Stand-alone photovoltaic (SAPV) systems are widely used in rural areas where there is no national grid or as a precaution against power outages. In this study, technical and economic analysis of a SAPV system was carried out using meteorological data for 75 province centers in seven geographical regions of Turkey. Obtained results for each province center were separated by geographical area. The averages of the centers for each region are taken as output. A calculation algorithm based on MsExcel has been established for these operations. The analyses made with the developed algorithm are repeated for five different scenarios that they cover periods of time when a constant strong load is active for all seasons (winter, spring, summer, and autumn) and all year round. The developed algorithm calculates the life-cycle cost, the unit energy cost, the electrical capacity utilization rate, the amount of generated/excess energy per month, the initial investment/replacement, and operating and maintenance (O&M) costs of each element. As a result, geographical regions of Turkey are compared in terms of these outputs graphically. Further investigations may include the sale of excess energy generated, small-scale PV system cost factors parallel to the grid, and the effects of government incentives.
Stand-alone photovoltaic (SAPV) systems are one of the best options for energy conversion, especially in areas where there is no national grid. Establishing small-scale stand-alone wind farms is often technically and economically disadvantageous. The efficiency of small-scale wind turbines is relatively low. The operating and maintenance (O&M) difficulties caused by the installation in the remote areas are also the most important deficiencies due to more failures than PV systems because of moving parts. Because of these reasons, PV systems with a quick settling time, long life, and lower risk of failure are preferred. SAPV systems typically have installed powers between 0.5 kW and 20 kW [
Basic components of SAPV plant.
In the economic analysis of these systems, the future costs of initial investment and recurrent purchases of fixed components of the project are also taken into account. This process is generally called life-cycle cost analysis (LCC). In this analysis, inflation and discount rates of the country are very effective. Therefore, the unit energy cost (UEC) of the system is also related to these ratios [
Al-falahi et al. have recently categorized the methods of designing and dimensioning SAPV used in today’s comprehensive review under three headings as classical, modern, and computer software [
Constant values of SAPV system components.
Life cycle (years) | System | 25 |
Module | 25 | |
Battery | 10 | |
Charge regulator | 15 | |
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Operating period (months) | 12 | |
Annually operating days |
365 | |
Annually operating hours |
8760 | |
Load power (VA) | 1000 | |
Module current (A) | 22.5 | |
Battery voltage (V) | 12 | |
Battery capacity (Ah) | 110 | |
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Efficiency | Battery | 0.95 |
Transmission line | 0.98 | |
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Unit price ($) | Module | 200 |
Battery | 150 | |
Charge regulator | 100 | |
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Daily load demand | (Ah/day) | 2148.23 |
(kVAh/day) | 25.78 | |
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Replacement numbers | Module | 0 |
Battery | 2 | |
Charge regulator | 1 | |
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Annual inflation rate | 0.12 | |
Annual discount rate | 0.18 |
Here, it is assumed that the electrical charge is fed by direct current. In the case of the load fed by the alternating current, for the inverter, variables such as efficiency, initial investment cost, and change numbers should be included in the calculation. The purpose of the study is to examine the changes in energy costs according to geographical regions. For this reason, the inverter and/or converter costs will have approximately the same effect as constant parameters for all geographic region calculations.
