SMART RENEWABLE HUBS FOR FLEXIBLE GENERATION · SMART RENEWABLE HUBS FOR FLEXIBLE GENERATION SOLAR GRID STABILITY D2.3 Non-synchronous technologies Validation Report The project has

SMART RENEWABLE HUBS

FOR FLEXIBLE GENERATION

SOLAR GRID STABILITY

D2.3

Non-synchronous technologies Validation Report

The project has received funding from the European Union’s Horizon 2020

research and innovation programme under grant agreement No 727362

D2.3: NON-SYNCHRONOUS TECHNOLOGIES VALIDATION REPORT 1

DOCUMENT HISTORY

ID & Title : D2.3 - Non-synchronous technologies Validation Report Number of pages: 19

Version v1.0

.1

Short Description (Max. 50 words):

This report will summarise the results of the validation of simplified libraries of static production models

for PV plants and wind farms. Validation will be carried out with experimental data and commercially

available software.

Revision History

Version Date Modifications’ nature Author

v0.1 10/10/2017 First version of the deliverable IDIE

v0.2 16/10/2017 Second version including COBRA

contribution

IDIE

v1.0 27/10/2017 Final version including COBRA and

TECNALIA comments and updates.

IDIE

Accessibility:

PU, Public

PP, Restricted to other program participants (including the Commission Services)

RE, Restricted to other a group specified by the consortium (including the Commission Services)

CO, Confidential, only for members of the consortium (including the Commission Services)

If restricted, please specify here the group:

Owner / Main responsible:

IDIE

Reviewer (s):

COBRA, SBP and TECNALIA


TABLE OF CONTENTS

LIST OF TABLES ...................................................................................................................................... 3

LIST OF FIGURES .................................................................................................................................... 4

LIST OF ACRONYMS AND ABBREVIATIONS .................................................................................................... 5

1. INTRODUCTION ................................................................................................................................. 6

1.1. SCOPE OF WORK ....................................................................................................................................6

1.2. LIST OF OUTPUT VARIABLES FOR VALIDATION ..............................................................................................6

2. METHODOLOGY ................................................................................................................................. 7

2.1. SELECTION OF VALIDATION BENCHMARKS ...................................................................................................7

2.1.1. Solar Photovoltaic .......................................................................................................................7

2.1.2. Wind Energy ...............................................................................................................................7

2.2. DEFINITION OF EXPERIMENTS ....................................................................................................................8

2.2.1. Identification of potential experimental factors ........................................................................8

2.2.2. Definition of factor’s levels (Comparative Validation) ...............................................................9

2.2.3. Definition of factor’s levels (Empirical Validation) .................................................................. 10

2.2.4. Identification of significant experimental factors (Screening) ................................................ 10

2.3. DEFINITION OF MODEL’S PERFORMANCE INDICATORS (MPI’S) FOR COMPARISON .......................................... 11

2.3.1. Root Mean Squared Error (RMSE) ........................................................................................... 11

2.3.2. F-Test ....................................................................................................................................... 11

3. SELECTED EXPERIMENTS .................................................................................................................... 12

3.1. SELECTED FACTORS AND LEVELS FOR SOLAR PHOTOVOLTAIC LIBRARY VALIDATION ......................................... 12

3.2. SELECTED FACTORS AND LEVELS FOR WIND ENERGY LIBRARY VALIDATION .................................................... 13

4. VALIDATION RESULTS ........................................................................................................................ 14

4.1. SOLAR PHOTOVOLTAIC LIBRARY (PV-SYST) .............................................................................................. 14

4.1.1. Conclusions (Solar Photovoltaic Library Validation) ................................................................ 15

4.2. WIND ENERGY LIBRARY ........................................................................................................................ 15

4.2.1. Empirical Validation (Cobra Experimental data) ..................................................................... 15

4.2.2. Comparative Validation (Wind Atlas and Application Program (WASP)) ................................ 16

4.2.3. Comparative Validation (System Advisor Model (SAM)) ......................................................... 16

4.2.4. Conclusions (Wind Library Validation) .................................................................................... 18

REFERENCES ....................................................................................................................................... 19


LIST OF TABLES

Table 1. Output Variables for the validation of Non-Synchronous technologies' models ............................6

Table 2. Non-Synchronous technologies' Potential Factors ..........................................................................8

Table 3. PV Comparative Validation Factors and levels ............................................................................. 12

Table 4: Variables Discarded for PV Validation .......................................................................................... 12

Table 5. Wind Energy Comparative Validation Factors and levels ............................................................. 13

