Australian Electricity Research Articles

Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market

AbstractIn this paper, we study the change of Australian electricity price dynamics that was observed before, during and after the two-year period in which a Carbon Pricing Mechanism was in force. We fit a two-states Markov Switching Model, representing a high- and a low-volatility state of the world. To avoid the interference due to periodic patterns, a deseasonalization process accounting for short- and long-term seasonality is carried out prior to the study of volatility. Estimation results highlight that, during the period when the carbon tax applies, the volatility level is lower for both the states of the world. Furthermore, the persistence in the low-volatility state is increased in the presence of the carbon tax. This conclusion is particularly relevant for macroeconomic and investment considerations because the increased uncertainty in electricity prices can significantly influence firms’ investment decisions and shape inflation expectations.

Scenarios on future electricity storage requirements in the Austrian electricity system with high shares of variable renewables

This paper presents three scenarios (policy, renewables and electrification and efficiency) for transitioning to a 100 % renewable electricity sector in Austria, based predominantly on wind and photovoltaics, alongside sector-specific electrification. Considering renewable expansion targets and three distinctive weather years from an overall system perspective, the core objective is to minimize variable costs of electricity storage and dispatchable power plants. The model developed determines their optimal dispatch for meeting the underlying electricity demand each hour. Within the scenarios for renewable expansion, a special focus lies on integrating short-duration (batteries), medium-duration (pumped storage hydro) and long-duration (hydrogen) energy storage. Our analysis reveals the significant impact of weather patterns on renewable electricity generation, particularly the differences between winter and summer generation quantities. This necessitates seasonal balancing and the mitigation of extremes like low wind power events, which require corresponding backup capacities. This contrast is particularly evident when comparing the years 2030–2050, wherein in the latter, certain dispatchable generators are only utilized in one of the three underlying weather years during extreme weather conditions. In our paper, we demonstrate how, especially for hydrogen production and storage, weather conditions influence production levels and the re-electrification demand. The results indicate the feasibility of achieving a fully decarbonized energy system in Austria through suitable policy measures and expanded renewable generation, with long-duration storage playing a crucial role in seasonal balance and compensating for the absence of fossil fuel generation. Strategic planning is essential to aligning the expansion of renewable energy generation with the necessary flexibility.

Green Hydrogen Driven by Wind and Solar—An Australian Case Study

The energy transition to wind and solar opens up opportunities for green hydrogen as wind and solar generation tend to bring electricity prices down to very low levels. We evaluate whether green hydrogen can integrate well with wind and solar PVs to improve the South Australian electricity grid. Green hydrogen can use membrane electrolysis plants during periods of surplus renewable energy. This hydrogen can then be electrified or used in industry. The green hydrogen system was analysed to understand the financial viability and technical impact of integrating green hydrogen. We also used system engineering techniques to understand the system holistically, including the technical, social, environmental, and economic impacts. The results show opportunities for the system to provide seasonal storage, grid firming, and reliability services. Financially, it would need changes to electricity rules to be viable, so at present, it would not be viable without subsidy.

Has benchmarking improved the performance of the Australian electricity distribution utilities? A meta-frontier model

This research evaluates the effects of performance benchmarking for public utilities using the Australian experience, providing empirical support for the significance of mechanism design in implementing revenue-cap regulation. We estimate the efficiency and productivity of Australian electricity distribution providers by utilizing the meta-frontier framework with panel data covering the period from 2007 to 2021. Our findings indicate that introducing benchmarking has facilitated technological progress and reduced efficiency gaps among companies. However, there is no statistically significant difference in the average efficiency between the periods before and after the reform, potentially due to strategic behavior exhibited by network service providers.

Robust capital cost optimization of generation and multitimescale storage requirements for a 100% renewable Australian electricity grid.

