CarbonPricedPlacementScenario
Sustainability scenarios · Implementation: mlsysim/engine/scenario_definitions/sustainability.py.
Construct this scenario with keyword inputs. The fields below show the types and defaults declared by this class.
A run of it_energy on fleet placed among regions with and without a carbon price.
Notes
Equation: result evaluates the configured solver; its contract owns the governing equations.
Assumptions: Regional grids and the fleet describe the same supplied IT-energy workload.
Excludes: Scheduling uncertainty, future grid changes, and geographic service constraints.
References
Derivation: The scenario binds declared inputs to CarbonPricedPlacementModel. Those implementations document their derivations and sources.
Examples
Execute the registered worked inputs; this is a modeled calculation.
>>> from mlsysim import Scenarios
>>> case = Scenarios.CarbonPricedRegionPlacement
>>> value = case.result
>>> value.best_region
'Quebec'Attributes
| Name | Description |
|---|---|
| carbon_price_per_ton | |
| fleet | |
| it_energy | |
| mfu | |
| reference_grid | |
| regions | |
| result | Evaluate the configured inputs with mlsysim.solvers.CarbonPricedPlacementModel.solve. |
fleet
Type: Fleet.
Declared default: required.
it_energy
Type: Quantity.
Declared default: required.
regions
Type: List[str].
Declared default: required.
carbon_price_per_ton
Type: float.
Declared default: required.
mfu
Type: float.
Declared default: required.
reference_grid
Type: Any.
Declared default: required.
result
engine.scenario_types.CarbonPricedPlacementScenario.result
Evaluate the configured inputs with mlsysim.solvers.CarbonPricedPlacementModel.solve.
The returned typed result retains the solver’s documented units, assumptions, and feasibility semantics; this property adds no measurement or safety claim. Equation: result = CarbonPricedPlacementModel().solve(...) using the fields shown below.