Evaluation of Technical Aspects of Solar Photovoltaic (PV) Power Installations on Farmland

Abstract

This research evaluates the technical and economic aspects of solar photovoltaic (PV) power installations on farmland, utilizing a simulation model in MATLAB to forecast annual system output based on nominal power and meteorological data. This study compares various configurations, including single-sided versus double-sided modules and fixed versus tracker structures, to determine their efficiency, losses, and economic viability. The findings indicate that, while theoretically superior technologies may offer better production rates, their economic feasibility varies significantly depending on specific project conditions. The main conclusions drawn from this research emphasize that land-based PV systems present a promising solution for sustainable energy generation. By addressing challenges such as solar energy intermittency and the need for supportive infrastructure, this study highlights the potential for these systems to significantly contribute to reducing greenhouse gas emissions and enhancing energy resilience. This analysis underscores the importance of optimizing configurations to maximize both technical performance and economic returns, ultimately supporting a transition towards a more sustainable energy future.

1. Introduction

The escalating global demand for clean and sustainable energy has catalyzed a remarkable surge in the adoption of photovoltaic (PV) systems for electricity generation [1,2,3,4,5,6,7]. By harnessing solar power, these systems offer a compelling solution to reduce greenhouse gas emissions and combat climate change. For instance, research by Belloni et al. (2023) demonstrates the application of machine learning techniques to optimize PV system performance, significantly enhancing efficiency and reducing operating costs. This paper conducts a comprehensive technical assessment of land-based PV systems, evaluating their viability, cost-effectiveness, and overall performance.

Land-based PV systems [8,9,10,11,12,13] provide notable advantages over other renewable energy sources, such as wind or hydroelectric power. They typically have a lower environmental impact, requiring less land clearance while preserving local ecosystems, as emphasized in studies by Gholami et al. (2020) and Kerekes et al. (2012), which highlight the environmental benefits of integrating PV technology with sustainable land use practices. Furthermore, their ability to integrate seamlessly into existing infrastructure makes them versatile and suitable for both urban and rural settings.

A decisive factor driving the adoption of land-based PV systems has been the substantial decline in PV panel costs over the past decade. Research by Bindi et al. (2022) confirms that falling costs have made PV systems increasingly competitive with traditional fossil fuels. Additionally, advancements discussed by Laudani et al. (2023) and Lucaferri et al. (2023) illustrate how innovations in PV technology have contributed to improved energy conversion efficiencies, thereby enhancing the economic viability of solar energy solutions. Beyond cost advantages, PV systems provide a reliable and sustainable energy source. Solar energy is abundant and renewable, offering a consistent electricity supply that reduces dependency on fossil fuels and strengthens the resilience of the energy grid, as highlighted in the works of Parise (2015, 2022) [14,15,16,17,18,19,20,21].

Despite these benefits, PV systems face challenges that must be addressed to fully realize their potential [22,23,24,25,26,27,28,29]. A primary concern is the intermittency associated with solar energy generation, which only occurs during daylight hours. Studies by Corti et al. (2023) and Faba et al. (2017) explore this limitation, suggesting that energy storage technologies, such as batteries or pumped hydro storage, can effectively mitigate these issues by storing excess energy generated during the day for use during periods of low sunlight. Additionally, the successful widespread adoption of PV systems requires adequate infrastructure and policy support, a theme highlighted in the research by Talluri et al. (2021) and Xiao et al. (2012) [30,31,32,33,34,35,36]. Their findings emphasize the need for strategic governance to promote financial incentives and regulatory frameworks that facilitate the integration of PV systems into the energy market. In conclusion, deploying land-based PV systems emerges as a promising strategy to address the growing demand for clean energy. Through a thorough technical and economic analysis, this paper aims to assess the feasibility and cost-effectiveness of these systems, identifying their potential benefits and challenges. By tackling these issues and leveraging advancements in PV technology, we can accelerate the transition towards a more sustainable energy future. This paper presents a novel contribution to the field of renewable energy by providing a comprehensive evaluation of the technical and economic aspects of solar photovoltaic (PV) power installations specifically on farmland. Unlike previous studies that primarily focus on general PV systems [37,38,39,40], this research uniquely emphasizes the integration of PV technology within agricultural contexts, addressing both performance and sustainability concerns. The novelty of this work lies in its dual approach: it not only assesses various configurations of PV systems—such as single-sided versus double-sided modules and fixed versus tracker structures—but also utilizes a simulation model in MATLAB R2024. This methodological framework allows for a detailed comparison of efficiency, losses, and economic viability across different setups, providing insights that are particularly relevant for stakeholders in agricultural sectors. Furthermore, this paper identifies critical factors influencing the design decisions for PV installations on farmland, highlighting the need for tailored solutions that consider local environmental conditions and agricultural practices. By bridging the gap between technical performance and economic feasibility, this research contributes valuable knowledge that can guide future developments in sustainable energy generation within agricultural landscapes.

