1 Introduction

Renewable energy sources offer a potential solution to address the escalating issues of global warming and climate change. Concerns surrounding these changes and their link to hydrocarbon emissions depleting the ozone layer have prompted worldwide apprehension. While numerous efforts have been made to minimize our environmental impact, we are still in the early stages of reducing harmful emissions. Sun, as the cleanest energy source, provides virtually infinite energy to heat the Earth, although sunlight distribution is not uniform across the globe. Photovoltaic (PV) power plants have gained significant popularity as the preferred solar solution. Due to the popularity of PV systems, there arises a need to assess their role in sustainable energy production and sustainable agriculture in terms of efficiency and production capacities, as well as to investigate their relationship with the process of global warming. Solar energy studies have gained increasing attention worldwide, with several countries, such as Jordan, Morocco, Serbia, and Iran, analyzing their solar energy potential (Hrayshat, 2009; Merouni et al., 2016; Doljak et al., 2017; Edalati et al., 2017).

Dagher et al. (2023) aimed to provide an assessment of the extent of energy poverty in Lebanon compared to other countries. Additionally, they employed a new approach to simulate the dynamic behavior of natural gas demand and obtain dynamic elasticities (Dagher, 2012). Dagher and Yacoubian (2012) investigated the dynamic causal relationship between energy consumption and economic growth in Lebanon from 1980 to 2009. Dagher and Hariri (2013) explored the dynamic linkages between oil prices and stock markets, known as the oil price-stock price nexus.

According to Hepbaslı et al. (2004), Türkiye stands as one of the best regions globally to harness sunlight, and they emphasize the significant increase in PV plant installations in Türkiye in recent years. Concentrating solar power technologies offer potential solutions to Türkiye’s growing energy demand (Kaygusuz, 2011). Kumar and Sudhakar (2015) highlight grid-connected systems as the optimal choice among renewable sources.

Literature on solar energy, particularly in Türkiye, has been a focus of several studies. Incekara and Ogulata (2017) project that solar energy will rank second in Türkiye’s electricity production by coming years. Insel et al. (2022) have generated three investment scenarios for Türkiye’s renewable energy sector and extensively analyzed the future of wind and solar energy from a macro-economic perspective. Türkiye has launched auctions for renewable energy resource zones to promote solar and wind technologies. Sirin and Sevindik (2021) examined the inaugural auction for Solar PV renewable energy resource zones, which set ambitious targets for increasing solar PV capacity and fostering domestic competence in solar technologies.

Boran et al. (2010) identified the optimal location for installing a grid-connected PV power plant in Turkey. They proposed a multi-criteria axiomatic design approach for evaluating sites for such plants. Four evaluation criteria, including sun hours, solar radiation, mean temperature, and topography, which significantly influence the selection of a site for a grid-connected photovoltaic power plant (GCPP), were considered. In the evaluation model, Bodrum ranked highest in terms of meeting the criteria for sun hours and mean temperature compared to other candidate sites. The findings suggest that Bodrum should be chosen as the most suitable site for installing a GCPP.

Researchers have also explored specific Turkish cities such as Izmir, Bolu, and Isparta (Bezir et al. 2010; Ulgen & Hepbaslı, 2003; Yilmaz, 2013).

Performance ratio serves as a widely accepted indicator for evaluating the efficiency of grid-connected PV plants among researchers. It is a unit-less quantity that has gained global recognition in assessing PV system performance. A higher performance ratio signifies better system performance (Khalid et al. 2016). Additionally, performance ratio, along with energy output, capacity factor, and levelized cost of electricity, is a crucial parameter for designing PV power plants and conducting economic analyses (Konde et al. 2022). Gopi et al. (2021) evaluated the variation of weather-corrected performance ratio across different locations with varying climates. Başoğlu et al. (2015) described performance ratio as a means of calculating the efficiency of a PV system, representing the proportion of energy available for transfer to the grid after accounting for losses. Comparative analysis of different PV module technologies can be performed at various locations using the performance ratio. Boulmrharj et al. (2022) defined the performance ratio as the ratio between the actual energy produced and the theoretical energy, serving as one of the key performance evaluation indicators.

