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Wind Generation and the Dynamics of Electricity Prices in Australia

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Abstract

Australia's National Electricity Market (NEM) is experiencing one of the world's fastest and marked transitions toward variable renewable energy generation. This transformation poses challenges to system security and reliability and has triggered increased variability and uncertainty in electricity prices. By employing an exponential generalized autoregressive conditional heteroskedasticity (eGARCH) model, we gauge the effects of wind power generation on the dynamics of electricity prices in the NEM. We find that a 1 GWh increase in wind generation decreases daily prices up to 1.3 AUD/MWh and typically increases price volatility up to 2%. Beyond consumption and gas prices, hydro generation also contributes to an increase in electricity prices and their volatility. The cross-border interconnectors play a significant role in determining price levels and volatility dynamics. This underscores the important role of strategic provisions and investment in the connectivity within the NEM to ensure the reliable and effective delivery of renewable energy generation. Regulatory interventions, such as the carbon pricing mechanism and nationwide lockdown restrictions due to COVID-19 pandemic, also had a measurable impact on electricity price dynamics.

Suggested Citation

  • Muthe Mathias Mwampashi & Christina Sklibosios Nikitopoulos & Otto Konstandatos & Alan Rai, 2020. "Wind Generation and the Dynamics of Electricity Prices in Australia," Research Paper Series 416, Quantitative Finance Research Centre, University of Technology, Sydney.
    • Handle: RePEc:uts:rpaper:416
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    Cited by:

    1. De Blauwe, Jilles & Deissenroth-Uhrig, Marc & Mantke, Henrik & Keles, Dogan, 2025. "Cross-border effects on electricity spot prices - a meta-study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    2. Pham, Son Duy & Do, Hung Xuan & Nepal, Rabindra & Jamasb, Tooraj, 2025. "Tail risk connectedness in the Australian National Electricity Markets: The impact of rare events," Energy Economics, Elsevier, vol. 141(C).
    3. Guo, Kun & Liu, Yu & Cao, Shanwei & Zhai, Xiangyang & Ji, Qiang, 2025. "Can climate factors improve the forecasting of electricity price volatility? Evidence from Australia," Energy, Elsevier, vol. 315(C).
    4. Petersen, Claire & Reguant, Mar & Segura, Lola, 2024. "Measuring the impact of wind power and intermittency," Energy Economics, Elsevier, vol. 129(C).
    5. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan, 2024. "From 30- to 5-minute settlement rule in the NEM: An early evaluation," Energy Policy, Elsevier, vol. 194(C).
    6. Maticka, Martin J. & Mahmoud, Thair S., 2025. "Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems," Renewable Energy, Elsevier, vol. 239(C).
    7. Sirin, Selahattin Murat & Camadan, Ercument & Erten, Ibrahim Etem & Zhang, Alex Hongliang, 2023. "Market failure or politics? Understanding the motives behind regulatory actions to address surging electricity prices," Energy Policy, Elsevier, vol. 180(C).
    8. Hosius, Emil & Seebaß, Johann V. & Wacker, Benjamin & Schlüter, Jan Chr., 2023. "The impact of offshore wind energy on Northern European wholesale electricity prices," Applied Energy, Elsevier, vol. 341(C).
    9. Olukunle O. Owolabi & Toryn L. J. Schafer & Georgia E. Smits & Sanhita Sengupta & Sean E. Ryan & Lan Wang & David S. Matteson & Mila Getmansky Sherman & Deborah A. Sunter, 2021. "Role of Variable Renewable Energy Penetration on Electricity Price and its Volatility Across Independent System Operators in the United States," Papers 2112.11338, arXiv.org, revised Nov 2022.
    10. Flottmann, Jonty & Simshauser, Paul & Wild, Phillip & Todorova, Neda, 2025. "The forward market dilemma in energy-only electricity markets," Energy Economics, Elsevier, vol. 148(C).
    11. Rai, Alan & Nunn, Oliver, 2020. "On the impact of increasing penetration of variable renewables on electricity spot price extremes in Australia," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 67-86.
    12. Zorana Zoran Stanković & Milena Nebojsa Rajic & Zorana Božić & Peđa Milosavljević & Ancuța Păcurar & Cristina Borzan & Răzvan Păcurar & Emilia Sabău, 2024. "The Volatility Dynamics of Prices in the European Power Markets during the COVID-19 Pandemic Period," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    13. Alfeus, Mesias & Mwampashi, Muthe M. & Nikitopoulos, Christina S. & Overbeck, Ludger, 2025. "Stochastic modelling and forecasting of wind capacity utilization with applications to risk management: The Australian case," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
    14. Andr s Oviedo-G mez & Sandra Milena Londo o-Hern ndez & Diego Fernando Manotas-Duque, 2023. "Directional Spillover of Fossil Fuels Prices on a Hydrothermal Power Generation Market," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 85-90, January.
    15. Mwampashi, Muthe Mathias & Nikitopoulos, Christina Sklibosios & Rai, Alan & Konstandatos, Otto, 2022. "Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies," Energy Economics, Elsevier, vol. 115(C).
    16. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    17. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    18. Shao, Zhen & Yang, Yudie & Zheng, Qingru & Zhou, Kaile & Liu, Chen & Yang, Shanlin, 2022. "A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis," Applied Energy, Elsevier, vol. 327(C).
    19. Davis, Dominic & Brear, Michael J., 2024. "Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets," Energy Economics, Elsevier, vol. 130(C).

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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