In the next step, the components of the SAPV system can be determined and cost calculations can be made. First, it is necessary to determine the number of battery and PV modules that change depending on the load rated power and the system location. The load demand (D.C.) is determined by
The number of storage days, the required battery capacity, and the number according to the selected battery type can be calculated by (
Practically
In (
With (
As is known, the present worth factor of an item that will be bought after “
In this case, the present worth of a purchase in the
The present worth of recurring purchases is also given as
Equation (
In this case, the LCC will be equal to the sum of the total present worth of each component. Initial investment, O&M, and replacement costs are essential components of LCC analysis. In many studies, (
In some literature examples, salvage cost is added negatively to (
In a SAPV system, total energy production is directly related to the PV array. Here, monthly energy generation will vary depending on the minimum peak sun hours. Monthly calculation results with developed algorithm are collected to obtain the annual energy production amount. Equation (
The demand energy by the load is obtained from
After this step, the capacity utilization rate (CUR) and unit energy cost (UEC) can be calculated by (
The above equations have been applied using meteorological data for 75 provincial centers of seven geographical regions in Turkey. The values obtained from the averages of provincial centers constituting each region were categorized and tabulated for each region to compare graphically. For this, an Excel-based calculation algorithm is established. The developed algorithm was applied to five different scenarios. In these scenarios, the load is assumed to be seasonal (spring, summer, autumn, and winter) and continuous service throughout the year. The calculation results and graphics given in the next section are also based on this supposition. Thus, the effects of seasonal climate changes on the cost factors of stand-alone PV systems as well as geographical regional differences have been examined. Table
Average sun hours of Turkey’s provinces [
Geographical region | Provinces | Average sun hours (hour/day) | |||||||||||
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Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | ||
Marmara | Balikesir | 4.05 | 5.05 | 5.92 | 7.26 | 9.23 | 10.99 | 11.44 | 10.65 | 8.84 | 6.53 | 4.69 | 3.64 |
Bilecik | 3.31 | 4.45 | 4.77 | 6.27 | 8.75 | 9.86 | 10.48 | 9.75 | 8.48 | 5.86 | 4.44 | 3.31 | |
