Receipt date: 04/05/2021 / Revision date: 06/15/2021 / Acceptance date: 06/30/2021

Introduction

Currently, world energy consumption has had a growth of 13,975 Mtoe, where 73% comes from fossil sources and 27% from renewable energies (ENERDATA, 2020), reaching a world energy consumption increase of 2.35% in 2018 (World Energy Markets Observatory, 2019), and in Mexico at the beginning of 2018 there were only 6,464 million barrels of oil as proven reserves (Kühne et al., 2019), which are enough for 9 years according to current consumption. Additionally, the increase in the cost of electricity tariffs over the last 10 years (Flores Contreras, 2018, p. 32), makes it necessary to look for electricity saving alternatives that reduce consumption and CO2 emissions.

Under these conditions, although some countries in various regions of the world are already implementing programs that contribute to energy sustainability and sustainability through renewable energies, there is still much area of opportunity, particularly in Mexico due to its high levels of solar radiation throughout its territory according to data from the Geographic Information System for Renewable Energy in Mexico and the Solar Radiation Observatory of the Institute of Geophysics of the UNAM (Institute of Electrical Research, 2010). Observing irradiation values in most of the states above 5 kW/m2 reaching maximum values of 6.89 kW/m2, reflecting the large amount of solar resource available during most of the year. This competitive advantage of Mexico motivates the generation of electric energy through the implementation of distributed generation photovoltaic solar systems.

In addition, “grid parity” has now been achieved, which means that photovoltaic systems (PVS) are economically and environmentally competitive and superior to conventional energy production systems.

Global warming, environmental pollution, and the growing need for energy demanded by the regions due to population growth and technological development, forces the search for solutions that ensure its availability at competitive prices. Particularly the case of Nuevo Laredo, located in the region of the Burgos basin, where there are large deposits of gas and oil, which to extract it the companies demand a great amount of energy and motivates the development of this type of studies. For this reason the government of Tamaulipas is very interested in training highly qualified human capital to carry out studies and energy projects, provide energy to companies that could be installed in the city with the objective of extracting these resources from the subsoil.

As an answer to the described problem, the development of a descriptive energy model is proposed to determine the feasibility of installing, interconnecting to the grid, and optimizing distributed generation PV systems in any region of the world. This is achieved by analyzing the input/output variables of PV systems with capacities from 1 kW and less than 0.5 MW, identifying the types of photovoltaic (PV) installations, considering the sizing of components, materials, and PV equipment, as well as human, material, and financial resources and the applicable specifications and regulations.

There is currently no methodology that encompasses and describes in a single block the most relevant aspects considered in PVS design, even though there is information from various sources such as studies, projects, technical reports, theoretical models, doctoral theses, and other sources on related topics. Most of them deal with specific topics without a global vision of the factors involved, which makes it possible to exclude important variables or aspects that should be considered, since omitting them would result in an incomplete final design that would affect the operation of the PV system.

Table 1 mentions research works taken as a reference and describes the additional aspects considered in the proposed energy model that are not addressed in these works and that are relevant to consider in the design and commissioning of distributed generation interconnected PV systems (IPVS).

Method

Design

The design and development of the energy model was defined through conceptual research, studies, measurements, and analysis of PV projects, integrating all this information into a single functional block. The model relates various aspects and input/output variables involved in the generation of PV electric energy, describing how each variable affects the PV generation process, allowing to reduce the effects and improve the overall efficiency of the PV system.

The orderly and methodological application of the model allows obtaining, plotting, analyzing, and comparing the values of the variables of the region under study, with the applicable standards and the specifications of PV component manufacturers, including the sizing of such components and interconnection regulations.

The model methodology involves preparing a technical report with the results obtained, which allows inferring whether or not it is feasible to install PVS in a particular region, or if it is possible to optimize the generation of energy from PVS in case it is operating.

As a particular case, the model is applied to demonstrate that in Nuevo Laredo, Tamaulipas, Mexico, the solar resource, meteorological parameters, and other components included in the model, are adequate to install distributed generation PVS, also promoting the use of renewable energy and mitigating the environmental pollution of the city, due to the daily crossing between Mexico and the United States of more than 12,930 cargo trucks (Duarte, 2017) that consume highly polluting diesel. Nuevo Laredo is located on the southern bank of the Rio Bravo border boundary between Mexico and the United States of America, with 405,000 inhabitants according to the 2018 census. The climate is the driest and most extreme in the state, an average annual temperature of 22.6 °C with large oscillations ranging from 2.5 °C in winter, to 40.50 °C in summer; its average annual rainfall is 472.5 mm and the prevailing winds come from the south.

