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Outdoor performance analysis of different PV panel types
Renewable and Sustainable Energy Reviews 67 (2017) 651–661
Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
Outdoor performance analysis of different PV panel types Erdem Elibol a,n, Özge Tüzün Özmen b, Nedim Tutkun a, Oğuz Köysal b a b
Dept. of Electrical & Electronics Engineering, Duzce University, Duzce, Turkey Dept. of Physics, Duzce University, Duzce, Turkey
art ic l e i nf o
a b s t r a c t
Article history: Received 12 November 2015 Received in revised form 1 June 2016 Accepted 9 September 2016
Photovoltaic (PV) panel efficiency has been tested in the laboratory at standard test conditions (STC) (25 °C, 1000 W/m2 and AM:1.5). However, PV panels are used in different regions and climatic conditions quite different from STC. Due to that, panel efficiency is not observed same with manufacturer catalogue data. This study focus on outdoor testing of PV panels performances at literature, in addition, one-year results of mono-crystalline (2.35 kW), polycrystalline (2.64 kW) and amorphous silicon (2.40 kW) photovoltaic panels were analysed. These PV panels were placed on the roof of Düzce University Scientific and Technological Researches Application and Research Centre (DUBİT) in Düzce Province, in Turkey, one of the countries with the highest solar power potential in Europe and connected to power grid. Amounts of energy produced by the panels over a day, a month and a year as well as inverter efficiency and performance ratios were calculated. Performance ratios were found out as 73%, 81% and 91% for a-Si, polycrystalline and mono-crystalline PV panels, respectively. Panel efficiency was calculated as 4.79%, 11.36% and 13.26% in the same order. All results were compared with Previous studies. Statistical analysis was made to state relationship between efficiency and performance ratios of panel types, environmental temperature, panel temperature and amount of radiation. As a result of the statistical analysis, it was observed that temperature increase of 1 °C increased the efficiency of a-Si panels 0.029% and the efficiency of polycrystalline panels 0.033%, yet, decreased the efficiency of mono-crystalline panels 0.084%. & 2016 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy PV systems PV panel types Yield factor
Contents 1. 2. 3.
4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 Description of location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 Experımental network set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1. Solar PV plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1.1. PV panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1.2. Inverter Sunnyboy 2500HF-30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1.3. Sunny webbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 3.1.4. TFA 35.1077 meterological station and SMA sensorbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 PV characterıstıc parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 4.1. PV efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 4.2. Specific yield factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.3. Reference yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.4. Performance rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.5. PV plant capacity factor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.6. Inverter efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657
n
Corresponding author. E-mail addresses: [email protected] (E. Elibol), [email protected] (Ö.T. Özmen), [email protected] (N. Tutkun), [email protected] (O. Köysal). http://dx.doi.org/10.1016/j.rser.2016.09.051 1364-0321/& 2016 Elsevier Ltd. All rights reserved.
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4.7. CO2 emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Results and discussıon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Industry that developed in parallel with scientific advances in the last century enabled national economies to foster and production to increase dramatically. These developments in economy and production has increased the need for more energy and this relation between production and energy turned into a vicious circle. The total energy consumption of the world reached 19,782 billion kW h/year in 2014 [1]. Besides, the energy need is expected to increase 35% by 2035 because of the fact that population grows so quickly and energy-based technology becomes more general [2]. 80% of the energy need around the world is met by fossil fuels. This causes CO2 amount emitted into the atmosphere to increase swiftly [3]. If the role of the fossil fuels to meet energy demand continues, it is foreseen that industrial CO2 emissions will reach 35 GT level by 2035 and the atmosphere temperature will increase 3.6 °C [4]. These negative results are cited in environmental reports and people have become more sensitive on environmental issues. Therefore, it is expected that some main problems like environmental pollution, global warming etc. will change political opinions and road maps of foremost leaders and fossil fuel use will decrease by 2040 [4]. This expectation paves the way for requirement for renewable energy resources that are environmentfriendly, clean, cheap and sustainable. Photovoltaic systems (PV) including all these main properties and capable of producing energy at a certain levels in ever place letting in solar radiation are the ones with the highest solar potential among renewable energy resources. Mousazadeh et al. showed that the energy to be produced yearly when 16% of the world surface was covered with PV panels of 10% efficiency would be two times more than the energy to be generated from fossil fuels [5]. In fact, annual energy potential reaching the world from the sun is 1,78x109 MW which is 50 times more than current coal fuels and 800 times more than oil reserves [6]. This potential makes PV panels to be more widely used and several large- and small-scale sun farms have been built with output power ranging between 1 kW and 550 MW [6–9]. Companies and scientists carry out lots of studies to increase the efficiency and quality of current PV panels and to reduce their unit price [10]. As is known, PV panels with several different manufacturing technologies are produced today. Not only spectral reactions, temperature coefficients, voltage and current values of different panel types are different, but also their reactions to environmental factors like radiation, temperature and wind speed differ. The efficiency of PV systems is tested by producing companies in a laboratory environment and in a controlled way under standard test conditions (STC) at 25 °C and at a radiation level of 1000 W/m2 [11–13]. However, solar spectrum changes depending location. Although it is known that energy efficiency and performance of PV panels change according to atmospheric conditions like temperature, radiation etc., determining the best efficient panel type is an important criterion [14]. Evaluating different panel type technologies according to atmospheric conditions and selecting the most efficient panel types have been the subject of many studies. Williams et al. compared the performances of polycrystalline silicon (p-Si), single and multi-junction silicon amorphous silicon (a-Si), CdTe modules and proved that environmental factors
657 657 659 660
influenced module temperature, Amount of incoming solar radiation onto module surface and module angle affected energy production performance in the study they made in Loughborough UK [15]. Sharma et al. studied on p-Si, hetero-junction with instruction thin layer silicon (HIT) and amorphous single junction silicon (aSi) modules in the solar energy centre during one-year period in India. They compared the energy efficiency and performance ratios (PR) of the modules with external environment measurements and the simulation results [16]. As a result of this performance comparison, it was put forward that the most suitable solar panel module were a-Si and HIT for the area where PV modules were built and energy efficiency of a-Si module was 14% more in summer months and 6% less in winter months compared to p-Si module. It was also noted that HIT modules generated 4–12% more energy than p-Si modules. Mondol et al. observed the PV system connected to power grid with 13 kW output power in Ireland during three-year period and analysed the data according to hours, days and months. They calculated the efficiency of radiation amount and inverter. They also observed that PV efficiency decreased approximately 10% when module surface temperature increased in summer months and PV efficiency decreased between 4% and 8% in low radiation amount [17]. In the study Minemoto et al. carried out to evaluate the performances of a-Si and mc-Si modules depending radiation distribution and temperature in the external environment, they observed that a-Si modules were rather efficient in blue-rich spectrum, yet, the efficiency of mc-Si modules changed depending temperature similarly, Ye et al. reported in the study that they carried out on five different types of PV models that a-Si and micro morph Si PV module performances were affected by radiation level and frequency fluctuation more than temperature change, yet dependency of mono-crystalline modules on temperature was much more higher [18]. Mieke compared polycrystalline and a-Si modules through photovoltaic systems installed in Australia. It was seen that a-Si PV modules generated 20% more electrical energy than polycrystalline modules [19]. Similarly, Akhmad proved as a result of 2-year observation that performances of polycrystalline surfaces exposed to high temperatures decreased at a certain level and a-Si modules were more useful for the climates with high temperatures [20]. Ayompe et al. analysed 13 months' data of the PV system with 1.72 kW output power installed on a roof of a building in Ireland. They observed module efficiency, daily and monthly energy production, system and the efficiency of inverter. As a result of the study, module efficiency was found as 14.9, the efficiency of system as 12.6 and inverter efficiency as 89.2 [21]. Canete et al. analysed 4 different module technologies, like a-Si, microcrystalline silicon, Cd and Amount of incoming solar radiation for a year in Spain and reported that thin-films were more productive than polycrystalline modules [22]. Carr and Pryor [14], and Del Cueto [23] evaluated various PV panel Technologies in Perth, Australia and stated that thin-films gave more productive results. Rehman and El-Amin showed in the study they made with polycrystalline modules that the efficiency of module depended on module temperature [24]. Milosovlojevic et al. analysed the system performance, capacity factor and the yield of solar panel