Bursa | 3.71 | 4.51 | 5.64 | 6.96 | 8.90 | 10.11 | 10.78 | 9.98 | 8.49 | 5.84 | 4.35 | 3.40 | |
Çanakkale | 4.21 | 5.50 | 6.28 | 7.77 | 9.54 | 11.56 | 11.85 | 11.01 | 9.00 | 6.81 | 4.93 | 3.83 | |
Edirne | 3.93 | 5.31 | 5.94 | 7.69 | 9.54 | 10.96 | 11.73 | 10.71 | 8.88 | 6.03 | 4.56 | 3.37 | |
Istanbul | 3.46 | 4.43 | 5.32 | 6.85 | 8.61 | 10.51 | 11.17 | 10.14 | 7.83 | 5.22 | 3.85 | 2.96 | |
Kirklareli | 3.68 | 5.09 | 5.43 | 7.37 | 8.93 | 11.47 | 12.50 | 11.23 | 8.30 | 5.32 | 4.22 | 2.87 | |
Kocaeli | 3.29 | 4.17 | 5.20 | 6.55 | 8.56 | 9.79 | 10.44 | 9.59 | 7.96 | 5.40 | 3.95 | 3.06 | |
Sakarya | 3.20 | 4.23 | 5.01 | 6.33 | 8.39 | 9.72 | 10.35 | 9.56 | 8.01 | 5.53 | 4.05 | 3.09 | |
Tekirdağ | 3.69 | 4.96 | 5.54 | 7.26 | 9.05 | 11.21 | 11.92 | 10.93 | 8.32 | 5.66 | 4.15 | 3.13 | |
Yalova | 3.27 | 4.25 | 5.41 | 6.84 | 8.78 | 9.96 | 10.70 | 9.75 | 8.15 | 5.53 | 4.05 | 3.04 | |
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Aegean | Afyon | 3.91 | 5.17 | 5.64 | 7.05 | 9.27 | 10.71 | 11.36 | 10.73 | 9.39 | 6.82 | 5.12 | 3.74 |
Aydin | 5.16 | 5.98 | 7.00 | 8.09 | 9.76 | 11.79 | 12.09 | 11.45 | 9.85 | 7.67 | 5.76 | 4.45 | |
Denizli | 4.88 | 5.75 | 6.86 | 7.90 | 9.64 | 11.36 | 11.83 | 11.19 | 9.73 | 7.35 | 5.61 | 4.23 | |
Izmir | 4.86 | 5.86 | 6.96 | 8.03 | 9.77 | 11.89 | 12.20 | 11.48 | 9.67 | 7.61 | 5.55 | 4.27 | |
Kütahya | 3.71 | 4.78 | 5.50 | 6.65 | 8.91 | 10.29 | 10.77 | 10.09 | 8.90 | 6.26 | 4.75 | 3.51 | |
Manisa | 4.60 | 5.45 | 6.57 | 7.62 | 9.49 | 11.32 | 11.77 | 11.06 | 9.26 | 7.11 | 5.22 | 3.94 | |
Muğla | 5.13 | 6.20 | 7.12 | 8.18 | 9.91 | 11.73 | 11.90 | 11.31 | 9.92 | 7.85 | 6.01 | 4.67 | |
Uşak | 4.60 | 5.33 | 6.46 | 7.48 | 9.37 | 10.83 | 11.42 | 10.76 | 9.38 | 6.93 | 5.14 | 3.98 | |
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Mediterranean | Adana | 4.67 | 5.65 | 6.97 | 7.84 | 9.72 | 11.29 | 11.77 | 11.22 | 10.15 | 7.78 | 5.86 | 4.21 |
Antalya | 4.95 | 6.10 | 7.24 | 8.29 | 9.70 | 11.55 | 11.84 | 11.29 | 9.80 | 7.68 | 5.97 | 4.55 | |
Burdur | 4.74 | 5.82 | 6.98 | 7.97 | 9.61 | 11.40 | 11.85 | 11.25 | 9.78 | 7.45 | 5.72 | 4.23 | |
Hatay | 5.09 | 6.22 | 7.17 | 8.28 | 10.23 | 11.14 | 10.89 | 10.47 | 9.80 | 7.86 | 6.37 | 4.99 | |
Isparta | 4.38 | 5.46 | 6.82 | 7.77 | 9.42 | 11.10 | 11.70 | 11.13 | 9.64 | 7.17 | 5.44 | 3.95 | |
Kahramanmaraş | 4.21 | 5.47 | 6.61 | 7.85 | 9.57 | 11.49 | 12.07 | 11.43 | 10.13 | 7.55 | 5.56 | 3.86 | |
Mersin | 4.99 | 6.04 | 7.35 | 8.38 | 9.94 | 11.18 | 11.45 | 11.03 | 10.02 | 7.91 | 6.15 | 4.64 | |
