Towards a Sustainable Spatial Planning Approach for PV Site Selection in Portugal
Abstract
:1. Introduction
2. Overview of the Decision-Support Framework for the Site Selection of PV Technologies
3. Stage I: Energy Roadmap for PV Deployment
3.1. Spatial Data Collection/Digitization and GIS Analysis of Portuguese Municipalities (Phase 1)
NSC No. | Name | Data Source(s) | Spatial Resolution | Unsuitable Land Areas |
---|---|---|---|---|
NSC.1 | Global horizontal irradiance (GHI) | Global Solar Atlas [29] | 250 m | <4 kWh/m²/day |
NSC.2 | Practical PV energy output (PVOUT) | Global Solar Atlas [29] | 1 km | <3.5 kWh/kWp/day |
NSC.3 | Distance from protected areas (including protected landscapes) | Protected Planet [30] | N/A | <500 m |
NSC.4 | Distance from touristic zones and coastline | OSM, CLMS, EMODnet [31,32,33] | N/A, 20 m, N/A | <500 m and <1000 m |
NSC.5 | Land availability/geographic extent of the municipalities | AMA [34] | N/A | ― |
NSC.6 | Distance from urban and residential areas | OSM, CLMS [31,32] | N/A, 20 m | <1000 m |
NSC.7 | Distance from agricultural land and croplands | OSM, CLMS [31,32] | N/A, 20 m | <100 m |
NSC.8 | Distance from vineyards and other tree plantations | OSM, CLMS [31,32] | N/A, 20 m | <100 m |
NSC.9 | Distance from forests | OSM, CLMS [31,32] | N/A, 20 m | <100 m |
3.2. GIS Assessment and Prioritization Analysis of the Municipalities of the Portuguese Mainland for PV Installations (Phase 2)
- (1)
- (2)
- Mean of PVOUT: The PVOUT layer was clipped into each MoM, and its mean value was estimated for each MoM using the statistics section in GIS.
- (3)
- Land availability: The geographic extent of each MoM was estimated in GIS. Then, by excluding all existing infrastructure and water surfaces (Table 2) from each municipality in GIS, the final land availability in all MoM was estimated. Proper buffer zones were also applied from road and railway networks in order to prevent impacts of glare on local drivers. In addition, with the aim of enhancing the accuracy of the prioritization results, an extra filtering of the final land available zones was performed using the latest available OpenStreet base map in GIS in order to further erase areas that may contain any type of infrastructure or water surface.
- Step 1.Establishment of a performance decision matrix: An × decision matrix ( = number of alternatives and = number of decision criteria) is generated, which contains the specific values of each alterative solution to the decision criteria. In this case, the alternatives are the MoM, and the decision criteria are the MoMSC presented in Table 2.
- Step 2.Normalization of the decision matrix: The normalized decision matrix can be structured using Equation (1). This decision matrix sets the decision criteria on a common, dimensionless basis and permits comparisons among them.
- Step 3.Estimation of the weighted normalized decision matrix: After determining the weight (,) of each decision criterion (by applying either AHP or ENTROPY or any other method), the weighted normalized decision matrix can be formulated using Equation (2). In this work, all decision criteria are considered equally important in order to prioritize all MoM according to their attributes on the selected decision criteria and to eliminate the subjectivity of the prioritization results.
- Step 4.Determination of the PIS (A+) and the NIS (A−): In this step, the function type, namely benefit or cost, of each decision criterion is identified. If the criterion represents a benefit function, the PIS receives the maximum value between the values of the alternative solutions and the NIS the minimum value, whereas if the criterion represents a cost function, the PIS receives the minimum value, and the NIS receives the maximum value. The values of the PIS () and NIS () can be estimated using Equations (3) and (4), respectively.
- Step 5.Calculation of the Euclidean Distance of the alternatives from the A+ and A− solutions: The following equations are used in order to estimate the Euclidean distances of the alternatives.
- Step 6.Calculation of the relative closeness () to the ideal solution: The of an -th alternative solution with respect to the ideal solution can be calculated using Equation (7).
