IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v146y2025ics014098832500221x.html
   My bibliography  Save this article

Energy transition and climate policy selection with stochastic demand: Evidence from Australian electricity generation expansion planning

Author

Listed:
  • Sun, Xiaotong
  • Anderson, Heather M.
  • Wei, Wei
  • Zhang, Xibin

Abstract

Australia’s Nationally Determined Contributions made in 2022, following the Paris Agreement in 2016, include a pledge to reduce greenhouse gas emissions to 43% below 2005 levels by 2050. This paper calibrates a General Expansion Planning (GEP) model to the Australian National Electricity Market (NEM) to forecast cost-efficient ways to meet future electricity demand while transitioning from fossil-fuel-based technologies to renewable energy sources. The model incorporates uncertainty in the Australian electricity market and employs the stochastic dual dynamic programming (SDDP) methodology to devise plans for the period from 2020 to 2050. By comparing the effects of carbon taxes and emissions caps, the study highlights differences between states with substantial initial coal resources and those without coal-powered plants. The imposition of carbon taxes or capped emissions can reduce emissions by substantial amounts, with carbon taxes being more cost-effective than emission caps for most states. The model predicts that without climate policies, emissions in NEM will initially fall by 32% from 2020 to 2035, but will then rise to 92% of 2020 levels by 2050. Conversely, implementing carbon taxes or emissions caps can reduce emissions by nearly 50% from 2020 to 2050.

Suggested Citation

  • Sun, Xiaotong & Anderson, Heather M. & Wei, Wei & Zhang, Xibin, 2025. "Energy transition and climate policy selection with stochastic demand: Evidence from Australian electricity generation expansion planning," Energy Economics, Elsevier, vol. 146(C).
  • Handle: RePEc:eee:eneeco:v:146:y:2025:i:c:s014098832500221x
    DOI: 10.1016/j.eneco.2025.108397
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S014098832500221X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2025.108397?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Anderson, Heather M. & Gao, Jiti & Turnip, Guido & Vahid, Farshid & Wei, Wei, 2023. "Estimating the effect of an EU-ETS type scheme in Australia using a synthetic treatment approach," Energy Economics, Elsevier, vol. 125(C).
    2. Xun Zhou & Zhao Yang Dong & Ariel Liebman & Geoff James, 2008. "Potential Impact of Emission Trading Schemes on the Australian National Electricity Market," Energy Economics and Management Group Working Papers 1-2008, School of Economics, University of Queensland, Australia.
    3. Pope, Jeff & Owen, Anthony D., 2009. "Emission trading schemes: potential revenue effects, compliance costs and overall tax policy issues," Energy Policy, Elsevier, vol. 37(11), pages 4595-4603, November.
    4. Bahadori, Alireza & Nwaoha, Chikezie & Zendehboudi, Sohrab & Zahedi, Gholamreza, 2013. "An overview of renewable energy potential and utilisation inAustralia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 582-589.
    5. Shapiro, Alexander, 2011. "Analysis of stochastic dual dynamic programming method," European Journal of Operational Research, Elsevier, vol. 209(1), pages 63-72, February.
    6. Gadea Rivas, María Dolores & Gonzalo, Jesús, 2020. "Trends in distributional characteristics: Existence of global warming," Journal of Econometrics, Elsevier, vol. 214(1), pages 153-174.
    7. Chapman, Andrew J. & McLellan, Benjamin C. & Tezuka, Tetsuo, 2018. "Prioritizing mitigation efforts considering co-benefits, equity and energy justice: Fossil fuel to renewable energy transition pathways," Applied Energy, Elsevier, vol. 219(C), pages 187-198.
    8. Li, Can & Conejo, Antonio J. & Liu, Peng & Omell, Benjamin P. & Siirola, John D. & Grossmann, Ignacio E., 2022. "Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1071-1082.
    9. De Rosa, Luca & Castro, Rui, 2020. "Forecasting and assessment of the 2030 australian electricity mix paths towards energy transition," Energy, Elsevier, vol. 205(C).
    10. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    11. Lan, Haifeng & Gou, Zhonghua & Lu, Yi, 2021. "Machine learning approach to understand regional disparity of residential solar adoption in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    12. E. R. Petersen, 1973. "A Dynamic Programming Model for the Expansion of Electric Power Systems," Management Science, INFORMS, vol. 20(4-Part-II), pages 656-664, December.
    13. Howard, Bahareh Sara & Hamilton, Nicholas E. & Diesendorf, Mark & Wiedmann, Thomas, 2018. "Modeling the carbon budget of the Australian electricity sector's transition to renewable energy," Renewable Energy, Elsevier, vol. 125(C), pages 712-728.
    14. Graham, Paul W. & Williams, David J., 2003. "Optimal technological choices in meeting Australian energy policy goals," Energy Economics, Elsevier, vol. 25(6), pages 691-712, November.
    15. Oscar Dowson & Lea Kapelevich, 2021. "SDDP.jl : A Julia Package for Stochastic Dual Dynamic Programming," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 27-33, January.
    16. Jan-Horst Keppler & Stefan Lorenczik, 2020. "Projected Costs of Generating Electricity: 2020 Edition," Working Papers hal-03998435, HAL.
    Full references (including those not matched with items on IDEAS)