For the study, two PV systems are considered, one of 3 kW and the other of 4 kW located in different parts of the city; both PV systems use polycrystalline technology and central inverters of the mentioned capacities.

Participants.

The human resources, composed of research professors, students, and through the support of some government agencies to provide information and private owners of the PVS used in the study. Additionally, the facilities of the Universidad Tecnológica de Nuevo Laredo to perform tests on the PVSs and measurements with laboratory equipment.

Instruments.

The variables defined in the model are collected using ad hoc formats and tested with the instruments shown in Table 2:

Data analysis

The data of each variable measured in situ are plotted for comparative analysis and interpretation, observing monthly and annual trends, inferring the degree of affectation in the generation of PVS and proposing actions for improvement of the system under study, the analysis allows:

1. Observe trends, behaviors, averages, ranges, and limits.
2. Establish comparisons with applicable standards and manufacturers' specifications to optimize the PVS.
3. Suggest improvement actions, proposing equipment and materials that will withstand the climatic conditions of the region, ensuring a good performance during the PVS's useful life.
4. Issue recommendations or redesign the PVS to operate optimally.

Variables

The variables described in the model are analyzed and plotted to determine their impact on PV generation, the data obtained with measurement instruments and those obtained from official sources such as RetScreen, Meteonorm, CONAGUA, or other sources available on the internet, are recorded in ad hoc tables.

The variables defined in the model are of the quantitative type (Hernández Sampieri et al., 2010), multiple measurements are taken at various times, being analyzed graphically to know their impact and take actions to optimize PV generation, as mentioned in related studies (Caamaño Martín, 1998) and (García Barrios, 2018). Similar studies analyze environmental variables and their effects on PVS (Vigil Galán et al., 2018).

PVS input variables

These are meteorological and solar irradiation variables available in the region under study, analyzed during the design stage of the PV system to know their tendency and that can be determinant to install PV systems in a certain location, refer to Table 3.

PVS output variables

The values are obtained when the PVS is operating, their analysis allows adjustments to be made for optimum performance. These variables are shown in Table 4 indicating their units of measurement, the monitoring period, the source of the measurements, and providing a brief explanation on the interpretation, analysis, and use of the data.

Results

The design and development of the energy model is based on a series of functional blocks composed of solar irradiation, climatological and electrical variables, sizing of PV components, applicable regulations, manufacturers' specifications, types of users, human, material, financial and technological resources, all compiled from various bibliographic sources and integrated into a single schematic diagram of information shown in Figure 1.

The model developed in addition to considering the analysis of input/output variables, also includes the study of other complementary aspects such as government regulations for grid interconnection, allowing a complete view of the inherent aspects that affect and/or benefit PVS performance coinciding with the study (Masa Bote, 2014) and complementing with the analysis of additional variables such as WS, RH, Pa, and PP.

Through the methodological application of each block and analyzing the trend of the variables referred to in the model, it can be inferred whether the region under analysis located anywhere in the world meets the necessary and sufficient conditions to install PV systems to the distributed generation network. This allows for improvement actions to be taken to the PV systems in operation if the results show vulnerable conditions that were not considered during the design stage of the system or due to the premature degradation of its components. Each block can be broken down according to the flow diagram in Figure 2 and existing calculation tools or software can be used to facilitate the analysis of the PVS. The function of the main blocks of the proposed model is described in the following sections.

1. Block description of the descriptive energy model

Figure 1 shows all the blocks involved in the solar photovoltaic electric power generation process, describing the function and impact of each one. It is important to point out that the correct application of the model implies considering the analysis of all the blocks avoiding omitting any of them, allowing to obtain a complete result that reflects the reality of the energy conditions of the region and/or the current PV system, which will determine the feasibility of installing new PV systems, or allow improvements to the existing ones.



Figure 1. Descriptive model of a grid-connected solar photovoltaic energy system.
Note: Source: Own elaboration.

2. Type of photovoltaic system

There are two relevant characteristics in every PVS depending on its connection to the installation that will feed the user's loads, being necessary to define whether the system will be autonomous or interconnected to the grid, since the components and sizing of the PVS will be different depending on each case.

