Accurate PV system simulators are implemented with expensive software platforms using paid irradiance data. The main purpose of this paper is to develop and validate a PV system simulator, beginning with a solar cell parameter extraction model, then test and validate long-term Irradiance data using free online source (Typical Meteorological Year TMY in (PVGIS) European website), and finally building full solar generator simulator to run in working real conditions. Comparing results with Accurate Paid PV simulators (which use the Muneer model) showed good accuracy of the proposed simulator. Work flow starts with the Irradiance model’s data processing, then solar cell 5 parameters model data processing (to extract cell parameters), and finally full system simulator. MATLAB coding programs in real working conditions are used for simulation. Results of solar cell parameter extraction show 99.6% to 99.99% matching with data sheet and cell performance under standard test conditions. System model simulation output shows 8% less yearly generated energy compared to the PVGIS 2022 long-term simulation (hourly basis (one-year time)). This is due to incident energy variations (between the years 2016 and 2022) of 4.02%. The novelty of the algorithm is the methodology, as it tests irradiance data on an hourly basis and validates results for a whole continuous year. Also, the 5-parameter solar cell model is used to be validated in long term analysis, not only STC conditions and could be applied on any PV solar cell. The algorithm and block diagram used are scalable, modular, and interchangeable with similar models to be tested. This simulator could test several methods and models in solar pv technology.

Most North African nations (NAN), especially Egypt, are suffering from water scarcity Abdelhaleem et al. [_{p} and a life time cycle of 20 years for the pv solar system and 10 years for the diesel pumping system) proves that PV pumping system saves more than 30% cost less than diesel generator pumping system cost. Parvaresh Rizi et al. [_{s}), the parallel resistant (R_{sh}), the saturation current of the diode (I_{o}), and lastly the photo or light current (I_{ph}). There are two main categorized methods to determine the 5 parameters: numerical methods, and analytical methods. In the numerical model as the equation of the solar cell is a transcendent equation and parameters R_{s}, R_{sh} (or R_{b}), n cannot be separated (due to the presence of an exponential term). A mathematical solution based on the Lambert W-function Ding et al. [

Modeling and testing of a solar generator at STC or during a short-term simulation period will not account for a large number of effects and variances that will dramatically affect the expected energy performance. The purpose of this literature review is to explain various models used individually in modeling PV solar system parts, however, none of those studies integrate all the models with all its representative equations to evaluate long term operation performance, which lead us to the significance of this proposal, the main novelty of this paper could be summarized as follows:

Processing and validating free typical meteorological year (TMY) raw Irradiance data (TMY is a set of meteorological data with data values for every hour in a year for a given geographical location. The data is selected from hourly data over a longer time period, normally 10 years or more), by calculating the global horizontal irradiance (GHI) in plan radiation using two irradiance models, then, comparing it with a GHI output of 2022 paid platform (novelty is the validation of the database available on the EU site using the proposed method under working conditions).

Validating methodology of calculating the airmass effect on the long term in plan radiation using coding in MATLAB.

Using and validating analytic approach to extract solar cell parameters in STC proving 99.9% matching, depending on these parameters, using panel model in PV solar generator under real working conditions for a whole year, and hourly yield data validation compared to (photovoltaic geographical information system of European union) PVGIS results.

Integrating all models (solar irradiance model results (long term GHI) and solar panel model based on extracted parameters) in solar generator simulation model, then coding all representative equation in MATLAB to generate hourly, daily, monthly and yearly basis solar yield. This result resolution and detailed analysis is not available in PVGIS site.

Validating PV system energy yield from this work compared to energy yield from PVGIS platform 2022.

Solar generator model proposed as a free tool for solar engineers to estimate system yield shows 8% less yearly generated energy compared to the PVGIS 2022 long-term simulation (hourly, daily and weekly basis generation analysis in PVGIS tool is not available).

The main contribution of this paper is to present solar generation result for a nominal power system using a novel solar simulator composed of integrated models one after another, the proposed system analyses solar generated energy hourly basis for one year. The use of this system allows engineers to do a much deeper analysis of solar yield of any system. Any brand of PV panels available in the market could be tested and analyzed for parameter extraction using proposed methodology. Other advantage over PVGIS tool that, while it uses one model (Muneer model) any model could be plugged in the proposed system architecture to be tested (2 models are tested actually). The parameters extraction of any solar cell could be done and compared with manufacturer data sheet while is not available in PVGIS platform. While PVGIS gives yield results in monthly basis the proposed system gives results and analysis in hourly-daily-weekly-monthly and finally yearly basis. To determine the reliability of the proposed system, output is compared to the only available monthly solar yield of modern online simulator PVGIS. Each step is carefully tested under actual working conditions. Final results show identical performance in the first level with data sheet results, second level (in plan GHI) shows 4.02%. The difference compared to 2022 PVGIS results (using data available up to 2016). The novelty of this system is the ability of long-term simulation for PV system yield under daily working conditions, and ability of prediction of all parameter beginning of cell level.

