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SIZE OPTIMIZATION OF WIND PHOTOVOLTAIC SYSTEMS Dr. Fatih Onur HOCAO - PowerPoint PPT Presentation

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SIZE OPTIMIZATION OF WIND PHOTOVOLTAIC SYSTEMS Dr. Fatih Onur HOCAO LU Afyon Kocatepe University Electrical Eng. Dept. Outline Solar Radiation Data Analysis Wind Speed Data Analysis Load Forecasting Size Optimization and

  1. SIZE OPTIMIZATION OF WIND PHOTOVOLTAIC SYSTEMS Dr. Fatih Onur HOCAO Ğ LU Afyon Kocatepe University Electrical Eng. Dept.

  2. Outline • Solar Radiation Data Analysis • Wind Speed Data Analysis • Load Forecasting • Size Optimization and System Modeling

  3. Solar Radiation Data Analysis

  4. Solar Radiation Data Analysis R R 2 x 44 – x 34 0.911 0.830 x 44 – x 24 0.894 0.799 x 44 – x 14 0.898 0.806 x 44 – x 43 0.938 0.879 x 44 – x 42 0.807 0.651 x 44 – x 14 0.630 0.397 x 44 – x 33 0.870 0.760 x 44 – x 22 0.740 0.548

  5. Solar Radiation Data Analysis

  6. Solar Radiation Data Analysis

  7. Solar Radiation Data Analysis 400 350 300 250 200 150 100 50 0 25 10 15 20 0 5

  8. Optimal Coefficient Linear Prediction Filters x x  i j , 1 i j ,  x    x ? i 1, j  i 1, j 1

  9. Optimal Coefficient Linear Prediction Filters          0    a a a 1 2 3       a r R R R 11 12 13 1 1        R R R a r       21 22 23 2 2       R R R a r       31 32 33 3 3   1 a R r

  10. Optimal Coefficient Linear Prediction Filters 1D-Filter 3 1D-Filter 1 1D-Filter 2 x 12 x 12 x 13 x 11 x 12 . . . . .x 1n x 11 . . . . .x 1n x 11 .. .x 1n . . . x 22 . x 22 . x 22 . . . . . . . . . . . . . . . . x m1 . . . x mn x m1 . . . x mn x m1 . . . x mn 1D-Filter 4 1D-Filter 5 1D-Filter 6 x 11 . . . . .x 1n x 11 . . . . .x 1n x 11 . . . . .x 1n x 21 x 21 x 22 . x 22 . x 21 x 22 . . . x 31 x 31 x 32 ... . . . . . . . . x 41 . x m1 . . . x mn x m1 . . . x mn x m1 . . . x mn

  11. Optimal Coefficient Linear Prediction Filters 2D-Filter 1 2D-Filter 2 2D-Filter 3 x 11 . x 1n x 11 x 12 x 13 . x 1n x 11 x 12 x 12 . . . . .x 1n x 22 x 21 x 21 x 22 x 21 x 22 . . . . . . . . . . . . . x m1 . . . x mn x m1 . . . x mn x m1 . . . x mn 1 N M     2 RMSE ( Rad i j ( , ) Rad i j ( , )) NM   i 1 j 1

  12. Testing the Performance of 2D Approach using OCLPF

  13. Novel Analytical Modeling Approach for Solar Radiation Data

  14. Novel Analytical Modeling Approach for Solar Radiation Data

  15. Novel Analytical Modeling Approach for Solar Radiation Data 2 2 ae    ( x b ) c g x ( ) 2 2 2 2       ( x b ) c ( x b ) c g x ( ) a e a e 1 1 2 2 1 2

  16. Novel Analytical Modeling Approach for Solar Radiation Data a b c D1 285.6000 12.9500 2.7280 D2 226.7000 13.4000 2.8410 D3 301.8000 12.9500 2.7710 D4 235.1000 12.1700 2.8930 D5 201.1000 14.5600 1.8690 D6 229.7000 12.7800 3.0010 D7 134.3000 13.2700 3.2470 D8 122.9000 12.9200 3.4460 D9 298.9000 12.1100 2.8850 D10 103.1000 12.3000 3.3970

  17. Novel Analytical Modeling Approach for Solar Radiation Data 1 Source Gauss 2 Source Gauss D1 0.9907 0.9981 D2 0.982 0.9971 D3 0.9881 0.9986 D4 0.9596 0.9949 D5 0.9242 0.9974 D6 0.9136 0.9889 D7 0.9293 0.9907 D8 0.9561 0.9964 D9 0.9882 0.9899 D10 0.9155 0.9978

  18. Novel Analytical Modeling Approach for Solar Radiation Data

  19. Novel Analytical Modeling Approach for Solar Radiation Data

  20. Novel Analytical Modeling Approach for Solar Radiation Data 1-Source 2-source Gauss Gauss RMSE 57.20 105.25

  21. A novel modeling approach using extraterrestrial solar radiations

  22. Extraterrestrial Formulation   2 I C sin( ) / 24 R    sin( ) sin( )sin( L D ) cos( )cos( L D )cos( ) h   h 15( h 12) c