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Black Sea | Amasya | 3.57 | 4.65 | 5.48 | 6.60 | 8.16 | 9.58 | 10.14 | 9.70 | 8.11 | 6.11 | 4.51 | 3.19 |
Artvin | 3.49 | 4.13 | 5.56 | 6.61 | 7.12 | 8.10 | 7.65 | 7.64 | 6.86 | 5.38 | 4.27 | 3.14 | |
Bartin | 3.31 | 4.30 | 5.29 | 6.66 | 7.81 | 9.94 | 10.79 | 9.94 | 7.64 | 5.41 | 4.08 | 2.96 | |
Bolu | 3.28 | 4.41 | 5.19 | 6.53 | 8.31 | 9.73 | 10.44 | 9.74 | 8.15 | 5.78 | 4.26 | 3.12 | |
Çorum | 3.60 | 4.79 | 5.92 | 6.99 | 8.29 | 9.91 | 10.66 | 10.16 | 8.32 | 6.19 | 4.57 | 3.21 | |
Düzce | 3.17 | 4.25 | 5.20 | 6.37 | 8.21 | 9.71 | 10.40 | 9.67 | 7.96 | 5.60 | 4.03 | 3.04 | |
Gümüşhane | 3.02 | 4.60 | 5.54 | 6.74 | 8.17 | 9.25 | 9.30 | 8.99 | 8.28 | 5.95 | 4.43 | 2.99 | |
Kastamonu | 3.39 | 4.44 | 5.45 | 6.65 | 7.86 | 9.82 | 10.66 | 9.87 | 7.67 | 5.58 | 4.26 | 3.10 | |
Ordu | 3.44 | 4.40 | 4.77 | 6.24 | 7.96 | 9.06 | 8.92 | 8.60 | 7.72 | 5.80 | 4.34 | 3.11 | |
Rize | 3.38 | 4.21 | 5.21 | 6.40 | 7.47 | 8.19 | 7.75 | 7.54 | 7.19 | 5.37 | 4.17 | 3.02 | |
Samsun | 3.60 | 4.41 | 5.17 | 6.43 | 7.92 | 9.15 | 9.52 | 8.97 | 7.61 | 5.72 | 4.32 | 3.22 | |
Sinop | 3.46 | 4.42 | 5.35 | 6.62 | 7.80 | 9.44 | 10.08 | 9.32 | 7.57 | 5.56 | 4.32 | 3.21 | |
Tokat | 3.60 | 4.72 | 5.50 | 6.71 | 8.27 | 9.74 | 10.12 | 9.79 | 8.40 | 6.33 | 4.62 | 3.20 | |
Trabzon | 3.17 | 4.29 | 4.88 | 6.16 | 7.64 | 8.35 | 7.90 | 7.62 | 7.43 | 5.42 | 4.20 | 2.99 | |
Zonguldak | 3.27 | 4.25 | 5.37 | 6.62 | 8.10 | 9.83 | 10.59 | 9.79 | 7.84 | 5.54 | 4.02 | 3.07 | |
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Central Anatolia | Ankara | 3.73 | 4.89 | 6.16 | 7.21 | 8.76 | 10.21 | 11.06 | 10.45 | 8.83 | 6.48 | 4.73 | 3.35 |
Aksaray | 4.11 | 5.40 | 6.80 | 7.97 | 9.36 | 11.27 | 12.12 | 11.53 | 9.81 | 7.36 | 5.45 | 3.73 | |
Çankiri | 3.69 | 4.75 | 6.24 | 7.08 | 8.27 | 9.90 | 10.70 | 10.09 | 8.23 | 6.05 | 4.47 | 3.21 | |
Karaman | 4.46 | 5.85 | 7.14 | 8.44 | 9.84 | 11.51 | 12.02 | 11.47 | 10.11 | 7.77 | 5.95 | 4.31 | |
Kayseri | 4.08 | 5.31 | 6.37 | 7.59 | 9.21 | 11.22 | 12.03 | 11.47 | 9.84 | 7.39 | 5.39 | 3.55 | |
Kirikkale | 3.79 | 4.99 | 6.57 | 7.48 | 8.66 | 10.32 | 11.17 | 10.63 | 8.84 | 6.57 | 4.76 | 3.31 | |
Kirşehir | 3.97 | 5.18 | 6.61 | 7.72 | 9.02 | 10.78 | 11.73 | 11.15 | 9.32 | 6.98 | 5.07 | 3.51 | |
Konya | 4.19 | 5.51 | 6.88 | 8.03 | 9.46 | 11.28 | 11.97 | 11.35 | 9.79 | 7.35 | 5.53 | 3.93 | |
Niğde | 4.41 | 5.48 | 6.74 | 7.85 | 9.51 | 11.39 | 12.16 | 11.57 | 10.06 | 7.61 | 5.65 | 3.90 | |
Nevşehir | 4.07 | 5.32 | 6.43 | 7.65 | 9.16 | 11.17 | 12.05 | 11.48 | 9.68 | 7.27 | 5.35 | 3.57 | |
Sivas | 3.76 | 5.02 | 5.99 | 7.20 | 8.73 | 10.50 | 11.09 | 10.65 | 9.23 | 6.80 | 4.97 | 3.30 | |