The Soğuksu PV power plant, a large-scale installation in the northwestern region of Türkiye, represents a new investment. Saka and Canbolat (2018) presented a theoretical investigation of hydrogen production based on the actual electricity generation of the power plant. Saka (2022) provided the first-year electricity generation data from this power plant to the literature. The study revealed that December had the highest number of cloudy days, with only 3.5% of the annual generation occurring in that month (Saka, 2019).

Cubukcu and Gumus (2020) conducted an analysis of a grid-connected PV power plant with a rated power of 2130.7 kWp situated in the eastern region of Türkiye. Duman and Güler (2020) presented an economic evaluation of grid-connected residential rooftop PV systems in Türkiye, considering the current feed-in tariff scheme. They highlighted the reluctance towards residential PV systems in Türkiye, which can be attributed to the lack of widespread campaigns. They suggested that a strong political commitment and public campaigns can facilitate the widespread adoption of grid-connected residential rooftop PV systems in the country. Syahindra et al. (2021) aimed to determine and compare the Performance Ratios (PR) of a rooftop solar PV system and a floating solar PV system installed on a lake to determine which system is more suitable. According to Kymakis et al. (2009) the performance ratio is calculated by dividing the final yield by the reference yield and represents the overall losses in the system when converting from DC rating to AC output. They reported an average annual performance ratio of 67.36% for a system.

Udemba et al. (2024) have examined Norway’s environmental performance in light of interactions among natural resources, foreign direct investment, inflow, and economic growth. The analysis suggests that policy formulation should focus on mitigating the negative impacts of fossil fuels on Norway’s environmental performance. It is demonstrated that natural resources play a significant role in attracting foreign direct investment and have a positive impact on Norway’s environment.

Kumar et al. (2024) have concentrated on the factors influencing the adoption of non-residential Rooftop Solar Photovoltaic (RSPV) systems, specifically focusing on innovation attributes and technology. The study found that environmental concerns, compatibility, environmental benefits, immediacy of rewards, and trialability were significant drivers of the diffusion and adoption of non-residential RSPV systems. The research concluded that a lack of financial incentives and high upfront costs posed significant barriers to the diffusion and adoption of non-residential RSPV systems.

Zhou et al. (2023) aimed to investigate the influence of disaggregated energy sources, economic globalization, natural resources, and economic growth on carbon emissions in the Regional Comprehensive Economic Partnership (RCEP) nations from 1990 to 2020.

Lund et al. (2024) investigated the relationship between natural gas consumption and driving factors using the Logarithmic Mean Division Index framework. They observed that fossil energy structure and non-clean energy structure were significant factors, followed by population, per capita Gross Domestic Product, and energy intensity.

Khan et al. (2022b) investigated the impact of the energy trilemma balance and clean energy transitions on economic expansion and environmental sustainability while moderating the role of clean energy and natural resources rents of the three trilemma leaders from 1990 to 2016.

Zakari et al. (2022) examined the relationship between sustainable economic development, financial development, and energy efficiency.

In this study, the alignment between the actual values generated by a solar PV power plant and the reported radiation values for the region has been investigated using the performance ratio. A new perspective has been introduced to the utilization of the performance ratio, highlighting situations where the performance ratio exceeds 100% as an indicator of global warming for the region. The power plant is of the grid-connected type and is located in Bursa, a northwestern city in Türkiye. As this region is a significant agricultural area for the country, the study investigates how much of the region’s agricultural energy needs the power plant can meet. The daily and monthly electricity generation data of the power plant from April 2017 to December 2022 are reported. The electricity generation data, recorded as DC, were obtained from the power plant’s library. The monthly global radiation in the region is relatively low. The generated energy transmitted to the electricity grid was observed under real weather conditions. The total daily and monthly power generation was compared with the global radiation values of the region, and the performance ratio of the power plant was demonstrated. Evaluation of the relationship between reported the global solar radiation values (GSRVs) and the performance ratios in the examined region have been done.