_{p} system, while the second is 8.25 KW_{p} system.

The methods which are used in this analysis will consist of 3 modeling stages. The output data from the first and second stages will be the input data for the next level or stage. The result used in these models will be validated by comparison. The comparison will be with either analytical results (from an approved research paper), or empirically (measured data in approved research papers). The first stage of modeling will be related to Irradiance models and Irradiance data sources for long-term analysis, PVGIS satellite Irradiance data TMY-(Typical Meteorological Year) will be used. This data has a very good accuracy compared to calibrated verified and checked ground weather stations around the world (Baseline Surface Radiation Network (BSRN)) [

Lui and Jordan Isotropic Irradiance Model [

Hay and Davies circumsolar Irradiance Model [

The output of these irradiance models will suffer from the air mass effect. To consider such an effect, we will use the airmass modifier calculations [_{sh} or (_{b}), Thermal voltage _{t}) validation will be conducted by comparing the output of the cell with empirically measured results of the Shell Solar SQ175-PC PV module Ma et al. [

The irradiance model utilized in this work will be the Isotropic Irradiance Model, the Hay and Davis model, along with the air mass modifier.

Using Lui and Jordan Isotropic Irradiance model [

where:

SVF Sky view factor.

DHI Diffuse horizontal irradiance [W/

GHI Is the global horizontal irradiance [W/

In above model the diffuse irradiance is isotopically distributed in the earth’s atmosphere. No circumsolar ring around the sun is considered or horizon brightening components.

As stated by Hay [

where:

S Extraterrestrial radiation on horizontal plan [Wh/

R Actual sun-Earth distance [m].

DNI Direct normal irradiance hourly basis [W/

DHI Diffuse horizontal irradiance hourly basis [W/

SF Shading factor.

SVF Sky view factor.

In this method diffuse Irradiation is considered to be composed of 2 components:

Isotropic diffuse irradiance.

circumsolar diffuse irradiance.

Airmass (AM) is defined as the mass of atmosphere above Earth in which the light will travel and suffering of dispersion and spectral distribution according to the wave length of the components [

the Airmass Modifier.

hourly index of the Airmass Modifier.

Total Irradiance on Module affected by airmass modifier [W/m^{2}].

Cubas et al. [

One diode 5 parameters solar cell model equation:

Short circuit boundary condition:

Open circuit boundary condition:

Maximum power point differentiation condition:

where:

I Solar cell total current [A].

V_{oc} Open circuit voltage of the solar cell [V].

R_{s} Series resistance of the solar cell [

R_{sh} Shunt resistance of the solar cell [

An implicit expression of the series resistor,

Final expression of the shunt resistor, R_{sh}, as a function of R_{s} and the initial parameters:

Maximum power point voltage [V].

Maximum power point current [A].

These equations are validated by applying the methodology and iterative algorithm suggested by Zekry et al. [

Initiating a value for ideality factor (a) from 1 to 1.5, increasing by 0.1 each time.

Solving symbolic Equation for series resistance using MATLAB coding program.

Solving symbolic equations for shunt resistance using MATLAB coded program.

Apply all values to MPP and use the data sheet of the manufacturer to get the best estimation of the parameters. The results were compared to Ma et al. [

Based on Zekry et al. model [

^{2}]; ^{−1} ]; _{max} by 0.97 × 0.97 to account of the 3 percent loss for both cabling and inverter.

As shown in

As we stated on section two, we use both types of models isotropic and Anisotropic Models we choose Lui and Jordan Isotropic Irradiance Model, Hay and Davies. The calculations based on 8760 data point entry per year (TMY 2007–2016). The results will output total 8760 hourly total insolation power in plan radiation. This data is integrated into daily, monthly and yearly results and compared with the total in plan insolation depicted by PVGIS European data tool for year 2016. The later (PVGIS) calculated incident power per square meter is 2282.1 KW/m^{2} for year 2016, and 2377.579 KW/m^{2} for year 2020, while incident power per square meter using proposed system, TMY data (incident power on horizontal plan TMY 2007–2016) simulated by isotropic model and anisotropic model are: 2163.1 and 2160.1 KW/m^{2}, respectively. Percentage difference PVGIS results year 2016 and Simulation is about 5.6477% and 5.5039% consecutively. While for 2020 percentage difference PVGIS results and Simulation is about 9.11% and 9.02% consecutively. Also, we notice deviation in incident power per square meter in plan radiation between year 2016 (proposed system) and 2020 (PVGIS) about 4.02% increasing.

