This page has only limited features, please log in for full access.

Prof. Sonia Leva
Politecnico di Milano - Department of Energy

Basic Info


Research Keywords & Expertise

0 microgrid
0 Renewable and Sustainable Energy
0 Electric vehicle technologies
0 Forecasting Methods
0 Photovoltaic Engineering

Fingerprints

microgrid
Forecasting Methods

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 13 May 2021 in Electronics
Reads 0
Downloads 0

This work proposes a methodology for the optimal training of rule-based management strategies, to be directly implemented in the industrial controller of hybrid off-grid microgrids. The parameters defining the control rules are optimally tuned resorting to different evolutionary algorithms, based on the expected operating conditions. The performance of the resulting management heuristics is compared with conventional approaches to optimal scheduling, including Mixed Integer Linear Programming (MILP) optimization, direct evolutionary scheduling optimization, and traditional non-trained heuristics. Results show how the trained heuristics achieve a performance very close to the global optimum found by the MILP solution, outperforming the other methods, and providing a single-layer commitment and dispatch algorithm which is easily deployable in the microgrid controller.

ACS Style

Luca Moretti; Lorenzo Meraldi; Alessandro Niccolai; Giampaolo Manzolini; Sonia Leva. An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids. Electronics 2021, 10, 1162 .

AMA Style

Luca Moretti, Lorenzo Meraldi, Alessandro Niccolai, Giampaolo Manzolini, Sonia Leva. An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids. Electronics. 2021; 10 (10):1162.

Chicago/Turabian Style

Luca Moretti; Lorenzo Meraldi; Alessandro Niccolai; Giampaolo Manzolini; Sonia Leva. 2021. "An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids." Electronics 10, no. 10: 1162.

Editorial
Published: 21 February 2021 in Forecasting
Reads 0
Downloads 0

Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications

ACS Style

Sonia Leva. Editorial for Special Issue: “Feature Papers of Forecasting”. Forecasting 2021, 3, 135 -137.

AMA Style

Sonia Leva. Editorial for Special Issue: “Feature Papers of Forecasting”. Forecasting. 2021; 3 (1):135-137.

Chicago/Turabian Style

Sonia Leva. 2021. "Editorial for Special Issue: “Feature Papers of Forecasting”." Forecasting 3, no. 1: 135-137.

Journal article
Published: 09 February 2021 in Processes
Reads 0
Downloads 0

Microgrids represent a flexible way to integrate renewable energy sources with programmable generators and storage systems. In this regard, a synergic integration of those sources is crucial to minimize the operating cost of the microgrid by efficient storage management and generation scheduling. The forecasts of renewable generation can be used to attain optimal management of the controllable units by predictive optimization algorithms. This paper introduces the implementation of a two-layer hierarchical energy management system for islanded photovoltaic microgrids. The first layer evaluates the optimal unit commitment, according to the photovoltaic forecasts, while the second layer deals with the power-sharing in real time, following as close as possible the daily schedule provided by the upper layer while balancing the forecast errors. The energy management system is experimentally tested at the Multi-Good MicroGrid Laboratory under three different photovoltaic forecast models: (i) day-ahead model, (ii) intraday corrections and (iii) nowcasting technique. The experimental study demonstrates the capability of the proposed management system to operate an islanded microgrid in safe conditions, even with inaccurate day-ahead photovoltaic forecasts.

ACS Style

Simone Polimeni; Alfredo Nespoli; Sonia Leva; Gianluca Valenti; Giampaolo Manzolini. Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results. Processes 2021, 9, 323 .

AMA Style

Simone Polimeni, Alfredo Nespoli, Sonia Leva, Gianluca Valenti, Giampaolo Manzolini. Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results. Processes. 2021; 9 (2):323.

Chicago/Turabian Style

Simone Polimeni; Alfredo Nespoli; Sonia Leva; Gianluca Valenti; Giampaolo Manzolini. 2021. "Implementation of Different PV Forecast Approaches in a MultiGood MicroGrid: Modeling and Experimental Results." Processes 9, no. 2: 323.