Transitioning from a fossil-fuel-dependent economy to one based on renewable energy requires significant investment and technological advancement. While wind and solar technologies provide lower cost electricity, enhanced energy storage and transmission infrastructure come at a cost for managing renewable intermittency. Energy storage systems vary in characteristics and costs, and future grids will incorporate multiple technologies, yet the optimal combination of storage technologies and the role of interconnectors in alleviating storage needs are not widely explored. This study focuses on optimal generation-storage capacity requirements to elucidate associated investments. We propose a multitimescale storage solution consisting of three storage categories and an interconnector between Australia's eastern and western grids. Subsequently, through an extensive sensitivity analysis, we investigate the impact of specific storage technologies and cost variations. Our findings demonstrate that the proposed interconnector offers a cost-effective solution, reducing generation and storage power capacity needs by 6 and 14%, respectively, resulting in 4% savings on overall investment costs. Moreover, the study's sensitivity analysis reveals that wind generation provides 50-70% of the energy demand for the least-cost solution. Despite storage inefficiencies, long-duration storage would need to be deployed to support power capacity for 2-4 days, representing 15-40% of peak demand, depending on future technology costs. Subsequently, achieving a fully renewable electricity sector in Australia requires a significant expansion of generation and storage infrastructure, with a 13-fold increase in storage power capacity and a 40-fold increase in storage energy capacity compared to existing levels.

Australian energy policy decisions in the wake of the 2022 energy crisis

Large economies are trying to shift their energy procurement from fossil dependence to renewable sources. In 2022 global commodity markets saw rapid price increases due to Russia's invasion of Ukraine and subsequent economic barriers on Russian commodity exports. Australia was not immune to these commodity price rises. Throughout the last decade Australia has become increasingly exposed to global energy prices through its commodity exports of LNG and thermal coal. Over this same decade Australia also experienced energy and climate policy discontinuity, and therefore policy uncertainty. These factors combined with a continued reliance on aging thermal plant resulted in a perfect storm of events which saw Australian electricity and gas prices reach all-time highs across the east-coast. The outcome resulted in long-term government intervention in markets to stabilise prices. This article will argue domestic factors helped fuel record high electricity and gas prices but have since subsided or are in the process of being mitigated. As a result, policymakers should reconsider how interaction occurs with electricity and gas markets to stabilise prices. Another decade of energy and climate policy discontinuity would not be a helpful development for the desired energy transition. A survey of market participants exemplifies concern that this outcome is materialising.

Understanding the lobbying actions taken by the Australian renewable energy industry

The fossil fuel industry has dominated Australia’s economy for decades and has built a strong alliance with governments in various spheres, benefiting from subsidies and influencing policymaking. With global pressures to reduce greenhouse gas emissions and the increasing penetration of renewable sources, the status quo of the Australian electricity market is being challenged. Within this study, we seek to understand how the emerging renewable industry is acting to accelerate the sustainable energy transition in Australia. Semi-structured interviews were carried out with professionals within the sector to provide evidence to investigate if there is an imbalance between their measures and those taken by the fossil fuel industry to participate in the formulation of policies. From the interviews, a qualitative analysis was conducted and key themes emerged and were grouped in three main dimensions: the “barriers” faced by RE in Australia, the “enablers” or opportunities identified by the participants to overcome the existing barriers, and “strategies” used to influence decision-making. Results suggest that, compared to the renewable energy companies, the dominant fossil fuel industry is highly resourced, companies have strong internal structures and are actively involved in lobbying. Despite this disparity, the renewable sector has put strategies in place to strengthen their voice and influence by forming interest groups, carefully selecting topics for discussion, and engaging consultants to provide advisory services to support policy engagement.

Distributional Regression for Data Analysis

The flexible modeling of an entire distribution as a function of covariates, known as distributional regression, has seen growing interest over the past decades in both the statistics and machine learning literature. This review outlines selected state-of-the-art statistical approaches to distributional regression, complemented with alternatives from machine learning. Topics covered include the similarities and differences between these approaches, extensions, properties and limitations, estimation procedures, and the availability of software. In view of the increasing complexity and availability of large-scale data, this review also discusses the scalability of traditional estimation methods, current trends, and open challenges. Illustrations are provided using data on childhood malnutrition in Nigeria and Australian electricity prices.

Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.

Energy flexibility of commercial buildings for demand response applications in Australia

Demand response (DR) is widely recognized as an important mechanism in the Australian electricity market, though large-scale uptake in commercial buildings is yet to occur, in part due to the difficulty of characterising the resource. This paper describes a bottom-up physics-based approach to characterise the DR potential of three types of commercial buildings (schools, offices, and data centres) under a global set-point temperature offset strategy. Representative models are calibrated with energy meter data and parametric analysis is used to assess sensitivity to different building, operating and system parameters. Parametric equations are provided for relative DR potentials as functions of temperature and time of day. School buildings were found to have the highest relative DR potential (∼40–45%) for ambient temperatures over 30 °C, followed by data centres (∼20–30%) and offices (∼20%). Location has the strongest relative influence for school and office buildings and equipment energy intensity for data centres. The Australia-wide combined DR potential for school, office and data centres is estimated to be between 551 and 647 MW. Office buildings have the highest aggregate potential at between 1.5 and 1.7 times that of school buildings and between 9 and 11 times that of data centres.

