Planning Power System Portfolio of Turkey through Analytic Hierarchy Process and Data Preprocessing Abstract

Year 2020, Volume: 35 Issue: 4, 1031 - 1046, 31.12.2020

Üzeyir Fidan , Mehmet Atak

https://doi.org/10.21605/cukurovaummfd.869173

Cited By: 1

Abstract

The purpose of this study is to suggest an investment plan for Turkey, which is dependent on foreign energy, that meets Turkey’s electrical energy demand between 2020-2039. Strategic investment plan for electricity generation is crucial in terms of ensuring sustainable supply security. Analytical Hierarchy Process (AHP), which is a multi-criteria decision-making method, was utilized to build this investment proposal. Under a series of data preprocessing techniques applied within the model, the subjectivity that is often observed in AHP has been largely eliminated, resulting in relatively more objective results. With the unique data preprocessing techniques and the AHP method applied within the study, a portfolio scenario that is in line with the strategic objectives of the Republic of Turkey Ministry of Energy and Natural Resources has been offered. With the obtained portfolio scenario, the research reveals findings that support the goals of ETKB. Accordingly in proportion to the energy sources used to meet increasing demand, it is predicted that while the use of coal, natural gas and hydroelectric within this production will decrease by 10.6%, 3%, and 4.3% respectively, the use of wind, sun and geothermal will increase by 1.68%, 4.34%, and 2.98% respectively. As a result, the suggested plan reduces the rates of external dependency, fossil fuel use, and emission values while increasing the use of renewable energy sources, employment potential, and security of supply. The results of the research are of great importance in terms of demonstrating that the goals in the 20-year strategic plan of ETKB are consistent.

Keywords

Sustainable energy planning, Energy efficiency, Decision theory, Data preprocessing, Analytical hierarchy process

Analitik Hiyerarşi Süreci ve Veri Önişleme Yoluyla Türkiye’nin Güç Sistemi Portföyünün Planlanması

Abstract

Bu çalışmada amaç, enerji konusunda dışa bağımlı bir ülke olan Türkiye’nin 2020-2039 yılları arasında ihtiyaç duyacağı elektrik enerjisini karşılayabilecek bir yatırım planı sunmaktır. Elektrik enerjisi üretimi için stratejik yatırım planı sürdürülebilir arz güvenliğinin sağlanması açısından kritik öneme sahiptir. Yatırım planının oluşturulması aşamasında çok kriterli karar verme yöntemlerinden Analitik Hiyerarşi Süreci (AHS) kullanılmıştır. Model oluşturulurken uygulanan bir dizi veri önişleme tekniği sayesinde AHS’nin öznellik yaklaşımı büyük ölçüde ortadan kaldırılarak görece daha nesnel sonuçların elde edilmesi sağlanmıştır. Çalışmaya özgünlük kazandıracak şekilde kullanılan veri önişleme teknikleri ve AHS yöntemiyle Türkiye Cumhuriyeti Enerji ve Tabii Kaynaklar Bakanlığı’nın (ETKB) stratejik hedefleri doğrultusunda bir portföy senaryosu ortaya konmuştur. Araştırma elde edilen portföy senaryosuyla ETKB’nin hedeflerini destekleyecek nitelikte bulgular ortaya koymaktadır. Buna göre, artan ihtiyacı karşılarken kullanılan enerji kaynaklarına göre elektrik üretiminde kömürün %10,6, doğal gazın %3, hidroelektrik kullanımının ise %4,3 azalacağı, rüzgârın %1,68, güneşin %4,34 ve jeotermal kullanımının da %2,98 artacağı öngörülmüştür. Sonuç olarak, yapılan planlama dışa bağımlılık oranını, fosil yakıt kullanım oranını ve emisyon değerlerini düşürürken, yenilenebilir enerji kaynaklarının kullanımını, istihdam potansiyelini ve arz güvenliğini arttırmaktadır. Bu araştırmanın sonuçları ETKB’nin 20 yıllık stratejik planında yer alan hedeflerin tutarlı olduğunu göstermesi açısından büyük önem arz etmektedir.

