Special Issue "Computational Intelligence in Photovoltaic Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 November 2018).

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A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Emanuele Ogliari
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Photovoltaics, among Renewable Energy Sources, has become more popular. However, in recent years, many research topics arose, mainly due to problems that are constantly faced in smart-grid and microgrid operations, output power plants production forecast, storage sizing, modelling, and control optimization of photovoltaic systems.

Computational Intelligence algorithms (evolutionary optimization, neural networks, fuzzy logic, etc.) have become more and more popular as alternative approaches to conventional techniques in solving problems such as modelling, identification, optimization, availability prediction, forecasting, sizing and control of stand-alone, grid-connected, and hybrid photovoltaic systems.

Applied Sciences is an international journal that is developing a Special Issue focused on the latest scientific results and methods belonging to both Computational Intelligence and optimization techniques in all possible photovoltaic applications. Therefore, our goal is to bring together scientists representing several approaches and various research communities working on these topics, with the aim to share as much as possible of top-level research and to promote research on the same advanced topics.

This Special Issue "Computational Intelligence in Photovoltaic Systems" is open to both original research articles and review articles covering all the relevant progress in these fields (though is not limited to the following):

  • forecasting techniques (deterministic, stochastic, etc.)
  • DC/AC converter control and maximum power point tracking techniques
  • sizing of the photovoltaic system components and their optimization
  • photovoltaics modelling and parameters estimation
  • maintenance and reliability modelling
  • decision process for grid operators

Prof. Dr. Sonia Leva
Dr. Emanuele Giovanni Carlo Ogliari
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computational Intelligence
  • Photovoltaics Systems
  • Modelling and Optimization
  • Control
  • Evolutionary Optimization
  • Artificial Neural Network
  • Fuzzy Logic
  • Renewable Energy Forecast

Published Papers (11 papers)

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Editorial

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Editorial
Computational Intelligence in Photovoltaic Systems
Appl. Sci. 2019, 9(9), 1826; https://0-doi-org.brum.beds.ac.uk/10.3390/app9091826 - 02 May 2019
Cited by 2 | Viewed by 825
Abstract
Photovoltaics, among renewable energy sources (RES), has become more popular [...] Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)

Research

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Article
Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models
Appl. Sci. 2018, 8(11), 2155; https://0-doi-org.brum.beds.ac.uk/10.3390/app8112155 - 04 Nov 2018
Cited by 28 | Viewed by 1713
Abstract
Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for [...] Read more.
Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
Appl. Sci. 2018, 8(4), 558; https://0-doi-org.brum.beds.ac.uk/10.3390/app8040558 - 04 Apr 2018
Cited by 27 | Viewed by 2246
Abstract
The increase of renewable energy usage in the last two decades, in particular photovoltaic (PV) systems, has opened up different solar plant configurations that need to operate and properly perform in terms of efficient power transfer with respect to all of the involved [...] Read more.
The increase of renewable energy usage in the last two decades, in particular photovoltaic (PV) systems, has opened up different solar plant configurations that need to operate and properly perform in terms of efficient power transfer with respect to all of the involved components, such as inverters, grid interface, storage, and other electrical loads. In such applications, the power characteristics of the plant modules all together represent the main components that are responsible for power extraction, depending on both external and internal factors. Conventional maximum power point tracking techniques may not have a proper conversion efficiency under particular external dynamic conditions. This paper proposes an evolutionary-based maximum power point tracking algorithm suitable to operate under dynamic partial shading conditions and compares its performance with classical maximum power point tracking methods in order to evaluate their conversion efficiency in partial shading scenarios with relevant and dynamic changes in the environmental conditions. Simulations taking into account the different dynamic shading conditions were carried out to prove the effectiveness and limitations of the proposed approach with respect to classical algorithms. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm
Appl. Sci. 2018, 8(3), 339; https://0-doi-org.brum.beds.ac.uk/10.3390/app8030339 - 27 Feb 2018
Cited by 50 | Viewed by 4392
Abstract
In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in [...] Read more.
In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning
Appl. Sci. 2018, 8(2), 228; https://0-doi-org.brum.beds.ac.uk/10.3390/app8020228 - 02 Feb 2018
Cited by 30 | Viewed by 2552
Abstract
The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present [...] Read more.
The relevance of forecasting in renewable energy sources (RES) applications is increasing, due to their intrinsic variability. In recent years, several machine learning and hybrid techniques have been employed to perform day-ahead photovoltaic (PV) output power forecasts. In this paper, the authors present a comparison of the artificial neural network’s main characteristics used in a hybrid method, focusing in particular on the training approach. In particular, the influence of different data-set composition affecting the forecast outcome have been inspected by increasing the training dataset size and by varying the training and validation shares, in order to assess the most effective training method of this machine learning approach, based on commonly used and a newly-defined performance indexes for the prediction error. The results will be validated over a one-year time range of experimentally measured data. Novel error metrics are proposed and compared with traditional ones, showing the best approach for the different cases of either a newly deployed PV plant or an already-existing PV facility. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
Online Identification of Photovoltaic Source Parameters by Using a Genetic Algorithm
Appl. Sci. 2018, 8(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/app8010009 - 22 Dec 2017
Cited by 10 | Viewed by 2194
Abstract
In this paper, an efficient method for the online identification of the photovoltaic single-diode model parameters is proposed. The combination of a genetic algorithm with explicit equations allows obtaining precise results without the direct measurement of short circuit current and open circuit voltage [...] Read more.
In this paper, an efficient method for the online identification of the photovoltaic single-diode model parameters is proposed. The combination of a genetic algorithm with explicit equations allows obtaining precise results without the direct measurement of short circuit current and open circuit voltage that is typically used in offline identification methods. Since the proposed method requires only voltage and current values close to the maximum power point, it can be easily integrated into any photovoltaic system, and it operates online without compromising the power production. The proposed approach has been implemented and tested on an embedded system, and it exhibits a good performance for monitoring/diagnosis applications. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
Appl. Sci. 2017, 7(11), 1107; https://0-doi-org.brum.beds.ac.uk/10.3390/app7111107 - 25 Oct 2017
Cited by 41 | Viewed by 2983
Abstract
This paper is proposing and analyzing an electric energy storage system fully integrated with a photovoltaic PV module, composed by a set of lithium-iron-phosphate (LiFePO4) flat batteries, which constitutes a generation-storage PV unit. The batteries were surface-mounted on the back side [...] Read more.
This paper is proposing and analyzing an electric energy storage system fully integrated with a photovoltaic PV module, composed by a set of lithium-iron-phosphate (LiFePO4) flat batteries, which constitutes a generation-storage PV unit. The batteries were surface-mounted on the back side of the PV module, distant from the PV backsheet, without exceeding the PV frame size. An additional low-emissivity sheet was introduced to shield the batteries from the backsheet thermal irradiance. The challenge addressed in this paper is to evaluate the PV cell temperature increase, due to the reduced thermal exchanges on the back of the module, and to estimate the temperature of the batteries, verifying their thermal constraints. Two one-dimensional (1D) thermal models, numerically implemented by using the thermal library of Simulink-Matlab accounting for all the heat exchanges, are here proposed: one related to the original PV module, the other related to the portion of the area of the PV module in correspondence of the proposed energy-storage system. Convective and radiative coefficients were then calculated in relation to different configurations and ambient conditions. The model validation has been carried out considering the PV module to be at the nominal operating cell temperature (NOCT), and by specific experimental measurements with a thermographic camera. Finally, appropriate models were used to evaluate the increasing cell batteries temperature in different environmental conditions. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
