1. Introduction
The spread of the Corona virus (COVID-19) and the lockdown around the world in 2020 caused a drop in fossil fuel consumption along with a drop in the prices, which contributed to the mitigation of greenhouse gases in that year [
1,
2]. However, the dependency on fossil fuels is still significant, with a tremendous amount of greenhouse emissions escalating the global warming consequences. For instance, huge forest fires have increased due to global warming [
3], in different countries around the world such as Algeria, Turkey, and Greece. This urgent global problem has crossed regional borders, and needs coordination and cooperation from all countries to solve it, as agreed upon at the Paris Agreement in 2016 [
4]. Accordingly, many countries have increased their energy sector share of clean and renewable energy resources such as solar, wind, hydropower, geothermal, tidal, and biomass [
5].
The Hashemite Kingdom of Jordan is one of the countries in the Middle East with significant concerns regarding its energy security, and fewer concerns about global warming. These concerns were raised due to the limited traditional energy resources [
6], with almost total dependency on imported energy, where 97% of Jordan’s energy demand is imported [
7]. This dependency causes significant pressure on the economy since a vast portion of the annual Jordanian budget is spent on importing this demand, which causes an outflow of foreign currency. On the other hand, similar to many countries in the region, Jordan is rich with renewable energy resources, especially solar energy in almost all of the country, and wind energy in some areas in the north [
6,
8]. Therefore, Jordan has recently started to increase the share of renewable energy sources in its energy market. For instance, between 2017 and 2020 the installed solar and wind capacities were raised almost threefold, as reported in [
9], where the installed solar capacities increased from 591 GWh in 2017 to 1645 GWh in 2020. At the same time, the installed wind capacities increased from 447 GWh to 1378 GWh between 2017 and 2020 [
9]. This significant increase is related to the change in energy policy in Jordan, as well as the drops in the prices of renewable energy systems, especially solar systems [
5]. The Jordanian movement towards increasing the share of renewables is projected to surge, as highlighted by the 2020–2030 strategic energy plan, where there is an intention to increase the share of renewable energy from 11% in 2020 to 48% in 2030 [
10].
As aforementioned, Jordan is located in an area with high solar potential, where the investments in solar energy projects have proven to be technically and economically feasible, especially in the southern part (Ma’an, Aqaba, Tafilah, and Karak) [
6,
11,
12]. For instance, these regions have an average sunshine duration of about 300 days per year [
13], and annual daily average solar radiation on a horizontal surface of 5–7 kWh/m
2 [
13,
14], which is one of the highest values in the world. Hence, most renewable energy investments in Jordan are in solar energy systems. It is reported that the solar systems have the largest contribution of almost 9% of the renewable energy share in Jordan [
9].
The energy production of PV plants is highly affected by the ambient conditions (other than the solar radiation [
15]), especially the ambient temperature [
16], which has been incorporated in the standard energy estimation models of PV plants [
17]. Other studies have highlighted the effect of wind speed [
18,
19,
20,
21,
22], relative humidity [
23,
24], and dust/soil accumulation [
24,
25] on PV production. For instance, excluding wind data from PV estimation models could underestimate the PV production by 3.5%, as reported in [
26]. Other studies such as [
27] reported the necessity to include the wind speed and direction to better estimate the PV production. Likewise, excluding the dust/soiling effect results in overestimating the energy production [
28,
29]. For example, Zaihidee et al., [
30] found that dust accumulation of 20 g/m
2 on a PV panel reduces its efficiency by 15–35%. Moreover, Ullah et al., [
31] reported a 10–40% decrease in the monthly power production due to soil accumulation in Pakistan. Other studies such as [
5] reported that in Oman the losses in the monthly energy production could reach 10.8% if proper cleaning is not maintained. Furthermore, studies in Jordan, specifically in Ma’an [
25], showed the importance of a monthly cleaning process to minimize power production losses to 2.2%. However, other studies showed that the most feasible cleaning period is 15 days for PV plants in Tafilah, Jordan [
16,
32].
The ability to estimate the PV production accurately by incorporating these ambient conditions is vital to policymakers and investors. Excluding these factors from the energy estimation models could over- or underestimate the energy production, and affect the system’s technical and economic feasibility. Therefore, it is crucial to incorporate the soiling and wind effects in estimating the energy production, especially from large-scale PV plants, where minor inaccuracies could propagate and result in estimation errors up to the plant size. A few studies have investigated the performance of PV energy production models, and benchmarked them against the actual production of fixed PV plants with capacities up to 20 MW [
33,
34,
35,
36,
37,
38]. However, to the best of the authors’ knowledge the literature lacks studies that:
Evaluated the performance of different energy estimation models of large-scale (larger than 20 MW) fixed and tracked PV power plants.
Investigated the effect of cleaning cycle frequency on the energy production of large-scale fixed and tracked PV plants.
Estimated the best cleaning frequency for large-scale fixed and tracked PV plants in arid and dusty climates.
Therefore, this study aims to:
Investigate the performance of five energy estimation models, and benchmark them against the actual energy production of large-scale fixed and single-axis tracked PV plants in Jordan with a total capacity of 103 MW.
Investigate the effect of cleaning cycle frequency on the annual energy production and the LCOE of the plant.
Find the best cleaning frequency that maximizes the annual energy production of the two plants, and compare it with the current cleaning frequency.
4. Conclusions
In this study, five PV energy estimation models were evaluated and compared to the actual energy produced from Jordan’s largest PV power plants with a capacity of 103 MW. The plant consists of two sub-plants: a 51.7 MW fixed panels plant and a 51.7 MW single-axis tracking panels plant. Root mean square error (RMSE) and the coefficient of determination (R2) were used to assess the performance of the estimation models. Moreover, the effect of cleaning frequency on the annual energy production as well as the levelized cost of electricity (LCOE) of the PV plant was investigated. Finally, the 103 MW PV plant’s best cleaning frequency was found that maximizes the annual energy production and minimizes the LCOE.
The investigated models in this study have very similar performance in predicting the energy production, with R2 varying between 0.93 and 0.98, and RMSE between 0.93–1.56 MWh for both sub-plants. The results indicate that model 2 slightly outperforms the rest of the models—including the standard model—in terms of R2 and RMSE. The deviation between the measured and estimated energy prediction is expected for many reasons, such as:
Local wind speeds on the PV modules, where the used empirical models were developed for fixed PV modules only.
The adopted models do not incorporate the effect of wind direction due to the unavailability of wind direction measurements at the PV plant.
The models adopted in this study were obtained at specific locations with certain ambient conditions that deviate from the ones in this study.
Finally, the dust\soiling accumulation rates and the drop amount in the PV performance due to this accumulation could be another source for the deviation.
The plant is located in the southern part of Jordan, which is considered a desert with a very hot and dry climate. Hence, regular cleaning of PV modules is required to ensure maximum performance of the PV panels. It is concluded in this study that cleaning the panels every two weeks is recommended, instead of every three months, whereby the new cleaning frequency will increase the annual energy by almost 5% and decrease the LCOE by nearly 4% for the fixed and tracked panels.