Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Integration of GIS and MCDM
- Multi-attribute decision making (MADM); and
- Multi-objective decision making (MODM).
2.3. Determination of Research Parameters
- Climate;
- Geomorphological;
- Spatial; and
- Environmental.
- —population number;
- —number of tourist nights;
- —average annual electricity consumption in Croatia;
- —population number in Croatia; and
- —administrative unit.
2.4. Determination of Weight Coefficients of Parameters with AHP
3. Results
- —GHI;
- —land cover;
- —slope;
- —orientation;
- —elevation;
- —distance to settlements;
- —distance to electricity network;
- —distance to road infrastructure;
- —distance to railway infrastructure;
- —electricity consumption;
- —population;
- —number of tourist nights; and
- —unemployment.
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Sub-Criteria | Settlements | Electricity Network | Road Infrastructure | Railway Infrastructure | Weight Coefficient |
---|---|---|---|---|---|
Settlements | 1 | 0.333 | 1 | 7 | 0.220 |
Electricity network | 3 | 1 | 3 | 9 | 0.529 |
Road infrastructure | 1 | 0.333 | 1 | 6 | 0.209 |
Railway infrastructure | 0.143 | 0.111 | 0.167 | 1 | 0.042 |
λ = 4.107 | Cl = 0.036 | CR = 0.040 |
Sub-Criteria | Barren Land | Grassland | Bushland | Pastures | Emerging Forests | Agricultural Land | Forest | Wetland | Weight Coefficient |
---|---|---|---|---|---|---|---|---|---|
Barren land | 1 | 2 | 2 | 3 | 5 | 7 | 9 | 9 | 0.300 |
Grassland | 0.5 | 1 | 2 | 3 | 5 | 6 | 7 | 9 | 0.239 |
Bushland | 0.5 | 0.5 | 1 | 2 | 3 | 5 | 7 | 9 | 0.173 |
Pastures | 0.333 | 0.333 | 0.5 | 1 | 2 | 3 | 5 | 7 | 0.110 |
Emerging forests | 0.2 | 0.2 | 0.333 | 0.5 | 1 | 3 | 5 | 7 | 0.083 |
Agricultural land | 0.143 | 0.167 | 0.2 | 0.333 | 0.333 | 1 | 3 | 5 | 0.049 |
Forest | 0.111 | 0.143 | 0.143 | 0.2 | 0.2 | 0.333 | 1 | 3 | 0.028 |
Wetland | 0.111 | 0.111 | 0.111 | 0.143 | 0.143 | 0.2 | 0.333 | 1 | 0.018 |
λ = 8.704 | Cl = 0.101 | CR = 0.071 |
Sub-criteria | Slope | Orientation | Elevation | Weight Coefficient |
---|---|---|---|---|
Slope | 1 | 3 | 5 | 0.633 |
Orientation | 0.333 | 1 | 3 | 0.260 |
Elevation | 0.2 | 0.333 | 1 | 0.106 |
λ = 3.055 | Cl = 0.028 | CR = 0.048 |
Sub-Criteria | Electricity Consumption | Population | Number of Tourist Nights | Unemployment | Weight Coefficient |
---|---|---|---|---|---|
Electricity consumption | 1 | 2 | 3 | 9 | 0.489 |
Population | 0.5 | 1 | 1 | 7 | 0.246 |
Number of tourist nights | 0.333 | 1 | 1 | 7 | 0.225 |
Unemployment | 0.111 | 0.143 | 0.143 | 1 | 0.040 |
λ = 4.093 | Cl = 0.031 | CR = 0.034 |
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Intensity of Importance | Definition | Explanation |
---|---|---|
1 | Equal importance | Two activities contribute equally to the objective |
3 | Moderate importance | Experience and judgment slightly favour one activity over another |
5 | Strong importance | Experience and judgment strongly favour one activity over another |
7 | Very strong or demonstrated importance | An activity is favoured very strongly over another; its dominance demonstrated in practice |
9 | Extreme importance | The evidence favouring one activity over another is of the highest possible order of affirmation |
2, 4, 6, 8 | Intermediate values | |
Reciprocals of above | In the case of reverse comparison of the relationship between the elements, reciprocal values are assigned to the intensity of importance |
Constraint Parameter | Type of Parameter | Criteria |
---|---|---|
Built-up areas | Environmental | < 100 m |
Water bodies | Spatial | < 100 m |
Protected areas (archaeological sites, military zones, forest areas, animal protection areas, biologically important areas) | Spatial/ Environmental | < 100 m |
Parameter | Type of Parameter | Criteria |
---|---|---|
Distance to settlements | Spatial | < 50,000 m |
Distance to road infrastructure | Spatial | < 3000 m |
Distance to railway infrastructure | Spatial | < 20,000 m |
Distance to the electricity network | Spatial | < 10,000 m |
Land Cover | Environmental | Barren land, grasslands, bushland, pastures, emerging forests, agricultural land, forest, wetland |
Slope | Geomorphological | < 25% |
Orientation | Geomorphological | North = 0.3 East, West = 0.6 South = 1 |
Elevation | Geomorphological | < 150 m 150 m–500 m |
GHI | Climate | Max = 1 Min = 0 |
Population | Socioeconomic | Max = 1 Min = 0 |
Number of tourist nights | Socioeconomic | Max = 1 Min = 0 |
Electricity consumption | Socioeconomic | Max = 1 Min = 0 |
Unemployment | Socioeconomic | Max = 1 Min = 0 |
Criteria | GHI | Land Cover | Terrain Geomorphology | Spatial Paramete | Socioeconomic Parameters | Weight Coefficient |
---|---|---|---|---|---|---|
GHI | 1 | 2 | 3 | 2 | 7 | 0.372 |
Land Cover | 0.5 | 1 | 3 | 0.5 | 5 | 0.204 |
Terrain geomorphology | 0.333 | 0,333 | 1 | 0.333 | 5 | 0.119 |
Spatial parameters | 0.5 | 2 | 3 | 1 | 5 | 0.265 |
Socioeconomic parameters | 0.143 | 0.2 | 0.2 | 0.2 | 1 | 0.041 |
λ = 5.274 | Cl = 0.069 | CR = 0.061 |
Value | F1 | F2 | F3 |
---|---|---|---|
Number of pixels | 240,276,905 | 240,276,905 | 240,276,905 |
No. suitable pixels | 217,313,988 | 217,313,988 | 217,313,988 |
No. unsuitable pixels | 22,962,917 | 22,962,917 | 22,962,917 |
Minimum (%) | 0.3686 | 0.2945 | 0.3992 |
Maximum (%) | 0.7736 | 0.6967 | 0.7188 |
Range (%) | 0.4049 | 0.4021 | 0.3196 |
Mean value (%) | 0.5984 | 0.4876 | 0.5387 |
Standard deviation (%) | 0.0508 | 0.0418 | 0.0417 |
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Gašparović, I.; Gašparović, M. Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia. Remote Sens. 2019, 11, 1481. https://doi.org/10.3390/rs11121481
Gašparović I, Gašparović M. Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia. Remote Sensing. 2019; 11(12):1481. https://doi.org/10.3390/rs11121481
Chicago/Turabian StyleGašparović, Iva, and Mateo Gašparović. 2019. "Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia" Remote Sensing 11, no. 12: 1481. https://doi.org/10.3390/rs11121481
APA StyleGašparović, I., & Gašparović, M. (2019). Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia. Remote Sensing, 11(12), 1481. https://doi.org/10.3390/rs11121481