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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. 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. 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. 3. >>> Choice of international customers PJM Interconnection BPA Bonneville Power Administration Tennessee Valley Authority
  4. 4. >>> Areas with operational forecasting experience about 80GW wind power 48GW solar power
  5. 5. >>> Central Questions  Why are power predictions necessary?
  6. 6. >>> Central Questions  Why are power predictions necessary?  Who needs wind and solar power predictions?
  7. 7. >>> Central Questions  Why are power predictions necessary?  Who needs wind and solar power predictions?  What are the recommendations to implement forecasts?
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. >>> Who needs wind and solar power forecasts?  grid operators (TSOs, DSOs) – balancing – dispatch / re-dispatch – load flow calculations – congestion management
  18. 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. 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. 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. 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. 22. >>> Integration of forecasting right from the beginning
  23. 23. >>> Integration of forecasting right from the beginning
  24. 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. 25. >>> The plant register for renewables in Germany Forecaster plant register
  26. 26. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency
  27. 27. >>> The plant register for renewables in Germany Forecaster plant register Federal Network Agency TSO
  28. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 39. >>> The plant register for renewables: the transparent way Forecaster plant register Federal Network Agency / TSO Wind farm owners Solar plant owners
  40. 40. >>> Integration of forecasting right from the beginning
  41. 41. >>> Integration of forecasting right from the beginning
  42. 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. 43. >>> Integration of forecasting right from the beginning
  44. 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. 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. 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. 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. 48. >>> How do numerical prediction models see the world?
  49. 49. >>> Gridded!
  50. 50. >>> With different spatial resolutions!
  51. 51. >>> With different spatial resolutions!
  52. 52. >>> With different spatial resolutions! approx. 40 km
  53. 53. >>> With different spatial resolutions! approx. 40 km approx.9 km
  54. 54. >>> Spatial interpolation needed grid points site to be forecasted
  55. 55. >>> Spatial interpolation needed grid points site to be forecasted
  56. 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. 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. 58. >>> Benefit of combination wind power
  59. 59. >>> combination measurement solar power prediction Benefit of combination
  60. 60. >>> Forecast improvement due to intelligent model combination ! combination measurement solar power prediction Benefit of combination
  61. 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. 62. >>> Different types of forecast errors Large amount of different types of errors:  … errors in meteorological forecasts
  63. 63. >>> Different types of forecast errors Large amount of different types of errors:  … errors in meteorological forecasts  … errors in power transformation
  64. 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. 65. >>>„Artificial“ forecasting errors due to unknown plant status Wind farm availability below 100 %
  66. 66. >>>„Artificial“ forecasting errors due to unknown plant status Wind farm availability below 100 % Wind farm shut down due to grid congestion
  67. 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. 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. 69. >>> Difficult meteorological situations: wind ROTOR BLADE ICING
  70. 70. >>> Difficult meteorological situations: wind STABILITY EFFECTS
  71. 71. >>> Difficult meteorological situations: wind TIMING OF COLD FRONTS
  72. 72. >>> Difficult meteorological situations: wind STORM – CUT OFF
  73. 73. >>> Difficult meteorological situations: wind SNOW ON PV MODULES
  74. 74. >>> Difficult meteorological situations: wind SAHARA DUST
  75. 75. >>> Difficult meteorological situations: wind FOG
  76. 76. >>> Difficult meteorological situations: wind CONVECTIVE CLOUDS AND THUNDERSTORMS
  77. 77. >>> Difficult meteorological situations: wind SOLAR ECLIPSE
  78. 78. >>> … some more literature Variable Renewable Energy Forecasting - Integration into Electricity Grids and Makets - A Best Practice Guide Release: Original publication July 2015
  79. 79. >>> Thanks for your attention! www.energymeteo.com Jan Schmelter jan.schmelter@energymeteo.com

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