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
system with 2 kW output power in Serbia and reported that performance ratio was about 93.6% and the efficiency of system reduced when temperature increased [25]. Diaz et al. tested 12 module technologies in a rural area with an off-grid installed solar system [26]. Park et al. examined electrical and temperature relationship of semi-transport PV modules. In this study, they showed that 1 °C increase in the temperature of module surface leaded to 0.48% reduction in powers of the modules reduced 0.48% under STC and 0.52% in the external environment [27]. Makrides et al. Reported in the study they made on grid on system in Cyprus that due to temperature increase, DC output power loss was 8% for mono-crystalline modules, 9% for multi-crystalline modules and 5% for thin-films [28]. As it is seen from literature, PV panels performance were analysed in different geographical conditions with varied temperature, irradiation and wind speed. Despite Turkey's high solar potential [29], there is only one study about outdoor PV panels performance comparing. At this study Başoğlu et al. analysed c-Si, mc-Si and CdTe PV module's performance in Izmit, Turkey [30] Results of this work showed that PR rates are 83.8%, 82.05% and 89.76% for mc-Si, c-Si and CdTe, respectively [30]. Solar energy studies are still very new in Turkey in spite of high solar potential [29] and Turkey's dependency on energy import, mainly petrol and natural gas, is increasing like all around the world [1]. While 26% of current energy demand is met by domestic resources, 74% of it is met by different foreign resources [31]. According to International Energy Agency (IEA) predictions, Turkey is the country whose energy demand may increase in the fastest way in short- and long term among member states. The studies show that total final energy demand and total first energy demand are expected to reach 170.3 and 222.4 MTEP, respectively, by 2020 with about double increase, the demand for electricity, natural gas and petrol are expected to reach 398–434 billion kW h, 59 billion cubic meters and 59 million tonnes, respectively [32]. With an important potential of renewable energy, Turkey is ranked as the seventh in the world and the first in Europe in terms of geothermal potential. Besides, developing hydro-electricity sources, wind and solar energy rank in priority. It is foreseen that 30% of Turkey's total energy demand will be met by renewable energy resources by 2023. Moreover, Turkey became a founding member of International Renewable Energy Agency (IRENA) through an agreement signed at the meeting held on 26 January 2009 in Bonn, which proves that Turkey attaches importance to developing renewable energy resources [32]. In Turkey, as of February 2015, installed solar energy is about 90 MW [33]. Basaran et al. predicted that this number would reach to 200 MW by 2020 and 39.200 MW by 2050 thanks to sun farms to be built increasingly every year [33]. Moreover, this study showed that $/kW h cost of electricity could be the cheapest with PV panels when company expenses and electrical energy generated during the system life were compared. This study includes literature review about outdoor PV module performance analysis and also analyses of hourly, daily and monthly performances of 2.40 kW a-Si, 2.64 kW polycrystalline and 2.35 kW mono-crystalline modules placed on the roof of Düzce University Scientific and Technological Researches Application and Research Centre (DUBIT) within Düzce University Campus in Düzce Province, for contribute to the literature. Besides, the efficiency of PV panels and inverter were compared with each other, with catalogue data of producing company and with previous studies, the most suitable panel was selected for current environmental conditions for study location. The study was organized for a-year period between January 2014 and December 2014.
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2. Description of location Düzce is located on the coast of Black Sea, at 40°50 N, 31°9,4 E, between Ankara, the capital city of Turkey, and Istanbul. The location of Düzce in Turkey and in the world is shown in Fig. 1. Düzce is the 81st and last city of Turkey. The population of this small province that has been trying to pick up the pieces after the big earthquake in 1999 is increasing rapidly due to its location and growing industry potential, the province hosts approximately 352.000 people. Annual electricity consumption in Düzce is 837.616 MW h which is 0.42% of energy consumption of Turkey. Düzce holds 0.08% of current installed power in Turkey most of which is met by hydroelectric and thermal power plants. Due to regional factors, wind speed in Düzce is about 1.5 m/s which is not enough to support power plants to be built. Among renewable energy systems, solar power ranks first with its potential. Hours of sunshine of Düzce per year are 1905 h/year. While average hours of sunshine per day decrease to 3.9 h in winter, it is about 8.4 h in summer [34].
3. Experımental network set up 3.1. Solar PV plant 24 amorphous silicon thin-film panels (a-Si), each with 100 W PV panel power and total 2400 W output power, 11 polycrystalline panels, each with 240 W PV panel power and total 2640 W output power and 10 mono-crystalline panels, each with 235 W PV panel power and total 2350 W output power were placed on the roof of DUBIT in Konuralp Campus in Düzce University in September 2013. a-Si modular series was installed in double row, polycrystalline and mono-crystalline panels in single row. The appearance of PV systems on the roof surface is given in Fig. 2. Every module series was connected to a different inverter with conducting wire. Electrical energy produced by PV modules and converted into AC currency (230 V and 50 Hz) via an inverter was transferred to power plant connected to DUBIT building. The inverters used in the system are shown in Fig. 3 and the system installed on the roof of DUBIT in Fig. 4. The data obtained from the system was recorded at five minute intervals via Sunny webbox with bluetooth. Meteorology recorder was added to the system to follow meteorological data, temperature, radiation and wind speed. 3.1.1. PV panels 24 Mitsubishi Solar a-Si Thin film (PIN single junction) panels (MA100T2) were connected in series whose size is 1414 1114 35 mm and total surface is 1.575 24 m2. The module's weight is 21 kg. 11 polycrystalline panels (Symphony Energy SE-M240) are used at this study. Physical properties of this PV panel are 19 kg weight and 1605 986 38 mm module size. 10 monocrystalline (Symphony Energy SE-S235) whose size is 1636 982 35 mm and weight is 19 kg. Electrical properties of these 3 PV modules are shown at Table 1. 3.1.2. Inverter Sunnyboy 2500HF-30 In this study, 3 Sunnyboy 2500 HF-30 single phase inverters were used. An inverter was placed at the output of every panel type. The efficiency achieved by 2600 W DC power inverters was 96.3%. It is possible to observe DC currency and voltage, converted into AC produced from PV panels and to follow daily, instant and total production information by the inverter with monitoring system. The size of the inverter is 348 580 145 mm and it weighs
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E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
Fig. 1. Location of Düzce province in Turkey and in the world.
Fig. 2. PV systems on DUBİT.
about 17 kg. Technical properties of Sunnyboy Inverter are shown in Table 2. 3.1.3. Sunny webbox As a central communication interface, Sunny webbox was connected to PV system. Webbox that can be connected to computer via a cable collects all data of the devices it is connected to, documents and enables PV system to be constantly watched. The data kept graphically can be displayed with Flash view presentation software. Webbox works between 25 and þ50 degrees and its size is 125 130 57 mm and it weighs 750 g. Its data storage capacity is 2 GB. 3.1.4. TFA 35.1077 meterological station and SMA sensorbox Meteorological parameters, ambient temperature (°C), the global solar energy per square meter on surface, wind speed (m/s), are measured by TFA 35.1077 Meteorological station which was
installed on roof of DUBIT. Meteorological data are recorded for every 5 min. TFA 35.1077 is tested to run under the ambient temperature 40 °C to þ65 °C. The station also forecast the weather condition from 2 h to 24 h. With the station, SMA sensor box was installed near PV panels with the same angle, 30°. SMA sensor box send the information about ambient temperature, surface temperature of PV panels and solar radiation to Sunny webbox for every 5 min with bluetooth. Sensorbox's power consumption is less than 1 W.
4. PV characterıstıc parameters PV characteristic parameters are described by IEC 61724:1998 standard. These parameters are PV energy efficiency, yield factor, performance rate (PR) and capacity factor (CF) [35].
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
655
Fig. 3. Connected inverters on DUBIT.
Fig. 4. Schematic of the PV plants installed on the roof of DUBIT.
4.1. PV efficiency
In this equation, ƞsys represents instant efficiency of the system,
Instant system efficiency is calculated as the energy produced by the system to amount of radiation reflecting onto PV surface ratio. The efficiency of PV panels is a ratio expressing how much of radiation reflecting onto total surface area of panel surface is converted into electrical energy by PV panel. PV panels have some efficiency differences resulting from production technologies and semiconductor technology [36]. PV efficiency can be calculated in instant, hourly, daily, monthly and yearly periods. Instant system efficiency is calculated as (1) [36–38]
ƞsys =
Esys S·Gopt
(1)
Esys power transferred to power plant by the system, S total surface area of the panels and Gopt radiation energy per unit area (W/m2). Daily, monthly or yearly system efficiency is described as the energy transferred to power plant in stated period to radiation energy reflecting onto total PV panel surface ratio. Hourly energy efficiency (2) is expressed as is [36,38–42].
ƞsys, h =
Esys, h S·Gopt, h
(2)
In this equation, Esys, h shows hourly AC power amount transferred to power plant by the system and Gopt , h hourly radiation energy reflecting onto unit area of panel surface. Daily efficiency analysis can be expressed with (3).
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to power plant, using various conversion steps, energy production and losses of PV system are normalized to installed power value under STC. One of these parameters is YR and is described in a way that radiation energy reflecting onto unit area of normalized panel surface is normalized to radiation in unit area described under STC and is expressed with (5) [30–35].
