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Following the style of analysis of Fagan and Webber’s Global Restructuring, this paper examines the marketisation, privatisation and globalisation of Australia’s electricity generation and transmission sectors. This paper shows how, in Australia’s spatially dispersed and politically fragmented space-economy, marketisation and privatisation policies tend to produce oligopolistic industries with the capacity to thwart market-based regulation. A case study of the vertically integrated and diversified firm AGL reveals the changing nature of globalisation, the shifting relationships between states and firms, and the limits of marketisation policies in small spatially dispersed economies.* [ABSTRACT FROM AUTHOR]

In Australia and many other countries, distribution network service providers (DNSPs) have an obligation to their customers to provide electrical power that is reliable and of high quality. Failure to do so may have significant implications ranging from financial penalties theoretically through to the loss of a license to distribute electricity. In order to ensure the reliability and quality of supply are met, DNSPs engage in monitoring and reporting practice. This paper provides an overview of a large long-running power-quality monitoring project that has involved most of Australia's DNSPs at one time or another. This paper describes the challenges associated with conducting the project as well as some of the important outcomes and lessons learned. A number of novel reporting techniques that have been developed as part of the monitoring project are also presented. A discussion about large-volume data management, and issues related to reporting requirements in future distribution networks is included. [ABSTRACT FROM PUBLISHER]

Proponents of low-carbon transformation face an uphill battle to reconfigure incumbent energy systems against powerful interests protecting the status quo. Australia's electricity sector is an example of such a system, wedded to fossil fuels and backed by much of the country's political establishment. This paper addresses the potential for low-carbon transformation in Australia's electricity sector. It explores the complex and uncertain dynamics shaping the sector and outlines how these can be understood through the lens of strategic agency – with an eye for opportunities to drive a shift to renewables. It reviews the dynamics of change and re-stability shaping Australia's electricity sector between 2006 and 2015 and applies a resource-based view of transformative agency to analyse these dynamics. Results show the sector suffers multiple stressors and emphasise disruptive changes ‘in the pipeline’. Critically, many of these transformative dynamics stem from factors outside the influence of the electricity sector and its supporters. While results do not point to a clear trajectory or outcome of transformation, they indicate the uptake of small-scale solar photovoltaic (PV) systems by households and small business will play a defining role in the sector's future configuration. Furthermore, the exploration of transformative dynamics affecting the electricity sector through a resource-based lens shows that many opportunities exist for strategic agents to intervene in support of a disruptive shift to renewables. The paper suggests a range of mechanisms that agents could use to undermine the strength of fossil energy in the electricity sector and encourage a shift to renewables. [ABSTRACT FROM AUTHOR]

The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting. [ABSTRACT FROM AUTHOR]

Injustice is said to be the cornerstone of collective action, but why is it so important, and how does the way in which it is framed for mobilisation affect the outcomes? This paper compares two lengthy disputes in the Australian electricity industry which demonstrate that a sense of injustice and a history of successful industrial action do not guarantee that a dispute will be resolved to employees’ satisfaction when the wider context is unfavourable. Although leaders of both disputes expressed confidence in success predicated on the outcomes of previous industrial activity, there were specific factors within the industrial, economic and political context which provoked determined employer and government counter mobilisation leading to unsuccessful outcomes for the workers in dispute. The reasons for the poor outcomes are discussed within the context of the framing of the injustice by leaders and the effect of the response of a determined government. [ABSTRACT FROM AUTHOR]

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price, managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tickbox interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of a carbon price on wholesale electricity prices and carbon-pass-through rates in the states comprising the Australian National Electricity Market (NEM). The methodology utilize an agent-based model, which contains many features salient to the NEM including intra-state and inter-state transmission branches, regional location of generators and load centres and accommodation of unit commitment features. The model uses a Direct Current Optimal Power Flow (DC OPF) algorithm to determine optimal dispatch of generation plant, power flows on transmission branches and wholesale prices. The results include sensitivity analysis of carbon prices on wholesale prices and carbon pass-through rates for different states within the NEM. [ABSTRACT FROM AUTHOR]