Table 6: Main Results Obtained from the PV Library validation (F-Test) ................................................... 14

Table 7. Annual Wind energy production results (COBRA's Experimental data and SRHM) ..................... 16

Table 8. Annual Wind energy production results (WASP and SRHM) ........................................................ 16

Table 9: Main Results Obtained from the Wind Library validation with SAM (Deviation and NRMSE) .... 16

Table 10: Main Results Obtained from the Wind Library validation with SAM (F-Test) ............................ 17


LIST OF FIGURES

Figure 1: Normalized RMSE Histogram (PV Results) .................................................................................. 15

Figure 2: Normalized deviation from SAM’s Wind Power results .............................................................. 17


LIST OF ACRONYMS AND ABBREVIATIONS

ACRONYM MEANING

PV Photovoltaic

WP Work Package

MPI Model’s Performance Indicator

DoE Design of Experiments

SAM System Advisor Model

SRHM Smart Renewable Hub Modeller


1. INTRODUCTION

1.1. SCOPE OF WORK

This deliverable summarizes the results of the Non-synchronous technologies libraries’ validation. This

validation (D2.3) relates to “Task 2.3 - Non-synchronous technologies modelling”, within the second work

package of the GRIDSOL project. Two libraries for renewable energy technologies are included in this

report:

Solar Photovoltaic Technology

Wind Energy Technology1

Each one of these validations will follow a common methodology. Firstly, suitable references are selected

for each model validation: when there are no physical components to test, the experiments will consist

on running GRIDSOL’s model and other benchmark simulation tools under the same conditions. Secondly,

an experimental campaign is defined, selecting the conditions for the experiments to be conducted. The

results from this campaign will be used for comparison. Finally, the measurements (performance

indicators) to quantify the closeness between model’s and reference results are defined. The definition

of both experiments and Model Performance Indicators (MPI’s) might differ depending on the available

validation benchmarks (experimental data or validated software results).

1.2. LIST OF OUTPUT VARIABLES FOR VALIDATION

Table 1 shows the models for non-synchronous technologies to be included in the deliverable D2.3. The

results to be compared with reference benchmarks are also shown in the right column of Table 1:

TABLE 1. OUTPUT VARIABLES FOR THE VALIDATION OF NON-SYNCHRONOUS TECHNOLOGIES' MODELS

Technology Output variables to validate

Solar Photovoltaic Total yearly production / Peak Power installed (GWh/MWp)

Wind Farm Wake Losses

Output Power during a year (hourly)

1 Although the number of wind turbines using synchronous generators is progressively increasing, the vast majority of the wind capacity installed use non-synchronous generators. Therefore, wind energy library has been included in the Deliverable 2.3 for “Non-synchronous technologies”.


2. METHODOLOGY

A common methodology has been used for the validation of GRIDSOL’s WP2’s models. This methodology

consists of three fundamental stages:

1. Selection of Validation Benchmarks - Which references will be used for comparison?

2. Definition of Experiments - Under which conditions are the models going to be tested against

benchmarks?

3. Definition of Model’s Performance Indicators (MPI’s) for comparison - What metrics are going to

be used to compare the results? Which values of those metrics are defined as validation thresholds?

2.1. SELECTION OF VALIDATION BENCHMARKS

Two basic approaches are considered to evaluate the accuracy of the GRIDSOL libraries. Each one has its

advantages and disadvantages:

Empirical validation: Comparison between the results obtained with GRIDSOL’s Models and

measured data from experiment tests. The main concern in this approach are the uncertainties in

measured inputs and results, and the limited data from tests.

Comparative validation: Comparison between the results obtained with GRIDSOL’s Models with

the results from reputable software (e.g. Thermoflex, PVsyst, etc.). Numerical methods offer high

accuracy in inputs definition and great flexibility in results evaluation. The uncertainties in this

approach come from the approximation of the real case into a simplified case (real conditions to

numerical conditions).

For the validation of GRIDSOL’s Models, the Empirical approach has been prioritized when experimental

data were available.

2.1.1. SOLAR PHOTOVOLTAIC

Only a comparative benchmark was available for the validation of the solar photovoltaic library. PVsyst is

a software package for the study, sizing, simulation and data analysis of complete PV systems.

2.1.2. WIND ENERGY

Both empirical and comparative benchmarks were available for the validation of the wind energy library.

On one hand, Cobra provided experimental data:

Power production data from eight wind turbines was available.

Wind resource (both wind direction and speed module) at each turbine hub was available.

