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Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition
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Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition

A special issue of Solar (ISSN 2673-9941).

Deadline for manuscript submissions: 20 July 2026 | Viewed by 5366

Special Issue Editors

Prof. Dr. Philippe Poure

E-Mail Website
Guest Editor
Institut Jean Lamour (UMR7198), Université de Lorraine, Campus Artem, BP 50840, F-54511 Nancy, France
Interests: energy harvesting; solar energy; power and energy architectures; energy conversion and storage; energy conversion and efficiency; maximum power point tracking techniques; energy management; DC–DC conversion; operation under faulty conditions; prognostics and diagnostics; fault-tolerant operation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shamsodin Taheri

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, QC, Canada
Interests: solar systems; microgrid operation and control; power system analysis; power electronics; energy conversion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite contributions to a Special Issue of the journal Solar on the subject of “Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition”. Solar photovoltaic (PV) energy harvesting towards electrical power generation appears to be one of the best solutions providing sufficient and clean electrical energy. Power electronic conversion greatly impacts the performance of PV systems. Additionally, in order to power electronic architectures for conventional central, string and multistring configurations of PV modules, distributed-module converter architectures are also increasingly being investigated (DC–DC power optimizers, DC–AC microinverters, modular cascaded DC–DC converters, etc.). Differential power processing architectures are also being addressed to mitigate mismatches in those modules of PV systems that are connected in series. Moreover, power electronics architectures with fault-tolerant capabilities are also being studied in order to ensure consistent electrical power generation. In all of these cases, the design of efficient and reliable power electronic converters has attracted the attention of the scientific community and of industrial interests. For example, DC–DC converters and the associated control methods are crucial to the achievement of critical global targets in energy efficiency and fault-tolerant operation.

Within this broader thematic area, this Special Issue focuses on conversion, control, and power electronics architectures for monitoring and ensuring the optimal and safe production of PV systems.

Topics of interest for publication include, but are not limited to, the following:

  • Isolated and non-isolated DC–DC converter architectures for PV systems;
  • Modeling and control for optimal electrical energy production by PV systems;
  • The design and optimization of efficient converters for PV systems;
  • Multi-input DC–DC converters for PV systems with energy storage;
  • Maximum power point tracking techniques;
  • Differential power processing converter architectures in PV systems with mismatched modules;
  • Distributed converter architectures at the PV module level;
  • Fault diagnosis and the fault-tolerant control of converter architectures for PV systems;
  • PV systems with fault-tolerant capabilities—power and energy architectures, energy management, advanced control methods for healthy and faulty conditions, and islanded PV systems.

Prof. Dr. Philippe Poure
Prof. Dr. Shamsodin Taheri
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Solar is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 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

  • power and energy architectures for PV systems
  • power electronic converters for PV systems
  • isolated and non-isolated DC–DC converters
  • energy conversion and efficiency
  • PV systems with mismatched modules
  • energy management
  • maximum power point tracking techniques
  • fault tolerant operation of PV systems: fault diagnosis and fault tolerant control
  • efficient and reliable converters under faulty conditions

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Related Special Issue

Published Papers (6 papers)

