User:RobbieIanMorrison/sandbox/work in progress 8
| Rogeaulito model |
- Draft:Open energy system models#start — current draft
- Draft:Open energy system models#Open energy system models — appropriate section
| Project | Host | License | Access | Coding | Documentation | Scope/type |
|---|---|---|---|---|---|---|
| Rogeaulito | The Shift Project | TBA | on application | Excel/VBA | manual | world energy scenarios |
Rogeaulito
[edit]| Project | Rogeaulito |
|---|---|
| Host | The Shift Project |
| Scope/type | world energy scenarios |
| Code license | TBA |
| Website | theshiftproject |
| Repository | {{share email
<!-- can set 'contents' to nil to suppress custom output and the default current page URL -->
| target = The%20Shift%20Project%20%3Ccommunication@theshiftproject.org%3E
| subject = Rogeaulito spreadsheet request
| contents = Please email the Rogeaulito spreadsheet file to this address.
| show = request spreadsheet
| icon = true
}}
|
| Documentation | theshiftproject |
Rogeaulito is a world energy scenario model, designed to systematically explore long-term energy choices out to 2100.[a] It allows the user to set supply-side constraints in a manner not normally found in economic models. Rogeaulito is being developed by the European nonprofit think tank, The Shift Project (TSP), based in Paris, France.[2] The model itself is programmed using Excel/VBA. The spreadsheet is available on request.[2] A technical description can be downloaded.[1]
Rogeaulito is designed to be simple, didactic, and user-driven and is suited to what-if questions. By requiring users and their clients to examine the physical necessities of future energy supply and demand, the model should help raise early warnings and reduce overconfidence in some energy futures. [3]
Rogeaulito classes as a physical accounting model implemented as a simple deterministic dynamical system. As an intentional design choice, the model does not embed price mechanisms or economic loops. The model starts from the year 2010 and projects forward, in yearly steps, using a set of predefined evolutionary rules. The opening trajectory is initialized using historical data from the IEA spanning the years 1990 to 2009.[4] The supply-side and demand-side are calculated independently and if future demand cannot be serviced, the model simply reports the missing (primary) energy supply (MES). The user can then adjust the scenario, using a trial-and-error process, to ensure that supply does cover demand in all years. This procedure allows the user to remain connected with the assumptions being made to generate the scenario.[3]
Rogeaulito supports linear, exponential, Gaussian (bell-shaped), sigmoid (s-shaped), and other mathematical formulas to drive evolution. The user can select and calibrate these to suit their expectations. For instance, the car ownership rate (a variable) might be assumed linear (a shape) with a growth rate of 1 vehicle per 1000 capita per year (an evolution parameter). The historical 2009 value of 125 vehicles per 1000 capita would then balloon to 217 vehicles per 1000 capita in 2100. Structurally, the model comprises four independent modules. The demand module determines the evolution of final energy demand across a range of sectors and sub-sectors, as classified by the IEA.[5] This demand is ultimately driven by social factors (such as per capita car ownership and residential living area) and end-use efficiencies (fuel efficiency and building energy performance). The supply module similarly determines the evolution of potential primary energy supply across a number of energy resources and carriers, again after the IEA. Depletable energy resources (such as conventional oil) can be represented using Hubbert curves. The conversion module stands between the demand and supply modules and accounts for the conversion and transport infrastructure, with associated losses. The core module calculates the induced demand for primary energy and identifies the energy gap or MES, if any, by difference with the potential primary energy supply. Planned developments include better support for costs and regional disaggregation.[3]
Notes
References
- ^ a b Benichou, Léo; Praz, Bastien; Hajjar, Joseph; Le Treut, Gaëlle; Meillan, Marie-Pierre; Waterhouse, Samuel; Franco, Damaris (May 2013). Rogeaulito: transparent energy scenario thinking: technical description of Rogeaulito model and framework — Version 1.3 (PDF). Paris, France: The Shift Project. Retrieved 2016-12-12.
- ^ a b "Rogeaulito". The Shift Project. Paris, France. Retrieved 2016-12-13.
- ^ a b c
Benichou, Léo; Mayr, Sebastian (2014). "Rogeaulito: a world energy scenario modeling tool for transparent energy system thinking". Frontiers in Energy Research. 1 (13): 1–11. doi:10.3389/fenrg.2013.00013. ISSN 2296-598X. Retrieved 2016-12-12.
{{cite journal}}: CS1 maint: unflagged free DOI (link)
- ^
IEA (2012). World energy statistics and balances (2012 edition). Paris, France: International Energy Agency (IEA). The 2016 edition is available as an online data service from data
.iea ..org - ^ IEA (2011). World energy outlook 2011 (PDF). Paris, France: International Energy Agency (IEA). ISBN 978-92-64-12413-4. Retrieved 2016-12-14.
| Junk |
- Benichou and Mayr (2014)
- evince ~/synk/pdfs/2014-benichou-and-mayr-rogeaulito-world-energy-scenario-modeling-tool.pdf &
"Only very few (but very visible and largely cited) open source energy models exist such as the integrated assessment model DICE (Nordhaus and Boyer 2000), or AIM (Kainuma et al. 1999), an integrated energy model, which was developed mainly to examine global warming response measures in the Asian-Pacific region. Besides open source code and date, these software packages are also license-free, non-commercial, and give permission to redistribute. This rather broad understanding of open source is based on DeCarolis et al. (2012). Benichou and Mayr (2014) (p4)
- Technical description
- evince ~/synk/pdfs/2013-benichou-etal-rogeaulito-technical-description-v13.pdf &
- IEA World energy outlook 2011
- evince ~/synk/pdfs/2011-iea-world-energy-outlook-2011.pdf &