Table 1 Electrical characteristic of 3 different PV panels. Parameter
Monocrystalline Polycrystalline
STC power rating PMP 235 W (W) Short circuit current 8.22 A ISC (A) Open circuit voltage 37.3 V VOC (V) Voltage at max. power 30.7 V VMP (V) 7.65 A Current at Max. Power IMP (A) Panel efficiency (ƞ) 14.6 Fill factor (FF) 76.6 Temp. coefficiency of 0.03%/°C ISC Temp. coefficiency of 0.34%/°C VOC Standard test condition
a-Si
240 W
100 W
8.71 A
1.17 A
36.6 V
141 V
30.0 V
108 V
8.01 A
0.93 A
14.9% 75.3 0.06%/°C
6.2% – 0.14%/°C
0.35%/°C
0.32%/°C
YR =
)
Table 2 Technical characteristic of Sunnyboy 2500 HF-30 inverter.
Max. DC power (@ cos φ¼ 1) Max. İnput voltage MPP voltage range/rated input voltage Min. İnput voltage/ nitial input voltage Max. İnput current Max. İnput current per string Number of independent MPP inputs/strings per MPP input Parameter (Output AC) Rated output power (@ 230 V, 50 Hz) Max. Apparent Ac power Nominal AC voltage/range AC power frequency/range Rated power frequency/rated power voltage Max. Output current Power factor at rated power Adjustable displacement factor Feed-in phases/connection phases Max. efficiency/european efficiency Weight
2600 W 700 V 175–560 V/530 V 175 V/220 V 15 A 15 A 1/2
ƞsys, d =
Reference Yield ( Yf shows the normalized power generation system. Yf is calculated as follow; the rate of generated AC power by PV panels to total power of PV panels which producing company's catalogue data under STC [36,38–42].
ƞsys, m =
(6)
4.4. Performance rate
2500 W 2500 VA 220,230,240 V/180 280 V 50, 60 Hz/ 4.5…þ 4.5 Hz 50 Hz/230 V 14.2 A 1 – 1/1 96.3%/95.3% 17 kg/37.4 Ib
Normalized PV is described as energy produced by the system to radiation energy reflecting onto normalized PC panel surface ratio. PR is used to compare and evaluate performances of PV systems in different regions and at different times. PR is calculated using (7) [36,38–42].
PR =
Yf YR
(7)
The Eq. (7) can be interpreted as the ratio between the energy produced by the real system and the efficiency values of PV panels described under STC and the energy produced by the ideal system. 4.5. PV plant capacity factor
(3)
Esys, h represents daily AC energy amount transferred to power plant by the system (kW h) and Gopt , h daily amount of radiation reflecting onto unit area of panel surface (kW h/m2). Average system efficiency per month can be expressed with (4) [36,38–42]. n ∑i = 1 (Esys, d )i n S· ∑i = 1 (Gopt, d )i
EAC Pmax, STC
In this equation, EAC expresses AC energy (inverter output) W h generated by PV system and transferred to power plant and Pmax, STC power values of total installed panel given by producing company's catalogue data under STC. The unit of energy produced by normalized system is hour or kW h/kWp.
Esys, d S·Gopt, d
(5)
4.3. Reference yield
Yf =
SunnyBoy 2500HF-30
1000(W /m2)
In this equation, Gopt represents radiation energy reflecting onto unit area and is 1000 W/m2 under STC.
Irradiation 1000 W/m2, temperature 25 °C, AM¼ 1.5
Parameter (Input DC)
Gopt (W h/m2)
(4)
In this equation, n expresses total number of days within a month. In this study, within the framework of the data got from Sunny Boy Webbox, TA weather station and Sunny Sensor Box, instant, hourly, daily and yearly efficiency of three PV panel systems were calculated using the equations numbered (1), (2), (3) and (4) and the efficiency values were compared with one another and with efficiency values stated in catalogue data.
Capacity factor (CF) expresses the ratio between real electrical energy produced and transferred to the plant by PV panels and electrical power value given under STC for 24 h during a year [32,33].
CF =
Yf
)
8760 ( h
=
Gopt ·PR EAC = Pmax, STC ·8760 Pmax, STC ·8760
(8)
8 7 6 5 4 3 2 1 0
4.2. Specific yield factor Month
To evaluate the energy performance of PV systems connected
Fig. 5. Daily average radiation amount according to months in 2014 in Düzce.
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
300
657
500
250
Poly
400
Poly
200
a-Si
450
a-Si
Mono
350
Mono
300
150
250
100
150
200 100
50
50 0
0
Month
Month Fig. 9. Total electrical energy produced according to months.
Fig. 6. Average daily radiation energy reflecting onto PV surfaces according to months. 35
Ambient Temp.
Panel Temp.
Wind Speed
1.6
30
1.4
25
1.2 1
20
0.8
15
0.6
10
0.4
5
0.2
0
0
Month
Fig. 7. Monthly average annual temperature, panel temperature and wind speed for 2014.
4.6. Inverter efficiency Inverter efficiency is the value of how much per cent of DC power coming over the inverter can be converted to AC and it is calculated in daily ( ƞinv, d ), monthly ( ƞinv, m ) and yearly ( ƞinv, y ) periods [17]. 4.7. CO2 emission While 0.6 kg CO2 is emitted to the atmosphere to generate 1 kW of energy using fossil fuels, this emission is avoided when PV panels are used to generate the same amount of energy [43]. CO2 emission avoided by three different PV panels installed on the roof of DUBIT during 2014 was calculated and the most profitable panel in terms of environmental criteria in one-year period was determined.
5. Results and discussıon In this part, the data produced between January 2014 and 16
a-Si
14
Poly
12
Mono
10
December 2014 by three different types of PV systems installed on the roof of DUBIT is explained in details. Data produced by three different panel types were compared with each other, with their equivalents in the literature and with the catalogue data of producing company. The radiation energy reflecting onto PV systems placed at an angle of 30 °C is shown in Fig. 5 in the light of the data obtained from TFA 35.1077 Meteorological Station and SMA Sensorbox. July is the month when average daily radiant energy had the highest value, 7068.9 W h/m2 and this value decreased to 1691.3 W h/m2 in December. Average daily radiation amount in 2014 was calculated as 4331.2 W h/m2. Average daily radiation energy reflecting onto the panel surfaces of three different PV systems placed at an angle of 30° on the roof of DUBIT is shown in Fig. 6. Total surface of 24 a-Si PV panels is 37.8 m2, total surfaces of polycrystalline and mono-crystalline panels are 17.06 m2 and 16.04 m2, respectively. Average daily environmental temperature, panel temperature and wind speed according to months in 2014 are given in Fig. 7. August is the month with the highest average daily temperature of 25.29 °C. This value decreases to 7.44 °C in January, which is the month with the lowest average daily temperature. The highest temperature was calculated as 44 °C in July. At the same time, the panel temperature was calculated as 66.5 °C. Annual average daily temperature is 16.12 °C and average panel temperature is 18.22 °C. Düzce does not have a big wind potential, yet, it was observed that Düzce had a wind speed of 1.10 m/s in 2014. Monthly average daily values of experimental output powers obtained from a-Si PV system with a total output power of 2400 W, polycrystalline PV system with a total output power of 2640 W and mono-crystalline PV system with a total output power of 2350 W according to datasheet are given in Fig. 8. Among three PV systems, maximum energy production was observed in July when radiation energy amount was also the highest. Compared average daily energy production in this month, the power values of 13.22 kW h, 14.16 kW h and 14.27 kW h were obtained for a-Si, polycrystalline and mono-crystalline panels, respectively. 18 16
(a-Si)
(poly)
14 12 10
8
8
6
6
4
4
2
2
0
0
Month
Fig. 8. Monthly average daily energy amount produced by PV panels.
Month
Fig. 10. Monthly energy efficiency.
( mono)
658
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
16
160 a-Sİ
Poly
Mono
14
120
12 10
80
8 40
6 (a-Si)
4
0
(poly)
2 0
Fig. 11. PV system specific yield factors ( Yf ). 1.2
a-Sİ
( mono)
0
Month
Poly
5
10
15 Tamb (°C)
20
25
30
Fig. 13. The relationship between temperature and monthly efficiency.
Mono
1 0.8 0.6 0.4 0.2 0
Month
Fig. 12. PR rate of three different PV panel types.
