Unit

Note

This section of the documentation is auto-generated from the code of the Julia-based core model. Refer to IESopt.jl for any further details (which may require some familiarity with Julia).

If you spot incorrect math-mode rendering, or similar issues, please file an issue, since rendering documentation from Julia to Python is not the easiest task.

Overview

A Unit allows transforming one (or many) forms of energy into another one (or many), given some constraints and costs.

Basic Examples

A Unit that represents a basic gas turbine:

gas_turbine:
  type: Unit
  inputs: {gas: gas_grid}
  outputs: {electricity: node, co2: total_co2}
  conversion: 1 gas -> 0.4 electricity + 0.2 co2
  capacity: 10 out:electricity

A Unit that represents a basic wind turbine:

wind_turbine:
  type: Unit
  outputs: {electricity: node}
  conversion: ~ -> 1 electricity
  capacity: 10 out:electricity
  availability_factor: wind_factor@input_data
  marginal_cost: 1.7 per out:electricity

A Unit that represents a basic heat pump, utilizing a varying COP:

heatpump:
  type: Unit
  inputs: {electricity: grid}
  outputs: {heat: heat_system}
  conversion: 1 electricity -> cop@inputfile heat
  capacity: 10 in:electricity

Parameters

`conversion`

The conversion expression describing how this Unit transforms energy. Specified in the form of “\(\alpha \cdot carrier_1 + \beta \cdot carrier_2\) -> \(\gamma \cdot carrier_3 + \delta \cdot carrier_4\)”. Coefficients allow simple numerical calculations, but are not allowed to include spaces (so e.g. (1.0/9.0) is valid). Coefficients are allowed to be NumericalInputs, resulting in column@data_file being a valid coefficient (this can be used e.g. for time-varying COPs of heatpumps).

mandatory: yes
default: \(-\)
values: string
unit: -

`capacity`

Maximum capacity of this Unit, to be given in the format X in/out:carrier where X is the amount, in or out (followed by :) specifies whether the limit is to be placed on the in- our output of this Unit, and carrier specifies the respective Carrier. Example: 100 in:electricity (to limit the “input rating”).

mandatory: yes
default: \(-\)
values: value dir:carrier
unit: -

`outputs`

Dictionary specifying the output “ports” of this Unit. Refer to the basic examples for the general syntax.

mandatory: yes
default: \(-\)
values: dict
unit: -

`inputs`

Dictionary specifying the input “ports” of this Unit. If not specified (= no explicit input), the conversion has to follow the form of conversion: ~ -> ..., indicating an “open” input. This may, e.g., be used for renewable energy sources, where the primary energy input (e.g., solar) is not explicitly modeled.

mandatory: no
default: \(-\)
values: dict
unit: -

`availability`

Time series (or fixed value) that limits the available capacity. If, e.g., capacity: 100 out:electricity and availability: 70, the available capacity will only be 70 electricity. Can be used to model non-availability of power plants, e.g., due to maintenance. For time-varying availability of intermittent generators (e.g., wind), it’s recommended (most of the time) to use availability_factor instead.

mandatory: no
default: \(+\infty\)
values: numeric
unit: power

`availability_factor`

Similar to availability, but given as factor of capacity instead. If, e.g., capacity: 100 out:electricity and availability_factor: 0.7, the available capacity will only be 70 electricity. This is especially useful for intermittent generators, where the availability is not a fixed value, but depends on the weather, and can be passed, e.g., by setting availability_factor: wind@input_data_file.

mandatory: no
default: \(1\)
values: \in [0, 1]
unit: -

`adapt_min_to_availability`

If true, the minimal partial load will be influenced by the availability. Example: Consider a Unit with capacity: 100 out:electricity, a min_conversion of 0.4, and an availability_factor of 0.5. This entails having 50 electricity available, while the minimal partial load is 40 electricity. This results in the Unit at best operating only closely above the minimal partial load. Furthermore, an availability_factor below 0.4 would result in no feasible generation, besides shutting the Unit off. While this might be the intended mode of operation in many use cases, adapt_min_to_availability can change this: If set to true, this dynamically changes the minimal partial load. In the previous example, that means (100 * 0.5) * 0.4 = 20 electricity (the 50% minimum load are now based on the available 40), changing the overall behaviour (including efficiencies) as well as leading to feasible generations even when the availability_factor is below 0.4.

mandatory: no
default: \(false\)
values: true, false
unit: -

`marginal_cost`

Marginal cost of the consumption/generation of one unit of energy of the specified carrier. Has to be given in the format value per dir:carrier, e.g. 3.5 per out:electricity for a marginal cost of 3.5 monetary units per unit of electricity generated.

mandatory: no
default: \(0\)
values: value per dir:carrier
unit: monetary per energy

`enable_ramp_up`

Enables calculation of upward ramps. Ramping is based on the carrier specified in capacity.

mandatory: no
default: \(false\)
values: true, false
unit: -

`enable_ramp_down`

Enables calculation of downward ramps. Ramping is based on the carrier specified in capacity.

mandatory: no
default: \(false\)
values: true, false
unit: -

`ramp_up_cost`

Sets the cost of ramping up (increasing in-/output) by 1 unit of the capacity carrier.

mandatory: no
default: \(0\)
values: numeric
unit: monetary per power

`ramp_down_cost`

Sets the cost of ramping down (decreasing in-/output) by 1 unit of the capacity carrier.

mandatory: no
default: \(0\)
values: numeric
unit: monetary per power

`ramp_up_limit`

Limits the allowed ramping up based on this factor of the total capacity. If capacity: 100 in:electricity with ramp_up_limit: 0.2, this limits the total increase of usage of electricity (on the input) to 20 units (power) per hour. For example, starting at an input of 35, after one hour the input has to be lesser than or equal to 55. If a Snapshot’s duration is set to, e.g., two hours, this would allow a total increase of 40 units.

mandatory: no
default: \(1\)
values: \in [0, 1]
unit: -

`ramp_down_limit`

Limits the allowed ramping down based on this factor of the total capacity. See ramp_up_limit.

mandatory: no
default: \(1\)
values: \in [0, 1]
unit: -

`min_on_time`

Minimum on-time of the Unit. If set, the Unit has to be on for at least this amount of time, after turning on. It is highly recommended to only use this with unit_commitment: binary, unless you know why it’s fine to use with another mode.

mandatory: no
default: \(0\)
values: numeric
unit: hours

`min_off_time`

Minimum off-time of the Unit. If set, the Unit has to be off for at least this amount of time, after turning off. It is highly recommended to only use this with unit_commitment: binary, unless you know why it’s fine to use with another mode.

mandatory: no
default: \(0\)
values: numeric
unit: hours

`on_time_before`

Time that this Unit has already been running before the optimization starts. Can be used in combination with min_on_time.

mandatory: no
default: \(0\)
values: numeric
unit: hours

`off_time_before`

Time that this Unit has already been off before the optimization starts. Can be used in combination with min_off_time.

mandatory: no
default: \(0\)
values: numeric
unit: hours

`is_on_before`

Number of Units that should be considered to have been running before the optimization starts. Can be used in combination with on_time_before, especially for unit_count greater than 1.

mandatory: no
default: \(1\)
values: numeric
unit: -

`unit_commitment`

Controls how the unit commitment of this Unit is handled. linear results in the ability to startup parts of the unit (so 0.314159 is a feasible amount of “turned on unit”), while binary restricts the Unit to either be on (converting the conversion_at_min + possible additional conversion above that minimum) or off (converting nothing); integer is needed to consider binary unit commitment for Units with more than 1 “grouped unit” (see unit_count).

mandatory: no
default: \(off\)
values: off, linear, binary, integer
unit: -

`unit_count`

Number of units aggregated in this Unit. Besides interacting with the mode of unit_commitment, this mainly is responsible for scaling the output (e.g. grouping 47 of the same wind turbine, …).

mandatory: no
default: \(1\)
values: numeric
unit: -

`min_conversion`

If unit_commitment is not set to off, this specifies the percentage that is considered to be the minimal feasible partial load this Unit can operate at. Operating below that setpoint is not allowed, at that point the conversion_at_min coefficients are used, and above that they are scaled to result in conversion when running at full capacity.

mandatory: no
default: \(-\)
values: \in [0, 1]
unit: -

`conversion_at_min`

The conversion expression while running on the minimal partial load. Only applicable if unit_commitment is not off and min_conversion is explicitly set. Follows the same form as conversion.

mandatory: no
default: \(-\)
values: string
unit: -

`startup_cost`

Costs per startup (also applicable if startups are not binary or integer). This is necessary to allow conversion_at_min to have (at least partially) the effect that one expects, if unit_commitment: linear.

mandatory: no
default: \(0\)
values: numeric
unit: monetary per start

`build_priority`

Priority for the build order of components. Components with higher build_priority are built before. This can be useful for addons, that connect multiple components and rely on specific components being initialized before others.

mandatory: no
default: \(0\)
values: numeric
unit: -

Detailed reference

Expressions

Variables

`conversion`

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").var.conversion

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").var.conversion

Full implementation and all details: unit/var_conversion @ IESopt.jl

Add the variable describing the unit’s conversion to the model.

