Primary energy sources are running out due to the increase in electrical energy consumption. Environmental problems caused by primary energy sources are also increasing. Using more renewable energy resources (RES) can be considered as one of the most powerful solutions to address these problems. Today, required photovoltaic power systems (PVPS) and wind energy systems (WES) are widely used as RES for addressing these problems. Because of their high costs, feasibility studies are required for locating large systems associated with these resources. In this study, various suggestions are determined about location selection, which is an important stage in the PVPS’s establishment. Hence, the criteria for selecting the appropriate location are analyzed by the multicriteria decision making (MCDM) methods and the results are evaluated for 5 cities in the Central Anatolian Region of Turkey. In conclusion, it is determined which city is the most suitable place for installation of solar power plants.
Solar energy is one of the most important RES, and it is becoming more popular day by day for many reasons such as the purification of raw materials and the reduction of dependence on foreign oil and gas. Moreover, solar energy is an inexhaustible reliable source and it is harmless to the ecological environment. The choice of the appropriate solar energy location, which is important in their setup, depends on many factors. These factors should be optimized to get more energy as well as to reduce initial investment and operation costs. These operations should be considered during the first phase of solar energy installation to locate the plant accurately. Hence, many studies are performed in the literature locating the power plants in to the most appropriate places [
MCDM is a subbranch of a decision process. The decision process consists of the determination of different criteria for modelling goals, evaluation of alternatives, and getting results. To evaluate the alternatives based on criteria, different methods are used, such as analytic hierarchy process (AHP), analytic network process (ANP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Elimination and Choice Translating Reality English (ELECTRE), The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), and Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) [
There are many studies that use the MCDM methods to solve location problems in the literature. Kengpol et al. developed a decision support system for solar power plant site selection in Thailand. They applied fuzzy analytic hierarchy process (Fuzzy AHP) model for the problem [
In this study, four different MCDM methods are used to select the most suitable city among 5 cities in the Central Anatolian Region of Turkey for the establishment of solar power plant in order to get maximum power output and have minimum cost. Aksaray, Konya, Karaman, Nevşehir, and Niğde, which have the highest solar radiation, are selected for comparison. Three main criteria are defined for solar power plant location selection. These criteria rely on solar energy potential, feeder capacity, and surface slope. This study differs from other studies in terms of comparative use of all the MCDM methods. This situation has not been studied previously in the literature, especially when choosing suitable locations for PVPS. In addition, associating such a study with cities that have not been selected before is another contribution of this study. In conclusion of the study, it is observed that Karaman is determined as the most suitable city for the establishment of the solar plant station.
It has become important to determine the installation location of solar energy systems that are in the foreground among the RES. Since the lifetime of such systems is a long time in 25 years, the location of a solar power plant that can obtain maximum energy is significant. Moreover, it is not possible to change the place of the system after installation because of the construction costs.
There are different criteria that can be used to determine the solar power plant location. Solar energy potential, feeder capacity of the distribution center, and surface slope are the main criteria that have been used for the selection of the solar power plant location. These main criteria have subcriteria to examine the problem in detail. Subcriteria of energy potential criterion are photovoltaic (PV) solar radiation, sunshine duration, and the total amount of energy/PV area. The feeder capacity of the distribution center has subcriteria of total capacity and available quota. Subcriteria of the power plant surface are the surface slope, ice load, and wind potential. Each subcriterion has its own weight factor for the related main criterion. In the following, the above-mentioned main criteria for the related cities will be, respectively, explained.
The location where the solar power plant will be installed is highly related with the solar energy potential of the location. The information about the solar energy potential of a location can be determined from the global radiation values (kWh/m2-day), sunshine duration (hours), and PV-type area energy generation (kWh/year). In this study, these values of the cities are obtained from solar energy potential atlas (GEPA) of Directorate General of Renewable Energy in Turkey [
Konya province (a) global radiation values, (b) sunshine duration, and (c) total amount of energy/PV area.
Karaman province (a) global radiation values, (b) sunshine duration, and (c) total amount of energy/PV area.
Niğde province (a) global radiation values, (b) sunshine duration, and (c) total amount of energy/PV area.
Nevşehir province (a) global radiation values, (b) sunshine duration, and (c) total amount of energy/PV area.
Aksaray province (a) global radiation values, (b) sunshine duration, and (c) total amount of energy/PV area.
The data in the figures are used as inputs to the proposed methods. Since the cities are in the same region and are close to each other, the suitable location of the installation cannot be estimated from the figures easily. However, they are very useful while using together with other methods. For this reason, they have been thoroughly examined.