2 Methodology

2.1 System studied

The Soğuksu solar PV power plant has a total energy capacity of 7MWe. It consists of a large solar field divided into seven sectors, each having a capacity of 1 MWe. Inverters play a crucial role in maintaining high efficiency in the power plant. There are 20 inverters in each sector of the Soğuksu power plant. Additionally, the inverters are connected to strings, with each inverter responsible for 10 strings. Each string comprises a series connection of 22 PV panels. The panels chosen for the plant bear the commercial name UP-M270P. They are composed of polycrystalline silicon material. Each panel has a power generation capacity of 270 W, achieving an efficiency of 16.6%. There are a total of sixty PV cells incorporated on each panel.

2.2 Energy analysis

The study focuses on a system comprising three primary sections, as previously mentioned. Each section is characterized by mass and energy balance. The first section pertains to the instantaneous electricity generation of the Soğuksu solar power plant, which can be described by Eq. (1).

$${E}_{G}={N}_{P}{E}_{S}{\eta }_{P}{\eta }_{inv}{\eta }_{los}$$
(1)

where, \({N}_{P}\) represents the total number of panels. \({E}_{S}\) refers to the incident solar radiation energy on the panels, which varies based on the specific day and time. The cell surface area determines this value. However, the actual cell surface area is smaller than the total panel area due to the presence of the frame. Additionally, the panel conversion efficiency, denoted as \({\eta }_{P}\), is provided as follows:

$${\eta }_{P}=\frac{{E}_{el}}{{E}_{S}}$$
(2)

where, \({E}_{el}\) represents the electricity generated by the panels. The inverter efficiency, denoted as \({\eta }_{inv}\), ranges from 95 to 98%. PV power plants may experience losses in electricity generation due to various factors such as parameter mismatches, panel dust, conduction losses, wiring issues, humidity, and soiling. The overall efficiency, denoted as \({\eta }_{los}\), takes into account these losses. Maximizing efficiency in PV power plants leads to increased electricity production. In this study, the reported electricity generation values consider the aforementioned losses. The electrical properties of PV panels under standard test conditions (STC) can be described by Eq. (3). STC refers to solar irradiance of 1000 W/m2 and a cell temperature of 25 °C.

$${E}_{el}={I}_{dc}{V}_{dc}$$
(3)

In the DC phase of the panels, Idc represents the current, and Vdc represents the voltage. The maximum voltage of the panels, as stated in the producer catalog under standard test conditions, is 31.4 V, and the maximum current is 8.6 A.

When installing a power plant, suitable land area and financial considerations are essential, as is the case with any investment. The Soğuksu solar power plant is an official facility that possesses all the necessary legal permits. Table 1 provides an overview of the general characteristics of the solar field.

The Soğuksu solar power plant has been constructed on a 17-hectare area and incorporates a total of 30,800 solar panels. The power generated by a solar power plant is theoretically dependent on the surface area of the solar panels. Moreover, factors such as the day numbers of production, panel and inverter efficiencies are crucial considerations.

Table 1 General information about the power plant
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The power plant is situated in Bursa, specifically between Yenişehir and Kestel, with a closer proximity to Yenişehir. Therefore, when conducting comparisons, global radiation values from Yenişehir were utilized. The power plant is strategically positioned on a high hill relative to the city center, offering the advantage of natural ventilation. Figure 1 displays a map illustrating the solar radiation values of the region. The approximate solar radiation value for the region throughout the year is 1400 kWh/m2.

Fig. 1
figure 1

Solar radiation values of the Bursa province in Türkiye

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To assess the average solar potential of a specific region, solar potential maps have been developed. Figure 2 showcases the GSRVs specifically for the Yenişehir district, which range from 1.3 to 6.14 kWh/m2 per day (https://gepa.enerji.gov.tr/MyCalculator/ ). The yearly average solar energy incident value in the region is comparatively lower than the national average. By utilizing these GSRVs, it is possible to calculate the electricity potential and performance ratio of the power plant.

