Italian Power Exchange Research Articles

Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework

This paper addresses a self-scheduling model for a multi-energy virtual power plant (MEVPP) to optimize its day-ahead energy and reserve schedules considering the participation in joint markets. The coordination of energy and reserve services is realized by developing a holistic market framework. MEVPP trades electric energy in day-ahead market and natural gas in natural gas market under the uniform price scheme. MEVPP provides reserve in ancillary service market under the pay-as-bid scheme considering uncertain market clearing prices. Reserve regulations are modeled for the reserve quality provided by MEVPP. MEVPP can sign contracts in capacity market for capacity adequacy. The electricity and natural gas imbalance payments and unsupplied reserve penalty resulting from uncertain PV generation are calculated in real-time. The case studies, based on the practical data from Italian power exchange and transmission system operator, show the economic achievements of MEVPP with multiple markets participation. The advantages of multi-energy coupling in improving flexibility and economic profit are numerically analyzed. MEVPP is proven to be a promising reserve service supplier for TSO. Because through reasonable regulations, the reserve quality of MEVPP can be improved with little impact on its total cost.

Spatial merit order effects of renewables in the Italian power exchange

Estimating the merit order effect of renewable energy sources provides key information inputs in several domains, notably in the design of green energy policies. Given the spatial nature of electricity supply and demand, it is important to disentangle the direct effect of renewables on zonal electricity prices from spatial spillovers. In this paper, we estimate spatial econometric models of the Italian zonal electricity prices, using IPEx daily average prices in the time window 2009–2015, for 6 market zones. The best specification is a spatial Durbin model including wind power, solar power, and hydropower production, as well as control variables. The model is estimated under different assumptions on the spatial-weighting matrix and in 3 sub-periods. Direct and indirect merit order effects are found stronger over time, especially for wind power; but the estimated merit order effect is smaller than in non-spatial models, suggesting that our analysis is capable of accounting for spatial spillovers.

COVID-19 lockdown and market power in the Italian electricity market

The Italian electricity market was characterized by a remarkable decrease in demand during the COVID-19 lockdown (10 March to 2 June 2020). There were also negative peaks of over 50% and record low prices of about 20 Euro/MW. This paper aims to investigate the exercise of market power in the Italian power exchange during the pandemic, explicitly considering transmission line congestion, and disentangling the measure of the unilateral market power from congestion rent and re-dispatching costs.We computed the Zonal Lerner Index for every hour in January–June 2020 for the main operators on the supply and the demand side in the Italian day-ahead market. Furthermore, we analyzed the correlation between market power, congestion, and the effect of COVID-19. The results showed that the exercise of market power on the supply (mark-up) and demand (mark-down) sides were considerably weakened during peak hours by massive demand reduction. However, it was surprisingly reinforced during emergency periods in specific off-peak hours.Overall, the main players have somehow increased the intensity of market power during the COVID-19 pandemic crisis, which calls for more pro-competitive action by the regulatory authority and paying more attention to heterogeneous players’ behaviors.

The value of carbon emission reduction induced by Renewable Energy Sources in the Italian power market

In this paper we investigate the role of Renewable Energy Sources (RES) on the Italian power exchange. The purpose of this analysis is to assess the impact of electricity generation from RES on the reduction of CO2 emissions and on the value of the power supply. The study is based on hourly zonal micro-data for 2018 from the Italian power market and identifies the amount of avoided carbon emissions related both to crowded-out thermal units and to potential “load-shedding” situations. Finally, the investigation leads to the assessment of both the economic value of RES penetration and to the economic value of the CO2 emissions avoided by renewable power generation. The results show that the annual savings of carbon emissions nationwide amount to nearly 22 Mt CO2 whereas the value of CO2 reduction is estimated at €348 million. The economic savings from large and small-scale wind and solar generation in 2018 account for nearly €19 billion and welfare is increased by 44%, thus confirming the net positive effect arising from RES promotion.

