Reliability of 100 % Renewable Electricity Supply in the Australian National Electricity Market

Author(s):  
Ben Elliston ◽  
Mark Diesendorf ◽  
Iain MacGill
Energy ◽  
2011 ◽  
Vol 36 (5) ◽  
pp. 2952-2960 ◽  
Author(s):  
P. Finn ◽  
C. Fitzpatrick ◽  
D. Connolly ◽  
M. Leahy ◽  
L. Relihan

2004 ◽  
Vol 70 (1) ◽  
pp. 123-136 ◽  
Author(s):  
Judy Johnston

When governments open up opportunities for private investment in traditional public sector areas, it is increasingly clear that a useful range of performance management information needs to be available to both government and business. Government needs to know how it is performing, comparatively, within and beyond its own domain, for the development of public policy and productivity enhancement. Business needs to know, understand and monitor the industry environment in which investment is contemplated or has already taken place. Performance measurement and monitoring is especially important where governments wish to attract foreign direct investment (FDI) to their shores. Whether governments manage performance and information well or are still constrained by bureaucratic and political thinking is still at issue. Using the example of the contrived national electricity market in Australia, this article, through literature and document review, examines the likely value to government and business of performance information, now available in the public domain. First, the article considers some of the changes to the Australian electricity industry. Second, specific performance indicators relevant to the national electricity market are examined in terms of their utility for government and business decision-making. Third, the impact of the political environment on performance management information is explored. The article concludes that while some important quantitative performance management information is available in a rational sense, other more political, qualitative indicators also need to be taken into account.


Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 43 ◽  
Author(s):  
Mesbaholdin Salami ◽  
Farzad Movahedi Sobhani ◽  
Mohammad Ghazizadeh

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.


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