Microsoft Word - ETASR_V13_N2_pp10608-10621 Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10608 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in Northern Cyprus: A Case Study Youssef Kassem Department of Mechanical Engineering, Engineering Faculty, Near East University, Cyprus | Energy, Environment, and Water Research Center, Near East University, Cyprus yousseuf.kassem@neu.edu.tr (corresponding author) Huseyin Gοkcekus Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University, Cyprus | Energy, Environment, and Water Research Center, Near East University, Cyprus huseyin.gokcekus@neu.edu.tr Osama Abduljalil Mohammad Hamad Biotechnology Research Center, Libyan Authority for Scientific Research, Libya osamaabdli387@gmail.com Faed Mahmod Buojaylah Fayid Department of Botany, Qubbah Branch, University of Derna, Libya faed.mahmoud.fayid@gmail.com Received: 1 February 2023 | Revised: 22 February 2023 | Accepted: 28 February 2023 ABSTRACT In this paper, an in-depth analysis of small-scale PV in Northern Cyprus is conducted for the first time at 37 locations in Northern Cyprus. No previous study has investigated the viability of off-grid PV systems with various sun-tracking systems in Northern Cyprus. In order to achieve this, NASA POWER data were used for the evaluation of the solar resource in the selected locations. The results showed that the selected locations are suitable for the installation of various scales of PV systems due to the high global horizontal solar radiation. The mathematical modeling method was utilized for the design and analysis of PV systems with various sun-tracking systems and for the assessment of their economic viability and feasibility. Energy production, capacity factor, payback period, and cost of energy production were calculated. The results indicate that the proposed systems are very promising for all the selected locations. The PV projects with a 2-axis sun-tracking system produce a large amount of energy and have a low electricity cost. It was found that the electrical energy cost of the developed systems was within the range of 0.4851-0.6641TL/kWh. The payback periods varied from 4.57 years to 8.49 years, depending on the type of solar PV panel and sun- tracking system. This study provides some useful recommendations for decision-makers regarding the development and deployment of PV energy technology in the country in order to achieve sustainable development goals. Keywords-Northern Cyprus; solar energy potential; off-grid; small-scale PV system; sun-tracking system; mathematical modeling method I. INTRODUCTION Population growth and the rising of people’s living standards have led to the growing global demand for energy, primarily electrical, from fossil fuels. It is known that environmental pollution impacts human health through the emissions of harmful gases caused by conventional energy generation from fossil fuels. Thus, finding alternative energy sources to reduce fossil fuel consumption and environmental pollution is essential [1]. Solar energy has been widely utilized globally to generate electricity and reduce harmful gas emissions. It is clean energy from an inexhaustible and Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10609 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … environmentally friendly energy source. Generally, solar photovoltaic (PV) panels produce electrical power directly from the sunlight. Authors in [2] found that solar power will reduce gas emissions by about 69-100 million tons of CO2, 68- 99 thousand tons of NOX, and 126-184 thousand tons of SO2 by 2030. Authors in [3] reported that solar energy has a significant potential to meet the global electricity requirements in the future. Authors in [4] found that solar energy technologies have a huge potential to mitigate climate change by reducing energy- related emissions. The solar power system is most commonly used today due to improvements in the harvesting of solar energy [5], greater awareness about this energy [6], and low electricity cost compared to feed-in tariffs [7]. Generally, PV power systems are classified into two types (on-grid and off- grid PV systems). They, especially small-scale PV systems, are an effective solution to generate electricity for urban and rural regions [8, 9]. Globally, the share of PV generation capacity could be increased from 2% in 2017 with nearly 400GW installed to 10% in 2040 due to falling PV costs [10]. In Northern Cyprus, fossil fuels are the main source of electrical energy. The electrical energy is produced by steam power plants (Teknecik Steam Unit No. 1 and Teknecik Steam Unit No. 2), diesel electricity generating plants (Kalecik Diesel and Teknecik Diesel), and Serhatköy PV plant [11]. According to Cyprus Turkish Electricity Authority (KIB-TEK), 44.17%, 26.53%, 11.67%, and 11.95% of electricity is generated by Kalecik Diesel, Teknecik Diesel, Teknecik Steam Unit No. 1, and Teknecik Steam Unit No. 2, respectively, whereas the rest of the energy is produced by Serhatköy PV plant (0.1%) in 2021. The total electricity production increased from 1444.01GWh in 2015 to 1551.06GWh in 2019 with an average annual increase percentage of 7.41%. Furthermore, the average annual increase is approximately 3.4% in terms of electricity consumption per capita. Besides, the total installed capacity for these plants is 482.3MW according to KIB-TEK and Northern Cyprus Renewable Energy Board (YEK-Kurulu). Recently, Northern