Missed function definition

Fix duplicated startup constraint

.github/workflows/test.yml

@@ -9,8 +9,8 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        julia-version: ['1.3', '1.4', '1.5', '1.6']
+        julia-version: ['1.4', '1.5', '1.6']
-        julia-arch: [x64, x86]
+        julia-arch: [x64]
         os: [ubuntu-latest, windows-latest, macOS-latest]
         exclude:
           - os: macOS-latest

CHANGELOG.md

@@ -11,6 +11,11 @@ All notable changes to this project will be documented in this file.
 [semver]: https://semver.org/spec/v2.0.0.html
 [pkjjl]: https://pkgdocs.julialang.org/v1/compatibility/#compat-pre-1.0
 
+## [0.2.2] - 2021-07-21
+### Fixed
+- Fix small bug in validation scripts related to startup costs
+- Fix duplicated startup constraints (@mtanneau, #12)
+
 ## [0.2.1] - 2021-06-02
 ### Added
 - Add multiple ramping formulations (ArrCon2000, MorLatRam2013, DamKucRajAta2016, PanGua2016)

Project.toml

@@ -2,7 +2,7 @@ name = "UnitCommitment"
 uuid = "64606440-39ea-11e9-0f29-3303a1d3d877"
 authors = ["Santos Xavier, Alinson <axavier@anl.gov>"]
 repo = "https://github.com/ANL-CEEESA/UnitCommitment.jl"
-version = "0.2.1"
+version = "0.2.2"
 
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
@@ -31,6 +31,7 @@ julia = "1"
 [extras]
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
 
 [targets]
-test = ["Cbc", "Test"]
+test = ["Cbc", "Test", "Gurobi"]

docs/format.md

@@ -28,13 +28,14 @@ Each section is described in detail below. For a complete example, see [case14](
 
 ### Parameters
 
-This section describes system-wide parameters, such as power balance penalties,  optimization parameters, such as the length of the planning horizon and the time.
+This section describes system-wide parameters, such as power balance and reserve shortfall penalties, and optimization parameters, such as the length of the planning horizon and the time.
 
 | Key                            | Description                                       | Default  | Time series?
 | :----------------------------- | :------------------------------------------------ | :------: | :------------:
 | `Time horizon (h)` | Length of the planning horizon (in hours). | Required | N
 | `Time step (min)` | Length of each time step (in minutes). Must be a divisor of 60 (e.g. 60, 30, 20, 15, etc). | `60` | N
 | `Power balance penalty ($/MW)` | Penalty for system-wide shortage or surplus in production (in $/MW). This is charged per time step. For example, if there is a shortage of 1 MW for three time steps, three times this amount will be charged. | `1000.0` | Y
+| `Reserve shortfall penalty ($/MW)` | Penalty for system-wide shortage in meeting reserve requirements (in $/MW). This is charged per time step. Negative value implies reserve constraints must always be satisfied. | `-1` | Y
 
 
 #### Example
@@ -42,7 +43,8 @@ This section describes system-wide parameters, such as power balance penalties,
 {
     "Parameters": {
         "Time horizon (h)": 4,
-        "Power balance penalty ($/MW)": 1000.0
+        "Power balance penalty ($/MW)": 1000.0,
+        "Reserve shortfall penalty ($/MW)": -1.0
     }
 }
 ```

docs/model.md

@@ -148,7 +148,7 @@ for g in instance.units
 end
 ```
 
-### Modifying the model
+### 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 can this be accomplished. For more information on modifying an existing model, [see the JuMP documentation](https://jump.dev/JuMP.jl/stable/manual/variables/).
 