- Step 7.Determination of the preference order of the alternative solutions based on themeasure: The results of all steps are concentrated in a final overall matrix, and all alternative solutions are prioritized in a preference order based on the measure. The alternatives with the highest scores are the most preferred. In this case, the specific SI (i.e., value) was determined for all alternative solutions (MoM). Then, the MoM were prioritized on multiple spatial scales (i.e., national and regional scales) in order to determine the most and least suitable municipalities on all possible scales and contribute to a well-informed energy roadmap for PV deployment in Portugal.
4. Stage II—PV Site-Selection Analysis and Assessment
4.1. Identification of Suitable Sites for PV Installations in the Municipality with the Highest PV Suitability Index (Phase 3)
PVSC No. | Name | Data Source(s) | Spatial Resolution | Siting Aspect | Unsuitable Land Areas |
---|---|---|---|---|---|
PVSC.1 | Geographic boundaries | AMA [34] | N/A | Geographic/legal | Administration Boundaries |
PVSC.2 | Global horizontal irradiance (GHI) | Global Solar Atlas [29] | 250 m | Economic | <4 kWh/m2/day |
PVSC.3 | Distance from protected areas | Protected Planet [30] | N/A | Environmental | <500 m |
PVSC.4 | Distance from important bird areas (IBAs) | SPEA [52] | N/A | Environmental | <500 m |
PVSC.5 | Distance from urban and residential areas | OSM, CLMS [31,32] | N/A, 20 m | Social/cultural | <1000 m |
PVSC.6 | Distance from the road network | OSM [31] | N/A | Technical/economic | <150 m and >5000 m |
PVSC.7 | Distance from the railway network | OSM [31] | N/A | Technical/social | <150 m |
PVSC.8 | Average air temperature | Global Solar Atlas [29] | 1 km | Technical/economic | >25 °C |
PVSC.9 | Slope of terrain | CLMS [53] | 25 m | Technical/economic | > 5% |
PVSC.10 | Distance from civil/military aviation areas | OSM, CLMS [31,32] | N/A, 20 m | Political/technical | <3000 m |
PVSC.11 | Distance from water surfaces | OSM, CLMS [31,32] | N/A, 20 m | Environmental | <150 m |
PVSC.12 | Distance from the electricity grid | Esri’s Basemaps [54] | 0.2 m | Technical/economic | <150 m and >25,000 m |
PVSC.13 | Elevation | CLMS [53] | 25 m | Economic/environmental | >1500 m |
PVSC.14 | Military zones | OSM, EMODnet [31,33] | N/A | Political | ALL |
PVSC.15 | Distance from agricultural land and croplands | OSM, CLMS [31,32] | N/A, 20 m | Social/economic | <100 m |
PVSC.16 | Vineyards and other tree plantations | OSM, CLMS [31,32] | N/A, 20 m | Social/economic | ALL |
PVSC.17 | Distance from religious sites | OSM [31] | N/A | Social/cultural | <100 m |
PVSC.18 | Distance from touristic zones | OSM [31] | N/A | Social/economic | <100 m |
PVSC.19 | Distance from existing RE installations | EEP [55] | 0.2 m | Technical/economic | <500 m |
PVSC.20 | Mineral extraction sites | OSM, CLMS [31,32] | N/A, 20 m | Technical | ALL |
PVSC.21 | Industrial zones and economic activities | OSM, CLMS [31,32] | N/A, 20 m | Social/economic | ALL |
PVSC.22 | Distance from archaeological, historical and cultural heritage sites | OSM [31] | N/A | Social/cultural | <1000 m |
PVSC.23 | Distance from forests | OSM, CLMS [31,32] | N/A, 20 m | Environmental | <100 m |
PVSC.24 | Farm minimum required area | ― | ― | Economic | <0.15 km2 |
4.2. Determination of the Suitability Index of the Eligible Sites for PV Farm Installations (Phase 4)
- The AHP method;
- The ENTROPY method; and
- The equal weights approach.