3. Skilled labor, companies, and PVS suppliers

The methodology establishes the importance of having specialized labor in the installation of PV systems, and it is convenient to check the availability of certified personnel in the region. Likewise, the local existence of installation service companies or suppliers of PV equipment and materials is relevant in order to obtain a better cost-benefit and post-service during the operation of the PV system and to facilitate the application of guarantees.

4. Use of specialized software

Although the model establishes the measurement of variables in situ, it also considers the use of historical data of these variables obtained from a database using free or licensed software, allowing averages to be obtained between these values and those measured in situ, which is why the model's methodology refers to their use.

5. PVS sizing in alternating current and direct current (AC/DC)

The PV design in commercial, industrial, or residential applications must consider DC or AC loads, since if only DC loads will be fed, it is not necessary to install an inverter because they can be connected directly to the DC circuit. If the PVS will feed AC loads, the inverter must be sized correctly. Using software or manual calculations, the PVS components are sized as listed in Figure 1 (modules, batteries, controller, inverter, wiring, protection systems, electrical and mechanical structures, and accessories).

6. Design based on the regulations applicable to PVS

This block highlights the importance of designing PV systems in accordance with the applicable standards according to the region or country where they will be installed, including both national and international standards applicable to equipment, materials, the level of distortion allowed for electrical variables, including the personal safety standards that must be observed when installing the PV system. The consideration of all applicable standards ensures reliability and functionality of the project, facilitating the registration and connection to the PV system network by complying with all the requirements required by governmental agencies.

7. PVS location data on the Earth (latitude, longitude, and altitude)

It is important to define the place where the VFS will be installed, being necessary to know the geographic coordinates of the place defined by latitude, longitude, and altitude. Particularly for Nuevo Laredo we have the following values:

North Latitude: 27º29'48'' (27.43º),

West Longitude: 99º30'01'' (-99.56º),

Altitude=138 meters above sea level (masl).

These three parameters are constant values used to locate a point on Earth; however, for the exact choice of the location either at ground level, on the roof, or on facades, it implies considering other factors described in section h.

8. For greater solar gain

The model suggests considering the main variables that affect the capture of solar radiation during the operation of the PVS, some of them being the inclination of the PVM according to the season of the year and user needs, and the orientation towards the south for systems located in the northern hemisphere or oriented towards the north for PVS located in the southern hemisphere.

Another variable that affects the collection is the projection of shadows on the PVM, being necessary to carry out a shadow study to analyze possible affectations. Specialized software is available to estimate the losses caused by shading of the PVMs, in case they cannot be completely avoided due to physical obstacles such as buildings or trees.

9. Type of users

Considering that this research refers to the study of PV systems with capacities of less than 0.5 MW of distributed generation, it is important to distinguish that the largest market of users for the installation of PV systems comes from residential users and small or medium-sized businesses. For this reason, government support programs to promote renewable energies should be focused according to the type of users to which they are directed.

10. Analysis of solar resource and meteorological variables

This relevant block of the model includes the data collection and analysis of each variable of the region under study as indicated in the flow chart in Figure 2, particularly exemplified for Nuevo Laredo.



Figure 2. Flow diagram of a grid-connected PV energy system.
Note: Source: Own elaboration

j1: Solar irradiance (G)

Obtaining this variable according to the methodology described in previous sections shows that Nuevo Laredo has an average irradiation of 4.81 kWh/m2 during the year, exceeding this value in the months of May to August with values of up to 5.19 kWh/m2 according to (Electric Research Institute, 2010).

Figure 3 shows the solar irradiation values measured in situ and those obtained using the RETScreen and Meteonorm software in monthly averages, showing a data range of 0.70 kWh/m2 during the 12 months of the year. Figure 3 shows the congruence of the data from the sources referred to, which can be used to calculate PVS production, reaffirming the great potential for global and diffuse radiation and albedo in the region (De Juana Sardón et al., 2009).

Figure 3 also shows solar radiation readings taken in situ during the 12 months of the year under clear to slightly cloudy sky conditions, using the Amprobe Solar-100 meter. Average values of 940 W/m2 and 1,163 W/m2 measured at 27º and perpendicular to the sun respectively were observed, higher than the STC (standard test conditions) parameter of 1,000 W/m2 used by manufacturers to test the efficiency of the PVMs and a NOCT (nominal operating cell temperature) of 800 W/m2 (Messenger and Ventre, 2005).

