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Solar, Volume 5, Issue 2 (June 2025) – 14 articles

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30 pages, 2539 KiB  
Article
Photovoltaic Farm Power Generation Forecast Using Photovoltaic Battery Model with Machine Learning Capabilities
by Agboola Benjamin Alao, Olatunji Matthew Adeyanju, Manohar Chamana, Stephen Bayne and Argenis Bilbao
Solar 2025, 5(2), 26; https://doi.org/10.3390/solar5020026 - 6 Jun 2025
Viewed by 50
Abstract
This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. Microgrids integrating PV face challenges such as solar irradiance variability, temperature fluctuations, and intermittent generation, which impact grid stability and battery storage [...] Read more.
This study presents a machine learning-based photovoltaic (PV) model for energy management and planning in a microgrid with a battery system. Microgrids integrating PV face challenges such as solar irradiance variability, temperature fluctuations, and intermittent generation, which impact grid stability and battery storage efficiency. Existing models often lack predictive accuracy, computational efficiency, and adaptability to changing environmental conditions. To address these limitations, the proposed model integrates an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a multi-input multi-output (MIMO) prediction algorithm, utilizing historical temperature and irradiance data for accurate and efficient forecasting. Simulation results demonstrate high prediction accuracies of 95.10% for temperature and 98.06% for irradiance on dataset-1, significantly reducing computational demands and outperforming conventional prediction techniques. The model further uses ANFIS outputs to estimate PV generation and optimize battery state of charge (SoC), achieving a consistent minimal SoC reduction of about 0.88% (from 80% to 79.12%) over four different battery types over a seven-day charge–discharge cycle, providing up to 11 h of battery autonomy under specified load conditions. Further validation with four other distinct datasets confirms the ANFIS network’s robustness and superior ability to handle complex data variations with consistent accuracy, making it a valuable tool for improving microgrid stability, energy storage utilization, and overall system reliability. Overall, ANFIS outperforms other models (like curve fittings, ANN, Stacked-LSTM, RF, XGBoost, GBoostM, Ensemble, LGBoost, CatBoost, CNN-LSTM, and MOSMA-SVM) with an average accuracy of 98.65%, and a 0.45 RMSE value on temperature predictions, while maintaining 98.18% accuracy, and a 31.98 RMSE value on irradiance predictions across all five datasets. The lowest average computational time of 17.99s was achieved with the ANFIS model across all the datasets compared to other models. Full article
21 pages, 376 KiB  
Article
Barriers and Challenges in the Implementation of Decentralized Solar Water Disinfection Treatment Systems—A Case of Ghana
by Abdul-Rahaman Afitiri and Ernest Kofi Amankwa Afrifa
Solar 2025, 5(2), 25; https://doi.org/10.3390/solar5020025 - 31 May 2025
Viewed by 185
Abstract
Decentralized solar water disinfection systems (DSODIS) in continuous flow systems are alternatives for large-scale improved water access in rural contexts. However, DSODIS in rural Ghana are limited. An exploratory sequential mixed-methods design was used to explore the enablers of and barriers to, as [...] Read more.
