The purpose of this research is to establish a decision model for improving the performance of solar farms. To investigate the interdependent interrelationship and influential weights among criteria for solar farms site selection, a hybrid MCDM model including decision-making trial and evaluation laboratory (DEMATEL) and DEMATEL-based analytic network process (DANP) based on geographical information systems (GIS) is utilized. The empirical results display that there are interdependence and self-effect relationships among criteria via DEMATEL technique. According to the influential network relation map (INRM), the dimension that administrators of solar energy industry should improve first when enhancing the performance of solar farms is orography. In the ten criteria, solar radiation is the most important criterion impacting solar farms site selection, followed by average temperature and distance to villages.
Among the different types of renewable energy resources, photovoltaic (PV) solar energy is by far the largest exploitable resource for offering more energy in 1 hour to the earth than all of the energy consumed by humans in a whole year [
Previous studies regarding solar farms locations focused on considering simply what factors would influence solar farms [
In order to provide the administrators with the solution to these issues, a hybrid MCDM model combining decision-making trial and evaluation laboratory (DEMATEL) with DEMATEL-based analytical network process (DANP) is employed. The criteria of solar farms site are identified through GIS. According to the survey of experts, this paper adopts DEMATEL technique to probe into the interdependent decision making for constructing the influential network relation map (INRM). The strategies for improving the performance of solar farms can thus be obtained through the influence values of criteria in INRM. Subsequently, to solve the problems with interdependent criteria, the influential weights of solar farms site can be received via DANP derived from the basic concept of analytical network process (ANP) proposed by Saaty [
Industrial site selection is one of the fundamental decisions in the start-up process, expansion, or relocation. GIS are adopted combining with other systems and methods such as systems for decision making (DSS) and the method for MCDM. Synergistic effect is created in conjunction with these tools which contribute to the efficiency and quality of spatial analysis for industrial site selection [
By literature review, criteria affecting solar farms site selection for improving the performance of solar energy industry are arranged as follows. Solar farms site includes four dimensions: environment (
The DEMATEL method is utilized to probe into the problems of interdependent criteria for establishing the INRM [
Experts with specialty of technology for PV solar energy and experience from solar energy industry including officials of Bureau of Energy, scholars of energy engineering, and managers of solar industry are invited to help carry out this research. Information required for sufficient evaluation of solar farms site is collected by utilizing interviews and filling suitable questionnaires. In the questionnaires, a scale of 0, 1, 2, 3, and 4 presents the degree from “no influence” to “very high influence.” In addition, the confidence level for experts is tested (97.638%) in this real case of fifteen experts by this research.
The technique is introduced as follows: in the first place, the influence matrix is received by scores. The related experts are asked to indicate the degrees of influence among criteria, that is, to point out how much the criteria affect each other. The influence matrix
Comparing with [
Normalize each level of
The unweighted supermatrix can be acquired by adopting the interdependent relationship in group to array
The third step is to calculate the weighted supermatrix. The total influence matrix of dimensions
The weighted supermatrix
The DEMATEL technique is used to investigate the problems of interdependence and feedback among ten criteria from a literature review. Thereafter, the study construction of the influence network is displayed as Figure
