IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i4p940-d322758.html
   My bibliography  Save this article

Solar Energy Technology for Northern Cyprus: Assessment, Statistical Analysis, and Feasibility Study

Author

Listed:
  • Youssef Kassem

    (Department of Mechanical Engineering, Engineering Faculty, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus
    Department of Civil Engineering, Civil and Environmental Engineering Faculty, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus)

  • Hüseyin Çamur

    (Department of Mechanical Engineering, Engineering Faculty, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus)

  • Salman Mohammed Awadh Alhuoti

    (Department of Mechanical Engineering, Engineering Faculty, Near East University, 99138 Nicosia (via Mersin 10, Turkey), Cyprus)

Abstract

Solar power is the fastest-growing energy source in the world. New technologies can help to generate more power from solar energy. The present paper aims to encourage people and the government to develop solar energy-based power projects to achieve sustainable energy infrastructures, especially in developing countries. In addition, this paper presents a solar energy road map to attract investors to invest in clean energy technology to help reduce the effect of global warming and enhance sustainable technological development. Therefore, the first objective of the paper is to analyze and compare the monthly global solar radiation for five different locations in Northern Cyprus using the measured data collected from the Meteorological Department and estimated values collected from the satellite imagery database. In addition, the mean hourly meteorological parameters including global solar radiation, air temperature, sunshine, and relative humidity are analyzed statistically and the type of distribution functions are selected based on skewness and kurtosis values. Accordingly, estimating global solar radiation improves solar power generation planning and reduces the cost of measuring. Therefore, models of a surface were analyzed by means of polynomial adjustments considering the values of R-squared . Finally, this study provides a comprehensive and integrated feasibility analysis of a 100 MW grid-connected solar plant project as an economic project in the selected region to reduce electricity tariffs and greenhouse gas (GHG) emissions. RETScreen Expert software was used to conduct the feasibility analysis in terms of energy production, GHG emissions, and financial parameters for the best location for the installation of a 100 MW grid-connected photovoltaic (PV) plant. Finally, the results concluded that the proposed solar system could be used for power generation in Northern Cyprus.

