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This paper describes very short-term wind prediction for power generation, utilizing a case study from Tasmania, Australia. Windpower presently is the fastest growing power generation sector in the world. However, windpower is intermittent. To be able to trade efficiently, make the best use of transmission line capability, and address concerns with system frequency in a re-regulated system, accurate very short-term forecasts are essential. The research introduces a novel approach—the application of an adaptive neuro-fuzzy inference system to forecasting a wind time series. Over the very short-term forecast interval, both windspeed and wind direction are important parameters. To be able to be gain the most from a forecast on this time scale, the turbines must be directed toward on oncoming wind. For this reason, this paper forecasts wind vectors, rather than windspeed or power output. [ABSTRACT FROM AUTHOR]

The Lake Margaret Power Scheme located on Tasmania's rugged west coast commenced operations in November 1914. It is a rare example of an early 20th century hydroelectric scheme in Australia, and was the largest private generator in the state prior to the start of large-scale public generation in Tasmania with the commissioning of Waddamana in 1916. This paper examines the history and heritage significance of the Lake Margaret Power Scheme, together with a description of recent developments that will see a refurbished and restored scheme continue to operate well into the future. The history includes a description of major refurbishments, replacements and upgrades that have taken place through the life of the scheme. The paper is based on information drawn from early papers written about the scheme, information supplied by Hydro Tasmania and the personal knowledge of Peter Schulze who worked at Mt Lyell from 1961 to 1988. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is essential to power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate the intrinsic time‐related dimensions of electric load data and the decomposition methods into machine learning models so that their prediction accuracy and robustness still have much room for improvement. To solve this problem, this paper proposes a decomposition‐based multi‐time dimension long short‐term memory (DB‐MTD‐LSTM) model for short‐term electric load forecasting (STELF). In DB‐MTD‐LSTM, empirical mode decomposition with adaptive noise (CEEMDAN) is first introduced to smooth non‐linear non‐stationary electric load data and constrain the modal aliasing or noise of decomposed electric load data in the traditional decomposed method. A joint relevant time dimensions method (JRTDM) is then developed using autocorrelation analysis to rationally extract the temporal characteristics of decomposed data in multiple time dimensions. An improved LSTM called MTD‐LSTM is developed by combining JRTDM with LSTM, which can effectively apply multi‐dimensional time characteristics of the decomposed load to improve the predictive accuracy and robustness. Several datasets from Australia and China are performed to check the predictive performance of DB‐MTD‐LSTM. Experimental results verify that DB‐MTD‐LSTM has better predictive accuracy and satisfactory robustness compared with state‐of‐the‐art and conventional predictive models. [ABSTRACT FROM AUTHOR]

Owing to the technological development of natural gas exploration and the increasing penetration of gas-fired power generation, gas and power systems inevitably interact with each other from both physical and economic points of view. In order to effectively assess the two systems' interdependency, this paper proposes a systematic modeling framework and constructs simulation platforms for coupled gas and power systems in an energy market environment. By applying the proposed approach to the Australian national electricity market (NEM) and gas market, the impacts of six types of market and system factors are quantitatively analyzed, including power transmission limits, gas pipeline contingencies, gas pipeline flow constraints, carbon emission constraints, power load variations, and non-electric gas load variations. The important interdependency and infrastructure weakness for the two systems are well studied and identified. Our work provides a quantitative basis for grid operators and policy makers to support and guide operation and investment decisions for electric power and natural gas industries. [ABSTRACT FROM AUTHOR]

Abstract: Over the past decade, several new approaches have emerged to renewable energy education that seek to address the needs of the 21st century for sustainable energy supply systems. In addition, unlike conventional architectural education which does not cover renewable energy issues in its curriculum, these days, renewable energy education has an identity of its own within the discipline of architecture. This paper will clarify academic obstacles that are impeding the development of sustainable architectural education. These obstacles include: ambiguous definitions of sustainable architecture, confusion over the meaning of sustainability, and lack of experts in this field. In the last part of this paper we compare academic architectural education in the field of renewable energy in two countries, Iran as a developing country in Asia and Australia as a developed country in Oceania. Lessons can be drawn from these case studies in regard to the obstacles and progress in the development of sustainable architecture education. [Copyright &y& Elsevier]

The Australian electricity sector is expected to undergo major changes to reduce the current sectoral CO2 emission of 190 million tons to 96 million tons by 2050. This paper summarizes two major modeling studies that have been undertaken since 2006 to understand the full set of investment, price and financial impact on existing generators, especially coal-based generators. We have used a long-term capacity and dispatch optimization model to comprehensively analyze both least-cost and market-led expansion of the system to accommodate alternative emission reduction targets from a shallow cut to a deep cut. The modeling results demonstrate the cost of meeting the target can be over $30 billion in NPV terms. A mix of clean coal technologies, gas and nuclear technologies is expected to displace the existing stock of conventional coal which varies significantly depending on the emission reduction target. The analysis also show that mandatory renewable targets or technology standards are inefficient policies in comparison to a market-based policy such as the proposed emissions trading scheme. [ABSTRACT FROM AUTHOR]

In this paper, a property rights model for electric transmission is proposed and its properties analyzed. The proposed rights, called "border flow rights," support financial hedging of transmission risk and merchant transmission expansion through associated financial rights, called "contracts for differences of differences." These financial rights allow for forward trading of both energy and transmission by a unified exchange, avoiding the bifurcation in current markets between decentralized long-term energy trading and centralized long-term transmission trading. Such long-term trading can help to support the financing of both generation and transmission assets. We consider incentive properties of such a right in the absence of lumpiness, economies of scale, and market power. [ABSTRACT FROM AUTHOR]

