IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i5p4618-d1088019.html
   My bibliography  Save this article

Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation

Author

Listed:
  • Mahdi Asadi

    (Department of Energy Systems Engineering, School of Advanced Technologies, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Iman Larki

    (Department of Energy Systems Engineering, School of Advanced Technologies, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Mohammad Mahdi Forootan

    (Department of Energy Systems Engineering, School of Advanced Technologies, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Rouhollah Ahmadi

    (Department of Energy Systems Engineering, School of Advanced Technologies, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Meisam Farajollahi

    (Department of Energy Systems Engineering, School of Advanced Technologies, Iran University of Science and Technology, Tehran 16846-13114, Iran)

Abstract

Electricity plays a vital role in the economic development and welfare of countries. Examining the electricity situation and defining scenarios for developing power plant infrastructure will help countries avoid misguided policies that incur high costs and reduce people’s welfare. In the present research, three scenarios from 2021–2040 have been defined for Iran’s electricity status. The first scenario continues the current trend and forecasts population, electricity consumption, and carbon dioxide emissions from power plants with ARIMA and single and triple exponential smoothing time series algorithms. As part of the second scenario, only non-hydro renewable resources will be used to increase the electricity supply. By ensuring the existence of potential, annual growth patterns have been defined, taking into account the renewable electricity generation achieved by successful nations. The third scenario involves integrating operating gas turbines into combined cycles in exchange for buyback contracts. Economically, this scenario calculates return on investment through an arrangement of various contracts for the seller company and fuel savings for the buyer.

Suggested Citation

  • Mahdi Asadi & Iman Larki & Mohammad Mahdi Forootan & Rouhollah Ahmadi & Meisam Farajollahi, 2023. "Long-Term Scenario Analysis of Electricity Supply and Demand in Iran: Time Series Analysis, Renewable Electricity Development, Energy Efficiency and Conservation," Sustainability, MDPI, vol. 15(5), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4618-:d:1088019
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/5/4618/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/5/4618/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xue, Jian & Ding, Jing & Zhao, Laijun & Zhu, Di & Li, Lei, 2022. "An option pricing model based on a renewable energy price index," Energy, Elsevier, vol. 239(PB).
    2. Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam & Harijan, Khanji & Walasai, Gordhan Das & Mondal, Md Alam Hossain & Sahin, Hasret, 2018. "Long-term electricity demand forecast and supply side scenarios for Pakistan (2015–2050): A LEAP model application for policy analysis," Energy, Elsevier, vol. 165(PB), pages 512-526.
    3. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    4. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    5. Sen, Parag & Roy, Mousumi & Pal, Parimal, 2016. "Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization," Energy, Elsevier, vol. 116(P1), pages 1031-1038.
    6. 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.
    7. Mina Masoomi & Mostafa Panahi & Reza Samadi, 2022. "Demand side management for electricity in Iran: cost and emission analysis using LEAP modeling framework," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(4), pages 5667-5693, April.
    8. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    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. Yuanbo Hu & Weilun Huang & Aibi Dai & Xuemeng Zhao, 2024. "Determinants of Non-Hydro Renewable Energy Consumption in China’s Provincial Regions," Energies, MDPI, vol. 17(16), pages 1-44, August.

    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. Syed Aziz Ur Rehman & Yanpeng Cai & Rizwan Fazal & Gordhan Das Walasai & Nayyar Hussain Mirjat, 2017. "An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan," Energies, MDPI, vol. 10(11), pages 1-23, November.
    2. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    3. Jacob Hale & Suzanna Long, 2020. "A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition," Energies, MDPI, vol. 14(1), pages 1-14, December.
    4. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
    5. Shuyu Li & Xuan Yang & Rongrong Li, 2019. "Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models," Sustainability, MDPI, vol. 11(3), pages 1-19, January.
    6. Tanoto, Yusak & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2021. "Reliability-cost trade-offs for electricity industry planning with high variable renewable energy penetrations in emerging economies: A case study of Indonesia’s Java-Bali grid," Energy, Elsevier, vol. 227(C).
    7. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
    8. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    9. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    10. El-Sayed, Ahmed Hassan A. & Khalil, Adel & Yehia, Mohamed, 2023. "Modeling alternative scenarios for Egypt 2050 energy mix based on LEAP analysis," Energy, Elsevier, vol. 266(C).
    11. Hussain, Arif & Perwez, Usama & Ullah, Kafait & Kim, Chul-Hwan & Asghar, Nosheen, 2021. "Long-term scenario pathways to assess the potential of best available technologies and cost reduction of avoided carbon emissions in an existing 100% renewable regional power system: A case study of G," Energy, Elsevier, vol. 221(C).
    12. Karakurt, Izzet, 2021. "Modelling and forecasting the oil consumptions of the BRICS-T countries," Energy, Elsevier, vol. 220(C).
    13. Aysha Malik & Ejaz Hussain & Sofia Baig & Muhammad Fahim Khokhar, 2020. "Forecasting CO2 emissions from energy consumption in Pakistan under different scenarios: The China–Pakistan Economic Corridor," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(2), pages 380-389, April.
    14. Minglu Ma & Zhuangzhuang Wang, 2019. "Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models," Energies, MDPI, vol. 13(1), pages 1-15, December.
    15. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    16. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques," Energy, Elsevier, vol. 161(C), pages 821-831.
    17. Cao, Xin & Liu, Chang & Wu, Mingxuan & Li, Zhi & Wang, Yihan & Wen, Zongguo, 2023. "Heterogeneity and connection in the spatial–temporal evolution trend of China’s energy consumption at provincial level," Applied Energy, Elsevier, vol. 336(C).
    18. Xiwen Cui & Shaojun E & Dongxiao Niu & Dongyu Wang & Mingyu Li, 2021. "An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target," Sustainability, MDPI, vol. 13(15), pages 1-21, August.
    19. Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn, 2018. "Relationships between Causal Factors Affecting Future Carbon Dioxide Output from Thailand’s Transportation Sector under the Government’s Sustainability Policy: Expanding the SEM-VECM Model," Resources, MDPI, vol. 7(4), pages 1-18, December.
    20. Anca Mehedintu & Mihaela Sterpu & Georgeta Soava, 2018. "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, MDPI, vol. 10(5), pages 1-22, May.

    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:jsusta:v:15:y:2023:i:5:p:4618-:d:1088019. 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.