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Stable electric load forecasting plays a significant role in power system operation and grid management. Improving the accuracy of electric load forecasting is not only a hot topic for energy managers and researchers of the power system, but also a fair challenging and difficult task due to its complex nonlinearity characteristics. This paper proposes a new combination model, which uses the least squares support vector machine, extreme learning machine, and generalized regression neural network to predict the electric load in New South Wales, Australia. In addition, the model employs a heuristic algorithm–whale optimization algorithm to optimize the weight coefficient. To verify the usability and generalization ability of the model, this paper also applies the proposed combined model to electricity price forecasting and compares it with the benchmark method. The experimental results demonstrate that the combined model not only can get accurate results for short-term electric load forecasting, but also achieves fine accuracy for the same period of electricity price forecasting. [ABSTRACT FROM AUTHOR]

This paper proposes a novel peak load management scheme for rural areas. The scheme transfers certain customers onto local nonembedded generators during peak load periods to alleviate network under voltage problems. This paper develops and presents this system by way of a case study in Central Queensland, Australia. A methodology is presented for determining the best location for the nonembedded generators as well as the number of generators required to alleviate network problems. A control algorithm to transfer and reconnect customers is developed to ensure that the network voltage profile remains within specification under all plausible load conditions. Finally, simulations are presented to show the performance of the system over a typical maximum daily load profile with large stochastic load variations. [ABSTRACT FROM AUTHOR]

Abstract-Harmonic monitoring has become an important tool for harmonic management in distribution system. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical medium-voltage distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial, and industrial load sectors. Data over a three year period have been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of the measurement data are of importance. Based on this information, a closer inspection of smaller data sets can then be carried out to determine the reasons for its detection. In this paper, we classify the measurement data using unsupervised learning based on clustering techniques using the minimum message length technique, which can provide the engineers with a rapid, visually oriented method of evaluating the underlying operational information contained within the clusters. Supervised learning is then used to describe the generated clusters and to predict the occurrences of unusual clusters in future measurement data. [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]

Gradual technical advancement and rapidly decreasing costs have led to widespread deployment of solar photovoltaic (PV) systems. In Australia, distributed PV systems make up the vast majority of installed PV capacity. In remote communities, distributed PV systems offer a supplementary solution to existing diesel-based electricity generation. However, remote electricity networks, being different from urban networks primarily due to their limited generation capacity and spinning reserve are likely to be critically affected by the variability characteristics of PV generation. The integration of distributed PV systems into conventional remote electricity networks has noteworthy impacts on their technical and non-technical operations that pose new challenges for PV deployment. Significant technical issues are observed in these networks as PV penetration levels increase, such as reduced power quality, inadequate diesel generator dispatch for spinning reserve, increased complexities in network operation and management, unintended islanding and even system blackouts. Utility adopted operational and control strategies significantly influence PV penetration levels in different remote networks around the world, including Australia. This paper reviews the current electrification scenarios in remote Australian networks and focuses on the impacts and technical challenges of distributed PV deployment and the control strategies adopted by remote area power utilities. Some suggestions and recommendations, such as the development of robust control mechanisms incorporating PV forecasting technology, modern network equipment, real-time measurements and network ancillary services provided by inverter coupled generation systems to enhance networks' operational stability and reliability are also presented. This review is of benefit to scientific researchers, investors and different stakeholders, who wish to have a better understanding of distributed PV systems' deployment scenarios into remote electricity networks. As the inherent characteristics of Australian remote electricity networks are similar to those in African and Asian rural electricity networks; the findings, reviews and recommendations presented are also relevant to those networks. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the details of a theoretical study of the economic advantages of using large-scale energy storage to complement a wind farm in a base-load dominated electricity grid. A computer model is developed which simulates the operation of several energy storage systems when used with the 190-MW Portland Wind Farm (PWF) located in Portland, Victoria, Australia. A variety of operating strategies are compared with the results of a dynamic programming model which finds the maximum possible revenue which a given system can generate for a set of input conditions. Three energy storage systems are modelled and costed: Pumped Seawater Hydro Storage (PSHS), Compressed Air Energy Storage (CAES), and Thermal Energy Storage (TES). It is found that CAES is the most profitable storage medium, requiring a capital expenditure of A$140M and generating a rate of return (ROR) of 15.4%. The ROR for PSHS was 9.6%, and for TES was 8.0%. Therefore, a significant investment opportunity exists for the installation of an energy storage system in this wind farm. It is therefore highly recommended that CAES is investigated further with the aim of introducing large-scale energy storage to PWF and other similar wind turbine installations. [Copyright &y& Elsevier]