Causality analysis of electricity market liberalization on electricity price using novel Machine Learning methods

TL;DR

This paper applies novel Causal Machine Learning methods to analyze how electricity market liberalization affects prices in the US, revealing a short-term 7% price decrease and demonstrating the effectiveness of DeepProbCP.

Contribution

It introduces Causal Machine Learning as a new approach for analyzing energy policy impacts and compares ML-based models, highlighting DeepProbCP's superior performance.

Findings

DeepProbCP outperforms other frameworks in causal impact analysis.

Market liberalization and new entrants led to a 7% short-term price decrease.

Causal ML methods provide valuable insights for energy policy decisions.

Abstract

Relationships between the energy and the finance markets are increasingly important. Understanding these relationships is vital for policymakers and other stakeholders as the world faces challenges such as satisfying humanity's increasing need for energy and the effects of climate change. In this paper, we investigate the causal effect of electricity market liberalization on the electricity price in the US. By performing this analysis, we aim to provide new insights into the ongoing debate about the benefits of electricity market liberalization. We introduce Causal Machine Learning as a new approach for interventions in the energy-finance field. The development of machine learning in recent years opened the door for a new branch of machine learning models for causality impact, with the ability to extract complex patterns and relationships from the data. We discuss the advantages of causal ML methods and compare the performance of ML-based models to shed light on the applicability of causal ML frameworks to energy policy intervention cases. We find that the DeepProbCP framework outperforms the other frameworks examined. In addition, we find that liberalization of, and individual players' entry to, the electricity market resulted in a 7% decrease in price in the short term.