Modeling the renewable energy development in Türkiye with optimization
Due to increasing electricity consumption in Türkiye, energy supply had to increase significantly in the last few decades. As Türkiye has been highly dependent on energy imports, this had an effect on country’s economy. However, especially in the last two decades, Türkiye increased the utilization of renewables. This can significantly improve the energy security and the economy of the country. This paper investigates the development of renewable energy in Türkiye and aims to determine the main drivers behind the significant development recorded in the last decades. In order to do that, past data have been analyzed and a constrained optimization problem has been constructed. This optimization problem aims to minimize the Mean Absolute Error (MAE) between the real and estimated renewable electricity capacity levels in Türkiye. In order to analyze the effect of increasing the number of modeling parameters, this paper considers three cases with different number of modeling parameters. The results in each case reveal the optimum weights and the modeling parameters that should be preferred for modeling the installed renewable energy capacity in Türkiye. The results of the proposed approach, called Mean Absolute Error based OPTimization (MAEOPT), are also compared with that of multiple linear regression (MLR). The obtained MAE values for the best six models reported in this paper (three for MAEOPT and three for MLR) are less than 5%, which indicates an excellent performance. The results also indicate the superiority of MAEOPT over MLR, in terms of MAE and Mean Absolute Percentage Error (MAPE).
[1] Kaygusuz, K. (2020). The role of economic growth and energy prices for renewable energy development in Türkiye. Journal of Engineering Research and Applied Science, 9(2), 1612-1617.
[2] IEA (2022). Electricity consumption, Republic of Turkiye 1990-2020 [online]. International Energy Agency. Available from: https://www.iea.org/data-and-statistics/data-browser?country=Trkiye&fuel=Energy%20consumption&indicator=TotElecCons [Accessed August 2022].
[3] Erdil, A., & Erbıyık, H. (2015). Renewable Energy Sources of Türkiye and Assessment of Sustainability. Procedia - Social and Behavioral Sciences, 207, 669 – 679.
[4] WEC (2022). T¨urkiye, Country Profile [online]. World Energy Council. Available from: https://trilemma.worldenergy.org/#!/country-profile?country=Trkiye&year=2021 [Accessed August 2022].
[5] Bulut, U., & Muratoglu, G. (2018). Renewable energy in Türkiye: Great potential, low but increasing utilization, and an empirical analysis on renewable energy-growth nexus. Energy Policy, 123, 240-250. https://doi.org/10.1016/j.enpol.2018.08.057
[6] Aksoy, A. (2019). Integrated model for renewable energy planning in Türkiye. International Journal of Green Energy, 16 (1), 34-48.
[7] REN21. (2018). Renewables 2018 Global Status Report. Paris: REN21 Secretariat.
[8] REN21. (2019). Renewables 2019 Global Status Report. Paris: REN21 Secretariat.
[9] REN21. (2020). Renewables 2020 Global Status Report. Paris: REN21 Secretariat.
[10] B¨ol¨uk, G., & Mert, M. (2015). The renewable energy, growth and environmental Kuznets curve in Türkiye: An ARDL approach. Renewable and Sustainable Energy Reviews, 52, 587-595. https://doi.org/10.1016/j.rser.2015.07.138
[11] Pata, U. K. (2018). Renewable energy consumption, urbanization, financial development, income and CO2 emissions in Türkiye: Testing EKC hypothesis with structural breaks. Journal of Cleaner Production, 187, 770-779. https://doi.org/10.1016/j.jclepro.2018.03.236
[12] Mukhtarov, S., Y¨uksel, S., & Din，cer, H. (2022). The impact of financial development on renewable energy consumption: Evidence from Türkiye. Renewable Energy, 187, 169-176. https://doi.org/10.1016/j.renene.2022.01.061
[13] Deveci, K., & G¨uler, ¨ O. A. (2020). CMOPSO based multi-objective optimization of renewable energy planning: Case of Türkiye. Renewable Energy; 155,578-590. https://doi.org/10.1016/j.renene.2020.03.033
[14] Ozmen, A. (2022). Multi-objective regression modeling for natural gas prediction with ridge regression and CMARS. An International Journal of Optimization and Control: Theories & Applications 12,56-65. https://doi.org/10.11121/ijocta.2022.1084
[15] Perin, D., Karao˘glan, A.D., Yılmaz, K. (2024). Rotor design optimization of a 4000 rpm permanent magnet synchronous generator using moth flame optimization algorithm. An International Journal of Optimization and Control: Theories & Applications 14,123-133. https://doi.org/10.11121/ijocta.1407
[16] REN21. (2017). Renewables 2017 Global Status Report. Paris: REN21 Secretariat.