Journal article
Published: 05 February 2021 in IEEE Journal of Photovoltaics
Reads 0
Downloads 0

Organic photovoltaic (OPV) modules have significant advantages over conventional PV technologies drawing the attention of R&D activities. The OPV efficiency is increasing closing the gap against silicon-based modules. This article describes an experimental campaign performed at SolarTech LAB to assess the performance of six OPV modules in real environmental conditions (module nominal power 17.5 W p ). The first part of the activity was dedicated to the photoactivation process, which is a well-known phenomenon of this kind of modules. Measurements pointed out that the OPV modules reach stable conditions after collecting 10 kWh/m 2 of solar radiation independently from the module conditions during the procedure. A second relevant result is about the reversibility of the photoactivation process: experiments showed that activated modules left in the dark for several days lose the activation indicating the reversibility of the process. Finally, in the second part, the performances of the six OPV modules have been analyzed and benchmarked against silicon (c-Si) and CIS photovoltaic technology. The measured electric efficiency of the six OPV modules under real environmental conditions was below 4%, which is significantly lower than 20% and 15% measured for c-Si and CIS modules under the same conditions.

ACS Style

Alberto Dolara; Giulia di Fazio; Sonia Leva; Giampaolo Manzolini; Riccardo Simonetti; Andrea Terenzi. Outdoor Assessment and Performance Evaluation of OPV Modules. IEEE Journal of Photovoltaics 2021, 11, 391 -399.

AMA Style

Alberto Dolara, Giulia di Fazio, Sonia Leva, Giampaolo Manzolini, Riccardo Simonetti, Andrea Terenzi. Outdoor Assessment and Performance Evaluation of OPV Modules. IEEE Journal of Photovoltaics. 2021; 11 (2):391-399.

Chicago/Turabian Style

Alberto Dolara; Giulia di Fazio; Sonia Leva; Giampaolo Manzolini; Riccardo Simonetti; Andrea Terenzi. 2021. "Outdoor Assessment and Performance Evaluation of OPV Modules." IEEE Journal of Photovoltaics 11, no. 2: 391-399.

Journal article
Published: 13 January 2021 in Electronics
Reads 0
Downloads 0

Electric mobility can represent a game changing technology for the long-term sustainability of the transportation sector. Pursuing this target, a model to simulate an Electric Vehicle (EV) for Formula SAE Electric competition is herein proposed: all the subsystems of the EV and the hybrid storage of the Li-ion batteries and Ultra-Capacitors (UCs) are implemented, in order to store the kinetic energy of the regenerative braking in the storage system through the Kinetic Energy Recovery System (KERS). A bidirectional DC-DC resonant converter is herein applied to the KERS to manage the UC pack. The operational limits of the proposed system, keeping the soft-switching properties, are discussed, and the results show the capability of the converter to operate under resonant mode in both boost and buck mode. A drawback is the presence of high current peaks in the resonant inductor. The use of more than one converter in interleaving and the adoption of a suitable capability factor ensure the proper operation of the system.

ACS Style

Alberto Dolara; Sonia Leva; Giacomo Moretti; Marco Mussetta; Yales Romulo De Novaes. Design of a Resonant Converter for a Regenerative Braking System Based on Ultracap Storage for Application in a Formula SAE Single-Seater Electric Racing Car. Electronics 2021, 10, 161 .

AMA Style

Alberto Dolara, Sonia Leva, Giacomo Moretti, Marco Mussetta, Yales Romulo De Novaes. Design of a Resonant Converter for a Regenerative Braking System Based on Ultracap Storage for Application in a Formula SAE Single-Seater Electric Racing Car. Electronics. 2021; 10 (2):161.

Chicago/Turabian Style

Alberto Dolara; Sonia Leva; Giacomo Moretti; Marco Mussetta; Yales Romulo De Novaes. 2021. "Design of a Resonant Converter for a Regenerative Braking System Based on Ultracap Storage for Application in a Formula SAE Single-Seater Electric Racing Car." Electronics 10, no. 2: 161.