  23. 2D Representation of Extraterrestrial Data

  24. Modeling Solar Radiations from Extraterrestrial Radiations

  25. Modeling Solar Radiations from Extraterrestrial Radiations

  26. Model: Markov Processes   0 p 1 ij   p 1 ij  j 1   p p p ... p 11 12 13 1 n   p p p ... p   21 22 23 2 n    A . . . ... .   . . . ... .     p p p ... p   n 1 n 2 n 3 nn

  27. Wind Speed Modeling using Markov Approach m ij   p i j , 1,2,..., n  ij m ij j

  28. Wind Speed Modeling using Markov Approach 0.61 0.29 0.08 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.47 0.28 0.06 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.20 0.46 0.23 0.06 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.05 0.20 0.45 0.22 0.06 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.26 0.42 0.20 0.04 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.07 0.28 0.41 0.18 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.07 0.29 0.39 0.21 0.02 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.09 0.30 0.38 0.17 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.04 0.12 0.33 0.39 0.08 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.03 0.18 0.27 0.37 0.08 0.05 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.09 0.23 0.41 0.09 0.00 0.00 0.00 0.00 0.00 0.08 0.08 0.00 0.00 0.15 0.31 0.08 0.31 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 0.00 0.00

  29. Syntetic generation of wind speed data

  30. Wind Speed Modeling using Markov Approach 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ...... 0.00 0.61 0.18 0.10 0.05 0.03 0.02 0.00 0.00 0.00 0.00 0.00 ...... 0.00 0.25 0.24 0.24 0.14 0.06 0.04 0.01 0.00 0.00 0.00 0.00 ...... 0.00 0.12 0.18 0.28 0.23 0.11 0.05 0.03 0.01 0.01 0.00 0.00 ...... 0.00 0.05 0.11 0.15 0.23 0.19 0.12 0.07 0.02 0.03 0.00 0.00 ...... 0.00 0.02 0.04 0.10 0.18 0.30 0.16 0.10 0.04 0.02 0.01 0.00 ...... 0.00 0.01 0.03 0.05 0.10 0.17 0.28 0.17 0.10 0.04 0.02 0.01 ...... 0.00 0.00 0.00 0.02 0.04 0.10 0.20 0.25 0.18 0.11 0.05 0.02 ...... 0.00 0.00 0.00 0.00 0.02 0.06 0.12 0.23 0.25 0.13 0.10 0.05 ...... 0.00 0.00 0.00 0.00 0.01 0.02 0.05 0.12 0.22 0.25 0.18 0.07 ...... 0.00 0.00 0.00 0.00 0.00 0.01 0.03 0.05 0.13 0.22 0.23 0.16 ...... 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.04 0.05 0.15 0.22 0.21 ...... ...... ...... ...... ...... ...... ...... ...... ...... ...... ...... ...... ...... ......

  31. Wind Speed Modeling using Markov Approach k   P p ik ij  j 1 P is transition probability in the i th row at the k th state ik

  33. Wind Speed Modeling using Markov Approach 0.61 0.90 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.18 0.64 0.92 0.98 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.04 0.23 0.69 0.92 0.98 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.01 0.06 0.26 0.72 0.93 0.99 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.06 0.32 0.75 0.95 0.99 1.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.01 0.08 0.36 0.77 0.95 0.99 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.01 0.02 0.09 0.38 0.76 0.97 0.99 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.01 0.03 0.12 0.42 0.80 0.98 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.02 0.05 0.17 0.50 0.89 0.97 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.01 0.04 0.22 0.49 0.86 0.95 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.18 0.27 0.50 0.91 1.00 1.00 0.00 0.00 0.00 0.00 0.08 0.15 0.15 0.15 0.31 0.62 0.69 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00

  34. Wind Speed Modeling using Markov Approach

  35. Wind Speed Modeling using Markov Approach

  36. Wind Speed Modeling using Markov Approach

  37. Wind Speed Modeling using Markov Approach Generated Data from Generated Data from Observed Data Model-1 Model-2 Min 0.00 0.02 0.00 Max 12.47 11.44 12.77 Mean 3.52 3.16 3.61 Median 3.35 2.95 3.47 Std 2.11 1.91 2.02

  38. Wind Speed Modeling using Markov Approach Observed data Generated data from Model-1 Generated data from Model-2 20 15 Probability (%) 10 5 0 1 3 5 7 9 11 13 15 17 Wind Speed (m/s)

  39. Load Forecasting • İ t is of vital importance to forecast the possible load that the system will meet for proper size determination. • Once the load forecasted, the alteration curve of energy needs must be obtained. • In general the resulation of this curve is in hours.

  40. Size Optimization and System Modeling • Open the pdf File for details

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