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Due to their particular feature, DC Electric Arc Furnace (EAF) installations are peculiar loads that cause moderate to severe Power Quality (PQ) disturbances in the feeding power systems. Among them, voltage fluctuations and waveform distortions are the most impactful ones, and they should be adequately addressed in order to mitigate the detrimental effects. Several types of models have been developed in order to evaluate the effects of EAFs on networks, and chaotic models have been specifically recognized as suitable tools to evaluate the impact of EAFs in terms of PQ disturbances. This paper compares the performance of three chaotic models (Chua, Lorenz and Rssler) aiming at estimating PQ indices values of DC EAFs. The procedure exploits a block diagramming tool of the DC EAF installation and minimizes the deviation of estimated PQ indices from the actual ones through a new Monte Carlo optimization procedure, performed upon the parameters of the three chaotic models. To comply with the time-varying nature of the current and voltage waveforms in a DC EAF installation and with the wide presence of interharmonics, traditional and advanced PQ indices are considered in this paper. Actual data collected at an Italian DC EAF installation are used to conduct a numerical comparative analysis and to validate the effectiveness of the models in estimating the PQ disturbances.
Antonio Bracale; Pierluigi Caramia; Guido Carpinelli; Pasquale Defalco; Angela Russo. Comparison of DC Electric Arc Furnace Chaotic Models for Power Quality Indices Assessment. IEEE Transactions on Industry Applications 2021, PP, 1 -1.
AMA StyleAntonio Bracale, Pierluigi Caramia, Guido Carpinelli, Pasquale Defalco, Angela Russo. Comparison of DC Electric Arc Furnace Chaotic Models for Power Quality Indices Assessment. IEEE Transactions on Industry Applications. 2021; PP (99):1-1.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Guido Carpinelli; Pasquale Defalco; Angela Russo. 2021. "Comparison of DC Electric Arc Furnace Chaotic Models for Power Quality Indices Assessment." IEEE Transactions on Industry Applications PP, no. 99: 1-1.
Modern distribution systems are characterized by increasing penetration of photovoltaic generation systems. Due to the uncertain nature of the solar primary source, photovoltaic power forecasting models must be developed in any energy management system for smart distribution networks. Although point forecasts can suit many scopes, probabilistic forecasts add further flexibility to any energy management system, and they are recommended to enable a wider range of decision making and optimization strategies. Real-time probabilistic photovoltaic power forecasting is performed in this paper by using an approach based on Bayesian bootstrap. Particularly, the Bayesian bootstrap is applied to three probabilistic forecasting models (i.e., linear quantile regression, gradient boosting regression tree and quantile regression neural network) to provide sample bootstrap distributions of the predictive quantiles of photovoltaic power. The heterogeneous nature of the selected models allows evaluating the performance of the Bayesian bootstrap within different forecasting frameworks. Several benchmarks and error indices and scores are used to assess the performance of Bayesian bootstrap in probabilistic photovoltaic power forecasting. Tests carried out on two actual photovoltaic power datasets for probabilistic forecasting demonstrates the effectiveness of the proposed approach.
Mokhtar Bozorg; Antonio Bracale; Mauro Carpita; Pasquale De Falco; Fabio Mottola; Daniela Proto. Bayesian bootstrapping in real-time probabilistic photovoltaic power forecasting. Solar Energy 2021, 225, 577 -590.
AMA StyleMokhtar Bozorg, Antonio Bracale, Mauro Carpita, Pasquale De Falco, Fabio Mottola, Daniela Proto. Bayesian bootstrapping in real-time probabilistic photovoltaic power forecasting. Solar Energy. 2021; 225 ():577-590.
Chicago/Turabian StyleMokhtar Bozorg; Antonio Bracale; Mauro Carpita; Pasquale De Falco; Fabio Mottola; Daniela Proto. 2021. "Bayesian bootstrapping in real-time probabilistic photovoltaic power forecasting." Solar Energy 225, no. : 577-590.
