(Machine) Learning from the COVID-19 Lockdown about Electricity Market Performance with a Large Share of Renewables
Christoph Graf, Federico Quaglia, Frank A. Wolak

TL;DR
This paper analyzes the impact of COVID-19 lockdown-induced demand reductions on Italy's electricity market, revealing increased re-dispatch costs and potential market power exercises by suppliers with controllable units, highlighting challenges for renewable integration.
Contribution
It introduces a deep-learning model to estimate re-dispatch costs during low demand periods and links market power exercises to low net demand conditions with high renewable share.
Findings
Lockdown reduced day-ahead prices by 45%.
Re-dispatch costs increased by 73%.
Predicted re-dispatch costs were only 26% higher during lockdown.
Abstract
The negative demand shock due to the COVID-19 lockdown has reduced net demand for electricity -- system demand less amount of energy produced by intermittent renewables, hydroelectric units, and net imports -- that must be served by controllable generation units. Under normal demand conditions, introducing additional renewable generation capacity reduces net demand. Consequently, the lockdown can provide insights about electricity market performance with a large share of renewables. We find that although the lockdown reduced average day-ahead prices in Italy by 45%, re-dispatch costs increased by 73%, both relative to the average of the same magnitude for the same period in previous years. We estimate a deep-learning model using data from 2017--2019 and find that predicted re-dispatch costs during the lockdown period are only 26% higher than the same period in previous years. We argue…
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