Journal article
Published: 22 November 2020 in Electronics
Reads 0
Downloads 0

One of the common problems faced by Telecommunication (TLC) companies is the lack of power supply, usually for those appliances with scarce chances of grid connection often placed in remote zones. This issue is more and more critical if the radio network has the specific task of guaranteeing the so-called “mission-critical communications”. This manuscript aims to propose and assess a viable solution to optimize the power supply and maintenance operations required to assure the proper functionality in such critical and remote sites. In particular, the main goals are defining a method to select the critical sites in an extensive and composite radio system and designing the hybrid power system in a way to improve the service availability and technical-economic benefits of the whole mission-critical TLC system. Finally, the proposed method and related procedures are tested and validated in a real scenario.

ACS Style

Sonia Leva; Francesco Grimaccia; Marco Rozzi; Matteo Mascherpa. Hybrid Power System Optimization in Mission-Critical Communication. Electronics 2020, 9, 1971 .

AMA Style

Sonia Leva, Francesco Grimaccia, Marco Rozzi, Matteo Mascherpa. Hybrid Power System Optimization in Mission-Critical Communication. Electronics. 2020; 9 (11):1971.

Chicago/Turabian Style

Sonia Leva; Francesco Grimaccia; Marco Rozzi; Matteo Mascherpa. 2020. "Hybrid Power System Optimization in Mission-Critical Communication." Electronics 9, no. 11: 1971.

Journal article
Published: 11 June 2020 in Energies
Reads 0
Downloads 0

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.

ACS Style

Gabriel Mendonça De Paiva; Sergio Pires Pimentel; Bernardo Pinheiro Alvarenga; Enes Marra; Marco Mussetta; Sonia Leva. Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks. Energies 2020, 13, 3005 .

AMA Style

Gabriel Mendonça De Paiva, Sergio Pires Pimentel, Bernardo Pinheiro Alvarenga, Enes Marra, Marco Mussetta, Sonia Leva. Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks. Energies. 2020; 13 (11):3005.

Chicago/Turabian Style

Gabriel Mendonça De Paiva; Sergio Pires Pimentel; Bernardo Pinheiro Alvarenga; Enes Marra; Marco Mussetta; Sonia Leva. 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks." Energies 13, no. 11: 3005.

Review
Published: 09 January 2020 in Applied Sciences
Reads 0
Downloads 0

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic.

ACS Style

Adel Mellit; Alessandro Massi Pavan; Emanuele Ogliari; Sonia Leva; Vanni Lughi. Advanced Methods for Photovoltaic Output Power Forecasting: A Review. Applied Sciences 2020, 10, 487 .

AMA Style

Adel Mellit, Alessandro Massi Pavan, Emanuele Ogliari, Sonia Leva, Vanni Lughi. Advanced Methods for Photovoltaic Output Power Forecasting: A Review. Applied Sciences. 2020; 10 (2):487.

Chicago/Turabian Style

Adel Mellit; Alessandro Massi Pavan; Emanuele Ogliari; Sonia Leva; Vanni Lughi. 2020. "Advanced Methods for Photovoltaic Output Power Forecasting: A Review." Applied Sciences 10, no. 2: 487.

Journal article
Published: 01 January 2020 in IEEE Access
Reads 0
Downloads 0
ACS Style

Sonia Leva; Alfredo Nespoli; Silvia Pretto; Marco Mussetta; Emanuele Giovanni Carlo Ogliari. PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset. IEEE Access 2020, 8, 194428 -194440.

AMA Style

Sonia Leva, Alfredo Nespoli, Silvia Pretto, Marco Mussetta, Emanuele Giovanni Carlo Ogliari. PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset. IEEE Access. 2020; 8 ():194428-194440.

Chicago/Turabian Style

Sonia Leva; Alfredo Nespoli; Silvia Pretto; Marco Mussetta; Emanuele Giovanni Carlo Ogliari. 2020. "PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset." IEEE Access 8, no. : 194428-194440.

