Repository containing supplementary data and code for the paper "Risk- and Variance-Aware Electricity Pricing" by Robert Mieth, Jip Kim and Yury Dvorkin. If you use this code or its for academic purposes please cite this paper.
Instructions:
The optimization model was implemented by using JuMP and auxiliary packages in the Julia programming language. Additionally, we used Mosek 8.1.0.63 in our numerical experiments. Mosek is a commercial solver which must be installed and licensed. The solver was chosen for its specific features for second-order conic programming. For more information on solvers, see the JuMP documentation.
The experiments require Julia 1.1. All necessary packages and their respective versions are distributed in the Project.toml file. The packages can be loaded in a new Julia environment directly via the Julia package manager IDE or running:
julia> import Pkg
julia> Pkg.activate(".")
julia> Pkg.instantiate()
Running the code:
All necessary functions are provided by the files src/model_definition.jl, src/input.jl, src/output.jl and src/tools.jl. All necessary data is provided in the casedata folder.
An exemplary case study that showcases the model implementation is provided as a Jupyter Notebook uncertain_dlmp_case_study.ipynb.
To run the notebook install the IJulia package:
julia> Pkg.add("IJulia")
and then in your terminal:
jupyter notebook uncertain_dlmp_case_study.ipynb
More explanation on the model is given in the notebook.