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Basic Modelling of a Residential Photovoltaic System

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This paper provides a brief description of the workings of a residential solar system with the aim to create basic mathematical models of said system and compare the results with a real-life example, to analyze how accurate are the models in comparison to reality, using knowledge acquired during the Systems Engineering class of the Mechatronics Engineering undergraduate course at Óbuda University.
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Basic Modelling of a Residential Photovoltaic
System
Alexandre Oliveira de Almeida
Don´
at B´
anki Faculty
´
Obuda University
Budapest, Hungary
aoalmeida@stud.uni-obuda.hu
Abstract
This paper provides a brief description of the workings of a residential solar system with the aim to create basic mathematical
models of said system and compare the results with a real-life example, to analyze how accurate are the models in comparison
to reality, using knowledge acquired during the Systems Engineering class of the Mechatronics Engineering undergraduate course
at ´
Obuda University.
Index Terms
Mathematical model, photovoltaic energy, residential photovoltaic system.
I. INTRODUCTION
With the rising concerns about climate change and carbon emissions, the search for alternate, non-polluting energy sources
has seen an increase, and, along with a worldwide rise in energy costs, many people are looking for an alternative system to
power their homes. The solar photovoltaic (PV) system is the most popular among residential users, mainly due to its easiness
of installation and use, and cost effectiveness. A report produced in 2014 by the International Energy Agency predicted that
the sun could be the largest electricity source by 2050 [1], with solar photovoltaic systems accounting up to sixteen percent
of the world’s electricty demand in the same timeframe.
The most basic PV system comprises of an array photovoltaic cells (solar panels), which generate energy through the
photovoltaic effect, and one or more inverters. Solar panels produce energy in direct current (DC) form, and in order for it
to be used it needs to be converted to alternating current (AC), hence the need of a PV inverter. The inverter will basically
convert the DC energy produced by the solar panels to AC, so that it can either be used by the household or fed back to the
grid, offsetting energy costs in the electricity bill, for example. The array of PV cells may be divided onto ”strings”, which
allow the inverter to better optimize the energy output from the panels. Other components may be added to the system, such as
the string-box, for example, used as a security device, composed of surge protectors and circuit breakers that offer short-circuit
and overcurrent protections. If an anomaly is detected by the string-box, the energy flow is stopped, preventing the anomaly
from damaging the inverter and other devices connected to the energy grid.
II. SY STE M ANA LYSIS
As stated in the previous section, a basic photovoltaic system has two main components: the photovoltaic cells and the
inverter. In order to analyze the system from an engineering point-of-view, the PV system will be simplified: we will ignore
the inverter and focus only on the solar panels, to create mathematical models that can estimate daily and monthly energy
production of the system. The inverter is left out of the analysis to simplify the modelling, as most solar inverters already have
a high efficiency (over 98%), which won’t impact the analysis of the system.
Fig. 1. Simple diagram of a PV system. Adapted from [2]
For comparing the accuracy of the models, we will utilize a real world PV system installed at the author’s household in Brazil.
It has an array of 23 solar panels, distributed along two strings, connected to two solar power inverters, which characterizes a
multiple input, multiple output system (MIMO). To simplify the modelling, the system used for analysis will consider a single
solar panel array and string consisting of 23 solar panels, characterizing a multiple input, single output system (MISO).
(a) Aerial view of the household (b) Overview of the electrical system
(c) Connections of the first inverter (d) String box of the first inverter
(e) Second inverter (f) String box of the second inverter
Fig. 2. Electrical installation of the PV system
A. Characteristics of the Real System
The real world system used for comparison is composed of an array of 23 solar panels, 20 with a peak power of 360W and
three with a peak power of 400W, distributed along two strings, connected to two inverters. The solar panels are inclined at
a 19° angle, 19° East from the geographic North coordinate (azimuth). The efficiency of the solar panels at Standard Testing
Conditions (STC) are 18.51% and 19.90%, respectively.
By analysing the datasheet of both types of solar panels used in the system, it’s very important to note that the peak power
previously mentioned is measured at STC, which specifies an irradiance of 1000 W/m² and cell temperature at 25°C. The peak
power is slightly reduced at Nominal Operating Cell Temperature (NOCT), as this condition considers an irradiance of 800
W/m², cell temperature at 45°C, ambient temperature at 20°C and wind speed at 1 m/s. The peak power values for NOCT are
268W for the 360W modules and 297W for the 400W ones.
