Australian Electricity Market Research Articles

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.

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.

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%.

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.

Multivariable short-term electricity price forecasting using artificial intelligence and multi-input multi-output scheme

In deregulated power markets, electricity price forecasting is the most valuable tool. However, with inherent electricity price characteristics, such as high frequency and volatility, constructing an electricity price forecasting model remains a difficult task for decision-makers and scholars. Accurate electricity price point forecasting (PF) can guide market participants in maximizing benefits. Moreover, appropriate interval forecasting (IF) can provide further information based on PF. Accordingly, a novel electricity price multi-bi-forecasting system using multivariable and multi-input multi-output structures is formulated. The system has three stages: data preprocessing, combination forecasting, and performance evaluation. The data preprocessing stage removes and smooths the high-frequency electricity price and load data. Because the load series has a more regular cycle and smoother fluctuation than the electricity price series, two variables, electricity price and load, are employed for forecasting using a multivariable data arrangement rolling forecast mechanism. In addition, a multi-input multi-output structure is utilized by three member models (back propagation, bidirectional long short-term memory, and gated recurrent unit) to derive PF and IF results concurrently. The final results are obtained using a combined strategy based on the multi-objective salp swarm algorithm. Finally, three experiments are conducted in the Australian electricity market to evaluate the proposed system quantitatively. Results show that the designed system has superior ability in forecasting electricity price and practical application in real situations.

Multi-Market Bidding Behavior Analysis of Energy Storage System Based on Inverse Reinforcement Learning

The bidding behaviors of the energy storage systems (ESS) are complicated due to time coupling and market coupling limited by their capacity states. The existing research is mainly based on optimization models and reinforcement learning (RL) models, which are idealized with analytical objective functions, rational decisions, and virtual historical data. This leads to mismatches between theoretical results and actual bidding behaviors, and the obtained results cannot be generalized to a real-world market. In recent years, the disclosure of market data has enabled data-driven analysis, while the complexity of actual data makes the analysis process challenging and calls for more suitable methods. On the basis of our investigation of ESS bidding behaviors and market data, we propose a novel inverse RL (IRL)-based framework to identify the bidding decision objective function of ESS in coupled multi-market through their historical bidding records and operation status. The identification results can be used as the model parameters to help RL-based simulation models available for a real-world market. Based on the proposed framework, we conduct an empirical analysis on the real-world data of a battery ESS in the Australian electricity market. The results show how the ESS balances its objective among different markets and maintains its state of charge (SOC) to gain more profits. This framework can help us model ESS behaviors by relying on historical data to better understand the ESS bidding decision-making mechanism, which will facilitate market equilibrium analysis and mechanism design.

Deep Reinforcement Learning-Based Trading Strategy for Load Aggregators on Price-Responsive Demand.

With the development of the Internet of things and smart grid technologies, modern electricity markets seamlessly connect demand response to the spot market through price-responsive loads, in which the trading strategy of load aggregators plays a crucial role in profit capture. In this study, we propose a deep reinforcement learning-based strategy for purchasing and selling electricity based on real-time electricity prices and real-time demand data in the spot market, which maximizes the revenue of load aggregators. The deep deterministic policy gradient (DDPG) is applied through a bidirectional long- and short-term memory (BiLSTM) network to extract the market state features that are used to make trading decisions. The effectiveness of the method is validated using datasets from the New England electricity market and Australian electricity market by introducing a bidirectional LSTM structure into the actor-critic network structure to learn hidden states in partially observable Markov states through memory inference. Comparative experiments of the method show that the method can provide greater yield results.

Short-term Electricity Price Forecasting Based on Empirical Mode Decomposition and Deep Neural Network

Accurate short-term price forecasting is crucial for the power system and electricity market. This paper proposes a hybrid short-term electricity price forecasting model based on empirical mode decomposition (EMD) and deep neural network (DNN). Firstly, EMD is used to denoise the data set. Next, the reconstructed data are input into the DNN composed of a convolutional neural network (CNN) and long-short-term memory (LSTM) neural network to analyze the characteristics and output the prediction results. Finally, the superiority of this model is verified by comparing the electricity price data of the Australian electricity market with a single LSTM network, EMD-CNN model, and CNN-LSTM model.

Bidding strategy evolution analysis based on multi-task inverse reinforcement learning

Benefiting from the opening of electricity market data in recent years, the analysis of bidding behaviors based on actual data has gradually attracted increasing attention. The data-driven analysis methods could overcome the ideal assumption problem compared with traditional optimization-based methods. In this paper, we focus on the dynamic changes in bidding strategies and propose a multi-task inverse reinforcement learning-based analysis framework. It can identify several bidding objectives adopted by the participant in different time periods and label the adopted objective in each day, according to historical bidding records and market status. Moreover, analyzing the relationship between bidding objective functions and their influencing factors will enrich our understanding of the bidding mechanism and make the market operation simulation closer to reality. An empirical analysis conducted on Australian electricity market data demonstrates the feasibility and effectiveness of our framework. The results also show how a typical hydropower station changes its bidding strategies according to water storage.