Cyprus has experienced outages with duration of more than one to two hours [12] due to the fast growth of population and constructions. According to T-VINE [13], Northern Cyprus has suffered multiple blackouts due to outages at the power plants inability to produce sufficient electricity to cope with the excessive demands during the intense summer heat. In addition, approximately 30% of total energy consumption and 20% of electricity consumption are spent on residential and commercial buildings, respectively, according to KIB-TEK. Table I lists the costs of electricity in Northern Cyprus according to KIB-TEK. KIB-TEK utilizes the net metering system for allowing end users to generate and export energy surplus to the utility grid. Four new solar PV plants have been constructed and utilized at various locations in Northern Cyprus [14]. Northern Cyprus's climate is moderate and suitable for investing in solar energy, as there are 300 sunny days throughout the year. This increases the efficiency of relying on solar energy as an energy source. Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) values are within the range of 4.80- 5.44kWh/m 2 /day and 4.89-6.15kWh/m 2 /day, respectively according to the solar atlas map. Numerous studies have investigated the solar energy potential in various locations in Northern Cyprus as shown in Table II. Based on the results of the previous studies, the following can be concluded: (1) solar energy is expected to contribute to electricity generation in Northern Cyprus in the future, (2) PV systems can be a good solution for reducing the fossil fuel consumption and CO2 emissions in the island, (3) the use of solar power systems could help the island to become more independent from fossil fuels, and (4) PV systems have the ability to reduce the household electricity bills in Northern Cyprus. TABLE I. ENERGY COSTS IN NORTHERN CYPRUS Before March 2022 Energy consumption based on time Period Cost 0.7:00-17:00 0.98 TL/kWh 17:00-22:00 1.29 TL/kWh 22:00-07:00 0.65 TL/kWh 22:00-00:00 (Weekend) 0.98 TL/kWh Presently Energy consumption based on kW Energy consumption Cost 0-250 kW 0.98 TL/kWh 250-500kW 1.70 TL/kWh 501-750 kW 2 TL/kWh 751-1000kW 2.25 TL/kWh Greater than 1000kW 3.3TL/kWh Therefore, this paper represents a first effort to use a strategic perspective to explore how viable solar energy systems are in Northern Cyprus. The present study aims to investigate the techno-economic and environmental sustainability of rooftop PV systems and large-scale PV in various locations in Northern Cyprus. To achieve this, the solar radiation data are analyzed to classify the solar resource in the selected locations. Moreover, the techno-economic viability of an off-grid PV system with different sun-tracking systems including fixed tilt and 2-axis systems is evaluated using the mathematical modeling approach. II. MATERIALS AND METHODS The proposed methodology aims to assess the solar energy potential in 37 locations in Northern Cyprus to identify suitable locations for future installations of solar PV systems. The economic viability of these PV systems is evaluated. Figure 1 illustrates the methodology used in this study. A. Study Area This study aims to identify the best locations for installing solar PV power systems in Northern Cyprus. Figure 2 shows the selected locations. Table III provides the names and geographical coordinates of the selected locations. Due to the lack of actual weather data, satellite database helps in estimating the potential of wind and solar energy. It has led to the development of satellite-generated or interpolated synthetic meteorological data, which are available in grids with various spatiotemporal resolutions [39]. The gridded database NASA/POWER provides global coverage of complete weather data with a horizontal resolution of 1° of latitude and longitude [40] thus being a potential source for the study of the potentiality of renewable (wind and solar) energies [41, 42]. Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10610 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … TABLE II. SUMMARY OF PREVIOUS STUDIES FOCUSING ON SOLAR ENERGY POTENTIAL IN NORTHERN CYPRUS Reference Year Description/aim Remarks and Key Findings [15] 2011 Investigation of the feasibility of a 1M grid-connected PV plant in 3 locations (Lefkoşa, Güzelyurt, Dipkarpaz) in Northern Cyprus. The feasibility of the developed system increased when the 2-axis PV system was utilized. [16] 2012 Assessment of the performance of Parabolic Trough (PT) and Fresnel PV systems in Northern Cyprus. Fresnel systems have higher absorption o solar radiation, lower CO2 emissions, and operating cost compared to PT systems. [17] 2014 Installation of 30MW hypothetical solar chimney power plant under Northern Cyprus conditions. The annual electricity production of the proposed system was found to be 94.5GWh, which can provide for the annual electricity needs of over 22,128 residences without any CO2, NOx, and SOx emissions. [18, 19] 2015 Build 1.275GW PV plant in Serhatköy site. The plant will improve the electricity sector and will be able to reduce emissions to an acceptable level. [20] 2016 Design and techno-economic analysis of a standalone PV system to meet the electricity of a house in the rural fringe of Famagusta. The electricity generation cost was found to be 0.43TL/kWh and the developed system is a viable technology for the electrification of a house in Cyprus. [21] 2016 