- A solar radiation simulation is conducted for the entire area considered, including parking spaces, vegetation and the surrounding built-up environment. GIS-based tools such as Area Solar Radiation on ArcGIS  or solaR on R  can be utilised with the Digital Elevation Model (DEM) datasets to estimate the amount of solar radiation on various earth surfaces within a given area. A further simulation is then conducted on the results to identify areas that are affected by shadows due to adjacent buildings or surrounding vegetation. The proportion of shadow-affected spaces in a given area is determined by two main factors, which are (a) height of surrounding buildings and their distance to the parking area and (b) latitude of the study area. Areas affected by shading are excluded from the assessment.
- In the next step, areas identified as not affected by shading are spatially analysed, identifying their shapes and main orientations. These variables are utilised in the design of carport arrangement, obtaining the optimal layout of parking lots in order to make the best use of available spaces. As car parking spaces are normally constructed in the form of arrays, their carports in a single car park are likely to be constructed with a same orientation (ΩC). This orientation is required to be tested by using existing applications such as PV-GIS  to verify whether it is appropriate for PV applications. If not, as shown in Figure 2, an alternative orientation will need to be re-proposed and repeat the GIS spatial analysis.
- The geometrics of the carport structure are then configured to identify optimal slope for PV systems. Based on orientations (ΩC) that are obtained from previous steps, existing applications such as PV-GIS are used to specify the optimal angle of the slope . In addition, as carports are regularly constructed with a back-to-back structure, their configuration needs to consider PV systems facing both directions.
- The last step is to estimate the electricity generation from potential PV-embedded on carports. The results can be used in economic analysis to assess investment requirements and payback period for the development. Such considerations can then be linked to the consumption profile of the relevant entity owning/occupying the car parking areas. This latter step will inform the project owner of the scope of energy that can be displaced by the deployed PV systems for the investments made which will allow an informed decision making process.
Testing the Methodology
3. Case Study Analysis
3.1. Saudi Arabia
3.2. Campus Characteristics and Energy Consumption
Status of Energy Consumption of the KAU Campus
3.3. Estimating Appropriate Areas for PV Deployment
3.3.1. Campus Layout
3.3.2. Shading Influence
3.3.3. Geometrics of Potential Solar Canopies
3.3.4. Available Parking Areas and Potential of PV Deployment Capacity
3.3.5. Dynamic Simulation and Energy Yield
3.3.6. Case Study Economic Assessment
3.3.7. Case Study Conclusions
4. Economic Considerations
5. Overall Conclusions
Conflicts of Interest
- World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/SER.A/352); United Nations Department of Economic and Social Affairs Population Division: New York, NY, USA, 2014.
- Bailey, R.; Longhurst, J.W.S.; Hayes, E.T.; Hudson, L.; Ragnarsdottir, K.V.; Thumim, J. Exploring a city’s potential low carbon futures using Delphi methods: Some preliminary findings. J. Environ. Plan. Manag. 2012, 55, 1022–1046. [Google Scholar] [CrossRef]
- Byrne, J.; Taminiau, J.; Kurdgelashvili, L.; Nam, K. A review of the solar city concept and methods to assess rooftop solar electric potential, with an illustrative application to the city of Seoul. Renew. Sustain. Energy Rev. 2015, 41, 830–844. [Google Scholar] [CrossRef]
- Eicker, U.; Klein, M. Large-scale renewable energy integration within energy-efficient urban areas: Results from three German case studies. Int. J. Low Carbon Technol. 2012, 9, 202–213. [Google Scholar] [CrossRef]
- Theodoridou, I.; Karteris, M.; Mallinis, G.; Papadopoulos, A.M.; Hegger, M. Assessment of retrofitting measures and solar systems’ potential in urban areas using Geographical Information Systems: Application to a Mediterranean city. Renew. Sustain. Energy Rev. 2012, 16, 6239–6261. [Google Scholar] [CrossRef]
- Zhang, L.; Feng, Y.; Chen, B. Alternative Scenarios for the Development of a Low-Carbon City: A Case Study of Beijing, China. Energies 2011, 4, 2295–2310. [Google Scholar] [CrossRef]
- Araos, M.; Berrang-Ford, L.; Ford, J.D.; Austin, S.E.; Biesbroek, R.; Lesnikowski, A. Climate change adaptation planning in large cities: A systematic global assessment. Environ. Sci. Policy 2016, 66, 375–382. [Google Scholar] [CrossRef]
- Baiocchi, G.; Minx, J.; Hubacek, K. The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions in the United Kingdom. J. Ind. Ecol. 2010, 14, 50–72. [Google Scholar] [CrossRef]
- Zell, E.; Gasim, S.; Wilcox, S.; Katamoura, S.; Stoffel, T.; Shibli, H.; Engel-Cox, J.; Subie, M. Al Assessment of solar radiation resources in Saudi Arabia. Sol. Energy 2015, 119, 422–438. [Google Scholar] [CrossRef]
- Adam, K.; Hoolohan, V.; Gooding, J.; Knowland, T.; Bale, C.S.E.; Tomlin, A.S. Methodologies for city-scale assessment of renewable energy generation potential to inform strategic energy infrastructure investment. Cities 2016, 54, 45–56. [Google Scholar] [CrossRef]
- Clover, I. Third Phase of Dubai’s DEWA Solar Project Attracts Record Low Bid of US 2.99 Cents/kWh. Available online: https://www.pv-magazine.com/2016/05/02/third-phase-of-dubais-dewa-solar-project-attracts-record-low-bid-of-us-2-99-centskwh_100024383/ (accessed on 8 May 2017).