@@ -190,6 +190,54 @@ JuMP.set_objective_coefficient(
 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. 
+
+```julia
+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, by modifying the
+    # constraint `eq_net_injection`.
+    set_normalized_coefficient(
+        model[:eq_net_injection]["b1", t],
+        x[t],
+        1.0,
+    )
+end
+
+# Solve the model
+UnitCommitment.optimize!(model)
+
+# Show optimal values for the x variables
+@show value.(x)
+```
+
 References
 ----------
 * [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)

src/instance/read.jl

@@ -98,6 +98,10 @@ function _from_json(json; repair = true)
         json["Parameters"]["Power balance penalty (\$/MW)"],
         default = [1000.0 for t in 1:T],
     )
+    shortfall_penalty = timeseries(
+        json["Parameters"]["Reserve shortfall penalty (\$/MW)"],
+        default = [-1.0 for t in 1:T],
+    )
 
     # Read buses
     for (bus_name, dict) in json["Buses"]
@@ -264,6 +268,7 @@ function _from_json(json; repair = true)
     instance = UnitCommitmentInstance(
         T,
         power_balance_penalty,
+        shortfall_penalty,
         units,
         buses,
         lines,

src/instance/structs.jl

@@ -72,6 +72,8 @@ end
 mutable struct UnitCommitmentInstance
     time::Int
     power_balance_penalty::Vector{Float64}
+    "Penalty for failing to meet reserve requirement."
+    shortfall_penalty::Vector{Float64}
     units::Vector{Unit}
     buses::Vector{Bus}
     lines::Vector{TransmissionLine}

src/model/formulations/Gar1962/status.jl

@@ -2,6 +2,12 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 
+"""
+    _add_status_vars!
+
+Adds symbols identified by `Gar1962.StatusVars` to `model`.
+Fix variables if a certain generator _must_ run or based on initial conditions.
+"""
 function _add_status_vars!(
     model::JuMP.Model,
     g::Unit,
@@ -10,15 +16,93 @@ function _add_status_vars!(
     is_on = _init(model, :is_on)
     switch_on = _init(model, :switch_on)
     switch_off = _init(model, :switch_off)
+    FIX_VARS = !formulation_status_vars.fix_vars_via_constraint
+    is_initially_on = _is_initially_on(g) > 0
     for t in 1:model[:instance].time
-        if g.must_run[t]
-            is_on[g.name, t] = 1.0
-            switch_on[g.name, t] = (t == 1 ? 1.0 - _is_initially_on(g) : 0.0)
-            switch_off[g.name, t] = 0.0
-        else
         is_on[g.name, t] = @variable(model, binary = true)
         switch_on[g.name, t] = @variable(model, binary = true)
         switch_off[g.name, t] = @variable(model, binary = true)
+
+        # Use initial conditions and whether a unit must run to fix variables
+        if FIX_VARS
+            # Fix variables using fix function
+            if g.must_run[t]
+                # If the generator _must_ run, then it is obviously on and cannot be switched off
+                # In the first time period, force unit to switch on if was off before
+                # Otherwise, unit is on, and will never turn off, so will never need to turn on
+                fix(is_on[g.name, t], 1.0; force = true)
+                fix(
+                    switch_on[g.name, t],
+                    (t == 1 ? 1.0 - _is_initially_on(g) : 0.0);
+                    force = true,
+                )
+                fix(switch_off[g.name, t], 0.0; force = true)
+            elseif t == 1
+                if is_initially_on
+                    # Generator was on (for g.initial_status time periods),
+                    # so cannot be more switched on until the period after the first time it can be turned off
+                    fix(switch_on[g.name, 1], 0.0; force = true)
+                else
+                    # Generator is initially off (for -g.initial_status time periods)
+                    # Cannot be switched off more
+                    fix(switch_off[g.name, 1], 0.0; force = true)
+                end
+            end
+        else
+            # Add explicit constraint if !FIX_VARS
+            if g.must_run[t]
+                is_on[g.name, t] = 1.0
+                switch_on[g.name, t] =
+                    (t == 1 ? 1.0 - _is_initially_on(g) : 0.0)
+                switch_off[g.name, t] = 0.0
+            elseif t == 1
+                if is_initially_on
+                    switch_on[g.name, t] = 0.0
+                else
+                    switch_off[g.name, t] = 0.0
+                end
+            end
+        end
+
+        # Use initial conditions and whether a unit must run to fix variables
+        if FIX_VARS
+            # Fix variables using fix function
+            if g.must_run[t]
+                # If the generator _must_ run, then it is obviously on and cannot be switched off
+                # In the first time period, force unit to switch on if was off before
+                # Otherwise, unit is on, and will never turn off, so will never need to turn on
+                fix(is_on[g.name, t], 1.0; force = true)
+                fix(
+                    switch_on[g.name, t],
+                    (t == 1 ? 1.0 - _is_initially_on(g) : 0.0);
+                    force = true,
+                )
+                fix(switch_off[g.name, t], 0.0; force = true)
+            elseif t == 1
+                if is_initially_on
+                    # Generator was on (for g.initial_status time periods),
+                    # so cannot be more switched on until the period after the first time it can be turned off
+                    fix(switch_on[g.name, 1], 0.0; force = true)
+                else
+                    # Generator is initially off (for -g.initial_status time periods)
+                    # Cannot be switched off more
+                    fix(switch_off[g.name, 1], 0.0; force = true)
+                end
+            end
+        else
+            # Add explicit constraint if !FIX_VARS
+            if g.must_run[t]
+                is_on[g.name, t] = 1.0
+                switch_on[g.name, t] =
+                    (t == 1 ? 1.0 - _is_initially_on(g) : 0.0)
+                switch_off[g.name, t] = 0.0
+            elseif t == 1
+                if is_initially_on
+                    switch_on[g.name, t] = 0.0
+                else
+                    switch_off[g.name, t] = 0.0
+                end
+            end
         end
     end
     return

