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DEVELOPMENT OF NEW SOFTWARE
 TO ANALYSE AND PREDICT THE
    MODULE PERFORMANCE

      Susana Iglesias Puente
OUTLINE

 Performed tasks
  •   Indoor measurements
  •   Outdoor measurements
  •   Data treatment software

 Conclusions
 Suggested Software improvements
INDOOR MEASUREMENTS

 PASAN LAPSS Laboratory

 The module is characterised at each point on a matrix of
 Pmax (W) as a function of Tmod and irradiance

 Final result: empirical equation to estimate Pmax
       result:


                          a + b ⋅ ln Irr + c ⋅ T AMB
         Pmax =
                  1 + d ⋅ ln Irr + e ⋅ (ln Irr ) + f ⋅ T AMB
                                              2
INDOOR MEASUREMENTS: data fitting
                                             AI01 Indoor
                      Rank 68 Eqn 1132 z=(a+blnx+cy)/(1+dlnx+e(lnx)^2+fy)
                r^2=0.99974319 DF Adj r^2=0.99972731 FitStdErr=0.2585647 Fstat=76302.371
                               a=-0.93125496 b=0.40257114 c=-0.0040224985
                              d=-0.26053276 e=0.017375655 f =0.00010847924




           60                                                                              60

           50                                                                              50

           40                                                                              40




                                                                                                Pmax
    Pmax




           30                                                                              30

           20                                                                              20

           10                                                                              10

            0 0                                                                  0
             90 8 00700 00                                             3 5 30 25
                        6 500400 00                              45 40 od
                       Irr      3 200 100            0 60 55 5 0      Tm
OUTDOOR MEASUREMENTS

 Tracker:
 Tracker:    avoid   the
 effects of the angle of
 incidence.
 incidence.

 Irradiance measured by
 two different kinds of
 devices:
 devices: Pyranometer
 and ESTI sensor.
           sensor.
OUTDOOR MEASUREMENTS

 Rack: in-plane
       in-
 measurements.

 Result:
 Result:   text   files
 storing the different
 variables involved in
 module performance.
         performance.
OUTDOOR MEASUREMENTS: data treatment
        MEASUREMENTS:



          W/m2   ºC    W
DATA TREATMENT SOFTWARE

 Main task: development of specific software to
 treat the data from the outdoor measurements.

 Employed software: Matlab.
OUTDOOR MEASUREMENTS: data treatment
        MEASUREMENTS:

 The text files contain data for several years,
 therefore they are large.


         The data treatment using a
         spreadsheet is impractical.

  Solution: creation of special software to treat
  the outdoor data systematically.
DATA TREATMENT SOFTWARE

 Main objectives:
      objectives:
  •   Obtain the values of the measured and estimated
      energy produced by the module
  •   Obtain the energy coming from the sun (irradiation)
  •   To be able to calculate these at different time
      intervals, e.g. day, month, year, etc.
                                        etc.
  •   Compare measured and estimated energies, and
      other output results numerically and graphically
DATA TREATMENT SOFTWARE

 Software developed to treat the data:
    •   Solar_data_treatment
    •   Data_writing
    •   Data_plotting

    •   Eq_fit_params
    •   NOCT_estimation
    •   Montly_sum
    •   Month_teller
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.
                                    file.
  •   Obtain the parameters of the empirical equation.
                                                equation.
  •   Estimate Tmod from Tamb and Irradiance.
                                        Irradiance.
  •   Estimate Pmax values with the empirical equation.
                                                  equation.
  •   Integrate Pmax over the day and store the results in
      3-D arrays.
          arrays.
  •   Obtain the energy for every month and every year.year.
  •   Calculate the BIAS error and the module efficiency.
                                                  efficiency.
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.
                                     file.
  •   Obtain the parameters of the empirical equation.
                                              equation.
  •   Estimate Tmod from Tamb and irradiance.
                                      irradiance.
  •   Estimate Pmax values with the empirical equation.
                                                equation.
  •   Integrate Pmax over the day and store the results in
      3-D arrays.
          arrays.
  •   Obtaining the energy for every month and every year.
                                                        year.
  •   Calculate the BIAS error and the module efficiency.
                                                efficiency.
Prasentation pv module performance
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.   file.
  •   Obtain the parameters of the empirical equation.  equation.
  •   Estimation of Tmod with the empirical equation.
                                                    equation.
  •   Estimation of Pmax a + b ⋅ ln Irr + c ⋅ T AMB
       Used function: Eq_fit_params the empirical equation.
                              values with                  equation.
             Pmax =
  •   Integration of Pmax over theln Irr )andf storing the results
                     1 + d ⋅ ln Irr + e ⋅ ( day + ⋅ T AMB
                                                  2