- Yenneti, K.; Day, R.; Golubchikov, O. Spatial justice and the land politics of renewables: Dispossessing vulnerable communities through solar energy mega-projects. Geoforum 2016, 76, 90–99. [Google Scholar] [CrossRef]
- James, P.A.B.; Jentsch, M.F.; Bahaj, A.S. Quantifying the added value of BiPV as a shading solution in atria. Sol. Energy 2009, 83, 220–231. [Google Scholar] [CrossRef]
- Kanters, J.; Wall, M. A planning process map for solar buildings in urban environments. Renew. Sustain. Energy Rev. 2016, 57, 173–185. [Google Scholar] [CrossRef]
- Amado, M.; Poggi, F. Solar energy integration in urban planning: GUUD model. Energy Procedia 2014, 50, 277–284. [Google Scholar] [CrossRef]
- Yang, T.; Athienitis, A.K. A review of research and developments of building-integrated photovoltaic/thermal (BIPV/T) systems. Renew. Sustain. Energy Rev. 2016, 66, 886–912. [Google Scholar] [CrossRef]
- Delisle, V.; Kummert, M. Cost-benefit analysis of integrating BIPV-T air systems into energy-efficient homes. Sol. Energy 2016, 136, 385–400. [Google Scholar] [CrossRef]
- Masson, G.; Orlandi, S. Global Market Outlook for Solar Power 2015–2019; Solar Power Europe: Brussels, Belgium, 2014. [Google Scholar]
- Celik, B.; Karatepe, E.; Silvestre, S.; Gokmen, N.; Chouder, A. Analysis of spatial fixed PV arrays configurations to maximize energy harvesting in BIPV applications. Renew. Energy 2015, 75, 534–540. [Google Scholar] [CrossRef]
- Zomer, C.; Nobre, A.; Reindl, T.; Ruther, R. Shading analysis for rooftop BIPV embedded in a high-density environment: A case study in Singapore. Energy Build. 2016, 121, 159–164. [Google Scholar] [CrossRef]
- Brenna, M.; Dolara, A.; Foiadelli, F.; Gafaro, L.; Leva, S.; Longo, M. Solar energy exploitation for charging vehicles. UPB Sci. Bull. Ser. C Electr. Eng. 2015, 77, 277–284. [Google Scholar]
- Brenna, M.; Dolara, A.; Foiadelli, F.; Leva, S.; Longo, M. Urban scale photovoltaic charging stations for electric vehicles. IEEE Trans. Sustain. Energy 2014, 5, 1234–1241. [Google Scholar] [CrossRef]
- Davis, A.Y.; Pijanowski, B.C.; Robinson, K.; Engel, B. The environmental and economic costs of sprawling parking lots in the United States. Land Use Policy 2010, 27, 255–261. [Google Scholar] [CrossRef]
- Du, Y.; Wang, J.; Huang, W.; Fan, Z. Simulation and analysis on heat transfer and pre-cooling characteristics of new solar power vehicle parking ventilation system. In Proceedings of the 2015 IEEE International Transportation Electrification Conference (ITEC), Chennai, India, 27–29 August 2015. [Google Scholar]
- Bybee, H.; Durrant, J.; McNeel, A.; Millsap, R.; Vasquez, R.; Wiley, E. Feasibility Study to Install Photo Voltaic Structures at the University of Utah; University of Utah: Salt Lake City, UT, USA, 2010. [Google Scholar]
- Robinson, J.; Brase, G.; Griswold, W.; Jackson, C.; Erickson, L. Business models for solar powered charging stations to develop infrastructure for electric vehicles. Sustainability 2014, 6, 7358–7387. [Google Scholar] [CrossRef]
