Modelling and Analysis of Future Energy Scenarios on the Sustainability Axis

Öz

This study aims to propose a sustainable electricity mix option for Turkey by 2030. First, the electricity demand of Turkey by 2030 is estimated by employing a method that comprises MLP ANN and GPRM. Population, GDP, imports, exports, and IPI are considered independent variables used in the ANN model. The future values of each of the independent variables are predicted by a GPRM model based on a univariate time series approach. ANN model is then employed to predict electricity demand based on the future values of independent variables. Secondly, four diverse electricity mix scenarios are developed considering the forecasted electricity demand. The sustainability evaluation of the scenarios is performed using TOPSIS method considering ten different criteria classified into environmental, economic, technical, and social categories. Furthermore, four diverse weight sets are determined for the given categories, and also a sensitivity analysis is carried out. Turkey’s electricity demand is found out as ≈ 384,569 GWh according to the prediction for the year 2030. The Scenario-(C), which has a comparatively higher percent of nuclear energy generation, is determined as the most sustainable electricity mix scenario according to evaluation with the TOPSIS method.

Anahtar Kelimeler

• Energy
• Sustainability Assessment
• Artificial Neural Network
• Grey Prediction
• TOPSIS

Modelling and Analysis of Future Energy Scenarios on the Sustainability Axis

Öz

Bu çalışma Türkiye için 2030 yılına ait sürdürülebilir bir elektrik enerjisi karması seçeneği önerilmesini amaçlamaktadır. İlk olarak, MLP ANN ve GPRM’yi kapsayan bir yöntem kullanılarak Türkiye’nin 2030 yılı elektrik enerjisi talebi tahmin edilmiştir. ANN modelinde dikkate alınan bağımsız değişkenler nüfus, GDP, ithalat, ihracat ve IPI olmaktadır. Her bir bağımsız değişkenin gelecekteki değerleri tek değişkenli zaman serisi yaklaşımı temelinde bir GPRM modeli kullanılarak tahmin edilmiştir. ANN modeli daha sonra bağımsız değişkenlerin gelecekteki değerleri temelinde elektrik enerjisi talebinin tahmin edilmesinde kullanılmıştır. İkinci olarak, tahmin edilen elektrik talebi dikkate alınarak dört farklı elektrik karması senaryosu geliştirilmiştir. Senaryoların sürdürülebilirlik değerlendirilmesi TOPSIS kullanılarak çevresel, ekonomik, teknik ve sosyal kategorileri dâhilinde sınıflandırılmış on farklı kritere göre gerçekleştirilmiştir. Buna ek olarak, belirtilen kategoriler için dört farklı ağırlık seti belirlenmiş ve bir duyarlılık analizi de gerçekleştirilmiştir. 2030 yılı için yapılan tahmin işlemine göre Türkiye’nin elektrik enerjisi talebi ≈ 384,569 GWh olarak bulunmuştur. TOPSIS metodu ile yapılan değerlendirmeye göre, karşılaştırmalı olarak daha yüksek nükleer enerji üretim yüzdesine sahip olan Senaryo-(C) en sürdürülebilir elektrik enerjisi karması senaryosu olarak belirlenmiştir. 

Anahtar Kelimeler

• Enerji
• Sürdürülebilirlik Değerlendirmesi
• Yapay Sinir Ağı
• Gri Tahmin
• TOPSIS

Kaynakça

1. [1] Çunkaş, M. and Altun, A. A., “Long Term Electricity Demand Forecasting in Turkey Using Artificial Neural Networks”, Energy Sources, Part B: Economics, Planning, and Policy, 5(3): 279-289, (2010).
2. [2] Kavaklioglu, K., Ceylan, H., Ozturk, H. K. and Canyurt, O. E., “Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks”, Energy Conversion and Management, 50(11): 2719-2727, (2009).
3. [3] Cretì, A. and Fontini, F., “Economics of Electricity: Markets, Competition and Rules”, Cambridge University Press, Cambridge, (2019).
4. [4] http://www.teias.gov.tr/sites/default/files/2019-10/38ing.docx [20.01.2020].
5. [5] http://www.teias.gov.tr/sites/default/files/2019-10/3ing.docx [20.01.2020].
6. [6] https://cnpp.iaea.org/countryprofiles/Turkey/Turkey.htm [20.01.2020].
7. [7] Kaytez, F., Taplamacioglu, M. C., Cam, E. and Hardalac, F., “Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines”, International Journal of Electrical Power & Energy Systems, 67: 431-438, (2015).
8. [8] Hsu, C. and Chen, C., “Regional load forecasting in Taiwan––applications of artificial neural networks”, Energy Conversion and Management, 44(12): 1941-1949, (2003).