      in 3-D arrays.
             arrays.
  •   Obtaining the energy for every month and every year.     year.
  •   Calculation of the BIAS error and the module efficiency.
                                                           efficiency.
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.file.
  •   Obtain the parameters of the empirical equation. equation.
  •   Estimate Tmod from Tamb and Irradiance.     Irradiance.
  •   Estimate Pmax values with the empirical equation.  equation.
                              NOCT − day
      Used function:MODover the 20  ⋅ Irrand
      Integrate PmaxNOCT_estimation store the results in
  •
                       T   =              + T AMB
      3-D arrays.
           arrays.              800   

  •
      Nominal Operatingfor every month and every year.
      Obtain the energy Cell Temperature                       year.
  •   Calculate the BIAS error used in empirical eqn, efficiency.
        - Necessary because Tmod is
                                     and the module efficiency.
                                                          not Tamb
 NOCT − 20 
TMOD =             ⋅ G + T AMB
          800     


     DATA TREATMENT SOFTWARE: Solar_data_treatment
                    SOFTWARE:

           Actions carried out by the program:
                                      program:
            •    Import the data from the text file. file.
            •    Obtain the parameters of the empirical equation.
                                                               equation.
            •    Estimate Tmod with the empirical equation.equation.
            •    Estimate Pmax values with the empirical
                 equation.
            •    Integrate Pmax over the day and store the results in
                   - ESTI irrad (& measured Tmod)
                 3-DPyran irrad (& measured Tmod)
                   - arrays.
                      arrays.
                   - ESTI irrad (& estimated Tmod using NOCT)
            •    Obtain the energy for everyusing NOCT) every year.
                   - Pyran irrad (& estimated Tmod month and           year.
            •    Calculate the BIAS error and the module efficiency.
                                                                  efficiency.
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.
                                    file.
  •   Obtain the parameters of the empirical equation.
                                              equation.
  •   Estimate Tmod with the empirical equation.
                                          equation.
  •   Estimate Pmax values with the empirical equation.
  •   Integrate Pmax and irradiance over the day and
      store the results in 3-D arrays.
  •   Obtain the energy for every month and every year.
                                                      year.
      Used function: Monthly_sum
  •   Calculate the BIAS error and the module efficiency.
                                                 efficiency.
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:


  Data storage:
    One 12x31 matrix containing the energy for every day
    for each year.
    These matrices are stored in the same variable to form
    a 3-D array (tensor) for a number of years.
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.
                                    file.
  •   Obtain the parameters of the empirical equation.
                                              equation.
  •   Estimate Tmod with the empirical equation.
                                          equation.
  •   Estimate Pmax values with the empirical equation.
                                                 equation.
  •   Integrate Pmax over the day and store the results in
      3-D arrays.
          arrays.
  •   Obtain the energy for every month and every year.  year.
  •   Calculate the BIAS error and the module efficiency.
                                                 efficiency.
DATA TREATMENT SOFTWARE: Solar_data_treatment
               SOFTWARE:

 Actions carried out by the program:
                            program:
  •   Import the data from the text file.
                                     file.
  •   Obtaining the parameters of the empirical equation.
                                                equation.
  •   Estimation of Tmod with the empirical equation.
                                            equation.
  •   Estimation of Pmax values with the empirical equation.
                                                   equation.
  •   Integration of Pmax and irradiance over the day and
      storing the results in 3-D arrays.
                                 arrays.
  •   Obtaining the energy for every month and every year.
                                                      year.
  •   Calculation of BIAS error and module efficiency.
DATA TREATMENT SOFTWARE: Data_writing
               SOFTWARE:

 Actions carried out by the program:
    Creating M-files to store the calculated variables.

 These data can be easily imported to a
 spreadsheet (e.g. Excel) for further analysis.
DATA TREATMENT SOFTWARE: Data_plotting
               SOFTWARE:

 Actions carried out by the program:
    Plotting the different variables of interest to study the
    module performance.