Monthly total electrical energy generated by PV systems is shown in Fig. 9. The month with the highest electric power production is July. The electricity values for a-Si, polycrystalline and mono-crystalline panels were 410.0 kW, 439.2 kW and 442.4 kW, respectively. Energy conversion efficiencies of PV panel types were calculated with (2) and in the light of the data, time graph of instant efficiency within a day was drawn. PV panel energy conversion efficiencies according to months in 2014 were calculated with (3) (Fig. 10). The maximum value of energy conversion efficiency for monocrystalline PV panels was about 14.5% in February and the minimum value was 12.3% in April. Although this value for polycrystalline panels decreased to 9.8% in December, its efficiency remained at 11% during the whole year. The minimum value of energy conversion efficiency for a-Si PV panel was 3.8% in December and the maximum value was 5.2% in March. Monthly average energy efficiency in 2014 were 4.8%, 11.4% and 13.3% for a-Si, polycrsytalline and mono-crystalline PV panels, respectively. Monthly mean value of Specific yield factor ( Yf ) and Table 3 Compare the correlations.
Ambient temp. Pearson Correlation Sig. (2-tailed) N Panel temp. Pearson Correlation Sig. (2-tailed) N Irradiation Pearson Correlation Sig. (2-tailed) N
Ambient temp.
Panel temp.
Irradiation
1.00
0.99
0.87
0.00 12.00 0.99
0.00 12.00 1.00
0.00 12.00 0.89
0.00 12.00 0.87
12.00 0,89
0.00 12.00 1.00
0.00 12.00
0,00 12.00
12.00
performance rate (PR) are shown respectively by Figs. 11 and 12. Yf rates have parallel results with radiation rate. Both of this rate have maximum value in July and have minimum value at December. The PR of 3 different PV panels are shown in Fig. 12. PR of mono-crystalline (PRm) PV panel's values change in range of 1.09– 0.84. PRm panels have better results in winter months. Yearly average value of mono-crystalline PR is determined 0.92. Although value of polycrystalline panel's PR (PRp) is resolved in range of 0.85–0.75, its PR can be more near-constant. PR range of a-Si (PRa) PV panel values are determined in range of 0.81–0.55. While its minimum results are observed in December (0.559) and January (0.62), its maximum result is in April with value of 0.81. Average PRp and PRa is repectively 0.82, 0.72. To see the changes in efficiency and performance ratios of PV panel types towards environmental temperature, panel temperature and radiation energy, they were made via PASW v.18 packet program and significance level was taken as 0.05. The efficiency and performances of the panels were compared via One-Way ANOVA, LSD post hoc test was applied to state the groups significantly different. Correlations of temperature, radiation, efficiency and performance values were examined via Pearson Correlation Analysis. Linear regression models were created to predict efficiency and performance values in terms of temperature and radiance. Comparison of correlation is given in Table 3. comparing correlations to see the connection between environmental temperature, panel temperature and radiation. it was concluded that three parameters version were significantly related with each other. There is a strong positive correlation between temperature, panel temperature (r ¼0998, p o 0001) and radiation (r ¼0872, po 0001). Similarly, there is a strong positive correlation between panel temperature and radiation, too (r ¼0896, p o0001). In the light of these results, it was found out that it would be suitable to compare panel types in terms of efficiency through environmental temperature, to compare PR through radiation. As a result of the statistical analysis to observe the influences of environmental temperature change on panel efficiency values, it is possible to express the changes of the values ƞm (a-Si), ƞm (poly) ve ƞm (mono) via following three equations, there is no strong statistical correlation, though.
ƞm( a Si ̵ )=4. 26697 + 0. 02878·Tamb
(9)
ƞm( poly)=10. 6605 + 0. 03279·Tamb
(10)
ƞm( mono)=14. 4471 − 0. 08445·Tamb
(11)
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
659
Table 4 The place of the study in the literature. Location
PV type
ƞy (%)
PR (%) Inverter efficiency
Serbia Greece Algeria Germany Calabria,Italy Ballymena, Ireland Düzce, Turkey
mc-Si pc-Si mc-Si pc-Si pc-Si mc-Si
11.35 – 10.1 – 7.6 7.5–10.0
93.6 67.4 – 66.5 – 60–62
– – 88.1 – 84.8 87
[25] [44] [45] [8] [46] [17]
a-Si pc-Si mc-Si mc-Si a-Si pc-Si mc-Si a-Si pc-Si
4.79 11.36 13.26 13.7 3.7 – 14.9 4–5 8.3
73 81 91 89.5 42 68 81.5 60–80 74
87.3 90.7 89.4 89.5 64.5 90 89.2 – –
Present Study
mc-Si pc-Si a-Si
10.1 9.2 5.4
76.8 72 73
– – 80
[51]
Castile, Spain UK Dublin, Ireland Poland Katar-Kalan, India South Korea Bangkok. Thailand
Ref.
[47] [48] [17] [21] [49] [50]
[52]
radiation change and PR. The relationship between PR value and temperature of each panel was given in (12), (13) and (14).
Fig. 14. Statistical relationship between PR and Gopt .
As it is obvious from three equations, change in the value of Tamb causes PV panel efficiency to change. Increase in the value of Tamb leads to increase in the values of ƞm( a Si ̵ ) and ƞm( poly), yet decrease in the value of ƞm( mono). As it is seen from (9), 1 °C increase in temperature makes the value of ƞm( a Si ̵ ) to increase 0.029%. Similarly, as it is given in (10), 1 °C increase in temperature of Tamb causes the value of ƞm( poly) to increase about 0.033%. The influence of temperature increase on ƞm( mono) is negative (11) shows that 1 °C increase leads efficiency value to decrease 0.085%. The graph of the study is given in Fig. 13. As it is understood from this graph including percentage changes of monthly average efficiency ( ƞm) of three panel types according to temperature, a-Si and poly crystalline PV panels are influenced positively but monocrystalline PV panel negatively by temperature change. A similar statistical analysis was also made between performance ratios of PV panel types and the values of Gopt . As a result of the study, there was a statistically strong correlation between
PRa=0. 66029 + 0. 27105·Gopt
(12)
PRp=0. 73722 + 0. 015432·Gopt
(13)
PRm=0. 96923 − 0. 013659·Gopt
(14)
These three equations explain how the change in the value of Gopt influences PR values. Increase in the value of Gopt affects PRa and PRp values positively, but PRm value negatively. (12),(13) and (14) show that increase of 1 kW per square meter in radiation value increases the value of PRa 0.271%, the value of PRp 0.015%, but decreases the value of PRm 0.014%. In the light of the results, the graphs showing the relationship between PR and Gopt are given in Fig. 14 for PRm, PRp and PRa respectively. Toroidal values in this graph represent the values measured, straight lines passing through them show their linear regression in relation to radiance value. The outmost lines show the safe range. Performance rate, efficiency, final efficiency, inverter efficiency values were compared with the studies in various countries and geographical areas and are shown in Table 4.
6. Conclusion This work is the first detailed performance analysis of three different PV panels carried out in the north west of Turkey. In the light of the findings, solar power potential of this region is quite useful for generating electrical energy. In addition, the results obtained from three PV panel systems installed on the roof of DUBIT in Duzce University Campus follow as:
Average temperature was calculated as 16.12 °C, wind speed as
1.10 m/s and radiation amount as 4.44 kW/m2 in 2014 in Duzce. As a result of 12-month-observation between January 2014 and December 2014, total electrical energy generated by a-Si, polycrystalline and mono-crystalline panels are 2942.82 kW h, 3141.15 kW h and 3364.46 kW h, respectively.
660
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
Average performance rate of 2240 W a-Si panel was calculated
as 73%, 81% and 91% for 2640 W polycrystalline and 2350 W mono-crystalline panels, respectively. In the light of mathematical equations obtained from simple linear regression, it was concluded that increase in solar radiation causes the performance rate of a-Si and polycrystalline panels to increase, but the performance rate of mono-crystalline panels to decrease. Energy conversion rates of PV panels on the roof of DUBIT in 2014 were calculated as 4.79%, 11.36% and 13.26% for a-Si, polycrystalline and mono-crystalline panels, respectively. When the efficiency values were compared with the catalogue data of producing company, it was seen that they were 10% less efficient. PV panel performance rates and energy efficiency were compared with similar studies as it is obvious in Table 4. The results are similar for three panel types. When changes in panel energy conversion efficiency according to temperature were examined, it was observed that 1 °C increase in temperature increased the efficiency of a-Si panels 0.029% and the efficiency of polycrsytalline panels 0.033%, yet, decreased the efficiency of mono-crystalline panels 0.084%. The inverter efficiency for 3 different PV systems were obtained for a-Si, polycrystalline and monocrystalline, repectively, 87.3%, 90.7% and 89.4% as shown in Table 4 Each 1 kW electric power generated by PV panels decreases 0.6 kg CO2 emission in the atmosphere. Thanks to PV systems installed on the basis of this fact, it was calculated that 20,348,7 kg CO2 emission was reduced in a year. 5669.1 kg of this decrease was ensured by a-Si panels, 7138.4 kg by polycrsytalline panels and 7541.2 kg by mono-crystalline panels.