Journal article
Published: 12 December 2019 in Energies
Reads 0
Downloads 0

Photovoltaic (PV) plant monitoring and maintenance has become an often critical activity: the high efficiency requirements of the new European policy have often been in contrast with the many low-quality plants installed in several countries over the past few years. In actual industrial practices, heterogeneous information is produced, and they are often managed in a fragmented way. Several software tools have been developed for obtaining reliable and valuable information from the PV plant’s raw data. With the aim of gathering and managing all these data in a more complex and integrated manner, an information managing system is proposed in this work—it is composed of a structured database, called the Photovoltaic Indexed Database, and a user interface, called the Digital Map, that allows for easy access and completion of the information present in the database. This information managment system and PV plant digitalization process is able to analyze and properly index the IR in the database, as well as the visual images obtained in photovoltaic plant monitoring.

ACS Style

Alessandro Niccolai; Francesco Grimaccia; Sonia Leva. Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping. Energies 2019, 12, 4736 .

AMA Style

Alessandro Niccolai, Francesco Grimaccia, Sonia Leva. Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping. Energies. 2019; 12 (24):4736.

Chicago/Turabian Style

Alessandro Niccolai; Francesco Grimaccia; Sonia Leva. 2019. "Advanced Asset Management Tools in Photovoltaic Plant Monitoring: UAV-Based Digital Mapping." Energies 12, no. 24: 4736.

Journal article
Published: 01 December 2019 in Electronics
Reads 0
Downloads 0

Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.

ACS Style

Alfredo Nespoli; Marco Mussetta; Emanuele Ogliari; Sonia Leva; Luis Fernández-Ramírez; Pablo García-Triviño. Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study. Electronics 2019, 8, 1434 .

AMA Style

Alfredo Nespoli, Marco Mussetta, Emanuele Ogliari, Sonia Leva, Luis Fernández-Ramírez, Pablo García-Triviño. Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study. Electronics. 2019; 8 (12):1434.

Chicago/Turabian Style

Alfredo Nespoli; Marco Mussetta; Emanuele Ogliari; Sonia Leva; Luis Fernández-Ramírez; Pablo García-Triviño. 2019. "Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study." Electronics 8, no. 12: 1434.

Journal article
Published: 27 November 2019 in Energies
Reads 0
Downloads 0

The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.

ACS Style

Simone Sala; Alfonso Amendola; Sonia Leva; Marco Mussetta; Alessandro Niccolai; Emanuele Ogliari. Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant. Energies 2019, 12, 4520 .

AMA Style

Simone Sala, Alfonso Amendola, Sonia Leva, Marco Mussetta, Alessandro Niccolai, Emanuele Ogliari. Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant. Energies. 2019; 12 (23):4520.

Chicago/Turabian Style

Simone Sala; Alfonso Amendola; Sonia Leva; Marco Mussetta; Alessandro Niccolai; Emanuele Ogliari. 2019. "Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant." Energies 12, no. 23: 4520.

Journal article
Published: 17 May 2019 in Renewable Energy
Reads 0
Downloads 0

The high uncertainty associated with renewable power production limits renewable energy penetration in off-grid systems. Advanced control strategies allow for a more effective exploitation of non-dispatchable sources. This paper presents a two-layer predictive management strategy for an off-grid hybrid microgrid featuring controllable and non-controllable generation units and a storage system. The upper layer deals with the unit commitment, while the second layer regulates real-time operation, applying a response filter to smooth out genset load variation. The algorithm is tested on data from a real rural microgrid in Somalia, performing minute-by-minute simulations. Results are compared to the currently deployed management strategy and to a new improved heuristic algorithm. The two new methods attain a fuel consumption reduction with respect to the previous management system of about 15%. Finally, a new configuration for the Somalian microgrid is evaluated, in the two cases where the predictive or heuristic management strategies are adopted. The comparison of the two optimal solutions demonstrates that the adoption of the proposed predictive strategy leads to a 6.5% cut of the overall system cost, ensuring at the same time a 24.1% fuel consumption reduction with respect to the best heuristic solution and attaining a renewable penetration as high as 65.1%.

ACS Style

L. Moretti; S. Polimeni; L. Meraldi; P. Raboni; S. Leva; G. Manzolini. Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids. Renewable Energy 2019, 143, 1439 -1453.

