Electricity Market In Australia Research Articles

Taking a Portfolio approach to wind and solar deployment: The case of the National Electricity Market in Australia

We formulate a new, computationally inexpensive framework based on Mean–Variance Portfolio Theory to determine the optimal allocation of wind and solar capacity, which can leverage the complementarity between intermittent solar and wind generation to meet electricity demand at the lowest cost, with gas acting as reserve generation in times of insufficient wind and solar output, and the variance of the mismatch between demand and the output of wind and solar is taken as the risk indicator. We then apply this framework to the context of the National Electricity Market in Australia. Our result reveals that, assuming sufficient network capacity, the estimated lowest generation cost achievable is $45.26/MWh, where the expected percentage of unserved energy (USE) is 0.0044%. For a risk-averse planner, a level of USE less than 0.0001% may be achieved at the expense of almost doubling the cost to $88.23/MWh. Under the current network capacity constraints, we estimate these costs to increase to $53.09/MWh and $89.90/MWh respectively, while the USE could rise to 0.017%. Moreover, network expansions in Queensland are found to be the most beneficial. On the other hand, a 4-hour battery storage system with power capacity equivalent to 20% of the peak load could help reduce the cost by up to 6.8% alongside further decrease in the USE. Our results are also shown to differ drastically from the conventional, output-based approach. Since the two approaches address two different questions depending on whether demand is flexible, demand flexibility is also an important factor.

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.

Integrated Policies to Reduce Australia’s Electricity Sector Greenhouse Gas Emissions to Net Zero by 2050

Recent events within the Australian National Electricity Market have demonstrated that the system of an energy-only market (a market that only compensates power that has been produced) is no longer fit for purpose. The rate of change in installed capacity and generation requires better planning to ensure reliability is maintained at the lowest total system cost during the transition to net zero. Australian National Electricity Market participants will need sufficient incentives and confidence to invest in new capacity. This paper assesses a “no constraints” scenario and recommends a range of policy and market mechanisms that could be utilized to achieve a net zero National Electricity Market in Australia by 2050. This paper adopts the perspective of total system cost, which allows multiple factors relating to decision-making to be incorporated. In the absence of a carbon price, this paper seeks to put forward technology-based policy and market mechanisms to incentivise the changes required. The “Modelling Energy and Grid Services” model used in this study has shown that this “no constraints” future grid will need to contain approximately 100 GW of variable renewable energy, almost 20 GW of firm, low-emissions generation, such as carbon capture, utilisation and storage, bioenergy with carbon capture and storage, hydroelectric power, or nuclear power. It will also require more than 10 GW of storage, including pumped hydro energy storage and other energy storage technologies, and over 30 GW of firm, dispatchable peaking plants, including thermal power generation.

The status of and opportunities for utility-scale battery storage in Australia: A regulatory and market perspective

Australia's electricity market is rapidly adding renewable energy generation. Utility-scale batteries could have a major role in facilitating these transitions; however, their deployment is still largely state-subsidized. We summarize the current and future roles for batteries from a legal-economic perspective in the context of Australia's electricity market framework. We find that the future of batteries in Australia is not only a function of the large-scale deployment of renewables, their cost development and the comparative future cost of competing gas turbines but also of national electricity market and state policy reforms focusing on reliability.

Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

A Multi-Feature Based Approach Incorporating Variable Thresholds for Detecting Price Spikes in the National Electricity Market of Australia

Detecting electricity price spikes is crucial as it helps market participants to gain confidence and formulate appropriate strategy to maximize their benefits. In this paper, a multi-feature based approach with the incorporation of variable thresholds is developed to detect electricity price spikes in the national electricity market of Australia. The variable thresholds, which are determined using a weighted sliding window average and an adjusted standard deviation, help to segregate spikes from normal price variations. Also, significant features are extracted from the market after thoroughly analyzing the underlying causes resulting into the price spikes. These features are employed as inputs to a support vector machine to classify electricity prices as spikes or non-spikes. A case study is conducted using a dataset acquired from the state of New South Wales, Australia. The results show that the proposed method can successfully detect the price spikes with high accuracy and confidence.

Issues associated with the possible contribution of battery energy storage in ensuring a stable electricity system

The purpose of this paper is to provide a consideration of the role of battery energy storage in enhancing electricity grid stability at a time of increased use of non-hydro renewable generation sources, focusing on the Australian case. Besides the creation of a strategic reserve one measure that might reduce this instability that have being increasingly attracted much attention by both the operators of power systems and electricity distribution networks is the use of battery energy storage. Together, the multitude of regulatory and advisory bodies operating in and around the National Electricity Market of Australia (NEM), and the diverse and sometimes contrary opinions as to the best underlying foundational architecture for the NEM, overlaid by ongoing disagreement at the government policy level, point to continuing uncertainty and difficulties for an Australian electricity system in a state of transition.

