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<li class="first-level active"><a href="#benchmark-model">Benchmark Model</a></li>
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<li class="second-level"><a href="#building-and-solving-the-model">Building and Solving the Model</a></li>
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<li class="second-level"><a href="#decision-variables">Decision Variables</a></li>
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<li class="second-level"><a href="#accessing-the-solution">Accessing the Solution</a></li>
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<li class="second-level"><a href="#modifying-the-model">Modifying the Model</a></li>
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<li class="second-level"><a href="#reference">Reference</a></li>
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<div class="col-md-9" role="main">
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<h1 id="benchmark-model">Benchmark Model</h1>
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<p>UnitCommitment.jl includes a reference Mixed-Integer Linear Programming
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(MILP), built with <a href="https://github.com/JuliaOpt/JuMP.jl">JuMP</a>, which can
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either be used as-is to solve instances of the problem, or be extended to
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build more complex formulations.</p>
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<h2 id="building-and-solving-the-model">Building and Solving the Model</h2>
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<p>Given an instance and a JuMP optimizer, the function <code>build_model</code> can be used to
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build the reference MILP model. For example:</p>
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<pre><code class="julia">using UnitCommitment, JuMP, Cbc
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instance = UnitCommitment.load("ieee_rts/case118")
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model = build_model(instance, with_optimizer(Cbc.Optimizer))
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</code></pre>
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<p>The model enforces all unit constraints described in <a href="@ref">Unit Commitment
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Instances</a>, including ramping, minimum-up and minimum-down times. Some
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system-wide constraints, such as spinning reserves, are also enforced. The
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model, however, does not enforce transmission or N-1 security constraints,
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since these are typically generated on-the-fly.</p>
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<p>A reference to the JuMP model is stored at <code>model.mip</code>. After constructed, the model can
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be optimized as follows:</p>
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<pre><code class="julia">optimize!(model.mip)
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</code></pre>
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<h2 id="decision-variables">Decision Variables</h2>
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<p>References to all decision variables are stored at <code>model.vars</code>.
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A complete list of available decision variables is as follows:</p>
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<table>
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<thead>
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<tr>
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<th align="left">Variable</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td align="left"><code>model.vars.production[gi,t]</code></td>
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<td align="left">Amount of power (in MW) produced by unit with index <code>gi</code> at time <code>t</code>.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.reserve[gi,t]</code></td>
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<td align="left">Amount of spinning reserves (in MW) provided by unit with index <code>gi</code> at time <code>t</code>.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.is_on[gi,t]</code></td>
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<td align="left">Binary variable indicating if unit with index <code>gi</code> is operational at time <code>t</code>.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.switch_on[gi,t]</code></td>
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<td align="left">Binary variable indicating if unit with index <code>gi</code> was switched on at time <code>t</code>. That is, the unit was not operational at time <code>t-1</code>, but it is operational at time <code>t</code>.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.switch_off[gi,t]</code></td>
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<td align="left">Binary variable indicating if unit with index <code>gi</code> was switched off at time <code>t</code>. That is, the unit was operational at time <code>t-1</code>, but it is no longer operational at time <code>t</code>.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.unit_cost[gi,t]</code></td>
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<td align="left">The total cost to operate unit with index <code>gi</code> at time <code>t</code>. Includes start-up costs, no-load costs and any other production costs.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.cost[t]</code></td>
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<td align="left">Total cost at time <code>t</code>.</td>
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</tr>
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<tr>
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<td align="left"><code>model.vars.net_injection[bi,t]</code></td>
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<td align="left">Total net injection (in MW) at bus with index <code>bi</code> and time <code>t</code>. Net injection is defined as the total power being produced by units located at the bus minus the bus load.</td>
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</tr>
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</tbody>
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</table>
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<h2 id="accessing-the-solution">Accessing the Solution</h2>
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<p>To access the value of a particular decision variable after the
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optimization is completed, the function <code>JuMP.value(var)</code> can be used. The
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following example prints the amount of power (in MW) produced by each unit at time 5:</p>
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<pre><code class="julia">for g in instance.units
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@show value(model.vars.production[g.index, 5])
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end
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</code></pre>
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<h2 id="modifying-the-model">Modifying the Model</h2>
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<p>Prior to being solved, the reference model can be modified by using the variable references
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above and conventional JuMP macros. For example, the
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following code can be used to ensure that at most 10 units are operational at time 4:</p>
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<pre><code class="julia">using UnitCommitment, JuMP, Cbc
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instance = UnitCommitment.load("ieee_rts/case118")
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model = build_model(instance, with_optimizer(Cbc.Optimizer))
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@contraint(model.mip,
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sum(model.vars.is_on[g.index, 4]
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for g in instance.units) <= 10)
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optimize!(model.mip)
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</code></pre>
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<p>It is not currently possible to modify the constraints included in the
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reference model.</p>
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<h2 id="reference">Reference</h2>
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<p><code>@docs
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UnitCommitment.build_model</code></p></div>
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