Waveforms distortion is a pressing concern in Smart Grids where a massive presence of new technologies in distributed energy resources and in advanced smart metering systems is expected. In this context, the increasing diffusion of high switching frequencies static converters and the growing usage of Power Line Communication push for research dealing with the assessment of waveforms with spectral components up to 150 kHz. The analysis of such waveforms is a challenge for researchers due to the contemporaneous presence of a high number of spectral components in the range of low- (up to 2 kHz) and high- (up to 150 kHz) frequencies, with their opposite needs in term of time window length (and frequency resolution). The main idea of this paper is to improve the performances of existing methods by using a joint method of analysis based on a profitable strategy of divide and conquer; the method guarantees the best compromise between accuracy and computational efforts. A Discrete Wavelet Transform initially divides the original waveform to obtain two frequency bands: the wavelet suitability for conducting multi-resolution time-frequency analysis on waveforms in different frequency bands with different frequency resolution is effectively exploited. Then, the sliding-window modified ESPRIT method and the sliding-window Discrete Fourier Transform which uses a Nuttal window are used for the analysis of the low- and high-frequency bands, respectively; the positive characteristics of each method are exploited, minimizing the drawbacks and integrating their behavior so that the whole joint method allows an accurate estimation of each low- and high-frequency spectral component with the required acceptable computational efforts. The proposed method is tested on synthetic and measured waveforms in terms of accuracy and computational efforts. The analysis of the numerical application results clearly reveals that the proposed method improves the performances of existing methods of analysis in the examined cases.
Guido Carpinelli; Antonio Bracale; Pietro Varilone; Tomasz Sikorski; Pawel Kostyla; Zbigniew Leonowicz. A new advanced method for an accurate assessment of harmonic and supraharmonic distortion in power system waveforms. IEEE Access 2021, 9, 1 -1.
AMA StyleGuido Carpinelli, Antonio Bracale, Pietro Varilone, Tomasz Sikorski, Pawel Kostyla, Zbigniew Leonowicz. A new advanced method for an accurate assessment of harmonic and supraharmonic distortion in power system waveforms. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleGuido Carpinelli; Antonio Bracale; Pietro Varilone; Tomasz Sikorski; Pawel Kostyla; Zbigniew Leonowicz. 2021. "A new advanced method for an accurate assessment of harmonic and supraharmonic distortion in power system waveforms." IEEE Access 9, no. : 1-1.
Operating electrical networks by Dynamic Transformer Rating (DTR) unlocks the capacity of grids and allows to increase the exploitation of distributed generation and renewables. However, there is risk associated with the operation of transformers by DTR. Energy dispatch and/or load curtailment must be scheduled before the actual energy delivery, thus DTR and load should be predicted prior the actual transformer loading. Since both are random variables, this problem is prone to be addressed by stress-strength analysis. In this paper, the novel comprehensive probabilistic tool “SmarTrafo” is presented. It allows predicting the probability of the DTR to be greater than the load (i.e., the probability of success) through an exact analytic stress-strength model, and formulating an alarm-setting strategy in order to establish a warning if the expected probability is greater than a threshold. Specifically, the threshold is optimized in a multi-objective formulation, exploiting three different indices which differently evaluate the predictive skill of alarm-setting strategy. Numerical experiments based on actual data confirm the suitability of the proposal in predicting the probability of success and in establishing high-performance alarms based on such predictions.
Antonio Bracale; Pierluigi Caramia; Guido Carpinelli; Pasquale De Falco. SmarTrafo: A Probabilistic Predictive Tool for Dynamic Transformer Rating. IEEE Transactions on Power Delivery 2020, 36, 1619 -1630.
AMA StyleAntonio Bracale, Pierluigi Caramia, Guido Carpinelli, Pasquale De Falco. SmarTrafo: A Probabilistic Predictive Tool for Dynamic Transformer Rating. IEEE Transactions on Power Delivery. 2020; 36 (3):1619-1630.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Guido Carpinelli; Pasquale De Falco. 2020. "SmarTrafo: A Probabilistic Predictive Tool for Dynamic Transformer Rating." IEEE Transactions on Power Delivery 36, no. 3: 1619-1630.
Industrial load takes a big portion of the total electricity demand. Skilled probabilistic industrial load forecasts allow for optimally exploiting energy resources, managing the reserves, and market bidding, which are beneficial to transmission and distribution system operators and their industrial customers. Despite its importance, industrial load forecasting has never been a popular subject in the literature. Most existing methods operate on the active power alone, partially or totally neglecting the reactive power. This paper proposes a multivariate approach to probabilistic industrial load forecasting, which addresses active and reactive power simultaneously. The proposed method is based on a two-level procedure, which consists of generating probabilistic forecasts individually for active and reactive power through univariate probabilistic models, and combining these forecasts in a multivariate approach based on a multivariate quantile regression model. The procedure to estimate the parameters of the multivariate quantile regression model is posed in this paper under a linear programming problem, to facilitate the convergence to the optimal solution. The proposed method is validated using actual load data collected at an Italian factory, under comparison with several probabilistic benchmarks. The proposed multivariate method enhances the skill of forecasts by 6% to 13.5%, with respect to univariate benchmarks.