In regions with high solar irradiation, cells with medium performance and affordable cost technologies can be used (Vigil Galán et al., 2018), still maintaining high levels of generation motivating local investment in PV projects by installing PVS with good performance and of lower cost. PV technologies with high yields are more expensive and are recommended in places with low levels of solar irradiation.

To install stand-alone PVSs, an average solar radiation of 3 kW/m2/day to 4 kW/m2/day is required and, for PVSs interconnected to the grid, a solar radiation higher than 4 kW/m2/day (Vanegas Chamorro et al., 2015), a condition that is met for Nuevo Laredo.

Figure 3. Solar irradiance and radiation measurements
Note: Source: Own elaboration with data from RetScreen software, Meteonorm, and in situ measurements.

j2: Temperature (T)

The obtaining of this variable according to the methodology previously described is shown graphically in Figure 4, reaching maximum average temperatures of 40.50 ºC in the hottest months and 2.5 ºC in the coldest months, causing possible effects on PV generation if the selection of PV components is not adequate, since at operating temperatures above 25 ºC, appreciable losses are generated in the performance of the cells (Mazón Hernández, 2014).

The actual operating temperature of a PVM normally reaches between 15 ºC and 20 ºC above ambient temperature (Pérez Regalado, 2010), making it necessary to use PVMs with high thermal coefficients to withstand the temperature level of the region. High temperatures reduce the voltage and power generated and, at temperatures of zero or below 0 ºC the voltage generated increases according to a factor specified in article 690.7 of the National Electric Code by up to 20%, which can affect the operation of the inverter and cause failures of the entire PVS. There is a wide range of PVM and commercial inverters with temperature coefficients wide enough to be used in every region and affect the performance of the PVS to a lesser degree.

Figure 4. Tendencia mensual de la temperatura.
Note: Source: Own elaboration with data from EMAINL and in situ measurements.

Figure 5 shows that 47% of 42 PVM manufacturers analyzed have a power temperature coefficient (Pmax) higher than 0.4%/oC, meaning that for every degree Celsius above 25 ºC in the cell working temperature, 0.4% of the nominal power generated by each PVM cell is lost.

Figure 5. PVM temperature coefficients (Isc: short circuit current; Voc: open circuit voltage)
Note: Source: Own elaboration with data from manufacturer's data sheets.

It is recommended to use modules with low power temperature coefficients, avoiding those with higher coefficients since they present higher losses reducing power. For this reason, it is suggested that modules with temperature coefficients of -0.4%/oC or lower be used in Nuevo Laredo, guaranteeing better performance. The nominal operating temperature of the PVMs also affects their performance. Figure 6 shows that 78% of the PVMs of the 42 manufacturers mentioned above withstand operating temperatures from -40 ºC to +85 ºC, 5 % from -40 ºC to +80 ºC, and 17 % withstand operating temperatures from -40 ºC to +85 ºC.

According to the temperature analysis shown above, it is observed that the PVMs of the 42 manufacturers comply with the temperature parameter required for Nuevo Laredo, even if the module temperature reaches 60.50 ºC (40.50 ºC +20 ºC), a value within the operating range that any PVM can withstand as shown in Figure 6, a condition that is also widely met for temperatures below zero.

Figure 6.PVM operating temperature
Note: Source: Own elaboration with data from manufacturer's data sheets.

j3: Wind speed (WS)

Wind speed and its mechanical force of impact (Reguera Gil, 2015) is another relevant variable to be considered when planning and developing distributed or centralized photovoltaic generation projects; this variable is obtained according to the methodology previously described in this article. Figure 7 shows that the average maximum WS oscillates between 39 km/h with maximum gusts of up to 55 km/h, still being a moderate speed without major risk as a mechanical force of impact on the PVMs and their structures, since it is within the limits indicated by the manufacturers, acting only as a natural cooling system of the PVS when its speed increases (Mazón Hernández, 2014).

Knowing the average WS and the maximum gusts of the region, in this case for Nuevo Laredo, they are compared with the specifications of different PVMs in terms of their capacity to withstand wind loads. Figure 8 shows the load capacity caused by wind and snow forces supported by the PVMs according to the specifications of 42 manufacturers, showing that they support wind forces from 2,400 Pa to 5,400 Pa, in accordance with the European standard IEC 61215, which indicates a standard value of 2,400 Pa. The maximum value of 5,400 Pa is relevant in areas where the WS is very high during the year, which is not the case for Nuevo Laredo. For these WS, the use of anodized aluminum structures is recommended for mounting the PVMs due to their high resistance to mechanical strength and corrosion.

