Italian Electricity Market Research Articles

Joint Component Estimation for Electricity Price Forecasting Using Functional Models

This work considers the issue of modeling and forecasting electricity prices within the functional time series approach. As this is often performed by estimating and predicting the different components of the price dynamics, we study whether jointly modeling the components, able to account for their inter-relations, could improve prediction with respect to a separate instance of modeling. To investigate this issue, we consider and compare the predictive performance of four different predictors. The first two, namely Smoothing Splines-Seasonal Autoregressive (SS-SAR) and Smoothing Splines-Functional Autoregressive (SS-FAR) are based on separate modeling while the third one is derived from a single-step procedure that jointly estimates all the components by suitably including exogenous variables. It is called Functional Autoregressive with eXogenous variables (FARX) model. The fourth one is a combination of the SS-FAR and FARX predictors. The predictive performances of the models are tested using electricity price data from the northern zone of the Italian electricity market (IPEX), both in terms of forecasting error indicators (MAE, MAPE and RMSE) and by means of the Diebold and Mariano test. The results point out that jointly estimating the components leads to significantly more accurate predictions than using a separate instance of modeling. In particular, the MAE, MAPE, and RMSE values for the best predictor, based on the FARX(3,0,4) model, are 4.25, 9.28, and 5.38, respectively. The percentage error reduction is about 20% with respect to SS-SAR(3,1) and about 10% with respect to SS-FAR(5). Finally, this study suggests that the forecasting errors are generally higher on Sunday and Monday, from hours 3 to 6 in the morning and 14 to 15 in the afternoon, and in June and December. On the other hand, prices are relatively lower on Wednesday, Thursday, and Friday, from hour 20 to 1 a.m., and in January and February.

Structural changes in contagion channels: the impact of COVID-19 on the Italian electricity market

AbstractOperating on electricity markets requires accurately identifying, quantifying, and measuring risk coupled with their corresponding return: this appears as a crucial point, particularly during and after the COVID-19 pandemic. The aim of the present paper is twofold. First, we propose a novel econometric approach to identifying relevant market factors that capture several elements of the risk transmission mechanism inherent in energy systems. The proposed model extends Bayesian graphical models with change points to a multiple-layer set-up. Multilayer graphs encompass the two relevant channels of shock transmission: volatility and price contagion effects. The choice of these two layers seems natural because electricity prices and their spiky nature, coupled with inherent volatility, constitute essential influential elements for market players to maximize their profits. The change-point specification allows for detecting relevant changes in the electricity market. Second, we apply the proposed econometric framework to the Italian zonal markets analyzing the effects of returns and volatility contagion in several periods detected by the model. The last time intervals identified by the change-point methodology overlap the COVID-19 pandemic period. The model captures relevant abrupt changes in prices and volatility in the zonal electricity market and provides new evidence of interconnections in the zones of the Italian market related to the risk alone, price process alone, and risk versus price process relationship and their interactions.

Measuring Switching Costs in the Italian Residential Electricity Market

Residential electricity markets in European countries are still characterized by low consumer engagement, especially where regulated and liberalized markets coex- ist. Using an original dataset on 2015–2018 prices for the Italian electricity mar- ket, augmented with the number of residential consumers, we study the presence and magnitude of switching costs—i.e., time-based and cognitive-based costs on consumers changing providers—in the liberalized market. We find that switch- ing from the incumbent involves high costs—almost as high as the yearly energy expenditure—while switching from competitors is less expensive. We also carry out two counterfactual analyses. In the first, we show that consumers would have incurred lower average switching costs over the years had the market been less concentrated. In the second, we simulate how switching costs could evolve once regulated prices are phased out, and the market is fully liberalized.

Taxing Carbon Emissions and Green Transition: The Case of the Italian Electricity Market

Taxing Carbon Emissions and Green Transition: The Case of the Italian Electricity Market

Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique

Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges.

Investment and Bidding Strategies for Optimal Transmission Management Dynamics: The Italian Case

This paper explores the allocation process of Financial Transmission Rights (FTRs) in the Italian electricity market. FTRs are financial instruments allowing market participants to hedge against transmission congestion, also playing a critical role in ensuring the efficient use of the transmission system. We present a linear programming (LP) model that simulates the FTRs allocation process, taking into account the transmission capacity limits of the electric network when the total revenue is considered as the utility function. Obtained results highlight that our solution allows us to develop better investment and bidding strategies for optimal transmission management dynamics. In particular, numerical simulations show good results, with an overall MAPE of approximately 7%, indicating that the model accurately predicts the allocation of transmission rights across the network. Overall, the paper provides insights into the inner workings of the FTR allocation process in Italy, also providing improved market efficiency while increasing revenue for market participants.