Yozgat | 3.80 | 5.12 | 6.10 | 7.29 | 8.72 | 10.60 | 11.41 | 10.92 | 9.10 | 6.85 | 4.99 | 3.33 | |
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Eastern Anatolia | Ağri | 4.10 | 5.43 | 6.25 | 7.62 | 9.08 | 10.82 | 11.32 | 10.79 | 9.45 | 7.14 | 5.42 | 3.96 |
Ardahan | 3.75 | 4.31 | 6.01 | 6.93 | 7.12 | 8.76 | 8.85 | 8.94 | 7.33 | 5.90 | 4.68 | 3.42 | |
Bingöl | 4.08 | 4.93 | 6.02 | 7.18 | 9.08 | 11.01 | 11.51 | 10.87 | 9.54 | 6.87 | 4.89 | 3.45 | |
Bitlis | 3.46 | 4.72 | 5.56 | 7.13 | 9.27 | 10.95 | 11.31 | 10.62 | 9.81 | 6.86 | 5.19 | 3.59 | |
Elaziğ | 4.13 | 5.14 | 6.37 | 7.56 | 9.39 | 11.45 | 12.01 | 11.33 | 9.86 | 7.14 | 5.12 | 3.56 | |
Erzincan | 3.73 | 4.85 | 6.15 | 7.14 | 8.63 | 10.29 | 10.67 | 10.23 | 9.12 | 6.52 | 4.71 | 3.27 | |
Erzurum | 3.85 | 4.71 | 5.82 | 6.95 | 8.28 | 9.86 | 10.30 | 9.91 | 8.50 | 6.24 | 4.57 | 3.31 | |
Hakkari | 6.65 | 8.17 | 8.92 | 9.95 | 11.31 | 12.71 | 12.61 | 11.85 | 10.70 | 8.69 | 7.42 | 6.42 | |
Iğdir | 5.88 | 7.34 | 8.43 | 9.64 | 10.87 | 12.34 | 12.59 | 11.65 | 10.25 | 8.33 | 6.86 | 5.61 | |
Kars | 3.82 | 4.74 | 6.24 | 7.04 | 7.90 | 9.89 | 10.55 | 10.36 | 8.15 | 6.46 | 4.82 | 3.44 | |
Malatya | 4.23 | 5.30 | 6.59 | 7.86 | 9.41 | 11.43 | 12.09 | 11.44 | 9.96 | 7.28 | 5.26 | 3.64 | |
Muş | 3.66 | 4.80 | 5.56 | 7.25 | 9.13 | 10.83 | 11.42 | 10.76 | 9.60 | 6.89 | 4.98 | 3.47 | |
Tunceli | 4.02 | 4.99 | 6.25 | 7.27 | 9.00 | 10.88 | 11.43 | 10.86 | 9.49 | 6.85 | 4.88 | 3.41 | |
Van | 5.27 | 6.40 | 7.39 | 8.50 | 10.11 | 11.55 | 11.65 | 10.97 | 10.31 | 7.65 | 6.16 | 4.93 | |
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Southeastern Anatolia | Adiyaman | 4.51 | 5.49 | 6.74 | 8.08 | 9.70 | 11.78 | 12.25 | 11.52 | 10.17 | 7.56 | 5.56 | 4.01 |
Batman | 3.92 | 5.02 | 6.16 | 7.64 | 9.71 | 11.72 | 12.09 | 11.34 | 10.05 | 7.33 | 5.48 | 3.92 | |
Diyarbakir | 3.73 | 4.89 | 6.16 | 7.21 | 8.76 | 10.21 | 11.06 | 10.45 | 8.93 | 6.48 | 4.73 | 3.35 | |
Gaziantep | 4.60 | 5.78 | 6.82 | 8.10 | 9.93 | 11.63 | 11.74 | 11.07 | 10.03 | 7.80 | 5.98 | 4.38 | |
Kilis | 4.70 | 5.92 | 6.80 | 8.12 | 10.15 | 11.48 | 11.43 | 10.86 | 9.81 | 7.86 | 6.14 | 4.58 | |
Mardin | 4.35 | 5.45 | 6.74 | 7.90 | 10.01 | 12.52 | 12.84 | 12.03 | 10.07 | 7.59 | 5.83 | 4.43 | |
Siirt | 3.84 | 5.00 | 6.04 | 7.38 | 9.64 | 11.52 | 11.78 | 11.07 | 9.99 | 7.19 | 5.53 | 4.02 | |
Şanliurfa | 4.68 | 5.62 | 6.92 | 8.14 | 9.96 | 12.24 | 12.42 | 11.66 | 10.11 | 7.71 | 5.87 | 4.40 |
Flowchart of the calculation algorithm.
As mentioned above, calculations are made for five different scenarios. Table
Calculations for the all-year (8760 hours) scenario.