5. Results and Discussion
5.1. Classification of the Municipalities of Portugal and Suitability Selection
5.2. Prioritization Results of the Municipalities of the Portuguese Mainland
5.3. Identification of Suitable Sites for PV Installation in the Municipality of Mértola
5.4. PV Site-Suitability Analysis and Assessment Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- REN21. Renewables 2022 Global Status Report; REN21 Secretariat: Paris, France, 2022. [Google Scholar]
- Liang, J.; Irfan, M.; Ikram, M.; Zimon, D. Evaluating Natural Resources Volatility in an Emerging Economy: The Influence of Solar Energy Development Barriers. Resour. Policy 2022, 78, 102858. [Google Scholar] [CrossRef]
- Statista Portugal: Renewable Energy Capacity 2021. Available online: https://www.statista.com/statistics/864765/total-renewable-capacity-in-portugal/ (accessed on 10 May 2022).
- IRENA. Renewable Energy Statistics 2022; The International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2022. [Google Scholar]
- Sánchez-Lozano, J.M.; García-Cascales, M.S.; Lamata, M.T. Comparative TOPSIS-ELECTRE TRI Methods for Optimal Sites for Photovoltaic Solar Farms. Case Study in Spain. J. Clean. Prod. 2016, 127, 387–398. [Google Scholar] [CrossRef]
- Charabi, Y.; Gastli, A. PV Site Suitability Analysis Using GIS-Based Spatial Fuzzy Multi-Criteria Evaluation. Renew. Energy 2011, 36, 2554–2561. [Google Scholar] [CrossRef]
- Merrouni, A.A.; Elalaoui, F.E.; Mezrhab, A.; Mezrhab, A.; Ghennioui, A. Large Scale PV Sites Selection by Combining GIS and Analytical Hierarchy Process. Case Study: Eastern Morocco. Renew. Energy 2018, 119, 863–873. [Google Scholar] [CrossRef]
- Noorollahi, E.; Fadai, D.; Shirazi, M.A.; Ghodsipour, S.H. Land Suitability Analysis for Solar Farms Exploitation Using GIS and Fuzzy Analytic Hierarchy Process (FAHP)—A Case Study of Iran. Energies 2016, 9, 643. [Google Scholar] [CrossRef] [Green Version]
- Al-Shammari, S.; Ko, W.; Al Ammar, E.A.; Alotaibi, M.A.; Choi, H.-J. Optimal Decision-Making in Photovoltaic System Selection in Saudi Arabia. Energies 2021, 14, 357. [Google Scholar] [CrossRef]
- Spyridonidou, S.; Sismani, G.; Loukogeorgaki, E.; Vagiona, D.G.; Ulanovsky, H.; Madar, D. Sustainable Spatial Energy Planning of Large-Scale Wind and PV Farms in Israel: A Collaborative and Participatory Planning Approach. Energies 2021, 14, 551. [Google Scholar] [CrossRef]
- Fang, H.; Li, J.; Song, W. Sustainable Site Selection for Photovoltaic Power Plant: An Integrated Approach Based on Prospect Theory. Energy Convers. Manag. 2018, 174, 755–768. [Google Scholar] [CrossRef]
- Liu, J.; Xu, F.; Lin, S. Site Selection of Photovoltaic Power Plants in a Value Chain Based on Grey Cumulative Prospect Theory for Sustainability: A Case Study in Northwest China. J. Clean. Prod. 2017, 148, 386–397. [Google Scholar] [CrossRef]
- Sánchez-Lozano, J.M.; Antunes, C.H.; García-Cascales, M.S.; Dias, L.C. GIS-Based Photovoltaic Solar Farms Site Selection Using ELECTRE-TRI: Evaluating the Case for Torre Pacheco, Murcia, Southeast of Spain. Renew. Energy 2014, 66, 478–494. [Google Scholar] [CrossRef]
- Majumdar, D.; Pasqualetti, M.J. Analysis of Land Availability for Utility-Scale Power Plants and Assessment of Solar Photovoltaic Development in the State of Arizona, USA. Renew. Energy 2019, 134, 1213–1231. [Google Scholar] [CrossRef]