However, atmospheric pressure and temperature were not available, and density was assumed

constant.

As Cobra’s wind power experimental data was confidential, Cobra performed the comparison

between reference and software results. The results of this comparison are shown in this report.

Results from WASP (Wind Atlas and Application Program) for the same case were also included.


On the other hand, NREL’s System Advisor Model (SAM) was used to complement the empirical validation

with a multivariable analysis.

2.2. DEFINITION OF EXPERIMENTS

To check the validity of the WP2 models, their results will be compared with reference data. The models

depend on a series of inputs that influence the results. It is necessary to verify that the effects of these

inputs conform to those found in reference data.

To that end, systematic procedures (experiments) will be carried out to discover the effects of these inputs

(factors) in the models, covering the factors’ range of variation at different values (levels). These

experiments will be defined following the three basic stages of Design of Experiments (DOE) method [1]:

1. Identification of potential experimental factors

2. Definition of factor’s levels

3. Identification of significant experimental factors (Screening)

2.2.1. IDENTIFICATION OF POTENTIAL EXPERIMENTAL FACTORS

To cover the full spectrum of factors of each model, all the potential factors that might influence in the

model’s results were initially considered. These inputs are included in Table 2.

TABLE 2. NON-SYNCHRONOUS TECHNOLOGIES' POTENTIAL FACTORS

Technology Factor category Potential Factors

PV Module Sizing PV Power (MWe)

Conditions & Characteristics Module Characteristics.

Sun tracking.

GCR

Azimuth.

Elevation

Inverter Characteristics.

Losses (Wiring, inverter, transformer).

Nº ARRAYS x MODULE PER ARRAY (parallel x serial)

Location & Meteorological GHI (kWh/(m2·year)) (Hourly)

Temperature (ºC) (Hourly)

Wind Speed (m/s) (Hourly)

Ground Reflectance (%)


Latitude (º)

Longitude (º)

Altitude (m)

Wind Farm Sizing Wind Farm Size (MWe)

Conditions & Characteristics Wind turbine type (MWe)

Hub Height (m)

Downwind distance between turbines (m)

Location & Meteorological Wind Speed module (m/s)

Wind speed direction (radians)

Temperature (ºC)

Altitude (m)

Atmospheric Pressure (Pa)

2.2.2. DEFINITION OF FACTOR’S LEVELS (COMPARATIVE VALIDATION)

Factors can only assume a limited number of possible values, known as factor levels. These levels will be

within a range, limited by a maximum and minimum value. For the comparative validation, this threshold

will be stablished differently depending on the category of each factor:

SRH’s technology sizes:

Different nominal sizes are being considered for GRIDSOL implementation, depending on the type

of power system (non-interconnected or interconnected), renewable share, available land, etc.

Minimum and maximum levels (for each technology) will be obtained from the preliminary

analysis performed by GRIDSOL WP2, 5 and 6.

Operating conditions and Design characteristics

Both threshold limits are defined using reference cases, expanding them (i.e. the limits) to

contemplate wider ranges of validation.

Meteorological and geographical factors

Several potential locations are being contemplated for GRIDSOL’s implementation. Factor ranges

for each factor will be obtained from the TMY’s files obtained from each location. Upper limits

will be the maximum value among all the values and the lower value will be the minimum.

The following locations will be considered for this task:

Crete, Greece

Puglia, Italy

Fuerteventura, Canary Islands, Spain

Marseille, Aix-en-Provence, France

Plataforma Solar de Almería, Spain


Badajoz, Extremadura, Spain

Sicily, Italy

Peloponnese, Greece

Faro, Portugal

Madeira Island, Portugal

Cyprus

Atacama, Chile

Kuwait

Ouarzazate, Morocco

Once both upper and lower limits are stablished for each factor. Three levels defined as follows:

Maximum value (+)

Average value (0)

Minimum value (-)

2.2.3. DEFINITION OF FACTOR’S LEVELS (EMPIRICAL VALIDATION)

Experimental values offer a high level of accuracy and a great benchmark for validation. The procedure to

define the factor’s levels will be analogous to that explained in section 2.2.2.

However, experimental data may be limited. The number of test performed and available for validation

could be insufficient to cover all the factor’s ranges. Therefore, the procedure for levels’ definition will be

as follows:

1. Filter and discard experimental data whose factor’s values are excessively out of factor’s ranges

(defined in section 2.2.2).

2. Find and use experiments in levels near the values established in section 2.2.2

3. If there is no experimental data to roughly cover the levels’ ranges established in section 2.2.2, use

reference software data to complement experimental data for validation.