Techno-economic and environmental feasibility of 40MW PV power plant and 40MW PT power plant in Nicosia and Famagusta. Power plants in Nicosia have higher profitability than in Famagusta and the utilizing solar power plant will reduce large amounts of CO2 emissions. [22] 2017 Development of a hybrid system to generate electricity for a household in Nicosia The PV-wind hybrid system needs less capital cost, which is considered a more attractive option to set up in the selected location. [11] 2018 Development of a 12MW grid-connected wind farms and fixed-tilt PV power plants in Lefkoşa and Girne. The selected locations were found suitable to build a PV system for energy production and reduce the fuel consumption [23] 2018 Proposing a 1MW grid-connected PV power plant with various sun-tracking systems in Lefke town. The electricity cost was found within the range of 0.109-0.150 $/kWh and annual GHG emission reduction varied from 1321 to 1829 tCO2. [24] 2018 Investigation of the utilization of solar energy in Famagusta. Famagusta has a huge solar energy potential, but the city is not able to generate the required amount of solar energy due to its inappropriate urban design. [25] 2018 Finding the best location for installing a PV power plant based on the highest profitability of the project. Among 19 locations, Güzelyurt was the best to install a PV power plant due to the net present value, payback period and internal rate of return, and levelized cost of electricity. [26] 2018 Proposal of a 4.85 kW grid-connected PV rooftop systems in Nicosia, Morphou, and Dipkarpaz All selected locations have a high solar energy potential. [27] 2019 Development of a 6.4kW grid-connected PV wind system for a household in Lefkoşa, Girne, and Gazimağusa The PV systems are a more economical option for generating electricity in the selected locations compared to wind systems due to the low electricity production cost [28] 2019 Proposition of a 45kW rooftop-building grid-connected PV power system in Lefke town. The electricity cost of the proposed system was found to be 0.056$/kWh, which is lower than the energy cost of traditional energy (0.15$/kWh) [29] 2019 Using of PV as a shading device for solving the heating problems in the residential sector in Famagusta. Utilizing the PV as a shading device can provide up to 50% of the electricity demand of the single-family building and raises the comfort level of the building by about 20%. [30] 2019 Evaluation of the performance of a 110kW grid-connected PV system with different PV technologies based on performance ratio, CF, and energy cost. CdTe PV system has a higher performance ratio compared to other PV technologies and the proposed system can help reduce green gas emissions and supply electricity to the Near East University. [31] 2019 Evaluation of the performance of a 110kW grid-connected PV system in 25 locations. The annual average performance ratio was within the range of 75-80%. [32] 2019 Techno-economic feasibility evaluation of a solar-powered seawater desalination plant in Güzelyurt. Solar desalination could be a profitable investment with governmental incentives and can be considered a sustainable option for solving water scarcity and reducing greenhouse gas emissions. [33] 2019 Technical, environmental, and economic aspects for developing PV/wind hybrid system in the Middle East Technical University Northern Cyprus Campus The proposed system will reduce the annual fuel consumption of the island by 9920 barrels which will also reduce the annual CO2 emissions by 3622 tons. [34] 2020 Development of a 30kW grid-connected PV system for Near East University grand library with various types of PV systems used in building PV technologies. The performance of a freestanding mounting position system with thin film (CdTe) was found better than building-integrated PV. [35] 2020 Techno-economic feasibility of 100MW grid-connected PV system in Lefkoşa. The proposed system would reduce fuel consumption, electricity tariffs, and greenhouse gas emissions. [36] 2020 Development of a 6kW PV-wind hybrid system to meet a single household electricity demands in Güzelyurt. The proposed system provided alternative incentives to the consumers and it is economically feasible. [37] 2021 Investigation of the techno-economic feasibility of small- scale PV systems with different sun-tracking systems for producing household electricity and desalination systems in the Güzelyurt region. The small-scale grid-connected PV systems will reduce the electricity bill. [14] 2021 Design of a PV system for covering the power demands in Near East University Hospital The PV system will help solve the electricity crisis and reduce the fossil fuel consumption. [38] 2021 Evaluation of the techno-economic feasibility of a PV- battery system for a house in Güzelyurt. The electricity generation cost is less than the grid tariff rate. Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10611 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … TABLE III. SUMMARY OF PREVIOUS STUDIES FOCUSING ON SOLAR ENERGY POTENTIAL IN NORTHERN CYPRUS Location No. Name Elevation [m] Latitude [°N] Longitude [°E] Optimum orientation angles Fixed-tilt Vertical Inclined Slope angle [°] Azimuth angle [°] Slope angle [°] Slope angle [°] 1 Akdeniz 89 35.300 32.965 31.0 2.0 52.0 34.0 2 Camlibel 277 35.316 33.071 31.0 3.0 52.0 33.0 3 Lapta 168 35.336 33.163 29.0 -5.0 51.0 31.0 4 Girne 10 35.342 33.331 31.0 -1.0 52.0 33.0 5 Beylerbeyi 225 35.297 33.354 28.0 -22.0 46.0 29.0 6 Bogaz 300 35.288 33.285 31.0 0.0 52.0 34.0 7 Tatlisu 168 35.225 33.451 32.0 -3.0 52.0 34.0 8 Kantara 480 35.401 33.914 31.0 -4.0 51.0 33.0 9 Esentepe 183 35.333 33.579 31.0 0.0 52.0 33.0 10 Guzelyurt 52 35.189 32.982 31.0 0.0 52.0 34.0 11 Gaziveren 19 35.173 32.922 31.0 0.0 52.0 34.0 12 Lefke 129 35.097 32.841 30.0 -8.0 52.0 32.0 13 Yesilirmak 20 35.166 32.737 30.0 -2.0 52.0 33.0 14 Ercan 119 35.159 33.502 32.0 -4.0 52.0 34.0 15 Serdarli 111 35.252 33.610 32.0 -5.0 52.0 34.0 16 Degirmenlik 168 35.253 33.472 31.0 -1.0 51.0 33.0 17 Gecitkale 45 35.233 33.729 32.0 -2.0 52.0 34.0 18 Gonendere 75 35.270 33.657 32.0 -3.0 52.0 34.0 19 Vadili 54 35.139 33.652 32.0 -2.0 52.0 34.0 20 Beyarmudu 87 35.047 33.696 31.0 0.0 52.0 34.0 21 Cayirova 67 35.349 34.031 31.0 2.0 52.0 34.0 22 Iskele 39 35.286 33.884 31.0 0.0 52.0 34.0 23 Mehmetcik 99 35.422 34.078 31.0 2.0 52.0 34.0 24 Gazimagusa 10 35.136 33.936 31.0 2.0 52.0 34.0 25 Salamis 6 35.181 33.897 31.0 1.0 52.0 34.0 26 Alevkaya 623 35.286 33.535 31.0 2.0 51.0 33.0 27 Zumrutkoy 129 35.174 33.049 31.0 0.0 52.0 34.0 28 Alaykoy 166 35.185 33.257 32.0 -2.0 53.0 34.0 29 Lefkosa 134 35.196 33.352 32.0 -3.0 52.0 34.0 30 Ziyamet 82 35.454 34.125 30.0 3.0 51.0 33.0 31 Dipkarpaz 136 35.599 34.379 31.0 5.0 51.0 33.0 32 Yenierenkoy 123 35.536 34.189 31.0 5.0 52.0 33.0 33 Dortyol 54 35.179 33.759 32.0 -1.0 52.0 34.0 34 Kalecik 7 35.334 33.981 29.0 49.0 55.0 36.0 35 Sınırüstü 28 35.276 33.856 34.0 49.0 55.0 36.0 36 Sadrazamköy 68 35.383 32.949 32.0 51.0 54.0 35.0 37 Selvilitepe 879 35.321 33.158 27.0 49.0 54.0 35.0 Fig. 1. Flowchart of the proposed methodology. Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10612 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … Fig. 2. The selected locations. B. Classification of Solar Potential The potential of solar energy is divided into two classifications, namely geographical potential and technical potential [43]. In general, the annual amounts of total solar radiation at a specific location, factoring in existing geographical, social, environmental, and cultural constraints, are the factors that define the geographical potential. At the same time, the technical potential of a specific location is defined as the amount of geographical potential that can be translated into electricity using a PV [43]. According to [44], the solar energy potential was classified based on the annual value of GHI and DNI. In general, global solar radiation is considered applicable for the evaluation of the energy generation for the flat PV system. Table IV shows the classification of solar energy. TABLE IV. CLASSIFICATION OF SOLAR ENERGY Class Annual GHI [kWh/m 2 ] Annual DNI [kWh/m 2 ] 1 (poor) <1191.8 <936.9 2 (marginal) 1191.8-1419.7 936.9-1255.7 3 (fair) 1419.7-1641.8 1255.7-1546.8 4 (good) 1641.8-1843.8 1546.8-1840.9 5 (excellent) 1843.8-2035.9 1840.9-2149.9 6 (outstanding) 2035.9-2221.8 2149.9-2533.7 7 (superb) >2221.8 >2533.7 TABLE V. DEFINITION OF THE FREQUENCY DISTRIBUTION TYPE ACCORDING TO THE RANGE OF THE SKEWNESS AND KURTOSIS VALUES Distribution type number Distribution curve Skewness range Kurtosis range I Normal -0.4 < S < 0.4 -0.8�: (5) where � �3 is the referenced efficiency of the PV panels, < �3 is the temperature coefficient of the PV panel, �?@!� is the incident solar radiation for the NOCT test, is the ambient temperature, �3 is the reference temperature of the PV panels, ?@!� is the nominal operating cell temperature at test conditions, ,?@!� is the ambient NOCT, <� , =� , and >� are the hourly beam, reflected and diffuse component of the solar radiation on a tilted surface, and ��� is the efficiency of the solar panels provided by: ��� = � �3 A1 − < �3 B − �3 + � ?@!� − ,?@!� $ �C�DEFCGH (6) ������ is considered the first parameter that needs to be estimated. It can be expressed as [51]: ������ = I JKF����LMNOMPQRMST �5 ��� (7) where ��� is the PV modules efficiency, �576 is the inverter efficiency; 4 is the temperature coefficient, and �U is the battery efficiency. Moreover, designing a battery system is required due to the limitation of daily hours of sunshine and the stochastic nature of solar energy and to support electricity generation sustainability and energy storage [52]. Consequently, the number of autonomy dat (( V ), the battery efficiency (�U ), the inverter efficiency (�576 ), the depth of discharge (>V�V ), and the size of the electrical load (WX! ) should be considered for designing the battery system. Equation (18) is used to determine the Battery Storage Capacity (BSC) in Wh [51]: V�V is the battery storage system depth of discharge, cU is the voltage of the battery block, and dU is the battery storage system's nominal capacity in kWh provided by: dU = ?ZbJKFMPQRM[`Lb\[][ (10) Additionally, a Charge Controller (CC) has been designed to support the battery of the off-grid system. The CC should be designed with at least 25% more of the ������ current to permit cold temperatures [20]. The CC is equipped with a Maximum Power Point Tracker (MPPT) that is required to charge and protect the batteries avoiding both the risk of overcharging and reaching excessive discharge level [20]. The main function of CC is to support electrical load power consumption and promote the battery's lifespan [54]. As mentioned above, an inverter is a component of the system. The capacity of the inverter should be higher than P����� by 20% in order to be able to handle the