2. Materials and Methods

2.1. Comparison of Array Configurations

The study described here arose from the desire to generate a simulation model that has the ability to indicate the most convenient design choices based on the available terrain and the needs of a hypothetical client. It is therefore a question of identifying the best configuration, both from a technical and economic point of view, among a series of plant types examined. In particular, this research focuses on the differences between single-sided and double-sided modules and between fixed and tracker structures. While it is to be expected that, reasoning from the point of view of production, a system with tracker is more promising than one with a fixed structure, this does not mean that the same is true in economic terms. The same statement is valid for the combination of single-sided and double-sided modules: double-sided modules are more efficient because they also exploit the rear side of the cell; however, they are slightly more expensive at present. To carry out an appropriate study, it is therefore necessary to define the production advantage of one technology over the other, and then to understand whether this advantage is also maintained at an economic level. To determine purely technical and then economic differences, four PV system macro-families were created and listed here:

• Single-sided module, fixed structure;
• Single-sided module with tracker;
• Double-sided module, fixed structure;
• Double-sided module with tracker.

As already mentioned, the intention was to create a general model applicable to as many cases as possible. In this specific case, the model was used on a hypothetical piece of land of approximately 13 hectares, with a maximum permitted AC input power of 6 MW. To the extent that the land is understood to be at the fence, a percentage fraction to be subtracted from the total land was defined as 12%. The useful land for the installation of the photovoltaic panels is therefore 11.5 hectares. The parameters described are summarized in

Table 1. Plant characteristics.

Fence Surface Area	130,000 m2
Useful Module Area	115,000 m2
AC input power	6 MW

,

Table 2. Technical characteristics of single-sided modules (TSM-DE14A (II)).

Rated Power [W]	350	370	400
Efficiency [%]	18	19	21
$U_{OC}$ [V]	47	48	49.7
$U_{MPP}$ [V]	38.5	39.4	41
$I_{SC}$ [A]	9.7	9.8	10.2
$I_{MPP}$ [A]	9.1	9.4	9.7

and

Table 3. Technical characteristics of double-sided modules(JAM72D00 355-375).

Rated Power [W]	360	375	390
Efficiency [%]	18	18	20
$U_{OC}$ [V]	47.6	48.2	49.3
$U_{MPP}$ [V]	39.2	39.4	40
$I_{SC}$ [A]	9.5	9.6	10.0
$I_{MPP}$ [A]	8.9	9.2	9.6

.

Each module of those listed was studied both in fixed structure and with tracker for a total of 6 module–structure combinations for single-sided technology and 6 for double-sided technology. Having established the characteristic parameters of the land and the point of feed-in point (Table 1), the optimal configuration was determined for each case study by varying the installed DC power. This results in a change in the DC/AC ratio (R), as the AC feed-in power is set at 6 MW. Since the available installation area does not vary, the DC/AC ratio becomes a function of the ground cover ratio (GCR). The GCR is defined as the ratio of the panel width to the distance between rows of panels (distance B). The ground cover ratio gives an indication of the ground cover: a small value means that the rows are far apart, while a high value means a shorter distance between rows of modules. The theoretical limit values derived from the definition of the GCR are as follows:

• GCR = 0 would be obtained if the distance between the rows tended to an infinite value.
• GCR = 1 if the rows were at a distance equal to the width of the modules.

