PowerGenerator

A PowerGenerator represents any dispatchable power source in your energy system -- think gas turbines, diesel generators, or even a simplified solar/wind unit with a fixed capacity.

Basic usage

from odys.energy_system_models.assets.generator import PowerGenerator

gen = PowerGenerator(
    name="gas_turbine",
    nominal_power=100.0,
    variable_cost=50.0,
)

That's really all you need. The optimizer will figure out the dispatch (how much power to produce at each timestep) to minimize total cost while meeting demand.

Fields

Field	Type	Required	Default	Description
name	str	Yes	-	Unique identifier for the generator
nominal_power	float	Yes	-	Maximum power output (in your chosen power unit)
variable_cost	float	Yes	-	Cost per unit of energy produced (currency/MWh)
min_power	float	No	0.0	Minimum power output when the generator is on
ramp_up	float	No	None	Max increase in power per hour
ramp_down	float	No	None	Max decrease in power per hour
min_up_time	int	No	1	Minimum number of timesteps the generator must stay on
min_down_time	int	No	1	Minimum number of timesteps the generator must stay off
startup_cost	float	No	0.0	Cost incurred each time the generator starts up
shutdown_cost	float	No	None	Cost incurred each time the generator shuts down

Ramp constraints

If your generator can't change output instantly, set ramp limits:

gen = PowerGenerator(
    name="slow_gen",
    nominal_power=200.0,
    variable_cost=30.0,
    ramp_up=50.0,   # can increase by at most 50 MW/h
    ramp_down=40.0,  # can decrease by at most 40 MW/h
)

When ramp_up or ramp_down is None (the default), there's no ramp constraint -- the generator can jump from 0 to full power in a single step.

Minimum up/down times

These prevent the optimizer from toggling the generator on and off every timestep:

gen = PowerGenerator(
    name="coal_plant",
    nominal_power=500.0,
    variable_cost=25.0,
    min_up_time=4,   # once on, stays on for at least 4 steps
    min_down_time=2,  # once off, stays off for at least 2 steps
)

Startup and shutdown costs

You can penalize switching the generator on or off:

gen = PowerGenerator(
    name="peaker",
    nominal_power=50.0,
    variable_cost=80.0,
    startup_cost=500.0,
    shutdown_cost=100.0,
)

This makes the optimizer think twice before toggling the generator, which is realistic for many thermal plants.

Available capacity profiles

In a Scenario, you can limit the generator's available capacity per timestep using available_capacity_profiles. This is useful for modeling things like planned maintenance or variable renewable output:

from odys.energy_system_models.scenarios import Scenario

scenario = Scenario(
    available_capacity_profiles={
        "gas_turbine": [100, 100, 50, 50, 100, 100, 100],
    },
    load_profiles={"load": [80, 90, 70, 60, 85, 95, 80]},
)

The key in the dict must match the generator's name.

Results

After optimization, access generator results through result.generators:

result = energy_system.optimize()

result.generators.power     # dispatch per timestep
result.generators.status    # on/off status (1 or 0)
result.generators.startup   # startup events
result.generators.shutdown  # shutdown events

Each of these is a pandas.DataFrame.