Solar doi: 10.3390/solar6030025

Authors: Xianren Ai Honggang Li Yuqian Wang Qishun Zhang Jie Peng Feifan Li Chulun Cheng

In recent years, cascaded hydropower (CHP) has been extensively leveraged to enhance the grid-connected penetration of photovoltaic (PV) generation. However, the inherent stochasticity and volatility of high-penetration PV often lead to significant renewable curtailment. To address this challenge, this paper proposes a coordinated day-ahead and intra-day scheduling model that incorporates curtailment risk assessment. The proposed framework employs a two-stage optimization architecture: the day-ahead stage establishes a baseline dispatch schedule with the objective of maximizing total energy production, while the intra-day stage refines this plan through multi-scenario optimization that explicitly accounts for curtailment risk. This synergistic mechanism achieves the objective of “maximizing day-ahead economic benefits and ensuring intra-day renewable accommodation”. Case studies on a specific river basin demonstrate the effectiveness of the proposed model. Simulation results indicate that, compared to conventional energy-maximization approaches, the proposed model significantly reduces intra-day curtailment rates and substantially enhances the integrated accommodation capacity of the hydro–solar hybrid system.

Solar doi: 10.3390/solar6030024

Authors: Artur Zaporozhets Vitalii Babak Mykhailo Kulyk Viktor Denysov

This paper examines an algorithm and evaluates the upper limits of technical parameters for step-by-step management of forecast coverage for aggregated generation from solar power plants (SPPs) in Ukraine, given the high share of renewable energy sources in the integrated power system of Ukraine. The relevance of the research is due to the growth in the installed capacity of SPPs, stricter requirements for forecasting accuracy, and the full financial responsibility of producers for imbalances in accordance with the current electricity market model. The problem is formulated as a special case of a hierarchically controlled quasi-dynamic power system, accounting for technological, energy, and economic constraints. The objective function is defined as the minimisation of the total hourly measure of discrepancy between the forecast and actual volumes of electricity supplied, whilst ensuring power balance through energy storage systems and flexible generation. The numerical implementation was carried out using the “SOPS” software and information complex. The input data used were hourly indicators of the forecasted and actual generation of Ukraine’s solar power plants for 2021–2025, published by the state-owned enterprise “Guaranteed Buyer”. Hourly, daily and monthly operating parameters for aggregated solar power generation in 2025 have been calculated. The calculations show that the maximum hourly mismatch between forecasted and actual solar generation in 2025 reached 3116 MW, while the maximum daily mismatch exceeded 19.8 GWh. Under the assumed operating conditions, an energy storage system with 30,000 MWh capacity and flexible generation of up to 7500 MW enabled full imbalance compensation, achieving IMB(t) = 0 for all hourly intervals in the analysed case. The required volumes of flexible generation and the operating parameters of the storage systems have been determined. The practical significance of the results lies in their potential use for operational planning of the operating modes of solar power plants, energy storage systems, and flexible generation on a daily and hourly basis, as well as for justifying technical and economic decisions aimed at reducing imbalances. The results obtained confirm the effectiveness of the proposed step-by-step control algorithm and demonstrate the potential to minimise imbalances through the rational coordination of solar power plants, energy storage systems, and flexible generation capacities.

Solar doi: 10.3390/solar6030023

Authors: Oluwaseyi O. Alabi Oluwatoyin J. Gbadeyan Oludolapo A. Olanrewaju

Solar Water Heating Systems (SWHS) are increasingly recognized as vital technologies for reducing dependence on conventional energy sources and supporting sustainable thermal energy solutions. This study reviews recent advancements in the numerical modeling and simulation of SWHS, with a particular focus on improving heat transfer efficiency and overall system performance. The primary aim is to evaluate how Computational Fluid Dynamics (CFD) and other simulation approaches accurately predict thermal behavior, fluid flow characteristics, and energy storage dynamics. The study identifies key objectives, including the analysis of critical design parameters, collector geometry, material properties, working fluid selection, and operating conditions, and their impact on thermal efficiency. This review integrates heat transfer, fluid dynamics, and energy storage within a unified numerical modeling framework. The current study also emphasizes advanced simulation techniques, including multi-physics analysis and optimization to enhance prediction accuracy and reduce computational cost. The outcomes indicate that validated numerical models provide reliable performance predictions under varying operating conditions and facilitate the development of high-efficiency, cost-effective SWHS for residential, commercial, and industrial applications. The findings also outline future research directions, including transient analysis, experimental validation, and advanced optimization frameworks, thereby contributing to the next generation of solar thermal technologies.

Solar doi: 10.3390/solar6030022

Authors: Volodymyr Derii Artur Zaporozhets Tetiana Nechaieva Yaroslav Havrylenko

Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between electricity production and demand timing. Without appropriate storage solutions, surplus energy remains unused. Although battery energy storage systems are increasingly applied to improve the flexibility and reliability of power systems, there is still a research gap in forecasting the optimal power and storage capacity of solar power plant–battery energy storage system energy complexes operating in parallel with the grid under short-term forecasting conditions, particularly when economic aspects such as partial leasing of storage capacity are considered. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems are widely used due to their availability, high response speed, significant energy density, and sufficient power capacity; however, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model uses a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results shows that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. The results confirm that the proposed methodology provides a reliable basis for determining optimal parameters of solar power plant–battery energy storage system energy complexes and enables economically efficient use of storage capacity through short-term leasing mechanisms. Although the proposed methodology is applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients differ, but the methodology itself and the sequence of its application remain the same.

Solar doi: 10.3390/solar6030021

Authors: Chahrazad Zargane Anas Kabbori Azidine Guezzaz Said Benkirane Mourade Azrour

Moroccan installations of solar photovoltaic panels experience operational difficulties due to shading and vegetation-related soiling, which reduce energy output by 15–30%. Most monitoring systems depend upon a single vegetation index, which can reduce the accuracy of detecting even moderate anomalies. This paper presents a novel integration of multi-criteria hierarchical labeling with dual-branch deep learning for enhanced vegetation anomaly detection. We combined MODIS (2000–2015) and Sentinel-2 (2015–2025) images and NASA POWER weather records to study a 25-year vegetation record using multi-source satellite data in 5 of Morocco’s ecologically diverse zones. We introduced a three-class hierarchical labeling scheme (normal, moderate, severe) for dynamic vegetation models based on combined vegetation indices (NDVI, EVI, NDWI) and meteorological thresholds. The proposed dual-branch architecture uses independent data streams for unfused data, which include temporal multi-scale CNNs (TMSCNN) for spatiotemporal modeling and bidirectional LSTMs for weather-integrated vegetation data. Systematic ablation studies show improvements from using NDVI (68.98%) to multispectral indices (77.74%), meteorological integration (81.02%), and a final accuracy of 82.34% ± 0.88%. The moderate anomaly class exhibits lower precision (65%), demonstrating the challenge of operationalizing severity-based anomaly classification. This work integrates hierarchical, multi-criteria labeling and hybrid deep learning for solar photovoltaic vegetation monitoring.

Solar doi: 10.3390/solar6030020

Authors: Pengju Duan Yu Jin Boda Song

To address the demands of large-scale production in the photovoltaic industry for laminators with a small footprint, low energy consumption, and high encapsulation quality, this paper presents research on the structural design, simulation optimization, and performance validation of a multi-layer laminator for photovoltaic modules. Different from existing single-layer or double-layer structures, this paper proposes for the first time an eight-layer, three-stage overall scheme, develops modular lamination units, completes the design of core systems, and achieves multi-chamber coordination. Simulation validation was conducted on the temperature uniformity of the heating plates and the thermo-mechanical coupling under vacuum conditions. A prototype, model HCDL2743DSiT, was developed and subjected to a 30-day production trial. The results show that the equipment reaches a vacuum degree of 92 Pa within 100 s and drops to 38 Pa within 120 s; the temperature uniformity error of the heating plates is ±1.3 °C; the maximum positioning deviation of the transmission is ±2.8 mm. All core indicators meet the design requirements, and the module encapsulation pass rate reaches 99.9%. At the same production rate, the footprint is reduced by approximately 72% compared with that of a traditional double-layer laminator, achieving dual optimization of space utilization and energy consumption and providing technical equipment support for the high-efficiency encapsulation of photovoltaic modules.

Solar doi: 10.3390/solar6030019

Authors: Cheng-Ting Han Hsin-Mei Lin

Dye-sensitized solar cells (DSSCs) remain attractive as low-cost photovoltaic devices; however, their practical efficiency is still constrained by electron-transport losses, interfacial recombination, and incomplete light harvesting in conventional titanium dioxide (TiO2) photoanodes. The effects of TiO2 film thickness, multi-walled carbon nanotube (MWCNT) incorporation, and multilayer oxide interface engineering on DSSC performance were examined. Degussa P25-TiO2 photoanodes were first optimized with respect to thickness, after which controlled MWCNT loadings and sequential compact sol–gel TiO2 and tin dioxide (SnO2) sublayers were introduced. The optimum pristine P25-TiO2 photoanode thickness was 9.11 μm, yielding an open-circuit voltage of 0.74 ± 0.01 V, a short-circuit current density of 14.10 ± 0.40 mA/cm2, a fill factor of 56.24 ± 1.00%, and a power-conversion efficiency of 5.93 ± 0.20%. The incorporation of 0.025 wt% MWCNTs increased the efficiency to 6.04 ± 0.20%, corresponding to an absolute gain of 0.11 percentage points. The best performance was obtained with the sol–gel SnO2/sol–gel TiO2/P25-CNT multilayer photoanode, which delivered 0.74 ± 0.02 V, 16.22 ± 0.40 mA/cm2, 57.59 ± 1.00%, and 6.89 ± 0.30%, respectively. FE-SEM, EIS, XRD, Heated Ultrasonic Cleaner and UV–visible analyses indicate that the multilayer architecture preserves porosity, enhances light harvesting, and suppresses interfacial recombination, while the CNT network facilitates charge transport.