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Contents lists available at ScienceDirect
Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
Outdoor performance analysis of different PV panel types Erdem Elibol a,n, Özge Tüzün Özmen b, Nedim Tutkun a, Oğuz Köysal b a b
Dept. of Electrical & Electronics Engineering, Duzce University, Duzce, Turkey Dept. of Physics, Duzce University, Duzce, Turkey
art ic l e i nf o
a b s t r a c t
Article history: Received 12 November 2015 Received in revised form 1 June 2016 Accepted 9 September 2016
Photovoltaic (PV) panel efficiency has been tested in the laboratory at standard test conditions (STC) (25 °C, 1000 W/m2 and AM:1.5). However, PV panels are used in different regions and climatic conditions quite different from STC. Due to that, panel efficiency is not observed same with manufacturer catalogue data. This study focus on outdoor testing of PV panels performances at literature, in addition, one-year results of mono-crystalline (2.35 kW), polycrystalline (2.64 kW) and amorphous silicon (2.40 kW) photovoltaic panels were analysed. These PV panels were placed on the roof of Düzce University Scientific and Technological Researches Application and Research Centre (DUBİT) in Düzce Province, in Turkey, one of the countries with the highest solar power potential in Europe and connected to power grid. Amounts of energy produced by the panels over a day, a month and a year as well as inverter efficiency and performance ratios were calculated. Performance ratios were found out as 73%, 81% and 91% for a-Si, polycrystalline and mono-crystalline PV panels, respectively. Panel efficiency was calculated as 4.79%, 11.36% and 13.26% in the same order. All results were compared with Previous studies. Statistical analysis was made to state relationship between efficiency and performance ratios of panel types, environmental temperature, panel temperature and amount of radiation. As a result of the statistical analysis, it was observed that temperature increase of 1 °C increased the efficiency of a-Si panels 0.029% and the efficiency of polycrystalline panels 0.033%, yet, decreased the efficiency of mono-crystalline panels 0.084%. & 2016 Elsevier Ltd. All rights reserved.
Keywords: Renewable energy PV systems PV panel types Yield factor
Contents 1. 2. 3.
4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 Description of location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 Experımental network set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1. Solar PV plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1.1. PV panels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1.2. Inverter Sunnyboy 2500HF-30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653 3.1.3. Sunny webbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 3.1.4. TFA 35.1077 meterological station and SMA sensorbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 PV characterıstıc parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654 4.1. PV efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 4.2. Specific yield factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.3. Reference yield . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.4. Performance rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.5. PV plant capacity factor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656 4.6. Inverter efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657
n
Corresponding author. E-mail addresses: [email protected] (E. Elibol), [email protected] (Ö.T. Özmen), [email protected] (N. Tutkun), [email protected] (O. Köysal). http://dx.doi.org/10.1016/j.rser.2016.09.051 1364-0321/& 2016 Elsevier Ltd. All rights reserved.
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4.7. CO2 emission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Results and discussıon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction Industry that developed in parallel with scientific advances in the last century enabled national economies to foster and production to increase dramatically. These developments in economy and production has increased the need for more energy and this relation between production and energy turned into a vicious circle. The total energy consumption of the world reached 19,782 billion kW h/year in 2014 [1]. Besides, the energy need is expected to increase 35% by 2035 because of the fact that population grows so quickly and energy-based technology becomes more general [2]. 80% of the energy need around the world is met by fossil fuels. This causes CO2 amount emitted into the atmosphere to increase swiftly [3]. If the role of the fossil fuels to meet energy demand continues, it is foreseen that industrial CO2 emissions will reach 35 GT level by 2035 and the atmosphere temperature will increase 3.6 °C [4]. These negative results are cited in environmental reports and people have become more sensitive on environmental issues. Therefore, it is expected that some main problems like environmental pollution, global warming etc. will change political opinions and road maps of foremost leaders and fossil fuel use will decrease by 2040 [4]. This expectation paves the way for requirement for renewable energy resources that are environmentfriendly, clean, cheap and sustainable. Photovoltaic systems (PV) including all these main properties and capable of producing energy at a certain levels in ever place letting in solar radiation are the ones with the highest solar potential among renewable energy resources. Mousazadeh et al. showed that the energy to be produced yearly when 16% of the world surface was covered with PV panels of 10% efficiency would be two times more than the energy to be generated from fossil fuels [5]. In fact, annual energy potential reaching the world from the sun is 1,78x109 MW which is 50 times more than current coal fuels and 800 times more than oil reserves [6]. This potential makes PV panels to be more widely used and several large- and small-scale sun farms have been built with output power ranging between 1 kW and 550 MW [6–9]. Companies and scientists carry out lots of studies to increase the efficiency and quality of current PV panels and to reduce their unit price [10]. As is known, PV panels with several different manufacturing technologies are produced today. Not only spectral reactions, temperature coefficients, voltage and current values of different panel types are different, but also their reactions to environmental factors like radiation, temperature and wind speed differ. The efficiency of PV systems is tested by producing companies in a laboratory environment and in a controlled way under standard test conditions (STC) at 25 °C and at a radiation level of 1000 W/m2 [11–13]. However, solar spectrum changes depending location. Although it is known that energy efficiency and performance of PV panels change according to atmospheric conditions like temperature, radiation etc., determining the best efficient panel type is an important criterion [14]. Evaluating different panel type technologies according to atmospheric conditions and selecting the most efficient panel types have been the subject of many studies. Williams et al. compared the performances of polycrystalline silicon (p-Si), single and multi-junction silicon amorphous silicon (a-Si), CdTe modules and proved that environmental factors
657 657 659 660
influenced module temperature, Amount of incoming solar radiation onto module surface and module angle affected energy production performance in the study they made in Loughborough UK [15]. Sharma et al. studied on p-Si, hetero-junction with instruction thin layer silicon (HIT) and amorphous single junction silicon (aSi) modules in the solar energy centre during one-year period in India. They compared the energy efficiency and performance ratios (PR) of the modules with external environment measurements and the simulation results [16]. As a result of this performance comparison, it was put forward that the most suitable solar panel module were a-Si and HIT for the area where PV modules were built and energy efficiency of a-Si module was 14% more in summer months and 6% less in winter months compared to p-Si module. It was also noted that HIT modules generated 4–12% more energy than p-Si modules. Mondol et al. observed the PV system connected to power grid with 13 kW output power in Ireland during three-year period and analysed the data according to hours, days and months. They calculated the efficiency of radiation amount and inverter. They also observed that PV efficiency decreased approximately 10% when module surface temperature increased in summer months and PV efficiency decreased between 4% and 8% in low radiation amount [17]. In the study Minemoto et al. carried out to evaluate the performances of a-Si and mc-Si modules depending radiation distribution and temperature in the external environment, they observed that a-Si modules were rather efficient in blue-rich spectrum, yet, the efficiency of mc-Si modules changed depending temperature similarly, Ye et al. reported in the study that they carried out on five different types of PV models that a-Si and micro morph Si PV module performances were affected by radiation level and frequency fluctuation more than temperature change, yet dependency of mono-crystalline modules on temperature was much more higher [18]. Mieke compared polycrystalline and a-Si modules through photovoltaic systems installed in Australia. It was seen that a-Si PV modules generated 20% more electrical energy than polycrystalline modules [19]. Similarly, Akhmad proved as a result of 2-year observation that performances of polycrystalline surfaces exposed to high temperatures decreased at a certain level and a-Si modules were more useful for the climates with high temperatures [20]. Ayompe et al. analysed 13 months' data of the PV system with 1.72 kW output power installed on a roof of a building in Ireland. They observed module efficiency, daily and monthly energy production, system and the efficiency of inverter. As a result of the study, module efficiency was found as 14.9, the efficiency of system as 12.6 and inverter efficiency as 89.2 [21]. Canete et al. analysed 4 different module technologies, like a-Si, microcrystalline silicon, Cd and Amount of incoming solar radiation for a year in Spain and reported that thin-films were more productive than polycrystalline modules [22]. Carr and Pryor [14], and Del Cueto [23] evaluated various PV panel Technologies in Perth, Australia and stated that thin-films gave more productive results. Rehman and El-Amin showed in the study they made with polycrystalline modules that the efficiency of module depended on module temperature [24]. Milosovlojevic et al. analysed the system performance, capacity factor and the yield of solar panel