Suggested Citation

  • Youssef Kassem & Hüseyin Çamur & Salman Mohammed Awadh Alhuoti, 2020. "Solar Energy Technology for Northern Cyprus: Assessment, Statistical Analysis, and Feasibility Study," Energies, MDPI, vol. 13(4), pages 1-29, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:940-:d:322758
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/4/940/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/4/940/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. M. Mujahid Rafique & Shafiqur Rehman & Md. Mahbub Alam & Luai M. Alhems, 2018. "Feasibility of a 100 MW Installed Capacity Wind Farm for Different Climatic Conditions," Energies, MDPI, vol. 11(8), pages 1-18, August.
    2. Rashwan, Sherif S. & Shaaban, Ahmed M. & Al-Suliman, Fahad, 2017. "A comparative study of a small-scale solar PV power plant in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 313-318.
    3. Rao K, D.V. Siva Krishna & Premalatha, M. & Naveen, C., 2018. "Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 248-258.
    4. Egelioglu, F. & Mohamad, A.A. & Guven, H., 2001. "Economic variables and electricity consumption in Northern Cyprus," Energy, Elsevier, vol. 26(4), pages 355-362.
    5. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
    6. Almarshoud, A.F., 2016. "Performance of solar resources in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 694-701.
    7. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    8. Nematollahi, Omid & Kim, Kyung Chun, 2017. "A feasibility study of solar energy in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 566-579.
    9. Mohammadi, Kasra & Shamshirband, Shahaboddin & Kamsin, Amirrudin & Lai, P.C. & Mansor, Zulkefli, 2016. "Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 423-434.
    10. Bocca, Alberto & Bottaccioli, Lorenzo & Chiavazzo, Eliodoro & Fasano, Matteo & Macii, Alberto & Asinari, Pietro, 2016. "Estimating photovoltaic energy potential from a minimal set of randomly sampled data," Renewable Energy, Elsevier, vol. 97(C), pages 457-467.
    11. Ilkan, M. & Erdil, E. & Egelioglu, F., 2005. "Renewable energy resources as an alternative to modify the load curve in Northern Cyprus," Energy, Elsevier, vol. 30(5), pages 555-572.
    12. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    13. Mohammadi, Kasra & Naderi, Mahmoud & Saghafifar, Mohammad, 2018. "Economic feasibility of developing grid-connected photovoltaic plants in the southern coast of Iran," Energy, Elsevier, vol. 156(C), pages 17-31.
    14. John Emmanuel Ogbeba & Ercan Hoskara, 2019. "The Evaluation of Single-Family Detached Housing Units in terms of Integrated Photovoltaic Shading Devices: The Case of Northern Cyprus," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
    15. Radmehr, Mehrshad & Willis, Ken & Kenechi, Ugo Elinwa, 2014. "A framework for evaluating WTP for BIPV in residential housing design in developing countries: A case study of North Cyprus," Energy Policy, Elsevier, vol. 70(C), pages 207-216.
    16. Kalogirou, S.A. & Pashiardis, S. & Pashiardi, A., 2017. "Statistical analysis and inter-comparison of erythemal solar radiation for Athalassa and Larnaca, Cyprus," Renewable Energy, Elsevier, vol. 111(C), pages 580-597.
    17. Maghami, Mohammad Reza & Hizam, Hashim & Gomes, Chandima & Radzi, Mohd Amran & Rezadad, Mohammad Ismael & Hajighorbani, Shahrooz, 2016. "Power loss due to soiling on solar panel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1307-1316.
    18. Chang, Byungik & Starcher, Ken, 2019. "Evaluation of wind and solar energy investments in Texas," Renewable Energy, Elsevier, vol. 132(C), pages 1348-1359.
    19. Shahzad, M. Kashif & Zahid, Adeem & ur Rashid, Tanzeel & Rehan, Mirza Abdullah & Ali, Muzaffar & Ahmad, Mueen, 2017. "Techno-economic feasibility analysis of a solar-biomass off grid system for the electrification of remote rural areas in Pakistan using HOMER software," Renewable Energy, Elsevier, vol. 106(C), pages 264-273.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Youssef Kassem & Hüseyin Çamur & Ramzi Aateg Faraj Aateg, 2020. "Exploring Solar and Wind Energy as a Power Generation Source for Solving the Electricity Crisis in Libya," Energies, MDPI, vol. 13(14), pages 1-29, July.
    2. Ewa Klugmann-Radziemska, 2020. "Shading, Dusting and Incorrect Positioning of Photovoltaic Modules as Important Factors in Performance Reduction," Energies, MDPI, vol. 13(8), pages 1-12, April.