In practice, generally, the GCR has values between 0.3 and 0.6. Using the definition of GCR, the following equation can be written:

$$\mathrm{R}={\displaystyle \frac{\mathrm{S}·\eta ·\mathrm{GCR}·{\mathrm{G}}_{\mathrm{STC}}}{(1+\mathrm{c})·{\mathrm{P}}_{\mathrm{AC}}}}$$

(1)

• ${\mathrm{P}}_{\mathrm{AC}}$: total installed power;
• $\eta$: module efficiency;
• S: gross area, understood as at enclosure;
• c: useful land reduction coefficient (10%);
• $G_{STC}$: radiation under standard conditions of 1000 W/m² S.

For each case study, a sensitivity analysis was performed by varying the land cover and, consequently, the DC/AC ratio. In

Table 4. DC/AC ratios for single-sided modules, varying GCR.

		Ground Cover Ratio
Power [W]	$\mathit{\eta }$ [%]	0.4	0.47	0.5	0.55
350	18	1.03	1.14	1.24	1.35
370	19	1.07	1.20	1.30	1.44
400	21	1.13	1.3	1.4	1.55

and

Table 5. DC/AC ratios for double-sided modules, varying GCR.

		Ground Cover Ratio
Power [W]	$\mathit{\eta }$ [%]	0.4	0.47	0.5	0.55
360	18	1.08	1.18	1.34	1.4
375	18	1.10	1.27	1.36	1.47
390	20	1.3	1.3	1.5	1.6

, the values of the DC/AC ratio at varying GCRs, calculated using (1), are shown. Values in red are outside the considered study range: DC/AC ratios below 1.1 do not optimize production, values above 1.55 can be dangerous for the inverter. Apart from these values, all the resulting configurations mentioned above were simulated.

2.2. Electricity Production Calculation Models

To calculate the productivity, reference is made to the calculation of the available power, considering real operating conditions, such as the following:

• The hourly average global irradiance incident on the modules;
• The temperature of the cells;
• The availability of the system.

For simplicity’s sake, a plant availability of 100% has been assumed. Given the operating conditions, system losses must be taken into account for the calculation of the actual power fed into the grid. These relate to the individual module, the string, the inverter, the Joule effect in the cables, and the transformer. Figure 1 shows the representative flowchart of the model implemented in MATLAB. In summary, the input variables are (in hourly values): the real incident radiation, expressed as a ratio of the radiation under STC (Greal/GSTC); the ambient temperature (Tamb); and the shading factor on the direct radiation (Kshad). These hourly values are used to calculate the losses which depend on them, each of which is represented by a block.

The losses are given as a percentage of the power available in

Table 6. Loss percentages for each block.

	Single Fixed	Single Tracker	Double Fixed	Double Tracker
Module	8.5%	8.6%	8.3%	8.8%
String	1.4%	1.4%	3.3%	2.4%
DC/AC	1.3%	1.2%	1.0%	1.1%
Transformer	0.8%	0.6%	0.7%	0.9%

, which is calculated in proportion to the incident radiation for all the general blocks shown in Figure 1.

2.3. Economic Technical Evaluation for the Different Configurations

The economic analysis is calculated over an operating period of thirty years. Useful data for simulations are as follows:

• Global Horizontal Irradiance [W/m²].
• Horizontal diffuse irradiance [W/m²].
• Wind speed [m/s].
• Temperature [^∘C].

In this study, one data source was considered to estimate the output of the various configurations: Photovoltaic Geographical Information System (PVGIS). The solar radiation and temperature information provided by PVGIS is mainly calculated from satellite data. This provides information for any location over large geographical areas, with hourly temporal resolution. The graphs in Figure 2 and Figure 3 show the monthly horizontal global radiation and average temperature.