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
system with 2 kW output power in Serbia and reported that performance ratio was about 93.6% and the efficiency of system reduced when temperature increased [25]. Diaz et al. tested 12 module technologies in a rural area with an off-grid installed solar system [26]. Park et al. examined electrical and temperature relationship of semi-transport PV modules. In this study, they showed that 1 °C increase in the temperature of module surface leaded to 0.48% reduction in powers of the modules reduced 0.48% under STC and 0.52% in the external environment [27]. Makrides et al. Reported in the study they made on grid on system in Cyprus that due to temperature increase, DC output power loss was 8% for mono-crystalline modules, 9% for multi-crystalline modules and 5% for thin-films [28]. As it is seen from literature, PV panels performance were analysed in different geographical conditions with varied temperature, irradiation and wind speed. Despite Turkey's high solar potential [29], there is only one study about outdoor PV panels performance comparing. At this study Başoğlu et al. analysed c-Si, mc-Si and CdTe PV module's performance in Izmit, Turkey [30] Results of this work showed that PR rates are 83.8%, 82.05% and 89.76% for mc-Si, c-Si and CdTe, respectively [30]. Solar energy studies are still very new in Turkey in spite of high solar potential [29] and Turkey's dependency on energy import, mainly petrol and natural gas, is increasing like all around the world [1]. While 26% of current energy demand is met by domestic resources, 74% of it is met by different foreign resources [31]. According to International Energy Agency (IEA) predictions, Turkey is the country whose energy demand may increase in the fastest way in short- and long term among member states. The studies show that total final energy demand and total first energy demand are expected to reach 170.3 and 222.4 MTEP, respectively, by 2020 with about double increase, the demand for electricity, natural gas and petrol are expected to reach 398–434 billion kW h, 59 billion cubic meters and 59 million tonnes, respectively [32]. With an important potential of renewable energy, Turkey is ranked as the seventh in the world and the first in Europe in terms of geothermal potential. Besides, developing hydro-electricity sources, wind and solar energy rank in priority. It is foreseen that 30% of Turkey's total energy demand will be met by renewable energy resources by 2023. Moreover, Turkey became a founding member of International Renewable Energy Agency (IRENA) through an agreement signed at the meeting held on 26 January 2009 in Bonn, which proves that Turkey attaches importance to developing renewable energy resources [32]. In Turkey, as of February 2015, installed solar energy is about 90 MW [33]. Basaran et al. predicted that this number would reach to 200 MW by 2020 and 39.200 MW by 2050 thanks to sun farms to be built increasingly every year [33]. Moreover, this study showed that $/kW h cost of electricity could be the cheapest with PV panels when company expenses and electrical energy generated during the system life were compared. This study includes literature review about outdoor PV module performance analysis and also analyses of hourly, daily and monthly performances of 2.40 kW a-Si, 2.64 kW polycrystalline and 2.35 kW mono-crystalline modules placed on the roof of Düzce University Scientific and Technological Researches Application and Research Centre (DUBIT) within Düzce University Campus in Düzce Province, for contribute to the literature. Besides, the efficiency of PV panels and inverter were compared with each other, with catalogue data of producing company and with previous studies, the most suitable panel was selected for current environmental conditions for study location. The study was organized for a-year period between January 2014 and December 2014.
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2. Description of location Düzce is located on the coast of Black Sea, at 40°50 N, 31°9,4 E, between Ankara, the capital city of Turkey, and Istanbul. The location of Düzce in Turkey and in the world is shown in Fig. 1. Düzce is the 81st and last city of Turkey. The population of this small province that has been trying to pick up the pieces after the big earthquake in 1999 is increasing rapidly due to its location and growing industry potential, the province hosts approximately 352.000 people. Annual electricity consumption in Düzce is 837.616 MW h which is 0.42% of energy consumption of Turkey. Düzce holds 0.08% of current installed power in Turkey most of which is met by hydroelectric and thermal power plants. Due to regional factors, wind speed in Düzce is about 1.5 m/s which is not enough to support power plants to be built. Among renewable energy systems, solar power ranks first with its potential. Hours of sunshine of Düzce per year are 1905 h/year. While average hours of sunshine per day decrease to 3.9 h in winter, it is about 8.4 h in summer [34].
3. Experımental network set up 3.1. Solar PV plant 24 amorphous silicon thin-film panels (a-Si), each with 100 W PV panel power and total 2400 W output power, 11 polycrystalline panels, each with 240 W PV panel power and total 2640 W output power and 10 mono-crystalline panels, each with 235 W PV panel power and total 2350 W output power were placed on the roof of DUBIT in Konuralp Campus in Düzce University in September 2013. a-Si modular series was installed in double row, polycrystalline and mono-crystalline panels in single row. The appearance of PV systems on the roof surface is given in Fig. 2. Every module series was connected to a different inverter with conducting wire. Electrical energy produced by PV modules and converted into AC currency (230 V and 50 Hz) via an inverter was transferred to power plant connected to DUBIT building. The inverters used in the system are shown in Fig. 3 and the system installed on the roof of DUBIT in Fig. 4. The data obtained from the system was recorded at five minute intervals via Sunny webbox with bluetooth. Meteorology recorder was added to the system to follow meteorological data, temperature, radiation and wind speed. 3.1.1. PV panels 24 Mitsubishi Solar a-Si Thin film (PIN single junction) panels (MA100T2) were connected in series whose size is 1414 1114 35 mm and total surface is 1.575 24 m2. The module's weight is 21 kg. 11 polycrystalline panels (Symphony Energy SE-M240) are used at this study. Physical properties of this PV panel are 19 kg weight and 1605 986 38 mm module size. 10 monocrystalline (Symphony Energy SE-S235) whose size is 1636 982 35 mm and weight is 19 kg. Electrical properties of these 3 PV modules are shown at Table 1. 3.1.2. Inverter Sunnyboy 2500HF-30 In this study, 3 Sunnyboy 2500 HF-30 single phase inverters were used. An inverter was placed at the output of every panel type. The efficiency achieved by 2600 W DC power inverters was 96.3%. It is possible to observe DC currency and voltage, converted into AC produced from PV panels and to follow daily, instant and total production information by the inverter with monitoring system. The size of the inverter is 348 580 145 mm and it weighs
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Fig. 1. Location of Düzce province in Turkey and in the world.
Fig. 2. PV systems on DUBİT.
about 17 kg. Technical properties of Sunnyboy Inverter are shown in Table 2. 3.1.3. Sunny webbox As a central communication interface, Sunny webbox was connected to PV system. Webbox that can be connected to computer via a cable collects all data of the devices it is connected to, documents and enables PV system to be constantly watched. The data kept graphically can be displayed with Flash view presentation software. Webbox works between 25 and þ50 degrees and its size is 125 130 57 mm and it weighs 750 g. Its data storage capacity is 2 GB. 3.1.4. TFA 35.1077 meterological station and SMA sensorbox Meteorological parameters, ambient temperature (°C), the global solar energy per square meter on surface, wind speed (m/s), are measured by TFA 35.1077 Meteorological station which was
installed on roof of DUBIT. Meteorological data are recorded for every 5 min. TFA 35.1077 is tested to run under the ambient temperature 40 °C to þ65 °C. The station also forecast the weather condition from 2 h to 24 h. With the station, SMA sensor box was installed near PV panels with the same angle, 30°. SMA sensor box send the information about ambient temperature, surface temperature of PV panels and solar radiation to Sunny webbox for every 5 min with bluetooth. Sensorbox's power consumption is less than 1 W.
4. PV characterıstıc parameters PV characteristic parameters are described by IEC 61724:1998 standard. These parameters are PV energy efficiency, yield factor, performance rate (PR) and capacity factor (CF) [35].
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
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Fig. 3. Connected inverters on DUBIT.
Fig. 4. Schematic of the PV plants installed on the roof of DUBIT.
4.1. PV efficiency
In this equation, ƞsys represents instant efficiency of the system,
Instant system efficiency is calculated as the energy produced by the system to amount of radiation reflecting onto PV surface ratio. The efficiency of PV panels is a ratio expressing how much of radiation reflecting onto total surface area of panel surface is converted into electrical energy by PV panel. PV panels have some efficiency differences resulting from production technologies and semiconductor technology [36]. PV efficiency can be calculated in instant, hourly, daily, monthly and yearly periods. Instant system efficiency is calculated as (1) [36–38]
ƞsys =
Esys S·Gopt
(1)
Esys power transferred to power plant by the system, S total surface area of the panels and Gopt radiation energy per unit area (W/m2). Daily, monthly or yearly system efficiency is described as the energy transferred to power plant in stated period to radiation energy reflecting onto total PV panel surface ratio. Hourly energy efficiency (2) is expressed as is [36,38–42].
ƞsys, h =
Esys, h S·Gopt, h
(2)
In this equation, Esys, h shows hourly AC power amount transferred to power plant by the system and Gopt , h hourly radiation energy reflecting onto unit area of panel surface. Daily efficiency analysis can be expressed with (3).
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to power plant, using various conversion steps, energy production and losses of PV system are normalized to installed power value under STC. One of these parameters is YR and is described in a way that radiation energy reflecting onto unit area of normalized panel surface is normalized to radiation in unit area described under STC and is expressed with (5) [30–35].