maximum power of electric loads [55]. E. Off-Grid Photovoltaic System In the literature, several methods and software simulations have been utilized for the evaluation of the life cycle cost of standalone and hybrid PV systems [17, 20, 51, 55-59]. For instance, authors in [17] developed an economic model to evaluate the cost-benefit of an off-grid PV system in rural Gusau, Nigeria. Author in [20] used mathematical modeling to assess the techno-economic feasibility of an off-grid PV system to electrify a house in the rural fringe of Famagusta, Cyprus. Authors in [51] examined the feasibility of an off-grid PV system to drive the electricity consumption of a residential building in Jos, Nigeria using mathematical modeling. Authors in [58] used HOMER software to evaluate the feasibility of wind–PV–diesel hybrid power systems for a village in Saudi Arabia. Authors in [57] investigated the economic viability of a hybrid system that can be used to supply electricity to a rural community in Sri Lanka using HOMER. Authors in [58] evaluated the performance of residential solar PV systems with utility backup in Nagpur, India using mathematical modeling and software simulation. Authors in [55] utilized mathematical modeling to evaluate the feasibility of an off-grid PV system for the electrification of a single residential household in Faisalabad, Pakistan. Authors in [59] investigated the techno- economic feasibility of a stand-alone solar PV system for an isolated locality in Bangladesh using spreadsheet modeling. The mathematical equations that are used in the current study to evaluate the life cycle analysis, which covers all the PV system life stages including the initial capital cost or acquisition cost and the operation and maintenance costs, are expressed below. Cost of a PV array (*�� ) in US$: *�� = ������ × unit price of PV module (11) Initial cost of batteries (ICB) in US$: �*< = BSC × unit price of battery (12) Present worth of the battery for various battery lifespans ((): �*< = *< × I yz5yzVT ? (13) where { is the inflation rate, 9 is the discount rate, and *< is the cost of the battery in US$. Cost of an inverter system (*576 ) in US$: *576 = ������ × unit price of inverter (14) Cost of a charge controller (*!! ) in US$: *!! = Size of charge controller × unit cost of charge controller (15) Cost of a PV installation (*��� ) is US$: Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10614 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … *��� = 10% × initial cost of PV (16) Maintenance cost (*. ): *. = 2% × initial cost of PV (17) Furthermore, the Present Worth (PW) of the *. is estimated by considering the depreciation rate, the inflation rate, and the PV system life span. ��*. = I��7�^�� T × I yz5yzVT × �y�I ��P��[T D y�I ��P��[T � (18) where 9 is the depreciation rate percentage. Levelized cost (�**) in US$: �** = *�� + CB + *576 + *!! + *. + *��� (19) Annualized life cycle cost (1�**): 1�** = �** � y�I ��P��[Ty�I ��P��[T?� (20) Unit electricity cost (�J! ) in US$/kWh: �J! = X�!!���×JKF (21) Annual Savings (AS) in US$: 1Y = Annual Energy Generation × feedin tariff rate for solar power purchase (22) Payback Period (PP): �� = ������� ����������X# (23) F. Solar Panel and Inverter A large number of PV modules with different specifications are available on the market. Mono-crystalline PV modules have the highest efficiency compared to poly-crystalline cells and amorphous silicon modules [54]. Furthermore, cell type, system cost, warranty, size, and power are important factors to select the best PV panel [60]. Therefore, a selection criterion is needed to select a specified PV module to be used in a specific project. A survey of the characteristics of most of the available PV modules from different manufacturers and technologies is done (Table VI). Based on the previous studies, several Panel Selection (PS) criteria are used to select the suitable PV module: �Y =  ¡ �¢£¤�� ¥�¦�¥��§×¨¢£¤�� �©©�¥���¥§ �¢£¤�� ¦ª�¥� שª��� �ª�� ¢© �¢£¤�� (24) �Y =  ¡ �¢£¤�� ¥�¦�¥��§ «ª��� �ª�� ¢© �¢£¤�� (25) �Y =  ¡ �¢£¤�� ¥�¦�¥��§ �¢£¤�� ¦ª�¥� שª��� �ª�� ¢© �¢£¤�� (26) �Y = ¬¢�� ¢© y­®  ¡ �§���� ¯��ª°§ ¦ª¢£¤¥��¢� ¢© y ­®  ¡ �§���� (27) Based on the calculations (Figure 3), UZ158MHC340-60, AE410HM6-72, and NS-290P6 solar modules offer a better ratio compared to other models. It should be noted that these models have been selected based on the selection criteria among the PV modules surveyed. TABLE VI. CHARACTERISTICS OF MOST AVAILABLE PV MODULES Module No Manufacturer Module type Maximum Power [W] Efficiency Module area [m2] Module Price [$] 1 Jinko JKM330M-60-V 330 19.78 1.668 115 2 Solar Fabrik - 340 20.14 1.687 237 3 Panasonic - 325 19.4 1.674 257 4 Ankara Solar AS-M60-310W 310 19 1.63 77 5 AXITEC AC-430MH/144V 430 19.33 2.174 120.4 6 Suntech STP325-24/ Vfw 335 17.2 1.944 81 7 SunLink - 435 20 2.1073 105 8 AE Solar AE410HM6-72 410 20.69 1.674 115 9 Tide Solar - 450 20.4 2.208 90 10 Regitec Solar RMH60/380S1 380 20.9 1.853 110 11 Austa Energy AU410-27V-MHB 410 20.97 1.955 99 12 Horay Solar HS166-380-120M 380 20.5 1.823 84 13 München Energieprodukte GmbH - 440 19.9 2.209 110 14 Fortunes Solar FDS-M6M-60-355BK 355 19.26 1.868 82 15 Sunket SKT375M6-20/HC 375 20.1 1.868 90 16 Smart Solar System HCM60X9-345W 345 20.4 1.689 72.75 17 Mysolar MS400PM5-66SA 400 21.3 1.876 83.51 18 AE solar AE M6-60 320W 320 19.27 1.661 62.42 19 UzinSemi UZ158MHC340-60 340 20.1 1.695 57 20 Betop (EU) Tech GmbH SR-325-340-120M 