From the point of view of the technical analysis, whose objective is to relatively assess the productivity of different plants and thus to identify the best performing of them all, the choice of a weather database is not relevant: the best plant of all will be the same in all cases. For the economic analysis, on the contrary, the production is in absolute value. A further analysis was carried out on the fixed structure to find the optimal tilt angle for each ground cover ratio value. In fact, it is logical to expect that the closer the rows of the plant are to each other, the more lower inclinations are required to avoid mutual shading as much as possible. To quantitatively confirm this hypothesis, fixed-structure systems were simulated at different inclinations. For systems with single-sided modules, productions (expressed in equivalent annual hours) were simulated at inclinations of ${20}^{\circ }$, ${25}^{\circ }$ and ${30}^{\circ }$. For the bifacial modules, an inclination of ${35}^{\circ }$ was also added to check whether the production of this technology is favored at more vertical positions. The results are shown in the following

Table 7. Production expressed in annual equivalent hours (kWh/kWp/year) at varying of tilt angle and GCR, for single-sided modules.

Module Power	GCR	Tilt
[W]	[%]	20°	25°	30°
350	42	1435	1451	1463
350	45	1423	1434	1429
350	48	1393	1398	1379
370	36	1446	1463	1468
370	40	1438	1451	1455
370	44	1412	1421	1418
370	48	1372	1373	1357
400	38	1473	1489	1494
400	42	1454	1466	1469
400	46	1420	1427	1421
400	49	1371	1370	1352

and

Table 8. Production expressed in annual equivalent hours (kWh/kWp/year) at varying of tilt angle and GCR, for double-sided modules.

Module Power	GCR	Tilt
[W]	[%]	20°	25°	30°	35°
360	44	1524	1540	1544	1539
360	49	1499	1511	1512	1500
360	53	1449	1453	1439	1424
375	40	1517	1532	1536	1531
375	44	1485	1497	1501	1490
375	49	1433	1436	1421	1414
375	53	1377	1366	1357	1346
390	40	1536	1550	1556	1553
390	44	1495	1502	1500	1484
390	49	1429	1428	1410	1399

and were obtained via the PVGIS database. For each row, which indicates a configuration, the highest specific production value at the same GCR and varying the inclination of the modules is shown in bold.

The results in Table 7, which concerns single-sided modules, confirm the hypothesis made earlier, i.e., that for lower GCRs it is convenient to choose higher inclinations, although for higher GCR values there is not a great difference between productions with inclination of ${20}^{\circ }$ and ${25}^{\circ }$. For example, for the 370 W module, it appears that the inclination is better with the GCR at 48% is ${25}^{\circ }$. For bifacial modules, looking at Table 8, it can be deduced that, for low GCRs, an inclination does not exceed ${30}^{\circ }$. It is interesting to note that, in some cases, such as for the 375 W module with 49% GCR, the difference between an installation at ${25}^{\circ }$ and ${30}^{\circ }$ amounts to 15 equivalent hours, which corresponds to about 10% of the annual total, and that in installations of between 8 and 10 MW such as those under consideration, this difference is of the order of hundreds of MWh produced. The choice of the optimal inclination is therefore not arbitrary and is highly dependent on the value of the GCR. For this reason, in the following simulations for each fixed structure, the best inclination will be set, based on the results of the previous tables.

2.4. Economic Analysis

The criteria for life-cycle investment must be met for the market to adopt the use of bifacial modules on industrial-scale installations. In fact, the additional output obtained from bifacial modules is not a sufficient piece of information to decide whether bifacial is a cost-effective solution compared to single-sided modules. The idea is to check whether, in a given economic context, bifacial modules might be a viable option. In the following, the costs for a turnkey photovoltaic system and the O&M (Operation and Maintenance) costs are described. The economic calculation was performed over a period of 30 years, taking into account the results of the technical analysis referring only to the PVGIS database.

2.4.1. Capital Expenditure (CAPEX)

For the calculation of the investment costs of the plants under consideration, some features were considered with variable costs depending on the type of module and structure, others with a fixed value, the same for all plants.

Table 9. Variable costs based on the module.

