Australia Energy Market
  • Research on the dynamics of the Australian energy market

Research on the dynamics of the Australian energy market

Regional, Temporal, and Demand Dynamics Shaping the Market

Author

Yao Jin

Australian energy markets exhibit fascinating and complex patterns influenced by time, region, demand and import/export trade. Analysis of these dynamics, based on data from 2018 to 2019, to uncover the patterns can provide important insights for policymakers, businesses and consumers to optimize energy use and manage costs.

Temporal Volatility in Energy Prices

Energy prices across Australian states are subject to temporal fluctuations.

Monthly trends reveal seasonal peaks in energy costs, for example NSW and VIC experience significant price spikes in January and July, likely due to seasonal heating and cooling demands. Meanwhile, QLD sees a notable peak in December.

Weekly patterns further highlight price variations, with weekdays generally exhibiting higher prices than weekends. These temporal variations underscore the need for strategic planning in energy consumption.

Regional Disparities in Pricing

Energy costs are not uniform across states.

VIC consistently records the highest average energy prices, while QLD boasts the lowest.

This disparity reflects regional factors such as infrastructure costs, local energy policies, and supply chain dynamics. Such insights emphasize the importance of tailored regional strategies to manage energy affordability and efficiency.

Price-Demand Relationships

Examining the relationship between price and demand reveals unique regional patterns.

For instance, NSW shows a positive correlation, where higher prices align with higher demand, likely reflecting economic activities.

In contrast, TAS presents a weaker correlation, suggesting other factors, like renewable energy contributions, play a more significant role.

Demand Variations Across Time and Regions

Energy demand patterns differ significantly between states and seasons.

NSW and QLD account for the bulk of total energy demand, with NSW peaking in winter and QLD maintaining steadier demand year-round.

Such variability highlights the importance of analysing state-specific trends to ensure effective energy distribution and storage strategies.

Net Export and Trade Balances

The net export of energy paints a diverse picture of trade balances among states.

NSW and VIC have relatively symmetrical distributions centered around zero, suggesting balanced trade. In contrast, QLD and SA show a skew towards negative net exports, indicating a tendency towards higher imports than exports.

TAS has a bimodal distribution, indicating variability in trade balances with peaks at both negative and positive net export values.

The relationship between demand and net exports further underscores regional differences, with NSW and VIC exhibiting a negative correlation, where higher demand leads to reduced net exports, likely due to increased local consumption.

Implications of this research in the long term:

The intricacies of price, region, demand and imports/exports in Australia’s energy markets Identifying these trends can give stakeholders a powerful aid to policy and business strategy.

For example, energy storage and distribution can be optimized to meet different demand patterns. Consider regional differences and develop targeted pricing models. Invest in infrastructure and renewable resources to stabilize prices and improve sustainability.

This analysis paves the way for a resilient and efficient energy future, balancing economic, environmental and social needs across Australia.

Translated with DeepL.com (free version)

Citation

  • Wickham, H., Averick, M., Bryan, J., Chang, W., D’Agostino McGowan, L., François, R., … & Woo, K. (2019). tidyverse: Easily install and load ‘tidyverse’ packages. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686

  • Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://doi.org/10.1007/978-3-319-24277-4

  • Müller, K. (2020). here: A simpler way to find your files. (R package version 1.0.1). https://CRAN.R-project.org/package=here

  • Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2023). dplyr: A grammar of data manipulation. (R package version 1.1.4). https://CRAN.R-project.org/package=dplyr

  • Arnold, J. B. (2024). ggthemes: Extra themes, scales and geoms for ‘ggplot2’. (R package version 5.1.0). https://CRAN.R-project.org/package=ggthemes

  • Pedersen T, Robinson D (2024). _gganimate: A Grammar of Animated Graphics_. (R package version 1.0.9). https://CRAN.R-project.org/package=gganimate

  • Ooms J, Kornel Lesiński, Authors of the dependency Rust crates (2024). _gifski: Highest Quality GIF Encoder_. (R package version 1.32.0-1). https://CRAN.R-project.org/package=gifski

  • Urbanek S (2022). _png: Read and write PNG images_. (R package version 0.1-8). https://CRAN.R-project.org/package=png

  • Data: energydata.csv