TABLE I
SOL AR PAN EL S PARAMETERS
Module 360W (STC) 360W (NOCT) 400W (STC) 400W (NOCT)
Max Power – Pmax (W) 360 268 400 297
Open-circuit Voltage – VOC (V) 47.4 43.8 48.7 46.48
Short-circuit Current – ISC (A) 9.7 7.8 10.79 8.35
Voltage at Max. Power – Vmp (V) 38.5 36 40.70 37.73
Current at Max. Power – Imp (A) 9.36 7.46 9.84 7.88
Module Efficiency – η(%) 18.55 — 19.90 —
With the peak power values at NOCT, it is also possible to calculate the efficiency at the same conditions, using simple
rules of three, as seen in (1) and (2).
η360W=268W·18.51%
360W= 13.78% (1)
η400W=297W·19.90%
400W= 14.78% (2)
B. Mathematical Models
As this paper focuses on creating basic models to help estimate daily and monthly energy production of a PV system, the
ambient temperature, wind speed and cell temperature variables included in NOCT conditions will be skipped. Although some
previous Physics knowledge may be required to create the mathematical models, the analyzed system can be considered as a
gray box type, as we have some knowledge of the inner workings of the system. As previously mentioned, the analyzed system
consists of a solar array of 23 solar panels. It is worth to simplify it even more, considering the solar array of 23 panels as a
single entity; in other words, the system used will be a single input, single output system (SISO).
One frequently asked question by owners of PV systems is ”How much energy will the system produce in a month?”. In
order produce an answer to this question, we propose the creation of a very simple mathematical model that can estimate the
monthly production of a photovoltaic system. This model will be expressed by (3). The dimensional analysis can be seen in
(4).
Emonth =H·A·n·ηavg (3)
where: H =the average solar irradiation of the month
A=the solar panel surface area,
n=the number of solar panels,
ηavg =the average efficiency of the solar panels.
kW h =kW h
m2
·m2
·n1
1
·ηavg
1
1(4)
The average efficiency of the solar panels can be calculated using a weighted arithmetic mean, as there are two types of
modules being used in the system. The result is 18.73% for STC and 13.91% for NOCT and can be verified in (5) and (6).
ηavg =20 ·18.55% + 3 ·19.90%
23 = 18.73% (5)
ηavg =20 ·13.78% + 3 ·14.78%
23 = 13.91% (6)
At first sight, it may seem that the model is missing an important piece of information: the peak power output of the
solar panels. Honsberg and Bowden [3] have shown that the efficiency is a function of the maximum power of the solar cell
multiplied by its fill factor, where the fill factor is defined as the ratio between the maximum power output of the solar panel
and the product of its open-circuit voltage and short-circuit current. Therefore the peak power output parameter can be replaced
by the efficiency of the solar cells.
The final missing piece of the model is the solar irradiation parameter (H), which can be obtained through the PVGIS
application [5] or through the CRESESB website [6]. We will utilize both to compare how accurate they are in relation to the
real world PV system.
To obtain the solar irradiation from PVGIS, we need to set a few parameters in the application. The location of the house
is selected in the map. Then we have to choose the solar radiation database from two options: the National Solar Radiation
Database (NSRDB), by the National Renewable Energy Laboratory, or the SARAH database, provided by the EUMETSAT
Climate Monitoring Satellite Application Facility (CM SAF). Other than operational differences, the NSRDB provides data up
until 2015, whereas the SARAH database provides data until 2016. Both databases will be used.
After selecting the database, we have to select the date range of the irradiation data and define the data we want to receive.
The date range will be the latest year where data is available (2015 for NSRDB, 2016 for SARAH), and we will check ”Global
horizontal irradiation” and ”Global irradiation at angle”, with a value of 19° for the latter setting.
Fig. 3. Configuration settings of the PVGIS application
For the CRESESB website, a similar approach is needed. The coordinates of the location are inserted into the website and it
gives us three databases to choose from. The selected database will be ”Belo Horizonte”, and the options ”Plano Horizontal”
(horizontal plane) and ” ˆ
Angulo igual a latitude” (plane with angle corresponding to latitude) will be checked.
Fig. 4. Data output from CRESESB
With the data in our hands, it is now possible to create the graphs for the mathematical model.
(a) Daily average (b) Monthly sum of daily average
Fig. 5. Solar irradiation obtained from the databases
(a) Daily production average (b) Monthly production
Fig. 6. Comparison of estimated and real energy production
Fig. 7. Comparison of STC and NOCT efficiencies for estimating monthly production
It is important to note that the different databases have different solar irradiation values for every month. By comparing
with data obtained from the inverters of the PV system during the months of September to November of 2021, we can see
that only in September the production output surpassed the estimates, whereas it was lower in the other months. Finally, the
difference between the NOCT and STC efficiencies when applied in the mathematical model can be seen in Fig. 7.