Keywords

Sürdürülebilir enerji planlaması, Enerji verimliliği, Karar teorisi, Veri önişleme, Analitik hiyerarşi süreci

References

• 1. Mansur, E.T., 2008. Measuring Welfare in Restructured Electricity Markets. The Review of Economics and Statistics, 90(2), 369-386.
• 2. Kurlinski, R.E., Lave, L., Ilic, M.D., 2008. Creating Reliability Choice: How Building Less Reliability into Electric Power Grids Could Improve the Welfare of all Customers. In 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century (1-8). IEEE.
• 3. Özcan, E.C., Ünlüsoy, S., Tamer, E., 2017. ANP ve TOPSIS Yöntemleriyle Türkiye’de Yenilenebilir Enerji Yatırım Alternatiflerinin Değerlendirilmesi. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 5(2), 204-219.
• 4. Ürün, E., Soyu, E., 2016. Türkiye’nin Enerji Üretiminde Yenilenebilir Enerji Kaynakları Üzerine Bir Değerlendirme. Dumlupinar University Journal of Social Science/Dumlupinar Üniversitesi Soysyal Bilimler Dergisi.
• 5. Çoban, O., Kılınç, N.Ş., 2016. Enerji Kullanımının Çevresel Etkilerinin İncelenmesi. Marmara Coğrafya Dergisi, (33), 589-606.
• 6. Bayrak, M., Esen, Ö., 2014. Türkiye’nin Enerji Açığı Sorunu ve Çözümüne Yönelik Arayışlar. Ataturk University Journal of Economics ve Administrative Sciences, 28(3).
• 7. Furuncu, Y., 2016. Türkiye’nin Enerji Bağımlılığı ve Akkuyu Nükleer Enerji Santralı. Fen Bilimleri Dergisi (CFD), 37.
• 8. Temurçin, K., Aliağaoğlu, A., 2003. Nükleer Enerji ve Tartışmalar Işığında Türkiye’de Nükleer Enerji Gerçeği. Coğrafi Bilimler Dergisi, 1(2), 25-39.
• 9. Wang, J.J., Jing, Y.Y., Zhang, C.F., Zhao, J.H., 2009. Review on Multi-criteria Decision Analysis Aid in Sustainable Energy Decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263-2278.
• 10. Lin, Y., Luo, H., Wang, D., Guo, H., Zhu, K., 2017. An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-term Electric Load Forecasting. Energies, 10(8), 1186.
• 11. Zhang, Z., Hong, W.C., 2019. Electric Load Forecasting by Complete Ensemble Empirical Mode Decomposition Adaptive Noise and Support Vector Regression with Quantum-based Dragonfly Algorithm. Nonlinear Dynamics, 98(2), 1107-1136.
• 12. Metaxiotis, K., Kagiannas, A., Askounis, D., Psarras, J., 2003. Artificial Intelligence in Short Term Electric Load Forecasting: a State-of-the-art Survey for the Researcher. Energy Conversion and Management, 44(9), 1525-1534.
• 13. Alfares, H.K., Nazeeruddin, M., 2002. Electric Load Forecasting: Literature Survey and Classification of Methods. International journal of Systems Science, 33(1), 23-34.
• 14. Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J., 1991. Electric Load Forecasting Using an Artificial Neural Network. IEEE transactions on Power Systems, 6(2), 442-449.
• 15. Roques, F.A., Newbery, D.M., Nuttall, W.J., 2008. Fuel Mix Diversification Incentives in Liberalized Electricity Markets: A Mean– Variance Portfolio Theory Approach. Energy Economics, 30(4), 1831-1849.
• 16. Campbell, C.J., Duncan, R.C., 1988. The Coming Oil Crisis. Brentwood, Essex, England: Multi- Science Publishing.
• 17. Salameh, M.G., 2001. A Third Oil Crisis?. Survival, 43(3), 129-144.
• 18. Karagöl, E., Erbaykal, E., Ertuğrul, H.M., 2007. Türkiye’de Ekonomik Büyüme ile Elektrik Tüketimi İlişkisi: Sınır Testi Yaklaşımı.
• 19. Cohen, G., Joutz, F., Loungani, P., 2011. Measuring Energy Security: Trends in the Diversification of Oil and Natural Gas Supplies. Energy Policy, 39(9), 4860-4869.
• 20. Rugman, A.M., 1979. International Diversification and the Multinational Enterprise.
• 21. Fidan, Ü., Atak, M., 2020. Elektrik Üretim Teknolojileri Özelinde Çok Kriterli Karar Verme Yöntemleri Sonuçlarının Karşılaştırılması, Enerji Bilimlerinde Güncel Araştırmalar, Ankara: Iksad Yayınevi.
• 22. Şengül, Ü., Eren, M., Shiraz, S.E., Gezder, V., Şengül, A.B., 2015. Fuzzy TOPSIS Method for Ranking Renewable Energy Supply Systems in Turkey. Renewable Energy, 75, 617-625.
• 23. Baltuttis, D., Töppel, J., Tränkler, T., Wiethe, C., 2020. Managing the Risks of Energy Efficiency Insurances in a Portfolio Context: An Actuarial Diversification Approach. International Review of Financial Analysis, 101313.