AMA Style

L. Moretti, S. Polimeni, L. Meraldi, P. Raboni, S. Leva, G. Manzolini. Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids. Renewable Energy. 2019; 143 ():1439-1453.

Chicago/Turabian Style

L. Moretti; S. Polimeni; L. Meraldi; P. Raboni; S. Leva; G. Manzolini. 2019. "Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids." Renewable Energy 143, no. : 1439-1453.

Editorial
Published: 02 May 2019 in Applied Sciences
Reads 0
Downloads 0

Photovoltaics, among renewable energy sources (RES), has become more popular

ACS Style

Sonia Leva; Emanuele Ogliari. Computational Intelligence in Photovoltaic Systems. Applied Sciences 2019, 9, 1826 .

AMA Style

Sonia Leva, Emanuele Ogliari. Computational Intelligence in Photovoltaic Systems. Applied Sciences. 2019; 9 (9):1826.

Chicago/Turabian Style

Sonia Leva; Emanuele Ogliari. 2019. "Computational Intelligence in Photovoltaic Systems." Applied Sciences 9, no. 9: 1826.

Journal article
Published: 29 April 2019 in Energies
Reads 0
Downloads 0

We compare the 24-hour ahead forecasting performance of two methods commonly used for the prediction of the power output of photovoltaic systems. Both methods are based on Artificial Neural Networks (ANN), which have been trained on the same dataset, thus enabling a much-needed homogeneous comparison currently lacking in the available literature. The dataset consists of an hourly series of simultaneous climatic and PV system parameters covering an entire year, and has been clustered to distinguish sunny from cloudy days and separately train the ANN. One forecasting method feeds only on the available dataset, while the other is a hybrid method as it relies upon the daily weather forecast. For sunny days, the first method shows a very good and stable prediction performance, with an almost constant Normalized Mean Absolute Error, NMAE%, in all cases (1% < NMAE% < 2%); the hybrid method shows an even better performance (NMAE% < 1%) for two of the days considered in this analysis, but overall a less stable performance (NMAE% > 2% and up to 5.3% for all the other cases). For cloudy days, the forecasting performance of both methods typically drops; the performance is rather stable for the method that does not use weather forecasts, while for the hybrid method it varies significantly for the days considered in the analysis.

ACS Style

Alfredo Nespoli; Emanuele Ogliari; Sonia Leva; Alessandro Massi Pavan; Adel Mellit; Vanni Lughi; Alberto Dolara. Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies 2019, 12, 1621 .

AMA Style

Alfredo Nespoli, Emanuele Ogliari, Sonia Leva, Alessandro Massi Pavan, Adel Mellit, Vanni Lughi, Alberto Dolara. Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies. 2019; 12 (9):1621.

Chicago/Turabian Style

Alfredo Nespoli; Emanuele Ogliari; Sonia Leva; Alessandro Massi Pavan; Adel Mellit; Vanni Lughi; Alberto Dolara. 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques." Energies 12, no. 9: 1621.

Journal article
Published: 07 November 2018 in IEEE Transactions on Industrial Electronics
Reads 0
Downloads 0

The employment of solar micro-converter allows a more detailed monitoring of the PV output power at the single module level; thus, machine learning techniques are capable to track the peculiarities of modules in the PV plants such as regular shadings. In this way it is possible to compare in real-time the day-ahead forecast power with the actual one in order to better evaluate faults or anomalous trends which might have occurred in the PV plant. This paper presents a method for an effective fault diagnosis; this method is based on the day-ahead forecast of the output power from an existing PV module, linked to a micro-converter, and on the outcome of the neighbor PV modules. Finally, this paper proposes also the analysis of the most common error definitions with new mathematical formulations, by comparing their effectiveness and immediate comprehension, in view of increasing power forecasting accuracy and performing both real-time and offline analysis of PV modules performance and possible faults.

ACS Style

Sonia Leva; Marco Mussetta; Emanuele Ogliari. PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast. IEEE Transactions on Industrial Electronics 2018, 66, 3928 -3937.