- Aghbashlo, M.; Tabatabaei, M.; Rahnama, E.; Rosen, M.A. A New Systematic Decision Support Framework Based on Solar Extended Exergy Accounting Performance to Prioritize Photovoltaic Sites. J. Clean. Prod. 2020, 256, 120356. [Google Scholar] [CrossRef]
- Al Garni, H.Z.; Awasthi, A. Solar PV Power Plant Site Selection Using a GIS-AHP Based Approach with Application in Saudi Arabia. Appl. Energy 2017, 206, 1225–1240. [Google Scholar] [CrossRef]
- Awan, A.B.; Zubair, M.; P., P.R.; Abokhalil, A.G. Solar Energy Resource Analysis and Evaluation of Photovoltaic System Performance in Various Regions of Saudi Arabia. Sustainability 2018, 10, 1129. [Google Scholar] [CrossRef] [Green Version]
- Arán Carrión, J.; Espín Estrella, A.; Aznar Dols, F.; Zamorano Toro, M.; Rodríguez, M.; Ramos Ridao, A. Environmental Decision-Support Systems for Evaluating the Carrying Capacity of Land Areas: Optimal Site Selection for Grid-Connected Photovoltaic Power Plants. Renew. Sustain. Energy Rev. 2008, 12, 2358–2380. [Google Scholar] [CrossRef]
- Charabi, Y.; Gastli, A. Integration of Temperature and Dust Effects in Siting Large PV Power Plant in Hot Arid Area. Renew. Energy 2013, 57, 635–644. [Google Scholar] [CrossRef]
- Dias, L.; Gouveia, J.P.; Lourenço, P.; Seixas, J. Interplay between the Potential of Photovoltaic Systems and Agricultural Land Use. Land Use Policy 2019, 81, 725–735. [Google Scholar] [CrossRef]
- Doljak, D.; Stanojevic, G. Evaluation of Natural Conditions for Site Selection of Ground-Mounted Photovoltaic Power Plants in Serbia. Energy 2017, 127, 291–300. [Google Scholar] [CrossRef] [Green Version]
- Fernandez-Jimenez, L.A.; Mendoza-Villena, M.; Zorzano-Santamaria, P.; Garcia-Garrido, E.; Lara-Santillan, P.; Zorzano-Alba, E.; Falces, A. Site Selection for New PV Power Plants Based on Their Observability. Renew. Energy 2015, 78, 7–15. [Google Scholar] [CrossRef]
- Gunderson, I.; Goyette, S.; Gago-Silva, A.; Quiquerez, L.; Lehmann, A. Climate and Land-Use Change Impacts on Potential Solar Photovoltaic Power Generation in the Black Sea Region. Environ. Sci. Policy 2015, 46, 70–81. [Google Scholar] [CrossRef]
- Ozdemir, S.; Sahin, G. Multi-Criteria Decision-Making in the Location Selection for a Solar PV Power Plant Using AHP. Measurement 2018, 129, 218–226. [Google Scholar] [CrossRef]
- Lee, A.H.I.; Kang, H.-Y.; Lin, C.-Y.; Shen, K.-C. An Integrated Decision-Making Model for the Location of a PV Solar Plant. Sustainability 2015, 7, 13522–13541. [Google Scholar] [CrossRef] [Green Version]
- Kereush, D.; Perovych, I. Determining Criteria for Optimal Site Selection for Solar Power Plants. Geomat. Landmanagement Landsc. 2017, 4, 39–54. [Google Scholar] [CrossRef]
- Haurant, P.; Oberti, P.; Muselli, M. Multicriteria Selection Aiding Related to Photovoltaic Plants on Farming Fields on Corsica Island: A Real Case Study Using the ELECTRE Outranking Framework. Energ. Policy 2011, 39, 676–688. [Google Scholar] [CrossRef]
- Habib, S.M.; Emam Suliman, A.E.-R.; Al Nahry, A.H.; El Rahman, E.N.A. Spatial Modeling for the Optimum Site Selection of Solar Photovoltaics Power Plant in the Northwest Coast of Egypt. Remote Sens. Appl. Soc. Environ. 2020, 18, 100313. [Google Scholar] [CrossRef]
- Global Solar Atlas 2.0: Company Solargis s.r.o. on Behalf of the World Bank Group, with Funding Provided by the Energy Sector Management Assistance Program (ESMAP). Available online: https://globalsolaratlas.info/download?c=25.482951,-3.691406,4 (accessed on 9 April 2022).
