right from the beginning
- 42. >>> Grid code requirements The grid code should contain the following data for every unit: power output (real power output) available active power (power output due to meteorological conditions) Information on scheduled availability of wind farms and solar plants in terms of effective installed power to cover e.g. scheduled maintenance, known outages of machines Information on current availability of units Information on scheduled curtailment, i.e. limits to power output Information on currently activated curtailment by grid operators or dispatch centres Meteorological data that measure the available resource Wind farms: wind speed and direction close to hub height Solar plants: solar irradiation (direct and diffuse separately if possible)
- 43. >>> Integration of forecasting right from the beginning
- 44. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work? Why is a power prediction difficult?
- 45. >>> Goal is power conversion: transfer meteorological variables into power output of wind or solar plants Basic approaches to wind and solar power prediction
- 46. >>> Goal is power conversion: transfer meteorological variables into power output of wind or solar plants state of the art: use of numerical weather prediction models (NWP) Basic approaches to wind and solar power prediction
- 47. >>> State of the art: prediction systems physical model statistical post processing different NWPs standing data of power plants final forecast online data forecaster external data
- 48. >>> How do numerical prediction models see the world?
- 49. >>> Gridded!
- 50. >>> With different spatial resolutions!
- 51. >>> With different spatial resolutions!
- 52. >>> With different spatial resolutions! approx. 40 km
- 53. >>> With different spatial resolutions! approx. 40 km approx.9 km
- 54. >>> Spatial interpolation needed grid points site to be forecasted
- 55. >>> Spatial interpolation needed grid points site to be forecasted
- 56. >>> … vertical interpolation is also needed 100 m 10 m wind speed height hub height unstable NWP data stable NWP data wind profile changes with atmospheric conditions
- 57. >>> Combination of weather models Weighting factors according to capabilities in different weather situations Weather classification Weather data wind power prediction Single wind power predictions
- 58. >>> Benefit of combination wind power
- 59. >>> combination measurement solar power prediction Benefit of combination
- 60. >>> Forecast improvement due to intelligent model combination ! combination measurement solar power prediction Benefit of combination
- 61. >>> Central Questions Why are power predictions necessary? Who needs wind and solar power predictions? What are the recommendations to implement forecasts? How does a power prediction work? Why is a power prediction difficult?
- 62. >>> Different types of forecast errors Large amount of different types of errors: … errors in meteorological forecasts
- 63. >>> Different types of forecast errors Large amount of different types of errors: … errors in meteorological forecasts … errors in power transformation
- 64. >>> Different types of forecast errors Large amount of different types of errors: … errors in meteorological forecasts … errors in power transformation … unknown status of power plants
- 65. >>>„Artificial“ forecasting errors due to unknown plant status Wind farm availability below 100 %
- 66. >>>„Artificial“ forecasting errors due to unknown plant status Wind farm availability below 100 % Wind farm shut down due to grid congestion
- 67. >>> To be considered: curtailment and scheduled outages Prediction using curtailment information (wind farm limited to 80 MW) Prediction assuming full availability of wind farm (200 MW installed capacity) Time schedules for planned outages or curtailments can be transmitted to Previento to be considered in the forecasts.
- 68. >>> Different types of forecast errors Large amount of different types of errors: … errors in meteorological forecasts … errors in power transformation … unknown status of power plants … difficult meteorological situations
- 69. >>> Difficult meteorological situations: wind ROTOR BLADE ICING
- 70. >>> Difficult meteorological situations: wind STABILITY EFFECTS
- 71. >>> Difficult meteorological situations: wind TIMING OF COLD FRONTS
- 72. >>> Difficult meteorological situations: wind STORM – CUT OFF
- 73. >>> Difficult meteorological situations: wind SNOW ON PV MODULES
- 74. >>> Difficult meteorological situations: wind SAHARA DUST
- 75. >>> Difficult meteorological situations: wind FOG
- 76. >>> Difficult meteorological situations: wind CONVECTIVE CLOUDS AND THUNDERSTORMS
- 77. >>> Difficult meteorological situations: wind SOLAR ECLIPSE
- 78. >>> … some more literature Variable Renewable Energy Forecasting - Integration into Electricity Grids and Makets - A Best Practice Guide Release: Original publication July 2015
- 79. >>> Thanks for your attention! www.energymeteo.com Jan Schmelter jan.schmelter@energymeteo.com