In this 5-day course, we will learn about the economics of electricity markets and how to use code to model them. The class is taught by Mar Reguant.
The goal of the course is threefold:
Get familiar with trends and changes in the electricity market.
Get familiar with how to use data and models of electricity markets.
Get familiar with how machine learning tools can help with the above.
Preliminaries
We will be using Julia and Jupyter Notebooks to run the exercises. For this, you will need to install Julia in your computer, and Jupyter Notebooks.
You can follow this guide to add Julia to Jupyter Notebooks.
❗ It is highly recommended to make sure you can use Julia and Jupyter Notebooks before class starts. We will also spend time the first day to get this going.
ℹ You can test the installation by checking this small workbook plotting installed wind capacity in a few select countries. Download the exercise file and data file in the same folder, and open the exercise file in Jupyter Notebooks to get started.
Further optional prep
We will be using JuMP, a mathematical programming library for Julia. You do not need to actively prep for this, but if you are eager to learn about it, you can find a great JuMP tutorial here.
Flow
Each day, we will cover topics in the first half of the class, and then we will do examples in the second half. You will be able to modify the exercises at home for additional practice.
In each day page you will find the necessary material:
Day 1: Introduction
Day 2: Supply side I
Day 3: Supply side II
Day 4: Demand side I
Day 5: Demand side II
Additional Julia resources
Quantitative Economics with Julia (macro focus) https://julia.quantecon.org
MIT Introduction to Computational Thinking https://computationalthinking.mit.edu/Spring21/
PowerSystems.jl and other libraries - Julia libraries to build electricity models, developed at NREL https://www.nrel.gov/analysis/siip.html
A tutorial to learn JuMP (also referenced above) - https://github.com/jump-dev/JuMPTutorials.jl
With the support of:
