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<li class="first-level active"><a href="#benchmark-solver">Benchmark Solver</a></li>
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<li class="second-level"><a href="#usage">Usage</a></li>
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<h1 id="benchmark-solver">Benchmark Solver</h1>
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<p>Solving an instance of the Unit Commitment problem typically involves more
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than simply building a Mixed-Integer Linear Programming and handing it over to the
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solver. Since the number of transmission and N-1 security constraints can
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easily exceed hundreds of millions for large instances of the problem, it is often
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necessary to iterate between MILP optimization and contingency screening, so
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that only necessary transmission constraints are added to the MILP.</p>
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<p><code>UnitCommitment.jl</code> includes a fast implementation of the contingency
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screening method described in
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<a href="https://doi.org/10.1109/TPWRS.2019.2892620">[1]</a>, which is able to
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efficiently handle even ISO-scale instances of the problem. The method makes
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use of Injection Shift Factors (ISFs) and Line Outage Distribution Factors
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(LODFs) to model DC power flows and N-1 contingencies. If Julia is configured
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to use multiple threads (through the environment variable <code>JULIA_NUM_THREADS</code>)
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then multiple contingency scenarios are evaluated in parallel.</p>
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<h2 id="usage">Usage</h2>
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<p>To solve one of the benchmark instances using the included benchmark solver, use the method <code>UnitCommitment.solve</code>
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as shown in the example below.</p>
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<pre><code>julia> UnitCommitment.solve("ieee_rts/case118")
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[ Info: Loading instance: ieee_rts/case118
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[ Info: 54 units
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[ Info: 118 buses
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[ Info: 186 lines
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[ Info: Scaling problem (0.6 demands, 1.0 limits)...
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[ Info: Using Cbc as MILP solver (0.001 gap, 4 threads)
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[ Info: Computing sensitivity factors (0.001 ISF cutoff, 0.0001 LODF cutoff)...
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[ Info: Building MILP model (24 hours, 0.01 reserve)...
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[ Info: Optimizing...
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[ Info: Optimal value: 4.033106e+06
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[ Info: Solved in 8.73 seconds
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</code></pre>
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<p>With default settings, the solver does not consider any transmission or
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security constraints, and the peak load is automatically set to 60% of the
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installed capacity of the system. These, and many other settings, can be
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configured using keyword arguments. See the reference section below for more
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details. Sample usage:</p>
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<pre><code>julia> UnitCommitment.solve("ieee_rts/case118", demand_scale=0.7, security=true)
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[ Info: Loading instance: ieee_rts/case118
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[ Info: 54 units
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[ Info: 118 buses
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[ Info: 186 lines
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[ Info: Scaling problem (0.7 demands, 1.0 limits)...
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[ Info: Using Cbc as MILP solver (0.001 gap, 4 threads)
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[ Info: Computing sensitivity factors (0.001 ISF cutoff, 0.0001 LODF cutoff)...
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[ Info: Building MILP model (24 hours, 0.01 reserve)...
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[ Info: Optimizing...
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[ Info: Verifying flow constraints...
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[ Info: Optimal value: 4.888740e+06
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[ Info: Solved in 4.50 seconds
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</code></pre>
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<p>When transmission or N-1 security constraints are activated, the solver uses an
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iterative method to lazily enforce them. See <a href="https://doi.org/10.1109/TPWRS.2019.2892620">this
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paper</a> for a detailed description
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of the method. Injection Shift Factors (ISF) and Line Outage Distribution
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Factors (LODF) are used for the computation of DC power flows. </p>
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<div class="admonition note">
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<p class="admonition-title">Note</p>
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<p>Many of the benchmark instances were not originally designed for N-1
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security-constrained studies, and may become infeasible if these constraints
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are enforced. To avoid infeasibilities, the transmission limits can be
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increased through the keyword argument <code>limit_scale</code>.</p>
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</div>
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<p>By default, the MILP is solved using <a href="https://github.com/coin-or/Cbc">Cbc, the COIN-OR Branch and Cut
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solver</a>. If <code>UnitCommitment</code> is loaded after
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either <a href="https://github.com/JuliaOpt/CPLEX.jl">CPLEX</a> or
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<a href="https://github.com/SCIP-Interfaces/SCIP.jl">SCIP</a>, then these solvers will be
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used instead. A detailed solver log can be displayed by setting <code>verbose=true</code>.</p>
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<h2 id="reference">Reference</h2>
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<p><code>@docs
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UnitCommitment.solve</code></p></div>
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