Figure 7. Average, maximum wind speed and maximum wind gusts.
Note: Source: Own elaboration with data from EMAINL and in situ measurements.

Figure 8. Resistance to mechanical loads of PVMs
Note: Source: Own elaboration with data from manufacturer's data sheets.

J4: Relative humidity (RH)

Figure 9 shows the monthly and annual behavior of relative humidity in Nuevo Laredo with data taken in situ and from the Nuevo Laredo international airport weather station (EMAINL). It is observed that the general average is between 55% to 60%, which is acceptable due to the fact that Nuevo Laredo has an extreme dry climate with large temperature oscillations from -3 ºC in winter to 46 ºC in summer, with August being the hottest month and January the coldest month (Institute for Competitiveness and Foreign Trade of Nuevo Laredo, 2021). Although the maximum humidity value ranges between 85%, the frequency of occurrence per month is from 1 to 3 days and mostly occurs in the coldest months of the year, prevailing average RH values that do not cause premature oxidation or failure of the PVMs by potential induced degradation (PID) caused by moisture seepage in the encapsulated PVMs.

Figure 9. Relative humidity trend in Nuevo Laredo.
Note: Source: Own elaboration with data measured in situ and from EMAINL.

J5: Atmospheric pressure

The atmospheric pressure above sea level is 1,013.25 hPa and at higher elevations is reduced to less than 700 hPa, given that Nuevo Laredo has an altitude above sea level that varies from 138 m to 150 m, the effect is negligible and is not considered to have a major effect on the performance of the PVS to be installed in this region of Tamaulipas. Lowering the pressure below the local atmospheric pressure generates storms or bad weather, which is not typical in Nuevo Laredo where most of the time the weather is dry and with low precipitation.

Figure 10 shows a very stable trend in pressure variation which is largely attributed to the semi-desert climate in Nuevo Laredo, maintaining a monthly average of 997 hPa. This variable is not considered in the manufacturers' specifications because it does not directly affect the PVM, but rather through its effects, especially when it presents a very wide range of variation.

Figure 10. Atmospheric pressure trend in Nuevo Laredo.
Note: Source: Own elaboration with data measured in situ and from EMAINL.

J6: Rainfall Analysis

Precipitation in Nuevo Laredo varies from 550 mm to 600 mm (Tamaulipas Civil Protection, 2011), showing in Figure 11 that during the months of May to October precipitation is higher, reaching an annual average of 553.8 mm. Due to Nuevo Laredo's semi-desert climate, rainfall during most of the year is scarce with a daily average of approximately 45 days during a 20-year analysis period, i.e., only 12% of the days of the year are rainy.

Rain is a natural way of cleaning the PVMs, but when it is very frequent it causes oxidation in the support structures, for this reason, anodized aluminum is frequently used and, in rare cases, iron structures are used. According to this analysis, precipitation in Nuevo Laredo does not represent a major problem that causes premature oxidation or PID problems in the PVMs. However, to ensure long life, the use of anodized aluminum structures for the attachment of the PVMs is recommended.

Figure 11. Rainfall trend in Nuevo Laredo.
Note: Source: Own elaboration with data from different database sources.

J7: Hail and snow analysis

Figure 12 shows the trend of hail fall in Nuevo Laredo and the region, showing a low recurrence being a low risk variable; however, it is convenient that the PVMs meet certain standards to the impact force. In the referred figure, an annual maximum of 2 days per year is observed, being normally one day per year, even during several years there is no hail fall.

Figure 12. Annual behavior of hail fall in Nuevo Laredo.
Note: Source: Own elaboration with data from the National Meteorological System (SMN).

As a regional reference, the hail fall in 2 neighboring municipalities located less than 50 km away is plotted, showing a similar trend.

Nuevo Laredo is not a snowfall area and on rare occasions a light layer of snow has fallen, so its analysis is irrelevant.

J8: Peak solar hours

The PSH (Pérez Martínez et al., 2017) is a variable obtained from the solar irradiance of the region divided by the STC of the PVMs equivalent to 1,000 W/m2. The average PSH in Nuevo Laredo is obtained from the average insolation divided by the STC value resulting in 5.0 h according to monitoring data taken in situ during a period of 18 months and of 5.027 h in annual average according to SAM software (system advisor model) as shown in Figure 13, being both values similar and acceptable for modules with efficiencies higher than 16% to be installed in Nuevo Laredo.

