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| | |Full_Model_Name=EnergyNumbers-Balancing | | |Full_Model_Name=EnergyNumbers-Balancing |
| | |author_institution=UCL Energy Institute | | |author_institution=UCL Energy Institute |
| − | |authors=Andrew ZP Smith | + | |authors=[http://wiki.openmod-initiative.org/wiki/User:Andrew_ZP_Smith Andrew ZP Smith] |
| | |contact_persons=Andrew ZP Smith | | |contact_persons=Andrew ZP Smith |
| | |contact_email=andrew.smith@ucl.ac.uk | | |contact_email=andrew.smith@ucl.ac.uk |
Revision as of 18:58, 8 September 2015
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EnergyNumbers-Balancing
by UCL Energy Institute
Authors:
[[authors::Andrew ZP Smith]]
Contact:
Andrew ZP Smith
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The model uses historic demand data, and historic (half-)hourly capacity factors for PV and wind, to simulate the extent to which demand could be met by some combination of wind, PV and storage. Please do email me if you'd like to request early access to the source.
Based on Fortran, PHP, Javascript, HTML, CSS. Using Matlab, Python for data processing.
Website / Documentation
Download
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 Open Source
GNU General Public License version 3.0 (GPL-3.0)
 Not directly downloadable
Some input data shipped
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| Model Scope |
Model type and solution approach |
| Model class
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Simulating storage and exogenously-variable renewables
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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
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| Regions
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Germany, Britain
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| Geographic Resolution
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National
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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
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Historic (half-)hourly wind/PV capacity factors are scaled up to meet a scenario's specified aggregate penetration levels, and compared to historic (half-)hourly demand. Storage is dispatched period-by-period based on surplus/deficits of energy.
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| Variables
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28
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| Computation time
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0.00001 minutes (runtime ~1ms to simulate 4.5 years half-hourly)
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| Objective
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| Uncertainty modeling
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Deterministic
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| Suited for many scenarios / monte-carlo
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Yes
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References
Scientific references
Example research questions
In Britain, how much wind & PV generation would be constrained, and what proportion of demand would get met in real time, assuming half-hourly demand as it was 2011-2015, and X% aggregate wind penetration, Y% aggregate PV penetration, and power-to-gas storage with an input efficiency of A%, an output efficiency of B%, storage capacity of C TWh, an input capacity of D GW, and an output capacity of E GW.
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