Power [Wp]	350	370	400	360	375	390
Module [EUR/Wp]	0.28	0.285	0.29	0.293	0.295	0.3
Structure with tracker [EUR/Wp]	0.100	0.095	0.088	0.099	0.097	0.093
Fixed structure [EUR/Wp]	0.055	0.052	0.049	0.054	0.051	0.049
DC cables [EUR/Wp]	0.01	0.009	0.009	0.010	0.010	0.009
Electrical installation [EUR/Wp]	0.020	0.019	0.018	0.020	0.019	0.018
Mechanical installation [EUR/Wp]	0.040	0.038	0.035	0.039	0.039	0.037

shows the costs dependent on module power and technology (single-sided or double-sided). Finally, the cost of grid connection was taken into account, following the procedure indicated by the “Testo Integrato delle Connessioni Attive” (TICA), according to which the fee for the connection, expressed in euros, is the lower of

$$A=C{P}_A·P+C{M}_A·P·D_A+100$$

(2)

$$B=C{P}_B·P+C{M}_B·P·D_B+6000$$

(3)

where:

• $C{P}_A$ = 35 EUR/kW;
• $C{M}_A$ = 90 EUR/(kW km);
• $C{P}_B$ = 4 EUR/kW;
• $C{M}_B$ = 7.5 EUR/(kW km);
• P = power for connection purposes, expressed in kW;
• $D_A$ = the distance as the crow flies between the connection point and the nearest medium/low-voltage transformer substation of the grid operator which has been in operation for at least five years, expressed in km to two decimal places;
• $D_B$ = distance as the crow flies between the connection point and the nearest high/medium voltage transformer station of the grid operator in service for at least five years, expressed in km to two decimal places.

2.4.2. Operational Expenditure (OPEX)

Following the calculation of the initial investment cost for the turnkey plant, it is necessary to consider the operating and management costs that are present throughout the life of the plant.

Table 10. Operational and management costs.

Fixed structures [EUR/Wp]	0.007
Tracker [EUR/Wp]	0.008
Ground [EUR/ha]	2500
Asset management [EUR/MWp]	2000
Electric energy [EUR/MWp]	2000
IMU [EUR/Wp]	500
Accounting [EUR/Wp]	5000
Security services [EUR/Wp]	3000

shows the items contributing to this expense, on an annual basis. As can be seen from the table, some costs depend on the installed power, others are fixed. The cost of the land depends only on the area “at the fence”, which is fixed at 13 ha for all plants, so it can be included in the expenses considered fixed and amounts to EUR 30,000/year.

2.5. Economic Calculation

The aim of the economic analysis is to identify the plant with the lowest energy cost but also represents the best investment. For the first evaluation, the total expenditure over thirty years was calculated and divided by the total production over the same time period. For the second evaluation, the net present value (NPV) and the internal rate of return (IRR) were calculated. The NPV is the value of a sum of cash flows discounted to time zero at a rate equal to the opportunity cost of financial capital and is calculated using the following formula:

$$\mathrm{NPV}=-\mathrm{I}+\sum _{\mathrm{t}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{B}}_{\mathrm{t}}}{{(1+\mathrm{i})}^{\mathrm{t}}}}$$

(4)

where:

• I = initial investment;
• n = duration of the investment project, in this case 30 years;
• i = opportunity cost of capital;
• ${\mathrm{B}}_{\mathrm{t}}$ = cash flow at year t-th.

The initial investment is the one resulting from the CAPEX calculation. In this case, it was assumed that the weighted average opportunity cost of capital, known by the acronym WACC (Weighted Average Cost of Capital), is a weighted average of the cost of equity capital and the cost of debt capital, which is evaluated using the following formula:

$$\mathrm{WACC}={\mathrm{K}}_{\mathrm{e}}·{\displaystyle \frac{\mathrm{E}}{\mathrm{D}+\mathrm{E}}}+{\mathrm{K}}_{\mathrm{d}}·{\displaystyle \frac{\mathrm{D}}{\mathrm{D}+\mathrm{E}}}$$

(5)

where:

• ${\mathrm{K}}_{\mathrm{e}}$ = cost of equity capital;
• ${\mathrm{K}}_{\mathrm{d}}$ = cost of debt capital;
• E = equity capital (${\mathrm{E}}_{\mathrm{quity}}$);
• D = debt capital (${\mathrm{D}}_{\mathrm{ebt}}$).