9. [9] Tso, G. and Yau, K., “Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks”, Energy, 32(9): 1761-1768, (2007).
10. [10] Sözen, A., Arcaklioğlu, E. and Özkaymak, M., “Turkey’s net energy consumption”, Applied Energy, 81(2): 209-221, (2005).
11. [11] Sözen, A., Akçayol, M. A. and Arcaklioğlu, E., “Forecasting Net Energy Consumption Using Artificial Neural Network”, Energy Sources, Part B: Economics, Planning, and Policy, 1(2): 147-155, (2006).
12. [12] Sözen, A. and Arcaklioglu, E., “Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey”, Energy Policy, 35(10): 4981-4992, (2007).
13. [13] Sözen, A., Arcaklioglu, E. and Tekiner, Z., “Estimation of Net Energy Consumption in Turkey Using Different Indicators”, Energy Sources, Part B: Economics, Planning, and Policy, 4(3): 261-277, (2009).
14. [14] Sözen, A. and Arcaklioğlu, E., “Prospects for Future Projections of the Basic Energy Sources in Turkey”, Energy Sources, Part B: Economics, Planning, and Policy, 2(2): 183-201, (2007).
15. [15] Kankal, M., Akpınar, A., Kömürcü, M. İ. and Özşahin, T. Ş., “Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables”, Applied Energy, 88(5): 1927-1939, (2011).
16. [16] Uzlu, E., Kankal, M., Akpınar, A. and Dede, T., “Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm”, Energy, 75: 295-303, (2014).
17. [17] Geem, Z. W. and Roper, W. E., “Energy demand estimation of South Korea using artificial neural network”, Energy Policy, 37(10): 4049-4054, (2009).
18. [18] Akay, D. and Atak, M., “Grey prediction with rolling mechanism for electricity demand forecasting of Turkey”, Energy, 32(9): 1670-1675, (2007).
19. [19] Boran, F. E., “Forecasting Natural Gas Consumption in Turkey Using Grey Prediction”, Energy Sources, Part B: Economics, Planning, and Policy, 10(2): 208-213, (2015).
20. [20] G. Li, D. Yamaguchi, H. Lin and M. Nagai, “The Simulation Modeling About The Developments of GDP, Population and Primary Energy Consumption in China Based on MATLAB”, 2006 IEEE Conference on Cybernetics and Intelligent Systems, Bangkok, 1-6, (2006).
21. [21] Pao, H., Fu, H. and Tseng, C., “Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model”, Energy, 40(1): 400-409, (2012).
22. [22] Wang, C. and Hsu, L., “Using genetic algorithms grey theory to forecast high technology industrial output”, Applied Mathematics and Computation, 195(1): 256-263, (2008).
23. [23] Boran, F. E., Boran, K. and Dizdar, E., “A Fuzzy Multi Criteria Decision Making to Evaluate Energy Policy Based on an Information Axiom: A Case Study in Turkey”, Energy Sources, Part B: Economics, Planning, and Policy, 7(3): 230-240, (2012).
24. [24] Boran, F. E., Dizdar, E., Toktas, I., Boran, K., Eldem, C. and Asal, Ö., “A Multidimensional Analysis of Electricity Generation Options with Different Scenarios in Turkey”, Energy Sources, Part B: Economics, Planning, and Policy, 8(1): 44-55, (2013).
25. [25] Boran, K., “An evaluation of power plants in Turkey: Fuzzy TOPSIS method”, Energy Sources, Part B: Economics, Planning, and Policy, 12(2): 119-125, (2017).
26. [26] Boran, F. E., “A new approach for evaluation of renewable energy resources: A case of Turkey”, Energy Sources, Part B: Economics, Planning, and Policy, 13(3): 196-204, (2018).
27. [27] Atmaca, E. and Basar, H. B., “Evaluation of power plants in Turkey using Analytic Network Process (ANP)”, Energy, 44(1): 555-563, (2012).
28. [28] Atilgan, B. and Azapagic, A., “An integrated life cycle sustainability assessment of electricity generation in Turkey”, Energy Policy, 93: 168-186, (2016).
29. [29] Kuleli Pak, B., Albayrak, Y. E. and Erensal, Y. C., “Evaluation of sources for the sustainability of energy supply in Turkey”, Environmental Progress & Sustainable Energy, 36(2): 627-637, (2017).