Solar doi: 10.3390/solar6030018

Authors: Duc-Huy Nguyen Jérémy Martin Arnaud Gaillard Quoc-Tuan Tran

The demand for a more sustainable energy system is driving the development of renewable energy sources and green technologies within the electrical sector. However, integrating these technologies is challenging due to the increased complexity of the system components and grid architectures. This paper provides an overview of power electronic conversion systems that facilitate the connection of renewable energy sources (photovoltaic power plants) and direct-current energy storage systems to three-phase medium-voltage alternating-current grids. This paper presents a comprehensive study of the state-of-the-art converter architectures and proposes modifications and technological alternatives, providing insight into the future development of grid-interface power converters for hybrid energy systems.

Solar doi: 10.3390/solar6020017

Authors: Homar Santillan Gomez Wildor Gosgot Angeles Merbelita Yalta Chappa Fernando Isaac Espinoza Canaza Yasmin Delgado Rodríguez Manuel Oliva Cruz Oscar Gamarra Torres Miguel Ángel Barrena Gurbillón

This study experimentally evaluates the thermal performance of a compact parabolic trough solar collector (PTSC) operating under actual solar conditions in Chachapoyas, a high-Andean city in northern Peru characterized by frequent cloud cover and variable irradiance. Despite the growing interest in solar thermal systems, few studies have assessed PTC behavior under high-altitude, diffuse radiation conditions typical of Andean regions. The PTSC, aligned along the north–south axis and equipped with a manual solar tracking system, was monitored for 30 consecutive days. Solar irradiance, ambient temperature, and water inlet/outlet temperatures were recorded at 30 min intervals using a DAVIS Vantage Pro Plus weather station and infrared thermometers (±0.5 °C accuracy). Thermal efficiency was determined from the ratio of useful heat gain to incident solar energy, based on instantaneous irradiance data. Results showed peak irradiance values of 1000 W m−2 and maximum outlet water temperatures of 85 °C, achieving an average efficiency of 68 ± 2.5%. The collector maintained stable operation even under fluctuating radiation, confirming its suitability for domestic hot-water and low-temperature industrial applications. These findings provide the first experimental evidence of efficient solar-thermal conversion in cloudy highland environments of Peru, supporting the deployment of decentralized renewable energy systems in the Andean region.

Solar doi: 10.3390/solar6020016

Authors: Piotr Kraska Krzysztof Hanzel

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.

Solar doi: 10.3390/solar6020015

Authors: Monica De Riso Gerardo Saggese Pierluigi Guerriero Santolo Daliento Vincenzo d’Alessandro

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.

Solar doi: 10.3390/solar6020014

Authors: Simon Oskar Weber Philip Leistner

Façade-integrated solar cooling technologies enable the utilisation of façade surface potential and the provision of resilient cooling. This work compares three solar cooling scenarios, positioning a solar cooling element in the west and east façades. The 2ACE scenario is based on a compact adsorption cooling concept, while the 2PV scenario directly drives a compression chiller with photovoltaic elements, and 2PVB incorporates an additional battery. All Modelica system models are implemented in Modelon Impact and operated using dynamic optimisation for the hottest day of the year. Results indicate that the 2ACE scenario, utilising the adsorbent Silica Gel SG123, achieves near to double the self-sufficiency compared to Zeolite 13X. No clear optimal area balance between west and east elements is apparent. The 2PV scenario only surpasses the 2ACE scenario’s self-sufficiency with increased total element area, whereas 2PVB enables a drastic increase and complete self-sufficiency. This highlights the limitation of the adsorption cooling scenario due to its inability to compensate for ventilation’s electrical energy consumption. However, photovoltaic scenarios are heavily reliant on the assumed energy efficiency ratio. Additionally, slender buildings with a low AV ratio require less façade area to achieve the same self-sufficiency level as wider buildings.

Solar doi: 10.3390/solar6020013

Authors: Khuloud Ali Ghayth Tintawi Mohamad Khaled Bassma

Achieving meaningful reductions in residential heating demand requires design strategies that can respond to climate-specific solar availability and envelope performance. Although passive solar principles are well established, their effectiveness remains highly context-dependent, and simplified prescriptive approaches may not capture interactions across different climates. This study presents an AI-guided evolutionary optimization framework for passive solar residential design, focusing exclusively on the reduction in annual space heating demand under standardized assumptions. A standardized single-story residential prototype is simulated across three climatic contexts: hot–dry (Riyadh), temperate (Barcelona), and cold (Toronto). Dynamic building performance simulations are conducted using EnergyPlus, coupled with DesignBuilder’s built-in Non-Dominated Sorting Genetic Algorithm II (NSGA-II) evolutionary optimization engine. Envelope-related variables, including the window-to-wall ratio, orientation, glazing configuration, and thermal mass, are optimized with a single objective: minimizing the annual heating load under idealized heating conditions. The results demonstrate substantial climate-dependent reductions in heating demand. In Toronto, the annual heating demand is reduced from approximately 16,900 kWh to 9600 kWh (≈43%). In Barcelona, a reduction from approximately 5650 kWh to 1990 kWh (≈65%) is achieved, while in Riyadh, heating demand is reduced from approximately 990 kWh to 39 kWh (>95%). The optimized solutions reveal distinct climate-specific design logic rather than universal passive rules. The results demonstrate that evolutionary optimization can support early-stage envelope design by revealing climate-specific heating strategies under clearly defined and comparable assumptions.

Solar doi: 10.3390/solar6020012

Authors: Nick Pelekas Stefanos Keskinis Ioannis E. Kosmadakis Costas Elmasides

This paper investigates the integration of on-site photovoltaic (PV) systems in the industrial sector under a zero feed-in configuration, where all generated electricity is consumed locally without export to the grid. The analysis follows the current Greek regulatory framework and uses real operating data from an insulation materials manufacturing plant. Twelve months of measured electricity demand were combined with Typical Meteorological Year (TMY) solar data to simulate PV systems of 500, 1000, 1500, and 2000 kWp. Annual PV production ranges from approximately 739 MWh (500 kWp) to 2970 MWh (2000 kWp), and it is all fully self-consumed by the factory due to its high and continuous load. However, given the plant’s large annual electricity use, the PV systems offset 1.0–2.8% of total consumption. The avoided grid purchases correspond to 40–160 MWh/year of net energy savings, delivering positive Net Present Value (NPV) when electricity tariffs exceed EUR 0.15/kWh. The results confirm that zero feed-in PV deployment is technically feasible and economically attractive for industrial facilities facing high electricity prices, while also enhancing sustainability by reducing dependency on the public grid.

Solar doi: 10.3390/solar6010011

Authors: Rodolfo Y. Salas-Bernal Pablo Sosa-Flores Armando Piña-Ortiz Carlos A. Pérez-Rábago Agustín Brau-Ávila Rafael E. Cabanillas-López Ricardo A. Pérez-Enciso

Recent advances in design, manufacturing and development techniques have been very relevant to making solar collectors feasible for production in a variety of applications. In the field of concentrated solar thermal technologies, several techniques have been developed to achieve high levels of radiation concentration. The generation of concave curvature geometry through the polishing of the reflective surface or through specialized machining is one of the most common methods. However, the way in which these bends are obtained can vary significantly, depending on the required quality of optical concentration for the application. This study presents a simple parametric technique to achieve a parabolic curvature for solar concentration applications. To do this, a controlled bending deformation was applied to a metal hollow profile beam supported by a pin and roller at each of the ends, and only two symmetric point loads were applied to generate a bending moment to induce a bending of a curved shape. It was found that, for a given load configuration, a parabolic geometry was generated along a partial center section of the beam. The analysis carried out showed that under the load configuration analyzed, up to 66% of the beam length adopted a fully parabolic geometry. The technique proposed in this work allows for the creation of parabolas with variable focal distances, offering versatility in the design of solar concentrating systems. It also allows corrective adjustments to be made during the assembly of the complete solar concentrator system.

Solar doi: 10.3390/solar6010010

Authors: Leonel Díaz-Tato Luis Angel Iturralde Carrera Carlos D. Constantino-Robles Fernando Banda-Muñoz José M. Álvarez-Alvarado Marcos Aviles Juvenal Rodríguez-Reséndiz

This study aimed to evaluate the thermal performance and operational behavior of an evacuated-tube solar collector field installed in a coastal hotel under real industrial conditions. The work analyzed temperature, irradiance, and mass-flow data to determine instantaneous efficiency and identify performance deterioration associated with fouling. A multivariable regression model was developed to predict collector efficiency as a function of operating parameters. The results showed an average efficiency of 40–55%, with a noticeable decrease attributed to soiling effects. The methodology and findings contribute to improving monitoring-based maintenance strategies and optimizing the energy performance of large-scale domestic hot-water systems.