 Bar graphs were chosen instead of scatter/line
 graphs.
 Month_teller gives the month name that is being
 plotted.
DATA_PLOTTING:
                              DATA_PLOTTING: Energy for ai01 in 2003
                                                              Module surface area=0.49 m2

                                             Year 2003                                                        NORMALISED MONTHLY ESTIMATES 2003
               9000                                                                                9000

               8000                                                                                8000

               7000                                                                                7000

               6000                                                                                6000




                                                                                    Energy (W·h)
Energy (W·h)




               5000                                                                                5000

               4000                                                                                4000

               3000                                                                                3000
                                                                                                                     Mean monthly energy
               2000                                                                                2000              Measured energy
                                  Measured energy
                                                                                                                     Empirical ESTI energy
                                  Empirical ESTI energy
                                                                                                                     Empirical ESTI & Tmod energy
               1000               Empirical ESTI & Tmod energy                                     1000

                 0                                                                                   0
                      1   2   3   4     5    6       7    8      9   10   11   12                         2      4            6            8        10   12
                                                 Months                                                                           Months




                             Main differences:                                      Corrected values: divided by the
                          Jun, Aug, Oct, Nov, Dec                                   number of days actually measured
                                                                                    per month
DATA_PLOTTING:
                 DATA_PLOTTING: Energy for ai01, Jan 2003

                              JANUARY 2003
               400
                                        Measured energy
                                                                       • Days without
               350
                                        Empirical ESTI energy
                                        Empirical ESTI & Tmod energy     measurements
               300
                                                                       • Measurements
               250
                                                                         not carried out
Energy (W·h)




               200                                                       the same
               150                                                       amount of
               100                                                       hours every
               50
                                                                         day.
                0
                     5   10     15        20         25         30
                                 Days
DATA_PLOTTING:
DATA_PLOTTING: Energy for ai01, May 2003

                                    MAY 2003
                     400


                     350


                     300


                     250
      Energy (W·h)




                     200


                     150


                     100
                                             Measured energy
                                             Empirical ESTI energy
                     50                      Empirical ESTI & Tmod energy


                      0
                           5   10    15        20         25         30
                                      Days
DATA_PLOTTING:
                      DATA_PLOTTING: Efficiency for ai01 in 2003
                                           Year 2003                                                                    AMBIENT AND MODULE TEMPERATURE FOR AI01 IN 2003
                                                                                                           50
                                 Mean efficiency ESTI
                 14              Measured energy <> ESTI irrad
                                                                                                           45
                                 Estimated ESTI energy <> ESTI irrad
                                 Estimated ESTI & Tmod energy <> ESTI irrad
                 12                                                                                        40

                                                                                                           35
                 10




                                                                                       Tem perature (ºC)
Efficiency (%)




                                                                                                           30


                                                             Energy produced by the module (W ⋅ h )
                 8

                          Efficiency (% ) =
                                                                                    25
                                                                                                                   ⋅ 100
                 6                                                                 (
                                                 Energy coming from the sun W ⋅ h / 20 2 ⋅ module surface area m 2
                                                                                     m                              )                                      ( )
                                                                                                           15
                 4
                                                                                                           10                                Ambient temperature
                 2                                                                                                                           Measured module temperature
                                                                                                           5                                 Estimated module temperature

                 0                                                                                         0
                      2         4          6            8         10          12                                1       2     3    4     5     6       7      8      9      10   11
                                               Months                                                                                        Months




                                    The efficiency is lower in summer time
                                    when the module temperature is higher
DATA_PLOTTING:
DATA_PLOTTING: Efficiency for ai01, Jan 2003

                                        JANUARY 2003
                                       Measured energy <> ESTI irrad
                         14            Estimated ESTI energy <> ESTI irrad
                                       Estimated ESTI & Tmod energy <> ESTI irrad

                         12


                         10
        Efficiency (%)



                         8


                         6


                         4


                         2


                         0
                              5   10        15         20         25         30
                                             Days
DATA_PLOTTING:
DATA_PLOTTING: Efficiency for ai01, May 2003

                                          MAY 2003
                                       Measured energy <> ESTI irrad
                         14            Estimated ESTI energy <> ESTI irrad
                                       Estimated ESTI & Tmod energy <> ESTI irrad

                         12


                         10
        Efficiency (%)



                         8


                         6


                         4


                         2


                         0
                              5   10       15          20         25         30
                                            Days
OVERVIEW ON NUMERICAL RESULTS

      Energy comparison for AI01 (polycrystalline)
                        Annual energy (W·h)        Relative error (%)