2.2.4. IDENTIFICATION OF SIGNIFICANT EXPERIMENTAL FACTORS (SCREENING)

The number of experiments for validation increases exponentially with the number of inputs (or factors).

Therefore, a screening process is highly advisable, to identify the key influential factors and reduce the

number of tests. Plackett-Burman method [2] has been used for this purpose.

Plackett-Burman experimental design consists of a carefully fraction of the experimental runs of a full

factorial design. All the factors whose measured effects on results are of the same magnitude of

experimental error will be discarded.

To determine experimental error, the Plackett–Burman design considers insignificant dummy variables,

with no influence on results. Once the experiments are run and the samples measured, the data from the

experiments are used to calculate the effects and to determine the statistical significance of those effects.


A two-level full Plackett-Burman design has been used to screen for the principal factors, presuming the

relationship between the variables is linear. Only a fraction of factor’s ranges may be used depending on

the factor (latitude, longitude, etc.).

2.3. DEFINITION OF MODEL’S PERFORMANCE INDICATORS (MPI’S) FOR COMPARISON

Two statistical tools (MPI’s) will be used to measure the differences between GRIDSOL’s models and

reference data.

2.3.1. ROOT MEAN SQUARED ERROR (RMSE)

Root Mean Squared Error (RMSE) will be used to quantify the differences between the GRIDSOL models

and reference results. This quantity will be normalized with the maximum value minus the minimum value

of the measured data to obtain the Normalized Root Mean Squared Error (NRMSE):

𝑅𝑀𝑆𝐸 = √∑ (𝑋𝑜𝑏𝑠,𝑖 − 𝑋𝐺𝑅𝐼𝐷𝑆𝑂𝐿 𝑀𝑜𝑑𝑒𝑙,𝑖)2𝑛

𝑖=1

𝑛

𝑁𝑅𝑀𝑆𝐸 =𝑅𝑀𝑆𝐸

𝑋𝑜𝑏𝑠,𝑚𝑎𝑥 − 𝑋𝑜𝑏𝑠,𝑚𝑖𝑛

EQUATION 1. ROOT MEAN SQUARED ERROR EQUATIONS

The validation threshold for Normalized Root Mean Squared Error (NRMSE) could differ between

measured outputs. However, as a default value, maximum allowed NRSME value is fixed at 10%.

2.3.2. F-TEST

Normalized Root Mean Squared Error (NRMSE), as other similar statistics, doesn’t provide a direct

conclusion about the validity (or not) of GRIDSOL’s models. Therefore, “Hypothesis Testing” will also be

used. A hypothesis test examines two opposing hypotheses about two samples of, supposedly, the same

population (in this case, GRIDSOL’s models results and either experimental data or results from the

reference validation software). In this case, samples’ variance will be compared using F-Test [3], [4]:

Null hypothesis (𝐻0)

Both samples’ variances are equal. That would mean GRIDSOL’s model results agree with reference

data.

Alternative Hypothesis (𝐻1)

The variance differs from one sample to another.

Once the tests are performed and both reference’s and models’ results are available, F-Statistic will be

calculated using both populations’ (i.e. experiments’ results) variances. Instead of a maximum admissible

error (like the one used for NRMSE), a level of significance is used instead.

The level of significance has been chosen at 95%. The significance level is the probability of rejecting the

null hypothesis when it is false.

http://www.statisticshowto.com/what-does-it-mean-to-reject-the-null-hypothesis/

http://www.statisticshowto.com/what-does-it-mean-to-reject-the-null-hypothesis/


3. SELECTED EXPERIMENTS

In section 2.2, the general methodology for experiments definition was explained. In summary, all the

potential factors (input variables) are initially considered. Secondly, factor levels are defined from each

location TMY and typical values from reference. Finally, a screening method (Plackett-Burman) is used to

screen the main factors, only the ones that have significant influence on the results.

3.1. SELECTED FACTORS AND LEVELS FOR SOLAR PHOTOVOLTAIC LIBRARY VALIDATION

Once evaluated the different potential variables for the validation, the variables selected are shown in

Table 3 . For each technical factor, three levels have been selected.

On the other hand, regarding the meteorological data, a Typical Meteorological Year has been selected

for each one of the locations specified at section 2.2.2.

