Intended for

Undergraduate degree in Economics, Statistics, or equivalent. Familiarity with Julia/Python/R or similar programming language is encouraged to be able to follow the class. This course is self-contained but it can also be taken in conjunction with The Economics and Econometrics of Climate Change Policy

Prerequisites

Undergraduate degree in Economics, Statistics, or equivalent. Familiarity with Julia/Python/R or similar programming language is encouraged to be able to follow the class.

Overview

The objective of this course is to introduce the participants to key challenges in the energy sector as we decarbonize our economies. The class will be centered on quantitative tools that can assist us in modeling the rapid transformation of the energy sector, with a main focus on electricity markets. We will cover tools of mixed integer programming and machine learning that can assist the quantitative modeling of these complex systems. Each lecture will contain a practical application with data and coding exercises.

Topics

The climate change challenge and trends in energy: introduction and facts
Supply side and wholesale pricing: intro and modeling basics
Machine learning in the supply side: dimensionality reduction of complex models
Demand side and retail pricing: basics intro and modeling basics
Machine learning in the demand side: demand response and energy efficiency

Mar Reguant is an Associate Professor in Economics at Northwestern University. Previously, she
worked at Stanford GSB. She received her Ph.D. from MIT in 2011. Her research uses high frequency data to study the impact of auction design and environmental regulation on electricity markets and energy intensive industries. She is a Research Associate at the NBER and the Industrial Organisation Programme Director at CEPR. She was awarded an NSF CAREER grant in 2015, a Sloan Research Fellowship in 2016, the Sabadell Prize for Economic Research in 2017, and the EAERE Award for Researchers in Environmental Economics under the Age of Forty in 2019. She has been recently awarded an ERC Consolidator grant to develop machine learning tools to understand the energy transition.