Figure 13. Monthly and annual trend of PSH in Nuevo Laredo.
Note: Source: Own elaboration with data taken in situ and from SAM software.

J9: Correlation analysis between variables

The analysis referred to in the energy model considers reviewing the correlation between variables with the objective of analyzing the behavior of the PVS. Figure 14 shows the hourly electric power generation capacities of two PVSs used as a sample, located at a distance of 25 km from each other, during a sampling period from 31-May to 23-June-2020.

Figure 14. Comparison of meteorological variables and PV-generated energy.
Note: Source: Own elaboration with data from https://www.meteoblue.com/es/tiempo/archive/export/nuevo-laredo and Apsystem.

It is observed that as the irradiation increases so does the temperature and the energy generated in both PVS (Granda Gutiérrez et al., 2013); however, after a certain time of the day, although the temperature still remains high, the radiation is reduced and also the energy generated in both PVS in a proportional way. Proving that the energy generated depends directly on the radiation and, in this case, the temperature has a minor effect due to the fact that the temperature coefficient of the modules used is less than -0.4%/oC.

Discussion and conclusions

Discussion

The detailed analysis of the variables referred to in the proposed energy model allows considering the most important parameters when designing a PVS to ensure optimal generation, selecting the materials and equipment with the most appropriate specifications to withstand the environmental conditions of the region under study, and determining whether or not the site is viable for installing PVS.

The model considers most of the variables that affect or optimize PV power generation to ensure a robust, reliable design that guarantees a return on investment for residential, commercial, service, and industrial users through financial analysis (Caamaño Martín, 1998).

Table 5 shows the parameters obtained by applying the methodology according to the proposed model, noting that the site is viable for installing PVS.

By using this model as a methodological guide for the design and development of PV projects, the risks of system malfunction during its lifetime can be reduced by considering manufacturers' specifications. In the study "Improvement strategies for distributed electricity generation with solar, wind, or hybrid equipment" (Cadena et al., 2012), the feasibility of using renewable resources to cover the demand for electricity is mentioned by analyzing the available resource in the region; however, it only refers to certain variables involved in PV systems, which are already considered in the proposed model.

This model aims to bridge the existing gap between systems that integrate most of the conditions necessary to design and install PVS and the information from various studies that currently exist separately, i.e., the existing information is isolated and is not concentrated or integrated into a single functional block that allows a comprehensive or holistic view of the aspects to be considered.

The use of commercial or free software complements and facilitates the development of each block of the model, resulting in a final technical report that can be used to decide to install or improve the PV project under study.

Conclusions and future work

The analysis of each block of the proposed model applied to Nuevo Laredo, allows inferring that the region meets the necessary and sufficient conditions for the installation of PV systems according to Table 5. It is shown that most of the variables analyzed are within the acceptable ranges when complying with the commercial specifications of PV equipment, ensuring the feasibility of the project.

By breaking down and developing each block of the proposed model, performing the measurements and calculations of the indicated variables, comparing them with applicable standards and regulations according to the region or country, considering the specifications of PV equipment manufacturers, differentiating the types of users to perform demand planning studies (Falcón Roque, 2018), and correctly sizing the system, a robust and complete design that makes the development of the PV project viable will be ensured.

Additionally, the model requires in situ data collection of solar radiation, information that is lacking in most regions of Mexico, relying only on data obtained from measurement stations closer to the study area that are several kilometers away, or satellite data that have certain percentages of error (Grossi Gallegos, 1999). The study (Osseweijer et al., 2017) considers the importance of the involvement of industry (suppliers, consumers), academia (to conduct research and training of skilled labor) and, the government (to establish regulations) as stakeholders in the use and promotion of PVS, conditions that are included in the proposed study.

Currently, in Nuevo Laredo, there are more than 100 PV systems installed with capacities from 1 kW to 202 kW. However, there is still a captive market of 126,127 domestic users, 9161 in PDBT6 tariff (small demand in low voltage), 1754 with GDMTO6 tariff (high demand medium ordinary voltage), and 644 users in GDMTH6 (high demand medium hourly voltage) to implement distributed generation PV projects.

By developing and integrating in a final report all the aspects included in each block of the model and according to the flow diagram in Figure 2 previously mentioned, the results, in addition to being used for the purposes described, could also be used as a reference and complement for the development of future research that requires analyzing the variables and other characteristics and aspects inherent to PVS, resulting in more complete studies.