[17] Tekbıyık Ersoy, N. (2024). Energy Efficiency and Renewable Energy Policies. Synthesis Lectures on Renewable Energy Technologies. Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-64305-7
[18] Bahadıro˘glu, A., Atay, B. N., & Basakcılardan Kabakcı S. (2022). Agricultural Residues in Türkiye: Energy Potential and Evaluation of Existing Biomass Power Plants. Yuzuncu Yil University Journal of Agricultural Sciences. 32 (1), 119-131. https://doi.org/10.29133/yyutbd.1030233
[19] IRENA. (2022). IRENASTAT Online Data Query Tool [online]. International Renewable Energy Agency (IRENA). Available from: https://pxweb.irena.org/pxweb/en/IRENASTAT/IRENASTAT__Power%20Capacity%20and%20Generation/ELECCAP_2022_cycle2.px/ [Accessed August2022].
[20] IRENA. (2023). IRENASTAT Online Data Query Tool (online). International Renewable Energy Agency (IRENA). Available from: Link: https://pxweb.irena.org/pxweb/en/IRENASTAT/IRENASTAT [Accessed August 2023].
[21] T.C. Enerji ve Tabii Kaynaklar Bakanlı˘gı. (2019). T.C. Enerji ve Tabii Kaynaklar Bakanlı˘gı 2019- 2023 Stratejik Planı. Ankara, Türkiye.
[22] IRENA. (2024). IRENASTAT Online Data Query Tool (online). International Renewable Energy Agency (IRENA). Available from: https://pxweb.irena.org/pxweb/en/IRENASTAT/IRENASTAT__Power%20Capacity%20and%20Generation/Country_ELECSTAT_2024_H2.px/ [Accessed August 2024].
[23] Vural, G. (2020). Investigation of the effects of GDP per capita, carbon dioxide emission, trade and technological innovation on the electricity generation from renewable energy in Turkey. International Marmara Social Sciences Congress (Imascon 2020 – Autumn) Proceedings Book, 175-178.
[24] World Bank (2024). World Development Indicators (online). The World Bank. Available from: https://databank.worldbank.org/source/world-development-indicators# [Accessed August2024].
[25] IEA. (2024). Net energy imports, Republic of Turkiye, 1990-2022 (online). International Energy Agency. Available from: hhttps://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=TURKEY&fuel=Key%20indicators&indicator=NetImports [Accessed August 2024].
[26] IEA. (2024). Coal imports vs. exports, Republic of Turkiye, 1990-2022 (online). International Energy Agency. Available from: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=TURKEY&fuel=Imports%2Fexports&indicator=CoalImportsExports [Accessed August 2024].
[27] IEA. (2024). Natural gas final consumption, Republic of Turkiye, 1990-2021 (online). International Energy Agency. Available from: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=TURKEY&fuel=Natural%20gas&indicator=NatGasCons [Accessed August 2024].
[28] IEA. (2024). Electricity consumption, Republic of Turkiye, 1990-2022 (online). International Energy Agency. Available from: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=TURKEY&fuel=Key%20indicators&indicator=TotElecCons [Accessed August 2024].
[29] IEA. (2024). CO2 emissions per capita, Republic of Turkiye, 1990-2022 (online). International Energy Agency. Available from: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=TURKEY&fuel=Key%20indicators&indicator=CO2PerCap [Accessed August 2024].
[30] Qazi, I. A., Abd, A. A., Hasan, M. M., & Othman, M. R. (2023). Optimizing hydrogen purification performance by membrane from industrial waste of methanol production. Chemical Engineering Journal Advances, 14 (100490). https://doi.org/10.1016/j.ceja.2023.100490