  Measured value                70345                       —
  Estimate ESTI                 70471                      0.18
  Estimate Pyran                70740                      0.53
  Estimate
                                70890                      0.77*
  ESTI & Tmod
  Estimate
                                 71162                     1.16*
  Pyran & Tmod
    *including December 2003 NOCT estimation with bad Tamb data
OVERVIEW ON NUMERICAL RESULTS

    Energy comparison for LE02 (monocrystalline)
                                             Relative
                    Energy, 7 months (W.h)
                                              error (%)
   Measured value          29624                —
   Estimate ESTI           29401              -0.75
   Estimate Pyran          29219              -1.37
   Estimate
                           29395              -0.77
   ESTI & Tmod
   Estimate
                           29213              -1.39
   Pyran & Tmod
ENERGY PREDICTION ON PV-GIS WEB SITE
                     PV-

Solar irradiation map    Energywe use monthlyon
                          Can prediction based
(of T. Huld & M. Suri)   empirical model of c-Si module
                          averages for energy
                          rating?
                             T. Huld new calculations:
                             Monthly averages based on
                             our meteo tower data (2003 &
                             2004).
                             Assumption: in a month the
                             energy is the same for every
                             day.
                             Calculate expected
                             instantaneous values from
                             sun position & airmass.
ENERGY PREDICTION ON PV-GIS WEB SITE
                     PV-


             Measured    Estimate     Relative     Estimate    Relative
    2003
            energy (Wh) PV-GIS (Wh)   error (%)   Pyran (Wh)   error (%)

     Jan       4500        4928          9.5        4648          3.3
     Feb       6007        6650         10.7        6214          3.4
     Mar       7831        6032         -23.0       8018          2.4
     Apr       6751        6949          2.9        6854          1.5
    May        8659        8644         -0.2        8593         -0.8
     Jun
            “PV-
            “PV-GIS type” prediction is good for a long
               7006       8033    14.7      6925    -1.2
     Jul      period of time but not for single months
               8202       8344     1.7      8256    -0.8
     Aug       7471        8056          7.8        7354         -1.6
     Sep       5554        6983         21.2        5513         -0.7
     Oct       3546        4229         19.3        3553          0.2
     Nov       1328        1964         47.9        1339          0.8
     Dec       3513        2835         -14.7       3594          2.3
    TOTAL     70367        73398         4.3        70740         0.5
CONCLUSIONS (Software)

 Systematic treatment of the outdoor measurement data.
                                                 data.

 Nevertheless, the program is flexible as it can be easily
 modified by adding new functions.
                        functions.

 The program can function correctly with missing data.
                                                 data.
 The results are obtained in far less time than employing
 a spreadsheet, and different data sets of different
 lengths and from different modules can be easily
 analysed.
 analysed.
 At the same time, the results are more reliable.
                                        reliable.
Suggested Software improvements

  Check the number of hours during which the
  measurements were done for every day.
                                   day.

  If ∆t > 6 min, the integration of Pmax is not precise.
                                                precise.

  More parameters should be plotted, e.g. irradiance,
  BIAS error, mean values, etc.
                           etc.
 There should be taken into account that the empirical
 equation to estimate Pmax can change depending on
 the module.
     module.
CONCLUSIONS (Predictions)
 The empirical equation from solar simulator gives good
 predictions compared with long term outdoor
 measurements.
 measurements.

 2 Crystalline (mono and poly) modules have been
 analysed.
 analysed.

 Comparisons with estimates based on average
 irradiance and temperature data (i.e. PV-GIS) are very
                                 (i. PV-
 encouraging – proves the validity of using monthly
 averages for Energy Rating purposes
Thanks to the RE Unit for giving me the
opportunity of participating in their projects

Thanks to Thomas Huld and all my other
    colleagues for their assistance

     Special thanks to my supervisor
              Robert Kenny

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Prasentation pv module performance