Energy efficiency and environmental impacts of horizontal small wind turbines in Austria

Small wind turbines (SWT) can contribute to the urgent need of transforming the energy system. Nevertheless, due to the heterogeneity of SWT technology, a high variety in environmental impacts of SWT exist. To evaluate whether the use of horizontal SWT can efficiently contribute to a renewable energy system, Life Cycle Assessments of two SWT, a commercial Schachner SW5 and a do-it yourself turbine in the Piggot Pig2F design, were conducted. The energy payback time was calculated using performance measurements from a rural and a suburban site in Austria. Results show that SWTs can reduce greenhouse gas emissions of the Austrian electricity production with calculated global warming potentials of 62gCO2eq/kWh (SW5) and 94gCO2eq/kWh (Pig2F) respectively. For both SWTs, the sub-system tower with foundation contributes about half to their overall global warming potential as well as to the cumulative energy demand. An improved shaping of SWT technology is therefore exemplified by both SWTs. The analysis shows that 19 out of 20 considered impact categories can be reduced by using secondary (recycled) or re-used steel, especially for the tower. The rural and suburban use of horizontal SWT can potentially contribute to reduce the environmental impacts of Austrian electricity production. Nevertheless, it becomes evident that a more sustainable shaping of SWT technology is highly recommendable.

Decarbonising Australian Electricity Markets: Regulatory Roadblocks and Transformative Opportunities for Achieving Net Zero

With a national emissions profile revealing a persistent role for fossil fuels in the energy and electricity fuel mixes, Australia's ability to realise its net zero emission ambitions is inherently linked to how the country manages and supports the decarbonisation of its energy sector to ensure the realisation of net zero goals by 2030, 2050, and beyond. Rapid growth in distributed energy resources is now challenging Australia’s electricity system and the regulatory framework, both of which were established on assumptions of centralized electricity supply from large generators to passive consumers. This article recognises that while regulatory frameworks – policies, laws, other instruments – can be critical enablers of desirable transitions, the current regulatory regime governing Australia’s energy markets has been a significant impediment to the energy sector decarbonisation and emission mitigation. Aspects of the regulatory framework that can encourage or hinder the development of future high DER markets and the decarbonisation transition across the national energy market are also explored.

Regional electricity market price forecasting based on an adaptive spatial–temporal convolutional network

The accurate prediction of electricity prices has great significance for the power system and the electricity market, regional electricity prices are difficult to predict due to congestion issues in regional transmission lines. A regional electricity price prediction framework is proposed based on an adaptive spatial–temporal convolutional network. The proposed framework is expected to better explore regional electricity prices’ spatial–temporal dynamic characteristics in the electricity spot market and improve the predictive accuracy of regional electricity prices. First, different areas of the electricity market are regarded as nodes. Then, each area’s historical electricity price data are used as the corresponding node’s characteristic information and constructed into a graph. Finally, a graph containing the spatial–temporal information on electricity prices is input to the adaptive spatial–temporal prediction framework to predict the regional electricity price. Operational data from the Australian electricity market are adopted, and the prediction results from the proposed adaptive spatial–temporal prediction framework are compared with those of existing methods. The numerical example results show that the predictive accuracy of the proposed framework is better than the existing baseline and similar methods. In the twelve-step forecast example in this paper, considering the spatial dependence of the spot electricity price can improve the forecast accuracy by at least 10.3% and up to 19.8%.

A Comparative Analysis of Electricity Companies’ Sustainability Reporting in Australia and India

This study compares the sustainability reporting efforts by electricity companies in developed (Australia) and emerging (India) economies. Using the GRI G4 sector-specific guidelines for electric utilities on the 2018/2019 annual reports, stand-alone sustainability reports, corporate social responsibility (CSR) reports, and corporate websites, the study finds that the compliance rates with the categories of sustainability reporting in the Indian electricity companies are far low compare to the Australian electricity companies. Overall, the disclosure is low in both countries as the highest compliance rate in Australia is 63.7% and the lowest compliance rate is 43.6%, while in the case of India, the highest compliance rate is 45.7% and the lowest compliance rate is 28.5%. Considering that electric utilities provide essential services to society and have a significant influence on sustainable development, such a low disclosure, particularly by Indian companies, could lead the efforts to gain stakeholders’ legitimacy and sustainable development to crumble.

