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Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand‐side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application‐oriented review of state‐of‐the‐art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre‐processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper‐parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained. [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]

• This paper presents a review on the state-of-the-art electric vehicle charging technologies i.e., fast, super-fast, and ultra-super-fast charging stations that are under development. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. • EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper has highlighted these issues and their impacts to power grids. • This paper investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. • The charging stations are modeled for three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and Chargepoint Express 20), with separate CC-CV charging modes of the DC fast chargers incorporated. To quantify the impacts, case studies are conducted on the simulation platform for two different scenarios. • The impact on the electric power grid is evaluated in terms of voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) experienced by the corresponding transformer connected to the charging stations. • The results have revealed that random and fluctuating EV fast-charging loads pose much more intense impacts on the power systems than the normal EV slow charging loads. The era of the electrified transportation system is fast approaching. Although the socioeconomic and environmental benefits of electric vehicles (EVs) have contributed to their large-scale utilization, it has also created a huge load demand on the existing power grids throughout the world. Moreover, fast, super-fast, and ultra-super-fast charging stations are under development, some of which are now in the markets. These have the potential to cause power quality issues such as charging transients, rapid voltage fluctuations, and harmonics in the power grids. Moreover, EVs can participate as mobile storage to provide vehicle-to-grid (V2G) support and ancillary services. There are still some barriers to the wide implementation of V2G systems. This paper addresses these issues and provides a review of the state-of-the-art EV technologies and their impacts on power grids. This paper also investigates the impacts of random and fluctuating EV fast-charging loads on the electric power grids, mainly considering the random connection of EVs to the power grids through DC fast-charging stations as the principal source of fluctuating EV loads. A practical electrical grid of Wollongong, New South Wales, Australia has been considered in this work to separately analyze the impacts of constant current (CC) and constant voltage (CV) charging modes upon the grid. Furthermore, design and modeling of three different commercial DC fast charger connections (CHAdeMO, SAE CCS, and ChargePoint Express 200), with separate CC-CV charging modes of the DC fast chargers have been incorporated. To quantify the impacts, two separate scenarios were examined using a simulation platform, with case studies conducted to determine the impacts on the power grid. The first scenario involved three fast charging stations, while the second scenario featured ten stations that were able to charge six and twenty electric vehicles respectively, with various load combinations considered. Each of these scenarios was analyzed under different conditions to evaluate their impact on the grid, using factors such as voltage drops, maximum power demand, current, and voltage total harmonic distortion (THD) for the transformer that was connected to the charging stations. The study results indicated that the power systems were affected more significantly by random and fluctuating EV fast-charging loads, compared to normal EV slow-charging loads. [ABSTRACT FROM AUTHOR]

South East Queensland's (SEQ's) centralised electricity system is under great pressure to adapt. Climate change is converging with socio-economic, demographic and technological changes to create a 'perfect storm' for the region's electricity system. Distribution networks are particularly affected, with these factors contributing to tremendous peak demand growth, about double the rate of growth in average demand in recent years. This paper reviews how Australia's electricity system is adapting to multiple drivers of peak electricity demand. We use socio-technical transitions theory to understand the temporal interconnected social and technical dimensions of adaptation in this setting. Specifically, we present an historical narrative of the emergence of centralisation in Australia and outline the peak demand problem in SEQ and review adaptation options from the international literature. We also analyse the interactions between key social groups and their adaptation responses over the past decade. Our analysis shows that adaptation has become a contested process between supply-chain actors and end-users, each with different economic objectives, adaptation needs and capacities. The resulting adaptation dynamic that is emerging shows worrying signs of maladaptation. Implications for market governance and urban policy and research are discussed. [ABSTRACT FROM AUTHOR]

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of −1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the 'most' appropriate system when a review or upgrade of the network infrastructure is required. [ABSTRACT FROM AUTHOR]