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Research

21 pages, 3509 KB  
Article
Comparison of Electricity Production Prediction Models Based on Meteorological Data for PV Farms in Poland—Challenges and Problems
by Piotr Kraska and Krzysztof Hanzel
Solar 2026, 6(2), 16; https://doi.org/10.3390/solar6020016 - 11 Mar 2026
Viewed by 562
Abstract
In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on [...] Read more.
In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
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22 pages, 4880 KB  
Article
Cell-Level Modeling Approach for Accurate Irradiance Estimation in Bifacial Photovoltaic Modules
by Monica De Riso, Gerardo Saggese, Pierluigi Guerriero, Santolo Daliento and Vincenzo d’Alessandro
Solar 2026, 6(2), 15; https://doi.org/10.3390/solar6020015 - 11 Mar 2026
Viewed by 354
Abstract
Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool [...] Read more.
Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool benefits from a refined numerical evaluation of ground-reflected irradiance performed through a view-factor-based cell-level approach within a realistic three-dimensional (3D) Sun-module-shadow geometry. This allows capturing both vertical and lateral nonuniformities in the irradiance distributions over the module surfaces, which are neglected in conventional module-level models. The irradiances incident on the cells are subsequently supplied to a circuit-based block, operating with a cell-level granularity as well, which computes the I–V characteristics and the maximum power point (MPP) at selected time instants. Simulations performed on a simplified tool variant assuming uniform albedo irradiance show that this approximation leads to a non-negligible overestimation of power output. An extensive comparison against state-of-the-art tools, including the previous version of our framework, allows us to conclude that the proposed method is especially advantageous for standalone modules or short-row configurations under medium-to-high albedo conditions. Moreover—like its previous version—the tool can handle a large variety of detrimental effects, namely, partial architectural shading, localized snow coverage, bird droppings, and faulty cells. Additionally, a non-zero elevation from the ground can be effectively described. It is also found that south-oriented 30°-tilted bifacial modules suffer from appreciable albedo-induced mismatch losses on the rear surface during summer under medium-albedo conditions, whereas vertically-mounted West- and East-oriented configurations are less affected by such losses. Experimental validation confirms the accuracy of the proposed framework. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
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21 pages, 2769 KB  
Article
Study of a University Campus Smart Microgrid That Contains Photovoltaics and Battery Storage with Zero Feed-In Operation
by Panagiotis Madouros, Yiannis Katsigiannis, Evangelos Pompodakis, Emmanuel Karapidakis and George Stavrakakis
Solar 2026, 6(1), 8; https://doi.org/10.3390/solar6010008 - 3 Feb 2026
Viewed by 721
Abstract
Smart microgrids are localized energy systems that integrate distributed energy resources, such as photovoltaics (PVs) and battery storage, to optimize energy use, enhance reliability, and minimize environmental impacts. This paper investigates the operation of a smart microgrid installed at the Hellenic Mediterranean University [...] Read more.
Smart microgrids are localized energy systems that integrate distributed energy resources, such as photovoltaics (PVs) and battery storage, to optimize energy use, enhance reliability, and minimize environmental impacts. This paper investigates the operation of a smart microgrid installed at the Hellenic Mediterranean University (HMU) campus in Heraklion, Crete, Greece. The system, consisting of PVs and battery storage, operates under a zero feed-in scheme, which maximizes on-site self-consumption while preventing electricity exports to the main grid. With increasing PV penetration and growing grid congestion, this scheme is an increasingly relevant strategy for microgrid operations, including university campuses. A properly sized PV–battery microgrid operating under zero feed-in operation can remain financially viable over its lifetime, while additionally it can achieve significant environmental benefits. The study performed at the HMU Campus utilizes measured hourly data of load demand, solar irradiance, and ambient temperature, while PV and battery components were modeled based on real technical specifications. The study evaluates the system using financial and environmental performance metrics, specifically net present value (NPV) and annual greenhouse gas (GHG) emission reductions, complemented by sensitivity analyses for battery technology (lead–carbon and lithium-ion), load demand levels, varying electricity prices, and projected reductions in lithium-ion battery costs over the coming years. The findings indicate that the microgrid can substantially reduce grid electricity consumption, achieving annual GHG emission reductions exceeding 600 tons of CO2. From a financial perspective, the optimal configuration consisting of a 760 kWp PV array paired with a 1250 kWh lead–carbon battery system provides a system autonomy of 46% and achieves an NPV of EUR 1.41 million over a 25-year horizon. Higher load demands and electricity prices increase the NPV of the optimal system, whereas lower load demands enhance the system’s autonomy. The anticipated reduction in lithium-ion battery costs over the next 5–10 years is expected to provide improved financial results compared to the base-case scenario. These results highlight the techno-economic viability of zero feed-in microgrids and provide valuable insights for the planning and deployment of similar systems in regions with increasing renewable penetration and grid constraints. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
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30 pages, 3873 KB  
Article