As Example of solar yield monthly,

As stated in

Characteristics | Value |
---|---|

Open-Circuit Voltage (V_{oc}) |
44.6 V |

Voltage at maximum power point (V_{mp}) |
35.4 V |

Short-Circuit Current (I_{sc}) |
5.43 A |

Current at maximum power point (I_{mp}) |
4.95 A |

Maximum power at STC (P_{max}) 175 W_{p} |
175 w |

Number of cells connected in series | 72 |

Temperature coefficient of I_{sc} (alpha) |
0.8 mA/°C |

Temperature coefficient of V_{oc} (beta) |
145 mV/°C |

Temperature coefficient of P_{mpp} (gamma) |
0.43%/°C |

As shown in

Cell parameter | R_{sh} or (R_{b}) |
V_{t} |
n | |||
---|---|---|---|---|---|---|

Tao Ma results | 5.449 | 1.20E-09 | 0.010 | 2.725 | 0.028 | 1.086 |

This work results | 5.4493 | 1.30E-09 | 0.0097 | 2.7354 | 0.02567 | 1.09 |

Here we consider the term ((I * _{sh}) <<) and would be neglected, on the other hand we will keep the term I * R_{s} in the exponential diode current term. this new arrangement of equation will ignore the whole shunt current in the solar cell and will show the increase of the cell power as a simplified model.

The used panel used for this analysis is SUNTECH poly crystalline panel STP275-20/Wfw with following Electrical Characteristics as shown in

Maximum Power at STC (P_{max}) |
275 W |

Optimum Operating Voltage (V_{mp}) |
31.2 V |

Optimum Operating Current (I_{mp}) |
8.82 A |

Open Circuit Voltage (V_{oc}) |
38.1 V |

Short Circuit Current (I_{sc}) |
9.27 A |

Module Efficiency | 16.8% |

Operating Module Temperature | −40°C to +85°C |

Maximum System Voltage | 1000 V DC (IEC) |

Maximum Series Fuse Rating | 20 A |

Power Tolerance | 0/+5 W |

The following

Until this point in the article we only tested the solar cell/panel/array in lab conditions only. That we use insolation power with in controlled conditions to feed and test the model. These conditions do not reflect environmental working daily conditions. In the next section we will start feeding the model with all predicted data based on satellite imagery system in hourly basis to analyze and predict the real performance of these systems in several applications. This will allow us to know limitations of these systems in daily life use and reliability of solar stand-alone systems or on-grid systems for certain application. The motivation of this model is to merge it in future work in solar pumping irrigation system to test ability of solar system in agricultural sector as stand-alone systems. Also, another prospective will be studied the effect of using these systems in the economy of developing countries, which suffering from energy problems.

Based on Zekry model, we use the equations stated above to represent the solar array, and fed the solar generator with TMY irradiation data from years 2007 to 2016 in hourly basis, after conditioning and preparing the data to the suitable format, we deduce the in-plan radiation and apply airmass modifier to it, furthermore, the losses due to inverter and cables are considered, then the total generated power is compared to the generated power simulated by PVGIS European web site (JRC tools). This model (PVGIS) is based on Muneer model. And radiation data of year 2022. We found that the total generated power by PVGIS model was 14828.71 KWh per year for the nominal power of 8.25 KW_{p} system on the other hand the same nominal power of the system simulated in this work, gives yield of 13644.33 KWh per year the ratio between the two system was found to be 92% as following equation:

Month | Generated energy in KWh for 1 KW_{p} system monthly |
---|---|

January | 117.9052 |

February | 112.635 |

March | 151.8479 |

April | 142.6993 |

May | 141.458 |

June | 143.4121 |

July | 150.565 |

August | 158.7076 |

September | 145.6952 |

October | 147.9126 |

November | 109.3154 |

December | 131.7055 |

In next _{p} is taken as a case study for data analysis. It shows final yield simulated by PVGIS platform (of European union) and yield of the same system (8.25