Antonio Bracale; Pierluigi Caramia; Pasquale De Falco; Tao Hong. A Multivariate Approach to Probabilistic Industrial Load Forecasting. Electric Power Systems Research 2020, 187, 106430 .
AMA StyleAntonio Bracale, Pierluigi Caramia, Pasquale De Falco, Tao Hong. A Multivariate Approach to Probabilistic Industrial Load Forecasting. Electric Power Systems Research. 2020; 187 ():106430.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Pasquale De Falco; Tao Hong. 2020. "A Multivariate Approach to Probabilistic Industrial Load Forecasting." Electric Power Systems Research 187, no. : 106430.
The paper investigates the renewal of a hybrid trolley-bus, powered by a 600 V DC overhead electrical grid. The analysis focuses on replacing the on-board internal combustion engine (ICE) with a battery-based power unit. A novel two-step optimization procedure is proposed for this purpose. The procedure compares the solutions in terms of the total cost sustained by the ownership. By means of an iterative method, the optimal size of the battery unit is designed as a function of power and energy requirements, taking into account cycle life, depth of discharge, working temperature, replacement, and the requirements of the transport service operator. Using the real measurements taken on a trolley-bus operating in the city center of Naples (Italy), several numerical simulations are performed. The simulations examine three alternative Lithium high specific power and energy batteries. The comparison of the results allows to select the best solution among the different technologies for the proposed application.
Luisa Alfieri; Antonio Bracale; Pierluigi Caramia; Diego Iannuzzi; Mario Pagano. Optimal battery sizing procedure for hybrid trolley-bus: A real case study. Electric Power Systems Research 2019, 175, 105930 .
AMA StyleLuisa Alfieri, Antonio Bracale, Pierluigi Caramia, Diego Iannuzzi, Mario Pagano. Optimal battery sizing procedure for hybrid trolley-bus: A real case study. Electric Power Systems Research. 2019; 175 ():105930.
Chicago/Turabian StyleLuisa Alfieri; Antonio Bracale; Pierluigi Caramia; Diego Iannuzzi; Mario Pagano. 2019. "Optimal battery sizing procedure for hybrid trolley-bus: A real case study." Electric Power Systems Research 175, no. : 105930.
Photovoltaic systems (PVSs) are among the most diffuse Distributed Generators based on renewable energy sources. PVSs contribute to the short-circuit currents during a fault, modifying the short-circuit capacity of the distribution systems. Then, the contribution of PVSs to the fault current must be adequately modeled to extend the traditional short-circuit analysis to distribution networks with PVSs. In this paper an analytical model based on the phase-coordinates approach is proposed to evaluate the fault contributions of three-phase PVSs connected to unbalanced distribution networks in presence of symmetrical and asymmetrical shunt short-circuits, with and without fault impedance. The model of the PVS takes into account the environmental conditions, the inverter control system, the maximum current that can flow through the inverter switching devices, the filter, the interface transformer and the self-protections imposed by the Standard IEEE 1547. Both a first cycles and a steady-state model of the PVS are developed accounting for inverter control systems equipped with VAr or LVRT control schemes or inverter control system without reactive power regulation. The model of the PVS is applied to a typical North American MV network and the results of the proposed approach are compared with time-domain simulations.
G. Carpinelli; A. Bracale; P. Caramia; A.R. Di Fazio. Three-phase photovoltaic generators modeling in unbalanced short-circuit operating conditions. International Journal of Electrical Power & Energy Systems 2019, 113, 941 -951.
AMA StyleG. Carpinelli, A. Bracale, P. Caramia, A.R. Di Fazio. Three-phase photovoltaic generators modeling in unbalanced short-circuit operating conditions. International Journal of Electrical Power & Energy Systems. 2019; 113 ():941-951.
Chicago/Turabian StyleG. Carpinelli; A. Bracale; P. Caramia; A.R. Di Fazio. 2019. "Three-phase photovoltaic generators modeling in unbalanced short-circuit operating conditions." International Journal of Electrical Power & Energy Systems 113, no. : 941-951.