30. [30] Shen, Y., Lin, G., Li, K. and Yuan, B., “An assessment of exploiting renewable energy sources with concerns of policy and technology”, Energy Policy, 38(8): 4604-4616, (2010).
31. [31] San Cristóbal, J. R., “Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method”, Renewable Energy, 36(2): 498-502, (2011).
32. [32] Ribeiro, F., Ferreira, P. and Araújo, M., “Evaluating future scenarios for the power generation sector using a Multi-Criteria Decision Analysis (MCDA) tool: The Portuguese case”, Energy, 52: 126-136, (2013).
33. [33] Hong, S., Bradshaw, C. J. A. and Brook, B. W., “Evaluating options for the future energy mix of Japan after the Fukushima nuclear crisis”, Energy Policy, 56: 418-424, (2013).
34. [34] Santoyo-Castelazo, E. and Azapagic, A., “Sustainability assessment of energy systems: integrating environmental, economic and social aspects”, Journal of Cleaner Production, 80: 119-138, (2014).
35. [35] Brand, B. and Missaoui, R., “Multi-criteria analysis of electricity generation mix scenarios in Tunisia”, Renewable and Sustainable Energy Reviews, 39: 251-261, (2014).
36. [36] Kaya, T. and Kahraman, C., “Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology”, Expert Systems with Applications, 38(6): 6577-6585, (2011).
37. [37] Maxim, A., “Sustainability assessment of electricity generation technologies using weighted multi-criteria decision analysis”, Energy Policy, 65: 284-297, (2014).
38. [38] Nunes Silva, I., Hernane Spatti, D., Andrade Flauzino, R., Liboni, L.H.B. and dos Reis Alves, S.F., “Artificial Neural Networks: A Practical Course”, Springer, Cham, (2017).
39. [39] Baliyan, A., Gaurav, K. and Mishra, S. K., “A Review of Short Term Load Forecasting using Artificial Neural Network Models”, Procedia Computer Science, 48: 121-125, (2015).
40. [40] Haykin, S., “Neural Networks and Learning Machines”, Pearson, New Jersey, (2009).
41. [41] Hagan, M. T., Demuth, H. B., Beale, M. H. and De Jesús, O., “Neural Network Design”, Martin Hagan, (2014).
42. [42] Ju-Long, D., “Control problems of grey systems”, Systems & Control Letters, 1(5): 288-294, (1982).
43. [43] Julong, D., “Introduction to Grey system theory”, The Journal of Grey System, 1: 1-24, (1989).
44. [44] Kayacan, E., Ulutas, B. and Kaynak, O., “Grey system theory-based models in time series prediction”, Expert Systems with Applications, 37(2): 1784-1789, (2010).
45. [45] Hwang C. L. and Yoon, K., “Multiple Attribute Decision Making”, Springer-Verlag, Berlin, (1981).
46. [46] Huang, X., Zhang, J., Luo, L., Tang, Q., Luo, B., Zhang, W., Deng, S., Shen, F., Yao, X. and Xiao, H., “The influence of GDP, population, and net export value on energy consumption”, Energy Sources, Part B: Economics, Planning, and Policy, 12(9): 815-821, (2017).
47. [47] http://www.tuik.gov.tr/PreIstatistikTablo.do?istab_id=1588 [20.01.2020].
48. [48] Say, N. P. and Yücel, M., “Energy consumption and CO2 emissions in Turkey: Empirical analysis and future projection based on an economic growth”, Energy Policy, 34(18): 3870-3876, (2006).
49. [49] Soytas, U. and Sari, R., “Energy consumption and GDP: causality relationship in G-7 countries and emerging markets”, Energy Economics, 25(1): 33-37, (2003).
50. [50] Lise, W. and Van Montfort, K., “Energy consumption and GDP in Turkey: Is there a co‐integration relationship?”, Energy Economics, 29(6): 1166-1178, (2007).
51. [51] Altinay, G. and Karagol, E., “Structural break, unit root, and the causality between energy consumption and GDP in Turkey”, Energy Economics, 26(6): 985-994, (2004).
52. [52] Yalta, A. T., “Analyzing energy consumption and GDP nexus using maximum entropy bootstrap: The case of Turkey”, Energy Economics, 33(3): 453-460, (2011).
53. [53] Altinay, G. and Karagol, E., “Electricity consumption and economic growth: Evidence from Turkey”, Energy Economics, 27(6): 849-856, (2005).