Decentralized solar water disinfection systems (DSODIS) in continuous flow systems are alternatives for large-scale improved water access in rural contexts. However, DSODIS in rural Ghana are limited. An exploratory sequential mixed-methods design was used to explore the enablers of and barriers to, as well as reported barrier perceptions to, the effective implementation of DSODIS in the Sawla-Tuna-Kalba (STK) District of Ghana. The qualitative data (26 respondents) were analyzed thematically, and the quantitative data (1155 household heads) were subjected to Poisson regression analyses. Enablers were categorized into themes such as willingness to pay for DSODIS, household and community participation, and willingness to use water from DSODIS. Similarly, the barriers include environmental barriers, technological barriers, economic barriers, and political and legal barriers. Household characteristics such as main water source and income, age group, education, marital status, household size, being born in the community, and years living in the community are statistically associated with reported barrier perceptions. Households with unimproved water sources and high income (IRR = 1.432, p = 0.000) and improved water sources and high income (IRR = 1.295, p = 0.000) are 43% and 30% more likely, respectively, to report more barrier perceptions compared with households with unimproved water sources and low income. Females (IRR = 1.070, p = 0.032) are marginally more likely to report more barrier perceptions compared with males. The model output also indicates that household heads with higher educational attainment (IRR = 1.152, p = 0.001) are 15% more likely to report more barrier perceptions compared with those with no formal education. These findings provide valuable information for policymakers and stakeholders aiming to provide quality water in rural Ghana where centralized systems cannot be installed. Full article
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17 pages, 5647 KiB  
Article
Solar Photovoltaic Diagnostic System with Logic Verification and Integrated Circuit Design for Fabrication
by Abhitej Divi and Shuza Binzaid
Solar 2025, 5(2), 24; https://doi.org/10.3390/solar5020024 - 30 May 2025
Viewed by 188
Abstract
Solar photovoltaic (PV) panels are the best solution to reduce greenhouse gas emissions by fossil fuel combustion, with global capability now exceeding 714 GW due to rapid technological advances in solar panels (SPs). However, SPs’ efficiency and lifespan remain limited due to the [...] Read more.
Solar photovoltaic (PV) panels are the best solution to reduce greenhouse gas emissions by fossil fuel combustion, with global capability now exceeding 714 GW due to rapid technological advances in solar panels (SPs). However, SPs’ efficiency and lifespan remain limited due to the absence of advanced fault-detection systems, and they are prone to short circuits (SC), open circuits (OC), and power degradation. Therefore, this large-scale production requires reliable, real-time fault diagnosis to maintain panel performance. However, traditional diagnostic methods implemented using MPPT, neural networks, or microcontroller-based systems often rely on complex computational algorithms and are not cost-effective. So, this paper proposes a diagnostic system composed of six functional blocks to address this issue. The proposed system was initially verified using an Intel DE-10 Lite FPGA board. Once its functionality was confirmed, an ASIC design was proposed for mass production, offering a significantly lower implementation cost and reduced hardware complexity than prior methods. Different circuit designs were developed for each of the six blocks. All designs were created using Cadence software and TSMC 180 nm technology files. The basic components used in these designs include PMOS transistors with 300 nm channel length and 2 µm width, NMOS transistors with 350 nm channel length and 2 µm width, as well as resistors and capacitors. Differential amplifiers with a gain of 40 dB were used for voltage and current sensing from the SP. The chip activation signal generator circuit was designed with an adjustable frequency and generated 120 MHz and 100 MHz signals in this work. The decision-making block, Logic Driver Circuit, was innovatively implemented using a reduced number of transistors. A custom memory block with a reset switch was also implemented to store the fault value detected at the SP. Finally, the proposed ASIC was implemented for fabrication, which is highly cost-effective in mass production and does not require complex computational stages. Full article
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1 pages, 167 KiB  
Correction
Correction: Cardoso et al. Solar Resource and Energy Demand for Autonomous Solar Cooking Photovoltaic Systems in Kenya and Rwanda. Solar 2023, 3, 487–503
by João P. Cardoso, António Couto, Paula A. Costa, Carlos Rodrigues, Jorge Facão, David Loureiro, Anne Wambugu, Sandra Banda, Izael Da Silva and Teresa Simões
Solar 2025, 5(2), 23; https://doi.org/10.3390/solar5020023 - 21 May 2025
Viewed by 263
Abstract
Following publication, the Editorial Office became aware that the original article [...] Full article
9 pages, 679 KiB  
Article
Policies for Promising Prospects of Photovoltaics
by Lucie McGovern and Bob van der Zwaan
Solar 2025, 5(2), 22; https://doi.org/10.3390/solar5020022 - 19 May 2025
Viewed by 218
Abstract
As photovoltaics’ (PVs) capacity will probably rapidly expand to tens of terawatts globally, the diversification of the PV technology portfolio becomes essential. Perovskite technology proffers promise for expanding solar energy market segments like building-integrated PVs and flexible PVs for the residential and industrial [...] Read more.