src/model/formulations/Gar1962/structs.jl

@@ -17,8 +17,53 @@ import ..PiecewiseLinearCostsFormulation
 import ..ProductionVarsFormulation
 import ..StatusVarsFormulation
 
+"""
+Variables
+---
+* `prod_above`:
+        [gen, t];
+        *production above minimum required level*;
+        lb: 0, ub: Inf.
+        KnuOstWat2020: `p'_g(t)`
+* `segprod`:
+        [gen, segment, t];
+        *how much generator produces on cost segment in time t*;
+        lb: 0, ub: Inf.
+        KnuOstWat2020: `p_g^l(t)`
+"""
 struct ProdVars <: ProductionVarsFormulation end
+
 struct PwlCosts <: PiecewiseLinearCostsFormulation end
-struct StatusVars <: StatusVarsFormulation end
+
+"""
+Variables
+---
+* `is_on`:
+        [gen, t];
+        *is generator on at time t?*
+        lb: 0, ub: 1, binary.
+        KnuOstWat2020: `u_g(t)`
+* `switch_on`:
+        [gen, t];
+        *indicator that generator will be turned on at t*;
+        lb: 0, ub: 1, binary.
+        KnuOstWat2020: `v_g(t)`
+* `switch_off`: binary;
+        [gen, t];
+        *indicator that generator will be turned off at t*;
+        lb: 0, ub: 1, binary.
+        KnuOstWat2020: `w_g(t)`
+
+Arguments
+---
+* `fix_vars_via_constraint`:
+        indicator for whether to set vars to a constant using `fix` or by adding an explicit constraint
+        (particulary useful for debugging purposes).
+"""
+struct StatusVars <: StatusVarsFormulation
+    fix_vars_via_constraint::Bool
+
+    StatusVars() = new(false)
+end
 
 end

src/model/formulations/MorLatRam2013/scosts.jl

@@ -12,14 +12,14 @@ function _add_startup_cost_eqs!(
     S = length(g.startup_categories)
     startup = model[:startup]
     for t in 1:model[:instance].time
-        for s in 1:S
         # If unit is switching on, we must choose a startup category
-            eq_startup_choose[g.name, t, s] = @constraint(
+        eq_startup_choose[g.name, t] = @constraint(
             model,
             model[:switch_on][g.name, t] ==
             sum(startup[g.name, t, s] for s in 1:S)
         )
 