Solar doi: 10.3390/solar6010009

Authors: Bo Yuan Yuan Li Jiachao Li Mengjing Guo Miaojie Li Shuguang Xie

CPVG (Utility-scale photovoltaic generation) is expanding rapidly worldwide, yet its cumulative ecological effects remain insufficiently quantified. This review synthesizes current evidence to clarify how CPVG influences ecosystems through linked mechanisms of energy redistribution, biogeochemical cycling disturbance, and ecological responses. CPVG alters surface radiation balance, modifies microclimate, and disrupts carbon–nitrogen–water fluxes, thereby driving vegetation shifts, soil degradation, and biodiversity decline. These impacts accumulate across temporal scales—from short-term construction disturbances to long-term operational feedbacks—and propagate spatially from local to regional and watershed levels. Ecological outcomes differ substantially among deserts, grasslands, and agroecosystems due to contrasting resilience and limiting factors. Based on these mechanisms, we propose a multi-scale cumulative impact assessment framework integrating indicator development, multi-source monitoring, coupled modelling, and ecological risk tiering. A full-chain mitigation pathway is further outlined, emphasizing optimized siting, disturbance reduction, adaptive management, and targeted restoration. This study provides a systematic foundation for evaluating and regulating CPVG’s cumulative ecological impacts, supporting more sustainable solar deployment.

Solar doi: 10.3390/solar6010008

Authors: Panagiotis Madouros Yiannis Katsigiannis Evangelos Pompodakis Emmanuel Karapidakis George Stavrakakis

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.

Solar doi: 10.3390/solar6010007

Authors: Shiyao Hu Manyi Yang Guozhen Ma Xiaobin Xu Hangtian Li Chuanfeng Xie

Uncovering the interdependencies among barrier factors and pinpointing the most critical obstacles are essential to overcoming the resistance encountered by photovoltaic (PV) integration into electricity markets. This study first employs grounded theory to identify and categorize the key barriers impeding PV participation, thereby constructing a comprehensive barrier factor model. Subsequently, Interpretive Structural Modeling (ISM) is applied to systematically analyze the interrelations and hierarchical structure among these barriers. The results reveal that: (1) The complex system of PV participation comprises 15 distinct barriers, which can be grouped into 4 overarching categories: economic and cost-related challenges, policy and regulatory uncertainties, technological and infrastructure constraints, and environmental and resource limitations. (2) These barriers form a six-tier hierarchical structure, reflecting their layered influence. (3) Root-level barriers—such as inadequate government fiscal support and the absence of a comprehensive coordination mechanism—play a foundational role in hindering progress. In response, this study proposes policy recommendations, including establishing a unified and effective coordination framework to align renewable energy policies and formulating standardized guidelines for PV panel recycling.

Solar doi: 10.3390/solar6010006

Authors: Muhammet Tahir Guneser Sakir Kuzey Bayram Kose

The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine learning (ML) algorithms for the detection and classification of AC-side faults (inverter and grid faults) in GCPV systems. We utilized three commonly employed algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), and Artificial Neural Networks (ANNs), to develop fault detection models. These models were trained using a monthly electrical dataset obtained from the AYCEM-GES-GCPV power plant in Giresun, Turkiye, and their performance was rigorously evaluated using classification accuracy, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) analyses. The results demonstrate that the algorithms are highly effective in fault detection, with AUC values consistently exceeding the critical threshold. The obtained accuracies for KNN, LR, and ANN were 0.9826, 0.782, and 0.7096, respectively. These findings emphasize the high effectiveness of ML algorithms, with KNN exhibiting the best performance, for identifying AC-side faults in GCPV installations. While the study focused on AC-side fault detection, subsequent work developed a smart card module to identify complex DC side electrical faults and built a PV array for experimental testing.

Solar doi: 10.3390/solar6010005

Authors: Dimitra Tsirigoti Konstantinos Gkyrtis

In Mediterranean cities, high solar radiation combined with limited shading and vegetation intensifies the urban heat island (UHI) phenomenon. As the road network often covers a large portion of the cities’ surfaces and is mostly constructed using asphalt pavements, it can significantly affect the urban microclimate, leading to low thermal comfort and increased energy consumption. Recycled and waste materials are increasingly used in the construction of pavements in accordance with the principle of sustainability for minimizing waste and energy to produce new materials based on a circular economy. The scope of this study is to evaluate the effect of recycled or waste materials used in road pavements on the urban microclimate. The surface and ambient temperature of urban pavements constructed with conventional asphalt and recycled/waste-based mixtures are assessed through simulation. Two study areas comprising large street junctions near metro stations in the city of Thessaloniki, in Greece, are examined under three scenarios: a conventional hot mix asphalt, an asphalt mixture containing steel slag, and a high-albedo mixture. The results of the research suggest that the use of steel slag could reduce the air temperature by 0.9 °C at 15:00, east European summer time (EEST), while the high-albedo scenario could reduce the ambient temperature by 1.6 °C at 16:00. The research results are useful for promoting the use of recycled materials, not only as a means of sustainably using resources but also for the improvement of thermal comfort in urban areas, the mitigation of the UHI effect, and the reduction of heat stress for human health.

Solar doi: 10.3390/solar6010004

Authors: Ghadeer Badran Vlado K. Lazarov Mahmoud Dhimish

Bypass diode failure, particularly in the short-circuit mode, remains an under-addressed reliability issue in photovoltaic (PV) modules, causing severe power suppression and often leading to premature disposal of otherwise functional units. This study presents a non-destructive, field-applicable plug-in repair protocol for restoring modules affected by short-circuited bypass diodes. From twenty-two field-deployed modules, nine were analyzed in detail under healthy, single-fault, and dual-fault conditions. Controlled diode faults were introduced and subsequently repaired using commercially available plug-in bypass diodes. Electroluminescence (EL) imaging, current–voltage (I–V) testing, and extraction of series and shunt resistances were performed before and after repair. Results show that a single shorted diode deactivates one substring, reducing power by ~34–37%, while dual faults suppress over two-thirds of the active area, causing power losses above 67%. After repair, power deviation decreased to <3% for single faults and <7% for dual faults, with shunt resistance increasing by 52–262%, confirming removal of diode-induced leakage paths. Series resistance remained largely unchanged except in modules with irreversible cell-level damage accumulated during prolonged faulty operation. The findings demonstrate that short-circuited bypass diode faults are readily repairable and that component-level intervention can restore module performance, extend operational lifetime, and reduce unnecessary PV recycling.

Solar doi: 10.3390/solar6010003

Authors: Svetlana Kunskaja Artur Budzyński

The deployment of solar and other renewable energy technologies (RETs) plays a central role in the global energy transition and the pursuit of sustainable development. Beyond reducing greenhouse gas emissions, these technologies generate far-reaching societal co-benefits that shape environmental quality, social equity, and economic growth. This study systematically reviews peer-reviewed literature published between 2009 and 2025 to identify, integrate, and assess empirical evidence on how RET deployment contributes to societal welfare. Following the SALSA framework and PRISMA guidelines, 147 studies were selected from Scopus and Web of Science. The evidence reveals a consistent welfare triad: environmental gains (emission and pollution reduction, climate mitigation), social gains (improved health, affordability, energy security, and inclusion), and economic gains (employment and income growth, local development). These benefits are, however, heterogeneous and depend on enabling conditions such as policy stability, financial development, grid integration, innovation capacity, and social acceptance. The review highlights that solar energy, in particular, acts as both an environmental and social catalyst in advancing sustainable welfare outcomes. The findings provide a comprehensive basis for policymakers and researchers seeking to design equitable and welfare-enhancing renewable energy transitions.

Solar doi: 10.3390/solar6010002

Authors: Ezra Nash Eazaz Sadeghvaziri

While there is currently a significant opportunity for the construction of photovoltaic solar farms in the Southeastern United States, there is also a need for proper spatial planning that has not been adequately addressed by the existing literature. The objective of this study is to examine the adaptability of geographic information system-based multiple criteria decision analysis models developed for foreign contexts to the United States. This was accomplished through the application of a model developed originally for Thailand to the study area of Bibb County, Georgia, United States. Model results were analyzed to identify trends and provide concrete recommendations for future work. Using a six-rank classification scheme, 93% of Bibb County was found to have moderate suitability, while 5% and 2% had moderate-to-low and moderate-to-high suitability, respectively. Of the 11 model criteria, land usage and power line distance were found to have the largest impact on the area’s suitability. Statistical analysis identified positive trends indicating that these criteria explained 21% and 10% of the variance in the model’s output, respectively. Empirical verification proved the model structure to be viable for application in the Southeastern United States; however, additional examination of the model’s results found that there is room to improve the model for the local context. These improvements could potentially be realized through the reweighting of criteria and the re-establishment of evaluation benchmarks, allowing for the development of a truly robust model for the region.

Solar doi: 10.3390/solar6010001

Authors: Yoon Ko Dana Duong Reidar Stølen Janne Siren Fjærestad

This study investigates the fire behaviour of building-integrated photovoltaic (PV) claddings, focusing on the progression from glass failure to ignition under different cavity conditions. Experimental tests were conducted on two common PV cladding types: bifacial dual-glass (GG) and monofacial glass–plastic (GP) modules. Results revealed that GP modules exhibited faster burning and higher peak heat release rates (HRR), reaching up to 600 kW, while GG modules burned more slowly with peak HRR between 50 and 100 kW. Cavity conditions, including depth, ventilation, and operational energization, were found to be vital in determining glass breakage, occurring between 400 and 550 °C, and cavity ignition and subsequent flame spread. The relationship between cavity fire dynamics and glass breakage suggests the importance of system design, particularly regarding cavity ventilation and flame barriers, for mitigating upward fire propagation. These results establish a basis for advancing numerical fire models through integration of critical parameters such as material properties, glass breakage, cavity ignition, and cavity configuration. This approach supports comprehensive real-scale analysis to guide the development of effective design recommendations, ultimately improving fire safety in PV-integrated construction.