The resolution for the year 2021 is not ideal, so we need to adjust some parameters of the model. The PV system had 20
360W and three 400W solar panels installed in September 2021; the 400W solar panels were installed during the month of
September 2021. Therefore, to facilitate the analysis of the accuracy of the mathematical model, the amount of solar panels
will change from 23 to 20, and the average NOCT efficiency will be set to that of the 360W solar panels, 13.91%, as defined
in (1).
(a) Daily production average (b) Monthly production
Fig. 8. Comparison of estimated and real energy production from August 2020 to August 2021
TABLE II
VARIA NC E PE RC EN TAGE O F RE AL WO RL D SY ST EM DATA CO MPA RE D TO MATH EM ATIC AL M OD EL S
Month PVGIS-SARAH PVGIS-NSRDB CRESESB
Aug ’20 3.4940 -4.1263 7.3079
Sep ’20 18.1239 12.05983 17.0403
Oct ’20 -7.4364 -11.9295 -3.2554
Nov ’20 5.1980 -1.6368 3.3809
Dec ’20 -0.3427 -4.8985 1.4228
Jan ’21 13.5045 8.4057 18.5277
Feb ’21 -25.6387 -24.4442 -24.4927
Mar ’21 7.7725 11.6612 15.8998
Apr ’21 10.8877 12.6584 14.0330
May ’21 2.8660 -2.4273 10.8621
Jun ’21 3.2593 -2.9969 4.8557
Jul ’21 7.9951 3.7680 17.0490
Aug ’21 5.7278 -2.0570 9.6240
We can calculate the average yearly solar irradiation by performing a sum on the monthly average values. It is also possible
to obtain the average ”real” irradiation by manipulating (3), as seen in (7).
H=Emonth
A·n·ηavg
(7)
TABLE III
AVERA GE YE AR LY SO LA R IR RAD IAT IO N (IN KWH/M² )
PVGIS-SARAH PVGIS-NSRDB CRESESB Real system
2181.3300 2269.0200 2108.7000 2060.0210
The variance of the ”real” irradiation compared to the data obtained from the PVGIS and CRESESB databases can be
calculated with the average yearly solar irradiation, as seen in Tab. IV.
TABLE IV
VARIA NC E PE RC EN TAGE O F RE AL S OLA R IR RA DI ATIO N CO MPA RE D TO VALU E OB TAI NED F RO M DATABAS ES
PVGIS-SARAH PVGIS-NSRDB CRESESB
-5.5612 -9.2110 -2.3085
C. Model Review
In the first revision of this paper, the databases utilized were a bit outdated, as the solar irradiation data they provided
were from 2015 and 2016. After some research, it was possible to obtain more recent data, from the Instituto Nacional de
Meteorologia (INMET) database [7]. The data utilized from INMET dates from 2020, thus it was possible to analyze the
production data in 2020 from the PV system. The previous modifications made to the base system are still in place.
However, the data provided by INMET does not consider the inclination angle of 19° of the solar panels. It is safe to assume
that the solar irradiation data considers a horizontal plane. Therefore, to use apply these new values in the model, a conversion
is needed. Luckily, Honsberg and Bowden [8] provide some equations to find the solar irradiation in an inclined plane, as seen
in (8), (9) and (10).
α= 90 ϕ+δ(8)
where: α=the elevation angle of the Sun,
ϕ=the latitude,
δ=the declination angle.
δ= 23.45sin 360
365(284 + d)(9)
where: d =the day of the year.
Hmodule =Hhorizontal ·sin (α+β)
sin α(10)
It is needed, however, to modify (8) to consider the Southern Hemisphere, as seen in 11.
α= 90 + ϕδ(11)
where: ϕ=the latitude, relative to the North (negative values for Southern Hemisphere).
With this modification in place, we need to calculate the average irradiation for every month by using a simple sum. After
calculating the average monthly irradiation, it is now possible to apply these values to the model, as seen in Fig. 9.
(a) Daily production average (b) Monthly production average
Fig. 9. Comparison of estimated and real energy production during the year of 2020.
III. CONCLUSION
The mathematical model is not always accurate, as shown by the data previously presented. In the best case scenario for
the first revision of this paper, the model produced a result 2.31% higher than the real data, and in the worst case it is off by
9.21%. Depending on the database chosen, the model can be considered accurate. There is also the weather factor, as weather
is, most of the times, unpredictable, as seen in the off curve point in February 2021 in Fig. 8 (a), where it was very cloudy
and rainy month that could not be predicted by the aforementioned databases.
