Assessing the Electricity Market Performance Through Data Analytics Approaches: The Italian Case

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Implementing Time-of-Use Demand Response Program in microgrid considering energy storage unit participation and different capacities of installed wind power

Penetration of wind units in Microgrid (MG) imposes remarkable challenges on MG operation. Demand Response Programs (DRPs) and Energy Storage Units are used by MG operators to address these challenges. This paper analyzes the effect of running the Time-of-Use Demand Response Program (TOU-DRP) on an isolated MG by considering different capacities of installed wind power with/without energy storage unit. The energy storage unit is deployed to cover the stochastic nature of wind generation unit. TOU-DRP is modeled based on price elasticity and customer benefit function in an isolated MG. Different levels of customers’ participation in TOU-DRP has also been studied and its effects on operation cost, unserved energy, and wind power spillage are investigated comprehensively. To verify the proposed model’s efficiency, the study is implemented on an 11-bus MG over a 24-h period for twelve detailed case studies. The case study results confirmed the effectiveness of the proposed model in running DRP and providing MG operator a general overview for optimal operation

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

Predictive methods of electricity price: An application to the Italian electricity market

Price forecasting is a crucial element for the members of the electricity markets and business decision making to maximize their profits. The electricity prices have an impact on the behavior of market participants, and thus, predicting prices for generation companies, and consumers is essential for both the short-term profits in the Day-Ahead, Intra-Day and Ancillary markets, and the long-term benefits in the future planning, investment, and risk management. Therefore, participants in the electricity market need to accurately and effectively predict the price signal to manage market risk. In this paper, different forecasting models have been compared, and the most promising ones have been employed to forecast the short term Italian electricity market clearing price for achieving forecasting accuracy. In particular, simulations are performed for four principal regression methods, including Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer Perceptron. The performance of predicted models is compared through several performance metrics, including MAE, RMSE, R, and the total number of percentage error anomalies. The results indicate the SVM is the best choice for forecasting the electricity market price on the Italian case study

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 play a crucial role. The more accurate the prediction is, the lower the market risk is. In this paper, several machine learning algorithms (Support Vector Machine, Gaussian Processes Regression, Regression Trees, and Multi-Layer 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 (MAE), R-index, Mean Absolute Percentage Error (MAPE), 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

Impacts of the COVID-19 pandemic on the Italian electricity demand and markets

In this paper, the short-, mid-and long-term effects of the COVID-19 pandemic on the Italian power system, particularly electricity consumption behavior and electricity market prices, are investigated by defining various metrics. The investigation reveals that COVID-19 lockdown caused a drop in load consumption and, consequently, a decrement in day-ahead market prices and an increase in ancillary service prices

Data analytics in the electricity market: a systematic literature review

In the last decade, data analytics studies have covered a wide range of fields across the entire value chain in the electricity sector, from production and transmission to the electricity market, distribution, and load consumption. It is essential to integrate and organize the wide range of current scientific publications to effectively allow researchers and specialists to implement and progress cutting-edge methodologies in the future. Because of the electricity market’s significance in the value chain of the electricity sector, in this study, we structure a systematic literature review of the data analytics-related works following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) framework to categorize the more common applications and approaches in the electricity market field. After refining the identified studies from the Web of Science database using the inclusion and exclusion criteria, 925 articles were chosen as the final pool of literature.Investigation of the extracted studies reveals that the application of data analytics in the electricity market can be clustered into four distinct groups: Prediction, Demand Side Management (DSM), Analysis of the market power, and Market simulation. Within the categorized applications, Prediction with 67% is the most frequent application of data analytics in the electricity market, followed by market simulation (14%), analysis of the market power (9%), DSM (7%), and other applications (3%)