Internet-of-thing based real-time electrical market monitoring system design

Electricity price forecasting is a good way of estimating the future electricity prices to a large extent in safe and peaceful conditions. Knowing an acceptable range of commodity prices in the future can positively reduce investment risk (for both producers and customers) and give more stability to the trading market. In this paper, the electricity price is forecasted using the autoregressive moving average method and the Generalized Autoregressive Conditional Heteroscedasticity model. These methods have been developed and applied to the Australian electricity market data, and in some case studies. The proposed learning model is capable of modeling the variance error in the highly correlated market data as well as dealing with the price of the electricity in real-time. The results are compared with the actual data that is collected with Internet of Things approach and show the convincing evidence of the model's validity and accuracy.

Deep Inverse Reinforcement Learning for Objective Function Identification in Bidding Models

Due to the deregulation of power systems worldwide, bidding behavior simulation research has gained prominence. One crucial element in these studies is accurately defining and modelling the individual reward function (or objective function). Considering the ubiquitous information barriers between market participants and researchers, the common way is to develop reward functions based on theoretical assumptions, which will inevitably cause deviations from the real world. However, since market data have gradually become transparent in recent years, especially data regarding historical bidding behaviors, it is feasible to introduce data-driven methods to identify the individual reward functions that are hidden in raw bidding data. Thus, this paper proposes a data-driven bidding objective function identification framework with three procedures. First, the bidding decision processes of participants are formulated as a standard Markov decision process. Second, a deep inverse reinforcement learning method that is based on maximum entropy is introduced to identify individual reward functions, whose high-dimensional nonlinearity could be saved in multilayer perceptions (MLPs). Third, a deep Q-network method is customized to simulate the individual bidding behaviors based on the obtained MLP-based objective functions. The effectiveness and feasibility of the proposed framework and methods are tested based on real market data from the Australian electricity market.

Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing

Real-time electricity pricing mechanisms are emerging as a key component of the smart grid. However, prior work has not fully addressed the challenges of multi-step prediction (Predicting multiple time steps into the future) that is accurate, robust and real-time. This paper proposes a novel Artificial Intelligence-based approach, Robust Intelligent Price Prediction in Real-time (RIPPR), that overcomes these challenges. RIPPR utilizes Variational Mode Decomposition (VMD) to transform the spot price data stream into sub-series that are optimized for robustness using the particle swarm optimization (PSO) algorithm. These sub-series are inputted to a Random Vector Functional Link neural network algorithm for real-time multi-step prediction. A mirror extension removal of VMD, including continuous and discrete spaces in the PSO, is a further novel contribution that improves the effectiveness of RIPPR. The superiority of the proposed RIPPR is demonstrated using three empirical studies of multi-step price prediction of the Australian electricity market.

Value of deterministic day-ahead forecasts of PV generation in PV + Storage operation for the Australian electricity market

During the last decade, numerous solar forecasting tools have been developed to predict the energy generation of photovoltaic (PV) farms. The quality of solar forecasts is assessed by comparing predictions with measured solar data. However, this methodology does not consider the added value of the forecasts for their applications. As a consequence, what value could be given to the improvement of forecasts considering this evaluation framework?To answer this question, this work compares the value of different operational solar forecasts for a specific application. The aim is to look for relationships between the economic value and the error metrics defined to evaluate the forecast quality.A new generation of large-scale PV plants integrates ESS. The aim is to add flexibility to the injection of the production into the grid and thus to maximize the profit by taking advantage of the possibilities offered by the electricity market, such as energy arbitrage. To optimize the operation of these specific ESS, forecasting of the solar production is of paramount importance. The study case considered in this work is a large-scale PV farm of several megawatts associated with Li-ion batteries in the Australian energy market context.For this specific case study, the results show that the metrics used to evaluate the forecast quality based on the mean absolute error (MAE) have an almost linear relationship with the economic gain brought by applying the forecast. More precisely, an improvement of 1% point in MAE results approximately in an increase of 2% points in economical gain.

The duck curve in a drying pond: The impact of rooftop PV on the Western Australian electricity market transition

Rooftop solar photovoltaics (PV) are significantly influencing the electricity market and system operability in Western Australia. Qualitative methods are used to determine likely impacts and solutions to associated technical and market challenges in this islanded electricity system. Solutions focus on flattening the load curve and addressing minimum system load issues, including via; tariff reform, new ancillary services, automation, storage and energy productivity; targeted markets to match energy supply to the new demand curve; together with enabling technologies such as; improved inverter functionality and control systems. A parsimonious model demonstrates the impacts of rooftop PV on the local mid-day wholesale energy prices.