Short-Term Forecasting for the Electricity Spot Prices With Extreme Values Treatment

Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, and extreme values (also known as jumps, spikes, or outliers). This study aims to provide a comprehensive analysis of electricity price forecasting by comparing several outlier filtering techniques followed by various modeling frameworks. To this end, extreme values are first treated with five different filtering techniques and are then replaced by four different outlier replacement approaches. Next, the spikes-free series is divided into deterministic and stochastic components. The deterministic component includes long-term trend, yearly and weekly seasonalities, and bank holidays and is estimated through parametric and nonparametric approaches. On the other hand, the stochastic component accounts for the short-run dynamics of the price time series and is modeled using different univariate and multivariate models. The one-day-ahead out-of-sample forecast results for the Italian Power Exchange (IPEX), obtained for a whole year, suggest that the outliers pre-filtering give a high accuracy gain. In addition, multivariate modeling for the stochastic component outperforms univariate models.

Renewable Energy and Market Power in the Italian Electricity Market

The Italian electricity market has been characterized by a remarkable increase of renewable energy source (RES) supply since 2010, which has determined relevant structural changes in the electric system. Noticeably, during favorable weather conditions, such as sunny or windy hours, increasing supply of RES generation exerts a downward pressure to the formation of the equilibrium price in the market and at the same time forces an increase in line congestion. The aim of this paper is to investigate whether such RES increase has affected the exercise of market power in the Italian Power Exchange (IPEX), explicitly considering transmission line congestion. We employ our approach to construct the residual demand curve and to disentangle the measure of the unilateral market power from the congestion rent. We compute the zonal Lerner index during the period 2009 to 2013 for the main generators in the Italian day-ahead market and we analyze the correlation among market power, congestion and RES supply. In particular, we investigate whether RES development has affected congestion and firm's strategic behavior, empirically testing whether structural changes have occurred in market power or in congestion rent. Our results show that the exercise of market power has been considerably weakened during peak hours by the massive competition of RES, but it has been surprisingly reinforced in specific off-peak hours, in the absence of solar RES and in specific zones, where congestion yields market splitting. These findings support pro-competitive market regulation and reform strategies, and shed light on the impact of RES and congestion on market outcomes.

Market Power and Transmission Congestion in the Italian Electricity Market

Analysis of market power in electricity markets is relevant for understanding the competitive development of the industry’s restructuring and liberalization process, but in the existing literature, there is not an adequate consideration of line transmission congestion. The aim of this paper is to propose a new approach to measuring market power in the Italian Power Exchange (IPEX), explicitly considering transmission line congestion. We construct a new measure of the residual demand curve to disentangle unilateral market power from congestion rent for the main Italian generators during the period April 2004 to December 2007. In Italy, this period was one of stable transmission network structure. Following the approach of Wolak (2003, 2009), we measure the unilateral market power with the Lerner index (LI), computed as the inverse of the residual demand elasticity. In conclusion, the correct modeling of the residual demand curve including transmission congestions enables us to compute the zonal LI and therefore more accurately measure the market power when congestion occurs. Our results show that various generators exercise market power only in specific zones. These findings provide deeper understanding of market outcomes in the presence of congestion, suggesting appropriate policy directions for market surveillance and competition regulation.

A System of Hourly Demand in the Italian Electricity Market

The purpose of this paper is to analyze and test demand behavior in the organized electricity market. According to a theoretical framework of heterogeneous agents’ behavior, we estimate a complete multi-stage system weakly separable using individual demand bid data in the Italian Power Exchange. The novel contribution of this paper is twofold. Firstly, we model hourly demand of heterogeneous groups of agents acting in the Italian electricity market with a simultaneous system for all 24 hours. Secondly, we empirically measure the entire structure of expenditure elasticities and cross price elasticities for all 24 hours of the day, ascertaining whether hourly electricity demands can be considered normal or luxury goods and substitutes or complements in an organized electricity market. Econometric estimation shows that price elasticity tends to be higher when hourly price peak. Moreover, electricity exhibits both substitutability and complementarity characteristic in different hours of the day, the former during the day and the latter during the night. Electricity appears to be a normal good during nighttime and a luxury good during daytime. The demand structure has welfare improving policy implications, because appropriate regulation can favor consumer behavior adjustment to shave consumption away from peak prices, thus yielding lower aggregate equilibrium expenditures. To this end, we advocate reforming the actual administered two-price tariff structure to introduce real time pricing options for Italian final users.

