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43c68a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
function optimize!(model::JuMP.Model, method::XavQiuWanThi2019.Method)::Nothing
if !occursin("Gurobi", JuMP.solver_name(model))
method.two_phase_gap = false
end
function set_gap(gap)
JuMP.set_optimizer_attribute(model, "MIPGap", gap)
@info @sprintf("MIP gap tolerance set to %f", gap)
end
initial_time = time()
large_gap = false
has_transmission = false
for sc in model[:instance].scenarios
if length(sc.isf) > 0
has_transmission = true
end
if has_transmission && method.two_phase_gap
set_gap(1e-2)
large_gap = true
end
end
while true
time_elapsed = time() - initial_time
time_remaining = method.time_limit - time_elapsed
if time_remaining < 0
@info "Time limit exceeded"
break
end
@info @sprintf(
"Setting MILP time limit to %.2f seconds",
time_remaining
)
JuMP.set_time_limit_sec(model, time_remaining)
@info "Solving MILP..."
JuMP.optimize!(model)
has_transmission || break
@info "Verifying transmission limits..."
time_screening = @elapsed begin
violations = []
for sc in model[:instance].scenarios
push!(
violations,
_find_violations(
model,
sc,
max_per_line = method.max_violations_per_line,
max_per_period = method.max_violations_per_period,
),
)
end
end
@info @sprintf(
"Verified transmission limits in %.2f seconds",
time_screening
)
violations_found = false
for v in violations
if !isempty(v)
violations_found = true
end
end
if violations_found
for (i, v) in enumerate(violations)
_enforce_transmission(model, v, model[:instance].scenarios[i])
end
else
@info "No violations found"
if large_gap
large_gap = false
set_gap(method.gap_limit)
else
break
end
end
end
return
end
explanation = """
What is the Two-Phase Gap?
Phase 1:
The solver is allowed to terminate early, accepting a looser optimality gap (e.g., 1%). This means the solver can stop as soon as it finds a solution within 1% of the best possible bound. This phase is fast and is used to quickly identify major issues, such as transmission constraint violations.
Phase 2:
If no violations are found (or after enforcing them), the solver is run again, but now with a tighter optimality gap (e.g., 0.1% or whatever is specified in method.gap_limit). This ensures the final solution is of high quality.
Benefit:
This approach saves time: you avoid spending a long time finding a very tight solution to a model that may need to be changed anyway (due to constraint violations found in screening).
Why is Gurobi Treated Differently?
Gurobi supports dynamic adjustment of the MIP gap and time limit during the solve, and is robust to repeated changes in these parameters.
Other solvers (like HiGHS, CBC, etc.) may not support dynamic gap adjustment as reliably, or may not respond as well to repeated changes in gap/time limit during iterative solves.
In the code:
If the solver is not Gurobi, the two-phase gap feature is disabled (method.two_phase_gap = false).
If Gurobi is used, the code will:
Set a loose gap (e.g., 1%) for the first phase.
After constraint screening, if no violations are found, it tightens the gap and resolves for a high-quality solution.
""" |