# Unit commitment

To incorporate technical detail about (clustered) unit-commitment statuses of units, the online, started and shutdown status of units can be tracked and constrained in SpineOpt. In the following, relevant relationships and parameters are introduced and the general working principle is described.

## Key concepts for unit commitment

Here, we briefly describe the key concepts involved in the representation of (clustered) unit commitment models:

• units_on is an optimization variable that holds information about the on- or offline status of a unit. Unit commitment restrictions will govern how this variable can change through time.
• units_on__temporal_block is a relationship linking the units_on variable of this unit to a specific temporal_block object. The temporal block holds information on the temporal scope and resolution for which the variable should be optimized.
• online_variable_type is a method parameter and can take the values unit_online_variable_type_binary, unit_online_variable_type_integer, unit_online_variable_type_linear. If the binary value is chosen, the units status is modelled as a binary (classic UC). For clustered unit commitment units, the integer type is applicable. Note that if the parameter is not defined, the default will be linear. If the units status is not crucial, this can reduce the computational burden.
• number_of_units defines how many units of a certain unit type are available. Typically this parameter takes a binary (UC) or integer (clustered UC) value. To avoid confusion the following distinction will be made in this document: unit will be used to identify a Spine unit object, which can have multiple members. Together with the unit_availability_factor, this will determine the maximum number of members that can be online at any given time. (Thus restricting the units_on variable). The default value for this parameter is $1$. It is possible to allow the model to increase the number_of_units itself, through Investment Optimization
• unit_availability_factor: (number value or time series). Is the fraction of the time that this unit is considered to be available, by acting as a multiplier on the capacity. A time series can be used to indicate the intermittent character of renewable generation technologies.
• start_up_cost: "number value". Cost associated with starting up a unit.
• shut_down_cost: "number value". Cost associated with shutting down a unit.

## Illustrative unit commitment examples

### Step 1: defining the number of members of a unit type

A spine unit can represent multiple members. This can be incorporated in a model by setting the number_of_units parameter to a specific value. For example, if we define a single unit in a model as follows:

• unit_1
• number_of_units: 2

And we link the unit to a certain node_1 with a unit__to_node relationship.

• unit_1_to__node_1

The single spine unit defined here, now represents two members. This means that a single unit_flow variable will be created for this unit, but the restrictions as imposed by the Ramping and Reserves framework will be adapted to reflect the fact that there are two members present, thus doubling the total capacity.

### Step 2: choosing the online_variable_type

Next, we have to decide the online_variable_type for this unit, which will restrict the kind of values that the units_on variable can take. This basically comes down to deciding if we are working in a classical UC framework (unit_online_variable_type_binary), a clustered UC framework (unit_online_variable_type_integer), or a relaxed clustered UC framework (unit_online_variable_type_linear), in which a non-integer number of units can be online.

The classical UC framework can only be applied when the number_of_units equals 1.

### Step 3: imposing a minimum operating point

The output of an online unit to a specific node can be restricted to be above a certain minimum by choosing a value for the minimum_operating_point parameter. This parameter is defined for the unit__to_node relationship, and is given as a fraction of the unit_capacity. If we continue with the example above, and define the following objects, relationships, and parameters:

• unit_1
• number_of_units: 2
• unit_online_variable_type: "unit_online_variable_type_integer"
• unit_1_to__node_1
• minimum_operating_point: 0.2
• unit_capacity: 200

It can be seen that in this case the unit_flow form unit_1 to node_1 must for any timestep $t$ be larger than $units\_on(t) * 0.2 * 200$

### Step 4: imposing a minimum up or down time

Spine units can also be restricted in their commitment status with minimum up- or down times by choosing a value for the min_up_time or min_down_time respectively. These parameters are defined for the unit object, and should be duration values. We can continue the example and add a minimum up time for the unit:

• unit_1
• number_of_units: 2
• unit_online_variable_type: "unit_online_variable_type_integer"
• min_up_time: 2h
• unit_1_to__node_1
• minimum_operating_point: 0.2
• unit_capacity: 200

Whereas the units_on variable was restricted (before inclusion of the min_up_time parameter) to be smaller than or equal to the number_of_units for any timestep $t$, it now has to be smaller than or equal to the number_of_units decremented with the units_started_up summed over the timesteps that include t - min_up_time. This implies that a unit which has started up, has to stay online for at least the min_up_time

To consider a simple example let's assume that we have a model with a resolution of 1h. Suppose that before t, there is no member of the unit online and in timestep t -> t + 1h, one member starts up. Another member starts up in timestep t + 1h \-> t + 2h. The first startup, along with the minimum up time of 2 hours implies that the units_on variable of this unit has now changed to $1$ in timestep t -> t + 1h and can not go back to $0$ in timestep t-> t + 1h -> t + 2h. The second startup further restricts the number of units that are allowed to be online, it can be seen that the following restrictions apply when both startups are combined with the minimum up time of 2h:

• t-> t + 1h : $units\_on = 1$
• t + 1h -> t + 2h: $units\_on = 2$
• t + 2h-> t + 3h: $units\_on \in {1,2}$
• t + 3h-> t + 4h: $units\_on \in {0,1,2}$

The minimum down time restrictions operate in very much the same way, they simply impose that units that have been shut down, have to stay offline for the chosen period of time.

### Step 5: allocationg a cost to startups or shutdowns

Costs can be allocated to startups or shutdowns by choosing a value for the start_up_cost or shut_down_cost respectively.

### Step 6: defining unit availabilities

By defining a unit_availability_factor, the fact that typical members are not available all the time can be reflected in the model.

Typically, units are not available $100$% of the time, due to scheduled maintenance, unforeseen outages, or other things. This can be incorporated in the model by setting the unit_availability_factor to a fractional value. For each timestep in the model, an upper bound is then imposed on the units_on variable, equal to number_of_units $*$ unit_availability_factor. This parameter can not be used when the online_variable_type is binary. It should also be noted that when the online_variable_type is of integer type, the aforementioned product must be integer as well, since it will determine the value of the units_available parameter which is restricted to integer values. The default value for this parameter is $1$.

The unit_availability_factor can also be taken as a timeseries. By allowing a different availability factor for each timestep in the model, it can perfectly be used to represent intermittent technologies of which the output cannot be fully controlled.