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Update jump-api pr (#294)
* prepare more tests * sketch * temp examples * remove printing * remove printing * remove duplicatie def * move definition * format * format * simplify conic model * force nlp on nlp tests * fix param tests * cleanup parameter usage * remove code * cleanup usage of parameters * format * add temp dep * format * add temp dep * fix PSDSquare * temp fix for tests * format
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‎docs/Project.toml

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@@ -9,6 +9,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
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MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
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MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
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SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

‎docs/make.jl

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# Use of this source code is governed by an MIT-style license that can be found
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# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
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import Pkg
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Pkg.add(;
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url = "https://github.com/jump-dev/ParametricOptInterface.jl",
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rev = "jg/newdo",
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)
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using Documenter
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using DiffOpt
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using Literate

‎docs/src/examples/Thermal_Generation_Dispatch_Example_new.jl

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# # Thermal Generation Dispatch Example
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#md # [![](https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white)](@__REPO_ROOT_URL__/examples/Thermal_Generation_Dispatch_Example.jl)
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# This example illustrates the sensitivity analysis of thermal generation dispatch problem.
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# This problem can be described as the choice of thermal generation `g` given a demand `d`, a price for thermal generation `c` and a penalty price `c_{ϕ}` for any demand not attended ϕ.
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# ```math
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# \begin{split}
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# \begin{array} {ll}
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# \mbox{minimize} & \sum_{i=1}^{N} c_{i} g_{i} + c_{\phi} \phi \\
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# \mbox{s.t.} & g_{i} \ge 0 \quad i=1..N \\
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# & g_{i} \le G_{i} \quad i=1..N \\
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# & \sum_{i=1}^{N} g_{i} + \phi = d\\
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# \end{array}
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# \end{split}
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# ```
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# where
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# - `G_{i}` is the maximum possible generation for a thermal generator `i`
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# ## Define and solve the Thermal Dispatch Problem
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# First, import the libraries.
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using Test
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using JuMP
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import DiffOpt
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import LinearAlgebra: dot
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import HiGHS
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import MathOptInterface as MOI
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import Plots
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# Define the model that will be construct given a set of parameters.
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function generate_model(
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d_data::Float64;
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g_sup::Vector{Float64},
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c_g::Vector{Float64},
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c_ϕ::Float64,
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)
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## Creation of the Model and Parameters
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model = DiffOpt.quadratic_diff_model(HiGHS.Optimizer)
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set_silent(model)
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I = length(g_sup)
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## Variables
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@variable(model, g[i in 1:I] >= 0.0)
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@variable(model, ϕ >= 0.0)
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## Parameters
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@variable(model, d in Parameter(d_data))
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## Constraints
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@constraint(model, limit_constraints_sup[i in 1:I], g[i] <= g_sup[i])
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@constraint(model, demand_constraint, sum(g) + ϕ == d)
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## Objectives
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@objective(model, Min, dot(c_g, g) + c_ϕ * ϕ)
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## Solve the model
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optimize!(model)
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## Return the solved model
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return model
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end
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# Define the functions that will get the primal values `g` and `\phi` and sensitivity analysis of the demand `dg/dd` and `d\phi/dd` from a optimized model.
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function diff_forward(model::Model, ϵ::Float64 = 1.0)
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## Initialization of parameters and references to simplify the notation
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vect_ref = [model[:g]; model[:ϕ]]
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## Get the primal solution of the model
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vect = value.(vect_ref)
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## Reset the sensitivities of the model
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DiffOpt.empty_input_sensitivities!(model)
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## Pass the perturbation to the DiffOpt Framework
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DiffOpt.set_forward_parameter(model, model[:d], ϵ)
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## Compute the derivatives with the Forward Mode
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DiffOpt.forward_differentiate!(model)
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## Get the derivative of the model
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dvect = DiffOpt.get_forward_variable.(model, vect_ref)
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## Return the values as a vector
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return [vect; dvect]
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end
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function diff_reverse(model::Model, ϵ::Float64 = 1.0)
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## Initialization of parameters and references to simplify the notation
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vect_ref = [model[:g]; model[:ϕ]]
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## Get the primal solution of the model
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vect = value.(vect_ref)
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## Set variables needed for the DiffOpt Backward Framework
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dvect = Array{Float64,1}(undef, length(vect_ref))
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## Loop for each primal variable
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for i in 1:I+1
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## Reset the sensitivities of the model
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DiffOpt.empty_input_sensitivities!(model)
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## Pass the perturbation to the DiffOpt Framework
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DiffOpt.set_reverse_variable.(model, vect_ref[i], ϵ)
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## Compute the derivatives with the Forward Mode
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DiffOpt.reverse_differentiate!(model)
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## Get the derivative of the model
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dvect[i] = DiffOpt.get_reverse_parameter(model, model[:d])
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end
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## Return the values as a vector
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return [vect; dvect]
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end
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# Initialize of Parameters
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g_sup = [10.0, 20.0, 30.0]
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I = length(g_sup)
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d = 0.0:0.1:80
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d_size = length(d)
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c_g = [1.0, 3.0, 5.0]
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c_ϕ = 10.0;
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# Generate models for each demand `d`
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models = generate_model.(d; g_sup = g_sup, c_g = c_g, c_ϕ = c_ϕ);
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# Get the results of models with the DiffOpt Forward and Backward context
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result_forward = diff_forward.(models)
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optimize!.(models)
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result_reverse = diff_reverse.(models);
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# Organization of results to plot
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# Initialize data_results that will contain every result
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data_results = Array{Float64,3}(undef, 2, d_size, 2 * (I + 1));
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# Populate the data_results array
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for k in 1:d_size
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data_results[1, k, :] = result_forward[k]
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data_results[2, k, :] = result_reverse[k]
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end
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# ## Results with Plot graphs
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# ### Results for the forward context
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# Result Primal Values:
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Plots.plot(
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d,
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data_results[1, :, 1:I+1];
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title = "Generation by Demand",
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label = ["Thermal Generation 1" "Thermal Generation 2" "Thermal Generation 3" "Generation Deficit"],
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xlabel = "Demand [unit]",
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ylabel = "Generation [unit]",
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)
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# Result Sensitivity Analysis:
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Plots.plot(
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d,
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data_results[1, :, I+2:2*(I+1)];
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title = "Sensitivity of Generation by Demand",
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label = ["T. Gen. 1 Sensitivity" "T. Gen. 2 Sensitivity" "T. Gen. 3 Sensitivity" "Gen. Deficit Sensitivity"],
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xlabel = "Demand [unit]",
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ylabel = "Sensitivity [-]",
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)
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# ### Results for the reverse context
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# Result Primal Values:
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Plots.plot(
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d,
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data_results[2, :, 1:I+1];
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title = "Generation by Demand",
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label = ["Thermal Generation 1" "Thermal Generation 2" "Thermal Generation 3" "Generation Deficit"],
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xlabel = "Demand [unit]",
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ylabel = "Generation [unit]",
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)
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# Result Sensitivity Analysis:
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Plots.plot(
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d,
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data_results[2, :, I+2:2*(I+1)];
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title = "Sensitivity of Generation by Demand",
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label = ["T. Gen. 1 Sensitivity" "T. Gen. 2 Sensitivity" "T. Gen. 3 Sensitivity" "Gen. Deficit Sensitivity"],
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xlabel = "Demand [unit]",
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ylabel = "Sensitivity [-]",
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)

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