As photovoltaics’ (PVs) capacity will probably rapidly expand to tens of terawatts globally, the diversification of the PV technology portfolio becomes essential. Perovskite technology proffers promise for expanding solar energy market segments like building-integrated PVs and flexible PVs for the residential and industrial sectors. In this perspective, we calculate that under reasonably attainable values for the module cost, conversion efficiency, and degradation rate, a levelized cost of electricity (LCOE) of 10 EURct/kWh can be reached for perovskite PV in 2035. Furthermore, if, in 2035, the conversion efficiency can be increased to 25% and the degradation rate falls to below 1%, with a module cost of 50 EUR/m2, the LCOE for perovskite PV could become around 8 EURct/kWh. For lower module costs, the LCOE would drop further, by which cost competitiveness with c-Si PV is in sight. We point out that even if the LCOE of perovskite solar modules may remain relatively high, they could still occupy an important role, particularly in the residential sector, thanks to their flexibility and lightweight properties, enabling a large suite of new applications. Overall, to push perovskite PVs towards successful commercialization, policy support will be indispensable. Full article
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1 pages, 135 KiB  
Correction
Correction: Topa Gavilema et al. Modeling and Energy Management of a Microgrid Based on Predictive Control Strategies. Solar 2023, 3, 62–73
by Alex Omar Topa Gavilema, Juan D. Gil, José Domingo Álvarez Hervás, José Luis Torres Moreno and Manuel Pérez García
Solar 2025, 5(2), 21; https://doi.org/10.3390/solar5020021 - 16 May 2025
Viewed by 327
Abstract
Following publication, the Editorial Office became aware that the original article [...] Full article
1 pages, 135 KiB  
Correction
Correction: Marotta et al. Towards Positive Energy Districts: Energy Renovation of a Mediterranean District and Activation of Energy Flexibility. Solar 2023, 3, 253–282
by Ilaria Marotta, Thibault Péan, Francesco Guarino, Sonia Longo, Maurizio Cellura and Jaume Salom
Solar 2025, 5(2), 20; https://doi.org/10.3390/solar5020020 - 15 May 2025
Viewed by 367
Abstract
Following publication, the Editorial Office became aware that the original article [...] Full article
1 pages, 133 KiB  
Correction
Correction: Fernández-Reche et al. Measuring Concentrated Solar Radiation Flux in a Linear Fresnel-Type Solar Collector. Solar 2022, 2, 401–413
by Jesús Fernández-Reche, Loreto Valenzuela and Diego Pulido-Iparraguirre
Solar 2025, 5(2), 19; https://doi.org/10.3390/solar5020019 - 14 May 2025
Viewed by 375
Abstract
Following publication, the Editorial Office became aware that the original article [...] Full article
1 pages, 134 KiB  
Correction
Correction: Estremera-Pedriza et al. Optical Characterization of a New Facility for Materials Testing under Concentrated Wavelength-Filtered Solar Radiation Fluxes. Solar 2023, 3, 76–86
by Noelia Estremera-Pedriza, Jesús Fernández-Reche and Jose A. Carballo
Solar 2025, 5(2), 18; https://doi.org/10.3390/solar5020018 - 14 May 2025
Viewed by 360
Abstract
Following publication, the Editorial Office became aware that the original article [...] Full article
20 pages, 13013 KiB  
Article
Impact of Surface Modification on Performance of Solar Concentrators
by Nikolaos Skandalos and Gudrun Kocher-Oberlehner
Solar 2025, 5(2), 17; https://doi.org/10.3390/solar5020017 - 6 May 2025
Viewed by 315
Abstract
This study analyzes the impact of powder-blasted surface modification on the performance of non-imaging solar concentrators and evaluates a ray-tracing simulation approach to virtual solar power measurements. Powder blasting was applied to poly(methyl methacrylate) (PMMA) sheets to create a rough, Lambertian-like scattering surface, [...] Read more.