    3. Waqas Ahmed & Jamil Ahmed Sheikh & Abbas Z. Kouzani & M. A. Parvez Mahmud, 2020. "The Role of Single End-Users and Producers on GHG Mitigation in Pakistan—A Case Study," Sustainability, MDPI, vol. 12(20), pages 1-12, October.
    4. Shahid Ali & Qingyou Yan & Muhammad Sajjad Hussain & Muhammad Irfan & Munir Ahmad & Asif Razzaq & Vishal Dagar & Cem Işık, 2021. "Evaluating Green Technology Strategies for the Sustainable Development of Solar Power Projects: Evidence from Pakistan," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    5. Hamza S. Abdalla Lagili & Aşkın Kiraz & Youssef Kassem & Hüseyin Gökçekuş, 2023. "Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets," Energies, MDPI, vol. 16(18), pages 1-53, September.
    6. Youssef Kassem & Hüseyin Gökçekuş & Ali Güvensoy, 2021. "Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus," Energies, MDPI, vol. 14(22), pages 1-27, November.
    7. Rami Alamoudi & Osman Taylan & Mehmet Azmi Aktacir & Enrique Herrera-Viedma, 2021. "Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches," Mathematics, MDPI, vol. 9(22), pages 1-24, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Youssef Kassem & Hüseyin Gökçekuş & Ali Güvensoy, 2021. "Techno-Economic Feasibility of Grid-Connected Solar PV System at Near East University Hospital, Northern Cyprus," Energies, MDPI, vol. 14(22), pages 1-27, November.
    2. Jiaojiao Feng & Weizhen Wang & Jing Li, 2018. "An LM-BP Neural Network Approach to Estimate Monthly-Mean Daily Global Solar Radiation Using MODIS Atmospheric Products," Energies, MDPI, vol. 11(12), pages 1-14, December.
    3. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
    4. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    5. Youssef Kassem & Hüseyin Çamur & Ramzi Aateg Faraj Aateg, 2020. "Exploring Solar and Wind Energy as a Power Generation Source for Solving the Electricity Crisis in Libya," Energies, MDPI, vol. 13(14), pages 1-29, July.
    6. Aziz, Ali Saleh & Tajuddin, Mohammad Faridun Naim & Adzman, Mohd Rafi & Mohammed, Mohd Fayzul & Ramli, Makbul A.M., 2020. "Feasibility analysis of grid-connected and islanded operation of a solar PV microgrid system: A case study of Iraq," Energy, Elsevier, vol. 191(C).
    7. Hossein Moayedi & Amir Mosavi, 2021. "An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework," Energies, MDPI, vol. 14(4), pages 1-18, February.
    8. Rami Alamoudi & Osman Taylan & Mehmet Azmi Aktacir & Enrique Herrera-Viedma, 2021. "Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches," Mathematics, MDPI, vol. 9(22), pages 1-24, November.
    9. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.
    10. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    11. Rao K, D.V. Siva Krishna & Premalatha, M. & Naveen, C., 2018. "Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 248-258.
    12. Fahad Alharbi & Denes Csala, 2020. "Saudi Arabia’s Solar and Wind Energy Penetration: Future Performance and Requirements," Energies, MDPI, vol. 13(3), pages 1-18, January.
    13. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    14. Qingpeng Cao & Moses Olabhele Esangbedo & Sijun Bai & Caroline Olufunke Esangbedo, 2019. "Grey SWARA-FUCOM Weighting Method for Contractor Selection MCDM Problem: A Case Study of Floating Solar Panel Energy System Installation," Energies, MDPI, vol. 12(13), pages 1-30, June.
    15. Shafiqur Rehman & Muhammad M. Rafique & Luai M. Alhems & Md. Mahbub Alam, 2020. "Development and Implementation of Solar Assisted Desiccant Cooling Technology in Developing Countries: A Case of Saudi Arabia," Energies, MDPI, vol. 13(3), pages 1-22, January.
    16. Weizhen Wang & Jiaojiao Feng & Feinan Xu, 2021. "Estimating Downward Shortwave Solar Radiation on Clear-Sky Days in Heterogeneous Surface Using LM-BP Neural Network," Energies, MDPI, vol. 14(2), pages 1-14, January.
    17. Hamza S. Abdalla Lagili & Aşkın Kiraz & Youssef Kassem & Hüseyin Gökçekuş, 2023. "Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets," Energies, MDPI, vol. 16(18), pages 1-53, September.
    18. Ağbulut, Ümit & Gürel, Ali Etem & Biçen, Yunus, 2021. "Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    19. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    20. Faris Alqurashi & Rached Nciri & Abdulrahman Alghamdi & Chaouki Ali & Faouzi Nasri, 2022. "Control of the Solar Radiation Reception Rate (SRRR) Using a Novel Poly-Tilted Segmented Panel (PTSP) in the Region of Makkah, Saudi Arabia," Energies, MDPI, vol. 15(7), pages 1-15, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:940-:d:322758. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.