Solar doi: 10.3390/solar5040058

Authors: Ibai de Juan Carmen Hidalgo-Giralt Antonio Palacios-García

In response to the pressing climate change emergency, the rapid expansion of renewable energies, particularly photovoltaic (PV) power in Spain, is reconfiguring national energy landscapes, thereby necessitating detailed spatial analysis. This study aims to characterise the spatial distribution of PV energy in the country. Specifically, it employed the Administrative Register of Electricity Production Facilities (RAIPEE) database from 2000 to 2023 and a review of Environmental Impact Statements (EIA) from 2014 to 2023 to generate a facility density cartography. Additionally, the spatial statistic Moran’s I was used to detect aggregation patterns. The results demonstrated an aggregation tendency for low and medium power facilities (up to 10 MW), while the distribution of higher-capacity facilities appeared random. Examination of the facility density cartographies suggest significant variability among provinces and distribution trends centred around the country’s main urban regions. This approach to understanding the spatial dynamics of PV energy offers novel and crucial geospatial insights for renewable energy planning.

Solar doi: 10.3390/solar5040057

Authors: Sean Elphick Duane A. Robinson Jonathan C. Knott Gerrard Drury

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.

Solar doi: 10.3390/solar5040056

Authors: Giacomo Cupertino Luciano Blasi Angelo Cipollini Ramiro Dell’Erba Luca Quattrucci Giuseppe Marco Tina

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.

Solar doi: 10.3390/solar5040055

Authors: Riccio Francisco Ruperto Pilacuan-Bonete Luis Plaza V. Ángel

Efficient utilization of solar energy in equatorial regions depends on accurately determining the optimal tilt and azimuth angles of fixed photovoltaic (PV) systems. This study presents a three-dimensional vector-based methodology that employs Direct Normal Irradiance (DNI) to estimate the mean direction of incident solar flux. Hourly DNI data from 2020–2024 for the city of Guayaquil, Ecuador, were transformed into spatial vectors and integrated to obtain a resultant vector representing the average orientation and elevation of direct solar radiation. The analysis yielded an optimal tilt angle of 5.73° and an azimuth of 59.15°, values consistent with Guayaquil’s equatorial latitude and previous studies conducted in tropical environments. The low tilt angle reflects the persistently high solar elevation typical of equatorial zones, while the slight northeastward orientation deviation corresponds to the asymmetric diurnal distribution of solar irradiance. The main contribution of this work lies in providing a geometrically rigorous and computationally efficient approach capable of synthesizing the directional behavior of solar flux without relying on complex transposition models. The proposed method enhances the optimization of PV system design, urban energy planning, and renewable microgrid modeling in data-scarce contexts, supporting the sustainable development of solar energy in equatorial regions.

Solar doi: 10.3390/solar5040054

Authors: Onon Bayasgalan Amarbayar Adiyabat Atsushi Akisawa

Accurate day-ahead solar forecasting is essential for grid stability and energy planning. This study introduces a specialized forecasting framework that enhances accuracy by training models on specific day-to-day sky condition transitions. The framework employs a dual-attention transformer model, which captures complex temporal and feature-wise relationships, using a dataset of approximately 5000 daily sequences from three sites in Mongolia (2018–2024). Our core contribution is a specialized training strategy where the dataset is first classified into nine distinct classes based on the sky condition transition from the previous day to the forecast day, such as ‘Clear’ to ‘Partly cloudy’. A dedicated transformer model is then trained for each transitional state, enabling it to become an expert on that specific weather dynamic. This specialized framework is benchmarked against a naive persistence model, a standard, generalized transformer trained on all data and a ‘cluster-then-forecast’ approach. Results show the proposed approach achieves superior performance improvement compared to baseline models (p < 0.001) across all error metrics, demonstrating the value of modeling inter-day weather dynamics. Furthermore, the framework is extended to probabilistic forecasting using quantile regression to generate 80% prediction intervals, providing crucial uncertainty information for operational decision-making in power grids.

Solar doi: 10.3390/solar5040053

Authors: Merbelita Yalta Chappa Wildor Gosgot Angeles Homar Santillan Gomez Humberto Jesus Hernandez Vilcarromero Diana Carina Mori Servan Manuel Oliva-Cruz Oscar Gamarra Torres Fernando Isaac Espinoza Canaza Carla Ordinola Ramírez Miguel Ángel Barrena Gurbillón

This study evaluates the thermal performance of a prototype vacuum-tube solar cooker adapted to the climatic conditions of the Amazon region, Peru. Four grain types (Zea mays L., Triticum aestivum, Zea mays var. morochon, and Hordeum vulgare) were tested to assess temperature evolution, exposure time, and incident solar radiation. Hordeum vulgare was selected as a food model for calibration due to its well-characterized thermophysical properties and reproducible heating behavior. The results showed individual thermal efficiencies ranging from 19.3% to 35.3%, with an average of 27.3% across the three tubes. The most efficient treatment, obtained with Zea mays L., reached 180 °C under an irradiance of approximately 980 W/m2. A direct relationship was observed between solar radiation intensity, exposure time, and thermal efficiency. These findings confirm that the proposed hybrid design combining a cylindrical parabolic collector with vacuum U-tubes achieves higher and more stable performance than conventional box-type cookers. The system allows complete grain cooking without fossil fuels, demonstrating its potential as a sustainable and low-cost solution for rural communities in the Andean Amazonian region, promoting clean energy adoption and reducing environmental impact.

Solar doi: 10.3390/solar5040052

Authors: Uliana Moshina Izabelle P. Chick Juliet E. Carlisle Daniel P. Ames

This study investigates the efficacy of large language models (LLMs) in supporting responsible and optimized geographic site selection for large-scale solar energy farms. Using Microsoft Bing (predecessor to Copilot), Google Bard (predecessor to Gemini), and ChatGPT, we evaluated their capability to address complex technical and social considerations fundamental to solar farm development. Employing a series of guided queries, we explored the LLMs’ “understanding” of social impact, geographic suitability, and other critical factors. We tested varied prompts, incorporating context from existing research, to assess the models’ ability to use external knowledge sources. Our findings demonstrate that LLMs, when meticulously guided through increasingly detailed and contextualized inquiries, can yield valuable insights. We discovered that (1) structured questioning is key; (2) characterization outperforms suggestion; and (3) harnessing expert knowledge requires specific effort. However, limitations remain. We encountered dead ends due to prompt restrictions and limited access to research for some models. Additionally, none could independently suggest the “best” site. Overall, this study reveals the potential of LLMs for geographic solar farm site selection, and our results can inform future adaptation of geospatial AI queries for similarly complex geographic problems.

Solar doi: 10.3390/solar5040050

Authors: Ziad Hunaiti Ayed Banibaqash Zayed Ali Huneiti

Solar panel deployment is vital to generate clean energy and reduce carbon emissions, but sustaining energy output requires regular monitoring and maintenance. This is particularly critical in countries with harsh environmental conditions, such as Qatar, where high dust density reduces solar radiation reaching panels, thereby lowering generating efficiency and increasing maintenance costs. This paper introduces a data-driven model that uses the relationship between generated and consumed energy to track changes in solar panel performance. By applying statistical analysis to real and simulated data, the model identifies when efficiency losses are within the parameters of normal variation (e.g., daily fluctuations) and when they are likely caused by dust accumulation or system ageing. The findings demonstrate that the model provides a reliable and cost-effective way to support timely cleaning and maintenance decisions. It offers decision-makers a practical tool to improve residential solar panel management, reducing unnecessary costs, and ensuring more consistent renewable energy generation.

Solar doi: 10.3390/solar5040051

Authors: Tomoyoshi Motohiro Kazuo Hasegawa

Cr content χ of 0.4 at% for a Cr doped Nd (1 at%): YAG laser rod (LR) gave a higher laser output (Ioutput) than that of 0.0, 0.7, and 1.0 at% in a specially designed compact solar-pumped laser (SPL) outdoors. Ioutputs were measured as a function of an 808 nm pumping laser’s power indoors, changing the transmittance of the output coupler. From the obtained slope efficiencies, round-trip resonator losses Ls for the four χs were estimated, and the best-fit function L(χ) was derived. From the experimentally estimated Cr-to-Nd effective energy transfer efficiency ηCr→Nd at the four χs, the best-fit function ηCr→Nd(χ) was derived. Using L(χ), ηCr→Nd(χ), and a wavelength λ- and χ-dependent absorption coefficient α(λ, χ), inferred from the literature, the power conversion efficiency ηpower(χ) under 1 Sun was estimated. The estimated ηpower(0.4) and ηpower(0.7) were reproduced in experimentally deduced factors at the mode-matching efficiency ηmode = 0.19. The estimated maximum ηpower(χ) appeared around χ = 0.2 at%, being 20% higher than that at χ = 0.4 at%. In addition to this, a composite LR (Cr, Nd: YAG core/Gd: YAG cladding) was found to achieve ηmode = 0.68 and ηpower = 0.064, ranking among the highest-class SPL ηpowers.

Solar doi: 10.3390/solar5040049

Authors: Gabriele Malgaroli Fabiana Matturro Andrea Cagnetti Aleandro Vivino Ludovico Terzi Alessandro Ciocia Filippo Spertino

Nowadays, bifacial photovoltaic (PV) technology has emerged as a key solution to enhance the energy yield of large-scale PV plants, especially when integrated with sun-tracking systems. This study investigates the quantification of bifaciality productivity for two multi-MW PV plants in southern Italy (Sicily) equipped with monocrystalline silicon bifacial modules installed on single-axis east–west tracking systems and aligned in the north–south direction. An optimized energy model was developed at the stringbox level, employing a dedicated procedure including data filtering, clear-sky condition selection, and numerical estimation of bifaciality factors. The model was calibrated using on-field measurements acquired during the first operational months to minimize uncertainties related to degradation phenomena. The application of the model demonstrated that the rear-side contribution to the total energy output is non-negligible, resulting in additional energy gains of approximately 5.3% and 3% for the two plants, respectively.