TABLE 3. PV COMPARATIVE VALIDATION FACTORS AND LEVELS

FACTORS

LEVELS

-1 0 1

Nominal Power (MW) 25 50 100

Tilt (º) 25 30 35

Azimuth (º) 170 180 190

GCR (%) 30 50 70

YEARLY EXPERIMENTS IN TOTAL 1215

According to the procedure explained in section 2.2.4, a screening method has been used to select only

the relevant factors for validation. The potential factors discarded for the validation are shown in Table 4.

TABLE 4: VARIABLES DISCARDED FOR PV VALIDATION

Module type Doesn´t change significantly the output

Inverter type Doesn´t change significantly the output

Losses Is a % of the energy production.

Sun tracking Hasn’t be implemented yet.

Strings / Modules per Array Is a design parameter of the model. Can´t be change


3.2. SELECTED FACTORS AND LEVELS FOR WIND ENERGY LIBRARY VALIDATION

Table 5 lists all the selected factors for the validation of the Wind energy library. Three values (levels) have

been selected for each factor (Maximum, mean and minimum value). Wind turbine’s type were selected

considered typical sizes in industry. In total, 19683 experiments were performed.

TABLE 5. WIND ENERGY COMPARATIVE VALIDATION FACTORS AND LEVELS

FACTORS

LEVELS

-1 0 1

Number of Turbine rows 1 3 5

Number of Turbines per row 10 30 50

Row Spacing (diameters) 4 8 12

Wind Turbine Hub Height (m) 50 75 100

Wind Turbine Type Nordic 1000 54m Gamesa G90 2MW NREL 5MW

Wind Speed module (m/s) 7.3 14.7 22

Wind speed direction (radians) 0 90 180

Temperature (ºC) -5.9 21.8 49.6

Atmospheric Pressure (atm) 0.82 0.92 1.02

EXPERIMENTS IN TOTAL 19683

According to the procedure explained in section 2.2.4, a screening method has been used to select only

the relevant factors for validation. No potential was discarded and all of them were included in the

experimental campaign.


4. VALIDATION RESULTS

4.1. SOLAR PHOTOVOLTAIC LIBRARY (PV-SYST)

For each one of the simulation, the normalized annual energy production was obtained and compared

with the reference results (PVsyst’s). This production values were normalized with the design power

installed (GWh/MWp). The MPI’s obtained from the simulations are:

TABLE 6: MAIN RESULTS OBTAINED FROM THE PV L IBRARY VALIDATION (F-TEST)

PVsyst (GWh/MW) SRHM (GWh/MW)

AVERAGE 1.606 1.538

STD 0.030 0.046

Nº SAMPLES 1215 1215

DEGREES OF FREEDOM 1214 1214

P value (F<=f) 2.2E-13

RMSE NORMALIZED 7%

In the Fisher’s test, the p value is used to decide if the null hypothesis is true or false. The greater the p-

value the greater the evidence against the null hypothesis. The level of significance, α=0.05 (95%), is

compared against this value.

As Table 6 shows:

PV-Syst average production is slightly higher than the one modelled in SRHM software (4%).

The standard deviation in SRHM software is 0.045 whereas in PV-Syst is 0.030 (0.015 GWh/MWp).

The two models can be considered as the same population case (F < Fcritical value), for the selected

level of significance (95%).

The normalized relative errors’ histogram is shown in Figure 1.


FIGURE 1: NORMALIZED RMSE HISTOGRAM (PV RESULTS)

4.1.1. CONCLUSIONS (SOLAR PHOTOVOLTAIC LIBRARY VALIDATION)

Both Fisher’s test (F-Test) and Root Mean Square Error (RMSE) show that the deviation of GRIDSOL PV

Model’s results is smaller than validation threshold. Reference power production (PV-Syst) is slightly

greater than the obtained with GRIDSOL PV Libraries. Further efforts should be made to reduce this

deviation.

4.2. WIND ENERGY LIBRARY

4.2.1. EMPIRICAL VALIDATION (COBRA EXPERIMENTAL DATA)

Using a ten minutes wind speed distribution, annual energy production was obtained with the software

and compared with experimental data provided by COBRA. The results are shown in Table 7. The error

from the experimental results is around 2%.


TABLE 7. ANNUAL WIND ENERGY PRODUCTION RESULTS (COBRA'S EXPERIMENTAL DATA AND SRHM)

Source Annual Production (GWh) Capacity Factor (%)

COBRA (Experimental data) 38.132 27.21

SRHM 37.416 26.67

The following source of uncertainty could lead to error in the empirical validation:

A constant density value (instead of an hourly distribution) has been considered for the simulation

between empirical and software data.