340 20 1.6873 64.89 21 Exiom Solution EX340M-120 340 20.1 1.692 70.98 22 EnergyPal ASP345P6-72 345 17.8 1.94 64.02 23 Betop (EU) Tech GmbH NS-290P6 290 17.8 1.626 47.24 24 Ankara Solar AS-P60-290W 290 17.83 1.63 30 25 Ankara Solar AS-P72-345W 345 17.78 1.94 35 26 Ankara Solar AS-B60 -320W 320 21 1.61 47 27 Canadian Solar mono-Si - CS6X-300M 300 15.63 1.919 249 28 Canadian Solar poly-Si - CS6X-310P 310 16.16 1.918 248 Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10615 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … Fig. 3. PS criteria and efficiency of the surveyed PV modules. Furthermore, output AC power, DC-AC conversion efficiency, and capital cost of the inverter are the main factors to select a suitable inverter. As mentioned above, this study aims to design a small-scale on-grid and off-grid PV system, thus, a hybrid inverter, the combination of an on-grid and off- grid solar inverter, is selected in this study. III. RESULTS AND DISCUSSION Mathematical modeling methods and RETScreen Expert software were utilized to evaluate the viability of small-scale rooftop and large-scale PV systems in different locations in Northern Cyprus. A. Solar Irradiation Characteristics Analysis In this section, the GHI data were analyzed. GHI is an essential parameter to analyze the solar resource at the project location to evaluate power generation for flat-panel PVs [51, 61]. The statistical description of GHI for the period of 2005- 2020 is listed in Table VII. The description includes the mean, standard deviation (SD), coefficient of variation (CV), minimum (Min.), maximum (Max.), Kurtosis (K), and Skewness (S). It can be seen that the mean monthly value of GHI is within the range of 142.0-162.3kWh/m 2 . The maximum and minimum values of mean GHI are recorded in Ercan and Beylerbeyi, respectively. The CV values are moderately low, ranging from 36.6% to 47.53%. Additionally, all S values are negative, indicating that all distributions are left skewed. The K values varied from -1.7 to -1.68. Moreover, Type VI, which is bimodal, and symmetrical with a flat peak, characterizes the mean monthly GHI. Figure 4 illustrates the annual value of GHI for all selected locations. It is seen that the annual GHI values varied from 1704.07 (Beylerbeyi) to 1948.00 (Ercan) kWh/m 2 , respectively. Furthermore, the solar resources at project locations are categorized as good, excellent, outstanding, and superb as shown in Table VIII. Based on the value of GHI, it is observed that the solar resource of all selected locations expect Beylerbeyi is categorized as class 5 (excellent). It is concluded that the selected locations are suitable for the installation of large- or small-scale PV systems due to the values of GHI and DNI. Accordingly, all selected areas are suitable for the installation of PV/flat panels and CSP systems. Fig. 4. Annual value of GHI for all selected locations. Fig. 5. Sun's path over a year for some selected locations. Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10616 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … TABLE VII. STATISTICAL ESTIMATORS OF THE MONTHLY GHI IN KWH/m2 FOR THE 2005-2020 PERIOD Location Mean SD CV Min. Max. S K Distribution type Akdeniz 159.5 61.1 38.3 75.4 238.2 -0.11 -1.68 Bimodal, symmetrical with a flat peak Camlibel 159.9 61.1 38.23 75.8 238.7 -0.11 -1.68 Bimodal, symmetrical with a flat peak Lapta 155.4 65.7 42.29 63.6 238.2 -0.15 -1.69 Bimodal, symmetrical with a flat peak Girne 160 61 38.1 76.1 238.5 -0.11 -1.68 Bimodal, symmetrical with a flat peak Beylerbeyi 142 67.5 47.53 51.5 227.8 -0.09 -1.7 Bimodal, symmetrical with a flat peak Bogaz 159.9 60.5 37.84 76.4 237 -0.12 -1.69 Bimodal, symmetrical with a flat peak Tatlisu 162 60.5 37.33 78.2 239.2 -0.12 -1.68 Bimodal, symmetrical with a flat peak Kantara 156.5 57.2 36.55 76.1 227.7 -0.16 -1.69 Bimodal, symmetrical with a flat peak Esentepe 160.5 60.8 37.87 76.2 238.6 -0.11 -1.68 Bimodal, symmetrical with a flat peak Guzelyurt 160.8 60.8 37.79 77.3 239.2 -0.11 -1.68 Bimodal, symmetrical with a flat peak Gaziveren 160.8 60.8 37.81 77.3 239.4 -0.1 -1.68 Bimodal, symmetrical with a flat peak Lefke 160.4 60.3 37.61 77.3 237.2 -0.12 -1.69 Bimodal, symmetrical with a flat peak Yesilirmak 159.7 60.1 37.66 76.8 236 -0.12 -1.69 Bimodal, symmetrical with a flat peak Ercan 162.3 60.2 37.11 78.4 238.8 -0.13 -1.68 Bimodal, symmetrical with a flat peak Serdarli 160 60.6 37.86 76.2 237.3 -0.12 -1.69 Bimodal, symmetrical with a flat peak Degirmenlik 159.7 60.7 37.99 75.8 237 -0.12 -1.69 Bimodal, symmetrical with a flat peak Gecitkale 162.2 60.3 37.17 78.1 238.7 -0.13 -1.68 Bimodal, symmetrical with a flat peak Gonendere 160.4 60.7 37.86 76.2 238.3 -0.11 -1.68 Bimodal, symmetrical with a flat peak Vadili 162.3 60.2 37.13 78.2 238.7 -0.13 -1.68 Bimodal, symmetrical with a flat peak Beyarmudu 162.2 60.2 37.13 78.2 238.6 -0.13 -1.68 Bimodal, symmetrical with a flat peak Cayirova 159.2 60.2 37.81 75.6 235.7 -0.12 -1.68 Bimodal, symmetrical with a flat peak Iskele 160.3 60.6 37.78 76.2 237.8 -0.12 -1.68 Bimodal, symmetrical with a flat peak Mehmetcik 159.2 60.2 37.82 75.6 235.7 -0.12 -1.68 Bimodal, symmetrical with a flat peak Gazimagusa 161.8 60 37.1 77.8 237.5 -0.13 -1.68 Bimodal, symmetrical with a flat peak Salamis 161.8 60 37.1 77.8 237.5 -0.13 -1.68 Bimodal, symmetrical with a flat peak Alevkaya 160.5 60.8 37.88 76.3 238.6 -0.11 -1.68 Bimodal, symmetrical with a flat peak Zumrutkoy 161.2 60.7 37.66 