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Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting

Author

Listed:
  • Gökay Yörük

    (Graduate School of Natural and Applied Sciences, Atilim University, 06830 Ankara, Turkey)

  • Ugur Bac

    (Department of Industrial Engineering, Atilim University, 06830 Ankara, Turkey)

  • Fatma Yerlikaya-Özkurt

    (Department of Industrial Engineering, Atilim University, 06830 Ankara, Turkey)

  • Kamil Demirberk Ünlü

    (Department of Industrial Engineering, Atilim University, 06830 Ankara, Turkey)

Abstract

This study examines Turkey’s energy planning in terms of strategic planning, energy policy, electricity production planning, technology selection, and environmental policies. A mixed integer optimization model is proposed for strategic electricity planning in Turkey. A set of energy resources is considered simultaneously in this research, and in addition to cost minimization, different strategic level policies, such as CO 2 emission reduction policies, energy resource import/export restriction policies, and renewable energy promotion policies, are also considered. To forecast electricity demand over the planning horizon, a variety of forecasting techniques, including regression methods, exponential smoothing, Winter’s method, and Autoregressive Integrated Moving Average methods, are used, and the best method is chosen using various error measures. The optimization model constructed for Turkey’s Strategic Electricity Planning is obtained for two different planning intervals. The findings indicate that the use of renewable energy generation options, such as solar, wind, and hydroelectric alternatives, will increase significantly, while the use of fossil fuels in energy generation will decrease sharply. The findings of this study suggest a gradual increase in investments in renewable energy-based electricity production strategies are required to eventually replace fossil fuel alternatives. This change not only reduces investment, operation, and maintenance costs, but also reduces emissions in the long term.

Suggested Citation

  • Gökay Yörük & Ugur Bac & Fatma Yerlikaya-Özkurt & Kamil Demirberk Ünlü, 2023. "Strategic Electricity Production Planning of Turkey via Mixed Integer Programming Based on Time Series Forecasting," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1865-:d:1123359
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    References listed on IDEAS

    as
    1. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    2. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    3. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
    4. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    5. Taylor, James W., 2010. "Triple seasonal methods for short-term electricity demand forecasting," European Journal of Operational Research, Elsevier, vol. 204(1), pages 139-152, July.
    6. Pina, André & Silva, Carlos A. & Ferrão, Paulo, 2013. "High-resolution modeling framework for planning electricity systems with high penetration of renewables," Applied Energy, Elsevier, vol. 112(C), pages 215-223.
    7. Pokharel, Shaligram & Chandrashekar, M., 1998. "A multiobjective approach to rural energy policy analysis," Energy, Elsevier, vol. 23(4), pages 325-336.
    8. Daniel, J. & Dicorato, M. & Forte, G. & Iniyan, S. & Trovato, M., 2009. "A methodology for the electrical energy system planning of Tamil Nadu state (India)," Energy Policy, Elsevier, vol. 37(3), pages 904-914, March.
    9. Ünal, Berat Berkan & Onaygil, Sermin & Acuner, Ebru & Cin, Rabia, 2022. "Application of energy efficiency obligation scheme for electricity distribution companies in Turkey," Energy Policy, Elsevier, vol. 163(C).
    10. Levin, Todd & Kwon, Jonghwan & Botterud, Audun, 2019. "The long-term impacts of carbon and variable renewable energy policies on electricity markets," Energy Policy, Elsevier, vol. 131(C), pages 53-71.
    11. Jiang, Weiheng & Wu, Xiaogang & Gong, Yi & Yu, Wanxin & Zhong, Xinhui, 2020. "Holt–Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption," Energy, Elsevier, vol. 193(C).
    12. Sun-Youn Shin & Han-Gyun Woo, 2022. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms," Energies, MDPI, vol. 15(13), pages 1-20, July.
    13. Huang, Yun-Hsun & Wu, Jung-Hua, 2008. "A portfolio risk analysis on electricity supply planning," Energy Policy, Elsevier, vol. 36(2), pages 627-641, February.
    14. Calikoglu, Umit & Aydinalp Koksal, Merih, 2022. "Green electricity and Renewable Energy Guarantees of Origin demand analysis for Türkiye," Energy Policy, Elsevier, vol. 170(C).
    15. Selçuklu, Saltuk Buğra & Coit, D.W. & Felder, F.A., 2023. "Electricity generation portfolio planning and policy implications of Turkish power system considering cost, emission, and uncertainty," Energy Policy, Elsevier, vol. 173(C).
    16. Antunes, C.Henggeler & Martins, A.Gomes & Brito, Isabel Sofia, 2004. "A multiple objective mixed integer linear programming model for power generation expansion planning," Energy, Elsevier, vol. 29(4), pages 613-627.
    17. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra, 2017. "Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation," Applied Energy, Elsevier, vol. 193(C), pages 287-296.
    18. Senatla, Mamahloko & Nchake, Mamello & Taele, Benedict M. & Hapazari, Innocent, 2018. "Electricity capacity expansion plan for Lesotho – implications on energy policy," Energy Policy, Elsevier, vol. 120(C), pages 622-634.
    19. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "An integrated model for long-term power generation planning toward future smart electricity systems," Applied Energy, Elsevier, vol. 112(C), pages 1424-1437.
    20. Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael, 2021. "Mixed-integer linear programming based optimization strategies for renewable energy communities," Energy, Elsevier, vol. 237(C).
    21. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Decision analysis in energy and environmental modeling: An update," Energy, Elsevier, vol. 31(14), pages 2604-2622.
    22. Kachoee, Mohammad Sadegh & Salimi, Mohsen & Amidpour, Majid, 2018. "The long-term scenario and greenhouse gas effects cost-benefit analysis of Iran's electricity sector," Energy, Elsevier, vol. 143(C), pages 585-596.
    23. Akbal, Yıldırım & Ünlü, Kamil Demirberk, 2022. "A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production," Renewable Energy, Elsevier, vol. 200(C), pages 832-844.
    24. Ramanathan, R. & Ganesh, L.S., 1995. "Energy alternatives for lighting in households: An evaluation using an integrated goal programming-AHP model," Energy, Elsevier, vol. 20(1), pages 63-72.
    25. Cormio, C. & Dicorato, M. & Minoia, A. & Trovato, M., 2003. "A regional energy planning methodology including renewable energy sources and environmental constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 7(2), pages 99-130, April.
    26. Şengül, Ümran & Eren, Miraç & Eslamian Shiraz, Seyedhadi & Gezder, Volkan & Şengül, Ahmet Bilal, 2015. "Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey," Renewable Energy, Elsevier, vol. 75(C), pages 617-625.
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