Parameter | Regions | |||||||
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Marmara | Aegean | Mediterranean | Black Sea | Central Anatolia | Eastern Anatolia | Southeastern Anatolia | ||
Total battery size (Ah) | 8115.42 | 6562.84 | 6695.41 | 8296.89 | 7679.91 | 7190.73 | 6967.24 | |
Battery number |
71.97 | 59.66 | 58.49 | 74.31 | 61.30 | 69.74 | 64.82 | |
Modified design current (A) | 740.80 | 536.39 | 553.04 | 769.42 | 670.96 | 628.42 | 582.23 | |
Module number |
32.92 | 23.84 | 24.58 | 34.20 | 29.82 | 27.93 | 25.88 | |
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Initial investment costs ($) | Module | 6672.23 | 4800.00 | 5028.57 | 6960.00 | 6066.67 | 5685.71 | 5300.00 |
Battery | 11,004.55 | 8850.00 | 9064.29 | 11,260.00 | 10,412.50 | 9750.00 | 9142.50 | |
Charge regulator | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | |
BOS | 667.27 | 480.00 | 502.60 | 696.00 | 606.67 | 568.57 | 530.00 | |
O&M | 333.64 | 240.00 | 251.43 | 348.00 | 303.63 | 284.29 | 265.00 | |
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LCC costs ($) | Module | 6672.73 | 4800.00 | 5028.57 | 6960.00 | 6066.67 | 5685.71 | 5300.00 |
Battery | 21,410.02 | 17,218.22 | 17,365.12 | 21,907.02 | 20,258.16 | 18,969.22 | 18,312.60 | |
Charge regulator | 145.71 | 145.71 | 145.71 | 145.71 | 145.71 | 145.71 | 145.71 | |
BOS | 667.27 | 480.00 | 502.86 | 696.00 | 606.67 | 568.57 | 530.00 | |
O&M | 4872.08 | 3504.71 | 3671.61 | 5081.84 | 4429.57 | 4151.42 | 4782.47 | |
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Lifetime total energy (to be produced) (MVAh) | 7448.87 | 6802.37 | 6831.41 | 6989.77 | 7328.96 | 6723.80 | 6970.57 | |
Lifetime total energy (to be consumed by load) (MVAh) | 235.23 | 235.23 | 235.23 | 235.23 | 235.23 | 235.23 | 235.23 | |
The capacity utilization rate (%) | 3.16 | 3.46 | 3.44 | 3.37 | 3.21 | 3.50 | 3.37 | |
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Excess energy (MVAh) | 7213.64 | 6567.14 | 6596.18 | 6754.54 | 7093.73 | 6488.57 | 6735.34 |
In all-year, winter, spring, and autumn scenarios, storage costs are 64% of LCC. 19% of the LCC is module cost. The remaining 17% is the sum of the charge regulator, BOS, and O&M costs. These ratios show very important differences only in the summer scenario. The share of storage costs within the total LCC is about 1% in summer. Module and O&M cost ratios are 49% and 36%, respectively. In particular, the effects on the replacement costs of inflation and discount rates in the country are particularly important. In [
Figure
Changes in LCCs by scenarios and geographical regions.
Figure
Comparison of unit energy costs by geographical areas and scenarios.
In this study, classical analytical calculation method is used. Similar methods are used for both technical and economic analyzes in many studies. In LCC and UEC calculations, in particular, the economic conditions of the country, the assumptions made, and the system life are important parameters. In addition, the unit costs of SAPV system components have been decreasing over the years. Besides, the incentives and subsidies given by the governments of the countries also show significant changes. In [
Comparison of studies in the literature in terms of UEC.
Ref. number | Authors | Year | Location | System life (years) | Discount rate (%) | Inflation rate (%) | System power (kWp) | UEC ($/kWh) |
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[ |
Nordin and Rahman | 2015 | Malaysia | 25 | 6.85 | 2.8 | 1.40 | 0.34 |
[ |
Chel and Tiwari | 2010 | India | 30 | 4 | n/a | 2.32 | 0.96 (0.8 €/kWh) |
[ |
Roy and Kabir | 2011 | Bangladesh | 20 | 10 | n/a | 0.4 | 1.046 |
14 | 0.582 | |||||||
[ |
Ghafoor and A. Munir | 2014 | Pakistan | 20 | 8 | 4 | 2 | 0.15 |
[ |
Kamalapur and Udaykumar | 2010 | India | 25 | n/a | n/a | 0.14 | 0.258 |
[ |
Hassan et al. | 2016 | Iraq | 20 | 9 | 3 | 3.1 | 0.51 |
[ |
Al-Karaghouli and Kazmerski | 2009 | Iraq | 30 | 6 | n/a | 6 | 0.444 |
Used model in this article | 2017 | Turkey | 25 | 18 | 12 | 1 | 0.11~0.15 (all-year sce.) | |
0.24~0.37 (spring sce.) | ||||||||
0.06~0.10 (summer sce.) | ||||||||
0.33~0.46 (autumn sce.) | ||||||||
0.45~0.60 (winter sce.) |
The CUR is related to the amount of excess energy produced. Since the load demand is a key element in determining all parameters of the system, SAPV plants usually have excessive energy. Therefore, the rate of electrical capacity utilization is always below 100%. As can be seen from Figure
Changes in electrical capacity utilization rates.