Short-term probabilistic load forecasting is essential to power systems management and optimization of power flows across transmission networks. Developing forecasting tools capable of providing accurate predictions must comply with their practical implementation in short-term operations, mainly in terms of fast computing and high efficiency. Many data-driven forecasting solutions are often unnecessarily verbose, thus making their practical value limited. This problem occurs more frequently when multiple loads have to be predicted simultaneously, as in power transmission system analysis and optimization. In this paper, we propose a new cooperative forecasting system that refines probabilistic forecasts of individual loads online. The refining procedure is based on a multivariate quantile regression, which is dynamically applied to the individual forecasts as new observations become available. The proposal is validated on the load data published by ISO New England for eight regions, covering six States of the United States. The quality of probabilistic forecasts is assessed in terms of reliability and sharpness, comparing the results to three benchmarks. The proposed method outperforms the best benchmark by up to 6% w.r.t. the reduction in pinball loss.
Antonio Bracale; Pierluigi Caramia; Pasquale De Falco; Tao Hong. Multivariate Quantile Regression for Short-Term Probabilistic Load Forecasting. IEEE Transactions on Power Systems 2019, 35, 628 -638.
AMA StyleAntonio Bracale, Pierluigi Caramia, Pasquale De Falco, Tao Hong. Multivariate Quantile Regression for Short-Term Probabilistic Load Forecasting. IEEE Transactions on Power Systems. 2019; 35 (1):628-638.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Pasquale De Falco; Tao Hong. 2019. "Multivariate Quantile Regression for Short-Term Probabilistic Load Forecasting." IEEE Transactions on Power Systems 35, no. 1: 628-638.
Electrical distribution networks are often inadequate to comply with the increasing load demand and to enable new installation of distributed energy resources. This inadequacy may result in upgrading existing transformers of in disconnecting the entire load during demand peaks, and into wasting a portion of the total renewable energy produced during favorable weather conditions. Increasing the capacity of electrical distribution network components is therefore mandatory in order to avoid these negative circumstances; however replacing lines and transformers is costly, and often the benefits are not worth the investments. Fortunately, the capacity of distribution networks can be unlocked by managing the electrical components by their dynamic ratings, allowing operating them even beyond their rated ampacity for a limited interval of time. This paper proposes a new risk-based procedure to manage distribution transformers by their dynamic rating. Probabilistic forecasts of loads and ambient temperatures are the inputs of the procedure, which are obtained in this paper from quantile regression forests. The procedure quantifies, in a probabilistic framework, the insulation thermal ageing due to the transformer (over)load in terms of the cost sustained for rewinding the transformer. The corresponding estimated risk is made available to the distribution system operator, who may therefore take a decision comparing it to an arbitrary level of risk coverage, favoring either the power delivery to customers or the reduction of the transformer ageing rate; this level is formulated in this paper having an intuitive physical meaning, thus enabling its setting also by non-expert operators. Comprehensive numerical experiments based on actual data are presented under different configurations and scenarios, in order to validate the proposal from an energetic and economic point of view.
Antonio Bracale; Guido Carpinelli; Pasquale De Falco. Probabilistic risk-based management of distribution transformers by dynamic transformer rating. International Journal of Electrical Power & Energy Systems 2019, 113, 229 -243.
AMA StyleAntonio Bracale, Guido Carpinelli, Pasquale De Falco. Probabilistic risk-based management of distribution transformers by dynamic transformer rating. International Journal of Electrical Power & Energy Systems. 2019; 113 ():229-243.
Chicago/Turabian StyleAntonio Bracale; Guido Carpinelli; Pasquale De Falco. 2019. "Probabilistic risk-based management of distribution transformers by dynamic transformer rating." International Journal of Electrical Power & Energy Systems 113, no. : 229-243.
This paper presents a probabilistic model for supporting the process of decision making about the value of new lighting systems in existing road tunnels when some data and parameters are affected by uncertainty. The proposed model, which we have called Probabilistic Energy Screening of Tunnel (PrEST), accounts for both the technical performance and the economic objectives of the new lighting systems. The technical performance is described on an adequate (x, y) plane that was defined by two indices. The first index measured the consumption of electricity per kilometre of tunnel lengths; the second index measured the performance of the lighting systems per unit of illuminated area. The economic results were measured by the net present value of the savings and by the payback period. Both the terms account for initial capital investments, energy and maintenance costs. PrEST was applied to two real road tunnels in service in Italy showing that the statistics of the results can support a final decision in function of the business strategy.