This study examines the effect of sample size on the mean productive efficiency of firms when the efficiency is evaluated using the non-parametric approach of Data Envelopment Analysis. By employing Monte Carlo simulation, we show how the mean efficiency is related to the sample size. The paper discusses the implications for international comparisons. As an application, we investigate the efficiency of the electricity distribution industries in Australia, Sweden and New Zealand. [ABSTRACT FROM AUTHOR]

Calculation of wind power capacity values for risk assessment of power system adequacy has attracted great attention in the literature. And the most popular approach has been the Effective Load Carrying Capability (ELCC) method which allows for the consideration of key factors such as wind capacity factor, forced outage rates (F.O.R) of conventional power stations, system reliability targets, and the correlation between wind availability and system load. However, comparatively little attention has been paid to analysing the effects of other factors such as the number of wind farms and wind installed capacity, the length of historic time series data on demand and wind resources. This paper provides an in-depth analysis of how these factors influence the calculation of capacity value for the Australian National Electricity Market (NEM) power system using metered half-hourly wind and load data for 2006 to 2013. The analysis incorporates the periods with extreme risk events. Our results show that capacity values depend greatly on the design of the simulation model used, and highlight the importance of capturing wind and load data points relating to extremely high demand periods. We compare our NEM-wide estimates to recent estimates for the State of South Australia. [ABSTRACT FROM AUTHOR]

This paper proposes an energy offering strategy for wind power producers. A new trading plan is presented through which a wind power producer can employ demand response (DR) to maximize its profit. To consider DR, a new DR scheme is developed here. The proposed plan includes two steps: The first step takes place on a day-ahead basis. The corresponding decisions involve an initial offering schedule and preliminary DR arrangements for the following day. The second step coincides with the day of the energy delivery. A consecutive approach is proposed in which the wind power producer determines its final energy offer during each trading interval. Simultaneously, the required DR agreements for that interval are also confirmed. This approach is repeated until all periods of the day are covered. The proposed plan is formulated as a stochastic programming approach, where its feasibility is evaluated on a case of the Australian National Electricity Market (NEM). [ABSTRACT FROM AUTHOR]

Abstract: Given the recent increasing public focus on climate change issues, there is a need for robust, sustainable and climate friendly power transmission and distribution systems that are intelligent, reliable, and green. Current power systems create environmental impacts as well as contributing to global warming due to their utilization of fossil fuels, especially coal, as carbon dioxide is emitted into the atmosphere. In contrast to fossil fuels, renewable energy is starting to be used as the panacea for solving climate change or global warming problems. This paper describes a feasibility study undertaken to investigate the potentialities of renewable energy including the prospective locations in Australia for renewable energy generation, in particular solar and wind energy. Initially, a hybrid model has been developed to investigate the prospects of wind energy for typical Australian region considering production cost, cost of energy, emission production and contribution from renewable energy using the Hybrid Optimization Model for Electric Renewable (HOMER), a computer model developed by the USA’s National Renewable Energy Laboratory (NREL). This model also explores suitable places around Australia for wind energy generation using statistical analysis. Subsequently, the usefulness of solar energy in the Australian context and suitable locations for solar energy generation are also investigated using a similar hybrid model. Finally, the model has been developed to investigate the prospects of renewable energy in particular wind and solar energy including specific locations in Australia that would be suitable for both wind and solar energy generation. From simulation analysis it is clearly observed that Australia has enormous potentialities for substantially increased use of renewable energy; a large penetration of renewable energy sources into the national power system would reduce CO2 emissions significantly, contributing to the reduction of global warming. [Copyright &y& Elsevier]

The article discusses the origin and development of hydraulic power equipment and systems in Great Britain and Australia. It discusses the formation of the Melbourne Hydraulic Power Co., a public utility company, in 1888 and also states its early success and competition faced by the company from electricity distributors. It also states the acquisition of the company through legislation by the Melbourne City Council, a City Corporation. The customer base of the company is also discussed.

Load forecasting is of crucial importance for operations of electric power systems. In recent years, deep learning based methods are emerging for load forecasting because their strong nonlinear approximation capabilities can provide more forecasting precision than conventional statistical methods. However, they usually suffer from some problems, e.g., the gradient vanishment and over-fitting. In order to address these problems, an unshared convolution based deep learning model with densely connected network is proposed. In this model, the backbone is the unshared convolutional neural network and a densely connected structure is adopted, which could alleviate the gradient vanishment. What is more, we use a regularization method named clipped $L_2$ -norm to overcome over-fitting, and design a trend decomposition strategy to address the possible distribution differences between the training and validation data. Finally, we conduct five case studies to verify the outperformance of our proposed deep learning model for deterministic and interval load forecasting. Two high-voltage and an medium-voltage real load datasets from Australia, Germany and America are used for model training and validation, respectively. Results show that the proposed model can achieve higher load forecasting accuracy, compared with other existing methods including the popular conventional methods such as naive forecast and generalized additive model, and deep learning methods, e.g., long short-term memory network, convolutional neural network, fully connected network, etc. [ABSTRACT FROM AUTHOR]

The large-scale integration of wind power into electrical power systems is likely to require significant improvements to transmission infrastructure over coming years. Planning for these developments may involve modeling of power systems using estimates of the time-varying power output of proposed wind farms. Such estimates may be derived from wind measurements, although it is rarely the case that measurements are available simultaneously for all sites of interest. In the present work, a method is developed to produce synthetic time-series of wind power at several locations based on a measured time-series of wind speed from a reference site. The stochastic properties of the modeled time-series are shown to be comparable with those of measured data from south-eastern Australia. It is anticipated that with appropriate tuning for local conditions, the method could be applied in other geographical regions. [ABSTRACT FROM PUBLISHER]