Abstract This paper reviews opportunities for, and barriers to, increasing photovoltaic (PV) deployment on apartment buildings, with a particular focus on the Australian experience. As rapid urbanisation drives increasing housing density, PV penetration in multi-occupancy housing has been limited by comparison with stand-alone housing in many jurisdictions, including in Australia despite its world-leading residential PV penetration. Given the growing commercial attractiveness of residential PV, this also raises equity concerns for apartment households. PV can potentially be installed to supply electricity to common property, to serve individual apartments, or as a resource shared between multiple apartments through embedded networks, local energy trading or 'behind the meter' deployment models. Our study undertook a review of the academic literature in this space and of specific Australian regulatory arrangements, as well as conducting a series of semi-structured interviews with a range of relevant stakeholders. Barriers identified include the huge variety amongst existing apartment building stock, demographic factors and knowledge issues. However, the Australian regulatory context - including governance of apartment buildings, regulation of the energy market, and electricity tariff policies - also impacts on the options available to apartment residents. New business models for deploying PV on apartments are emerging, including initiatives from retailers, developers and community energy organisations. While some issues are specific to the Australian context, or to buildings governed under strata-type arrangements, broader lessons can be taken from the Australian experience, including to inform the design of the regulatory framework required to facilitate widespread PV deployment across all residential housing types. Highlights • Photovoltaic deployment on apartment buildings faces multiple barriers. • A range of technical and organisational models is emerging. • Changes to the regulatory framework could support greater deployment. [ABSTRACT FROM AUTHOR]

The slow response of volume housebuilders to changes in demand has been cited as a contributing cause of a global crisis of housing affordability and allegations of land banking have persistently dogged the industry. This article reviews the supply responsiveness of speculative housebuilders in the United Kingdom and Australia through the neglected Marxian analytic category of absolute rent. Absolute rent directs attention to the relationship between the value of land and the cost of housing and models a market in which landowners may withdraw land from supply to inflate prices. Through the lens of absolute rent, the real estate practices of the housebuilding industry can be understood as a strategy of artificial scarcity straddling land and housing markets. The findings of this investigation demonstrate the insights to be gained by a return to absolute rent that valuably expand the current debate on the supply and cost of housing. [ABSTRACT FROM AUTHOR]

This paper is the second of a two part study that considers least cost, greenhouse gas abatement pathways for an electricity system. Part 1 of this study formulated a model for determining these abatement pathways, and applied this model to Australia's NEM (National Electricity Market) for a single reference scenario. Part 2 of this study applies this model to different scenarios and considers the policy implications. These include cases where nuclear power generation and CCS (carbon capture and storage) are implemented in Australia, which is presently not the case, as well as a more detailed examination of how an extended, RPS (renewable portfolio standard) might perform. The effect of future fuel costs and different discount rates are also examined. Several results from this study are thought to be significant. Most importantly, this study suggests that Australia already has utility scale technologies, renewable and non-renewable resources, an electricity market design and an abatement policy that permit continued progress towards deep greenhouse gas abatement in its electricity sector. In particular, a RPS (renewable portfolio standard) appears to be close to optimal as a greenhouse gas abatement policy for Australia's electricity sector for at least the next 10–15 years. [ABSTRACT FROM AUTHOR]

Voltage dips are considered to be one of the costliest of all power quality disturbances due to their disruption of industrial and commercial operations. The ability to characterise the voltage dip performance of distribution networks has many benefits for industry. There is considerable potential for industry to make large economic gains if voltage dip behaviour can be better understood and planned for. To put strategies in place to deal with voltage dips, there is a need for extensive data collection and analysis so that correct planning decisions can be made. This study details the characteristics of voltage dip data which has been collected at low-voltage sites in Australia over a 9 year period. The data is displayed in a number of different formats to aid analysis. [ABSTRACT FROM AUTHOR]

A survey of the visual effects of wind farms was undertaken in South Australia, with the aim of quantifying the impact of wind farms on the perceived scenic quality of landscapes. In the study a total of 311 participants assessed the scenic quality of a photographed landscape in 68 coastal and inland locations where wind farms could be located, both without wind farms and with wind farms digitally added to the scene. A key finding of the study was that wind farms generally had a negative effect on landscapes of higher scenic quality but a positive effect on landscapes of lower scenic quality. The negative visual effects of a wind farm did not reduce appreciably with distance. White, blue or grey coloured turbines were preferred over tan or rainbow hues, but this finding was based on a small number of scenes. Limitations of the study included using static views of wind farms and having an insufficient number of scenes to assess the influence of the number of turbines on scenic quality ratings. The study concluded that wind farms should avoid areas of higher perceived scenic quality, particularly on the coast, and be located in areas of lower scenic quality. The study adds to the growing body of international research on community attitudes to wind farms, and contributes useful knowledge for future planning of wind farms in Australia. These research results should assist communities, government agencies and the wind energy industry and improve the planning and design of wind farms by taking community perceptions into account. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM AUTHOR]

Investigates the divergence between electricity generation costs using current market prices of coal and natural gas in Australia. Application of the concept of full cost pricing to five different generation technologies in the electricity sector; Role of efforts to privatization and interconnection in promoting pricing closer to marginal private costs.