Antonio Bracale; Pierluigi Caramia; Pietro Varilone; Paola Verde. Probabilistic Estimation of the Energy Consumption and Performance of the Lighting Systems of Road Tunnels for Investment Decision Making. Energies 2019, 12, 1488 .
AMA StyleAntonio Bracale, Pierluigi Caramia, Pietro Varilone, Paola Verde. Probabilistic Estimation of the Energy Consumption and Performance of the Lighting Systems of Road Tunnels for Investment Decision Making. Energies. 2019; 12 (8):1488.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Pietro Varilone; Paola Verde. 2019. "Probabilistic Estimation of the Energy Consumption and Performance of the Lighting Systems of Road Tunnels for Investment Decision Making." Energies 12, no. 8: 1488.
Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score.
Antonio Bracale; Guido Carpinelli; Pasquale De Falco. Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method. Energies 2019, 12, 1011 .
AMA StyleAntonio Bracale, Guido Carpinelli, Pasquale De Falco. Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method. Energies. 2019; 12 (6):1011.
Chicago/Turabian StyleAntonio Bracale; Guido Carpinelli; Pasquale De Falco. 2019. "Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method." Energies 12, no. 6: 1011.
Antonio Bracale; Pierluigi Caramia; Pasquale De Falco; Andrea Michiorri; Angela Russo. Day-Ahead and Intraday Forecasts of the Dynamic Line Rating for Buried Cables. IEEE Access 2018, 7, 4709 -4725.
AMA StyleAntonio Bracale, Pierluigi Caramia, Pasquale De Falco, Andrea Michiorri, Angela Russo. Day-Ahead and Intraday Forecasts of the Dynamic Line Rating for Buried Cables. IEEE Access. 2018; 7 ():4709-4725.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Pasquale De Falco; Andrea Michiorri; Angela Russo. 2018. "Day-Ahead and Intraday Forecasts of the Dynamic Line Rating for Buried Cables." IEEE Access 7, no. : 4709-4725.
Reactive power forecasting is essential for managing energy systems of factories and industrial plants. However, the scientific community has devoted scant attention to industrial load forecasting, and even less to reactive power forecasting. Many challenges in developing a short-term reactive power forecasting system for factories have rarely been studied. Industrial loads may depend on many factors, such as scheduled processes and work shifts, which are uncommon or unnecessary in classical load forecasting models. Moreover, the features of reactive power are significantly different from active power, so some commonly used variables in classical load forecasting models may become meaningless for forecasting reactive power. In this paper, we develop several models to forecast industrial reactive power. These models are constructed based on two forecasting techniques (e.g., multiple linear regression and support vector regression) and two variable selection methods (e.g., cross validation and least absolute shrinkage and selection operator). In the numerical applications based on real data collected from an Italian factory at both aggregate and individual load levels, the proposed models outperform four benchmark models in short forecast horizons.
Antonio Bracale; Guido Carpinelli; Pasquale De Falco; Tao Hong. Short-term industrial reactive power forecasting. International Journal of Electrical Power & Energy Systems 2018, 107, 177 -185.
AMA StyleAntonio Bracale, Guido Carpinelli, Pasquale De Falco, Tao Hong. Short-term industrial reactive power forecasting. International Journal of Electrical Power & Energy Systems. 2018; 107 ():177-185.
Chicago/Turabian StyleAntonio Bracale; Guido Carpinelli; Pasquale De Falco; Tao Hong. 2018. "Short-term industrial reactive power forecasting." International Journal of Electrical Power & Energy Systems 107, no. : 177-185.
Due to their large usage of electricity, industrial factories stand out from domestic and commercial utilities for the enormous potential in providing services to power systems. Accurate energy consumption forecasts are required in order to exploit this outstanding capability, without incurring into operational mistakes. In this context, system operators prefer probabilistic load forecasts when dealing with decision-making processes, in order to fully account economical and technical risks. This paper tackles probabilistic industrial load forecasting from a dual point of view: active and reactive power forecasting. The strong correlation between active and reactive powers suggests to develop the proposed forecasting method by modeling the interaction effects between the variables; we investigate the convenience of such approach using both univariate and multivariate methods, i.e., quantile regression forests and vector autoregressive exogenous models, trained with actual data registered in an Italian factory. The results are compared to a naïve benchmark and a regression bootstrap benchmark.