  • 1. DEVELOPMENT OF NEW SOFTWARE TO ANALYSE AND PREDICT THE MODULE PERFORMANCE Susana Iglesias Puente
  • 2. OUTLINE Performed tasks • Indoor measurements • Outdoor measurements • Data treatment software Conclusions Suggested Software improvements
  • 3. INDOOR MEASUREMENTS PASAN LAPSS Laboratory The module is characterised at each point on a matrix of Pmax (W) as a function of Tmod and irradiance Final result: empirical equation to estimate Pmax result: a + b ⋅ ln Irr + c ⋅ T AMB Pmax = 1 + d ⋅ ln Irr + e ⋅ (ln Irr ) + f ⋅ T AMB 2
  • 4. INDOOR MEASUREMENTS: data fitting AI01 Indoor Rank 68 Eqn 1132 z=(a+blnx+cy)/(1+dlnx+e(lnx)^2+fy) r^2=0.99974319 DF Adj r^2=0.99972731 FitStdErr=0.2585647 Fstat=76302.371 a=-0.93125496 b=0.40257114 c=-0.0040224985 d=-0.26053276 e=0.017375655 f =0.00010847924 60 60 50 50 40 40 Pmax Pmax 30 30 20 20 10 10 0 0 0 90 8 00700 00 3 5 30 25 6 500400 00 45 40 od Irr 3 200 100 0 60 55 5 0 Tm
  • 5. OUTDOOR MEASUREMENTS Tracker: Tracker: avoid the effects of the angle of incidence. incidence. Irradiance measured by two different kinds of devices: devices: Pyranometer and ESTI sensor. sensor.
  • 6. OUTDOOR MEASUREMENTS Rack: in-plane in- measurements. Result: Result: text files storing the different variables involved in module performance. performance.
  • 7. OUTDOOR MEASUREMENTS: data treatment MEASUREMENTS: W/m2 ºC W
  • 8. DATA TREATMENT SOFTWARE Main task: development of specific software to treat the data from the outdoor measurements. Employed software: Matlab.
  • 9. OUTDOOR MEASUREMENTS: data treatment MEASUREMENTS: The text files contain data for several years, therefore they are large. The data treatment using a spreadsheet is impractical. Solution: creation of special software to treat the outdoor data systematically.
  • 10. DATA TREATMENT SOFTWARE Main objectives: objectives: • Obtain the values of the measured and estimated energy produced by the module • Obtain the energy coming from the sun (irradiation) • To be able to calculate these at different time intervals, e.g. day, month, year, etc. etc. • Compare measured and estimated energies, and other output results numerically and graphically
  • 11. DATA TREATMENT SOFTWARE Software developed to treat the data: • Solar_data_treatment • Data_writing • Data_plotting • Eq_fit_params • NOCT_estimation • Montly_sum • Month_teller
  • 12. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtain the parameters of the empirical equation. equation. • Estimate Tmod from Tamb and Irradiance. Irradiance. • Estimate Pmax values with the empirical equation. equation. • Integrate Pmax over the day and store the results in 3-D arrays. arrays. • Obtain the energy for every month and every year.year. • Calculate the BIAS error and the module efficiency. efficiency.
  • 13. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtain the parameters of the empirical equation. equation. • Estimate Tmod from Tamb and irradiance. irradiance. • Estimate Pmax values with the empirical equation. equation. • Integrate Pmax over the day and store the results in 3-D arrays. arrays. • Obtaining the energy for every month and every year. year. • Calculate the BIAS error and the module efficiency. efficiency.
  • 15. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtain the parameters of the empirical equation. equation. • Estimation of Tmod with the empirical equation. equation. • Estimation of Pmax a + b ⋅ ln Irr + c ⋅ T AMB Used function: Eq_fit_params the empirical equation. values with equation. Pmax = • Integration of Pmax over theln Irr )andf storing the results 1 + d ⋅ ln Irr + e ⋅ ( day + ⋅ T AMB 2 in 3-D arrays. arrays. • Obtaining the energy for every month and every year. year. • Calculation of the BIAS error and the module efficiency. efficiency.
  • 16. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file.file. • Obtain the parameters of the empirical equation. equation. • Estimate Tmod from Tamb and Irradiance. Irradiance. • Estimate Pmax values with the empirical equation. equation.  NOCT − day Used function:MODover the 20  ⋅ Irrand Integrate PmaxNOCT_estimation store the results in • T =  + T AMB 3-D arrays. arrays.  800  • Nominal Operatingfor every month and every year. Obtain the energy Cell Temperature year. • Calculate the BIAS error used in empirical eqn, efficiency. - Necessary because Tmod is and the module efficiency. not Tamb