Bayesian Analysis of Demand Elasticity in the Italian Electricity Market

The liberalization of the Italian electricity market is a decade old. Within these last ten years, the supply side has been extensively analyzed, but not the demand side. The aim of this paper is to provide a new method for estimation of the demand elasticity, based on Bayesian methods applied to the Italian electricity market. We used individual demand bids data in the day-ahead market in the Italian Power Exchange (IPEX), for 2011, in order to construct an aggregate demand function at the hourly level. We took into account the existence of both elastic and inelastic bidders on the demand side. The empirical results show that elasticity varies significantly during the day and across periods of the year. In addition, the elasticity hourly distribution is clearly skewed and more so in the daily hours. The Bayesian method is a useful tool for policy-making, insofar as the regulator can start with a priori historical information on market behavior and estimate actual market outcomes in response to new policy actions.

The effects of renewables in space and time: A regime switching model of the Italian power price

Renewable energy production can exercise a downward pressure on electricity prices by partly crowding out conventional units characterized by higher marginal costs (merit order effect). Yet, congestion induced by renewables would partly offset the merit order effect in the congested zone, unless renewables reduce the need for imports and allow the emergence of prosumers. These congestion effects of renewables are hereby jointly tested with the merit order effect by means of an endogenous regime-switching model wherein a regime corresponds to the observable status (congested/non-congested) of the grid. The model is taken to data from the Italian power exchange, observed in 2012 and 2013, with a focus on the line connecting Sicily with the South zone, a frequent bottleneck in the Italian transmission grid. The results confirm the merit order effect previously detected in the literature and highlight a negative congestion effect, i.e. renewables relieve congestion from Sicily, a systematic importer, but not from the Italian peninsula (the exporting region). This effect is mainly driven by the wind power in-feed.

Thermoeconomic analysis of a Compressed Air Energy Storage (CAES) system integrated with a wind power plant in the framework of the IPEX Market

Energy storage is regarded as a key factor to allow significant increase in the percentage of electricity generation from renewables. One of the most critical aspects related with energy storage is its economic feasibility, which intrinsically involves the analysis of the off-design conditions and the evaluation of the operating strategies using proper methodologies. This paper considers a promising system for mechanical energy storage constituted by a Compressed Air Energy Storage (CAES) integrated with a Hybrid Power Plant (HPP) and coupled with a wind farm. This system is modeled considering the South of Italy as the possible location. The HPP-CAES is simulated to operate on the Italian Power Exchange market, for one year, implementing suitable selling strategies. Cost analysis is performed using a thermoeconomic approach. Results show that reduced operating hours and large variations in the electricity production of the wind farm make the HPP-CAES cost-effective only when it is operated with the goal of solving local imbalances of the grid.

Market Power and Transmission Congestion in the Italian Electricity Market

Analysis of market power in electricity markets is relevant for understanding the competitive development of the industry's restructuring and liberalization process. Because the existing literature lacks adequate consideration of line transmission congestion, this paper sets forth a new approach to measuring market power in the Italian Power Exchange (IPEX) by explicitly taking into account transmission line congestion. We construct the residual demand curve to disentangle the measure of unilateral market power from congestion rent for the main Italian generators during the period of April 2004 to December 2007. In Italy, this was a period of stable transmission network structure. Following the approach of Wolak (2003, 2009), we measure the unilateral market power with the Lerner Index (LI) computed as the inverse of the residual demand elasticity. In conclusion, the correct modeling of the residual demand curve including transmission congestions allows us to compute the zonal LI and therefore more accurately measure the market power when congestion occurs. Our results show that various generators exercise market power in only some zones, which provides us with a deeper understanding of the IPEX and better support for designing appropriate market surveillance and competition regulation.