77.7 239.3 -0.11 -1.68 Bimodal, symmetrical with a flat peak Alaykoy 160.5 60.8 37.88 76.3 238.6 -0.11 -1.68 Bimodal, symmetrical with a flat peak Lefkosa 162.1 60.4 37.28 78.6 239.3 -0.12 -1.68 Bimodal, symmetrical with a flat peak Ziyamet 159 60.2 37.85 75.5 235.2 -0.12 -1.69 Bimodal, symmetrical with a flat peak Dipkarpaz 158 60 38 74.2 232.9 -0.13 -1.69 Bimodal, symmetrical with a flat peak Yenierenkoy 157.8 60.4 38.29 74 234.6 -0.12 -1.68 Bimodal, symmetrical with a flat peak Dortyol 161.9 60 37.09 77.9 237.5 -0.13 -1.68 Bimodal, symmetrical with a flat peak Kalecik 160.3 60.6 37.8 76.1 237.7 -0.12 -1.68 Bimodal, symmetrical with a flat peak Sınırüstü 160.3 60.6 37.78 76.2 237.8 -0.12 -1.68 Bimodal, symmetrical with a flat peak Sadrazamköy 159.5 61.1 38.33 75.4 238.3 -0.11 -1.68 Bimodal, symmetrical with a flat peak Selvilitepe 155.5 59 37.96 74.9 230.4 -0.11 -1.74 Bimodal, symmetrical with a flat peak TABLE VIII. SOLAR ENERGY CLASSIFICATION FOR ALL LOCATIONS BASED ON THE ANNUAL GHI IN KWh/m2. Location GHI Class Location GHI Class Akdeniz 1914.023 5 Beyarmudu 1946.873 5 Camlibel 1918.425 5 Cayirova 1910.374 5 Lapta 1865.277 5 Iskele 1923.826 5 Girne 1920.575 5 Mehmetcik 1910.203 5 Beylerbeyi 1704.069 4 Gazimagusa 1941.646 5 Bogaz 1919.021 5 Salamis 1941.734 5 Tatlisu 1944.539 5 Alevkaya 1925.431 5 Kantara 1877.795 5 Zumrutkoy 1933.877 5 Esentepe 1925.804 5 Alaykoy 1925.431 5 Guzelyurt 1929.479 5 Lefkosa 1945.419 5 Gaziveren 1929.953 5 Ziyamet 1908.114 5 Lefke 1925.094 5 Dipkarpaz 1896.059 5 Yesilirmak 1916.061 5 Yenierenkoy 1893.448 5 Ercan 1948.001 5 Dortyol 1942.426 5 Serdarli 1920.338 5 Kalecik 1923.242 5 Degirmenlik 1916.306 5 Sınırüstü 1923.958 5 Gecitkale 1946.526 5 Sadrazamköy 1913.993 5 Gonendere 1924.809 5 Selvilitepe 1866.323 5 Vadili 1947.005 5 The sun's direction for one year is illustrated in Figure 5 for some selected locations. The gray fills represent the terrain horizon, while the unit horizon (represented by blue fill) may have a shading effect on solar radiation. The black dots represent true solar time, while the blue markers indicate the local hourly time. A gray line is used in the illustration to depict three paths of the sun in three curves. The sun's lowest gray path is at the winter solstice, the mean curvature at the equinox, and the upper curvature at the summer solstice. The curves of the sun show that in winter, the sun has a low horizon and in summer, the sun has a high horizon. B. Electric Energy Production and Capacity Factor of 6.5kW Off-Grid PV Systems In this study, the PV system is designed according to the minimum value of GHI to satisfy the occupants' electricity demands for the whole year for the selected household. Based on the previous section, the maximum PV power is estimated to be 14.783kWh based on the minimum value of GHI on the horizontal surface for Beylerbeyi. According to the renewable energy board (Yek-Kurulu), the customers can install 5kW single-phase grid-connected and up to 8kW three-phase grid- connected PV systems. Consequently, the capacity of the developed system is assumed to be 6.5kW. Accordingly, the economic viability of off-grid PV systems with various sun- Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10617 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … tracking systems is discussed in this section. Based on the selection criteria, UZ158MHC340-60, AE410HM6-72, and NS-290P6 with 340W, 410W, and 290W peak capacity are selected in this study. In the following sections, the results obtained from the mathematical model are summarized. The performance of the PV systems in terms of PV output and Capacity Factor (CF) is dependent on the orientation angles [14]. Thus, the Electric Energy Production (EEP) and CF were estimated for the different sun-tracking systems. It should be noted that EEP and CF for the fixed-tilted, vertical axis and inclined system are determined at the optimum orientations angles. The mean annual values of EEP for all selected locations are shown in Figure 6. It is found that the maximum and minimum values of EEP are recorded in Sadrazamköy and Beylerbeyi, respectively. It should be noted that the monthly (MPVP) and annual (APVP) PV energy production can be determined by (28) and (29), respectively. As an example, the annual PV electrical energy production for Beylerbeyi and Sadrazamköy is shown in Figure 7. ±�c� = ��� × 1�� × ±²³�ℎµ¶ �·� (28) 1�c� = ∑ ��� × 1�� × ±²³�ℎµ¶ �·�y¹5ºy (29) Fig. 6. Annual EEP value of all selected locations. Among the PV models, the results indicate that the highest monthly/annual EEP is achieved when NS-290P6 is used, as shown in Figure 7. Besides, the solar tracking system can increase the annual energy production up to 20% and 40% using single-axis and 2-axis systems, respectively according to [62, 63]. It can be noticed that the amount of output power increases when the sun-tracking system is used. These results are supported by the findings in [54, 64]. As mentioned above, CF is an important parameter to evaluate the performance of PV systems. Figure 8 illustrates the annual value of CF for all selected locations. It is found that the CF values range from 22.43 to 39.77%. The highest average annual value of CF was recorded in Sadrazamköy while the lowest value was obtained in Beylerbeyi for a 2-axis PV system and a fixed-tilted PV system, respectively. Other researchers who analyzed the performance of off-grid PV systems in terms of CF can support these