This study analyzes the impact of powder-blasted surface modification on the performance of non-imaging solar concentrators and evaluates a ray-tracing simulation approach to virtual solar power measurements. Powder blasting was applied to poly(methyl methacrylate) (PMMA) sheets to create a rough, Lambertian-like scattering surface, enhancing light trapping and total internal reflection. The effects of this modification were systematically assessed using optical transmission spectroscopy, angular scattering measurements, and solar cell efficiency characterization under standard AM1.5 illumination. The results show that surface roughening significantly improves light redirection toward the concentrator’s edge, enhancing solar cell performance. OptisWorks ray-tracing simulations were employed to model the concentrator’s optical behavior, demonstrating strong agreement (within 5–10% deviation) with experimental data. These findings confirm that surface modification is crucial in optimizing concentrator efficiency and establishing ray tracing as a reliable tool for virtual performance evaluation in photovoltaic applications. Full article
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19 pages, 3724 KiB  
Article
Computational Fluid Dynamics–Discrete Element Method Numerical Simulation of Hydrothermal Liquefaction of Sewage Sludge in a Tube Reactor as a Linear Fresnel Solar Collector
by Artur Wodołażski
Solar 2025, 5(2), 16; https://doi.org/10.3390/solar5020016 - 28 Apr 2025
Viewed by 750
Abstract
This paper discusses the thermal and exergy efficiency analysis of the hydrothermal liquefaction (HTL) process, which converts sewage sludge into biocrude oil in a continuous plug–flow reactor using a linear Fresnel solar collector. The investigation focuses on the influence of key operational parameters, [...] Read more.
This paper discusses the thermal and exergy efficiency analysis of the hydrothermal liquefaction (HTL) process, which converts sewage sludge into biocrude oil in a continuous plug–flow reactor using a linear Fresnel solar collector. The investigation focuses on the influence of key operational parameters, including slurry flow rate, temperature, pressure, residence time, and the external heat transfer coefficient, on the overall efficiency of biocrude oil production. A detailed thermodynamic evaluation was conducted using process simulation principles and a kinetic model to assess mass and energy balances within the HTL reaction, considering heat and mass momentum exchange in a multiphase system using UDF. The reactor’s receiver, a copper absorber tube, has a total length of 20 m and is designed in a coiled configuration from the base to enhance heat absorption efficiency. To optimize the thermal performance of biomass conversion in the HTL process, a Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) coupling numerical method approach was employed to investigate improved thermal performance by obtaining a heat source solely through solar energy. This numerical modeling approach allows for an in-depth assessment of heat transfer mechanisms and fluid-particle interactions, ensuring efficient energy utilization and sustainable process development. The findings contribute to advancing solar-driven HTL technologies by maximizing thermal efficiency and minimizing external energy requirements. Full article
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35 pages, 411 KiB  
Article
Model Predictive Control of Electric Water Heaters in Individual Dwellings Equipped with Grid-Connected Photovoltaic Systems
by Oumaima Laguili, Julien Eynard, Marion Podesta and Stéphane Grieu
Solar 2025, 5(2), 15; https://doi.org/10.3390/solar5020015 - 25 Apr 2025
Viewed by 273
Abstract
The residential sector is energy-consuming and one of the biggest contributors to climate change. In France, the adoption of photovoltaics (PV) in that sector is accelerating, which contributes to both increasing energy efficiency and reducing greenhouse gas (GHG) emissions, even though the technology [...] Read more.
