Temporal dimension - WITNESS: Difference between revisions
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The underlying simulation mechanism could accommodate with smaller or bigger time steps (or adaptative with some developments to better capture shocks), but for models calibration purposes it was convenient to stick to 1 year steps as many data are available at a 1 year interval. | The underlying simulation mechanism could accommodate with smaller or bigger time steps (or adaptative with some developments to better capture shocks), but for models calibration purposes it was convenient to stick to 1 year steps as many data are available at a 1 year interval. | ||
The systems are at equilibrium at each steps, using | The systems are at equilibrium at each steps, using a [https://gemseo.readthedocs.io/en/stable/mdo/mdo_formulations.html Multi-Disciplinary Feasible (MDF) formulation] | ||
= Optimization = | = Optimization = | ||
Revision as of 09:56, 22 August 2024
Note: The documentation of WITNESS is 'in preparation' and is not yet 'published'!| Corresponding documentation | |
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| Previous versions | |
| No previous version available | |
| Model information | |
| Model link | |
| Institution | Open-Source for Climate (OS-C), N/A, https://os-climate.org/transition-analysis/., Linux Foundation (LF), N/A, https://www.linuxfoundation.org/. |
| Solution concept | Systems dynamics based approach |
| Solution method | OptimizationSimulation-based optimization |
| Anticipation | |
General principles
WITNESS temporal granularity is currently and typically 1 year steps.
The underlying simulation mechanism could accommodate with smaller or bigger time steps (or adaptative with some developments to better capture shocks), but for models calibration purposes it was convenient to stick to 1 year steps as many data are available at a 1 year interval.
The systems are at equilibrium at each steps, using a Multi-Disciplinary Feasible (MDF) formulation
Optimization
Optimization mechanisms typically will consider design variables as a vector of per time-step values, potentially driven by splines to reduce the actual # of control parameters and ensuring some continuity where needed.
For example in the basic framework settings, which optimize investment available from economy in various energy generation and CCUS technologies on a yearly timestep from now to 2100, you have a matrix of investment per year and per technology (80+) as design variables.