This can be accessed via unit.var.conversion[t]; this does not describe the full output of the Unit since that maybe also include fixed generation based on the ison variable.

`ison`

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").var.ison

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").var.ison

Full implementation and all details: unit/var_ison @ IESopt.jl

Add the variable describing the current “online” state of the unit to the model.

The variable can be further parameterized using the unit.unit_commitment setting (“linear”, “binary”, “integer”). It will automatically enforce the constraints \(0 \leq \text{ison} \leq \text{unitcount}\), with \(\text{unitcount}\) describing the number of units that are aggregated in this unit (set by unit.unit_count). This can be accessed via unit.var.ison[t].

`ramp`

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").var.ramp

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").var.ramp

Full implementation and all details: unit/var_ramp @ IESopt.jl

Add the variable describing the per-snapshot ramping to the model.

This adds two variables per snapshot to the model (if the respective setting unit.enable_ramp_up or unit.enable_ramp_down is activated). Both are preconstructed with a fixed lower bound of 0. This describes the amount of change in conversion that occurs during the current snapshot. These can be accessed via unit.var.ramp_up[t] and unit.var.ramp_down[t].

These variables are only used for ramping costs. The limits are enforced directly on the conversion, which means this variable only exists if costs are specified!

`startup`

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").var.startup

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").var.startup

Full implementation and all details: unit/var_startup @ IESopt.jl

Add the variable describing the per-snapshot startup to the model.

This adds a variable per snapshot to the model (if the respective setting unit.unit_commitment is activated). The variable can be further parameterized using the unit.unit_commitment setting (“linear”, “binary”, “integer”). It will automatically enforce the constraints \(0 \leq \text{startup} \leq \text{unitcount}\), with \(\text{unitcount}\) describing the number of units that are aggregated in this unit (set by unit.unit_count). This describes the startup that happens during the current snapshot and can be accessed via unit.var.startup.

Constraints

`conversion_bounds`

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").con.conversion_bounds

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").con.conversion_bounds

Full implementation and all details: unit/con_conversion_bounds @ IESopt.jl

Add the constraint defining the unit’s conversion bounds to the model.

This makes use of the current min_capacity (describing the lower limit of conversion; either 0 if no minimum load applies or the respective value of the minimum load) as well as the online_capacity (that can either be the full capacity if unit commitment is disabled, or the amount that is currently active).

Depending on how the “availability” of this unit is handled it constructs the following constraints:

if !isnothing(unit.availability)

\[\begin{split} \begin{aligned} & \text{conversion}_t \geq \text{capacity}_{\text{min}, t}, \qquad \forall t \in T \\ & \text{conversion}_t \leq \text{capacity}_{\text{online}, t}, \qquad \forall t \in T \\ & \text{conversion}_t \leq \text{availability}_t, \qquad \forall t \in T \end{aligned} \end{split}\]

This effectively results in \(\text{conversion}_t \leq \min(\text{capacity}_{\text{online}, t}, \text{availability}_t)\).

if !isnothing(unit.availability_factor)

\[\begin{split} \begin{aligned} & \text{conversion}_t \geq \text{capacity}_{\text{min}, t}, \qquad \forall t \in T \\ & \text{conversion}_t \leq \text{capacity}_{\text{online}, t} \cdot \text{availability}_{\text{factor}, t}, \qquad \forall t \in T \end{aligned} \end{split}\]

If no kind of availability limiting takes place, the following bounds are enforced:

`ison`

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").con.ison

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").con.ison

Full implementation and all details: unit/con_ison @ IESopt.jl

Construct the upper bound for var_ison, based on unit.unit_count, if it is handled by an external Decision.

`min_onoff_time`

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").con.min_onoff_time

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").con.min_onoff_time

Full implementation and all details: unit/con_min_onoff_time @ IESopt.jl

Add the constraints modeling min on- or off-time of a Unit to the model.