  • 17.  NOCT − 20  TMOD =   ⋅ G + T AMB  800  DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtain the parameters of the empirical equation. equation. • Estimate Tmod with the empirical equation.equation. • Estimate Pmax values with the empirical equation. • Integrate Pmax over the day and store the results in - ESTI irrad (& measured Tmod) 3-DPyran irrad (& measured Tmod) - arrays. arrays. - ESTI irrad (& estimated Tmod using NOCT) • Obtain the energy for everyusing NOCT) every year. - Pyran irrad (& estimated Tmod month and year. • Calculate the BIAS error and the module efficiency. efficiency.
  • 18. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtain the parameters of the empirical equation. equation. • Estimate Tmod with the empirical equation. equation. • Estimate Pmax values with the empirical equation. • Integrate Pmax and irradiance over the day and store the results in 3-D arrays. • Obtain the energy for every month and every year. year. Used function: Monthly_sum • Calculate the BIAS error and the module efficiency. efficiency.
  • 19. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Data storage: One 12x31 matrix containing the energy for every day for each year. These matrices are stored in the same variable to form a 3-D array (tensor) for a number of years.
  • 20. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtain the parameters of the empirical equation. equation. • Estimate Tmod with the empirical equation. equation. • Estimate Pmax values with the empirical equation. equation. • Integrate Pmax over the day and store the results in 3-D arrays. arrays. • Obtain the energy for every month and every year. year. • Calculate the BIAS error and the module efficiency. efficiency.
  • 21. DATA TREATMENT SOFTWARE: Solar_data_treatment SOFTWARE: Actions carried out by the program: program: • Import the data from the text file. file. • Obtaining the parameters of the empirical equation. equation. • Estimation of Tmod with the empirical equation. equation. • Estimation of Pmax values with the empirical equation. equation. • Integration of Pmax and irradiance over the day and storing the results in 3-D arrays. arrays. • Obtaining the energy for every month and every year. year. • Calculation of BIAS error and module efficiency.
  • 22. DATA TREATMENT SOFTWARE: Data_writing SOFTWARE: Actions carried out by the program: Creating M-files to store the calculated variables. These data can be easily imported to a spreadsheet (e.g. Excel) for further analysis.
  • 23. DATA TREATMENT SOFTWARE: Data_plotting SOFTWARE: Actions carried out by the program: Plotting the different variables of interest to study the module performance. Bar graphs were chosen instead of scatter/line graphs. Month_teller gives the month name that is being plotted.
  • 24. DATA_PLOTTING: DATA_PLOTTING: Energy for ai01 in 2003 Module surface area=0.49 m2 Year 2003 NORMALISED MONTHLY ESTIMATES 2003 9000 9000 8000 8000 7000 7000 6000 6000 Energy (W·h) Energy (W·h) 5000 5000 4000 4000 3000 3000 Mean monthly energy 2000 2000 Measured energy Measured energy Empirical ESTI energy Empirical ESTI energy Empirical ESTI & Tmod energy 1000 Empirical ESTI & Tmod energy 1000 0 0 1 2 3 4 5 6 7 8 9 10 11 12 2 4 6 8 10 12 Months Months Main differences: Corrected values: divided by the Jun, Aug, Oct, Nov, Dec number of days actually measured per month
  • 25. DATA_PLOTTING: DATA_PLOTTING: Energy for ai01, Jan 2003 JANUARY 2003 400 Measured energy • Days without 350 Empirical ESTI energy Empirical ESTI & Tmod energy measurements 300 • Measurements 250 not carried out Energy (W·h) 200 the same 150 amount of 100 hours every 50 day. 0 5 10 15 20 25 30 Days
  • 26. DATA_PLOTTING: DATA_PLOTTING: Energy for ai01, May 2003 MAY 2003 400 350 300 250 Energy (W·h) 200 150 100 Measured energy Empirical ESTI energy 50 Empirical ESTI & Tmod energy 0 5 10 15 20 25 30 Days
  • 27. DATA_PLOTTING: DATA_PLOTTING: Efficiency for ai01 in 2003 Year 2003 AMBIENT AND MODULE TEMPERATURE FOR AI01 IN 2003 50 Mean efficiency ESTI 14 Measured energy <> ESTI irrad 45 Estimated ESTI energy <> ESTI irrad Estimated ESTI & Tmod energy <> ESTI irrad 12 40 35 10 Tem perature (ºC) Efficiency (%) 30 Energy produced by the module (W ⋅ h ) 8 Efficiency (% ) = 25 ⋅ 100 6 ( Energy coming from the sun W ⋅ h / 20 2 ⋅ module surface area m 2 m ) ( ) 15 4 10 Ambient temperature 2 Measured module temperature 5 Estimated module temperature 0 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 11 Months Months The efficiency is lower in summer time when the module temperature is higher
  • 28. DATA_PLOTTING: DATA_PLOTTING: Efficiency for ai01, Jan 2003 JANUARY 2003 Measured energy <> ESTI irrad 14 Estimated ESTI energy <> ESTI irrad Estimated ESTI & Tmod energy <> ESTI irrad 12 10 Efficiency (%) 8 6 4 2 0 5 10 15 20 25 30 Days
  • 29. DATA_PLOTTING: DATA_PLOTTING: Efficiency for ai01, May 2003 MAY 2003 Measured energy <> ESTI irrad 14 Estimated ESTI energy <> ESTI irrad Estimated ESTI & Tmod energy <> ESTI irrad 12 10 Efficiency (%) 8 6 4 2 0 5 10 15 20 25 30 Days
  • 30. OVERVIEW ON NUMERICAL RESULTS Energy comparison for AI01 (polycrystalline) Annual energy (W·h) Relative error (%) Measured value 70345 — Estimate ESTI 70471 0.18 Estimate Pyran 70740 0.53 Estimate 70890 0.77* ESTI & Tmod Estimate 71162 1.16* Pyran & Tmod *including December 2003 NOCT estimation with bad Tamb data
  • 31. OVERVIEW ON NUMERICAL RESULTS Energy comparison for LE02 (monocrystalline) Relative Energy, 7 months (W.h) error (%) Measured value 29624 — Estimate ESTI 29401 -0.75 Estimate Pyran 29219 -1.37 Estimate 29395 -0.77 ESTI & Tmod Estimate 29213 -1.39 Pyran & Tmod
  • 32. ENERGY PREDICTION ON PV-GIS WEB SITE PV- Solar irradiation map Energywe use monthlyon Can prediction based (of T. Huld & M. Suri) empirical model of c-Si module averages for energy rating? T. Huld new calculations: Monthly averages based on our meteo tower data (2003 & 2004). Assumption: in a month the energy is the same for every day. Calculate expected instantaneous values from sun position & airmass.
  • 33. ENERGY PREDICTION ON PV-GIS WEB SITE PV- Measured Estimate Relative Estimate Relative 2003 energy (Wh) PV-GIS (Wh) error (%) Pyran (Wh) error (%) Jan 4500 4928 9.5 4648 3.3 Feb 6007 6650 10.7 6214 3.4 Mar 7831 6032 -23.0 8018 2.4 Apr 6751 6949 2.9 6854 1.5 May 8659 8644 -0.2 8593 -0.8 Jun “PV- “PV-GIS type” prediction is good for a long 7006 8033 14.7 6925 -1.2 Jul period of time but not for single months 8202 8344 1.7 8256 -0.8 Aug 7471 8056 7.8 7354 -1.6 Sep 5554 6983 21.2 5513 -0.7 Oct 3546 4229 19.3 3553 0.2 Nov 1328 1964 47.9 1339 0.8 Dec 3513 2835 -14.7 3594 2.3 TOTAL 70367 73398 4.3 70740 0.5
  • 34. CONCLUSIONS (Software) Systematic treatment of the outdoor measurement data. data. Nevertheless, the program is flexible as it can be easily modified by adding new functions. functions. The program can function correctly with missing data. data. The results are obtained in far less time than employing a spreadsheet, and different data sets of different lengths and from different modules can be easily analysed. analysed. At the same time, the results are more reliable. reliable.
  • 35. Suggested Software improvements Check the number of hours during which the measurements were done for every day. day. If ∆t > 6 min, the integration of Pmax is not precise. precise. More parameters should be plotted, e.g. irradiance, BIAS error, mean values, etc. etc. There should be taken into account that the empirical equation to estimate Pmax can change depending on the module. module.
  • 36. CONCLUSIONS (Predictions) The empirical equation from solar simulator gives good predictions compared with long term outdoor measurements. measurements. 2 Crystalline (mono and poly) modules have been analysed. analysed. Comparisons with estimates based on average irradiance and temperature data (i.e. PV-GIS) are very (i. PV- encouraging – proves the validity of using monthly averages for Energy Rating purposes
  • 37. Thanks to the RE Unit for giving me the opportunity of participating in their projects Thanks to Thomas Huld and all my other colleagues for their assistance Special thanks to my supervisor Robert Kenny