Repository containing supplementary data and code for "A Robust Approach to Chance Constrained Optimal Power Flow with Renewable Generation" by Lubin, Dvorkin, and Backhaus. DOI
Installation instructions:
The optimization model was implemented by using the JuMPChance extension to JuMP in the Julia programming language. Additionally, we used Gurobi 6.0 in our numerical experiments. Gurobi is a commercial solver which must be installed and licensed separately (one may easily use a different solver if Gurobi is not available, see the JuMP documentation).
The experiments require Julia 0.3 or later, and the following Julia packages:
- JuMP 0.9.0
- JuMPChance 0.1.1
- Gurobi.jl 0.1.26
- MAT
You should force the use of particular versions of these Julia packages with
julia> Pkg.pin("JuMP", v"0.9.0")
julia> Pkg.pin("JuMPChance", v"0.1.1")
julia> Pkg.pin("Gurobi", v"0.1.26")
Running the code:
The code for the experiments is contained in the codejl directory. Note that it is under 700 lines of code. The file input.jl contains routines and data structures for processing the input, and the file ccopfmodel_simulation.jl (heavily commented) contains the main simulation logic and optimization model.
You can run the robust chance-constrained OPF model by entering the run directory and executing:
julia ../codejl/ccopfmodel_simulation.jl bpa-season1-gamma1.0.dat
By default, this will run a small sample (10 hours) from the first season used in the experiment. See line 220 of ccopfmodel_simulation.jl to adjust this. The output of the simulation is a .mat file (ccopf_bpa_simulation_results_season1_gamma1.0.mat) which can be opened directly in MATLAB or loaded in Julia via the MAT package. Contained in the .mat file are the optimal objective values, solution times, solution status, and optimal values for the decision variables. See lines 323 and below of ccopfmodel_simulation.jl for more details on the output format.
The file bpa-season1-gamma1.0.dat specifies all of the input paths and parameters for the simulation. In particular, one can modify the robust_budget to chance the budget of uncertainty Γ for the parameters of the Gaussian distributions, as discussed in the paper.