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Open grid model of Australia's National Electricity Market allowing backtesting against historic data.

ABSTRACT: Rising electricity prices, concerns regarding system security, and emissions reduction are central to an energy policy debate under way in Australia. To better evaluate mechanisms that seek to address the nexus of engineering, economic, and environmental challenges facing the country's electricity system, we have constructed network and generator datasets describing the operation of Australia's largest transmission network. These data have been collated using open-source software, and are available under an open license. They include the geospatial locations of network elements, and have been designed to interface with a public database maintained by the Australian Energy Market Operator. This interface allows historic data, such as generator dispatch and regional load signals, to be integrated with market models. Interactive network maps, independent datasets, and power-flow models have been used to assess the completeness and functionality of the derived datasets. In the context of Australia, these data can be used to examine geospatial and temporal impacts of power injections from renewables. More generally, they allow market models to be benchmarked against realised outcomes.

SUBMITTER: Xenophon A

PROVIDER: S-EPMC6206589 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Open grid model of Australia's National Electricity Market allowing backtesting against historic data.

Xenophon Aleksis A Hill David D

Scientific data 20181023

Rising electricity prices, concerns regarding system security, and emissions reduction are central to an energy policy debate under way in Australia. To better evaluate mechanisms that seek to address the nexus of engineering, economic, and environmental challenges facing the country's electricity system, we have constructed network and generator datasets describing the operation of Australia's largest transmission network. These data have been collated using open-source software, and are availa ...[more]

PMID: 30351307

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Project description:BackgroundExamining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose.MethodsGeographical grids based on the Lambert Azimuthal Equal Area projection are well suited for spatial epidemiology because they preserve area: each cell of the grid has the same area. We describe how data is projected onto such a grid, as well as grid-based algorithms for spatial epidemiological data-mining. The software program (FGBASE), that we have developed, implements these grid-based methods.ResultsThe grid based algorithms perform extremely fast. This is particularly the case for cluster searches. When applied to a cohort of French Type 1 Diabetes (T1D) patients, as an example, the grid based algorithms detected potential clusters in a few seconds on a modern laptop. This compares very favorably to an equivalent cluster search using distance calculations instead of a grid, which took over 4 hours on the same computer. In the case study we discovered 4 potential clusters of T1D cases near the cities of Le Havre, Dunkerque, Toulouse and Nantes. One example of environmental analysis with our software was to study whether a significant association could be found between distance to vineyards with heavy pesticide. None was found. In both examples, the software facilitates the rapid testing of hypotheses.ConclusionsGrid-based algorithms for mining spatial epidemiological data provide advantages in terms of computational complexity thus improving the speed of computations. We believe that these methods and this software tool (FGBASE) will lower the computational barriers to entry for those performing epidemiological research.

Dataset Information

Open grid model of Australia's National Electricity Market allowing backtesting against historic data.

Publications

Open grid model of Australia's National Electricity Market allowing backtesting against historic data.

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