The excess energy produced varies inversely with the CURs. The values forming the graphs in Figure
Excess energy produced by PV array.
Calculation results for all scenarios.
Scenario | Parameter | Geographic regions | ||||||
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Marmara | Aegean | Mediterranean | Black Sea | Central Anatolia | Eastern Anatolia | Southeastern Anatolia | ||
All-year | LCC ($) | 33,767.81 | 26,148.64 | 26,983.87 | 34,790.57 | 31,506.77 | 29,520.64 | 29,070.78 |
CUR (%) | 3.16 | 3.46 | 3.44 | 3.37 | 3.21 | 3.50 | 3.37 | |
UEC ($/kVAh) | 0.14 | 0.11 | 0.11 | 0.15 | 0.13 | 0.13 | 0.12 | |
Excess energy (MVAh) | 7213.64 | 6567.14 | 6596.18 | 6754.54 | 7093.73 | 6488.57 | 6735.34 | |
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Spring | LCC ($) | 21,089.43 | 14,465.38 | 14,360.8 | 21,883.58 | 16,600.55 | 16,525.18 | 16,929.11 |
CUR (%) | 20.69 | 21.23 | 21.45 | 22.02 | 22.53 | 22.63 | 21.07 | |
UEC ($/kVAh) | 0.35 | 0.24 | 0.24 | 0.37 | 0.28 | 0.28 | 0.29 | |
Excess energy (MVAh) | 227.3 | 219.94 | 217.17 | 209.91 | 203.88 | 202.67 | 222.11 | |
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Summer | LCC ($) | 3806.91 | 4097.85 | 4150.13 | 5899.96 | 4274.67 | 4291.10 | 4462.92 |
CUR (%) | 25.54 | 24.66 | 24.19 | 26.77 | 25.28 | 26.38 | 24.41 | |
UEC ($/kVAh) | 0.06 | 0.07 | 0.07 | 0.10 | 0.07 | 0.07 | 0.08 | |
Excess energy (MVAh) | 172.90 | 181.17 | 185.84 | 162.20 | 175.27 | 165.47 | 183.61 | |
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Autumn | LCC ($) | 27,231.44 | 19,940.98 | 19,419.5 | 27,113.18 | 22,594.28 | 21,907.86 | 21,228.19 |
CUR (%) | 18.76 | 19.05 | 19.10 | 19.99 | 19.50 | 20.08 | 18.89 | |
UEC ($/kVAh) | 0.46 | 0.34 | 0.33 | 0.46 | 0.39 | 0.37 | 0.36 | |
Excess energy (MVAh) | 253.99 | 249.25 | 248.42 | 234.67 | 242.05 | 233.39 | 251.80 | |
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Winter | LCC ($) | 33,767.81 | 26,148,64 | 26,983.87 | 34,790.57234.67 | 31,506.77 | 29,588.47 | 29,070.78 |
CUR (%) | 23.30 | 23.37 | 23.32 | 24.03 | 23.28 | 24.15 | 24.12 | |
UEC ($/kVAh) | 0.58 | 0.45 | 0.46 | 0.60 | 0.54 | 0.51 | 0.50 | |
Excess energy (MVAh) | 190.89 | 190.14 | 190.39 | 183.39 | 191.11 | 182.12 | 182.47 |
The sale or use of excess-generated energy is generally not within the scope of this article. However, the use of excess energy in the all-year scenario will have a significant impact on the UEC of the system. According to the results, the energy consumed by the load was about 3% of the total energy production. For this reason, the effect of the excess energy usage on the UEC for the all-year scenario was taken into account. As shown in Figure
Effect of using excess energy on UEC in the all-year scenario.
