Motivation Methods and Data Empirical Analysis Outlook Designing a Wind Index for Assessing Wind Energy Potential M. Ritter Z. Shen B. L´ opez Cabrera L. Deckert M. Odening Humboldt-Universit¨ at zu Berlin 4initia Energy Finance Workshop May 7, 2014 Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 1/19 Motivation Methods and Data Empirical Analysis Outlook Contents Anhang 1 Motivation 2 Methods and Data 3 Empirical Analysis 4 Outlook Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 2/19 Motivation Methods and Data Empirical Analysis Outlook Wind Energy in Germany Current Situation Designing a Wind Index Wind Energy in Germany Source: DEWI Situation World’s third-largest producer of wind energy 8.2% of German energy production come from onshore wind energy Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 3/19 Motivation Methods and Data Empirical Analysis Outlook Wind Energy in Germany Current Situation Designing a Wind Index Current Situation Betreiber Datenbasis (BDB) Index Used for planning wind farms and compensation payments from the EEG Wind farms can report their production data Index depends on average production in one region Problems Firms report voluntarily, numbers not checked Regions too large Index changes with new firms Anhang Matthias Ritter Source: BDB Designing a Wind Index for Assessing Wind Energy Potential 4/19 Motivation Methods and Data Empirical Analysis Outlook Wind Energy in Germany Current Situation Designing a Wind Index Designing a Wind Index Aim: Developing a wind index to measure the potential of wind power for a wind farm The wind index should fulfil the following criteria: objective transparent reliable reflect long-term wind potential highly correlated to the actual wind production Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 5/19 Motivation Methods and Data Empirical Analysis Outlook Framework Data Contents Anhang 1 Motivation 2 Methods and Data Framework Data 3 Empirical Analysis 4 Outlook Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 6/19 Motivation Methods and Data Empirical Analysis Outlook Framework Data Framework Wind Speed Data Database Production Data Weather Stations Reanalysis Data Linear Log Profile Nearest Neighbour Four Nearest Neighbours Physical Transformation Statistical Transformation Vertical Extrapolation Horizontal Interpolation Convert Wind Speed to Production Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 7/19 Motivation Methods and Data Empirical Analysis Outlook Framework Data Data MERRA Modern-era retrospective analysis for research and applications (MERRA) NASA atmospheric reanalysis Reconstruction of atmospheric state by integrating data from different sources (conventional data, satellites) (Rienecker et al., 2011; Gunturu/Schlosser, 2012) Spatial resolution: 1/2◦ latitude × 2/3◦ longitude Time resolution: Hourly, 1979–present Wind speed in m/s at heights of 2M, 10M, 50M above ground Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 8/19 lated these data starting in 1948, as did ERA-40, starting Motivation and Data Framework and the time- in 1958. ArchivesMethods of conventional Empirical Analysis Data observations were preOutlook n Fig. 3. The served among a number of national, academic, and milir sources Dataare itary sources worldwide. Institutions such as NCAR and ol procedures, n, and the obd in R2008. nal assembly P preparations al. 2010), and –present) verERA-Interim; 9). While the nd CFSR are in the obserbelow. Obviof differences other sources at were used. observations FIG. 3. Summary of the observing system used for MERRA. eakdown by Fig.: MERRA observation system, source: Rienecker et al. (2011) Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 9/19 Motivation Methods and Data Empirical Analysis Outlook Framework Data Data Weather stations German Weather Service (DWD) Wind speed (in Bf) three times per day (6, 12, 18) 64 free DWD stations since at least 1996 Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 10/19 Motivation Methods and Data Empirical Analysis Outlook Framework Data Data Legend Weather stations German Weather Service (DWD) # MERRA grid ! DWD free # 64 free DWD stations since at least 1996 Wind energy production data Matthias Ritter # # ! # ! # # # ! # !# # # # # ! # # # # # # # # ! # # # # # # # # # # # ! # # # #! # # ! ! # # # # # # #! # # # # # # # # # # !# # # # # ! # !! # # # # !# # # # # #! # # # # # # # # # # # # #! # # # # # # # # # # # #! # # # # !# # # #! # # # # # ! ! ! # # # # # ! # # !# ! # # # # # # !# # # # # # !# # ! ! ! # # ! # # # # # # ! ! # # # # 0 55 ! ! ! ! ! # # # 110 ! !# # # # !# # # # # # # # # # ! ! # # ! ! # # # # # # ! #! # # ! # # ! ! # # ! # ! # #! # # ! ! # ! ! ! # Anhang ! # # 01/04/2010–31/05/2013 # # # Daily production in MWh of a German wind park in Saxony !