In relation to the most recent data obtained from INMET, the resolution is much better than the previous databases used in
the model. There are also off the curve points, such as the production in February 2020, which was a very rainy month as
well, as seen in Fig. 9 (a). The differences can also happen due to the location from which the solar irradiation was measured,
as the solar irradiation may be higher or lower depending on where it is measured, mainly due to cloud coverage, which the
data provided does not consider.
In the future, the model could be reworked to include the efficiency lost due to the rise in temperature of the solar cells.
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ResearchGate has not been able to resolve any citations for this publication.
Full-text available
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The first edition of the Brazilian Solar Energy Atlas was launched in 2006 based on ten years of data from the GOES series satellites and the BRAZIL-SR physical model of radiative transfer, validated with data observed at 98 weather stations operated by INMET (National Institute of Meteorology) and spread throughout the country. At the time of the launch, the SONDA network (Sistema de Organização Nacional de Dados Ambientais), operated by INPE, had recently entered into operation and contributed to the validation process with only three years of solarimetric data of the three components of solar irradiation on the surface: global horizontal, direct normal and diffuse. This pioneering edition of the Atlas was an important milestone in the history of solar energy in Brazil and is still employed today by several researchers and entrepreneurs in solar energy. After more than ten years, the Center for Terrestrial System Science (CCST) of the National Institute for Space Research (INPE), through its Laboratory for Modeling and Studies of Renewable Energy Resources (LABREN), is pleased to publish the second edition, expanded and revised, of the Brazilian Atlas of Solar Energy. It is an example of cooperative work between INPE and researchers from various institutions in Brazil: the Federal University of São Paulo (UNIFESP), the Federal University of Santa Catarina (UFSC), the Federal Technological University of Paraná (UTFPR), and the Federal Institute of Santa Catarina (IFSC). For this new edition, more than 17 years of satellite data have been used. Several advances have been implemented in the BRAZIL-SR radiative transfer model's parameterization, aiming to improve further the reliability and accuracy of the database produced and made available for public access. In addition to these advances, the new version contains analyses on confidence levels, on the spatial and temporal variability of the solar resource, and presents scenarios for the use of various solar technologies. Although the focus of the Atlas is on the energy area, the The data presented also serves users in several other areas of knowledge, such as meteorology, climatology, agriculture, hydrology, and architecture. This Atlas had the National Institute of Science and Technology for Climate Change (INCT-MC) scientific contribution through processes CNPq 573797/2008-0 and FAPESP 2008/57719-9, which supported the research phase, consolidation, and its final assembly. We can also thank and share this moment with the Petrobras Research Center (CENPES) which, through the ANEEL PD-0553-0013/2010 Project with INPE, provided the crucial financial support for the improvement of the BRAZIL-SR model, as well as for the expansion, operation, and maintenance of the SONDA solarimetric network. The credits are also addressed to the Brazilian Research Network on Global Climate Change, through the FINEP / Rede CLIMA 01.13.0353-00 agreement, for the support in the rounds phase of the model, and to other INPE colleagues, particularly the Laboratory of Meteorological Instrumentation (LIM), the Center for Weather Forecasting and Climate Studies (CPTEC), which provided logistical support to this work since the first edition of the Atlas. We are also grateful for the institutional support of the National Electrical Energy Agency (ANEEL) and the International Solar Energy Society (ISES) for recognizing the scientific merit of this publication as an important milestone for the penetration of solar technology in Brazil. Translated with www.DeepL.com/Translator (free version)
Photovoltaics Education Website
  • C B Honsberg
  • S G Bowden
C. B. Honsberg and S. G. Bowden. (2019) Photovoltaics Education Website. [Online]. Available: https://www.pveducation.org
Units and symbols in solar energy
"Units and symbols in solar energy," Solar Energy, vol. 61, no. 1, pp. III-IV, 1997. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0038092X97900165
Instituto Nacional de Meteorologia (INMET)
  • Dados Históricos
Dados Históricos. Instituto Nacional de Meteorologia (INMET), 2020. [Online]. Available: https://portal.inmet.gov.br/dadoshistoricos
Solar Radiation on a Tilted Surface -Photovoltaics Education Website
  • C B Honsberg
  • S G Bowden
C. B. Honsberg and S. G. Bowden. (2019) Solar Radiation on a Tilted Surface -Photovoltaics Education Website. [Online]. Available: https://www.pveducation.org/pvcdrom/properties-of-sunlight/solar-radiation-on-a-tilted-surface