Understanding the Energy Transition with a Machine Learning Toolbox

The goal of this proposal is to build tools to better understand the economic impacts of the rapid transformation of electricity markets, and to help better design electricity markets going forward.

I propose to develop and implement novel statistical tools and structural models that contribute to our understanding of this rapid transformation.

The proposed research focuses both on firm strategic responses and investment (supply-side), as well as consumer behavior and welfare and distributional impacts (demand-side).

The proposal presents several projects and methodologies that examine these issues in detail with unique high-frequency micro-data on firms and households.

The tools and models developed in this proposal can help understand the impacts of the energy transition, both on the supply and the demand side.

Among the expected methodological contributions, I plan to combine machine learning tools with more standard structural modeling.

On the supply side, the proposal emphasizes the need to understand how strategic behavior interacts with market design in the presence of intermittent resources.

On the demand side, the proposal highlights the importance of understanding the distributional implications of such changes with special attention to the residential sector and the most vulnerable socio-economic households.