When an electric energy production facility is installed in a region, the infrastructure of the region should be examined. Therefore, the transformer capacities, the number of lines, cable sections, and so forth are considered as the parameters. In this context, the allocated capacity should also be considered. The allocated capacity of the transformer center for solar and wind energy power plants within unlicensed electricity generation is obtained with the notification of Directorate General of Turkish Electricity Transmission Corporation (TEİAŞ) [
Another main criterion is surface slope. The slope of the surface where a solar power plant will be installed is usually kept below 5% [
In this study, the AHP, ELECTRE, TOPSIS, and VIKOR, submethods of the MCDM, are used to decide which of the above-mentioned cities is suitable for the PVPS installation. These methods are described next for a better understanding of the simulations.
AHP can be explained as the decision and the estimation method that is used for the identification of the decision hierarchy, and it gives percentage distribution of the decision points in terms of factors which affect the decision [
Rating scale of AHP method.
Intensity of importance | Definition | Explanation |
---|---|---|
1 | Equal importance | Two factors contribute equally to the objective. |
3 | More important | Experience and judgement slightly favour one over the other. |
5 | Much more important | Experience and judgement strongly favour one over the other. |
7 | Very much more important | Experience and judgement very strongly favour one over the other. |
9 | Absolutely more important | The evidence favouring one over the other is of the highest possible validity. |
2, 4, 6, 8 | Intermediate values | When compromise is needed. |
In the third step, percentage importance distribution of the factors is determined. Comparison matrix shows the importance level with respect to each factor. The column vector
The components of the column vector
The matrix
The percentage importance distribution that shows the relative importance of each factor can be obtained with the help of matrix
In the fourth step, consistency of factor comparison is measured. Consistency ratio (CR) determines whether the comparisons that are made by AHP method are true or not. Firstly, column vector
Main value related to each evaluation factor (EF) is obtained by dividing column vector
Mean value related to the comparison (
Then, consistency index (CI) and the (CR) are calculated as shown in (
The value of CR must be smaller than 0.10 to be consistent with comparison matrix [
Random index (RI) in (
The values of RI.
|
RI |
|
RI |
---|---|---|---|
1 | 0 | 6 | 1.24 |
2 | 0 | 7 | 1.32 |
3 | 0.58 | 8 | 1.41 |
4 | 0.90 | 9 | 1.45 |
5 | 1.12 | 10 | 1.49 |
In the final step, percentage importance distribution (PID) at
The decision matrix
As a result, the column vector
The method depends on dual superiority comparisons among the decision points for each evaluation factor. This method basically consists of 8 steps [
In the second step, standard decision matrix,
In the third step, the weighted standard decision matrix,
In the fourth step, consistency (
In the fifth step, consistency (
The matrix
In the sixth step, consistency superiority (
The matrix
The elements of the matrix
The matrix
The elements of the matrix
In the seventh step, total dominance matrix (
In the first step of this method, the decision matrix
In the second step, standard decision matrix
The elements of the matrix
In the third step, standard weighted decision matrix
In the fourth step, ideal
The smallest value of weighted evaluation factors of matrix
In the fifth step, discrimination measurements are calculated. The deviation values related to the decision points are calculated with the help of Euclidean distance approach. Ideal discrimination
In the final step, the relative proximity of the ideal solution is calculated. The ideal and nonideal discrimination values are used to calculate relative proximity of the ideal solution for each decision point. The calculation is shown in
In (
If
This method solves the problems by calculating the best and the worst values of all the criteria functions. The best (
Then, the values of
After that, the value of
Finally, the calculated values of
In this study, a simulation is implemented by using the MATLAB program to establish the location of the solar power plants for the suggested cities with the help of the methods that are described next. The results obtained from the methods according to the problem definition have been explained in this section.
In this method, the matrices to be found for the three main criteria described in the previous chapters will be shown in a tabular form. These matrices are the comparative matrices of the solar energy potential, the allocated capacity, and the surface slope. The data to be used for this purpose is taken from the study in [
Comparison matrix for solar energy potential.
Solar energy potential | Aksaray | Konya | Karaman | Nevşehir | Niğde |
---|---|---|---|---|---|
Aksaray | 1 | 1/3 | 1/7 | 3 | 1/5 |
Konya | 3 | 1 | 1/5 | 5 | 1/3 |
Karaman | 7 | 5 | 1 | 9 | 3 |
Nevşehir | 1/3 | 1/5 | 1/9 | 1 | 1/7 |
Niğde | 5 | 3 | 1/3 | 7 | 1 |
The comparison matrix that is formed by comparing the cities is shown in Table
After that, the CR value is calculated with the help of (
Similar to the above procedures, maximum allocated capacity values are found. The data of the maximum capacity are taken from the study in [
Comparison matrix for maximum capacity that can be allocated.