A Prototype Design and Development of the Smart Photovoltaic System Blind Considering the Photovoltaic Panel, Tracking System, and Monitoring System
Appl. Sci. 2017, 7(10), 1077; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101077 - 18 Oct 2017
Cited by 10 | Viewed by 2920
Abstract
This study aims to design and develop the prototype models of the smart photovoltaic system blind (SPSB). To achieve this objective, the study defined the properties in three ways: (i) the photovoltaic (PV) panel; (ii) the tracking system; and (iii) the monitoring system. [...] Read more.
This study aims to design and develop the prototype models of the smart photovoltaic system blind (SPSB). To achieve this objective, the study defined the properties in three ways: (i) the photovoltaic (PV) panel; (ii) the tracking system; and (iii) the monitoring system. First, the amorphous silicon PV panel was determined as a PV panel, and the width and length of the PV panel were determined to be 50 mm and 250 mm, respectively. Second, the four tracker types (i.e., fixed type, vertical single-axis tracker, horizontal single-axis tracker, and azimuth-altitude dual-axis tracker) was applied, as well as the direct tracking method based on the amount of electricity generated as a tracking system. Third, the electricity generation and environmental conditions were chosen as factors to be monitored in order to evaluate and manage the technical performance of SPSB as a monitoring system. The prototype model of the SPSB is designed and developed for providing the electricity generated from its PV panel, as well as for reducing the indoor cooling demands through the blind’s function, itself (i.e., blocking out sunlight). Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
Optimal Tilt Angle and Orientation of Photovoltaic Modules Using HS Algorithm in Different Climates of China
Appl. Sci. 2017, 7(10), 1028; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101028 - 06 Oct 2017
Cited by 27 | Viewed by 2337
Abstract
Solar energy technologies play an important role in shaping a sustainable energy future, and generating clean, renewable, and widely distributed energy sources. This paper determines the optimum tilt angle and optimum azimuth angle of photovoltaic (PV) panels, employing the harmony search (HS) meta-heuristic [...] Read more.
Solar energy technologies play an important role in shaping a sustainable energy future, and generating clean, renewable, and widely distributed energy sources. This paper determines the optimum tilt angle and optimum azimuth angle of photovoltaic (PV) panels, employing the harmony search (HS) meta-heuristic algorithm. In this study, the ergodic method is first conducted to obtain the optimum tilt angle and the optimum azimuth angle in several cities of China based on the model of Julian dating. Next, the HS algorithm is applied to search for the optimum solution. The purpose of this research is to maximize the extraterrestrial radiation on the collector surface for a specific period. The sun’s position is predicted by the proposed model at different times, and then solar radiation is obtained on various inclined planes with different orientations in each city. The performance of the HS method is compared with that of the ergodic method and other optimization algorithms. The results demonstrate that the tilt angle should be changed once a month, and the best orientation is usually due south in the selected cities. In addition, the HS algorithm is a practical and reliable alternative for estimating the optimum tilt angle and optimum azimuth angle of PV panels. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
Appl. Sci. 2017, 7(6), 622; https://0-doi-org.brum.beds.ac.uk/10.3390/app7060622 - 15 Jun 2017
Cited by 35 | Viewed by 2741
Abstract
Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, [...] Read more.
Since the beginning of this century, the share of renewables in Europe’s total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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Article
Novel Genetic Algorithm-Based Energy Management in a Factory Power System Considering Uncertain Photovoltaic Energies
Appl. Sci. 2017, 7(5), 438; https://0-doi-org.brum.beds.ac.uk/10.3390/app7050438 - 26 Apr 2017
Cited by 10 | Viewed by 2599
Abstract
The demand response and accommodation of different renewable energy resources are essential factors in a modern smart microgrid. This paper investigates the energy management related to the short-term (24 h) unit commitment and demand response in a factory power system with uncertain photovoltaic [...] Read more.
The demand response and accommodation of different renewable energy resources are essential factors in a modern smart microgrid. This paper investigates the energy management related to the short-term (24 h) unit commitment and demand response in a factory power system with uncertain photovoltaic power generation. Elastic loads may be activated subject to their operation constraints in a manner determined by the electricity prices while inelastic loads are inflexibly fixed in each hour. The generation of power from photovoltaic arrays is modeled as a Gaussian distribution owing to its uncertainty. This problem is formulated as a stochastic mixed-integer optimization problem and solved using two levels of algorithms: the master level determines the optimal states of the units (e.g., micro-turbine generators) and elastic loads; and the slave level concerns optimal real power scheduling and power purchase/sale from/to the utility, subject to system operating constraints. This paper proposes two novel encoding schemes used in genetic algorithms on the master level; the point estimate method, incorporating the interior point algorithm, is used on the slave level. Various scenarios in a 30-bus factory power system are studied to reveal the applicability of the proposed method. Full article
(This article belongs to the Special Issue Computational Intelligence in Photovoltaic Systems)
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