As future and complementary work to this research project, an abstract analysis of the variables and their mathematical representation as a function of those that are affected by the behavior of others is proposed, being an example the power generated from the PVS (P) as a direct function of the irradiation (G) incident on the PVMs and of the operating temperature of the system (T), that is, P→ (G, T). Further work should consider the use of equations for both sizing and calculation of meteorological and electrical variables.

The development of mathematical models on the degradation of PVMs (Reguera Gil, 2015), according to the region where they are installed, are also topics of interest and complementary to this project.

References

Becerra López, H., Agredano Díaz, J., y Huacuz Villamar, J. (2010). Guía de usuario Sistemas Fotovoltaicos Interconectados con la Red, Aplicaciones de Pequeña Escala (No. 1). Instituto de Investigaciones Eléctricas. https://www.ineel.mx/flipbook/libros/SFVI.pdf.

Caamaño Martín, E. (1998). Edificios fotovoltaicos conectados a la red eléctrica: caracterización y análisis. (Tesis doctoral). Universidad Politécnica de Madrid. http://oa.upm.es/1322/.

Cadena, C., Salazar, G., Javi, V., y Tilca, F. (2012). Estrategias de mejora para la generación de energía eléctrica distribuida con equipos solares, eólicos o híbridos. In IV Congresso Brasileiro de energía solar e V Conferencia de Latino-americana da ISES. Consejo Nacional de Investigaciones Científicas y Técnicas. https://ri.conicet.gov.ar/bitstream/handle/11336/15092/CONICET_Digital_Nro.17885.pdf?sequence=1&isAllowed=y.

Collado Fernández, E. (2009). Energía solar fotovoltaica, competitividad y evaluación económica, comparativa y modelos (Tesis doctoral). Universidad Nacional de Educación a Distancia http://www.ieec.uned.es/PersonalDIEEC/archivos/tesis-doctoral-eduardo-collado.pdf.

De Juana Sardón, J. M., de Francisco García, A., Fernández González, J., Santos García, F., Herrero García, M. A., y Crespo Martínez, A. (2009). Energías Renovables para el desarrollo (1ª. ed.). Paraninfo. Duarte, E. (2017, 23 mayo). Puente III de Nuevo Laredo, el gigante del comercio. T21mx. http://t21.com.mx/logistica/2017/05/23/puente-iii-nuevo-laredo-gigante-comercio.

ENERDATA. (2020). Anuario estadístico mundial de energía 2020: Cuota de energías renovables en la producción de electricidad. https://datos.enerdata.net/energias-renovables/produccion-electricidad-renovable.html.

Falcón Roque, E. J. (2018). Desarrollo de un modelo de planificación energética sostenible usando técnicas de optimización multicriterio: una aplicación a la provincia de Cajamarca, Perú. (Tesis doctoral). Universidad Politécnica de Madrid]. https://doi.org/10.20868/UPM.thesis.52109.

Flores Contreras, I. (2018). Nuevo esquema de tarifas eléctricas y su viabilidad en proyectos fotovoltaicos (CONERMEX, Ed.). https://www.conermex.com.mx/webinar/webinar-05-nuevo-esquema-tarifas%20ele%CC%81ctricas.html.

García Barrios, F. A. (2018). Efecto de los factores ambientales en la eficiencia de arreglos de paneles solares fotovoltaicos (Tesis de maestría). Universidad Autónoma del Caribe. http://hdl.handle.net/11619/3786.

Granda-Gutiérrez, E. E., Orta, O. A., Díaz-Guillén, J. C., Jiménez, M. A., Osorio, M., y González Albarrán, M. A. (Eds.). (2013). Modelado y simulación de celdas y paneles solares. In Congreso Internacional de Ingeniería Electrónica ELECTRO 2013. (17-22). Instituto Tecnológico de Chihuahua. https://www.researchgate.net/publication/265767005_MODELADO_Y_SIMULACION_DE_CELDAS_Y_PANELES_SOLARES?channel=doi&linkId=541ad2180cf25ebee988c4ec&showFulltext=true.

Grossi Gallegos, H. (1999, enero). Comparación de los valores satelitales del “surface solar energy (sse) data set versión 1.0” con datos de tierra de la red solarimétrica. Energías Renovables y Medio Ambiente, 6, 1-6. https://www.researchgate.net/publication/279752278.