Maximum capacity that can be allocated | Aksaray | Konya | Karaman | Nevşehir | Niğde |
---|---|---|---|---|---|
Aksaray | 1 | 1/3 | 3 | 5 | 8 |
Konya | 3 | 1 | 5 | 7 | 9 |
Karaman | 1/3 | 1/5 | 1 | 2 | 5 |
Nevşehir | 1/5 | 1/7 | 1/2 | 1 | 3 |
Niğde | 1/8 | 1/9 | 1/5 | 1/3 | 1 |
After that, the CR value is calculated with the help of
With the same repeated operations, the surface gradient matrix is also constructed using the data from the study [
Comparison matrix for surface slope.
Aksaray | Konya | Karaman | Nevşehir | Niğde | |
---|---|---|---|---|---|
Aksaray | 1 | 1/3 | 2 | 8 | 5 |
Konya | 3 | 1 | 5 | 9 | 7 |
Karaman | 1/2 | 1/5 | 1 | 6 | 3 |
Nevşehir | 1/8 | 1/9 | 1/6 | 1 | 1/3 |
Niğde | 1/5 | 1/7 | 1/3 | 3 | 1 |
The value of the CR for the surface slope criteria is calculated with (
Equation (
In this method, the decision matrix is formed as mentioned in (
Decision matrix.
Solar energy potential | Surface slope | Capacity | |
---|---|---|---|
Aksaray | 4 | 8 | 8 |
Konya | 6 | 10 | 10 |
Karaman | 10 | 6 | 6 |
Nevşehir | 2 | 2 | 4 |
Niğde | 8 | 4 | 2 |
After forming the decision matrix, total dominance matrix called matrix
Matrix
Aksaray | Konya | Karaman | Nevşehir | Niğde | |
---|---|---|---|---|---|
Aksaray | — | 0 | 0 | 1 | 0 |
Konya | 1 | — | 0 | 1 | 0 |
Karaman | 1 | 1 | — | 1 | 1 |
Nevşehir | 0 | 0 | 0 | — | 0 |
Niğde | 1 | 0 | 0 | 1 | — |
When results in Table
In this method, decision matrix is needed to obtain proximity values. Therefore, the decision matrix in Table
Proximity values based on ideal solution.
|
|
---|---|
Aksaray | 0.34 |
Konya | 0.56 |
Karaman | 0.83 |
Nevşehir | 0.07 |
Niğde | 0.62 |
The alternative, which has
In this method, the decision matrix in Table
Values of
|
Order of |
|
Order of |
|
Order of |
|
---|---|---|---|---|---|---|
Aksaray | 0.575 | 4 | 0.4875 | 4 | 0.6086 | 4 |
Konya | 0.325 | 2 | 0.325 | 3 | 0.2939 | 2 |
Karaman | 0.175 | 1 | 0.115 | 1 | 0 | 1 |
Nevşehir | 0.9425 | 5 | 0.65 | 5 | 1 | 5 |
Niğde | 0.4825 | 3 | 0.23 | 2 | 0.3077 | 3 |
According to the VIKOR method,
Table
Results with AHP, ELECTRE, TOPSIS, and VIKOR methods.
Method | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
AHP | Karaman | Konya | Niğde | Aksaray | Nevşehir |
ELECTRE | Karaman | Konya/Niğde | Konya/Niğde | Aksaray | Nevşehir |
TOPSIS | Karaman | Niğde | Konya | Aksaray | Nevşehir |
VIKOR | Karaman | Konya | Niğde | Aksaray | Nevşehir |
In this study, deciding on the most suitable location for a solar power plant installation is investigated. The results are obtained with the AHP, ELECTRE, TOPSIS, and VIKOR methods from MCDM submethods. The cities of Aksaray, Konya, Karaman, Nevşehir, and Niğde from the Central Anatolian Region of Turkey are selected for the study. The solar energy potential, the allocated feeder connection capacity, and the surface slope are chosen as criteria for the study. According to the chosen criteria, it has shown that Karaman has been identified as the most suitable city for solar power plant installation for all of the methods. Moreover, current practical works are also in the line with our study’s results. Therefore, this is a verification of the methods used in this study and they can be proposed for a solar power plant location selection.
The authors declare that there is no conflict of interests regarding the publication of this paper.