In this study, a SAPV system design algorithm was developed using a classical analytical method. Using the monthly average sun hours for 75 different locations of Turkey, the technoeconomic analysis of the SAPV system was realized for each geographical region. The developed algorithm groups the calculation results for each location according to the geographical regions of Turkey. In addition, the requested data is output graphically. The SAPV system components have been resized for five different operating period scenarios with a 1000 VA load. The numbers of module, battery, and charge regulator to be used were calculated. Depending on the selected components, BOS, O&M, and replacement costs were also calculated separately. The effects of LCC, UEC, CUR, and excess energy usage rates discussed in detail in the previous section were also examined.
The conclusions obtained can be briefly summarized as follows:
There are no significant differences between the geographical regions of Turkey in terms of LCC and UEC. Therefore, all locations of the country are suitable for SAPV installation with appropriate designs. However, especially the Mediterranean, Aegean, and Eastern Anatolian regions are the most advantageous geographical regions. In 2017, energy sales price (ESP) for residential consumers of Turkey is given as 42 krş/kWh (0.42 TL/kWh) including funds and taxes [ the average UEC for winter scenario is 43.3 times higher than ESP; for the autumn scenario, this value is calculated as 32.3 times of ESP; the cost of energy for the spring scenario is about 24.4 times higher than ESP; the all-year scenario is the second most advantageous time period and this value is calculated as 10.6 times of ESP; in the summer scenario, UEC is highly competitive. It is only 6.2 times more than the ESP. It can be said that LCC and UEC have become more advantageous at seasonal loads. Especially in the summer, these values are declining significantly at 87% compared to the winter scenario. Therefore, it is very important to correctly determine the operation period of the load of SAPV system. The operating period of the load also affects the components that make up the LCC. In SAPV systems, approximately 80% of LCC consists of storage and PV array components. However, in seasonal scenarios, the share of these components varies considerably. UECs in SAPV systems are considerably higher than in conventional systems. However, the environmental and economic benefits provided by these systems are also very critical for sustainable development. For this reason, many countries are taking measures to encourage SAPV investments. In this study, analyses made in different countries and dates are also compared in terms of UEC. UEC values vary depending on parameters such as economic conditions of the countries, discount, and inflation rates. Capacity utilization rates also vary significantly between scenarios. This value can be increased by selling SAPV-generated energy or by feeding seasonally additional loads. Thus, UEC can also be reduced. In this study, comparison was made in terms of the geographical regions of the country. Therefore, constant parameters such as inverter and salvage cost to be added to the system will not significantly change the results to be obtained above. The developed algorithm can be modified in the following studies to add options such as selling the excess energy to the national grid. Thus, the advantages of legal regulations allowing residential consumers to sell energy can be demonstrated. The proposed model can be used for any location depending on the meteorological data. In addition, it can realize economic parameter sensitivity analyses (e.g., different discounts, inflation rates, and government subsidies). Comparisons of LCCs for different brands and models of SAPV system components can also be performed with the exception of outputs from this study.
Daily load demand (Ah/day)
Load rated power (VA)
Yearly operating hour’s number
Yearly operating day’s number
Battery voltage (V)
Wiring efficiency
Battery efficiency
Number of storage days
Peak sun hours
Total battery capacity (Ah)
Temperature effect on battery correction factor
Charge-discharge correction factor of the battery
Percentage of battery discharge rate
Unit battery capacity (Ah)
Number of batteries
Total current drawn from PV array (A)
Number of PV modules
The current of the selected module (A)
Present worth factor
Inflation rate
Discount rate
System life cycle
The present worth of the cost for any “
Initial investment cost ($)
Total present worth ($)
Life-cycle cost ($)
Total initial investment cost ($)
Present worth of O&M costs ($)
Present worth of replacement costs ($)
Daily energy production of PV array (Ah/day)
Total energy production (kVAh)
Total energy demand (kVAh)
Electrical capacity usage rate
Unit energy cost ($/kVAh)
Energy sales price ($/kWh, TL/kWh).
The author declares that there is no conflict of interest regarding the publication of this paper.