This constructs the constraints

\[\begin{split} \begin{align} & \sum_{t' = t}^{t + \text{min\_on\_time}} ison_{t'} >= \text{min\_on\_time} \cdot (ison_t - ison_{t-1}) \qquad \forall t \in T \\ & \sum_{t' = t}^{t + \text{min\_off\_time}} (1 - ison_{t'}) >= \text{min\_off\_time} \cdot (ison_{t-1} - ison_t) \qquad \forall t \in T \end{align} \end{split}\]

respecting on_time_before and off_time_before, and is_on_before. See the code for more details.

Aggregated units

This is currently not fully adapted to account for Units with unit_count > 1.

`ramp`

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").con.ramp

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").con.ramp

Full implementation and all details: unit/con_ramp @ IESopt.jl

Add the auxiliary constraint that enables calculation of per snapshot ramping to the model.

Depending on whether ramps are enabled, none, one, or both of the following constraints are constructed:

\[\begin{split} \begin{aligned} & \text{ramp}_{\text{up}, t} \geq \text{conversion}_{t} - \text{conversion}_{t-1}, \qquad \forall t \in T \\ & \text{ramp}_{\text{down}, t} \geq \text{conversion}_{t-1} - \text{conversion}_{t}, \qquad \forall t \in T \end{aligned} \end{split}\]

This calculates the ramping that happens from the PREVIOUS snapshot to this one. That means that if:

out[5] = 100 and out[4] = 50, then ramp_up[5] = 50 and ramp_down[5] = 0

ramp_up[1] = ramp_down[1] = 0

`ramp_limit`

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").con.ramp_limit

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").con.ramp_limit

Full implementation and all details: unit/con_ramp_limit @ IESopt.jl

Add the constraint describing the ramping limits of this unit to the model.

This makes use of the maximum capacity of the unit, which is just the total installed capacity. Both, up- and downwards ramps can be enabled separately (via unit.ramp_up_limit and unit.ramp_down_limit), resulting in either or both of:

\[\begin{split} \begin{aligned} & \text{ramp}_{\text{up}, t} \leq \text{ramplimit}_\text{up} \cdot \text{capacity}_\text{max} \cdot \omega_t, \qquad \forall t \in T \\ & \text{ramp}_{\text{down}, t} \leq \text{ramplimit}_\text{down} \cdot \text{capacity}_\text{max} \cdot \omega_t, \qquad \forall t \in T \end{aligned} \end{split}\]

This does not make use of the ramping variable (that is only used for costs - if there are costs).

This calculates the ramping that happens from the PREVIOUS snapshot to this one. That means that if:

out[5] = 100 and out[4] = 50, then ramp_up[5] = 50 and ramp_down[5] = 0

ramp_up[1] = ramp_down[1] = 0

`startup`

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").con.startup

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").con.startup

Full implementation and all details: unit/con_startup @ IESopt.jl

Add the auxiliary constraint that enables calculation of per snapshot startup to the model.

Depending on whether startup handling is enabled, the following constraint is constructed:

\[ \begin{aligned} & \text{startup}_{\text{up}, t} \geq \text{ison}_{t} - \text{ison}_{t-1}, \qquad \forall t \in T \end{aligned} \]

This calculates the startup that happens from the PREVIOUS snapshot to this one. That means that if:

ison[5] = 1 and ison[4] = 0, then startup[5] = 1

Objectives

`marginal_cost`

How to access this objective?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").obj.marginal_cost

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").obj.marginal_cost

Full implementation and all details: unit/obj_marginal_cost @ IESopt.jl

Add the (potential) cost of this unit’s conversion (unit.marginal_cost) to the global objective function.

\[ \sum_{t \in T} \text{conversion}_t \cdot \text{marginalcost}_t \cdot \omega_t \]

`ramp_cost`

How to access this objective?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").obj.ramp_cost

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").obj.ramp_cost

Full implementation and all details: unit/obj_ramp_cost @ IESopt.jl

Add the (potential) cost of this unit’s ramping to the global objective function.

To allow for finer control, costs of up- and downwards ramping can be specified separately (using unit.ramp_up_cost and unit.ramp_down_cost):

\[ \sum_{t \in T} \text{ramp}_{\text{up}, t} \cdot \text{rampcost}_{\text{up}} + \text{ramp}_{\text{down}, t} \cdot \text{rampcost}_{\text{down}} \]

`startup_cost`

How to access this objective?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_unit").obj.startup_cost

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_unit").obj.startup_cost

Full implementation and all details: unit/obj_startup_cost @ IESopt.jl

Add the (potential) cost of this unit’s startup behaviour (configured by unit.startup_cost if unit.unit_commitment != :off).

\[ \sum_{t \in T} \text{startup}_t \cdot \text{startupcost} \]