The centre cannot (always) hold: Examining pathways towards energy system de-centralisation

‘Energy decentralisation’ means many things to many people. Among the confusion of definitions and practices that may be characterised as decentralisation, three broad causal narratives are commonly (implicitly or explicitly) invoked. These narratives imply that the process of decentralisation: i) will result in appropriate changes to rules and institutions, ii) will be more democratic and iii) is directly and causally linked to energy system decarbonisation. The principal aim of this paper is to critically examine these narratives. By conceptualising energy decentralisation as a distinct class of socio-technical transition pathway, we present a comparative analysis of energy decentralisation in Cornwall, South West UK, the French island of Ushant and the National Electricity Market in Australia. We show that, while energy decentralisation is often strongly correlated with institutional change, increasing citizen agency in the energy system, and enhanced environmental performance, these trends cannot be assumed as given. Indeed, some decentralisation pathways may entrench incumbent actors' interests or block rapid decarbonisation. In particular, we show how institutional context is a key determinant of the link between energy decentralisation and normative goals such as democratisation and decarbonisation. While institutional theory suggests that changes in rules and institutions are often incremental and path-dependent, the dense legal and regulatory arrangements that develop around the electricity sector seem particularly resistant to adaptive change. Consequently, policymakers seeking to pursue normative goals such as democratisation or decarbonisation through energy decentralisation need to look beyond technology towards the rules, norms and laws that constitute the energy governance system.

Review of system strength and inertia requirements for the national electricity market of Australia

Synchronous generators (SGs) are still making major contributions to the re-stabilization of a power system following voltage/frequency disturbances, attributed to their inherent capability of providing system strength and inertia. However, SGs powered by fossil fuels are operating to a lesser extent and scheduled for decommissioning in the National Electricity Market (NEM) of Australia due to the accelerating increase of low bidding priced asynchronous generation of wind and solar, which leads to the reduction and even in some cases, a shortage of system strength and inertia. This paper comprehensively reviews the requirements of system strength and inertia in the NEM from an operational security perspective. Australia is the first country that established the regulation rules of system strength and inertia to accommodate issues of an emerging high penetration level of non-synchronous renewable generation.

An Economic Analysis of the National Electricity Market

This paper is a technical report commissioned by Energy Consumers Australia, a statutory advocacy institution, as to the state of the electricity market in Australia. Using standard tools of economics, it finds that the combination of market power, vertical integration and a poor regulatory framework deliver a poor outcome in terms of social welfare, as suggested by steep and sustained price increases since 2008. The report advocates for more research, as well as for some already obvious remedies.

Modeling and Analysis of Lithium Battery Operations in Spot and Frequency Regulation Service Markets in Australia Electricity Market

Renewable share in the global total energy mix is predicted to grow, and this leads to an increase in the required capacity for frequency regulation. While an electric vehicle (EV) is gaining more popularity, a collection of retired EV battery packs provides an economic option for meeting the additional frequency regulation needs. In this paper, a battery market operation model is proposed to maximize financial return, and a battery operation cost estimator is built to evaluate the potential impacts of market operations on the battery lifespan. Specifically, the model is designed for retired EV lithium batteries under the Australian national electricity market framework. It predicts the automatic-generation-control energy due to the frequency regulation service offers. Battery cycle life cost and battery capacity degradation are considered in the model. It can be used to determine multimarket offers based on the expected profit. Nonetheless, the model can be generalized for other electricity market frameworks and battery types.

Forecasting quantiles of day-ahead electricity load

Accurate load forecasting plays a crucial role in the decision making process of many market participants, but probably is most important for the dispatch planning of an electricity market operator. Despite the competitive forecast accuracy achieved by existing point forecast models, point forecasts can only provide limited information relating to the expected level of future load. To account for the uncertainty of future load, and provide a more complete picture of the future load conditions for dispatch planning purposes, quantile forecasts can be useful. This paper proposes a computationally efficient approach to forecasting the quantiles of electricity load, which is then applied to forecasting in the National Electricity Market of Australia. The proposed model performs competitively in comparison with one industry standard and two recently proposed quantile forecasting methods. One of the main advantages of the proposed approach is the ease with the number of covariates can be expanded. This is a particularly important feature in the context of load forecasting where large numbers of important drivers are usually necessary to provide accurate load forecasts.

Pareto Optimal Prediction Intervals of Electricity Price

This letter proposes a novel Pareto optimal prediction interval construction approach for electricity price combing extreme learning machine and non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal prediction intervals are produced with respect to the formulated two objectives reliability and sharpness. The effectiveness of proposed approach has been verified through the numerical studies on Australia electricity market data.

The Controllability of Real Things: Planning for Wind Integration

The authors employed a novel optimization framework coupled with an econometric model of wind to study market performance. One conclusion: in circumstances with high uncertainty in the market – as with high penetration of renewables – relying more on a real-time market, similar to the National Electricity Market in Australia, may be a better way to deal with this uncertainty because it uses updated and more accurate information about the wind variability.