Hernández Gallegos, R. (2017). Análisis de factibilidad para la instalación de un sistema de energía limpia mediante celdas fotovoltaicas para la alimentación eléctrica del edificio 4 en el ITSLV. (Tesis de Maestría). CIATEQ México]. http://ciateq.repositorioinstitucional.mx/jspui/handle/1020/97.

Hernández Sampieri, R., Fernández Collado, C. y Baptista Lucio, M. P. (2010). Metodología de la investigación.Mc Graw Hill.

Instituto para la Competitividad y el Comercio Exterior de Nuevo Laredo. (2020, febrero). Prontuario socioeconómico binacional 2020 Región Laredos (No. 4). http://www.iccedenuevolaredo.org/prontuario/condiciones.html.

Kühne, K., Sánchez, L., Roth, J., Tornel, C., y Gerasimchuk, I. (2019, junio). Más allá de los combustibles fósiles: Transición fiscal en México. https://www.iisd.org/system/files/publications/combustibles-fosiles-transicion-fiscal-en-mexico.pdf?q=sites/default/files/publications/combustibles-fosiles-transicion-fiscal-en-mexico.pdf.

Masa Bote, D. (2014). Contribución a la integración de sistemas fotovoltaicos conectados a la red eléctrica: recurso solar y predicción de generación. (Tesis Doctoral). Universidad Politécnica de Madrid]. http://oa.upm.es/29110/1/DANIEL_MASA_BOTE.pdf.

Mazón Hernández, R. (2014). Estudio de la influencia de la refrigeración con aire de forma natural e inducida en el comportamiento de instalaciones fotovoltaicas (Tesis Doctoral). Universidad Politécnica de Cartagena Departamento de Ingeniería Térmica y de Fluidos. http://hdl.handle.net/10317/4780.

Messenger, R. A. y Ventre, J. (2005). Photovoltaic Systems Engineering. CRC Press LLC.

Osseweijer, F. J. W., van den Hurk, L. B. P., Teunissen, E. J. H. M., y van Sark, W. G. J. H. M. (2017). A review of the Dutch ecosystem for building integrated photovoltaics. ELSEVIER, 111, 974-981. https://www.researchgate.net/publication/315976225_A_Review_of_the_Dutch_Ecosystem_for_Building_Integrated_Photovoltaics.

Pérez Regalado, M. J. (2010). Estudio termográfico de la influencia de la temperatura en los módulos fotovoltaicos. (Tesis de Ingeniería). Universidad Carlos III de Madrid]. https://docplayer.es/8249240-Universidad-carlos-iii-de-madrid.html.

Pérez Martínez, M., Morales Rodríguez, I. C., y Castro, E. (Mayo, 2017). La hora solar pico equivalente, definición e interpretación. Revista de Ingeniería Energética, XXXVIII(2), 126-131. https://rie.cujae.edu.cu/index.php/RIE/article/view/506.

Protección Civil Tamaulipas. (2011). Atlas de riesgos del Municipio de Nuevo Laredo. https://www.tamaulipas.gob.mx/proteccioncivil/wp-content/uploads/sites/36/2011/12/ATLAS-DE-RIESGOS-DE-NUEVO-LAREDO.pdf.

Reguera Gil, F. J. (2015). Análisis de la degradación de módulos fotovoltaicos. (Tesis de Maestría). Universidad Internacional de Andalucía]. http://hdl.handle.net/10334/3524.

Sol Energy (Febrero, 2018). Degradación en Paneles Solares por Potencial Inducido (PID). https://www.solenergy.mx/es/principal/blog/degradacion-en-paneles-solares-por-potencial-inducido-pid#.

Vanegas Chamorro, M., Villicaña Ortíz, E., y Arrieta Viana, L. (2015, junio). Cuantificación y caracterización de la radiación solar en el departamento de La Guajira-Colombia mediante el cálculo de transmisibilidad atmosférica. PROSPECTIVA, 13(2). https://www.redalyc.org/pdf/4962/496250642007.pdf.

Vigil Galán, O., Jiménez Olarte, D., Contreras Puente, G. (2018). Efecto de las condiciones medioambientales de México en el desempeño de módulos fotovoltaicos de distintas tecnologías. Revista Eficiencia Energética, 4(17), 13-16. http://www.fide.org.mx/wp-content/uploads/2018/05/eficiencia_energetica-17/.

World Energy Markets Observatory. (2019). La crisis como oportunidad. https://www.capgemini.com/es-es/world-energy-markets-observatory-2019/.