Customizing the model

By default, build_model uses a formulation that combines modeling components from different publications, and that has been carefully tested, using our own benchmark scripts, to provide good performance across a wide variety of instances. This default formulation is expected to change over time, as new methods are proposed in the literature. You can, however, construct your own formulation, based on the modeling components that you choose, as shown in the next example.

using HiGHS
using UnitCommitment

import UnitCommitment:
    Formulation,
    KnuOstWat2018,
    MorLatRam2013,
    ShiftFactorsFormulation

instance = UnitCommitment.read_benchmark(
    "matpower/case118/2017-02-01",
)

model = UnitCommitment.build_model(
    instance = instance,
    optimizer = HiGHS.Optimizer,
    formulation = Formulation(
        pwl_costs = KnuOstWat2018.PwlCosts(),
        ramping = MorLatRam2013.Ramping(),
        startup_costs = MorLatRam2013.StartupCosts(),
        transmission = ShiftFactorsFormulation(
            isf_cutoff = 0.005,
            lodf_cutoff = 0.001,
        ),
    ),
)

Inspecting and modifying the model

Accessing decision variables

After building a model using UnitCommitment.build_model, it is possible to obtain a reference to the decision variables by calling model[:varname][index]. For example, model[:is_on]["g1",1] returns a direct reference to the JuMP variable indicating whether generator named "g1" is on at time 1. The script below illustrates how to build a model, solve it and display the solution without using the function UnitCommitment.solution.

using Cbc
using Printf
using JuMP
using UnitCommitment

# Load benchmark instance
instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")

# Build JuMP model
model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
)

# Solve the model
UnitCommitment.optimize!(model)

# Display commitment status
for g in instance.units
    for t in 1:instance.time
        @printf(
            "%-10s %5d %5.1f %5.1f %5.1f\n",
            g.name,
            t,
            value(model[:is_on][g.name, t]),
            value(model[:switch_on][g.name, t]),
            value(model[:switch_off][g.name, t]),
        )
    end
end

Fixing variables, modifying objective function and adding constraints

Since we now have a direct reference to the JuMP decision variables, it is possible to fix variables, change the coefficients in the objective function, or even add new constraints to the model before solving it.

The script below shows how the JuMP model can be modified after it is created. For more information on modifying an existing model, see the JuMP documentation.

using Cbc
using JuMP
using UnitCommitment

# Load benchmark instance
instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")

# Construct JuMP model
model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
)

# Fix the commitment status of the generator "g1" in time period 1 to 1.0
JuMP.fix(
    model[:is_on]["g1",1],
    1.0,
    force=true,
)

# Fix the production level of the generator "g1" above its minimum level in time period 1 and
# in scenario "s1" to 20.0 MW. Observe that the three-tuple dictionary key involves the scenario
# index "s1", as production above minimum is a second-stage decision variable.
JuMP.fix(
    model[:prod_above]["s1", "g1", 1],
    20.0,
    force=true,
)

# Enforce the curtailment of 20.0 MW of load at bus "b2" in time period 4 in scenario "s1".
JuMP.fix(
    curtail["s1", "b2", 4] =
    20.0,
    force=true,
)

# Change the objective function
JuMP.set_objective_coefficient(
    model,
    model[:switch_on]["g2",1],
    1000.0,
)

# Create a new constraint
@constraint(
    model,
    model[:is_on]["g3",1] + model[:is_on]["g4",1] <= 1,
)

# Solve the model
UnitCommitment.optimize!(model)

Adding new component to a bus

The following snippet shows how to add a new grid component to a particular bus. For each time step, we create decision variables for the new grid component, add these variables to the objective function, then attach the component to a particular bus by modifying some existing model constraints.

using Cbc
using JuMP
using UnitCommitment

# Load instance and build base model
instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
model = UnitCommitment.build_model(
    instance=instance,
    optimizer=Cbc.Optimizer,
)

# Get the number of time steps in the original instance
T = instance.time

# Create decision variables for the new grid component.
# In this example, we assume that the new component can
# inject up to 10 MW of power at each time step, so we
# create new continuous variables 0 ≤ x[t] ≤ 10.
@variable(model, x[1:T], lower_bound=0.0, upper_bound=10.0)

# For each time step
for t in 1:T

    # Add production costs to the objective function.
    # In this example, we assume a cost of $5/MW.
    set_objective_coefficient(model, x[t], 5.0)

    # Attach the new component to bus b1 in scenario s1, by modifying the
    # constraint `eq_net_injection`.
    set_normalized_coefficient(
        model[:eq_net_injection]["s1", "b1", t],
        x[t],
        1.0,
    )
end

# Solve the model
UnitCommitment.optimize!(model)

# Show optimal values for the x variables
@show value.(x)