Fig. 2
figure 2

Global solar radiation values of Yenişehir in Türkiye with methodology

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2.3. Performance ratio calculationThe performance ratio of the power plant for each month is calculated using Eq. (4) Syahindra et al. (2021). This equation enables a comparison between the actual energy generated and the calculated energy. The performance ratio is influenced by various factors, including the energy produced by the solar PV panels and the associated losses.

$$Performance\, Ratio=\frac{{E}_{G}}{{E}_{P}}$$
(4)
$${E}_{P}=GSRV*Days*{PV}_{surface}*{PV}_{efficiency}$$
(5)

The equation incorporates the following variables: EG represents the actual electricity production at the inverter output of the power plant, which is subsequently transferred to the grid. EP represents the potential electricity that could be generated by the power plant. To illustrate, a detailed calculation is provided below:

For the month of April 2017, the recorded actual generation is 1,118,160 kWh in DC. After accounting for the inverter’s overall efficiency, the electricity transferred to the grid at the inverter output (EG) amounts to 1,084,615.2 kWh in AC. In April, the Global Solar Radiation Value (GSRV) for the Yenişehir district is reported as 4.59 kWh/m2 per day, and the month consists of 30 days. The total PV surface area of the power plant is 50,108 m2, with each PV panel operating at an efficiency of 16.6%. Based on these values, the potential energy (EP) is calculated to be 1,145,378.7 kWh. Consequently, the performance ratio of the power plant for April 2017 is determined as 94.7%. This implies that if the actual production value is high within a given time period, the performance ratio may exceed 100% during that specific period. Figure 3 displays an aerial view of the Soğuksu solar plant.

Fig. 3
figure 3

The top view of Soğuksu solar plant

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Monthly radiation values for a region are formed by the sum of daily values, and it is possible to discuss an average value. However, due to weather conditions, it is certain that each month will have a different distribution depending on the days. In the region where the power plant under consideration is located, the month with the lowest radiation value is December. The average GSRH value for December is 1.3 kWh/m2day. Therefore, the lowest production value for the plant should be expected in this month. Conversely, the month with the highest GSRH value is June, with a value of 6.14 kWh/m2day, but depending on other factors, the month with the highest production could be July.

2.3 Data sources

In this study, it is possible to examine the data sources used in two parts. Firstly, the GSRH values published by the Turkish Ministry of Energy and Natural Resources will be discussed. Equation (5) given earlier is a function of GSRH values. Accessing GSRH values through GEPA (Solar Energy Potential Atlas), prepared by the Ministry, is possible. GEPA is a virtual map provided to the public via the internet. Thus, it is possible to access the radiation values and sunshine durations for all provinces and districts in Turkey online.

Another data source considered in the study is the real power values generated by the power plant and transmitted to the grid. These values are automatically recorded by the computers within the plant and are daily monitored by the plant personnel. The power values produced by the plant in its initial year were previously presented in the studies by Saka (2018) and Saka (2022) in the literature. However, this study will conduct an evaluation based on a five-year period.

2.4 Societal benefits

In today’s conditions in Turkey, there is increasing interest and investment in PV energy power plants. However, many investors are primarily concerned with the financial aspect of the matter, seeking support from the current industry conditions during the design and installation phases of the plants. This situation contributes more to the continuation rather than the development of PV energy power plants. PV energy power plants bring not only economic benefits to society but also environmental benefits. Among the environmental benefits of these plants, their focus on clean energy production and their ability to reduce carbon emissions are the best-known features. This study aims to draw attention to how power plants are affected by global warming, based on their energy production. Additionally, it is intended to emphasize that large-scale plants alone have the capability to meet a significant portion of the agricultural energy demand.

