mogottsch / amo-team-project

Two-Stage Stochastic Program for Electricity Grid Reinforcment And Scheduling

This repository contains the source code for a two-stage stochastic program of an electricity network.

Research Question

Our research question is inspired by the recent invasion of Ukraine by Russia, in which the electricity grid is repeatedly attacked, which leads to the suffering of innocent civilians. In order to prevent the damage we want to investigate the possibility of reinforcing the grid in a way that minimizes the impact on civilians. In addition we compare how an increase of renewable energy sources would affect such attacks.

Repository Structure

The source code of the model is contained in the src folder. The data folder contains the data for the model, which could take on different forms, depending on the parser used. Currently, we only implement one parser, which accepts an dataset generated from the RWTH Aachen University. In order to use this data, place the files Scenario_20XX.xlsx in the data folder. The results folder is intended to contain the results of the model/simulation, which will stored as .csv files.

file	description
rwth_parser.jl	loads the data from the RWTH Aachen University dataset
scenarios/	contains mulitple files intended for scenario generation and reduction
types.jl	contains the data types used in the model
process.jl	contains the model and the simulation (4 different electricity architectures in 12 different months)
main.jl	cli wrapper for the model and the simulation
notebooks/	contains jupyter notebooks
notebooks/01_single_model.ipynb	explains how to build and solve a single model
notebooks/01a_approximation_renewables.ipynb	explains conversion between wind speed/solar irradiance and maximum power generation
notebooks/02_simulation.ipynb	explains how to run a simulation consisting of multiple models
notebooks/02a_architectures.ipynb	explains the data used
notebooks/03_results.ipynb	visualizes results

How to run

1. Place the data in the data folder. (folder should contain Scenario_XXX.xlsx)
2. Install Gurobi or change the solver in src/model.jl.
3. Run julia ./src/main.jl ./results/sample/config.json to run a simulation. Look into the config.json file to see how to configure the simulation. For a description of the variables read ./notebooks/03_simulation.ipynb.

Run results.ipynb

The visualization of results is done with Python and requires additional setup:

1. Install conda
2. Run conda env create -f environment.yml
3. Run conda activate AMO