- Latif, Z.A.; Zaki, N.A.M.; Salleh, S.A. GIS-based estimation of rooftop solar photovoltaic potential using LiDAR. In Proceedings of the 2012 IEEE 8th International Colloquium Signal Processing and Its Applications (CSPA), Melaka, Malaysia, 23–25 March 2012; pp. 388–392. [Google Scholar]
- Carneiro, C.; Morello, E.; Desthieux, G. Assessment of Solar Irradiance on the Urban Fabric for the Production of Renewable Energy using LIDAR Data and Image Processing Techniques. In Advances in GIScience; Sester, M., Bernard, L., Paelke, V., Eds.; Lecture Notes in Geoinformation and Cartography; Springer: Berlin/Heidelberg, Germany, 2009; pp. 83–112. [Google Scholar]
- Catita, C.; Redweik, P.; Pereira, J.; Brito, M.C. Extending solar potential analysis in buildings to vertical facades. Comput. Geosci. 2014, 66, 1–12. [Google Scholar] [CrossRef]
- Nguyen, H.T.; Pearce, J.M. Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale. Sol. Energy 2012, 86, 1245–1260. [Google Scholar] [CrossRef]
- Lukač, N.; Seme, S.; Žlaus, D.; Štumberger, G.; Žalik, B. Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data. Energy 2014, 66, 598–609. [Google Scholar] [CrossRef]
- Jakubiec, J.A.; Reinhart, C.F. A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations. Sol. Energy 2013, 93, 127–143. [Google Scholar] [CrossRef]
- Hetrick, W.A.; Rich, P.M.; Barnes, F.J.; Alamos, L.; Weiss, S.B. GIS-based Solar Radiation Flux Models. Am. Soc. Photogramm. Remote Sens. Tech. Pap. 1993, 3, 132–143. [Google Scholar]
- Lamigueiro, O.P. Solar: Solar Radiation and Photovoltaic Systems with R. J. Stat. Softw. 2012, 50. [Google Scholar]
- Joint Research Centre (JRC) Photovoltaic Geographical Information System (PVGIS). Available online: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php# (accessed on 6 August 2015).
- McCormick, P.G.; Suehrcke, H. Diffuse fraction correlations. Sol. Energy 1991, 47, 311–312. [Google Scholar] [CrossRef]
- Reindl, D.T.; Beckman, W.A.; Duffie, J.A. Evaluation of hourly tilted surface radiation models. Sol. Energy 1990, 45, 9–17. [Google Scholar] [CrossRef]
- Choi, Y.; Rayl, J.; Tammineedi, C.; Brownson, J.R.S. PV Analyst: Coupling ArcGIS with TRNSYS to assess distributed photovoltaic potential in urban areas. Sol. Energy 2011, 85, 2924–2939. [Google Scholar] [CrossRef]
- Tulpule, P.J.; Marano, V.; Yurkovich, S.; Rizzoni, G. Economic and environmental impacts of a PV powered workplace parking garage charging station. Appl. Energy 2013, 108, 323–332. [Google Scholar] [CrossRef]
- U.S. Energy Information Administration. Country Analysis Brief: Saudi Arabia; U.S. Energy Information Administration: Washington, DC, USA, 2014.
- Alyahya, S.; Irfan, M.A. Role of Saudi universities in achieving the solar potential 2030 target. Energy Policy 2016, 91, 325–328. [Google Scholar] [CrossRef]
- Mansouri, N.Y.; Crookes, R.J.; Korakianitis, T. A projection of energy consumption and carbon dioxide emissions in the electricity sector for Saudi Arabia: The case for carbon capture and storage and solar photovoltaics. Energy Policy 2013, 63, 681–695. [Google Scholar] [CrossRef]
- Saudi Arabia Government Vision 2030; Saudi Arabia Government: Riyadh, Saudi Arabia, 2016.