- UNEP-WCMC Protected Area Profile for Portugal from the World Database on Protected Areas. Available online: https://www.protectedplanet.net/country/PRT (accessed on 10 May 2022).
- OSM: Geofabrik Download Server—Portugal. Available online: https://download.geofabrik.de/europe/portugal.html (accessed on 9 April 2022).
- Copernicus Land Monitoring Service: CLC 2018. Available online: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018 (accessed on 10 April 2022).
- EMODnet Human Activities. Available online: https://www.emodnet-humanactivities.eu/view-data.php (accessed on 10 April 2022).
- Municipalities of Portugal. Available online: https://data.europa.eu/data/datasets/5be9d5ac76892f1f0bf6087c?locale=en (accessed on 10 May 2022).
- Sabo, M.L.; Mariun, N.; Hizam, H.; Mohd Radzi, M.A.; Zakaria, A. Spatial Energy Predictions from Large Scale Photovoltaic Power Plants Located in Optimal Sites and Connected to a Smart Grid in Peninsular Malaysia. Renew. Sustain. Energy Rev. 2016, 66, 76–94. [Google Scholar] [CrossRef]
- NREL: Solar Resource Maps and Data. Available online: https://www.nrel.gov/gis/solar-resource-maps.html (accessed on 28 March 2022).
- Hwang, C.-L.; Lai, Y.-J.; Liu, T.-Y. A New Approach for Multiple Objective Decision Making. Comput. Oper. Res. 1993, 20, 889–899. [Google Scholar] [CrossRef]
- Hwang, C.L.; Yoon, K. Multiple Attribute Decision Making: Methods and Applications; Springer: New York, NY, USA, 1981. [Google Scholar]
- Merrouni, A.A.; Mezrhab, A.; Mezrhab, A. PV Sites Suitability Analysis in the Eastern Region of Morocco. Sustain. Energy Technol. Assess. 2016, 18, 6–15. [Google Scholar] [CrossRef]
- Sánchez-Lozano, J.M.; Teruel-Solano, J.; Soto-Elvira, P.L.; García-Cascales, M.S. Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) Methods for the Evaluation of Solar Farms Locations: Case Study in South-Eastern Spain. Renew. Sustain. Energy Rev. 2013, 24, 544–556. [Google Scholar] [CrossRef]
- Ali, S.; Taweekun, J.; Techato, K.; Waewsak, J.; Gyawali, S. GIS Based Site Suitability Assessment for Wind and Solar Farms in Songkhla, Thailand. Renew. Energy 2019, 132, 1360–1372. [Google Scholar] [CrossRef]
- Georgiou, A.; Skarlatos, D. Optimal Site Selection for Sitting a Solar Park Using Multi-Criteria Decision Analysis and Geographical Information Systems. Geosci. Instrum. Meth. 2016, 5, 321–332. [Google Scholar] [CrossRef] [Green Version]
- Huld, T.; Amillo, A.M.G. Estimating PV Module Performance over Large Geographical Regions: The Role of Irradiance, Air Temperature, Wind Speed and Solar Spectrum. Energies 2015, 8, 5159–5181. [Google Scholar] [CrossRef] [Green Version]
- Yelmen, B.; Çakir, M.T. Influence of Temperature Changes in Various Regions of Turkey on Powers of Photovoltaic Solar Panels. Energy Source Part A 2016, 38, 542–550. [Google Scholar] [CrossRef]
- Doorga, J.R.S.; Rughooputh, S.D.D.V.; Boojhawon, R. Multi-Criteria GIS-Based Modelling Technique for Identifying Potential Solar Farm Sites: A Case Study in Mauritius. Renew. Energy 2019, 133, 1201–1219. [Google Scholar] [CrossRef]