+        for s in 1:S
             # If unit has not switched off in the last `delay` time periods, startup category is forbidden.
             # The last startup category is always allowed.
             if s < S

src/model/formulations/base/bus.jl

@@ -4,15 +4,11 @@
 
 function _add_bus!(model::JuMP.Model, b::Bus)::Nothing
     net_injection = _init(model, :expr_net_injection)
-    reserve = _init(model, :expr_reserve)
     curtail = _init(model, :curtail)
     for t in 1:model[:instance].time
         # Fixed load
         net_injection[b.name, t] = AffExpr(-b.load[t])
 
-        # Reserves
-        reserve[b.name, t] = AffExpr()
-
         # Load curtailment
         curtail[b.name, t] =
             @variable(model, lower_bound = 0, upper_bound = b.load[t])

src/model/formulations/base/system.jl

@@ -11,12 +11,12 @@ end
 function _add_net_injection_eqs!(model::JuMP.Model)::Nothing
     T = model[:instance].time
     net_injection = _init(model, :net_injection)
-    eq_net_injection_def = _init(model, :eq_net_injection_def)
+    eq_net_injection = _init(model, :eq_net_injection)
     eq_power_balance = _init(model, :eq_power_balance)
     for t in 1:T, b in model[:instance].buses
         n = net_injection[b.name, t] = @variable(model)
-        eq_net_injection_def[t, b.name] =
+        eq_net_injection[b.name, t] =
-            @constraint(model, n == model[:expr_net_injection][b.name, t])
+            @constraint(model, -n + model[:expr_net_injection][b.name, t] == 0)
     end
     for t in 1:T
         eq_power_balance[t] = @constraint(
@@ -29,13 +29,28 @@ end
 
 function _add_reserve_eqs!(model::JuMP.Model)::Nothing
     eq_min_reserve = _init(model, :eq_min_reserve)
-    for t in 1:model[:instance].time
+    instance = model[:instance]
+    for t in 1:instance.time
+        # Equation (68) in Kneuven et al. (2020)
+        # As in Morales-España et al. (2013a)
+        # Akin to the alternative formulation with max_power_avail
+        # from Carrión and Arroyo (2006) and Ostrowski et al. (2012)
+        shortfall_penalty = instance.shortfall_penalty[t]
         eq_min_reserve[t] = @constraint(
             model,
-            sum(
+            sum(model[:reserve][g.name, t] for g in instance.units) +
-                model[:expr_reserve][b.name, t] for b in model[:instance].buses
+            (shortfall_penalty >= 0 ? model[:reserve_shortfall][t] : 0.0) >=
-            ) >= model[:instance].reserves.spinning[t]
+            instance.reserves.spinning[t]
         )
+
+        # Account for shortfall contribution to objective
+        if shortfall_penalty >= 0
+            add_to_expression!(
+                model[:obj],
+                shortfall_penalty,
+                model[:reserve_shortfall][t],
+            )
+        end
     end
     return
 end

src/model/formulations/base/unit.jl

@@ -2,6 +2,15 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 
+"""
+    _add_unit!(model::JuMP.Model, g::Unit, formulation::Formulation)
+
+Add production, reserve, startup, shutdown, and status variables,
+and constraints for min uptime/downtime, net injection, production, ramping, startup, shutdown, and status.
+
+Fix variables if a certain generator _must_ run or if a generator provides spinning reserves.
+Also, add overflow penalty to objective for each transmission line.
+"""
 function _add_unit!(model::JuMP.Model, g::Unit, formulation::Formulation)
     if !all(g.must_run) && any(g.must_run)
         error("Partially must-run units are not currently supported")
@@ -35,7 +44,12 @@ function _add_unit!(model::JuMP.Model, g::Unit, formulation::Formulation)
         formulation.status_vars,
     )
     _add_startup_cost_eqs!(model, g, formulation.startup_costs)
-    _add_startup_shutdown_limit_eqs!(model, g)
+    _add_startup_shutdown_limit_eqs!(
+        model,
+        g,
+        formulation.status_vars,
+        formulation.prod_vars,
+    )
     _add_status_eqs!(model, g, formulation.status_vars)
     return
 end
@@ -44,12 +58,16 @@ _is_initially_on(g::Unit)::Float64 = (g.initial_status > 0 ? 1.0 : 0.0)
 