Assessing the Transmission Expansion Cost With Distributed Generation: An Australian Case Study

Distributed generation (DG) is rapidly increasing its penetration level worldwide and is expected to play a more important role in providing power. An important benefit of DG is its ability to defer transmission investments. In this paper, a simulation model is implemented to conduct quantitative analysis on the effect of DG on transmission investment deferral. The transmission expansion model is formulated as a multi-objective optimization problem with comprehensive technical constraints, such as AC power flow and system reliability and/or security. The case study that was selected is the Queensland electricity market in Australia. Simulation results show that DG can reduce transmission investments significantly. This ability however is greatly influenced by a number of factors, such as the location of DG, the network topology, and power system technical constraints.

Green Electricity Markets? Constructed Realities, Prosumerism and Solar PV

Solar Photovoltaics (PV) on individual rooftops are a potentially revolutionary form of electricity generation. This paper critically analyses this potential in light of key debates on the impact of solar PV on the existing electricity market in Australia. These debates draw on electricity supply norms of reliability, accessibility and affordability together with a meta-norm of competition within liberalised markets. Certainly costs of this technology are declining rapidly yet the implications of solar PV on electricity go further. Further, neo-liberal reforms to electricity markets create opportunities for proponents to argue that solar PV can reduce the price of electricity to all, not just those with PV panels on their rooves. Not surprisingly these arguments have been subject to intense debate, with critics not only questioning these economic benefits of solar PV, but also appealing to social norms arguing that solar PV provides unreliable (intermittent) power that benefits the wealthy at the expense of the poor. Yet both sides of the argument may need to take greater account of new norms introduced into the market as PV transforms individual household orientation to the market from consumers to prosumers who both produce and consume electricity. A prosumerist sensibility together with technological advances suggests that solar PV has the potential to destabilise existing electricity markets, its associated norms and hence the interests of incumbent businesses.

A System Dynamics Conceptual Model on Retail Electricity Supply and Demand System to Minimize Retailer's Cost in Eastern Australia

In eastern Australia's electricity market, the retailers have to purchase a certain amount of power to fulfill their customers’ demand. When the purchased power through forward contracts exceeds the electricity demanded by customers it leads to retailer's loss. On the other hand, the demand is higher than the purchased power which urges the company to buy extra power from a peaking power generator at electricity market with volatile price. This research studies power supply and demand systems at electricity retail market. System Dynamics (SD) methodology is used to investigate the influence of weather and forward contract conditions on the fluctuation of energy supply and demand in order to minimize energy retailer's cost. A respective conceptual model has been built by using qualitative phase in SD approach which enables further development in the SD's quantitative phase.

A Price-Based Approach to Generation Investment Planning in Electricity Markets

A generation planning framework is developed for a competitive, energy-only electricity market. The framework developed combines decentralized merchant investment plans stemming from market signals and technological preferences, with centralized resource planning concepts to decide optimal time-ordered investment decisions. The investment decisions maximize the new entrants' net surpluses and minimize the consumer interruption cost, accounting for many uncertain variables and technical details. The optimal plan given by the main centralized framework represents the system planner's solution derived from the set of decentralized plans. An illustrative case study is given in the paper, applying the proposed framework to the Queensland region of the National Electricity Market of Australia.

The emergence of Australia's electricity market

The Australian energy market has developed in response to two sets of government decisions. The first was a policy of the Victorian Government to sell its state-owned electricity supply system and to do so in a way that introduced competition wherever this was possible. At the same time, the Commonwealth (federal) Government had determined on a course of introducing competitive provision of electricity, without any commitment to privatisation. The outcome has been a considerable increase in productivity and reliability accompanied by price reductions. Issues that remain include ensuring the provision of adequate transmission where this is regulated and competing with local regulation, the provision of which is market-determined. There is also a potentially unstable coexistence of private- and government-owned generation, with the latter appearing to be responsive to government policies that might jeopardise market-driven supply augmentations. Australia is also wrestling with responses to carbon emissions, which might create future investment uncertainties.

Quantifying the supply‐side benefits from forward contracting in wholesale electricity markets

AbstractThe assumption of expected profit‐maximizing bidding behavior in a multi‐unit, multi‐period auction with step‐function supply curves is used to estimate cost functions for electricity generation units and derive tests of expected profit‐maximizing behavior. Applying these techniques to data from the National Electricity Market in Australia reveals statistically significant evidence of output‐dependent marginal costs within and across half‐hours of the day, but no evidence against the hypothesis of expected profit‐maximizing behavior. These cost function estimates quantify the economic significance of output‐varying costs and how forward financial contract obligations impact the amount of these costs the generation unit owner incurs. This supplier's existing obligations imply average daily production costs that are 8% lower than the profit‐maximizing pattern of output with no forward contract obligations. Copyright © 2007 John Wiley & Sons, Ltd.