- Mensour, O.N.; Ghazzani, B.E.; Hlimi, B.; Ihlal, A. A Geographical Information System-Based Multi-Criteria Method for the Evaluation of Solar Farms Locations: A Case Study in Souss-Massa Area, Southern Morocco. Energy 2019, 182, 900–919. [Google Scholar] [CrossRef]
- Shiraishi, K.; Shirley, R.G.; Kammen, D.M. Geospatial Multi-Criteria Analysis for Identifying High Priority Clean Energy Investment Opportunities: A Case Study on Land-Use Conflict in Bangladesh. Appl. Energy 2019, 235, 1457–1467. [Google Scholar] [CrossRef]
- Hafeznia, H.; Yousefi, H.; Astaraei, F.R. A Novel Framework for the Potential Assessment of Utility-Scale Photovoltaic Solar Energy, Application to Eastern Iran. Energ. Convers. Manag. 2017, 151, 240–258. [Google Scholar] [CrossRef]
- Sultan, H.M.; Kuznetsov, O.N.; Diab, A.A.Z. Site Selection of Large-Scale Grid-Connected Solar PV System in Egypt. In Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow and St. Petersburg, Russia, 29 January–1 February 2018. [Google Scholar]
- Watson, J.J.W.; Hudson, M.D. Regional Scale Wind Farm and Solar Farm Suitability Assessment Using GIS-Assisted Multi-Criteria Evaluation. Landsc. Urban Plan. 2015, 138, 20–31. [Google Scholar] [CrossRef]
- Asakereh, A.; Soleymani, M.; Sheikhdavoodi, M.J. A GIS-Based Fuzzy-AHP Method for the Evaluation of Solar Farms Locations: Case Study in Khuzestan Province, Iran. Sol. Energy 2017, 155, 342–353. [Google Scholar] [CrossRef]
- Sociedade Portuguesa para o Estudo das Aves (SPEA): Programa IBAs. Available online: http://ibas-terrestres.spea.pt/pt/documentos-download/ (accessed on 9 May 2022).
- Copernicus Land Monitoring Service: EU-DEM v1.1. Available online: https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1 (accessed on 10 April 2022).
- Esri OpenStreetMap Vector Basemap. Available online: https://www.arcgis.com/home/item.html?id=fae788aa91e54244b161b59725dcbb2a (accessed on 9 May 2022).
- E2p—Endogenous Energies of Portugal. Available online: http://e2p.inegi.up.pt/?Lang=EN (accessed on 9 May 2022).
- Saaty, T.L. The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980. [Google Scholar]
- Saaty, T.L. Axiomatic Foundation of the Analytic Hierarchy Process. Manag. Sci. 1986, 32, 841–855. [Google Scholar] [CrossRef]
- Saaty, R.W. The analytic hierarchy process—What it is and how it is used. Math. Model. 1987, 9, 161–176. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Y.; Tian, D.; Yan, F. Effectiveness of Entropy Weight Method in Decision-Making. Math. Probl. Eng. 2020, 2020, e3564835. [Google Scholar] [CrossRef]
- Azevedo, F. Research for REGI Committee: The Economic, Social and Territorial Situation of the Azores (Portugal); Policy Department for Structural and Cohesion Policies, European Parliament: Strasbourg, France, 2017. [Google Scholar]
- Assembleia Da República. Constituição Da República Portuguesa VII Revisão Constitucional; Assembleia Da República: Lisbon, Portugal, 2005. [Google Scholar]
- Região Autónoma da Madeira. Proposta De Orçamento Da Região Autónoma Da Madeira; Região Autónoma da Madeira: Madeira, Portugal, 2018. [Google Scholar]