AMA Style

Sonia Leva, Marco Mussetta, Emanuele Ogliari. PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast. IEEE Transactions on Industrial Electronics. 2018; 66 (5):3928-3937.

Chicago/Turabian Style

Sonia Leva; Marco Mussetta; Emanuele Ogliari. 2018. "PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast." IEEE Transactions on Industrial Electronics 66, no. 5: 3928-3937.

Conference paper
Published: 01 July 2018 in 2018 International Joint Conference on Neural Networks (IJCNN)
Reads 0
Downloads 0

Application of Machine Learning in forecasting renewable energy sources (RES) is increasing: in particular, several neural networks have been employed to perform the day-ahead photo-voltaic output power forecast. The aim of this paper is to consider different training approaches in order to improve the accuracy of the PV power prediction, with particular attention to day-ahead and intra-day forecasts. Additionally, novel error metrics, specifically proposed for the defined task, are compared with traditional ones, showing the best approach for the different considered cases. The results will be validated over a 1-year time range of experimentally measured data, for a PV module installed in the Solar Tech Lab in the department of Energy at Politecnico di Milano.

ACS Style

Alfredo Nespoli; Emanuele Ogliari; Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta. Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts. 2018 International Joint Conference on Neural Networks (IJCNN) 2018, 1 -6.

AMA Style

Alfredo Nespoli, Emanuele Ogliari, Alberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta. Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts. 2018 International Joint Conference on Neural Networks (IJCNN). 2018; ():1-6.

Chicago/Turabian Style

Alfredo Nespoli; Emanuele Ogliari; Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta. 2018. "Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts." 2018 International Joint Conference on Neural Networks (IJCNN) , no. : 1-6.

Conference paper
Published: 01 July 2018 in 2018 IEEE Congress on Evolutionary Computation (CEC)
Reads 0
Downloads 0

Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.

ACS Style

Gabriel Paiva; Sergio Pires Pimentel; Sonia Leva; Marco Mussetta. Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -8.

AMA Style

Gabriel Paiva, Sergio Pires Pimentel, Sonia Leva, Marco Mussetta. Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-8.

Chicago/Turabian Style

Gabriel Paiva; Sergio Pires Pimentel; Sonia Leva; Marco Mussetta. 2018. "Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-8.

Journal article
Published: 07 June 2018 in Energies
Reads 0
Downloads 0

An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively).

ACS Style

Emanuele Ogliari; Alessandro Niccolai; Sonia Leva; Riccardo E. Zich. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed. Energies 2018, 11, 1487 .

AMA Style

Emanuele Ogliari, Alessandro Niccolai, Sonia Leva, Riccardo E. Zich. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed. Energies. 2018; 11 (6):1487.

Chicago/Turabian Style

Emanuele Ogliari; Alessandro Niccolai; Sonia Leva; Riccardo E. Zich. 2018. "Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed." Energies 11, no. 6: 1487.

Conference paper
Published: 01 June 2018 in 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Reads 0
Downloads 0

Nowadays grid connected PV plants are playing an important role in power systems. Although, the general application guide has been defined by standards, some important possible failures and their consequences are not considered in any standard. These failures can cause production stops and considerable damages to the system. This paper analyzes some real failures by studying three different PV power plants. In each cases, the unscheduled event has damaged a part of the PV power plant. Each failure case is described in detail with some practical solutions to solve it. Moreover, the last problem is investigated systematically and field records are demonstrated. Finally, it is suggested to include islanding overvoltage tests and maximum thresholds into grid code and standard guide.

ACS Style

Roberto Faranda; Hossein Hafezi; Sonia Leva. Case Studies on Possible Failures in PV Power Plants. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) 2018, 1 -6.

AMA Style

Roberto Faranda, Hossein Hafezi, Sonia Leva. Case Studies on Possible Failures in PV Power Plants. 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). 2018; ():1-6.

Chicago/Turabian Style

Roberto Faranda; Hossein Hafezi; Sonia Leva. 2018. "Case Studies on Possible Failures in PV Power Plants." 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) , no. : 1-6.