1,720,968 research outputs found
Empirical Analysis of Inter-Zonal Congestion in the Italian Electricity Market Using Multinomial Logistic Regression
Get PDFThe increasing integration of renewable energy sources (RESs) into the Italian electricity market has heightened inter-zonal congestion challenges as power flows vary across importing and exporting zones. Utilizing a Multinomial Logistic Regression model as an empirical approach, this study investigates the key factors driving inter-zonal congestion between zonal pairs from 2021 to 2023, focusing on how local and neighboring zones’ RES generation (wind, solar, and hydropower) and demand dynamics impact congestion probabilities. The findings reveal that increased local RES generation generally reduces the likelihood of congestion for importing regions but increases it for exporting zones. Specifically, higher wind and solar production in importing zones like CNOR and CSUD alleviates congestion by reducing the need for imports, while in exporting zones, such as NORD and CALA, increased RES generation can exacerbate congestion due to higher export volumes. Hydropower production shows similar trends, with local production mitigating congestion in importing zones but increasing it in exporting zones. In addition to the effects of local generation and demand within each zonal pair, the generation and demand from neighboring zones also have a notable and statistically significant impact. Although their marginal effects tend to be smaller, the contributions from neighboring zones are essential for comprehending the overall congestion dynamics. These insights underscore the need for strategic RES placement to enhance market efficiency and minimize congestion risks across the Italian zonal electricity market
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
Get PDFPenetration 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
Unsupervised Profiling of Operator Macro-Behaviour in the Italian Ancillary Service Market via Stability-Driven k-Means
Get PDFThe transition toward sustainability in the electric power sector, driven by increasingly renewable integration, has amplified the need to understand complex market dynamics. This study addresses a critical gap in the existing literature by presenting a systematic and reproducible methodology for profiling generating-unit operators’ macro-behaviour in the Italian Ancillary Services market (MSD). Focusing on the Northern zone (NORD) during the pivotal period of 2022–2024, a stability-driven k-means clustering framework is applied to a dataset of capacity-normalized features from the day-ahead market (MGP), intraday market (MI), and MSD. The number of clusters is determined using the Gap Statistic with a 1-SE criterion and validated with bootstrap stability (Adjusted Rand Index), resulting in a robust and reproducible 13-group taxonomy. The use of up-to-date data (2022–2024) enabled a unique investigation into post-2021 market phenomena, including the effects of geopolitical events and extreme price volatility. The findings reveal clear operator-coherent archetypes ranging from units that mainly trade in the day-ahead market to specialists that monetize flexibility in the MSD. The analysis further highlights the dominance of thermoelectric and dispatchable hydro technologies in providing ancillary services, while illustrating varying degrees of responsiveness to price signals. The proposed taxonomy offers regulators and policymakers a practical tool to identify inefficiencies, monitor concentration risks, and inform future market design and policy decisions
Clustering of electricity price: an application to the Italian electricity market
Get PDFAnalyzing the electricity price plays a vital role in market players in deregulated electricity markets. In this regard, proper clustering methods are more beneficial. In this paper, a comparative study of three major clustering algorithms, including K-Means, Fuzzy C-Means, and Hierarchical algorithm on the Italian National Single Price, has been carried out. Moreover, the impact of various parameters of the algorithms on the clustering results has been analyzed. The performance of the clustering methods has been compared through several clustering validity indexes, including the Silhouette index, Calinski-Harabasz index, and Davies-Bouldin indicator. Two distinct patterns consisting of working days and weekends (or holidays) are observable in the particular dataset
Applications of the Digital Twin and the Related Technologies Within the Power Generation Sector: A Systematic Literature Review
Get PDFDigital Twin (DT) technology has emerged as a valuable tool for researchers and en-gineers, enabling them to optimize performance and enhance system efficiency. This paper presents a comprehensive Systematic Literature Review (SLR) following the PRISMA framework to explore current applications of DT technology in the power generation sector while highlighting key advancements. A new framework is developed to categorize DTs in terms of time-scale horizons and applications, focusing on power plant types (emissive vs. non-emissive), operational behaviors (including condition monitoring, predictive maintenance, fault detection, power generation predic-tion, and optimization), and specific components (e.g., power transformers). The time-scale is subdivided into a six-level structure to precisely indicate the speed and time range at which it is used. More importantly, each category in the application is further subcategorized into a three-level framework: component-level (i.e., fundamental physical properties and operational characteristics), system-level (i.e., interac-tion of subsystems and optimization), and service-level (i.e., value-adding service outputs). This classification can be utilized by various parties, such as stakeholders, engineers, scientists, and policymakers, to gain both a general and detailed understanding of potential research and operational gaps. Addressing these gaps could im-prove asset longevity and reduce energy consumption and emissions
Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm
No full textAchieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price
Impact of Wind and Solar Generation on the Italian Zonal Electricity Price
Get PDFThis 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
Impacts of the COVID-19 pandemic on the Italian electricity demand and markets
No full textIn 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
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