Reaching New Lows? The Pandemic’s Consequences for Electricity Markets

Abstract

The large reductions in electricity demand caused by the COVID-19 crisis have disrupted electricity systems worldwide. This article draws insights from New York into the consequences of the pandemic for electricity markets. It disentangles the effects of the demand reductions, increased forecast errors, and fuel price drops on the day-ahead and real-time markets.

Does Automatic Wage Indexation Destroy Jobs? A Machine Learning Approach

“…Resce ( 2022 ) combines a difference-in-difference strategy with ML methods for counterfactual analysis, but limits the use of ML to assign observations to the treated or untreated group. Benatia and Gingras ( 2022 ), Cerqua et al ( 2021 ) and Cerqua and Letta ( 2022 ) investigate the effects of the COVID-19 crisis and, as in our case, cannot rely on a control group as everyone was affected by the pandemic. Benatia and Gingras ( 2022 ) use a neural network model to study the impact of COVID-19 measures on electricity demand in New York.…”

“…Benatia and Gingras ( 2022 ), Cerqua et al ( 2021 ) and Cerqua and Letta ( 2022 ) investigate the effects of the COVID-19 crisis and, as in our case, cannot rely on a control group as everyone was affected by the pandemic. Benatia and Gingras ( 2022 ) use a neural network model to study the impact of COVID-19 measures on electricity demand in New York. Cerqua et al ( 2021 ) test three different ML algorithms to estimate excess mortality in Italy.…”

“…Varian ( 2016 ) suggested that counterfactual building is essentially a predictive task for which ML is very well suited. Early literature with empirical applications using ML in a counterfactual setting has appeared (Abrell et al, 2019 ; Benatia & Gingras, 2022 ; Burlig et al, 2020 ; Cerqua & Letta, 2022 ; Cerqua et al, 2021 ; Resce, 2022 ; Souza, 2019 ). Abrell et al ( 2019 ), Burlig et al ( 2020 ) and Souza ( 2019 ) are in the energy economics field.…”