MoMSC No. | Name | Classification of MoMSC | Data Source(s) | Spatial Resolution | Unsuitable Land Areas |
---|---|---|---|---|---|
MoMSC.1 | Global horizontal irradiance (GHI) | Exclusion of low-GHI zones | Global Solar Atlas [29] | 250 m | <4 kWh/m2/day |
MoMSC.2 | Practical PV energy output (PVOUT) | Mean of PVOUT | Global Solar Atlas [29] | 1 km | ― |
MoMSC.3 | Urban and residential areas | Land availability | OSM, CLMS [31,32] | N/A, 20 m | ALL |
MoMSC.4 | Distance from road network | OSM [31] | N/A | <150 m | |
MoMSC.5 | Distance from railway network | OSM [31] | N/A | <150 m | |
MoMSC.6 | Civil/military aviation areas | OSM, CLMS [31,32] | N/A, 20 m | ALL | |
MoMSC.7 | Water surfaces | OSM, CLMS, EMODnet [31,32,33] | N/A, 20 m, N/A | ALL | |
MoMSC.8 | Industrial zones and economic activities | OSM, CLMS [31,32] | N/A, 20 m | ALL | |
MoMSC.9 | Military zones | OSM, EMODnet [31,33] | N/A | ALL | |
MoMSC.10 | Port areas | OSM, CLMS [31,32] | N/A, 20 m | ALL | |
MoMSC.11 | Solitary buildings and any infrastructure | OSM [31] | N/A | ALL | |
MoMSC.12 | Geographic extent of the municipality | AMA [34] | N/A | ― |
PVAC No. | Name | Evaluation Aspect | Function Type |
---|---|---|---|
PVAC.1 | Land availability (m2) | Economic | Benefit |
PVAC.2 | Existing land use (class) | Social/economic/environmental | Benefit |
PVAC.3 | PVOUT (kWh/kWp/day) | Economic | Benefit |
PVAC.4 | Distance from archaeological, historical and cultural heritage sites (m) | Social/cultural | Benefit |
PVAC.5 | Distance from protected areas (m) | Environmental | Benefit |
PVAC.6 | Distance from religious sites (m) | Social/cultural | Benefit |
PVAC.7 | Distance from agricultural land and croplands (m) | Social/economic | Benefit |
PVAC.8 | Distance from areas of landscape value (m) | Social/environmental | Benefit |
PVAC.9 | Distance from urban and residential areas (m) | Social/cultural | Benefit |
PVAC.10 | Distance from the road network (m) | Technical/economic | Cost |
PVAC.11 | Slope of terrain (%) | Technical/economic | Cost |
PVAC.12 | Distance from the electricity grid (m) | Technical/economic | Cost |
PVAC.13 | Water availability (m) | Technical/economic | Cost |
PVAC.14 | Average air temperature (°C) | Technical/economic | Cost |
PVAC.15 | Distance from civil/military aviation areas (m) | Political/technical/social | Benefit |
Suitability Class | Municipality Name | Region Name | Preference Order | Suitability Index |
---|---|---|---|---|
Excellent Suitability | Mértola | Beja | 1 | 0.996 |
Alcácer do Sal | Setúbal | 2 | 0.990 | |
Idanha-A-Nova | Castelo Branco | 3 | 0.989 | |
Montemor-O-Novo | Évora | 4 | 0.969 | |
Coruche | Santarém | 5 | 0.962 | |
Évora | Évora | 6 | 0.960 | |
Beja | Beja | 7 | 0.938 | |
Serpa | Beja | 8 | 0.935 | |
Odemira | Beja | 9 | 0.927 | |
Bragança | Bragança | 10 | 0.926 | |
High Suitability | Arouca | Aveiro | 269 | 0.741 |
Lousã | Coimbra | 270 | 0.740 | |
Valença | Viana do Castelo | 271 | 0.737 | |
Vieira do Minho | Braga | 272 | 0.732 | |
Mondim de Basto | Vila Real | 273 | 0.716 | |
Monção | Viana do Castelo | 274 | 0.668 | |
Ponte da Barca | Viana do Castelo | 275 | 0.642 | |