54. [54] Aslan, A., “Causality Between Electricity Consumption and Economic Growth in Turkey: An ARDL Bounds Testing Approach”, Energy Sources, Part B: Economics, Planning, and Policy, 9(1): 25-31, (2014).
55. [55] https://data.oecd.org/gdp/gross-domestic-product-gdp.htm [20.01.2020].
56. [56] Topcu, M. and Payne, J. E., “Further evidence on the trade-energy consumption nexus in OECD countries”, Energy Policy, 117: 160-165, (2018).
57. [57] Dedeoğlu, D. and Kaya, H., “Energy use, exports, imports and GDP: New evidence from the OECD countries”, Energy Policy, 57: 469-476, (2013).
58. [58] http://www.tuik.gov.tr/PreIstatistikTablo.do?istab_id=621 [20.01.2020].
59. [59] http://www.tuik.gov.tr/PreIstatistikTablo.do?istab_id=1579 [20.01.2020].
60. [60] Soytas, U. and Sari, R., “The relationship between energy and production: Evidence from Turkish manufacturing industry”, Energy Economics, 29(6): 1151-1165, (2007).
61. [61] Sun, S. and Anwar, S., “Electricity consumption, industrial production, and entrepreneurship in Singapore”, Energy Policy, 77: 70-78, (2015).
62. [62] https://data.oecd.org/industry/industrial-production.htm [20.01.2020].
63. [63] IEA, “Electricity Information 2018”, OECD Publishing, Paris, (2018).
64. [64] Wagner, H. J. and Mathur, J., “Introduction to Hydro Energy Systems: Basics, Technology and Operation”, Springer-Verlag, Berlin, (2011).
65. [65] Houghton, J. T., Jenkins, G. J. and Ephraums, J. J., “Climate change, The IPCC Scientific Assessment”, Cambridge University Press, Cambridge, (1990).
66. [66] Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T. and Minx, J.C., “IPCC, 2014: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change”, Cambridge University Press, Cambridge, (2014).
67. [67] Turconi, R., Boldrin, A. and Astrup, T., “Life cycle assessment (LCA) of electricity generation technologies: Overview, comparability and limitations”, Renewable and Sustainable Energy Reviews, 28: 555-565, (2013).
68. [68] Wang, J., Jing, Y., Zhang, C. and Zhao, J., “Review on multi-criteria decision analysis aid in sustainable energy decision-making”, Renewable and Sustainable Energy Reviews, 13(9): 2263-2278, (2009).
69. [69] IEA, "World Energy Outlook 2018", IEA, Paris, (2018).
70. [70] NEA/IEA/OECD, “Projected Costs of Generating Electricity 2015”, OECD Publishing, Paris, (2015).
71. [71] IFC, “Hydroelectric Power: A guide for Developers and Investors”, IFC, (2015).
72. [72] Evans, A., Strezov, V. and Evans, T. J., “Assessment of sustainability indicators for renewable energy technologies”, Renewable and Sustainable Energy Reviews, 13(5): 1082-1088, (2009).
73. [73] Afgan, N. H. and Carvalho, M. G., “Multi-criteria assessment of new and renewable energy power plants”, Energy, 27(8): 739-755, (2002).
74. [74] Santos, M., Ferreira, P., Araújo, M., Portugal-Pereira, J., Lucena, A. and Schaeffer, R., “Scenarios for the future Brazilian power sector based on a multi-criteria assessment”, Journal of Cleaner Production, 167: 938-950, (2017).
75. [75] M. J. Santos, P. Ferreira and M. Araújo, "Multicriteria scenario analysis on electricity production," 2015 12th International Conference on the European Energy Market (EEM), Lisbon, 1-5, (2015).
76. [76] Cartelle Barros, J. J., Lara Coira, M., de la Cruz López, M. P. and del Caño Gochi, A., “Comparative analysis of direct employment generated by renewable and non-renewable power plants”, Energy, 139: 542-554, (2017).
77. [77] Genoud, S. and Lesourd, J., “Characterization of Sustainable Development Indicators for Various Power Generation Technologies”, International Journal of Green Energy, 6(3): 257-267, (2009).
78. [78] MENR, “Mavi Kitap (Blue Book)”, Ministry of Energy and Natural Resources, (2016).
79. [79] Lewis, C. D., “Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting”, Butterworth Scientific, London, (1982).