3 Results and discussion

In this study, despite reaching many important conclusions through the analyses conducted, attention has been drawn to and solutions have been proposed for some noteworthy problems. One of these is the efficiency analysis. In this study, an efficiency analysis of a PV energy power plant, which is the largest capacity plant in its region, was aimed to be conducted. Different indicator parameters are available in the literature to express the efficiency level of PV power plants, among which the Performance Ratio (PR) is one. The production values of a PV power plant are affected by various parameters, including its location, weather conditions, and seasonal effects. The theoretical production values are a function of the Global Solar Radiation on a Vertical Surface (GSRV). The Performance Ratio, which is obtained by comparing the actual production values with the theoretical production values, provides an idea about the efficiency of the plant. During the analyses, it was found necessary to separately evaluate the efficient summer months from the inefficient winter months. Additionally, many instances were encountered where the PR exceeded 100%. PR exceeding 100% indicates that the actual production values exceed the theoretical production values. In general engineering approaches, since actual production values are smaller than theoretical production values, results indicating efficiency are below 100%. This situation is also valid for the summer months in this study. However, in much of the winter months, the actual production values exceeding the theoretical production values revealed the fact that the reported radiation values in the region are lower than the actual values. This situation is attributed to the relatively warmer weather conditions experienced during the winter months in the region, which is an indirect effect of global warming. Although numerous studies have been conducted on global warming, associating it with a numerical parameter would make many changes more expressible in this regard.

Another point addressed in the study is related to the proliferation of PV systems and their installation in areas with lower potential nationwide. Solar radiation values in Turkey vary greatly depending on the location. It is suitable to install PV energy production systems in locations with high radiation values as the primary investment areas. However, for investment activities to continue in areas with lower radiation, it is important to gain insight into the production values in these secondary areas. The power plant considered in this study is suitable for evaluation in this context due to its location, as other parts of the country have much higher radiation values. Especially during winter conditions in the northwest part of the country, it has become necessary to express how the production values of the plant are affected. The definition of Seasonal Effect Factors (SEF) has been proposed as a response to such problems. The ratio of the actual production values during the six-month summer period to the actual production values during the winter months of the other half of the year provides an insight into these issues.

Another point addressed in the study is to relate PV-generated electricity production to agricultural energy demand. The use of small PV systems at field edges is becoming widespread nationwide. However, there are few solutions proposed for large-capacity systems. The PV power plant considered is located in a productive agricultural area like Yenişehir plain. In this regard, the capacity of this plant to meet the agricultural energy demand of the plain has been investigated. Analyses have been conducted taking into account the cultivable areas of the Yenişehir plain for certain products with known water and energy requirements.

Figure 4 presents an example of the daily generation levels of the solar power plant, recorded during the first October after its initial operation. This study focuses on evaluating power generation over a span of five years. The figure demonstrates the impact of adverse conditions, such as cloudy weather, on the plant’s overall generation performance. The straight line depicted in the figure represents the average power generation of 27 MWh observed in the plant.

Fig. 4
figure 4

Daily generation levels of the power plant

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Figure 4 showcases the daily generation amounts in October at the Soğuksu solar power plant. It is observed that there were 12 days with cloudy weather during the month, resulting in electricity generation below the average. The total monthly generation amounted to 838.29 MWh, which accounts for 7.8% of the first year’s production. The inverter efficiency surpassed 98% for the month.

The period from April to September, spanning six months, experiences a Global Solar Radiation Value (GSRV) ranging from 4.32 to 6.14 kWh/m2 per day. This period is considered highly productive, as the electricity generation exceeds 1 GWh on a monthly basis. Consequently, the power plant predominantly generates electricity during this period. However, it should be noted that high power generation does not necessarily correlate with a high performance ratio. Figure 5 illustrates the performance ratio and electricity generation of the power plant during the productive period.