Electricity price modelling with stochastic volatility and jumps: An empirical investigation

Over the past few years, the electricity derivatives market has experienced a substantial growth in the volume of trade and the diversity of available products. This has led to a rich data environment that requires more sophisticated and accurate modelling approaches for electricity spot prices. This paper deals with an analysis of continuous-time stochastic volatility jump-diffusion processes in the context of pricing of futures contracts written on electricity spots. We formulate a variety of models which aim to capture the most prominent characteristics and stylised facts of the electricity spot market including mean reversion, seasonality, extreme volatility, and spikes. The proposed modelling framework extends the existing models by incorporating mean reversion, stochastic volatility, and jumps in both the underlying spot price process and its volatility. The modelling parameters are estimated using the Markov Chain Monte Carlo (MCMC) technique for the Australian electricity market. We find that incorporating stochastic volatility and jumps in both the underlying electricity spot price and its volatility is absolutely essential to accurately fit the observed electricity spot prices. We derive futures prices in a semi-closed form and confirm flexibility of the proposed models by their ability to fit the observed spot and futures prices in the Australian electricity market.

Online Sequential Extreme Learning Machine Algorithm for Better Predispatch Electricity Price Forecasting Grids

The predispatch price forecast plays a key element in the electricity market. However, such a forecast usually depends on the traditional offline batch-learning technologies, which cannot respond in time to the unexpected changes in the local power system environment. Further, the predispatch local price forecast is often affected by the dynamic price changes from the neighboring regions. This article proposes a novel online learning forecast approach to overcome the above issues to provide a better predispatch price forecast by using the online sequential extreme learning machine (OS-ELM) algorithm. The article proposes a novel data structure in the form of a 2-D orthogonal list and two corresponding OS-ELM modules. One module provides the rolling day-ahead price prediction and prediction intervals using the day-by-day online training update, while the other provides the rolling 30-min prediction using the 2-h-by-2-h online training update. The proposed approach can continuously perceive any unexpected events and any price fluctuations from the neighboring regions in the nonlinear patterns. The proposed approach is validated using simulation studies based on the data from the Australian electricity market, and the simulation results show that the proposed approach can help in improving the forecast accuracy, especially when unexpected changes occur both locally and in the neighboring area.

Impacts of LNG Export and Market Power on Australian Electricity Market Dynamics, 2016–2019

Purpose of ReviewSupply-side shocks in concentrated energy markets provide opportunity for exercise of market power, especially in markets undergoing transition due to imperatives such as decarbonisation. In Australia, the recent linkage of international gas markets with the electricity market provides a useful example to review these dynamics.Recent FindingsUsing the intersections between gas and electricity markets in Australia, we explore how (1) supply constraints associated with commencement of Liquefied Natural Gas (LNG) exports, and (2) market concentration due to closure of old coal plant, contributed to effect a doubling of wholesale electricity prices. The recent Australian experience highlights the disproportionate impact of gas generation on electricity prices due to its price-setting role. The price impact of the tightened gas market on electricity prices has been significantly exacerbated through the exercise of market power, especially via the practise of ‘shadow pricing’. In the Australian case, the potential adverse intersectional impacts of LNG exports could have been substantially adverted with enforced domestic reservation of about 6% of LNG export volumes.SummaryCompetition issues and gaming opportunities, which have accompanied the increased market concentration that accompanied the withdrawal of capacity, have exacerbated the price impacts of the supply-side shock stemming from the evolving gas market.

Trials and tribulations of market responses to climate change: Insight through the transformation of the Australian electricity market

We analyse the energy transition from coal to renewable. Our research contributes to the literature on transitions and grand challenges. Mitigating the effects of dangerous human-induced climate change requires Australia to adopt a ‘carbon budget’ of no more than 10 Gt between 2015 and 2050. To achieve this, the Australian electricity sector must reduce greenhouse gas emissions to at least net zero emissions by 2050. Australia’s strategic response to climate change will have a significant influence on greenhouse gas emissions across Asia and the Pacific. The transition to renewables has proved difficult. The Liddell case study, involving closure of an ageing coal-fired power station, shows how the transition was impeded by institutional decisions. While firm-level actors recognised opportunities, regulators resisted the transition. Our research illustrates that transitions for grand challenges may require a relational stakeholder review, beyond the concept of short-term win–wins. JEL Classification: L02, D02, Q05

Maintaining the security of supply in the Australian national electricity Market with higher levels of renewable energy

This paper examines security of supply concerns in an Australian electricity market under transition and considers potential policy pathways to address them. It details the current state of Australia’s electricity industry, considers the extent to which supply security in the electricity sector may be considered a public good, and examines the features likely to impact on the efficacy of current and potential future market designs.