Data-Driven Assessment of the Impact of Solar Photovoltaic Generation on Distribution Network Harmonic Distortion
by Sean Elphick, Duane A. Robinson, Jonathan C. Knott and Gerrard Drury
Solar 2025, 5(4), 57; https://doi.org/10.3390/solar5040057 - 5 Dec 2025
Viewed by 740
Abstract
As the penetration of distributed energy resources (DERs) continues to increase, there is conjecture concerning the power quality implications of the inverters used to interface these DERs with low-voltage (LV) electricity supply networks. As a power electronics converter, inverters are a known source [...] Read more.
As the penetration of distributed energy resources (DERs) continues to increase, there is conjecture concerning the power quality implications of the inverters used to interface these DERs with low-voltage (LV) electricity supply networks. As a power electronics converter, inverters are a known source of harmonic emissions. Using a combination of large-scale field measurements, laboratory evaluations of inverter performance, and power system modelling, this study applies an empirical data-driven approach to investigate the impact of small-scale solar PV inverters on LV harmonic distortion magnitudes. This multi-facetted approach, involving field data analysis, laboratory assessments of inverter performance, and power system simulation to evaluate the impact of small-scale DER on harmonic distortion in LV networks, is novel in comparison to other studies, which only utilise one or two of the analysis methods of simulation, laboratory evaluation, or analysis of field measurements but not all three. The analysis of field measurement data collected over the past decade does not indicate any significant changes in harmonic distortion magnitudes that can be attributed to the increasing penetration of DERs. Power system modelling, which incorporates data obtained from laboratory inverter performance evaluations, indicates that, even at very high levels of penetration, the harmonic current emissions from solar PV inverters are only sufficient to add modest levels of harmonic distortion to LV networks, a 0.25% increase in THD for 40% penetration and a 0.62% increase in THD for 100% penetration, providing an explanation for the findings of the field data analysis. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
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22 pages, 5312 KB  
Article
Feasibility on Operation and Maintenance in Floating Photovoltaic Power Plants Based on Cost-Effective Unmanned Surface Vehicles
by Giacomo Cupertino, Luciano Blasi, Angelo Cipollini, Ramiro Dell’Erba, Luca Quattrucci and Giuseppe Marco Tina
Solar 2025, 5(4), 56; https://doi.org/10.3390/solar5040056 - 4 Dec 2025
Viewed by 975
Abstract
Floating photovoltaic systems represent a promising solution for renewable energy generation, offering an alternative to agricultural land consumption. However, these installations have the potential to exert an effect on the aquatic ecosystem, emphasizing the necessity of effective monitoring strategies also related to system [...] Read more.
Floating photovoltaic systems represent a promising solution for renewable energy generation, offering an alternative to agricultural land consumption. However, these installations have the potential to exert an effect on the aquatic ecosystem, emphasizing the necessity of effective monitoring strategies also related to system management issues. In this paper, the use of an unmanned surface vehicle, which can also operate as an autonomous surface vehicle, is proposed to overcome many difficulties of maintenance and monitoring in aquatic environments. A review of the extant literature reveals the scarcity of a cohesive monitoring framework for these plants, highlighting the urgent need for standardized guidelines for plant management and water quality monitoring. The implementation of automated plants directly addresses this gap by providing a tool for efficient and sustainable monitoring tasks, enabling, at the same time, aquatic ecosystem protection and energy production optimization. To address these challenges, a low-cost prototype of an autonomous surface vehicle is proposed. Preliminary test results on trajectory control and obstacle recognition are reported. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
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22 pages, 3640 KB  
Article
Computational Intelligence-Based Modeling of UAV-Integrated PV Systems
by Mohammad Hosein Saeedinia, Shamsodin Taheri and Ana-Maria Cretu
Solar 2025, 5(4), 45; https://doi.org/10.3390/solar5040045 - 3 Oct 2025
Viewed by 998
Abstract
The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is [...] Read more.
The optimal utilization of UAV-integrated photovoltaic (PV) systems demands accurate modeling that accounts for dynamic flight conditions. This paper introduces a novel computational intelligence-based framework that models the behavior of a moving PV system mounted on a UAV. A unique mathematical approach is developed to translate UAV flight dynamics, specifically roll, pitch, and yaw, into the tilt and azimuth angles of the PV module. To adaptively estimate the diode ideality factor under varying conditions, the Grey Wolf Optimization (GWO) algorithm is employed, outperforming traditional methods like Particle Swarm Optimization (PSO). Using a one-year environmental dataset, multiple machine learning (ML) models are trained to predict maximum power point (MPP) parameters for a commercial PV panel. The best-performing model, Rational Quadratic Gaussian Process Regression (RQGPR), demonstrates high accuracy and low computational cost. Furthermore, the proposed ML-based model is experimentally integrated into an incremental conductance (IC) MPPT technique, forming a hybrid MPPT controller. Hardware and experimental validations confirm the model’s effectiveness in real-time MPP prediction and tracking, highlighting its potential for enhancing UAV endurance and energy efficiency. Full article
(This article belongs to the Special Issue Efficient and Reliable Solar Photovoltaic Systems: 2nd Edition)
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