observations. Authors in [65] found that the annual value of CF for the solar off-grid system is 24.6%. Authors in [66] found that the CF values of off-grid PV/hydrogen fuel cells are within the range of 16.4– 31.2%. Moreover, authors in [67] found that the value of CF lies in the range of 16.2-30.3%. Based on these findings, it can be concluded that the value gained during the current study for each location is consistent with the accepted values. Thus, it is technically sustainable to install off-grid PV systems at all the selected locations in Northern Cyprus. Fig. 7. Monthly PV energy production using UZ158MHC340-60 for Beylerbeyi and Sadrazamköy with various sun-tracking systems. C. Economic Viability of 6.5kW Off-Grid PV Systems The performance of the proposed systems is evaluated by estimating the economic factors of each system. In this study, the value of 4 is assumed to be 80% due to a 15–20% loss in efficiency as a result of increasing cell temperature to around 60℃ [20]. Moreover, the efficiency of the selected battery is 85% and the efficiency of the PV panels is listed in Table VI. Fig. 8. Annual CF value of all selected locations. Additionally, the required BSC is determined based on the assumption value of the ( V as 2 days and maximum >V�V as Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10618 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … 80%. Moreover, Trojan SAGM batteries were utilized in this study (nominal voltage = 12V, round trip efficiency = 85%, throughput = 2285.1kWh, capital cost = 538 USD, replacement cost = 500USD, and operating and maintenance cost = 8 USD/year). Fig. 9. Unit electricity cost for all the proposed PV systems (the prices are estimated based on the US dollar exchange rate during the wrtiting of the paper). Generally, assessing the economic viability of the proposed system is important. Based on the mathematical modeling method, the costs of PV modules, inverter, battery, charge controller, installation, and operation and maintenance were calculated. Further, the LCC, ALCC, PP, and �J! are mathematically estimated for various PV technologies and sun- tracking systems. It should be noted that the initial investment, battery replacement, and operation and maintenance of the system are determined using current market prices. Additionally, the financial parameters inflation rate and discount rate are 8% and 6%, respectively. The total Life Cycle Cost (LCC) of the proposed PV systems including the initial investment cost, the present value of battery replacement cost, and the present value of operation and maintenance cost was estimated. It was found that LCC values are within the range of 286903.39-656408.00 USD. Moreover, Figure 9 illustrates the value of PP and �J! for all selected locations. It was found that the �J! values varied from 0.4851TL/kWh to 0.6641TL/kWh. The lowest �J! value of 0.4851TL was obtained from the proposed system with a 2-axis system. These values are compared with the current electricity prices in Northern Cyprus (see Table I) and [20]. Authors in [20] estimated �J! to 0.43TL/kWh. Therefore, it is clear that the price of the electricity generating by PV systems is less than that of a conventional system. It can be concluded that the developed systems provide a significant insight into the economic feasibility of the project for all locations. Moreover, the PP for the off-grid PV systems with various sun-tracking systems and PV technologies is determined. It should be noted that the feed-in tariff rate varied from one country to another. For example, the value of the feed-in tariff during the first year after the system installation in Iran is 0.247$/kW for a PV system with a capacity smaller than 20kW [68]. According to [69, 70], the feed-in tariff for PV systems in Turkey is 13.3 $ cents/kWh, for an application period of 10 years. In this study, the feed-in tariff rate for solar power purchase is assumed to be 0.133 USD/kWh. It is observed that PP values varied from 4.57 to 8.49 years, depending on the type of the panel and the sun-tracking system, as shown in Figure 10. Fig. 10. Payback period of all the proposed PV systems. Engineering, Technology & Applied Science Research Vol. 13, No. 2, 2023, 10608-10621 10619 www.etasr.com Kassem et al.: Economic Viability of a 6.5kW Off-grid Solar PV with Various Sun-Tracking Systems in … IV. CONCLUSIONS The current study is one of the first attempts to assess the techno-economic validity of using an off-grid PV system with various sun-tracking modes in Northern Cyprus. To fulfill this objective, NASA POWER data were used to categorize the solar resource in the selected locations. Additionally, the mathematical modeling method was used to estimate the energy production, capacity factor, payback period, and the cost of energy production of the proposed systems. The main results of the current study are:  Ercan is the most suitable location for the installation of various sizes of PV systems.  The conducted analysis showed that the development of the proposed 6.5kW off-gird PV power system is very promising in all the selected locations.  An economic comparison between different tracking modes revealed that the use of a 2-axis tracking system is the most economical option for all locations.  The electricity price of the developed systems was found within the range of 0.4851-0.6641TL/kWh for off-grid PV systems. 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