Complementarity formulation of games with random payoffs

We consider an n-player non-cooperative game where the payoff function of each player follows a multivariate distribution. This formulation is adopted to model a zonal electricity market in which generators operate by running conventional and renewable-based plants. The players in the market compete as in a Cournot model. We formulate this problem as a chance-constrained game by defining the payoff function of each player using a chance constraint. A full empirical analysis has been conducted on the Italian electricity market to test the impact of renewable generators in the light of decarbonization of the market and the impact of the volatility of the cost of conventional plants, mainly related to the volatility of gas prices. We finally test the robustness of the chance constraint formulation with an out of sample analysis.

Evaluation of Cost-at-Risk related to the procurement of resources in the ancillary services market. The case of the Italian electricity market

Measuring the risk exposure of TSOs on the dispatching market is a crucial task for the correct management of liberalized electricity markets. To fill a gap in the literature, the notion of Cost-at-Risk (CaR) is defined in the context of the dispatching market. Moreover, we propose a set of semi-parametric and non-parametric models for the estimation of the Cost at Risk (CaR) for the Italian TSO (Terna) and evaluate the corresponding out-of-sample forecasting performance. The empirical analysis relies on a rich hourly dataset provided by Terna, including several costs’ drivers. The results, in terms of 1-day and 30-day ahead predictions, suggest that the model with the globally best performance is the semi-parametric GAM-GARCH model.

Energy Consumption Forecasts by Gradient Boosting Regression Trees

Recent years have seen an increasing interest in developing robust, accurate and possibly fast forecasting methods for both energy production and consumption. Traditional approaches based on linear architectures are not able to fully model the relationships between variables, particularly when dealing with many features. We propose a Gradient-Boosting–Machine-based framework to forecast the demand of mixed customers of an energy dispatching company, aggregated according to their location within the seven Italian electricity market zones. The main challenge is to provide precise one-day-ahead predictions, despite the most recent data being two months old. This requires exogenous regressors, e.g., as historical features of part of the customers and air temperature, to be incorporated in the scheme and tailored to the specific case. Numerical simulations are conducted, resulting in a MAPE of 5–15% according to the market zone. The Gradient Boosting performs significantly better when compared to classical statistical models for time series, such as ARMA, unable to capture holidays.

Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order P is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.

A Nature-Inspired Algorithm to Enable the E-Mobility Participation in the Ancillary Service Market

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach in identifying a suitable schedule for the charging requests of a large EV fleet (up to 3200 units), with acceptable computational effort. The benefits on the economic sustainability of the E-carsharing fleet given by the participation in the electricity market are also confirmed by an extensive sensitivity analysis.

Evaluation of Cost-at-Risk Related to the Procurement of Resources in the Ancillary Services Market. The Case of the Italian Electricity Market

Evaluation of Cost-at-Risk Related to the Procurement of Resources in the Ancillary Services Market. The Case of the Italian Electricity Market

A Review on Unit Commitment Algorithms for the Italian Electricity Market

This paper focuses on the state-of-the-art of unit commitment (UC) and economic dispatch (ED) algorithms suitable for the Italian electricity market. In view of the spread of renewable energy systems (RES), the desired UC algorithm should be able to properly consider the uncertainty affecting key input variables into the formulation of the problem, as well as the different capabilities of dispatched power plants to provide ancillary services (e.g., voltage regulation). The goal of this paper is to resume the developments in UC and ED algorithms which occurred in the last decades, having a particular focus on alternating current (AC) security constrained (SC) approaches and stochastic ones, highlighting the advantages and weakness of each technique. This review is useful for the Italian TSO (Terna) to investigate what is the best solution to formulate a new algorithm to be potentially adopted in the framework of the Italian Ancillary Service Market, striving for an explicit modelization of stochastic variables and voltage constraints. This review is also useful to all system operators (SOs), independently to the market environment in which they operate, because UC algorithms are widely adopted to ensure real-time security of power systems. In conclusion, an SC-UC algorithm which takes into account both stochastic variables and AC formulation does not exist.

