Energy Economics Summer School

Course 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 be paired with a practical application with data and coding exercises.

Course Outline

• 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

Selected References

• Borenstein, S. (2012). The Redistributional Impact of Nonlinear Electricity Pricing. American Economic Journal: Economic Policy, 4(3), 56–90.
• Burlig, F., Knittel, C., Rapson, D., Reguant, M., & Wolfram, C. (2020). Machine Learning from Schools about Energy Efficiency. Journal of the Association of Environmental and Resource Economists, 7(6), 1181–1217.
• Bushnell, J., Mansur, E., & Saravia, C. (2008). Vertical Arrangments, Market Structure, and Competition: An Analysis of Restructured US Electricity Markets. American Economic Review, 98(1), 237–266.
• Burlig, F., Knittel, C., Rapson, D., Reguant, M., & Wolfram, C. (2020). Machine Learning from Schools about Energy Efficiency. Journal of the Association of Environmental and Resource Economists, 7(6), 1181–1217.Cullen, J. (2013). Measuring the environmental benefits of wind-generated electricity. American Economic Journal: Economic Policy, 5(4), 107–133.
• Fabra, N., & Reguant, M. (2014). Pass-through of emissions costs in electricity markets. American Economic Review, 104(9).
• Ito, K., & Reguant, M. (2016). Sequential markets, market power, and arbitrage. American Economic Review, 106(7), 1921–1957.
• Jessoe, K., & Rapson, D. (2015). Commercial and Industrial Demand Response Under Mandatory Time-of-Use Electricity Pricing. Journal of Industrial Economics, 63(3), 397–421.
• Joskow, P. L. (2019). Challenges for wholesale electricity markets with intermittent renewable generation at scale: the US experience. Oxford Review of Economic Policy, 35(2), 291–331.
• Kellogg, R., & Reguant, M. (2021). Energy and Environmental Markets, Industrial Organization, and Regulation.
• Reguant, M. (2019). The Efficiency and Sectoral Distributional Impacts of Large-Scale Renewable Energy Policies. Journal of the Association of Environmental and Resource Economists, 6(S1), S129–S168.

About the Instructors:

Mar Reguant is an Associate Professor in Economics at Northwestern University and Research Fellow of the Barcelona School of Economics. 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.

Jacint Enrich is a Postdoctoral Researcher at the Barcelona School of Economics. He is Project Manager of the ERC Consolidator grant awarded to Professor Reguant aimed at understanding the economic impacts of the energy transition. He received his PhD from the Toulouse School of Economics in 2021. He has been a Teaching Assistant and Main Instructor at the graduate level.