4.2.2. COMPARATIVE VALIDATION (WIND ATLAS AND APPLICATION PROGRAM (WASP))

Additionally, WASP was used to simulate the same case from the experimental data. The results are shown

in Table 8. The NRMSE error in this case is 6.4%.

TABLE 8. ANNUAL WIND ENERGY PRODUCTION RESULTS (WASP AND SRHM)

Production (GWh) Capacity Factor (%)

Software 𝝆 = 𝟏. 𝟎𝟕 𝝆 = 𝟏. 𝟐𝟐𝟓 𝝆 = 𝟏.107 𝝆 = 𝟏. 𝟐𝟐𝟓

WASP 39.819 40.155 28.41 28.65

SRHM 37.4155 42.835 26.67 30.53

While this error is below the validation threshold, the results show how the variation of energy production

with density is much more pronounced for SRHM’s results than for WASP’s. Further efforts should be

made to review this aspect.

4.2.3. COMPARATIVE VALIDATION (SYSTEM ADVISOR MODEL (SAM))

For each one of the cases from 3.2, hourly production and wake losses were obtained and compared with

the reference results.

Table 9 shows the amount proportion of experiments depending on the normalized deviation obtained

from the hourly power production results. More than 70% of the experiments have a deviation smaller

than 1% and more than 95% have a RMSE smaller than 10%. The mean normalized deviation considering

all the experiments is 2.01%. The Normalized Root Mean Square Error (NRMSE) is 4.05%:

TABLE 9: MAIN RESULTS OBTAINED FROM THE WIND L IBRARY VALIDATION WITH SAM (DEVIATION AND NRMSE)

Normalized Deviation from reference

<1% 1-5% 5-10% 10-20% Total

Number of experiments 13851 2241 2646 945 19683

Proportion of the total amount (%) 70.4% 11.4% 13.4% 4.8% 100%


Mean Normalized Deviation 2.01%

Normalized Root Mean Square Error 4.05%

The normalized deviations are shown in Figure 2. Most of the simulations overestimate production,

comparing with reference SAM’s results.

FIGURE 2: NORMALIZED DEVIATION FROM SAM’S WIND POWER RESULTS

As Table 10 shows, p value is 1,8E-07. The two populations are similar enough to fail to reject null

hypothesis with a level of significance of 95% (0.05).

TABLE 10: MAIN RESULTS OBTAINED FROM THE WIND L IBRARY VALIDATION WITH SAM (F-TEST)

SAM (MW/MW installed) SRHM (MW/MW installed)

AVERAGE 0.561 0.583

STD 0.145 0.135

Nº SAMPLES 19683 19683

DEGREES OF FREEDOM 19682 19682

P value (F<=f) 1.8E-07


4.2.4. CONCLUSIONS (WIND LIBRARY VALIDATION)

The comparison between model’s and empirical results shows that the error obtained is smaller than 2%.

A constant value for density is considered and only one value is used for comparison (annual energy

production). Therefore, a comparative validation has been used to complement the empirical one.

Regarding the comparative validation against WASP’s, the normalized Root Mean Square Error (RMSE)

obtained (6.4%) is smaller than the maximum admissible value (10%).

Regarding the comparative validation against System Advisor Model’s results, the normalized Root Mean

Square Error (RMSE) obtained (4.05%) is much smaller than the maximum admissible value (10%). Also,

the level of significance (p-value=1.8e-7) for F-test is smaller than the one proposed as validation

threshold (0.05).

While further efforts will be made to improve the GRIDSOL’s wind energy library, the model already

obtains reliable results.


REFERENCES

[1] J. A. Wass, «First Steps in Experimental Design - The Screening Experiment,» The Journal of Validation

Technology, vol. Spring Issue, 2010.

[2] Analytical Methods Committee, AMCTB, No55, «Experimental design and optimisation (4): Plackett–

Burman designs,» Analytical methods, vol. 5, nº 8, pp. 1901-1903, March 2013.

[3] National Institute of Standards and Technology (NIST), «Engineering Statistics Handbook (F-Test for

Equality of Two Variances),» [En línea]. Available:

http://www.itl.nist.gov/div898/handbook/eda/section3/eda359.htm. [Último acceso: 26 July 2017].

[4] G. W. Snedecor y W. G. Cochran, Statistical Methods, Eighth ed., Iowa State University Press, 1989.

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SMART RENEWABLE HUBS FOR FLEXIBLE GENERATION · SMART RENEWABLE HUBS FOR FLEXIBLE GENERATION SOLAR GRID STABILITY D2.3 Non-synchronous technologies Validation Report The project has