- Al-Mostafa, Z.A.; Maghrabi, A.H.; Al-Shehri, S.M. Sunshine-based global radiation models: A review and case study. Energy Convers. Manag. 2014, 84, 209–216. [Google Scholar] [CrossRef]
- Baras, A.; Bamhair, W.; Alkhoshi, Y.; Alodan, M.; Engel-Cox, J. Opportunities and Challenges of Solar Energy in Saudi Arabia. In Proceedings of the World Renewable Energy Forum, Denver, CO, USA, 13–17 May 2012; Fellows, C., Ed.; American Solar Energy Society: Boulder, CO, USA, 2012; pp. 1–6. [Google Scholar]
- Sarver, T.; Al-Qaraghuli, A.; Kazmerski, L.L. A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches. Renew. Sustain. Energy Rev. 2013, 22, 698–733. [Google Scholar] [CrossRef]
- Hepbasli, A.; Alsuhaibani, Z. A key review on present status and future directions of solar energy studies and applications in Saudi Arabia. Renew. Sustain. Energy Rev. 2011, 15, 5021–5050. [Google Scholar] [CrossRef]
- Šúri, M.; Huld, T.A.; Dunlop, E.D. PV-GIS: A web-based solar radiation database for the calculation of PV potential in Europe. Int. J. Sustain. Energy 2005, 24, 55–67. [Google Scholar] [CrossRef]
- Kanyarusoke, K.; Gryzagoridis, J.; Oliver, G. Validation of TRNSYS modelling for a fixed slope photovoltaic panel. Turk. J. Electr. Eng. Comput. Sci. 2015, 24, 4763–4772. [Google Scholar] [CrossRef]
- Quesada, B.; Sánchez, C.; Cañada, J.; Royo, R.; Payá, J. Experimental results and simulation with TRNSYS of a 7.2 kWp grid-connected photovoltaic system. Appl. Energy 2011, 88, 1772–1783. [Google Scholar] [CrossRef]
- Department of Energy & Climate Change Official Statistics—Solar PV Cost Data (Online Dataset). Available online: https://www.gov.uk/government/statistics/solar-pv-cost-data (accessed on 8 May 2017).
- Jordan, D.; Kurtz, S. Overview of Field Experience—Degradation Rates & Lifetimes. In Proceedings of the Solar Power International 2015, Anaheim, CA, USA, 14–17 September 2015. [Google Scholar]
- Jordan, D.C.; Smith, R.M.; Osterwald, C.R.; Gelak, E.; Kurtz, S.R. Outdoor PV degradation comparison. In Proceedings of the 35th IEEE Photovoltaic Specialists Conference (PVSC’10), Honolulu, HI, USA, 20–25 June 2010; National Renewable Energy Laboratory: Golden, CO, USA, 2010; pp. 2694–2697. [Google Scholar]
- Jordan, D.C.; Kurtz, S.R. Photovoltaic degradation rates—An Analytical Review. Prog. Photovoltaics Res. Appl. 2013, 21, 12–29. [Google Scholar] [CrossRef]
- Mahapatra, S. Dubai Gets Record-Low Bid of 2.99 USC/kWh for 800 MW Solar PV Project. Available online: http://cleantechnica.com/2016/05/02/lowest-solar-price-dubai-800-mw-solar-project/ (accessed on 8 May 2017).
- Solar Server Chile Energy Tender: 12,430 GWh to Be Added, with Sensational Low Bids. Available online: http://www.solarserver.com/solar-magazine/solar-news/current/2016/kw34/chile-energy-tender-12430-gwh-to-be-added-with-sensational-low-bids.html?utm_source=REA+Members+Mailing+List&utm_campaign=1567a39b71-160826_REA_newsletter&utm_medium=email&utm_term=0_e (accessed on 26 August 2016).
- ICSU ISSC. Review of Targets for the Sustainable Development Goals: The Science Perspective; International Council for Science (ICSU): Paris, France, 2015. [Google Scholar]
|Carport Characteristics||Min||Max||Mean||Median||Standard Deviation|
|Ground Space (m2)||11.6||40.0||18.5||16.7||5.1|
|Orientation (Absolute Value)||0.1°||85.4°||38.2°||8.3°||36.5°|
|Total Car Park Area||Shaded (Cloth Canopies)||Unshaded|
|594,611 m2||400,996 m2||193,615 m2|
|Model Results||Shaded Car Parks||Unshaded Car Parks||Total|
|Mean utilisation factor||31%||50%||37%|
|Number of car ports||6772||5796 (estimated)||12,568|
|Estimated installation capacity, MWp||20.3||16.1||36.4|
|Model results||8° Southeast, ±20° Slope||83° Southwest, ±20° Slope|
|Energy yield (kWh/kWp)||south-facing||2031||1930||1848||1780|
|Expected ROI over 25 years||0%||50%||100%|
|Required export tariff||3.03||4.54||6.05|
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