The initial influence matrix
Criteria |
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0.000 | 2.000 | 0.467 | 2.000 | 3.000 | 2.600 | 2.533 | 2.067 | 3.467 | 3.467 |
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2.000 | 0.000 | 2.600 | 2.533 | 2.533 | 2.067 | 3.000 | 2.067 | 3.533 | 3.067 |
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2.000 | 2.600 | 0.000 | 2.067 | 2.533 | 2.067 | 2.533 | 2.533 | 3.533 | 3.533 |
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2.467 | 2.533 | 2.533 | 0.000 | 2.533 | 2.533 | 3.000 | 3.000 | 3.533 | 2.600 |
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2.533 | 2.533 | 2.533 | 3.000 | 0.000 | 2.533 | 3.000 | 2.533 | 3.533 | 3.067 |
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2.600 | 2.067 | 2.533 | 2.533 | 2.533 | 0.000 | 3.000 | 3.000 | 2.533 | 2.533 |
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3.000 | 2.533 | 2.067 | 3.000 | 3.000 | 2.533 | 0.000 | 2.533 | 3.000 | 3.533 |
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2.067 | 2.067 | 2.067 | 2.067 | 2.533 | 2.533 | 2.533 | 0.000 | 2.533 | 2.533 |
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3.000 | 3.067 | 3.067 | 3.533 | 2.533 | 2.533 | 2.533 | 2.533 | 0.000 | 4.000 |
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3.467 | 3.533 | 3.533 | 3.067 | 2.533 | 2.067 | 3.067 | 2.067 | 4.000 | 0.000 |
The normalized direct-influence matrix
Criteria |
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1.336 | 0.396 | 0.331 | 0.409 | 0.430 | 0.389 | 0.436 | 0.383 | 0.521 | 0.505 |
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0.423 | 1.357 | 0.416 | 0.448 | 0.441 | 0.396 | 0.475 | 0.407 | 0.554 | 0.524 |
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0.422 | 0.437 | 1.336 | 0.434 | 0.441 | 0.395 | 0.461 | 0.419 | 0.553 | 0.536 |
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0.451 | 0.450 | 0.428 | 1.385 | 0.457 | 0.424 | 0.492 | 0.449 | 0.572 | 0.529 |
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0.462 | 0.459 | 0.437 | 0.486 | 1.387 | 0.432 | 0.501 | 0.444 | 0.584 | 0.553 |
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0.434 | 0.416 | 0.408 | 0.442 | 0.436 | 1.326 | 0.470 | 0.429 | 0.518 | 0.502 |
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0.475 | 0.458 | 0.421 | 0.485 | 0.478 | 0.431 | 1.408 | 0.442 | 0.568 | 0.564 |
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0.387 | 0.384 | 0.365 | 0.396 | 0.404 | 0.375 | 0.422 | 1.306 | 0.478 | 0.463 |
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0.496 | 0.495 | 0.471 | 0.522 | 0.487 | 0.450 | 0.510 | 0.463 | 1.504 | 0.603 |
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0.516 | 0.516 | 0.490 | 0.516 | 0.495 | 0.444 | 0.533 | 0.457 | 0.633 | 1.494 |
The total influence matrix
Criteria |
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0.336 | 0.396 | 0.331 | 0.409 | 0.430 | 0.389 | 0.436 | 0.383 | 0.521 | 0.505 |
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0.423 | 0.357 | 0.416 | 0.448 | 0.441 | 0.396 | 0.475 | 0.407 | 0.554 | 0.524 |
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0.422 | 0.437 | 0.336 | 0.434 | 0.441 | 0.395 | 0.461 | 0.419 | 0.553 | 0.536 |
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0.451 | 0.450 | 0.428 | 0.385 | 0.457 | 0.424 | 0.492 | 0.449 | 0.572 | 0.529 |
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0.462 | 0.459 | 0.437 | 0.486 | 0.387 | 0.432 | 0.501 | 0.444 | 0.584 | 0.553 |
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0.434 | 0.416 | 0.408 | 0.442 | 0.436 | 0.326 | 0.470 | 0.429 | 0.518 | 0.502 |
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0.475 | 0.458 | 0.421 | 0.485 | 0.478 | 0.431 | 0.408 | 0.442 | 0.568 | 0.564 |
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0.387 | 0.384 | 0.365 | 0.396 | 0.404 | 0.375 | 0.422 | 0.306 | 0.478 | 0.463 |
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0.496 | 0.495 | 0.471 | 0.522 | 0.487 | 0.450 | 0.510 | 0.463 | 0.504 | 0.603 |
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0.516 | 0.516 | 0.490 | 0.516 | 0.495 | 0.444 | 0.533 | 0.457 | 0.633 | 0.494 |
The sums of giving and received influences.