Solar doi: 10.3390/solar5040048

Authors: Dmytro Matushkin Artur Zaporozhets Vitalii Babak Mykhailo Kulyk Viktor Denysov

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools.

Solar doi: 10.3390/solar5040047

Authors: Nina-Cristina Diţoiu Altan Abdulamit Radu Ştefan Tărău Dan Sebastian Săcui

The polder in this case study addresses several environmental issues, risk management concerns related to localities served by existing non-permanent dams, energy requirements that can meet a locality’s needs during the renewable energy transition, and their impacts on both rural and urban built environments. Cultural landscape preservation or solar regeneration on agricultural plots in Romania’s rural wetland areas focuses on traditionally inspired design, emphasising the technical versus humanistic approach as an optimal path through some inspiring “Dyads”. Briefly, the dyads are related to Bennett’s systematic approach to ensure the knowledge necessary for achieving understanding without experiencing it. With a two-way spiral, the defined methodology applies energy as solar photovoltaic technology to water-related natural aspects in the built environment without reducing or harming the relevant water management related to nature or built cultural heritage. The Solar Regeneration Monad “Nature -Energy- Built” is a holistic visual framework, replicable in any built environment for a “Built” regenerative culture, that enables the best solution to be identified for the conservation of cultural heritage values in an “Energy” transition context with “Nature”, biodiversity, or other water-related issues.

Solar doi: 10.3390/solar5040046

Authors: Osama A. Marzouk

The term SHIP (solar heat for industrial processes) or SHIPs (solar heat for industrial plants) refers to the use of collected solar radiation for meeting industrial heat demands, rather than for electricity generation. The global thermal capacity of SHIP systems at the end of 2024 stood slightly above 1 GWth, which is comparable to the electric power capacity of a single power station. Despite this relatively small presence, SHIP systems play an important role in rendering industrial processes sustainable. There are two aims in the current study. The first aim is to cover various types of SHIP systems based on the variety of their collector designs, operational temperatures, applications, radiation concentration options, and solar tracking options. SHIP designs can be as simple as unglazed solar collectors (USCs), having a stationary structure without any radiation concentration. On the other hand, SHIP designs can be as complicated as solar power towers (SPTs), having a two-axis solar tracking mechanism with point-focused concentration of the solar radiation. The second aim is to shed some light on the status of SHIP deployment globally, particularly in 2024. This includes a drop during the COVID-19 pandemic. The findings of the current study show that more than 1300 SHIP systems were commissioned worldwide by the end of 2024 (cumulative number), constituting a cumulative thermal capacity of 1071.4 MWth, with a total collector area of 1,531,600 m2. In 2024 alone, 120.3 MWth of thermal capacity was introduced in 106 SHIP systems having a total collector area of 171,874 m2. In 2024, 65.9% of the installed global thermal capacity of SHIP systems belonged to the parabolic trough collectors (PTCs), and another 22% of this installed global thermal capacity was attributed to the unevacuated flat plate collectors (FPC-Us). Considering the 106 SHIP systems installed in 2024, the average collector area per system was 1621.4 m2/project. However, this area largely depends on the SHIP category, where it is much higher for parabolic trough collectors (37,740.5 m2/project) but lower for flat plate collectors (805.2 m2/project), and it is lowest for unglazed solar collectors (163.0 m2/project). The study anticipates large deployment in SHIP systems (particularly the PTC type) in 2026 in alignment with gigascale solar-steam utilization in alumina production. Several recommendations are provided with regard to the SHIP sector.

Solar doi: 10.3390/solar5040045

Authors: Mohammad Hosein Saeedinia Shamsodin Taheri Ana-Maria Cretu

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.

Solar doi: 10.3390/solar5030044

Authors: Maroua Bouksaim Mohcin Mekhfioui Mohamed Nabil Srifi

Photovoltaic energy has become a key pillar in the transition to sustainable energy systems, driven by the need for efficient energy conversion and the reduction of dependency on fossil fuels. Maximum Power Point Tracking (MPPT) is central to optimizing the performance of photovoltaic systems by ensuring the maximum extraction of solar energy, even under fluctuating environmental conditions. This review provides a comprehensive analysis of MPPT algorithms developed and refined over the past decade (2015–2025), highlighting major breakthroughs in algorithmic approaches, from conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) to more advanced techniques incorporating artificial intelligence, fuzzy logic, and hybrid systems. The paper evaluates the evolution of MPPT techniques, focusing on their effectiveness in real-world applications, particularly in optimizing photovoltaic output under diverse operating conditions such as partial shading, temperature variations, and rapid irradiance changes. Furthermore, it discusses the ongoing challenges in the field and the promising directions for future research, aiming to further enhance the reliability and efficiency of solar power systems worldwide.

Solar doi: 10.3390/solar5030043

Authors: Dylan J. Colvin Cécile Molto Ryan M. Smith Manjunath Matam Peter Hacke Fang Li Brent A. Thompson James Barkaszi Govindasamy Tamizhmani Hubert P. Seigneur

Bifacial photovoltaic (PV) modules have the advantage of using light reflected off of the ground to contribute to power production. Predicting the energy gain is challenging and requires complex models to do so accurately. Often, module degradation over time is neglected in models for the sake of simplicity or is underestimated. Comparing outdoor and indoor current–voltage (I–V) performance for bifacial modules is more challenging than for monofacial modules, as there are additional variables to consider such as rear albedo non-uniformity, cell mismatch, and their effects on temperature. This challenge is compounded when heterogeneous degradation modes occur, such as polarization-type potential-induced degradation (PID-p). To examine the effects of PID-p on I–V predictions using an empirical data-driven approach, 16 bifacial PERC modules are installed outdoors on racks with different albedo conditions. A subset is exposed to high-voltage biases of −1500 V or +1500 V. Outdoor data are traced at irradiance ranges of 150–250 W/m2, 500–600 W/m2, and 900–1000 W/m2. These curves are corrected using control module temperature, wire resistivity, and module resistance measured indoors. We examine several methods to transform indoor I–V curves to accurately, and more simply than existing methods, approximate outdoor performance for bifacial modules without and with varying levels of PID-p degradation. This way, bifacial performance modeling can be more accessible and informed by fielded, degraded modules. Distributions of percent errors between indoor and outdoor performance parameters and Mean Absolute Percent Errors (MAPEs) are used to assess method quality. Results including low-irradiance data (150–250 W/m2) are discussed but are filtered for quantifying method quality as these data introduce substantial errors. The method with the most optimal tradeoff between low MAPE and analysis simplicity involves measuring the front side of a module indoors at an irradiance equal to plane-of-array irradiance plus the product of module bifaciality and albedo irradiance. This method gives MAPE values of 1–6.5% for non-degraded and 1.6–5.9% for PID-p degraded module performance.

Solar doi: 10.3390/solar5030042

Authors: Cameron Dolan Ryan M. Smith Henry Toal Michelle Wilber

Domestic water heaters traditionally use natural gas or electric resistance to heat stored water. A gas water heater relies on a non-renewable resource, while an electric water heater might rely on electricity generated by a non-renewable resource. This study analyzes the performance of an electric water heater featuring a novel heating element design based on a positive temperature coefficient (PTC) material powered directly by solar photovoltaic (PV) modules in a northern latitude installation. The project analyzes the operation of two different design temperatures of the PTC heating elements (50 °C, and 70 °C) when fed by three solar PV panels during the spring in the high-latitude location of Anchorage, Alaska (61.2° N). Our results show that both design temperatures of the PTC heating elements are able to achieve self-regulation at a sufficient and safe operating temperature for a domestic use case. Analysis of water heater performance directly connected to PV power showed that the PTC-equipped water heater had a limited period of heating when sufficient solar irradiance is available. Because of this, restrictive use of the water heater might be necessary during periods of non-daylight hours to preserve hot water in an insulated tank. However, this PV-to-PTC setup could be effectively used in industrial, commercial, and research settings.

Solar doi: 10.3390/solar5030041

Authors: Eduard Enasel Gheorghe Dumitrascu

The study investigates a hybrid energy system integrating photovoltaic (PV) panels, micro-CHP units, battery storage, and thermal storage to meet the winter energy demands of a residential building in Bacău, Romania. Using real-world experimental data from amorphous, polycrystalline, and monocrystalline PV panels, C++ Model 1 simulates building energy needs and PV system performance under varying irradiance levels. The results show that PV systems alone cannot meet the total winter demand, with polycrystalline slightly outperforming monocrystalline, yet still falling short. A second computational model (C++ Model 2) simulates hybrid energy flow, demonstrating how the CHP unit and storage systems can ensure off-grid autonomy. The model dynamically manages energy between components based on daily irradiance scenarios. The findings reveal critical thresholds for PV surplus, optimal CHP sizing, and realistic battery and thermal storage needs. This paper provides a practical framework for designing efficient, data-driven hybrid solar–CHP systems for cold climates. The novelty lies in the integration of real-world PV efficiency data with a dynamic irradiance-driven simulation framework, enabling precise hybrid system sizing for winter-dominant regions.