For this study, an equity amount of 30% of the total with a K of 8% and a resulting debt of 70% of the total, with a cost of debt equal to 4%, were considered. From this account, the WACC value corresponds to 5.2%. The cash flows were calculated taking into account the loan installment calculated over a period of 15 years and the selling price of energy, assumed to be EUR 60/MWh with an increase of 2% each year. In addition, an annual production decrease of 0.6% and an inflation value of 1% are considered. After calculating the NPV, the investment is valued in this way:

• Accepted if NPV > 0, because it indicates that the future return is higher than the opportunity cost opportunity cost of the capital invested.
• Rejected if NPV < 0, because the future return is less than the opportunity cost of the invested capital.

Furthermore, in the case of a choice between two investments both with positive NPVs, the project with the higher NPV is chosen. The internal rate of return (IRR) is the discount rate that makes the net present value of the cash flows generated by an investment project zero. The equation is the same as that of the NPV, where the NPV takes the value zero and the unknown becomes the discount rate i:

$$-\mathrm{I}+\sum _{\mathrm{t}=1}^{\mathrm{n}}{\displaystyle \frac{{\mathrm{B}}_{\mathrm{t}}}{{(1+\mathrm{i})}^{\mathrm{t}}}}=0$$

(6)

The IRR expresses the return on an investment, i.e., the maximum cost of capital for the investment to remain economically viable. When the IRR is higher than the WACC, then the return on the project under consideration exceeds the relative cost of financing and incremental wealth is generated. When the IRR is lower than the WACC, then the realization of the project would impose the occurrence of financing costs that could not be compensated by the incrementally generated flows. Therefore, in the latter case, the investment should not be made. Both methods of evaluating an investment (NPV, IRR) need the same initial information, i.e., the positive and negative cash flows of the investment. However, it is more appropriate for the purposes of economic analysis to use the NPV: this directly compares two alternative investments with the same investment risk profile. In contrast, the IRR cannot be used for comparative evaluations, but only to assess the overall return on investment and compare it with the cost of capital. Another parameter that is considered when evaluating an investment is the PayBack Time (PBT), which is the period required to compensate the investment. It is believed that, the higher the PBT, the higher the risk inherent in the investment, although choosing an investment solely on the basis of PBT may hamper the life-cycle effect of the project as it does not take into account its profitability. The following Figure 4 and Figure 5 show the initial investment values for all the configurations examined and the results of the economic calculation, represented by the NPV Figure 6 and Figure 7.

3. Results

In order to facilitate comparisons between different photovoltaic installations, the following performance indicators were used. These indicators are related to the incident energy in the plane of the collector and are normalized with respect to the nominal installed power under STC conditions, indicated by the module manufacturer, and are therefore independent of the size of the array. In these definitions, energy yields are expressed as [kWh/kWp/day]. In other words, these quantities are numerically equal to the equivalent operating time at a constant irradiance of 1 kW/m². According to this interpretation, it is possible to write the formula for daily, monthly, or annual productivity (${\mathrm{E}}_{\mathrm{AC}}$) as follows:

$${\mathrm{E}}_{\mathrm{AC}}={\mathrm{P}}_{\mathrm{N}}{\mathrm{Y}}_{\mathrm{R}}\mathrm{PR}={\mathrm{P}}_{\mathrm{N}}{\mathrm{Y}}_{\mathrm{F}}$$

(7)

Figure 8, Figure 9, Figure 10 and Figure 11 show the monthly electrical productivity for some examined configurations.

These performance indices are as follows:

• ${\mathrm{Y}}_{\mathrm{R}}$ reference yield or peak solar hours, intended as the ratio between the irradiation over a given period of time on the receiving plane (kWh/m²) and the radiation under STC conditions (kW/m²); it is expressed in h/day, h/month, or h/year;
• ${\mathrm{P}}_{\mathrm{R}}={\mathrm{Y}}_{\mathrm{F}}/{\mathrm{Y}}_{\mathrm{R}}$, the performance ratio is the efficiency of the overall system with respect to the nominal installed power and incident energy and is useful for comparing systems different plants;
• ${\mathrm{Y}}_{\mathrm{F}}$ yield is calculated as the ratio between energy output and installed power (expressed in h/day, h/month, and h/year).

The final yield was used as a comparison parameter between the models, and the results are shown in

Table 11. Comparison of annual final yield.