Low Suitability | Terras de Bouro | Braga | 276 | 0.376 |
Melgaço | Viana do Castelo | 277 | 0.281 | |
Arcos de Valdevez | Viana do Castelo | 278 | 0.066 |
Hybrid MCDM Application | Suitability Class | Number of Sites | Suitability Degree |
---|---|---|---|
ENTROPY AND TOPSIS | Excellent Suitability | 2 | 13.13% |
High Suitability | 36 | 76.49% | |
Moderate Suitability | 2 | 2.41% | |
Low Suitability | 4 | 7.97% | |
AHP and TOPSIS | Excellent Suitability | 0 | 0.00% |
High Suitability | 10 | 15.96% | |
Moderate Suitability | 25 | 67.7% | |
Low Suitability | 9 | 16.34% | |
Equal weights approach and TOPSIS | Excellent Suitability | 0 | 0.00% |
High Suitability | 6 | 33.22% | |
Moderate Suitability | 27 | 51.03% | |
Low Suitability | 11 | 15.75% |
PV Suitable Site | Land Availability (m2) | Existing Land Use (Land Class) | PVOUT (kWh/kWp/day) | Distance from Archaeological, Historical and Cultural Heritage Sites (m) | Distance from Protected Areas (m) | Distance from Religious Sites (m) | Distance from Agricultural Land and Croplands (m) | Distance from Areas of Landscape Value (m) | Distance from Urban and Residential Areas (m) |
---|---|---|---|---|---|---|---|---|---|
PVSite.1 | 1,873,161 | SV, TWS | 4.5–4.825 | 7930 | 1160 | 2050 | 538 | 47,500 | 4250 |
PVSite.2 | 1,639,859 | NIAL, TWS | 4.5–4.825 | 14,250 | 500 | 5650 | 997 | 47,500 | 2615 |
PVSite.3 | 1,315,962 | NG, NIAL, TWS | 4.5–4.825 | 5380 | 1000 | 4160 | 773 | 47,500 | 6070 |
PVSite.7 | 835,495 | NIAL, TWS | 4.5–4.825 | 13,520 | 2500 | 3470 | 530 | 47,500 | 4635 |
PVSite.8 | 824,534 | NIAL, TWS | 4.5–4.825 | 7300 | 3480 | 4270 | 537 | 47,500 | 7680 |
PVSite.19 | 337,425 | NIAL, TWS | 4.5–4.825 | 12,540 | 5670 | 2520 | 730 | 47,500 | 8295 |
PV Suitable Site | Distance from Road Network (m) | Slope of Terrain (%) | Distance from Electricity Grid (m) | Water Availability (m) | Average Air Temperature (°C) |
---|---|---|---|---|---|
PVSite.1 | 150 | 3.63 | 6950 | 1181 | 16.5 |
PVSite.2 | 180 | 3.34 | 23,970 | 1000 | 16.5 |
PVSite.3 | 150 | 3.20 | 11,550 | 1000 | 16.5 |
PVSite.7 | 150 | 3.42 | 13,450 | 1140 | 16.5 |
PVSite.8 | 150 | 3.22 | 11,075 | 1000 | 16.5 |
PVSite.19 | 150 | 3.61 | 9590 | 1000 | 16.5 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Spyridonidou, S.; Loukogeorgaki, E.; Vagiona, D.G.; Bertrand, T. Towards a Sustainable Spatial Planning Approach for PV Site Selection in Portugal. Energies 2022, 15, 8515. https://doi.org/10.3390/en15228515
Spyridonidou S, Loukogeorgaki E, Vagiona DG, Bertrand T. Towards a Sustainable Spatial Planning Approach for PV Site Selection in Portugal. Energies. 2022; 15(22):8515. https://doi.org/10.3390/en15228515
Chicago/Turabian StyleSpyridonidou, Sofia, Eva Loukogeorgaki, Dimitra G. Vagiona, and Teresa Bertrand. 2022. "Towards a Sustainable Spatial Planning Approach for PV Site Selection in Portugal" Energies 15, no. 22: 8515. https://doi.org/10.3390/en15228515
APA StyleSpyridonidou, S., Loukogeorgaki, E., Vagiona, D. G., & Bertrand, T. (2022). Towards a Sustainable Spatial Planning Approach for PV Site Selection in Portugal. Energies, 15(22), 8515. https://doi.org/10.3390/en15228515