Solar doi: 10.3390/solar5030040

Authors: Babak Ranjgar Alessandro Niccolai Sonia Leva

The expansion of solar photovoltaic (PV) energy is a central pillar of Italy’s climate and energy transition strategy. However, the actual availability of land for PV deployment is heavily influenced by a complex regulatory framework that imposes numerous spatial exclusions. This study presents a comprehensive geospatial analysis of exclusion zones for ground-mounted PV installations across Italy, integrating data from national regulations, environmental protection laws, and cultural heritage inventories. Using a vector-based overlay approach, we categorized constraints into six groups: natural assets, landscape protection, cultural heritage, natural hazards, environmental buffers, and infrastructural safety zones. The analysis shows that only approximately 26% of Italy’s land area remains available for PV development. Regional disparities are pronounced, with southern regions such as Sicilia and Puglia offering the highest availability, while northern and central regions face severe limitations due to dense overlays of protected landscapes and heritage sites. These findings offer quantitative support to the often-cited claim that Italy’s strict land-use regulations are a significant barrier to renewable energy development. The study highlights the need for more flexible, spatially informed regulatory frameworks to reconcile conservation priorities with the urgency of decarbonization.

Solar doi: 10.3390/solar5030039

Authors: Fahad Maoulida Kassim Mohamed Aboudou Rabah Djedjig Mohammed El Ganaoui

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions.

Solar doi: 10.3390/solar5030037

Authors: Alexander Axelevitch David Lugassy

Solar cells based on the hybrid perovskite absorbers had shown very high growth of their conversion efficiency almost reaching to the Shockley–Queisser limit during last decade. However, low stability prevents to widely use them in industry and in everyday life. Possible reasons and pathways to remedy of instability and degradation of the perovskite solar cells are considered in this review. Specific attention was paid to the thermodynamical analysis of the hybrid perovskite absorber.

Solar doi: 10.3390/solar5030038

Authors: Iddrisu Ibrahim Paul Tengey Kelci Mikayla Lawrence Joseph Atia Ayariga Fortune Akabanda Grace Yawa Aduve Junhuan Xu Robertson K. Boakai Olufemi S. Ajayi James Owusu-Kwarteng

In many developing African countries, milk safety is often managed through traditional methods such as fermentation or boiling over firewood. While these approaches reduce some microbial risks, they present critical limitations. Firewood dependency contributes to deforestation, depletion of agricultural residues, and loss of soil fertility, which, in turn, compromise environmental health and food security. Solar pasteurization provides a reliable and sustainable method for thermally inactivating pathogenic microorganisms in milk and other perishable foods at sub-boiling temperatures, preserving its nutritional quality. This study aimed to evaluate the thermal and microbial performance of a low-cost solar milk pasteurization system, hypothesized to effectively reduce microbial contaminants and retain milk quality under natural sunlight. The system was constructed using locally available materials and tailored to the climatic conditions of the Savanna ecological zone in West Africa. A flat-plate glass solar collector was integrated with a 0.15 cm thick stainless steel cylindrical milk vat, featuring a 2.2 cm hot water jacket and 0.5 cm thick aluminum foil insulation. The system was tested in Navrongo, Ghana, under ambient temperatures ranging from 30 °C to 43 °C. The pasteurizer successfully processed up to 8 L of milk per batch, achieving a maximum milk temperature of 74 °C by 14:00 GMT. Microbial analysis revealed a significant reduction in bacterial load, from 6.6 × 106 CFU/mL to 1.0 × 102 CFU/mL, with complete elimination of coliforms. These results confirmed the device’s effectiveness in achieving safe pasteurization levels. The findings demonstrate that this locally built solar pasteurization system is a viable and cost-effective solution for improving milk safety in arid, electricity-limited regions. Its potential scalability also opens avenues for rural entrepreneurship in solar-powered food and water treatment technologies.

Solar doi: 10.3390/solar5030036

Authors: Mohammad A. Khan Brian Maricle Zachary D. Franzel Gabe Gransden Matthew Vannette

Sources of renewable energy have attracted considerable attention. Their expanded use will have a substantial impact on both the cost of energy production and climate change. Solar energy is one efficient and safe option; however, solar energy harvesting sites, irrespective of the location, can impact the ecosystem. This experimental study explores the energy available inside and outside of novel miniature energy harvesting cages by measuring light intensity and power generated. Varying light intensity outside the cage has been utilized to study the remaining energy inside the cage of a flexible design, where the heights of the harvesting panels are parameters. Cages are built from custom photovoltaic panels arranged in a staircase manner to provide access to growing plants. The balance between power generation and biological development is investigated. Two different structures are presented to explore the variation of illumination intensity inside the cages. The experimental results show a substantial reduction in energy inside the cages. The experimental results showed up to 24% reduction in illumination inside the cages in winter. The reduction is even larger in summer, up to 57%. The results from the models provide a framework to study the possible impact on a biological system residing inside the cages, paving the way for practical farming with sustainable energy harvesting.

Solar doi: 10.3390/solar5030035

Authors: Lorenzo Miserocchi Alessandro Franco

Direct electrification and hydrogen utilization represent two key pathways for decarbonizing the glass industry, with their effectiveness subject to adequate furnace design and renewable energy availability. This study presents a techno-economic assessment for optimal solar energy integration in a representative 300 t/d oxyfuel container glass furnace with a specific energy consumption of 4.35 GJ/t. A mixed-integer linear programming formulation is developed to evaluate specific melting costs, carbon emissions, and renewable energy self-consumption and self-production rates across three scenarios: direct solar coupling, battery storage, and a hydrogen-based infrastructure. Battery storage achieves the greatest reductions in specific melting costs and emissions, whereas hydrogen integration minimizes electricity export to the grid. By incorporating capital investment considerations, the study quantifies the cost premiums and capacity requirements under varying decarbonization targets. A combination of 30 MW of solar plant and 9 MW of electric boosting enables the realization of around 30% carbon reduction while increasing total costs by 25%. Deeper decarbonization targets require more advanced systems, with batteries emerging as a cost-effective solution. These findings offer critical insights into the economic and environmental trade-offs, as well as the technical constraints associated with renewable energy adoption in the glass industry, providing a foundation for strategic energy and decarbonization planning.

Solar doi: 10.3390/solar5030034

Authors: Maryam Roodneshin Adrian Muros Alcojor Torsten Masseck

This study investigates strategies for urban-scale renewable energy integration through a photovoltaic-centric approach, with a case study of a district in Barcelona. The methodology integrates spatial and morphological data using a geographic information system (GIS)-based and clustering framework to address challenges of CO2 emissions, air pollution, and energy inefficiency. Rooftop availability and photovoltaic (PV) design constraints are analysed under current urban regulations. The spatial analysis incorporates building geometry and solar exposure, while an evolutionary optimisation algorithm in Grasshopper refines shading analysis, energy yield, and financial performance. Clustering methods (K-means and 3D proximity) group PV panels by solar irradiance uniformity and spatial coherence to enhance system efficiency. Eight PV deployment scenarios are evaluated, incorporating submodule integrated converter technology under a solar power purchase agreement model. Results show distinct trade-offs among PV scenarios. The standard fixed tilted (31.5° tilt, south-facing) scenario offers a top environmental and performance ratio (PR) = 66.81% but limited financial returns. In contrast, large- and huge-sized modules offer peak financial returns, aligning with private-sector priorities but with moderate energy efficiency. Medium- and large-size scenarios provide balanced outcomes, while a small module and its optimised rotated version scenarios maximise energy output yet suffer from high capital costs. A hybrid strategy combining standard fixed tilted with medium and large modules balances environmental and economic goals. The district’s morphology supports “solar neighbourhoods” and demonstrates how multi-scenario evaluation can guide resilient PV planning in Mediterranean cities.

Solar doi: 10.3390/solar5030033

Authors: Sachi Furukawa Hiroki Mikami Takehiro Okuji Koji Takamori

This study investigates the failure behavior of aluminum solar panel mounting structures subjected to uplift pressure, with particular focus on conditions not typically considered in conventional design, specifically, foundation defects. To clarify critical failure modes and evaluate potential countermeasures, full-scale pressure loading tests were conducted. The results showed that when even a single column base was unanchored, structural failure occurred at approximately half the design wind pressure. Although reinforcement measures—such as the installation of uplift-resistant braces—increased the failure pressure to 1.5 times the design value, they also introduced the risk of undesirable failure modes, including panel detachment. Additionally, four-point bending tests of failed members and joints, combined with structural analysis of the frame, demonstrated that once the ultimate strength of each component is known, the likely failure location within the structure can be reasonably predicted. To prevent panel blow-off and progressive failure of column bases and piles, specific design considerations are proposed based on both experimental observations and numerical simulations. In particular, avoiding local buckling in members parallel to the short side of the panels is critical. Furthermore, a safety factor of approximately two should be applied to column bases and pile foundations to ensure structural integrity under unforeseen foundation conditions.

Solar doi: 10.3390/solar5030032

Authors: Andrea Franzoni Chiara Leggerini Mariasole Bannò Mattia Avanzini Edoardo Vitto

Italy strives to meet its renewable energy targets for 2030 and 2050, with photovoltaic (PV) technology playing a central role. However, the push for increased solar adoption, spurred by past incentive schemes such as “Conto Energia” and “Superbonus 110%”, raises long-term challenges related to PV waste management. In this study, we present a multi-scale approach to forecast End-of-Life (EoL) PV waste across Italy’s 20 regions, aiming to support national circular economy strategies. Historical installation data (2008–2024) were collected and combined with socio-economic and energy-related indicators to train a Backpropagation Neural Network (BPNN) for regional PV capacity forecasting up to 2050. Each model was optimised and validated using R2 and RMSE metrics. The projections indicate that current trends fall short of meeting Italy’s decarbonisation targets. Subsequently, by applying a Weibull reliability function under two distinct scenarios (Early-loss and Regular-loss), we estimated the annual and regional distribution of PV panels reaching their EoL. This analysis provides spatially explicit insights into future PV waste flows, essential for planning regional recycling infrastructures and ensuring sustainable energy transitions.