Antonio Bracale; Pasquale De Falco; Guido Carpinelli. Comparing Univariate and Multivariate Methods for Probabilistic Industrial Load Forecasting. 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA) 2018, 1 -6.
AMA StyleAntonio Bracale, Pasquale De Falco, Guido Carpinelli. Comparing Univariate and Multivariate Methods for Probabilistic Industrial Load Forecasting. 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA). 2018; ():1-6.
Chicago/Turabian StyleAntonio Bracale; Pasquale De Falco; Guido Carpinelli. 2018. "Comparing Univariate and Multivariate Methods for Probabilistic Industrial Load Forecasting." 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA) , no. : 1-6.
Appropriate forecasts of the dynamic rating of buried cables can be exploited by networks operators to significantly increase the power transport capacity of medium voltage and low voltage distribution systems, without sacrificing the normal life of the cables. However, forecasting the effects of the soil conditions on the dynamic rating of the buried cable is not an easy task. In this paper, a detailed procedure is proposed to deal with this problem. The proposed procedure handles the changing environmental conditions by exploiting a thermal-hydraulic model of the soil and a model for the thermal exchange between the buried cable and the surrounding soil. The forecasts of external influencing variables, such as precipitations and soil temperature, and of cable currents, needed as inputs of the proposed procedure, are obtained by means of a Support Vector Regression technique. Numerical applications based on actual load and weather data confirmed the suitability of the procedure, encouraging for further research on the topic.
Antonio Bracale; Pierluigi Caramia; Pasquale De Falco; Angela Russo. A New Procedure to Forecast the Dynamic Rating of Buried Cables. 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) 2018, 1190 -1195.
AMA StyleAntonio Bracale, Pierluigi Caramia, Pasquale De Falco, Angela Russo. A New Procedure to Forecast the Dynamic Rating of Buried Cables. 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). 2018; ():1190-1195.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Pasquale De Falco; Angela Russo. 2018. "A New Procedure to Forecast the Dynamic Rating of Buried Cables." 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) , no. : 1190-1195.
The steady state thermal rating of overhead transmission lines is limited by the conductor’s maximum design temperature, which is related to the maximum sag and the loss of tensile strength of the conductors. Traditionally, this overhead transmission lines rating is computed using a deterministic approach, with reference to severe weather conditions. Thus, the application of this method leads to conservative results resulting in under-utilization of conductors. In this paper, a new method based on an hourly probabilistic index is proposed to predict the line thermal rating for each hour of the day; this index is evaluated using the conductor current limit probability density function (pdf). The method uses Bayesian time series models for the weather parameters (ambient temperature, wind speed and wind direction) and calculates the conductor current limit pdf using a Monte Carlo simulation. The probabilistic index is applied by considering measured weather data of both hot and cold seasons; the corresponding lines ratings are reported and analyzed.
Antonio Bracale; Amedeo Andreotti; Guido Carpinelli; Umberto De Martinis. Probabilistic Index for Increasing Hourly Transmission Line Ratings. ENERGYO 2018, 1 .
AMA StyleAntonio Bracale, Amedeo Andreotti, Guido Carpinelli, Umberto De Martinis. Probabilistic Index for Increasing Hourly Transmission Line Ratings. ENERGYO. 2018; ():1.
Chicago/Turabian StyleAntonio Bracale; Amedeo Andreotti; Guido Carpinelli; Umberto De Martinis. 2018. "Probabilistic Index for Increasing Hourly Transmission Line Ratings." ENERGYO , no. : 1.
In this paper, a probabilistic method is proposed to analyze the very short-term steady-state performance of an unbalanced distribution electrical system characterized by the presence of wind farms. This method, which can take into account the uncertainties of loads and wind productions, is based on a Monte Carlo simulation procedure applied to the non-linear three-phase load flow equations, including wind farm models. Bayesian time series models are used to predict the next hour's wind speed probability density functions, making possible a predictive evaluation of the very short-term system steady-state behavior. Numerical applications are presented and discussed with reference to the three-phase unbalanced IEEE 34-bus test distribution system in the presence of wind farms connected at different busbars.
Antonio Bracale; Pierluigi Caramia; Guido Carpinelli; Pietro Varilone. A Probability Method for Very Short-Term Steady State Analysis of a Distribution System with Wind Farms. ENERGYO 2018, 1 .
AMA StyleAntonio Bracale, Pierluigi Caramia, Guido Carpinelli, Pietro Varilone. A Probability Method for Very Short-Term Steady State Analysis of a Distribution System with Wind Farms. ENERGYO. 2018; ():1.