Electricity Market Price Prediction Based on Quadratic Hybrid Decomposition and THPO Algorithm

Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling of microgrids. However, the stochastic and periodic nature of electricity price sequences often results in low accuracy in electricity price forecasting. To address this issue, this study proposes a quadratic hybrid decomposition method based on ensemble empirical modal decomposition (EEMD) and wavelet packet decomposition (WPD), along with a deep extreme learning machine (DELM) optimized by a THPO algorithm to enhance the accuracy of electricity price prediction. To overcome the problem of the optimization algorithm falling into local optima, an improved optimization algorithm strategy is proposed to enhance the optimization-seeking ability of HPO. The electricity price series is decomposed into a series of components using EEMD decomposition and WPD decomposition, and the DELM model optimized by the THPO algorithm is built for each component separately. The predicted values of all the series are then superimposed to obtain the final electricity price prediction. The proposed prediction model is evaluated using electricity price data from an Australian electricity market. The results demonstrate that the proposed improved algorithm strategy significantly improves the convergence performance of the algorithm, and the proposed prediction model effectively enhances the accuracy and stability of electricity price prediction, as compared to several other prediction models.

Modeling Multi-horizon Electricity Demand Forecasts in Australia: A Term Structure Approach

The Australian Electricity Market Operator generates one-day ahead electricity demand forecasts for the National Electricity Market in Australia and updates these forecasts over time until the time of dispatch. Despite the fact that these forecasts play a crucial role in the decision-making process of market participants, little attention has been paid to their evaluation and interpretation. Using half-hourly data from 2011 to 2015 for New South Wales and Queensland, it is shown that the official half-hourly demand forecasts do not satisfy the econometric properties required of rational forecasts. Instead there is a relationship between forecasts and forecast horizon similar to a term structure model of interest rates. To study the term structure of demand forecasts, a factor analysis that uses a small set of latent factors to explain the common variation among multiple observables is implemented. A three-factor model is identified with the factors admitting interpretation as the level, slope and curvature of the term structure of forecasts. The validity of the model is reinforced by assessing the economic value of demand forecasts. It is demonstrated that simple adjustments to long-horizon electricity demand forecasts based on the three estimated factors can enhance the informational content of the official forecasts.

Modelling Australian electricity prices using indicator saturation

In our analysis of electricity price series from Australia's National Electricity Market (NEM), we employ the indicator saturation (IS) approach to simultaneously model the stylised facts of electricity prices, including extreme spikes, seasonality, level-shifts, and autocorrelation. The standard modelling methods described in the literature tend to use regime-switching models to cope with these characteristics, but these models cannot fully reflect the stylised facts of interest. Using a range of model-evaluation tools, our analysis finds that the IS method outperforms the regime-switching models in various settings. In addition to the statistical superiority of this approach, we detail the relevance of our findings to policymaking in the NEM and provide recommendations for the development of the electricity markets in Australia.

Assessing the impact of battery storage on Australian electricity markets

This paper empirically examines the impacts of grid-scale battery storage facilities on the frequency control ancillary services (FCAS) market that is used by energy market operators to maintain the frequency of the system within the normal operating band. Using a staggered introduction of grid-scale batteries in two Australian states, our difference-in-differences analysis shows that grid-scale batteries can significantly lower overall FCAS costs. We further show that the reduced FCAS costs are accentuated by the battery storage capacity, and that their impacts are most pronounced in reducing the costs of short-term FCAS markets (such as the regulation and 6-second services). These FCAS markets typically displace more expensive fossil-fuel participation. Given the large scale of both battery and renewable adoption in Australia, our results have policy implications for global energy markets.

A Day-Ahead Short-Term Load Forecasting Using M5P Machine Learning Algorithm along with Elitist Genetic Algorithm (EGA) and Random Forest-Based Hybrid Feature Selection

A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS approach consistently outperforms forecasters with larger feature sets.

Long Duration Energy Storage Requirement for Australian Electricity Grid with 100% Renewable Generation

Long Duration Energy Storage Requirement for Australian Electricity Grid with 100% Renewable Generation