Fig. 5
figure 5

Performance ratio and power generation of the plant by months

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In Fig. 6a, the performance ratios and electricity generation of the plant in April are displayed for different years. As depicted, these values exhibit a fluctuating trend. The maximum electricity generation occurred in 2018, reaching 1.28 GWh, whereas in 2019 it decreased to 0.84 GWh. The performance ratio for April 2019 was the lowest among the six years, standing at 71.2%. The average performance ratio for April over the six-year period is 89.1%. Figure 6b provides noteworthy information regarding the month of May. The performance ratio during this month fluctuates between 70.4% and 91.3%, with an average of 79% over the six-year period. In 2022, the power plant achieved its maximum electricity generation of 1.39 GWh with a performance ratio of 91.3%. Based on Fig. 6c, the month of June can be considered an ideal time period for the power plant. The nearly linear trend and minimal variation between years indicate that this month is less affected by environmental factors. Energy generation remains consistently around 1.2 GWh each year, while the performance ratio ranges from 75.2 to 81.5%. The highest performance ratio of 81.5% was recorded in the first year. In Fig. 6d, the performance ratio for July reached 90.7% in the first year but dropped to 70.9% in 2019. The maximum energy generation of 1.46 GWh with a performance ratio of 92.3% occurred in 2022. The six-year average performance ratio for July is 84.9%, with a corresponding electricity generation of 1.34 GWh. Figure 6e highlights August as another ideal time period after June. In 2018 and 2021, the performance ratio peaked at 95.4% and 93.3%, respectively. September marks the end of the productive period. In 2020 and 2021, the generation fell below 1 GWh, as illustrated in Fig. 6f. However, in the other years, energy generation remains around 1 GWh. The lowest performance ratio of 84.7% was observed in 2020. The six-year average performance ratio for September stands at 94.5%. This value indicates that September has the highest average performance ratio among the six months examined during the productive period.

Fig. 6
figure 6

Performance ratio and power generation of the plant by months

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In Fig. 6, the graphs present information for 36 individual production months over a total of six years. As mentioned earlier, the performance ratio, being a function of GSRVs, may occasionally exceed 100%. Among the 36 separate production months, only April 2018, September 2017 and September 2019 recorded performance ratios greater than 100%.

The period between October and March, spanning six months, is characterized by a lower GSRV of 3.14 kWh/m2day and below. This period is considered nonproductive as the electricity generation remains below 1 GWh monthly. Figure 5 illustrates the power plant’s performance ratio and electricity generation during this period.

Figure 5a presents the performance ratios for October along with the corresponding electricity generation for each year. The values exhibit a fluctuating but regular pattern. In 2017, 2019, and 2021, the electricity generation was approximately 1.06 GWh. The performance ratio reached its highest value of 97.2% in 2019. The number of days with a performance ratio above 100% is 3. Important information about November is depicted in Fig. 5b. The performance ratio exceeds 100% every year, with the highest production occurring in 2021 at 0.618 GWh. December, as shown in Fig. 5c, is one of the least productive months. However, the performance ratio was above 100% in 2017 and 2021. The lowest production value of all time was recorded in 2020 at 0.265 GWh. The five-year average generation for December stands at 0.32 GWh.

In Fig. 5d, the performance ratio for January dropped to 59.7% in 2019. The five-year average generation for the month is 0.39 GWh. The performance ratio exceeded 100% in 2018, 2020, and 2022. January and February have the same five-year average generation, as indicated in Fig. 5e. Figure 5f provides significant data for March, showing that the performance ratio exceeds 100% every year, similar to November.

The presented graphs in Fig. 5 provide information about thirty-two separate production months over a span of six years. It is observed that in many months, the performance ratio exceeded 100%. This result suggests the need for a reevaluation of the relationship between reported GSRVs and the performance ratio specifically for winter months in the examined region.

The final part of this study introduces a new criterion called the seasonal impact factor (SEF), which aims to compare the productive and unproductive periods of PV power plants. Considering the regional conditions of the power plant, SEF is defined as the ratio of the electricity generated between April and September (the first six-month period) to the electricity generated in the other six-month period within a year.

Table 2 presents the calculated seasonal impact factors for each year. In the first production year, the SEF was determined to be 2.14. Over the five-year period, the SEF reached its highest level of 2.85 in the second year.

Table 2 Seasonal effect factors of the power plant according to years
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Figure 7 depicts how the daily production values of the power plant change under real conditions. The production values for each day are represented with unique graphs corresponding to the specific day, as they occur under clear atmospheric conditions. Additionally, some of the results obtained in this study are presented in graphical format on the figure.