Table 1 Electrical characteristic of 3 different PV panels. Parameter
Monocrystalline Polycrystalline
STC power rating PMP 235 W (W) Short circuit current 8.22 A ISC (A) Open circuit voltage 37.3 V VOC (V) Voltage at max. power 30.7 V VMP (V) 7.65 A Current at Max. Power IMP (A) Panel efficiency (ƞ) 14.6 Fill factor (FF) 76.6 Temp. coefficiency of 0.03%/°C ISC Temp. coefficiency of 0.34%/°C VOC Standard test condition
a-Si
240 W
100 W
8.71 A
1.17 A
36.6 V
141 V
30.0 V
108 V
8.01 A
0.93 A
14.9% 75.3 0.06%/°C
6.2% – 0.14%/°C
0.35%/°C
0.32%/°C
YR =
)
Table 2 Technical characteristic of Sunnyboy 2500 HF-30 inverter.
Max. DC power (@ cos φ¼ 1) Max. İnput voltage MPP voltage range/rated input voltage Min. İnput voltage/ nitial input voltage Max. İnput current Max. İnput current per string Number of independent MPP inputs/strings per MPP input Parameter (Output AC) Rated output power (@ 230 V, 50 Hz) Max. Apparent Ac power Nominal AC voltage/range AC power frequency/range Rated power frequency/rated power voltage Max. Output current Power factor at rated power Adjustable displacement factor Feed-in phases/connection phases Max. efficiency/european efficiency Weight
2600 W 700 V 175–560 V/530 V 175 V/220 V 15 A 15 A 1/2
ƞsys, d =
Reference Yield ( Yf shows the normalized power generation system. Yf is calculated as follow; the rate of generated AC power by PV panels to total power of PV panels which producing company's catalogue data under STC [36,38–42].
ƞsys, m =
(6)
4.4. Performance rate
2500 W 2500 VA 220,230,240 V/180 280 V 50, 60 Hz/ 4.5…þ 4.5 Hz 50 Hz/230 V 14.2 A 1 – 1/1 96.3%/95.3% 17 kg/37.4 Ib
Normalized PV is described as energy produced by the system to radiation energy reflecting onto normalized PC panel surface ratio. PR is used to compare and evaluate performances of PV systems in different regions and at different times. PR is calculated using (7) [36,38–42].
PR =
Yf YR
(7)
The Eq. (7) can be interpreted as the ratio between the energy produced by the real system and the efficiency values of PV panels described under STC and the energy produced by the ideal system. 4.5. PV plant capacity factor
(3)
Esys, h represents daily AC energy amount transferred to power plant by the system (kW h) and Gopt , h daily amount of radiation reflecting onto unit area of panel surface (kW h/m2). Average system efficiency per month can be expressed with (4) [36,38–42]. n ∑i = 1 (Esys, d )i n S· ∑i = 1 (Gopt, d )i
EAC Pmax, STC
In this equation, EAC expresses AC energy (inverter output) W h generated by PV system and transferred to power plant and Pmax, STC power values of total installed panel given by producing company's catalogue data under STC. The unit of energy produced by normalized system is hour or kW h/kWp.
Esys, d S·Gopt, d
(5)
4.3. Reference yield
Yf =
SunnyBoy 2500HF-30
1000(W /m2)
In this equation, Gopt represents radiation energy reflecting onto unit area and is 1000 W/m2 under STC.
Irradiation 1000 W/m2, temperature 25 °C, AM¼ 1.5
Parameter (Input DC)
Gopt (W h/m2)
(4)
In this equation, n expresses total number of days within a month. In this study, within the framework of the data got from Sunny Boy Webbox, TA weather station and Sunny Sensor Box, instant, hourly, daily and yearly efficiency of three PV panel systems were calculated using the equations numbered (1), (2), (3) and (4) and the efficiency values were compared with one another and with efficiency values stated in catalogue data.
Capacity factor (CF) expresses the ratio between real electrical energy produced and transferred to the plant by PV panels and electrical power value given under STC for 24 h during a year [32,33].
CF =
Yf
)
8760 ( h
=
Gopt ·PR EAC = Pmax, STC ·8760 Pmax, STC ·8760
(8)
8 7 6 5 4 3 2 1 0
4.2. Specific yield factor Month
To evaluate the energy performance of PV systems connected
Fig. 5. Daily average radiation amount according to months in 2014 in Düzce.
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
300
657
500
250
Poly
400
Poly
200
a-Si
450
a-Si
Mono
350
Mono
300
150
250
100
150
200 100
50
50 0
0
Month
Month Fig. 9. Total electrical energy produced according to months.
Fig. 6. Average daily radiation energy reflecting onto PV surfaces according to months. 35
Ambient Temp.
Panel Temp.
Wind Speed
1.6
30
1.4
25
1.2 1
20
0.8
15
0.6
10
0.4
5
0.2
0
0
Month
Fig. 7. Monthly average annual temperature, panel temperature and wind speed for 2014.
4.6. Inverter efficiency Inverter efficiency is the value of how much per cent of DC power coming over the inverter can be converted to AC and it is calculated in daily ( ƞinv, d ), monthly ( ƞinv, m ) and yearly ( ƞinv, y ) periods [17]. 4.7. CO2 emission While 0.6 kg CO2 is emitted to the atmosphere to generate 1 kW of energy using fossil fuels, this emission is avoided when PV panels are used to generate the same amount of energy [43]. CO2 emission avoided by three different PV panels installed on the roof of DUBIT during 2014 was calculated and the most profitable panel in terms of environmental criteria in one-year period was determined.
5. Results and discussıon In this part, the data produced between January 2014 and 16
a-Si
14
Poly
12
Mono
10
December 2014 by three different types of PV systems installed on the roof of DUBIT is explained in details. Data produced by three different panel types were compared with each other, with their equivalents in the literature and with the catalogue data of producing company. The radiation energy reflecting onto PV systems placed at an angle of 30 °C is shown in Fig. 5 in the light of the data obtained from TFA 35.1077 Meteorological Station and SMA Sensorbox. July is the month when average daily radiant energy had the highest value, 7068.9 W h/m2 and this value decreased to 1691.3 W h/m2 in December. Average daily radiation amount in 2014 was calculated as 4331.2 W h/m2. Average daily radiation energy reflecting onto the panel surfaces of three different PV systems placed at an angle of 30° on the roof of DUBIT is shown in Fig. 6. Total surface of 24 a-Si PV panels is 37.8 m2, total surfaces of polycrystalline and mono-crystalline panels are 17.06 m2 and 16.04 m2, respectively. Average daily environmental temperature, panel temperature and wind speed according to months in 2014 are given in Fig. 7. August is the month with the highest average daily temperature of 25.29 °C. This value decreases to 7.44 °C in January, which is the month with the lowest average daily temperature. The highest temperature was calculated as 44 °C in July. At the same time, the panel temperature was calculated as 66.5 °C. Annual average daily temperature is 16.12 °C and average panel temperature is 18.22 °C. Düzce does not have a big wind potential, yet, it was observed that Düzce had a wind speed of 1.10 m/s in 2014. Monthly average daily values of experimental output powers obtained from a-Si PV system with a total output power of 2400 W, polycrystalline PV system with a total output power of 2640 W and mono-crystalline PV system with a total output power of 2350 W according to datasheet are given in Fig. 8. Among three PV systems, maximum energy production was observed in July when radiation energy amount was also the highest. Compared average daily energy production in this month, the power values of 13.22 kW h, 14.16 kW h and 14.27 kW h were obtained for a-Si, polycrystalline and mono-crystalline panels, respectively. 18 16
(a-Si)
(poly)
14 12 10
8
8
6
6
4
4
2
2
0
0
Month
Fig. 8. Monthly average daily energy amount produced by PV panels.
Month
Fig. 10. Monthly energy efficiency.
( mono)
658
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
16
160 a-Sİ
Poly
Mono
14
120
12 10
80
8 40
6 (a-Si)
4
0
(poly)
2 0
Fig. 11. PV system specific yield factors ( Yf ). 1.2
a-Sİ
( mono)
0
Month
Poly
5
10
15 Tamb (°C)
20
25
30
Fig. 13. The relationship between temperature and monthly efficiency.
Mono
1 0.8 0.6 0.4 0.2 0
Month
Fig. 12. PR rate of three different PV panel types.