Annual Final Yield	[kWh/kWp/Year]
Single fixed	1420
Single tracker	1580
Double fixed	1425
Double tracker	1687

. Firstly, all final yields meet expectations, at least in terms of the differences expected when comparing the characteristics of the configurations.

The best final performance is obtained by choosing a lower GCR and thus also a lower DC/AC ratio. This finding occurs for each module, regardless of its power and type (single-sided or double-sided). Among the single-sided modules, 400 W appears to be the best, regardless the supporting structure. The 390 W bifacial is the best of all, with a yield of up to 1722 kWh/kWp on a tracker structure. Installed on the same structure, the bifacial modules have a better yield than the monofacials, as was expected. However, the single-sided modules with tracker are performing better than the fixed double-sided ones, an outcome that could not be predicted beforehand. Figure 12 and Figure 13 show the final PVGIS yields as a function of GCR.

It can be observed that the gain from tracking is higher for the single-sided modules. This is due to the fact that bifacials can already benefit from a higher reference yield (Yr), due to the additional radiation received on the rear side, and also because the tracker significantly favors the front surface, thus reducing the contribution of the rear side. In general, the final yields decrease with increasing land cover and, as can be seen from the slope of the curves, the decrease is different depending on the power of the module. In fact, it can be seen that the magnitude of the variation is greater if the power of the module is higher; considering that the dimensions of the modules chosen for this study are approximately the same (gross area of approximately 21°), higher power also means higher module efficiency. This means that the more efficient modules are also those more sensitive to variations in GCR. Figure 14 and Figure 15 show the values of the annual energy produced by the same installations, from which it can be deduced that, by increasing the ground cover, and thus raising the DC/AC ratio, more energy is produced.

4. Discussion

The technical analysis shows that bifacial modules are better performers than single-sided modules, although they gain less than the latter by switching from a fixed structure to one with tracker. The result obtained regarding the change in ground cover (GCR) is consistent, as it is to be expected that there is a better specific output when the rows are further apart, just as it is consistent that the highest annual production occurs with more compact rows, as more power is installed. From the results of the technical analysis, it can be deduced that the most productive configuration is the 390 W bifacial, with a manufacturer-declared efficiency of 19.3% and a bifaciality factor of 70%, mounted on a tracker structure. A GCR of 39% gives the highest yield of 1725 kWh/kWp. For the economic analysis, it was taken into account that the costs of tracker structures are now close to those of fixed structures, and that the costs of bifacial modules are continually decreasing due to the increasing market penetration. The economic analysis therefore confirms the 390 W bifacial module with tracker as the one that best meets the needs of the customer, with the difference that the configuration that satisfies the economic analysis involves the most compact array of rows, with a GCR of 40%, slightly higher than that suggested by the technical analysis.

5. Conclusions

In conclusion, this research provides a thorough evaluation of the technical and economic aspects of solar photovoltaic (PV) power installations on farmland, establishing their potential as a sustainable energy solution. The findings indicate that, while advanced technologies, such as double-sided modules and tracking systems, can enhance energy production, their economic viability is heavily influenced by specific project conditions. Additionally, this study emphasizes the significant role of land-based PV systems in reducing greenhouse gas emissions and improving energy resilience through diversified energy sources. Looking forward, several avenues for future research are necessary to further enhance the understanding and effectiveness of PV installations. First, it is essential to conduct studies examining the influence of weather factors on electricity generation from photovoltaic stations. Such research will provide valuable insights that can inform design decisions and improve the economic feasibility of projects. Second, developing specific mathematical models that enable the calculation and forecasting of generated energy based on critical factors is crucial. Utilizing regression analysis to derive these models, along with corresponding determination coefficients, will enhance predictive accuracy. Moreover, to achieve the most precise predicted values of the parameters under consideration, it is advisable to incorporate machine learning techniques, particularly artificial neural networks. The integration of these advanced methodologies can significantly improve modeling accuracy and provide deeper insights into performance optimization. In summary, this research reinforces the notion that optimizing configurations and leveraging advancements in PV technology are vital for fostering a sustainable energy future. The exploration of weather influences, mathematical modeling, and machine learning applications will be instrumental in maximizing the potential benefits of land-based PV systems in addressing global energy challenges.