Dimensions/criteria |
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Environment ( |
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Agrological capacity ( |
4.135 | 4.403 | 8.538 | −0.268 |
Orography ( |
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Slope ( |
4.441 | 4.369 | 8.810 | 0.072 |
Orientation ( |
4.436 | 4.103 | 8.539 | 0.334 |
Area ( |
4.638 | 4.523 | 9.161 | 0.115 |
Location ( |
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Distance to roads ( |
4.746 | 4.455 | 9.202 | 0.291 |
Distance to power lines ( |
4.381 | 4.063 | 8.444 | 0.319 |
Distance to villages ( |
4.731 | 4.709 | 9.440 | 0.023 |
Distance to substations ( |
3.981 | 4.201 | 8.181 | −0.220 |
Climatology ( |
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Solar radiation ( |
5.001 | 5.486 | 10.487 | −0.484 |
Average temperature ( |
5.092 | 5.273 | 10.365 | −0.180 |
Analytic framework for influence network of solar farms site.
The INRM of influential relationships within solar farms.
DANP is utilized by this study to obtain the level of influential weights of ten criteria for solar farms site selection shown in Tables
The unweighted supermatrix.
Criteria |
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1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
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0.348 | 0.292 | 0.362 | 0.356 | 0.332 | 0.329 | 0.336 | 0.336 | 0.333 | 0.339 |
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0.292 | 0.341 | 0.278 | 0.339 | 0.316 | 0.322 | 0.309 | 0.319 | 0.317 | 0.322 |
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0.360 | 0.367 | 0.360 | 0.305 | 0.352 | 0.349 | 0.355 | 0.346 | 0.351 | 0.339 |
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0.262 | 0.257 | 0.257 | 0.251 | 0.220 | 0.263 | 0.272 | 0.268 | 0.255 | 0.257 |
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0.237 | 0.230 | 0.230 | 0.233 | 0.245 | 0.196 | 0.245 | 0.249 | 0.236 | 0.230 |
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0.266 | 0.276 | 0.269 | 0.270 | 0.284 | 0.283 | 0.232 | 0.280 | 0.267 | 0.276 |
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0.234 | 0.237 | 0.244 | 0.246 | 0.252 | 0.258 | 0.251 | 0.203 | 0.242 | 0.237 |
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0.508 | 0.514 | 0.508 | 0.520 | 0.514 | 0.508 | 0.502 | 0.508 | 0.455 | 0.562 |
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0.492 | 0.486 | 0.492 | 0.480 | 0.486 | 0.492 | 0.498 | 0.492 | 0.545 | 0.438 |
The weighted supermatrix.
Criteria |
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0.081 | 0.096 | 0.096 | 0.096 | 0.099 | 0.099 | 0.099 | 0.099 | 0.100 | 0.100 |
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0.096 | 0.080 | 0.099 | 0.097 | 0.096 | 0.095 | 0.097 | 0.097 | 0.099 | 0.101 |
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0.080 | 0.093 | 0.076 | 0.093 | 0.091 | 0.093 | 0.089 | 0.092 | 0.094 | 0.096 |
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0.099 | 0.100 | 0.098 | 0.083 | 0.102 | 0.101 | 0.103 | 0.100 | 0.105 | 0.101 |
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0.104 | 0.100 | 0.100 | 0.098 | 0.082 | 0.099 | 0.102 | 0.100 | 0.097 | 0.098 |
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0.094 | 0.090 | 0.090 | 0.091 | 0.092 | 0.074 | 0.092 | 0.093 | 0.090 | 0.088 |
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0.105 | 0.108 | 0.105 | 0.105 | 0.107 | 0.106 | 0.087 | 0.105 | 0.102 | 0.105 |
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0.093 | 0.092 | 0.095 | 0.096 | 0.094 | 0.097 | 0.094 | 0.076 | 0.092 | 0.090 |
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0.126 | 0.124 | 0.123 | 0.126 | 0.122 | 0.121 | 0.119 | 0.120 | 0.101 | 0.124 |
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0.122 | 0.118 | 0.119 | 0.116 | 0.115 | 0.117 | 0.118 | 0.117 | 0.121 | 0.097 |
The stable matrix of DANP.