• 24. Martins, F., Felgueiras, C., Smitkova, M., Caetano, N., 2019. Analysis of Fossil Fuel Energy Consumption and Environmental Impacts in European Countries. Energies, 12(6), 964.
• 25. Lak Kamari, M., Isvand, H., Alhuyi Nazari, M., 2020. Applications of Multi-Criteria Decision- Making (MCDM) Methods in Renewable Energy Development: A Review. Renewable Energy Research and Application, 1(1), 47-54.
• 26. Stein, E.W., 2013. A Comprehensive Multi- criteria Model to Rank Electric Energy Production Technologies, Renew. Sustain. Energy Rev. 22, 640–654.
• 27. Zavadskas, E.K., Turskis, Z., Kildienė, S., 2014. State of Art Surveys of Overviews on MCDM/MADM Methods. Technological and Economic Development of Economy, 20(1), 165-179.
• 28. Lee, A.H., Chen, H.H., Kang, H.Y., 2009. Multi- criteria Decision Making on Strategic Selection of Wind Farms. Renewable Energy, 34(1), 120-126.
• 29. Atilgan, B., Azapagic, A., 2016. An Integrated Life Cycle Sustainability Assessment of Electricity Generation in Turkey, Energy Pol. 93, 168-186.
• 30. Saaty, T.L., 1980. The Analytical Hierarchy Process, Planning, Priority. Resource allocation. RWS publications, USA.
• 31. Saaty, T.L., 1988. What is the Analytic Hierarchy Process?. In Mathematical models for decision support. Springer, Berlin, Heidelberg, 109-121.
• 32. Gabus, A., Fontela, E., 1972. World Problems, an Invitation to Further Thought Within the Framework of DEMATEL. Battelle Geneva Research Center, Geneva, Switzerland, 1-8.
• 33. Roy, B., Bertier, P., 1968. La Methode ELECTRE. Revue d’Informatique et. de Recherche Operationelle (RIRO), 8, 57-75.
• 34. Fishburn, P.C., 1974. Exceptional Paper- Lexicographic Orders, Utilities and Decision Rules: A Survey. Management Science, 20(11), 1442-1471.
• 35. Brans, J.P., 1982. L’ingénierie de la Décision: L’élaboration D’instruments D’aide a la Décision. Université Laval, Faculté des Sciences de L’administration.
• 36. Churchman, C.W., Ackoff, R.L., 1954. An Approximate Measure of Value. Journal of the Operations Research Society of America, 2(2), 172-187.
• 37. Yoon, K., 1987. A Reconciliation Among Discrete Compromise Solutions. Journal of the Operational Research Society, 38(3), 277-26.
• 38. Zavadskas, E.K., Turskis, Z., 2010. A New Additive Ratio Assessment (ARAS) Method in Multicriteria Decision‐making. Technological and Economic Development of Economy, 16(2), 159-172.
• 39. Zavadskas, E.K., Kaklauskas, A., 1996. Determination of an Efficient Contractor by Using the New Method of Multicriteria Assessment. In International Symposium for “The Organization and Management of Construction”. Shaping Theory and Practice, 2, 94-104.
• 40. Brauers, W.K., Zavadskas, E.K., 2006. The MOORA Method and its Application to Privatization in a Transition Economy. Control and Cybernetics, 35, 445-469.
• 41. Keršuliene, V., Zavadskas, E.K., Turskis, Z., 2010. Selection of Rational Dispute Resolution Method by Applying New Step‐wise Weight Assessment Ratio Analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258.
• 42. Zavadskas, E.K., Turskis, Z., Antucheviciene, J., Zakarevicius, A., 2012. Optimization of Weighted Aggregated Sum Product Assessment. Elektronika ir elektrotechnika, 122(6), 3-6.
• 43. Wang, T.C., Lee, H.D., 2009. Developing a Fuzzy TOPSIS Approach Based on Subjective Weights and Objective Weights. Expert Systems with Applications, 36(5), 8980-8985.
• 44. Yang, K., Ding, Y., Zhu, N., Yang, F., Wang, Q., 2018. Multi-criteria Integrated Evaluation of Distributed Energy System for Community Energy Planning Based on Improved Grey Incidence Approach: A Case Study in Tianjin. Applied Energy, 229, 352-363.
• 45. Özcan, E.C., Erol, S., 2013. Türkiye’de Elektrik Üretim Planlaması için Çok Amaçlı Bir Karışık Tam Sayılı Doğrusal Programlama Modeli, Selcuk Univ. J. Eng. Sci. Tech., 1(1), 41-54.
• 46. Alanne, K., Salo, A., Saari, A., Gustafsson, S.I., 2007. Multi-criteria Evaluation of Residential Energy Supply Systems. Energy and buildings, 39(12), 1218-1226.