Solar doi: 10.3390/solar5030031

Authors: Pere Antoni Bibiloni-Mulet Andreu Moià-Pol Jacinto Vidal-Noguera Iván Alonso Víctor Martínez-Moll Yamile Díaz Torres Vicent Canals Benito Mas Carles Mulet-Forteza

The University of the Balearic Islands is undertaking a significant energy transition toward a zero-emissions model, motivated by escalating energy costs and strong institutional commitments to climate neutrality. This study investigates the technical and operational feasibility of deploying 7.1 MWp of photovoltaic capacity across the campus, integrated with Li-FePO4 battery systems and thermal energy storage. Through a detailed analysis of hourly energy demand, PV generation profiles, and storage constraints, the research evaluates how these technologies can be optimized to meet campus needs. A linear optimization model is applied to assess system performance under the constraint of a 3 MW grid export limit. Furthermore, the potential of demand-side electrification, implemented via a centralized HVAC plant and a 4th–5th generation district heating and cooling network, is analyzed in terms of its ability to maximize on-site PV self-consumption and reduce reliance on grid electricity during non-generation periods.

Solar doi: 10.3390/solar5030030

Authors: Fernando Venâncio Mucomole Carlos Augusto Santos Silva Lourenço Lázaro Magaia

The characteristics and types of the sky can greatly influence photovoltaic (PV) power generation, potentially leading to a reduction in both the lifespan and efficiency of the entire system. Driven by the challenge of addressing fluctuations in solar PV energy utilization, the aim was to assess the solar energy potential by analyzing the clear sky index Kt* across elevation profiles. To achieve this, a theoretical model for determining Kt* was employed, which encapsulated the solar energy analysis. Initially, solar energy data collected from approximately 16 stations in various provinces of Mozambique, as part of the solar energy measurement initiatives by INAM, FUNAE, AERONET, and Meteonorm, was processed. Subsequently, the clear sky radiation was calculated, and Kt* was established. The statistical findings indicate a reduction in energy contribution from the predictors, accounting for 28% of the total incident energy; however, there are progressive increases averaging around ~0.02, with Kt* values ranging from 0.4 to 0.9, demonstrating a strong correlation between 0.7 and 0.9 across several stations and predictor parameters. No significant climate change effects were noted. The radiation flux is directed from areas with higher Kt* to those with lower values, as illustrated in the heat map. The region experiences an increase in atmospheric parameter deposition, with concentrations around ~0.20, yet there remains a substantial energy flow potential of 92% for PV applications. This interaction can also be applied in other locations to assess the potential for available solar energy, as the analyzed solar energy spectrum aligns closely with the theoretical statistical calibration of energy distribution relevant to the global solar energy population process.

Solar doi: 10.3390/solar5030029

Authors: Amina Laouid Amine Alaoui Belghiti Ali Abouais Krzysztof Wisniewski Mouhaydine Tlemçani Przemysław Płóciennik Abdelowahed Hajjaji Anna Zawadzka

In the purpose of enhancing solar cell efficiency and sustainability, zinc selenide (ZnSe) and silicon (Si) play indispensable roles, offering a compelling combination of stability and transparency while also highlighting their abundant availability. This study utilizes the SCAPS_1D tool to explore diverse heterojunction setups, aiming to solve the nuanced correlation between key parameters and photovoltaic performance, therefore contributing significantly to the advancement of sustainable energy solutions. Exploring the performance analysis of heterojunction solar cell configurations employing ZnSe and Si elements, various configurations including SnO2/ZnSe/p_Si/p+_Si, SnO2/CdS/p_Si/p+_Si, TiO2/ZnSe/p_Si/p+_Si, and TiO2/CdS/p_Si/p+_Si are investigated, delving into parameters such as back surface field thickness (BSF), doping concentration, operating temperature, absorber layer properties, electron transport layer properties, interface defects, series and shunt resistance. Among these configurations, the SnO2/ZnSe/p_Si/p+_Si configuration with a doping concentration of 1019 cm−3 and a BSF thickness of 2 μm, illustrates a remarkable conversion efficiency of 22.82%, a short circuit current density (Jsc) of 40.33 mA/cm2, an open circuit voltage (Voc) of 0.73 V, and a fill factor (FF) of 77.05%. Its environmentally friendly attributes position it as a promising contender for advanced photovoltaic applications. This work emphasizes the critical role of parameter optimization in propelling solar cell technologies toward heightened efficiency and sustainability.

Solar doi: 10.3390/solar5020028

Authors: Carlos A. Peña-Becerra Willian A. Pacheco-Cano Daniel F. Aragones-Vargas Agustín Irizarry-Rivera Marcel Castro-Sitiriche

The transition to renewable energy is critical to enhance Puerto Rico’s energy resilience and reduce dependence on imported fossil fuels. Rooftop photovoltaic (PV) systems provide a scalable opportunity to meet these objectives. This study evaluates the potential of rooftop PV systems across Puerto Rico using the National Renewable Energy Laboratory’s (NREL) PV Rooftop Database, processing detailed roof surface data to estimate installed capacity, energy generation, Levelized Cost of Electricity (LCOE), and solar resource potential at municipal and barrio levels. Findings reveal high solar rooftop capacity in urban neighborhoods, with areas like Sabana Abajo and Hato Tejas each exceeding 450 GWh/year in potential generation. Solar rooftop resource values peak at 3.67 kWh/kW in coastal areas, with LCOE values (0.071–0.215 USD/kWh) below current electricity rates. All municipalities demonstrate technical potential to meet their electricity demand with rooftop PV system alone. This research contributes through (1) developing Puerto Rico’s first comprehensive solar rooftop potential map; (2) providing unprecedented barrio-level analysis; (3) introducing a methodology for estimating missing post-disaster consumption data; and (4) integrating technical, economic, and equity indicators to inform energy policy. These findings demonstrate the importance of rooftop solar in achieving renewable energy goals and provide an understanding of spatial energy inequalities.

Solar doi: 10.3390/solar5020027

Authors: Antonio Famiglietti Ruben Abbas

Steam is widely used in industry as a heat carrier for thermal processes and is primarily generated by gas-fired steam boilers. The decarbonization of industrial thermal demand relies on the capability of clean and renewable technologies to provide steam through reliable and cost-effective systems. Concentrating solar thermal technologies are attracting attention as a heat source for industrial steam generation. In addition, electricity-driven high-temperature heat pumps can provide heat using either renewable or grid electricity by upgrading ambient or waste heat to the required temperature level. In this study, linear Fresnel solar collectors and high-temperature heat pumps driven by photovoltaics are considered heat sources for steam generation in industrial processes. Energetic and economic analyses are performed across the European countries to assess and compare their performances. The results demonstrate that for a given available area for the solar field, solar thermal systems provide a higher annual energy yield in southern countries and at lower costs than heat pumps. On the other hand, heat pumps driven by photovoltaics provide higher annual energy for decreasing solar radiation conditions (central and northern Europe), although it leads to higher costs than solar thermal systems. A hybrid scheme combining the two technologies is the favorable option in central Europe, allowing a trade-off between the costs and the energy yield per unit area.

Solar doi: 10.3390/solar5020026

Authors: Agboola Benjamin Alao Olatunji Matthew Adeyanju Manohar Chamana Stephen Bayne Argenis Bilbao

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.

Solar doi: 10.3390/solar5020025

Authors: Abdul-Rahaman Afitiri Ernest Kofi Amankwa Afrifa

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.

Solar doi: 10.3390/solar5020024

Authors: Abhitej Divi Shuza Binzaid

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.

Solar doi: 10.3390/solar5020023

Authors: João P. Cardoso António Couto Paula A. Costa Carlos Rodrigues Jorge Facão David Loureiro Anne Wambugu Sandra Banda Izael Da Silva Teresa Simões

Following publication, the Editorial Office became aware that the original article [...]

Solar doi: 10.3390/solar5020022

Authors: Lucie McGovern Bob van der Zwaan

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.

Solar doi: 10.3390/solar5020021

Authors: Alex Omar Topa Gavilema Juan D. Gil José Domingo Álvarez Hervás José Luis Torres Moreno Manuel Pérez García

Following publication, the Editorial Office became aware that the original article [...]

Solar doi: 10.3390/solar5020020

Authors: Ilaria Marotta Thibault Péan Francesco Guarino Sonia Longo Maurizio Cellura Jaume Salom

Following publication, the Editorial Office became aware that the original article [...]

Solar doi: 10.3390/solar5020019

Authors: Jesús Fernández-Reche Loreto Valenzuela Diego Pulido-Iparraguirre

Following publication, the Editorial Office became aware that the original article [...]

Solar doi: 10.3390/solar5020018

Authors: Noelia Estremera-Pedriza Jesús Fernández-Reche Jose A. Carballo

Following publication, the Editorial Office became aware that the original article [...]

Solar doi: 10.3390/solar5020017

Authors: Nikolaos Skandalos Gudrun Kocher-Oberlehner

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.

Solar doi: 10.3390/solar5020016

Authors: Artur Wodołażski

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.

Solar doi: 10.3390/solar5020015

Authors: Oumaima Laguili Julien Eynard Marion Podesta Stéphane Grieu

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.

Solar doi: 10.3390/solar5020014

Authors: Alain Foutche Tchouli Stephane Ndiya Ngasop Jean Hilaire Tchami Claude Bertin Nzoundja Fapi Hyacinthe Tchakounté

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.

Solar doi: 10.3390/solar5020013

Authors: Christof Schultz Markus Fenske Nicolas Otto Laura-Isabelle Dion-Bertrand Guillaume Gélinas Stéphane Marcet Janardan Dagar Rutger Schlatmann Eva Unger Bert Stegemann

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.