Fig. 7
figure 7

Graphical presentations of some findings

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4 Agrivoltaic aspects

The Yenişehir plain is one of the country’s productive agricultural areas. The cultivable agricultural land within the plain covers an area of 39,107 hectares, which represents half of the total surface area of the city. The distribution of surface areas comprising the city is provided in the following Table 3 (Orhan, 2015).

Table 3 Land Distribution in Yenişehir plain
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A study outlining the energy requirements for irrigation based on different crops specific to this plain has not been found in the literature. However, such a study has been conducted for the Konya plain, which is one of Türkiye’s major agricultural centers, focusing on sugar beets, dry beans, and carrot crops. There are no barriers to the production of the same crops in the Yenişehir plain. The only difference is that the region receives more rainfall compared to the Konya plain. This aspect, however, is considered an advantage, and no adjustments have been made when utilizing data from the Konya plain. Accordingly, the annual energy required to drive the pumping system for five different irrigation models per hectare has been determined (Yavuz et al., 2014). In Table 4, the amount of energy consumptions depending on the type of irrigation system with sufficiency rate of the power plant are given. There are five different irrigation systems;

  1. 1.

    Motopump,

  2. 2.

    Centrifugal pumping systems induced by power take-off shaft.

  3. 3.

    Vertical axle pump system induced by power take-off shaft.

  4. 4.

    Vertical axle pump induced by electric motor.

  5. 5.

    Submersible pumping systems.

There is a dam close to the city and irrigation is done with the help of deep wells. Therefore, it is possible to apply any of the irrigation models in the table. It was mentioned that the annual electricity generation of the power plant is approximately 10GWh. When the production rate of the plant is compared with the amount of energy required for irrigation depending on the irrigation diversity and three different products, The Soğuksu PV plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%.

Table 4 Energy consumptions of sprinkler systems (MJha-1year-1)
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5 Policy recommendations

Based on the research findings mentioned above, we have formulated the following policy recommendations:

  • Due to the effects of global warming, there is a need for more frequent revision of the global solar radiation values reported by administrations.

  • In order to achieve a cleaner environment and sustainable energy production, incentivizing the installation of PV power plants in regions with lower global solar radiation values should be implemented.

  • In addition to the agricultural use of small-capacity PV units, investors should be encouraged to use large-capacity systems.

  • Problems that cause investors to be hesitant about investing in PV power plants should be identified and addressed in order to continue investments in PV power plants.

6 Conclusions

This study focuses on the analysis of electricity generation in a PV grid-connected solar power station located in Bursa, with a total installed capacity of 7 MWe. The power station is equipped with 30,800 panels, each capable of generating 270 W of electricity. The analysis includes an assessment of the electricity generation, performance ratio, and seasonal effect factor of the PV power plant.

Based on the observed results, the annual electricity production of the power station amounts to approximately 10 GWh/year. During the six-month period from April to September, the electricity generation exceeds 1 GWh per month, while it is lower in the other six-month period. Among the examined six-month productive time period, September exhibits the highest average performance ratio of 94.5%. However, the maximum six-year average electricity generation occurs in July, reaching 1.34 GWh.

It is observed that in many months in winter period, the performance ratio exceeded 100%. This result suggests the need for a reevaluation of the relationship between reported GSRVs and the performance ratio specifically for winter months in the examined region.

To compare the productive and unproductive periods of PV power plants, a seasonal impact factor was proposed. In the first production year, the seasonal effect factor was determined to be 2.14. Over the course of the five-year period, the highest seasonal effect factor of 2.85 was observed in the second year, indicating the significance of seasonal variations in electricity generation. Also, the power plant alone can meet the agricultural irrigation energy requirement of the region in the range of 6.7–2.3%.

Overall, this study provides a comprehensive analysis of the PV power plant, including its electricity generation, performance ratio, and the proposed seasonal effect factor, shedding light on its operational characteristics and seasonal variations in output.