System used | Simulated | PVGIS | Ratio |
---|---|---|---|

Units | KWh | KWh | Ratio % |

January | 972.7183 | 1052.82 | 92.392% |

February | 929.2385 | 1042 | 89.178% |

March | 1252.745 | 1297.35 | 96.562% |

April | 1177.269 | 1307.35 | 90.050% |

May | 1167.028 | 1334.03 | 87.481% |

June | 1183.15 | 1323 | 89.429% |

July | 1242.161 | 1373.17 | 90.459% |

August | 1309.338 | 1370 | 95.572% |

Septemper | 1201.985 | 1272 | 94.496% |

October | 1220.279 | 1267.59 | 96.268% |

November | 901.8519 | 1097.7 | 82.158% |

December | 1086.57 | 1091.7 | 99.530% |

Total | 13644.33 | 14828.71 | 92.013% |

In this paper we tried to explain a method to practically evaluate the output of PV generator depending on available radiation data on the internet network for free, this method use sets of models for each level starting from irradiance data processing to solar parameter extraction and long term energy yield, using available TMY data from PVGIS website, although the raw data available ending in year 2016, the generated output power (solar energy yield) simulated, compared to the calculated energy yield in year 2022 was very near 8% difference (taking in consideration aging factor loss of efficiency 20 percent in 20 years), this tool could be used as a whole system simulator to accurately calculate the energy generated from PV solar water pumping system or any other system without any need to use paid tools or programs. Through coding the stated equations above in MATLAB programming language. And using the flow chart explained in details second step is to insert the TMY data, and some iterating procedure, the goal is to extract the solar cell 5 unknown parameters, then use the solar cell model in simulating program fed by climate data. The final results turned to be decently accurate, moreover if the available weather data is more recent it will reduce the difference between simulated output and PVGIS results. The novelty of this methodology that it uses all models from the very beginning (i.e., integration and testing of all level models under working conditions), Estimating and extracting of solar cell 5 parameters (i.e., 5 parameter model) through long term evaluation of energy yield in daily working conditions to design and to predict solar system output. Also, a novel method by applying all irradiance data to solar cell equation, this is will lead to get generated current hourly basis, then use current to estimate output power. This tool could be used by engineers to design solar systems and predict its performance in real working conditions. all models could be interchangeably used with different known models to test as many models as required for research. In future work, this model will be merged into solar pumping irrigation system to determine the effect of solar power nature on time sensitive application like agriculture and irrigation. In addition, the reliability of stand-alone solar pumping irrigation system should be discussed, and lastly feasibility of these system in developing countries like Egypt will be discussed as well. Limitation of this work, that it does not use latest irradiance data to predict exact yield in 2022, as the only available free irradiance data in the available resources is up to year 2016. Also, a 20-year degradation effect is taken in consideration so the actual output is degraded 0.5% each year with total degradation of 10% in the end of 20 year. All paid platforms design and simulate solar generator and upsizing the system to overcome this phenomenon.

Ideality factor of the solar cell

A string of constants for airmass modifier equation

Module azimuth [°]

Module altitude [°]

Sun azimuth [°]

Sun altitude [°]

Angle of incidence [°]

Diffuse horizontal irradiance [W/m^{2}]

Direct normal irradiance [W/m^{2}]

_{EX}

Hourly extraterrestrial solar radiation on horizontal surface empirical formula [W/m^{2}]

Band gap energy of the semiconductor [ev]

Energy [Wh]

Equivalent sun hours [h]

Yearly electricity yield [Wh/year. K

Fill factor [–]

Surface irradiance of the cell [W/m^{2}]

Total irradiance on module affected by airmass modifier [W/m^{2}]

Albedo irradiance [W/m^{2}]

Diffused irradiance [W/m^{2}]

Direct normal irradiance [W/m^{2}]

Total irradiance on square meter of the panel [W/m^{2}]

Irradiance under STC (=1000 W/m^{2})

Global horizontal irradiance [W/m^{2}]

Hourly index of the airmass modifier

Diode saturation current [A]

Nominal saturation current [A]

Short circuit current of the solar cell [A]

Short circuit current per cell at STC [A]

_{mp}

Max. power point current [A]

Boltzmann’s constant [J · K^{−1}]

Temperature coefficient of short circuit current [%/°C]

Temperature coefficient of open-circuit voltage [%/°C]

Airmass modifier

Diode ideality factor

_{max}

Maximum output power [W]

Power [W]

Power factor

_{s}

Series resistance of the solar cell [

_{sh}

_{b}

Shunt resistance of the solar cell [

Actual sun-Earth distance [m]

Sky view factor

Extraterrestrial radiation on horizontal plan [Wh/m^{2}]

Absolute temperature in Kelvin [k]

Thermal voltage of the diode [V]

_{oc}

Open circuit voltage of the solar cell [V]

_{mp}

Max. Power point voltage [V]

Albedo coefficient

Module tilt angel in degrees [°]

Number of modules [-]

Baseline Surface Radiation Network

Data acquisition system in LabVIEW application

Direct current

Egyptian Pound

Current voltage curve

_{p}

Kilo watt peak system size

Matrix laboratory

North African Nations

Photovoltaic

Photovoltaic Geographical Information System of European Union

Photovoltaic software PC package

Standard test conditions at which (

Solar water pumping system

A typical meteorological year

Levelized cost of electricity [€/KWh]

The authors acknowledge the supervising professors, the reviewers, the journal editorial Board, for providing valuable comments and helpful suggestions to improve the manuscript.

The authors received no specific funding for this study.

The authors declare that they have no conflicts of interest to report regarding the present study.