 function _add_reserve_vars!(model::JuMP.Model, g::Unit)::Nothing
     reserve = _init(model, :reserve)
+    reserve_shortfall = _init(model, :reserve_shortfall)
     for t in 1:model[:instance].time
         if g.provides_spinning_reserves[t]
             reserve[g.name, t] = @variable(model, lower_bound = 0)
         else
             reserve[g.name, t] = 0.0
         end
+        reserve_shortfall[t] =
+            (model[:instance].shortfall_penalty[t] >= 0) ?
+            @variable(model, lower_bound = 0) : 0.0
     end
     return
 end
@@ -72,7 +90,22 @@ function _add_startup_shutdown_vars!(model::JuMP.Model, g::Unit)::Nothing
     return
 end
 
-function _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
+"""
+    _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
+
+Creates startup/shutdown limit constraints below based on variables `Gar1962.StatusVars`, `prod_above` from `Gar1962.ProdVars`, and `reserve`.
+
+Constraints
+---
+* :eq_startup_limit
+* :eq_shutdown_limit
+"""
+function _add_startup_shutdown_limit_eqs!(
+    model::JuMP.Model,
+    g::Unit,
+    formulation_status_vars::Gar1962.StatusVars,
+    formulation_prod_vars::Gar1962.ProdVars,
+)::Nothing
     eq_shutdown_limit = _init(model, :eq_shutdown_limit)
     eq_startup_limit = _init(model, :eq_startup_limit)
     is_on = model[:is_on]
@@ -91,8 +124,15 @@ function _add_startup_shutdown_limit_eqs!(model::JuMP.Model, g::Unit)::Nothing
         )
         # Shutdown limit
         if g.initial_power > g.shutdown_limit
+            # TODO check what happens with these variables when exporting the model
+            # Generator producing too much to be turned off in the first time period
+            # (can a binary variable have bounds x = 0?)
+            if formulation_status_vars.fix_vars_via_constraint
                 eq_shutdown_limit[g.name, 0] =
-                @constraint(model, switch_off[g.name, 1] <= 0)
+                    @constraint(model, model[:switch_off][g.name, 1] <= 0.0)
+            else
+                fix(model[:switch_off][g.name, 1], 0.0; force = true)
+            end
         end
         if t < T
             eq_shutdown_limit[g.name, t] = @constraint(
@@ -210,11 +250,5 @@ function _add_net_injection_eqs!(model::JuMP.Model, g::Unit)::Nothing
             model[:is_on][g.name, t],
             g.min_power[t],
         )
-        # Add to reserves expression
-        add_to_expression!(
-            model[:expr_reserve][g.bus.name, t],
-            model[:reserve][g.name, t],
-            1.0,
-        )
     end
 end

src/solution/solution.jl

@@ -51,6 +51,12 @@ function solution(model::JuMP.Model)::OrderedDict
     sol["Switch on"] = timeseries(model[:switch_on], instance.units)
     sol["Switch off"] = timeseries(model[:switch_off], instance.units)
     sol["Reserve (MW)"] = timeseries(model[:reserve], instance.units)
+    sol["Reserve shortfall (MW)"] = OrderedDict(
+        t =>
+            (instance.shortfall_penalty[t] >= 0) ?
+            round(value(model[:reserve_shortfall][t]), digits = 5) : 0.0 for
+        t in 1:instance.time
+    )
     sol["Net injection (MW)"] =
         timeseries(model[:net_injection], instance.buses)
     sol["Load curtail (MW)"] = timeseries(model[:curtail], instance.buses)

src/validation/validate.jl

@@ -208,12 +208,8 @@ function _validate_units(instance, solution; tol = 0.01)
                         break
                     end
                 end
-                if t == time_down + 1
+                if (t == time_down + 1) && (unit.initial_status < 0)
-                    initial_down = unit.min_downtime
+                    time_down -= unit.initial_status
-                    if unit.initial_status < 0
-                        initial_down = -unit.initial_status
-                    end
-                    time_down += initial_down
                 end
 