Monthly total electrical energy generated by PV systems is shown in Fig. 9. The month with the highest electric power production is July. The electricity values for a-Si, polycrystalline and mono-crystalline panels were 410.0 kW, 439.2 kW and 442.4 kW, respectively. Energy conversion efficiencies of PV panel types were calculated with (2) and in the light of the data, time graph of instant efficiency within a day was drawn. PV panel energy conversion efficiencies according to months in 2014 were calculated with (3) (Fig. 10). The maximum value of energy conversion efficiency for monocrystalline PV panels was about 14.5% in February and the minimum value was 12.3% in April. Although this value for polycrystalline panels decreased to 9.8% in December, its efficiency remained at 11% during the whole year. The minimum value of energy conversion efficiency for a-Si PV panel was 3.8% in December and the maximum value was 5.2% in March. Monthly average energy efficiency in 2014 were 4.8%, 11.4% and 13.3% for a-Si, polycrsytalline and mono-crystalline PV panels, respectively. Monthly mean value of Specific yield factor ( Yf ) and Table 3 Compare the correlations.
Ambient temp. Pearson Correlation Sig. (2-tailed) N Panel temp. Pearson Correlation Sig. (2-tailed) N Irradiation Pearson Correlation Sig. (2-tailed) N
Ambient temp.
Panel temp.
Irradiation
1.00
0.99
0.87
0.00 12.00 0.99
0.00 12.00 1.00
0.00 12.00 0.89
0.00 12.00 0.87
12.00 0,89
0.00 12.00 1.00
0.00 12.00
0,00 12.00
12.00
performance rate (PR) are shown respectively by Figs. 11 and 12. Yf rates have parallel results with radiation rate. Both of this rate have maximum value in July and have minimum value at December. The PR of 3 different PV panels are shown in Fig. 12. PR of mono-crystalline (PRm) PV panel's values change in range of 1.09– 0.84. PRm panels have better results in winter months. Yearly average value of mono-crystalline PR is determined 0.92. Although value of polycrystalline panel's PR (PRp) is resolved in range of 0.85–0.75, its PR can be more near-constant. PR range of a-Si (PRa) PV panel values are determined in range of 0.81–0.55. While its minimum results are observed in December (0.559) and January (0.62), its maximum result is in April with value of 0.81. Average PRp and PRa is repectively 0.82, 0.72. To see the changes in efficiency and performance ratios of PV panel types towards environmental temperature, panel temperature and radiation energy, they were made via PASW v.18 packet program and significance level was taken as 0.05. The efficiency and performances of the panels were compared via One-Way ANOVA, LSD post hoc test was applied to state the groups significantly different. Correlations of temperature, radiation, efficiency and performance values were examined via Pearson Correlation Analysis. Linear regression models were created to predict efficiency and performance values in terms of temperature and radiance. Comparison of correlation is given in Table 3. comparing correlations to see the connection between environmental temperature, panel temperature and radiation. it was concluded that three parameters version were significantly related with each other. There is a strong positive correlation between temperature, panel temperature (r ¼0998, p o 0001) and radiation (r ¼0872, po 0001). Similarly, there is a strong positive correlation between panel temperature and radiation, too (r ¼0896, p o0001). In the light of these results, it was found out that it would be suitable to compare panel types in terms of efficiency through environmental temperature, to compare PR through radiation. As a result of the statistical analysis to observe the influences of environmental temperature change on panel efficiency values, it is possible to express the changes of the values ƞm (a-Si), ƞm (poly) ve ƞm (mono) via following three equations, there is no strong statistical correlation, though.
ƞm( a Si ̵ )=4. 26697 + 0. 02878·Tamb
(9)
ƞm( poly)=10. 6605 + 0. 03279·Tamb
(10)
ƞm( mono)=14. 4471 − 0. 08445·Tamb
(11)
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
659
Table 4 The place of the study in the literature. Location
PV type
ƞy (%)
PR (%) Inverter efficiency
Serbia Greece Algeria Germany Calabria,Italy Ballymena, Ireland Düzce, Turkey
mc-Si pc-Si mc-Si pc-Si pc-Si mc-Si
11.35 – 10.1 – 7.6 7.5–10.0
93.6 67.4 – 66.5 – 60–62
– – 88.1 – 84.8 87
[25] [44] [45] [8] [46] [17]
a-Si pc-Si mc-Si mc-Si a-Si pc-Si mc-Si a-Si pc-Si
4.79 11.36 13.26 13.7 3.7 – 14.9 4–5 8.3
73 81 91 89.5 42 68 81.5 60–80 74
87.3 90.7 89.4 89.5 64.5 90 89.2 – –
Present Study
mc-Si pc-Si a-Si
10.1 9.2 5.4
76.8 72 73
– – 80
[51]
Castile, Spain UK Dublin, Ireland Poland Katar-Kalan, India South Korea Bangkok. Thailand
Ref.
[47] [48] [17] [21] [49] [50]
[52]
radiation change and PR. The relationship between PR value and temperature of each panel was given in (12), (13) and (14).
Fig. 14. Statistical relationship between PR and Gopt .
As it is obvious from three equations, change in the value of Tamb causes PV panel efficiency to change. Increase in the value of Tamb leads to increase in the values of ƞm( a Si ̵ ) and ƞm( poly), yet decrease in the value of ƞm( mono). As it is seen from (9), 1 °C increase in temperature makes the value of ƞm( a Si ̵ ) to increase 0.029%. Similarly, as it is given in (10), 1 °C increase in temperature of Tamb causes the value of ƞm( poly) to increase about 0.033%. The influence of temperature increase on ƞm( mono) is negative (11) shows that 1 °C increase leads efficiency value to decrease 0.085%. The graph of the study is given in Fig. 13. As it is understood from this graph including percentage changes of monthly average efficiency ( ƞm) of three panel types according to temperature, a-Si and poly crystalline PV panels are influenced positively but monocrystalline PV panel negatively by temperature change. A similar statistical analysis was also made between performance ratios of PV panel types and the values of Gopt . As a result of the study, there was a statistically strong correlation between
PRa=0. 66029 + 0. 27105·Gopt
(12)
PRp=0. 73722 + 0. 015432·Gopt
(13)
PRm=0. 96923 − 0. 013659·Gopt
(14)
These three equations explain how the change in the value of Gopt influences PR values. Increase in the value of Gopt affects PRa and PRp values positively, but PRm value negatively. (12),(13) and (14) show that increase of 1 kW per square meter in radiation value increases the value of PRa 0.271%, the value of PRp 0.015%, but decreases the value of PRm 0.014%. In the light of the results, the graphs showing the relationship between PR and Gopt are given in Fig. 14 for PRm, PRp and PRa respectively. Toroidal values in this graph represent the values measured, straight lines passing through them show their linear regression in relation to radiance value. The outmost lines show the safe range. Performance rate, efficiency, final efficiency, inverter efficiency values were compared with the studies in various countries and geographical areas and are shown in Table 4.
6. Conclusion This work is the first detailed performance analysis of three different PV panels carried out in the north west of Turkey. In the light of the findings, solar power potential of this region is quite useful for generating electrical energy. In addition, the results obtained from three PV panel systems installed on the roof of DUBIT in Duzce University Campus follow as:
Average temperature was calculated as 16.12 °C, wind speed as
1.10 m/s and radiation amount as 4.44 kW/m2 in 2014 in Duzce. As a result of 12-month-observation between January 2014 and December 2014, total electrical energy generated by a-Si, polycrystalline and mono-crystalline panels are 2942.82 kW h, 3141.15 kW h and 3364.46 kW h, respectively.
660
E. Elibol et al. / Renewable and Sustainable Energy Reviews 67 (2017) 651–661
Average performance rate of 2240 W a-Si panel was calculated
as 73%, 81% and 91% for 2640 W polycrystalline and 2350 W mono-crystalline panels, respectively. In the light of mathematical equations obtained from simple linear regression, it was concluded that increase in solar radiation causes the performance rate of a-Si and polycrystalline panels to increase, but the performance rate of mono-crystalline panels to decrease. Energy conversion rates of PV panels on the roof of DUBIT in 2014 were calculated as 4.79%, 11.36% and 13.26% for a-Si, polycrystalline and mono-crystalline panels, respectively. When the efficiency values were compared with the catalogue data of producing company, it was seen that they were 10% less efficient. PV panel performance rates and energy efficiency were compared with similar studies as it is obvious in Table 4. The results are similar for three panel types. When changes in panel energy conversion efficiency according to temperature were examined, it was observed that 1 °C increase in temperature increased the efficiency of a-Si panels 0.029% and the efficiency of polycrsytalline panels 0.033%, yet, decreased the efficiency of mono-crystalline panels 0.084%. The inverter efficiency for 3 different PV systems were obtained for a-Si, polycrystalline and monocrystalline, repectively, 87.3%, 90.7% and 89.4% as shown in Table 4 Each 1 kW electric power generated by PV panels decreases 0.6 kg CO2 emission in the atmosphere. Thanks to PV systems installed on the basis of this fact, it was calculated that 20,348,7 kg CO2 emission was reduced in a year. 5669.1 kg of this decrease was ensured by a-Si panels, 7138.4 kg by polycrsytalline panels and 7541.2 kg by mono-crystalline panels.
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