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| | |source_download=https://github.com/ozsolarwind/siren | | |source_download=https://github.com/ozsolarwind/siren |
| | |logo=Sen logo 99.png | | |logo=Sen logo 99.png |
| − | |text_description=SIREN uses external datasets to model the potential for renewable energy generation for a geographic region. The approach is to model the data on an hourly basis for a desired year (ignoring leap days, that is, 8,760 hours). Users explore potential location and scale of renewable energy sources (stations, storage, transmission) to meet electricity demand. It is possible to model any geographic area and uses a number of open or publicaly available data sources: | + | |text_description=SIREN uses external datasets to model the potential for renewable energy generation for a geographic region. The approach is to model the data on an hourly basis for a desired year (ignoring leap days, that is, 8,760 hours). Users explore potential location and scale of renewable energy sources (stations, storage, transmission) to meet electricity demand. It is possible to model any geographic area and uses a number of open or publicly available data sources: |
| | o Maps can be created from OpenStreet Map (MapQuest) tiles | | o Maps can be created from OpenStreet Map (MapQuest) tiles |
| | o Weather data files can be created from NASA (MERRA2) satellite data | | o Weather data files can be created from NASA (MERRA2) satellite data |
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| | |modelling_software=Python, NREL SAM | | |modelling_software=Python, NREL SAM |
| | |processing_software=Python | | |processing_software=Python |
| − | |model_class=Electricity System Model, | + | |model_class=Electricity System Model, |
| | |sectors=Electricity, | | |sectors=Electricity, |
| | |technologies=Renewables | | |technologies=Renewables |
Revision as of 07:34, 28 June 2016
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SEN Integrated Renewable Energy Network Toolkit
by Sustainable Energy Now Inc
Authors:
Angus King
Contact:
Angus King
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SIREN uses external datasets to model the potential for renewable energy generation for a geographic region. The approach is to model the data on an hourly basis for a desired year (ignoring leap days, that is, 8,760 hours). Users explore potential location and scale of renewable energy sources (stations, storage, transmission) to meet electricity demand. It is possible to model any geographic area and uses a number of open or publicly available data sources:
o Maps can be created from OpenStreet Map (MapQuest) tiles
o Weather data files can be created from NASA (MERRA2) satellite data
o It uses US NREL SAM models to calculate energy generation
SIREN is available, packaged for Windows, on Sourceforge (https://sourceforge.net/projects/sensiren/). There's a help file (https://rawgit.com/ozsolarwind/siren/master/help.html) which describes "how it works"
Based on Python, NREL SAM. Using Python for data processing.
Website / Documentation
Download
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 Open Source
Affero General Public License v3 (AGPL-3.0)
Directly downloadable
Some input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Electricity System Model
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| Sectors
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Electricity
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| Technologies
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Renewables
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| Decisions
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dispatch, investment
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| Regions
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| Geographic Resolution
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| Time resolution
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Hour
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| Network coverage
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| Model type
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Simulation, Other
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User NREL SAM models to estimate hourly renewable generation for a range/number of renewable energy stations
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| Variables
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| Computation time
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minutes
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| Objective
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Match generation to demand and minimise cost
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| Uncertainty modeling
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| Suited for many scenarios / monte-carlo
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No
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References
Scientific references
Reports produced using the model
http://www.sen.asn.au/modelling_findings
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