    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. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Nolan, Tahlia, 2024. "Is pivoting offshore the right policy for achieving decarbonisation in the state of Victoria, Australia's electricity sector?," Energy Policy, Elsevier, vol. 190(C).
    3. Phillips, K. & Moncada, J.A. & Ergun, H. & Delarue, E., 2023. "Spatial representation of renewable technologies in generation expansion planning models," Applied Energy, Elsevier, vol. 342(C).
    4. Khorramfar, Rahman & Mallapragada, Dharik & Amin, Saurabh, 2024. "Electric-gas infrastructure planning for deep decarbonization of energy systems," Applied Energy, Elsevier, vol. 354(PA).
    5. Seyed Hamed Jalalzad Mahvizani & Hossein Yektamoghadam & Rouzbeh Haghighi & Majid Dehghani & Amirhossein Nikoofard & Mahdi Khosravy & Tomonobu Senjyu, 2022. "A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy," Energies, MDPI, vol. 15(3), pages 1-16, February.
    6. Arnab Bhattacharya & Jeffrey P. Kharoufeh & Bo Zeng, 2023. "A Nonconvex Regularization Scheme for the Stochastic Dual Dynamic Programming Algorithm," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1161-1178, September.
    7. Haoxiang Yang & Harsha Nagarajan, 2022. "Optimal Power Flow in Distribution Networks Under N – 1 Disruptions: A Multistage Stochastic Programming Approach," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 690-709, March.
    8. Ihsan, Abbas & Brear, Michael J. & Jeppesen, Matthew, 2021. "Impact of operating uncertainty on the performance of distributed, hybrid, renewable power plants," Applied Energy, Elsevier, vol. 282(PB).
    9. D. Ávila & A. Papavasiliou & N. Löhndorf, 2022. "Parallel and distributed computing for stochastic dual dynamic programming," Computational Management Science, Springer, vol. 19(2), pages 199-226, June.
    10. Kim, Sunwoo & Choi, Yechan & Park, Joungho & Adams, Derrick & Heo, Seongmin & Lee, Jay H., 2024. "Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    11. Fábio T. F. Silva & Alexandre Szklo & Amanda Vinhoza & Ana Célia Nogueira & André F. P. Lucena & Antônio Marcos Mendonça & Camilla Marcolino & Felipe Nunes & Francielle M. Carvalho & Isabela Tagomori , 2022. "Inter-sectoral prioritization of climate technologies: insights from a Technology Needs Assessment for mitigation in Brazil," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(7), pages 1-39, October.
    12. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    13. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    14. de Queiroz, Anderson Rodrigo, 2016. "Stochastic hydro-thermal scheduling optimization: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 382-395.
    15. Van Uffelen, N. & Taebi, B. & Pesch, Udo, 2024. "Revisiting the energy justice framework: Doing justice to normative uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    16. Lee, Jinkyu & Bae, Sanghyeon & Kim, Woo Chang & Lee, Yongjae, 2023. "Value function gradient learning for large-scale multistage stochastic programming problems," European Journal of Operational Research, Elsevier, vol. 308(1), pages 321-335.
    17. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    18. Kat, Bora, 2023. "Clean energy transition in the Turkish power sector: A techno-economic analysis with a high-resolution power expansion model," Utilities Policy, Elsevier, vol. 82(C).
    19. Moradi-Sepahvand, Mojtaba & Amraee, Turaj, 2021. "Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation," Applied Energy, Elsevier, vol. 298(C).
    20. Ismael Guerrero & Carlos del Cañizo & Yuanjie Yu, 2024. "Yield Performance of Standard Multicrystalline, Monocrystalline, and Cast-Mono Modules in Outdoor Conditions," Energies, MDPI, vol. 17(18), pages 1-13, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • P18 - Political Economy and Comparative Economic Systems - - Capitalist Economies - - - Energy; Environment
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

    Statistics

    Access and download statistics

    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:eee:eneeco:v:146:y:2025:i:c:s014098832500221x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    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.