Chicago/Turabian StyleAntonio Bracale; Pierluigi Caramia; Guido Carpinelli; Pietro Varilone. 2018. "A Probability Method for Very Short-Term Steady State Analysis of a Distribution System with Wind Farms." ENERGYO , no. : 1.
Distribution systems are undergoing significant changes as they evolve toward the grids of the future, which are known as smart grids (SGs). The perspective of SGs is to facilitate large-scale penetration of distributed generation using renewable energy sources (RESs), encourage the efficient use of energy, reduce systems’ losses, and improve the quality of power. Photovoltaic (PV) systems have become one of the most promising RESs due to the expected cost reduction and the increased efficiency of PV panels and interfacing converters. The ability to forecast power-production information accurately and reliably is of primary importance for the appropriate management of an SG and for making decisions relative to the energy market. Several forecasting methods have been proposed, and many indices have been used to quantify the accuracy of the forecasts of PV power production. Unfortunately, the indices that have been used have deficiencies and usually do not directly account for the economic consequences of forecasting errors in the framework of liberalized electricity markets. In this paper, advanced, more accurate indices are proposed that account directly for the economic consequences of forecasting errors. The proposed indices also were compared to the most frequently used indices in order to demonstrate their different, improved capability. The comparisons were based on the results obtained using a forecasting method based on an artificial neural network. This method was chosen because it was deemed to be one of the most promising methods available due to its capability for forecasting PV power. Numerical applications also are presented that considered an actual PV plant to provide evidence of the forecasting performances of all of the indices that were considered.
Antonio Bracale; Guido Carpinelli; Annarita Di Fazio; Shahab Khormali. Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power. ENERGYO 2018, 1 .
AMA StyleAntonio Bracale, Guido Carpinelli, Annarita Di Fazio, Shahab Khormali. Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power. ENERGYO. 2018; ():1.
Chicago/Turabian StyleAntonio Bracale; Guido Carpinelli; Annarita Di Fazio; Shahab Khormali. 2018. "Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power." ENERGYO , no. : 1.
The overall growth of electrical energy consumption and the spread of distributed generation pose severe problems to system planners and operators, mainly due to the difficulties in expanding and upgrading electrical distribution systems. These problems can be partially overcome through the smart operation of power system components. In particular, under the new smart grid paradigm, lines and transformers could be loaded beyond their nameplate (static) rating in favorable thermal conditions, without loss of rated life or breakdown. This paper deals with the loading of oil-immersed distribution transformers, and it proposes a probabilistic approach based on the monitoring of electrical and environmental conditions. The approach consists in forecasting the predictive distribution of the transformer dynamic rating, and then in forecasting the transformer allowable current. The probabilistic approach takes into account the unavoidable uncertainties involved in the thermal modeling of the transformer; indeed, it selects the allowable current through an index that takes into account both the probability of the allowable current to be higher than the predicted dynamic rating and the corresponding expected load curtailment. Numerical applications are performed on real data to evaluate the effectiveness of the proposed procedure.
Antonio Bracale; Guido Carpinelli; Mario Pagano; Pasquale De Falco. A Probabilistic Approach for Forecasting the Allowable Current of Oil-Immersed Transformers. IEEE Transactions on Power Delivery 2018, 33, 1825 -1834.
AMA StyleAntonio Bracale, Guido Carpinelli, Mario Pagano, Pasquale De Falco. A Probabilistic Approach for Forecasting the Allowable Current of Oil-Immersed Transformers. IEEE Transactions on Power Delivery. 2018; 33 (4):1825-1834.
Chicago/Turabian StyleAntonio Bracale; Guido Carpinelli; Mario Pagano; Pasquale De Falco. 2018. "A Probabilistic Approach for Forecasting the Allowable Current of Oil-Immersed Transformers." IEEE Transactions on Power Delivery 33, no. 4: 1825-1834.
Antonio Bracale; Guido Carpinelli; Pasquale De Falco. A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization. Renewable Energy 2017, 113, 1366 -1377.
AMA StyleAntonio Bracale, Guido Carpinelli, Pasquale De Falco. A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization. Renewable Energy. 2017; 113 ():1366-1377.
Chicago/Turabian StyleAntonio Bracale; Guido Carpinelli; Pasquale De Falco. 2017. "A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization." Renewable Energy 113, no. : 1366-1377.