Electricity Demand in Wholesale Italian Market

In this paper we pursue two objectives: firstly we construct a theory based behavioral model of electricity demand in the Italian market; secondly we measure demand elasticity at hourly level, directly from consumer behavior. This is a novel approach providing the first attempt in the literature to estimate demand elasticity using individual demand bid data in the Italian Power Exchange (IPEX). Econometric estimation allows us to identify robust results, showing that elasticity varies significantly with: time of the day; day of the week; season of the year; pattern of line congestion; as well as according to the level of equilibrium price. This has meaningful policy implications: fostering more competition on the supply side could yield lower equilibrium prices and proportionately much higher quantities, for a lower offer curve shifted to the right would intersect a flatter portion of the demand curve.

Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics

In European countries, the last decade has been characterized by a deregulation of power production and electricity became a commodity exchanged in proper markets. This resulted in an increasing interest of the scientific community on electricity exchanges for modeling both market activity and price process. This paper analyzes electricity spot-prices of the Italian Power Exchange IPEX and proposes three different methods to model prices time series: a discrete-time univariate econometric model ARMA-GARCH and two computational-intelligence techniques Neural Network and Support Vector Machine. Price series exhibit a strong daily seasonality, addressed by analyzing separately a series for each of the 24 hours. One-day ahead forecasts of hourly prices have been considered so to compare the prediction performances of three different methods, with respect to the canonical benchmark model based on the random walk hypothesis. Results point out that Support Vector Machine methodology gives better forecasting accuracy for price time series, closely followed by the econometric technique.

Assessing the impact of forward trading, retail liberalization, and white certificates on the Italian wholesale electricity prices

How do policy actions affect the dynamics of deregulated electricity prices? We investigate this issue in the context of the Italian Power Exchange (IPEX), using data on the daily average day-ahead price (PUN) between April 2004 and December 2008. Estimates of baseline time series models (SARMAX and SARMAX-EGARCH) and their forecasting performances suggest that the trend in natural gas prices, market power indicators, deterministic weekly patterns, perceived temperatures, persistence in conditional volatility, and the inverse leverage effect are essential features of the PUN dynamics. We then augment the best-performing models with dummies that account for changes in the market architecture, such as the introduction of contracts for differences (CfDs) to support renewables, trading of white certificates for energy efficiency, and the demand-side liberalization. The findings show that changes in the market architecture affected both the PUN level and its volatility. Specifically, wholesale electricity prices and volatility appear to have decreased upon the introduction of CfDs, only to be pushed upwards following the start of white certificates' trading and retail liberalization. Moreover, after controlling for reforms the inverse leverage effect vanishes, and the persistence in volatility is lower than in the baseline estimates.

Empirical assessment of strategic behavior in the Italian power exchange

AbstractIn this paper we examine the bidding behavior of Enel in 2004 to estimate the extent to which Enel was bidding opportunistically in temporal and geographic markets in which it was indispensible. Two types of competitive benchmark for bid levels were used. First, bids submitted by Enel in temporal and geographic markets in which it was pivotal were compared with Enel's bids submitted for similar generating units in markets where potential for market power exercise is low. Second, price bids of Enel were compared with bids submitted for comparable plants by other small generators that have no market power. This analysis has shown that over the period from April to August 2004 the bidding behavior of Enel varied across plant types. Both competitive benchmarks suggest that price bids of Enel submitted for Combined Cycle Gas plants were about 40–50% above the competitive level when Enel was pivotal. The excessive bidding was also present for Traditional Oil plants and Repowered Gas plants, although at a lower level, and was not established at all for Traditional Coal plants. We have verified whether this conduct could have had a material impact on market prices. An analysis of impact suggests that Enel's bidding during the peak hours was indeed 40% in excess of competitive levels for its CCGT units, 30% on Repowering units, and 20% on Oil units, such bidding have resulted in 20–30% higher average peak energy prices (PUN) in Italy. Copyright © 2010 John Wiley & Sons, Ltd.

Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models.

Contagion in Electricity Markets: Does it Exist?

This paper investigates the existence of contagion effects in electricity markets. The concept of contagion has been developed for high frequency financial markets, see the World Bank definition(Word Bank, 2000). Following Pick (2005) and Pesaran - Pick (2007) the paper presents a canonical, econometric model of contagion and investigates the conditions under which contagion can be distinguished from mere interdependence. The theoretical and empirical distinction between contagion and interdependence is based upon precise identification conditions, discussed in the paper. The empirical analysis is based on different regional markets in the Italian Power Exchange (IPX) and we focus only on pure contagion relationship in the IPX at the Italian regional level. This is a novel result in economic literature. The analysis and identification of contagion requires that each individual market equations contains market specific regressors, consequently we have to involve market specific variables in structural equations in order to correctly specify the model. Pesaran - Pick (2007, p. 1266) show that ignoring endogeneity and interdependence can introduce a substantial upward bias in estimation of contagion coefficient. In general, problems of endogeneity requires usage of instrumental variables (IV)estimation and, in agreement with Pick (2005), we obtain consistency by including regional market specific fundamentals. The most important conclusions of this paper are that contagion can be identified separately from interdependence and that effects are asymmetric.

A game theory simulator for assessing the performances of competitive electricity markets

In the last years, electricity markets were created all over the world following different basis concepts. Market structure, market rules, demand levels, market concentration and energy sources to produce electricity have a strong influence on market performances. Modifications on these aspects may significantly affect market outcomes. Sensitivity analyses need proper simulation tools. In this paper a medium run electricity market simulator (MREMS) based on game theory is presented. This simulator incorporates two different games, one for the unit commitment of thermal units and one for strategic bidding and hourly market clearing. Either a Forchheimer (one leader) or Bertrand (all player are leaders) or even intermediate model with a whatever number of leaders can be selected, in dependence on the strategic behavior of the producers, allowing for the simulation of markets with different levels of concentration. The simulator was applied to analyse producers’ behavior during the first operative year of the Italian power exchange. A comparison between simulation and true market results was carried out in order to test the simulator and validate its simplifying hypotheses. MREMS, yet capable to be used stand-alone, was conceived as the heart of a long-term market simulator (LREMS) allowing to simulate the long-run evolution of the generation park (investments in new plants, refurbishment and dismission of older ones). LREMS is a hierarchic simulator: a long-term “outer” game takes yearly investment decisions based on mid-term price projections provided by MREMS. Although this paper is mainly devoted to describe MREMS, one specific section will provide an overview of the “outer” game implemented by LREMS.

Optimal hydrogen storage sizing for wind power plants in day ahead electricity market

Wind energy generation does not often match energy market prices, which are tightly related to consumer demand. The coupling of wind energy conversion system with a storage medium improves the programmability of such generation plants, allowing suppliers to bid both in the energy and services market, and providing suitable profit margins for investors. Today, the ‘renewable hydrogen’ system, where hydrogen is produced by electrolysis, as an energy storage medium represents a way to fully exploit the economic opportunity of energy markets. Optimal sizing of a wind energy storage plant based on hydrogen technology has been analysed. Algorithms are applied in different simulations, varying some parameters such as efficiency and power rating of the plant's sub-systems, that is, electrolyser, fuel cell and storage tank (H2) with regard to their actual and forecasted cost. As a case study, a big wind farm in Sicily and the day ahead market prices in the Italian power exchange have been considered. The results show the incidence of the different components over the total system cost, fixing the possible values that make such a plant feasible considering the net present value.