The residential sector is energy-consuming and one of the biggest contributors to climate change. In France, the adoption of photovoltaics (PV) in that sector is accelerating, which contributes to both increasing energy efficiency and reducing greenhouse gas (GHG) emissions, even though the technology faces several issues. One issue that slows down the adoption of the technology is the “duck curve” effect, which is defined as the daily variation of net load derived from a mismatch between power consumption and PV power generation periods. As a possible solution for addressing this issue, electric water heaters (EWHs) can be used in residential building as a means of storing the PV power generation surplus in the form of heat in a context where users’ comfort—the availability of domestic hot water (DHW)—has to be guaranteed. Thus, the present work deals with developing model-based predictive control (MPC) strategies—nonlinear/linear MPC (MPC/LMPC) strategies are proposed—to the management of EWHs in individual dwellings equipped with grid-connected PV systems. The aim behind developing such strategies is to improve both the PV power generation self-consumption rate and the economic gain, in comparison with rule-based (RB) control strategies. Inasmuch as DHW and power demand profiles are needed, data were collected from a panel of users, allowing the development of profiles based on a quantile regression (QR) approach. The simulation results (over 6 days) highlight that the MPC/LMPC strategies outperform the RB strategies, while guaranteeing users’ comfort (i.e., the availability of DHW). The MPC/LMPC strategies allow for a significant increase in both the economic gain (up to 2.70 EUR) and the PV power generation self-consumption rate (up to 14.30%ps), which in turn allows the CO2 emissions to be reduced (up to 3.92 kg CO2.eq). In addition, these results clearly demonstrate the benefits of using EWHs to store the PV power generation surplus, in the context of producing DHW in residential buildings. Full article
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20 pages, 14556 KiB  
Article
Design and Improvement of an Automated Tool for Quality Control and Performance Assessment of Photovoltaic Modules
by Alain Foutche Tchouli, Stephane Ndiya Ngasop, Jean Hilaire Tchami, Claude Bertin Nzoundja Fapi and Hyacinthe Tchakounté
Solar 2025, 5(2), 14; https://doi.org/10.3390/solar5020014 - 16 Apr 2025
Viewed by 322
Abstract
Photovoltaic (PV) systems are at the heart of the energy transition, providing an essential source of clean, renewable energy for applications such as solar pumping, which is essential for irrigation and rural water supply. However, their efficiency depends directly on the quality and [...] Read more.
Photovoltaic (PV) systems are at the heart of the energy transition, providing an essential source of clean, renewable energy for applications such as solar pumping, which is essential for irrigation and rural water supply. However, their efficiency depends directly on the quality and performance of the modules, which are often affected by defects or unfavorable environmental conditions. This article presents the development of an innovative automated tool designed for advanced characterization of PV modules by analyzing key parameters such as voltage and current. The system integrates measurement sensors (voltage, current, temperature, etc.), an Arduino Mega board and an SD card, enabling real-time data collection, processing, and recording under various environmental conditions. The results of the experimental tests demonstrate a significant improvement in the PV panel selection process, ensuring optimized choices at the time of purchase and rigorous monitoring during operation. This innovation contributes to maximizing energy performance and extending panel longevity, reinforcing their role in the transition to a sustainable energy model. Full article
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13 pages, 3123 KiB  
Article
Loss Analysis of P3 Laser Patterning of Perovskite Solar Cells via Hyperspectral Photoluminescence Imaging
by Christof Schultz, Markus Fenske, Nicolas Otto, Laura-Isabelle Dion-Bertrand, Guillaume Gélinas, Stéphane Marcet, Janardan Dagar, Rutger Schlatmann, Eva Unger and Bert Stegemann
Solar 2025, 5(2), 13; https://doi.org/10.3390/solar5020013 - 11 Apr 2025
Viewed by 472
Abstract
Upscaling perovskite solar cells and modules requires precise laser patterning for series interconnection and spatial characterization of cell parameters to understand laser–material interactions and their impact on performance. This study investigates the use of nanosecond (ns) and picosecond (ps) laser pulses at varying [...] Read more.
Upscaling perovskite solar cells and modules requires precise laser patterning for series interconnection and spatial characterization of cell parameters to understand laser–material interactions and their impact on performance. This study investigates the use of nanosecond (ns) and picosecond (ps) laser pulses at varying fluences for the P3 patterning step of perovskite solar cells. Hyperspectral photoluminescence (PL) imaging was employed to map key parameters such as optical bandgap energy, Urbach energy, and shunt resistance. The mappings were correlated with electrical measurements, revealing that both ns and ps lasers can be utilized for effective series interconnections with minimal performance losses at optimized fluences. Our findings provide a deeper understanding of fluence-dependent effects in P3 patterning. Moreover, the results demonstrate that the process window is robust, allowing for reasonable cell performance even with deviations from optimal parameters. This robustness, coupled with the scalability of the laser patterning process, emphasize its suitability for industrial module production. Full article
(This article belongs to the Special Issue Developments in Perovskite Solar Cells)
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