Benchmark Solver

Solving an instance of the Unit Commitment problem typically involves more than simply building a Mixed-Integer Linear Programming and handing it over to the solver. Since the number of transmission and N-1 security constraints can easily exceed hundreds of millions for large instances of the problem, it is often necessary to iterate between MILP optimization and contingency screening, so that only necessary transmission constraints are added to the MILP.

UnitCommitment.jl includes a fast implementation of the contingency screening method described in [1], which is able to efficiently handle even ISO-scale instances of the problem. The method makes use of Injection Shift Factors (ISFs) and Line Outage Distribution Factors (LODFs) to model DC power flows and N-1 contingencies. If Julia is configured to use multiple threads (through the environment variable JULIA_NUM_THREADS) then multiple contingency scenarios are evaluated in parallel.

Usage

To solve one of the benchmark instances using the included benchmark solver, use the method UnitCommitment.solve as shown in the example below.

julia> UnitCommitment.solve("ieee_rts/case118")
[ Info: Loading instance: ieee_rts/case118
[ Info:           54 units
[ Info:          118 buses
[ Info:          186 lines
[ Info: Scaling problem (0.6 demands, 1.0 limits)...
[ Info: Using Cbc as MILP solver (0.001 gap, 4 threads)
[ Info: Computing sensitivity factors (0.001 ISF cutoff, 0.0001 LODF cutoff)...
[ Info: Building MILP model (24 hours, 0.01 reserve)...
[ Info: Optimizing...
[ Info: Optimal value: 4.033106e+06
[ Info: Solved in 8.73 seconds

With default settings, the solver does not consider any transmission or security constraints, and the peak load is automatically set to 60% of the installed capacity of the system. These, and many other settings, can be configured using keyword arguments. See the reference section below for more details. Sample usage:

julia> UnitCommitment.solve("ieee_rts/case118", demand_scale=0.7, security=true)
[ Info: Loading instance: ieee_rts/case118
[ Info:           54 units
[ Info:          118 buses
[ Info:          186 lines
[ Info: Scaling problem (0.7 demands, 1.0 limits)...
[ Info: Using Cbc as MILP solver (0.001 gap, 4 threads)
[ Info: Computing sensitivity factors (0.001 ISF cutoff, 0.0001 LODF cutoff)...
[ Info: Building MILP model (24 hours, 0.01 reserve)...
[ Info: Optimizing...
[ Info: Verifying flow constraints...
[ Info: Optimal value: 4.888740e+06
[ Info: Solved in 4.50 seconds

When transmission or N-1 security constraints are activated, the solver uses an iterative method to lazily enforce them. See this paper for a detailed description of the method. Injection Shift Factors (ISF) and Line Outage Distribution Factors (LODF) are used for the computation of DC power flows.

By default, the MILP is solved using Cbc, the COIN-OR Branch and Cut solver. If UnitCommitment is loaded after either CPLEX or SCIP, then these solvers will be used instead. A detailed solver log can be displayed by setting verbose=true.

Reference

@docs UnitCommitment.solve