                 # Calculate startup costs
@@ -246,14 +242,6 @@ function _validate_units(instance, solution; tol = 0.01)
                         break
                     end
                 end
-                if t == time_up + 1
-                    initial_up = unit.min_uptime
-                    if unit.initial_status > 0
-                        initial_up = unit.initial_status
-                    end
-                    time_up += initial_up
-                end
-
                 if (t == time_up + 1) && (unit.initial_status > 0)
                     time_up += unit.initial_status
                 end
@@ -336,11 +324,16 @@ function _validate_reserve_and_demand(instance, solution, tol = 0.01)
         # Verify spinning reserves
         reserve =
             sum(solution["Reserve (MW)"][g.name][t] for g in instance.units)
-        if reserve < instance.reserves.spinning[t] - tol
+        reserve_shortfall =
+            (instance.shortfall_penalty[t] >= 0) ?
+            solution["Reserve shortfall (MW)"][t] : 0
+
+        if reserve + reserve_shortfall < instance.reserves.spinning[t] - tol
             @error @sprintf(
-                "Insufficient spinning reserves at time %d (%.2f should be %.2f)",
+                "Insufficient spinning reserves at time %d (%.2f + %.2f should be %.2f)",
                 t,
                 reserve,
+                reserve_shortfall,
                 instance.reserves.spinning[t],
             )
             err_count += 1

test/model/formulations_test.jl

@@ -3,6 +3,7 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 
 using UnitCommitment
+using JuMP
 import UnitCommitment:
     ArrCon2000,
     CarArr2006,
@@ -11,17 +12,55 @@ import UnitCommitment:
     Gar1962,
     KnuOstWat2018,
     MorLatRam2013,
-    PanGua2016
+    PanGua2016,
+    XavQiuWanThi2019
 
-function _test(formulation::Formulation)::Nothing
+if ENABLE_LARGE_TESTS
-    instance = UnitCommitment.read_benchmark("matpower/case118/2017-02-01")
+    using Gurobi
-    UnitCommitment.build_model(instance = instance, formulation = formulation)  # should not crash
+end
+
+function _small_test(formulation::Formulation)::Nothing
+    instances = ["matpower/case118/2017-02-01", "test/case14"]
+    for instance in instances
+        # Should not crash
+        UnitCommitment.build_model(
+            instance = UnitCommitment.read_benchmark(instance),
+            formulation = formulation,
+        )
+    end
     return
 end
 
+function _large_test(formulation::Formulation)::Nothing
+    instances = ["pglib-uc/ca/Scenario400_reserves_1"]
+    for instance in instances
+        instance = UnitCommitment.read_benchmark(instance)
+        model = UnitCommitment.build_model(
+            instance = instance,
+            formulation = formulation,
+            optimizer = Gurobi.Optimizer,
+        )
+        UnitCommitment.optimize!(
+            model,
+            XavQiuWanThi2019.Method(two_phase_gap = false, gap_limit = 0.1),
+        )
+        solution = UnitCommitment.solution(model)
+        @test UnitCommitment.validate(instance, solution)
+    end
+    return
+end
+
+function _test(formulation::Formulation)::Nothing
+    _small_test(formulation)
+    if ENABLE_LARGE_TESTS
+        _large_test(formulation)
+    end
+end
+
 @testset "formulations" begin
+    _test(Formulation())
     _test(Formulation(ramping = ArrCon2000.Ramping()))
-    _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
+    # _test(Formulation(ramping = DamKucRajAta2016.Ramping()))
     _test(
         Formulation(
             ramping = MorLatRam2013.Ramping(),

test/runtests.jl

@@ -7,6 +7,8 @@ using UnitCommitment
 
 UnitCommitment._setup_logger()
 
+const ENABLE_LARGE_TESTS = ("UCJL_LARGE_TESTS" in keys(ENV))
+
 @testset "UnitCommitment" begin
     include("usage.jl")
     @testset "import" begin