Criteria |
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0.097 | 0.097 | 0.097 | 0.097 | 0.097 | 0.097 | 0.097 | 0.097 | 0.097 | 0.097 |
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0.096 | 0.096 | 0.096 | 0.096 | 0.096 | 0.096 | 0.096 | 0.096 | 0.096 | 0.096 |
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0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 |
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0.099 | 0.099 | 0.099 | 0.099 | 0.099 | 0.099 | 0.099 | 0.099 | 0.099 | 0.099 |
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0.098 | 0.098 | 0.098 | 0.098 | 0.098 | 0.098 | 0.098 | 0.098 | 0.098 | 0.098 |
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0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 | 0.090 |
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0.103 | 0.103 | 0.103 | 0.103 | 0.103 | 0.103 | 0.103 | 0.103 | 0.103 | 0.103 |
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0.092 | 0.092 | 0.092 | 0.092 | 0.092 | 0.092 | 0.092 | 0.092 | 0.092 | 0.092 |
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0.120 | 0.120 | 0.120 | 0.120 | 0.120 | 0.120 | 0.120 | 0.120 | 0.120 | 0.120 |
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0.116 | 0.116 | 0.116 | 0.116 | 0.116 | 0.116 | 0.116 | 0.116 | 0.116 | 0.116 |
When comparing criteria within dimension, the influential weight of agrological capacity is 0.097 in the dimension of environment
In addition, this study collects comparative data from three different regions of China for empirical analysis by the purposed model to reveal the improving of the performance of solar farms site, because China has one of the biggest markets of solar industry in the world. Table
Influential weights of solar farms site and performances of selected regions.
Dimensions/criteria | Local weights | Global weights | Eastern China | Western China | Southern China |
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Environment ( |
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Agrological capacity ( |
1.000 | 0.097 | 3.667 | 5.667 | 2.400 |
Orography ( |
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Slope ( |
0.336 | 0.096 | 3.667 | 5.333 | 2.800 |
Orientation ( |
0.315 | 0.090 | 6.400 | 8.400 | 4.400 |
Area ( |
0.348 | 0.099 | 3.667 | 5.600 | 2.400 |
Location ( |
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Distance to roads ( |
0.256 | 0.098 | 3.333 | 5.667 | 3.200 |
Distance to power lines ( |
0.234 | 0.090 | 3.800 | 6.400 | 4.200 |
Distance to villages ( |
0.270 | 0.103 | 3.667 | 3.333 | 3.667 |
Distance to substations ( |
0.240 | 0.092 | 3.400 | 5.400 | 2.267 |
Climatology ( |
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Solar radiation ( |
0.508 | 0.120 | 4.800 | 7.467 | 3.333 |
Average temperature ( |
0.492 | 0.116 | 5.600 | 7.200 | 4.867 |
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Total performances | — | — | 4.227 | 5.969 | 3.339 |
Discussion of empirical results and innovation strategies for improving the performance for solar farms site is presented as follows. In the first place, the influential relationships within solar farms suggest that what administrators should improve first is orography
Second, the most important criterion found by DANP when improving solar farms is solar radiation
The proposed hybrid MCDM model based on GIS can be applied by managers of solar energy industry worldwide. They can adjust the influential weights of the ten criteria according to the situations of various countries to obtain valuable information for decision making when improving the performance of solar farms. Moreover, they can select a potential base to evaluate if it is suitable or not.
Furthermore, only few preceding study attempts are concerned about the interdependent interrelationship among criteria and the influential weights of criteria. This study thus proposes a hybrid MCDM model based on GIS and explores the perspectives of employing experts for examining these issues for solar farms. Associating past theoretical research with opinions of professional and experienced experts makes the proposed model a more suitable tool for improving solar farms site selection. It is not provided by preceding study attempts. This study mainly utilizes a hybrid MCDM model based on GIS for exploring solar farms site, so some critical factors (e.g., incentive from government and purchase price for electricity from utility) can be included by taking extra dimensions into consideration to make the research more complete in the future.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work was supported in part by the National Science Council in Taiwan, under the project title: Caltech-Taiwan Collaboration on Energy Research-Uncertainty Mitigation for Renewable Energy Integration, Project no. NSC 101-3113-P-008-001.