• 47.https://enerjiapi.etkb.gov.tr//Media/Dizin/EIGM/R aporlar/Enerji_Talep_Projeksiyonu/114176- turkiye_elektrik_enerjisi_talep_projeksiyonu _raporu.pdf (Erişim Tarihi: 01.08.2020).
• 48. Millet, I., Saaty, T.L., 2000. On the Relativity of Relative Measures–accommodating Both Rank Preservation and Rank Reversals in the AHP. European Journal of Operational Research, 121(1), 205-212.
• 49. Leccese, F., Salvadori, G., Rocca, M., Buratti, C., Belloni, E., 2020. A Method to Assess Lighting Quality in Educational Rooms Using Analytic Hierarchy Process. Building and Environment, 168, 106501.
• 50. Lin, Y.C., Chen, T., 2020. A Multibelief Analytic Hierarchy Process and Nonlinear Programming Approach for Diversifying Product Designs: Smart Backpack Design as an Example. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(6-7), 1044-1056.
• 51. Han, Y., Zhou, R., Geng, Z., Bai, J., Ma, B., Fan, J., 2020. A Novel Data Envelopment Analysis Cross-model Integrating Interpretative Structural Model and Analytic Hierarchy Process for Energy Efficiency Evaluation and Optimization Modeling: Application to Ethylene Industries. Journal of Cleaner Production, 246, 118965.
• 52. Yüncü, V., Koparal, C., 2019. Is Cultural Environment a Determinant of Perceived Corporate Reputation?, Journal of Business Research- Turk, 11 (2), 1044-1056.
• 53. Kapasite Ölçümleri https://seffaflik.epias.com.tr/transparency/uretim/ planlama/kgup.xhtml (Erişim Tarihi: 01.08.2020).
• 54. Teiaş Elektrik Üretim Tüketim Raporları, 2020. https://www.teias.gov.tr/tr/elektrik-istatistikleri (Erişim Tairihi: 11.07.2020).
• 55. Easterling, R.G., 1979. Statistical Analysis of Power Plant Capacity Factors (Vol. 382). Nuclear Regulatory Commission, Office of Management and Program Analysis, Division of Technical Support, Applied Statistics Branch.
• 56. Eia Report, 2019. International Energy Outlook 2019 with Projections to 2050, U.S. Energy Information Administration.
• 57. Eia Report, 2020. Capital Cost and Performance Characteristic Estimates for Utility Scale Electric Power Generating Technologies, U.S. Energy Information Administration.
• 58. Krishna, S.M., Mahapatra, S., Raj, S., Daya, J.F., 2020. Estimation of Simple, Energy and Carbon Payback Periods of a 1 MWp Ground-Mounted Solar PV. Innovation in Electrical Power Engineering, Communication, and Computing Technology: Proceedings of IEPCCT 2019, 630, 439.
• 59. Fernández, J.C.R., 2019. Integration Capacity of Geothermal Energy in Supermarkets Through Case Analysis. Sustainable Energy Technologies and Assessments, 34, 49-55.
• 60. Dümenli, Ş., 2019. Rüzgar ve Güneş Enerjisi Santrali Yatırımlarının ve İşletme Faaliyetlerinin Tms/tfrs Çerçevesinde Muhasebeleştirilmesi. (Yüksek Lisans Tezi).
• 61. Yıldırım, H.H., 2017. Rüzgâr Enerjisi Santral Yatırımlarında Geri Ödeme Süresinin Monte Carlo Simülasyonu ile Belirlenmesi. Institute of Business Administration-Management Journal/İsletme İktisadi Enstitüsü Yönetim Dergisi, (82).
• 62. Karaman, G., 2015. Enerji İhtiyacının Karşılanmasında Doğalgaz ve Rüzgâr Kaynaklı Enerji Çevrim Santralleri; Yatırımlar Üzerine Stratejik Bir Analiz (Master’s thesis, Güven Karaman).
• 63. Kumar, R., Sharma, A.K., Tewari, P.C., 2015. Cost Analysis of a Coal-fired Power Plant Using the NPV Method. Journal of Industrial Engineering International, 11(4), 495-504.
• 64. Weißbach, D., Ruprecht, G., Huke, A., Czerski, K., Gottlieb, S., Hussein, A., 2013. Energy Intensities, EROIs (energy Returned on Invested), and Energy Payback Times of Electricity Generating Power Plants. Energy, 52, 210-221.
• 65. Maqbool, U., Tyagi, A., Tyagi, V.V., Kothari, R., 2020. Optimization of the Renewable-energy- based Micro-grid for Rural Electrification in Northern Region of India. Clean Technologies and Environmental Policy, 1-12.
• 66. https://sp.enerji.gov.tr/ETKB_2019_2023_Strateji k_Plani.pdf (Erişim Tairihi: 01.08.2020).
• 67. Bayer, P., Kennedy, R., Yang, J., Urpelainen, J., 2019. The Need for Impact Evaluation in Electricity Access Research. Energy Policy, 137, 111099.