The thesis is a collection of three empirical essays focussing on the Italian electricity market. The first chapter, titled “Assessing market power in the Italian electricity market: a synthetic supply approach”, is a joint work with Prof. Luigi Grossi and Prof. Michael. G. Pollitt and was published among the Energy Policy Research Group Working Papers (no. 1930). This chapter investigates the bidding behaviour of the leading firms in the Italian electricity market, in particular on the Italian day-ahead market. The methodology adopted is synthetic supply, proposed by Ciarreta et al (2010), which consists in a two-step procedure, i.e. 1) power plants association and 2) hourly bidding schedule “translation”. Thanks to synthetic supply it is possible to create hourly counterfactual supply curves and see if there are differences between actual and synthetic equilibria. In other words, the idea is to investigate the difference in mark-up between the bidding schedules of power plants with very similar features. For this reason, power plants were associated with very strict criteria: technology, energy efficiency and many others. Furthermore, an algorithm in R was developed to compute hourly equilibria in the day-ahead market. This way, it is possible to assess if there are any differences between the bidding behaviour of the leading operators and the bidding behaviour of smaller generators. The findings suggest that during the years under examination (2015-2018), the market underwent higher prices and a non-negligible consumer surplus loss, especially during the months when above average heating and cooling were required. The second chapter, titled “Detecting strategic capacity withholding through a synthetic supply approach - Cui Prodest?”, puts forward a new methodology in the field of market monitoring. The methodology of synthetic supply and the R code are employed. However, the way synthetic supply is used in this chapter is completely new. A four-step procedure is proposed to investigate strategic capacity withholding, i.e. 1) hourly supply curves are created and extra capacity is artificially added to the supply schedule, 2) synthetic and actual prices are compared and anomalous price spikes detected, 3) synthetic and actual revenues are compared, to see if any market operator could have obtained more revenues from a scenario where some capacity was withheld, 4) the SSCW index is proposed to better interpret the results. The chapter carries out an empirical analysis of the Italian day-ahead market in 2018. This approach is a significant contribution to the literature because it enables the analysis of manipulative behaviour with a different perspective compared to the methodology currently available. The third chapter, titled “Covid-19 and the Italian electricity market: impacts, developments and implications”, investigates the effects of the restrictive measures employed by the Italian Government in response to Covid-19 on the Italian electricity market. This chapter presents a data description of the main market variables of the electricity market and uses an econometric model to estimate the effects of geographical and production lockdowns on the zonal quantities purchased and on the PUN (nationwide unit price). Data suggests that both quantity purchased, and prices were affected by the lockdowns, especially in the bidding areas of Northern Italy. The bidding zones of Southern Italy seem to be considerably less affected by restrictive measures. This is also confirmed by the econometric model. In addition, fall in demand led to a smaller quantity purchased, compared to the corresponding weeks in previous years, leading to substantial changes in the mix of energy sources. This chapter proposes a complete description of the evolution of the electricity market during the pandemic and provides useful policy recommendations on how financial resources should be allocated to relieve the Italian economy.

Essays on the Italian electricity market

ROSSETTO, FRANCESCO;GROSSI, Luigi
2021

Abstract

The thesis is a collection of three empirical essays focussing on the Italian electricity market. The first chapter, titled “Assessing market power in the Italian electricity market: a synthetic supply approach”, is a joint work with Prof. Luigi Grossi and Prof. Michael. G. Pollitt and was published among the Energy Policy Research Group Working Papers (no. 1930). This chapter investigates the bidding behaviour of the leading firms in the Italian electricity market, in particular on the Italian day-ahead market. The methodology adopted is synthetic supply, proposed by Ciarreta et al (2010), which consists in a two-step procedure, i.e. 1) power plants association and 2) hourly bidding schedule “translation”. Thanks to synthetic supply it is possible to create hourly counterfactual supply curves and see if there are differences between actual and synthetic equilibria. In other words, the idea is to investigate the difference in mark-up between the bidding schedules of power plants with very similar features. For this reason, power plants were associated with very strict criteria: technology, energy efficiency and many others. Furthermore, an algorithm in R was developed to compute hourly equilibria in the day-ahead market. This way, it is possible to assess if there are any differences between the bidding behaviour of the leading operators and the bidding behaviour of smaller generators. The findings suggest that during the years under examination (2015-2018), the market underwent higher prices and a non-negligible consumer surplus loss, especially during the months when above average heating and cooling were required. The second chapter, titled “Detecting strategic capacity withholding through a synthetic supply approach - Cui Prodest?”, puts forward a new methodology in the field of market monitoring. The methodology of synthetic supply and the R code are employed. However, the way synthetic supply is used in this chapter is completely new. A four-step procedure is proposed to investigate strategic capacity withholding, i.e. 1) hourly supply curves are created and extra capacity is artificially added to the supply schedule, 2) synthetic and actual prices are compared and anomalous price spikes detected, 3) synthetic and actual revenues are compared, to see if any market operator could have obtained more revenues from a scenario where some capacity was withheld, 4) the SSCW index is proposed to better interpret the results. The chapter carries out an empirical analysis of the Italian day-ahead market in 2018. This approach is a significant contribution to the literature because it enables the analysis of manipulative behaviour with a different perspective compared to the methodology currently available. The third chapter, titled “Covid-19 and the Italian electricity market: impacts, developments and implications”, investigates the effects of the restrictive measures employed by the Italian Government in response to Covid-19 on the Italian electricity market. This chapter presents a data description of the main market variables of the electricity market and uses an econometric model to estimate the effects of geographical and production lockdowns on the zonal quantities purchased and on the PUN (nationwide unit price). Data suggests that both quantity purchased, and prices were affected by the lockdowns, especially in the bidding areas of Northern Italy. The bidding zones of Southern Italy seem to be considerably less affected by restrictive measures. This is also confirmed by the econometric model. In addition, fall in demand led to a smaller quantity purchased, compared to the corresponding weeks in previous years, leading to substantial changes in the mix of energy sources. This chapter proposes a complete description of the evolution of the electricity market during the pandemic and provides useful policy recommendations on how financial resources should be allocated to relieve the Italian economy.
2021
Inglese
160
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/115294
Il codice NBN di questa tesi è URN:NBN:IT:UNIVR-115294