# ! # Wind speed (in Bf) three times per day (6, 12, 18) # # # ! # 220 km Designing a Wind Index for Assessing Wind Energy Potential 10/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy Contents Anhang 1 Motivation 2 Methods and Data 3 Empirical Analysis Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy 4 Outlook Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 11/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy 1 10 0.8 8 0.6 6 RMSE Correlation Database: MERRA vs. Weather Station 0.4 2M 10M 50M 0.2 0 10 20 30 40 Station number 4 2 50 60 Fig: Correlation between weather station and MERRA data for 64 stations (4 nearest grid points weighted by distance) Anhang 2M 10M 50M Matthias Ritter 0 10 20 30 40 Station number 50 60 Fig: Root mean squared error (RMSE) between weather station and MERRA data for 64 stations (4 nearest grid points weighted by distance) Designing a Wind Index for Assessing Wind Energy Potential 12/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy Database: MERRA vs. Weather Station Legend Correlation (10M, 4NN) Results ( ! < 0.50 ( ! 0.50 - 0.70 ! ( 0.70 - 0.80 ( ! 0.80 - 0.85 # ! ( > 0.85 # # Correlation high (> 0.8) for most of the stations Correlation same for different heights RMSE changes a lot depending on height # # # 0 # # ! (# # # # # # # # # ! ( # # # ! ( # # # # ! (# # # # 55 ! ( # # !# ( (# ! # # # ! (# # ! ( # # # 110 # # ! ( # # ( ! # (# ! (# # ! ! ( # # # # ! ( (! ! (! ( # # # # ! ( # # # # # ! (# # # # # # # # # # ! ( # # # ! (# (# (# #! # #! ( ! ! ( # # # ( # # # #! (# ! ! ! ( ( # # # # # ! (# #!(!(# #!( # # # ! (# ! ( # ! ( # # ! ( # # ( ! # # # # # # # # # # # ( ! # # # ! (# ! (# ! ( # # # # # ! (# # # ! ( # # ! ( # # # # # # ! ( # # # # # Matthias Ritter # # # # ! ( # # # # (# ! ( #! ( ! # # Anhang # # Interpretation? # # # # # # # ! ( # # # ! (# ! (# ! ( # # # ! (# #( # !# ( ! # # ( ! # 220 km ( ! # (# ! # # # # ! ( # # # # ! (# # # # ( ! Designing a Wind Index for Assessing Wind Energy Potential 13/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy Extra-/Interpolation Vertical extrapolation Typical turbine height: 100M Advantage of MERRA data: Wind speeds available at 2M, 10M, 50M above surface → Extrapolation Linear Log wind profile: Vz = u∗ κ log (z−d) z0 Vz wind speed at height z, u∗ friction velocity, κ Von K´ arm´ an constant, d displacement height, z0 surface roughness Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 14/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy Extra-/Interpolation Vertical extrapolation Typical turbine height: 100M Advantage of MERRA data: Wind speeds available at 2M, 10M, 50M above surface → Extrapolation Linear Log wind profile: Vz = u∗ κ log (z−d) z0 Vz wind speed at height z, u∗ friction velocity, κ Von K´ arm´ an constant, d displacement height, z0 surface roughness Horizontal interpolation Weather stations: nearest neighbour MERRA: combination of four nearest neighbours weighted by horizontal distance Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 14/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy From Speed to Energy: Physical Transformation Wind power density: 1 ρ CP V 3 2 – ρ: air density – CP : Betz limit (=16/27) – V : wind speed at turbine height 4000 Energy production [MWh] WPD = WPD Production Actual Production 3000 2000 1000 0 2011 – Unit: W/m2 2012 Date 2013 MERRA 2 R RMSE Anhang Matthias Ritter 0.7585 377.4896 Designing a Wind Index for Assessing Wind Energy Potential 15/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy From Speed to Energy: Statistical Transformation I Linear function: Production = a + b ∗ V + 1000 Energy production [MWh] Energy production [MWh] 1000 800 600 400 200 0 −200 0 2 R RMSE Anhang 600 400 200 0 −200 5 10 15 MERRA wind speed [m/s] MERRA Production Actual Production 800 2011 MERRA DWD 0.8441 85.4783 0.7548 107.1882 Matthias Ritter 2012 Date 2013 Designing a Wind Index for Assessing Wind Energy Potential 16/19 Motivation Methods and Data Empirical Analysis Outlook Database: MERRA vs. Weather Station Extra-/Interpolation From Speed to Energy From Speed to Energy: Statistical Transformation II Piecewise function: cubic with cut-in speed and rated power speed 1000 Energy production [MWh] Energy production [MWh] 1000 800 600 400 200 0 0 2 R RMSE Anhang 600 400 200 0 5 10 15 MERRA wind speed [m/s] MERRA Production Actual Production 800 2011 MERRA DWD 0.8845 73.6194 0.7809 101.4062 Matthias Ritter 2012 Date 2013 Designing a Wind Index for Assessing Wind Energy Potential 17/19 Motivation Methods and Data Empirical Analysis Outlook Contents Anhang 1 Motivation 2 Methods and Data 3 Empirical Analysis 4 Outlook Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 18/19 Motivation Methods and Data Empirical Analysis Outlook Outlook Next steps Comparison between hourly production and hourly wind Calculation for more wind farms Potential outcomes General wind index Wind energy potential in Germany Wind derivatives Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 19/19 Appendix Bibliography Appendix: Bibliography Gunturu, U. B. and Schlosser, C. A. (2012). Characterization of wind power resource in the United States. Atmospheric Chemistry and Physics, 12(20):9687–9702. Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., et al. (2011). MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. Journal of Climate, 24(14). Anhang Matthias Ritter Designing a Wind Index for Assessing Wind Energy Potential 20/19
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