Electrification of the residential heat demand: An analysis of the power market potential to accommodate heat pumps

The share of renewables in the electric power generation is rapidly increasing and shifting the buildings heating demand to electricity represents a sustainable solution to decrease the fossil fuel dependency. In this paper, the maximum share of heating demand that can be switched to electricity by using heat pumps, according to the power market capacity, is estimated. By determining market price, plants generation and fuel consumption, the optimal share is calculated in terms of carbon emissions minimization. The methodology is developed with the support of a bid stack model (BISM) that performs an hourly simulation of the electricity market. Firstly, the analysis is led considering values of the heat pumps coefficient of performance, COP, in the range between 2 and 4. Then a focus is made on the COP dependence on local climatic conditions. In addition, three different time schedules of heat pumps activity are modelled to simulate the final users’ habits. Italy is considered as a case study to test the model. Italian market conditions are particularly favourable for the heat pumps utilization. The analysis is developed ex-post for the year 2019 as well as an outlook for the 2030 is provided. For the 2019 results show that the Italian electricity market allows a penetration of heat pumps in the range of 10%–56% for COP values between 2 and 4. In 2030 switching rates in the order of 5%–10% are estimated due to tighter market conditions.

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.

Forecasting Electricity Price in Different Time Horizons: An Application to the Italian Electricity Market

Electricity price is a crucial element for market players to maximize their profits. In this context, the forecast of the hour-ahead, day-ahead, and week-ahead electricity prices plays a crucial role. The more accurate the prediction is, the lower the market risk is. In this article, several machine learning algorithms (support vector machine, Gaussian processes regression, regression trees, and multilayer perceptron) with different structures have been adopted to forecast Italian wholesale electricity prices. Considering different time horizons (hourly, daily, and weekly), their performances have been compared through several performance metrics, including mean absolute error, R-index, mean absolute percentage error, and the number of anomalies in which the forecast error passes a threshold. The investigation reveals that, in general, SVM and tree-based models outperform other models at different time horizons.

The arithmetic of stepwise offer curves

In liberalized electricity markets, aggregated stepwise supply and demand curves are at the core of many relevant processes. Efficient and meaningful representations of the offer curves is an essential procedure for agents participating in those markets. However, there is not a formal framework that allows operating with those offer curves using basic arithmetic operations. In this paper we first formalize the concept of stepwise offer curve by explicitly defining the standard True Offer Curve (TOC). To overcome the inherit difficulties of this non-continuous TOC, we propose the Encoded Offer Curve (EOC), a continuous piecewise version that approximates the steps of the TOC with high accuracy. We present fast and simple specialized algorithms to obtain both TOC and EOC models, as well as a formal framework to deal with elementary mathematical operations involving TOCs and EOCs. The proposed framework has been tested in the Italian electricity market, computing the residual demand curves of the producers in a particular Market Zone; and in the Iberian electricity market, quantifying the differences in the bidding behavior of market agents in different stages of the COVID-19 pandemic.

Impact of Wind and Solar Generation on the Italian Zonal Electricity Price

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.

Modeling risk contagion in the Italian zonal electricity market

Ensuring the security of stable, efficient and reliable energy supplies has intensified the interconnections between energy markets. Imbalances between supply and demand due to operational failures, congestion and other sources of risk faced by market connections can lead to a system that is vulnerable to the spread of risk and its spill-over. The main contribution of this paper is the development and estimation of a Bayesian Graphical Vector-AutoRegression and a Bayesian Graphical Structural Equation Modelling with external regressors - BG-VARX and BG-SEMX, respectively - enhancing the proper analysis of market connections.The Italian electricity market has been chosen because it is a clear example of a zonal market where risk can spread over connected zones. We estimate, for the first time, within-day and across-day zonal market interconnections with a multivariate time series of hourly prices, actual and forecast power demand and forecast wind generation over the period 2014–2019 and evaluate the dynamics and persistence of zonal market connections, examining the spread of risk in the zones of the Italian electricity market.Our findings provide an improved, accurate explanation of risk contagion, identifying the zones that are most influential in terms of hub centrality (major transmitters) and authority centrality (major recipients), respectively, for intra-day and inter-day risk propagation in the Italian electricity market. In addition, the policy implications in terms of market-monitoring are discussed.

Competition in the Italian electricity market: The unforeseen social welfare losses of reform

The worldwide wave of reforms investing power industry has created new challenges to both supply demand side management. After deregulation, electric utilities restructured their opera-tions from vertically integrated mechanisms to open market systems in order to establish a new competitive sector. Reform has involved also the Italian power sector, but competition, as lar-gely shown by the empirical literature particularly in the first years of reform, has been far to be reached, and the electricity markets has been characterized by conditions of oligopoly and exercise of market power. This paper aims to analyze welfare loss and deviation from the competitive equilibrium recorded in the day ahead Italian electricity market after the first wave of reforms was almost implemented. The study presents a theoretical and empirical model to construct a competitive equilibrium, estimating market power, both, on the supply and demand sides of the day ahead electricity market. Results show the effect of non-competitive equilibriums for the hourly markets in the period 2013-2014. In an ideal competitive market, prices would be lower than historical prices by about 2-5% and quantities would be higher by about 0.5-1%.