Solar doi: 10.3390/solar5010012

Authors: Ruben Bartali Emanuele De Bona Michele Bolognese Alessandro Vaccari Matteo Testi Luigi Crema

Solar thermal energy is one of the most interesting sustainable solutions for decarbonizing the energy sector. Integrating solar collectors with other energy sources is common, as seen in domestic heating, where solar collectors are combined with common heaters to reduce fuel consumption (gasoline, electricity, gas, and biomass) and therefore, the energy cost. Similarly, this concept can be applied to nuclear energy, where the reduction in nuclear fuel consumption is very strategic for decreasing not only its cost but also the risk in handling, transportation, and storage (both the fuel and the nuclear waste as well). Nuclear energy, on the other hand, seems to be very useful in reducing the land occupation of concentrated solar power plants (CSPs) and helping a more constant production of electricity, both points being two important bottlenecks of CSP technologies. CSP and nuclear reactors, on the other hand, share common heating technologies and both can produce energy without CO2 emissions. Solar and nuclear energy, especially with the advent of the fourth generation of small modular reactors (SMRs), present a compelling opportunity for sustainable electricity generation. In this work, we present a brief review of CSP technology, a brief review of SMR concepts and development, and a brief overview of the combination of these two technologies. The review shows that in general, combined SMR + CSP technologies offer several advantages in terms of a strong reduction in the solar field extension areas, improved dispatchability of energy, improved efficiency of the SMRs, and, in particular, lower nuclear fuel consumption (hence, e.g., with a lowered refueling frequency).

Solar doi: 10.3390/solar5010011

Authors: Bingying Zheng Yihua Hu Mohammed Alkahtani

Urban soiling, consisting of dust, industrial byproducts, and other pollutants, presents a significant risk to the effectiveness and safety of solar energy systems. To achieve the goal of net zero, having renewable energy systems such as solar panels in urban environments can help. This review will examine the composition and variety of urban soiling and evaluate its impact on PV installation. The study will analyze the efficiency loss attributable to soiling, focusing on its impact on small-scale installations such as rooftops, building integrated photovoltaics (BIPVs), and large-scale urban solar installations. Furthermore, this study will also investigate various developing technologies and strategies to reduce the effects of urban soiling. This encompasses the examination of automated cleaning systems and robotic maintenance, with a specific focus on their potential effectiveness. This review aims to underline the importance of addressing urban soiling within the framework of sustainable urban development and the expansion of solar energy, with further research into the development of soiling mitigation technologies. Finally, soil management and further research gaps will be discussed.

Solar doi: 10.3390/solar5010010

Authors: Francesca Giuliano Andrea Pronti

In the decarbonization process, the solar energy sector will play a crucial role, representing one of the key technologies for reducing greenhouse gas emissions. In Italy, photovoltaics stands out as the fastest-growing energy sector, thanks to the combination of favorable climatic conditions, supportive policies, and a growing interest in renewable energy sources. In this context, renewable energy communities (RECs) emerge as potential strategic tools for promoting the development of photovoltaics nationally and at the European level. Therefore, this study aims to examine the policy and regulatory frameworks governing RECs in Europe and Italy, highlighting their impact on the establishment, operation, and evolution of these communities. Through a critical analysis of legislative documents at both the European and national levels, this research identifies the key factors shaping the growth and functionality of RECs, such as governance structures, economic incentives, and social inclusivity. This study underscores the dual influence of comprehensive regulation and a certain degree of flexibility in fostering RECs’ adaptability to diverse contexts. Additionally, it identifies existing challenges, including regional implementation disparities, legal ambiguities, and potential conflicts with other renewable energy policies. The findings contribute to the ongoing discourse on decentralized energy systems, providing insights for policymakers to refine frameworks and maximize RECs’ contributions to sustainable energy transitions.

Solar doi: 10.3390/solar5010009

Authors: Ahmed Rachid Talha Batuhan Korkut Jean-Sebastien Cardot Cheikh M. F. Kébé Ababacar Ndiaye Léonide Michael Sinsin François Xavier Fifatin

This paper presents findings from the LEOPARD project, part of the LEAP-RE program, a joint European Union (EU) and African Union initiative to advance renewable energy solutions. The study employs a simulation-based approach to optimize solar-integrated microgrid configurations for rural electrification. The project deployed a solar-integrated pilot microgrid at the Songhai agroecological center in Benin to address key challenges, including load profile estimation, energy balancing, and diesel dependency reduction. A hybrid methodology integrating predictive modeling, real-time solar and weather data analysis, and performance simulations was employed, leading to a 65% reduction in diesel reliance and an LCOE of EUR 0.47/kWh. Quality control measures, including compliance with IEC 61215 and IEC 62485-2 standards, ensured system reliability under extreme conditions. Over 150 days, the system consistently supplied energy, preventing 10.16 tons of CO2 emissions. Beyond the Benin pilot, the project conducted feasibility assessments in Senegal to evaluate microgrid replicability across different socio-economic and environmental conditions. These analyses highlight the scalability potential and the economic viability of expanding solar microgrids in rural areas. Additionally, this research explores innovative business models and real-time diagnostics to enhance microgrid sustainability. By providing a replicable framework, it promotes long-term energy access and regional adaptability. With a focus on community involvement and capacity building, this study supports efforts to reduce energy poverty, strengthen European–African collaboration, and advance the global clean energy agenda.

Solar doi: 10.3390/solar5010008

Authors: Nicolene Botha Toshka Coleman Gert Wessels Maximilian Kleebauer Stefan Karamanski

The global energy landscape is experiencing growing challenges, with energy crises in regions such as South Africa underscoring the drive to accelerate the shift toward renewable energy solutions. This paper presents an approach for improving solar energy planning, specifically focusing on leveraging the capabilities of the ATlite software in conjunction with custom data. Using mathematical models, ATlite (which was initially developed by the Renewable Energy Group at the Frankfurt Institute for Advances Studies) is a Python software package that converts historical weather data into power generation potentials and time series for renewable energy technologies such as solar photovoltaic (PV) panels and wind turbines. The software efficiently combines atmospheric and terrain data from large regions using user-defined weights based on land use or energy yield. In this study, European Centre for Medium-Range Weather Forecasts reanalysis data (ERA5) data was modified using Kriging to enhance the resolution of each data field. This refined data was applied in ATlite, instead of utilizing the standard built-in data download and processing tools, to generate solar capacity factor maps and solar generation time series. This was utilized to identify specific PV technologies as well as optimal sites for solar power. Thereafter, a simulated power generation time series was compared with measured solar generation data, resulting in a root mean square error (RMSE) of 19.6 kW for a 250 kWp installation. This approach’s flexibility and versatility in the inclusion of custom data, led to the conclusion that it could be a suitable option for renewable energy planning and decision making in South Africa and globally, providing value to solar installers and planners.

Solar doi: 10.3390/solar5010007

Authors: Olufemi Olayiwola Umit Cali Miles Elsden Poonam Yadav

The rapid acceptance of solar photovoltaic (PV) energy across various countries has created a pressing need for more coordinated approaches to the sustainable monitoring and maintenance of these widely distributed installations. To address this challenge, several digitization architectures have been proposed, with one of the most recently applied being the digital twin (DT) system architecture. DTs have proven effective in predictive maintenance, rapid prototyping, efficient manufacturing, and reliable system monitoring. However, while the DT concept is well established in fields like wind energy conversion and monitoring, its scope of implementation in PV remains quite limited. Additionally, the recent increased adoption of autonomous platforms, particularly robotics, has expanded the scope of PV management and revealed gaps in real-time monitoring needs. DT platforms can be redesigned to ease such applications and enable integration into the broader energy network. This work provides a system-level overview of current trends, challenges, and future opportunities for DTs within renewable energy systems, focusing on PV systems. It also highlights how advances in artificial intelligence (AI), the internet-of-Things (IoT), and autonomous systems can be leveraged to create a digitally connected energy infrastructure that supports sustainable energy supply and maintenance.

Solar doi: 10.3390/solar5010006

Authors: Rahima Khanam Tahreem Asghar Muhammad Hussain

The reliable operation of photovoltaic (PV) systems is essential for sustainable energy production, yet their efficiency is often compromised by defects such as bird droppings, cracks, and dust accumulation. Automated defect detection is critical for addressing these challenges in large-scale solar farms, where manual inspections are impractical. This study evaluates three YOLO object detection models—YOLOv5, YOLOv8, and YOLOv11—on a comprehensive dataset to identify solar panel defects. YOLOv5 achieved the fastest inference time (7.1 ms per image) and high precision (94.1%) for cracked panels. YOLOv8 excelled in recall for rare defects, such as bird drops (79.2%), while YOLOv11 delivered the highest mAP@0.5 (93.4%), demonstrating a balanced performance across the defect categories. Despite the strong performance for common defects like dusty panels (mAP@0.5 > 98%), bird drop detection posed challenges due to dataset imbalances. These results highlight the trade-offs between accuracy and computational efficiency, providing actionable insights for deploying automated defect detection systems to enhance PV system reliability and scalability.

Solar doi: 10.3390/solar5010005

Authors: Ernesto Anguera Romero Nidia Aracely Cisneros-Cárdenas Claudio A. Estrada Gasca

A theoretical and experimental evaluation was conducted on a prototype radiative flux homogenizer (HOFRAC) specifically designed for the Solar Furnace at Instituto de Energías Renovables (HoSIER) of Universidad Nacional Autónoma de México. The development of HOFRAC included three versions (HOFRAC-PRO, HOFRAC-PRI, and HOFRAC-PRIK); each iteration incorporated improvements based on theoretical modeling and experimental results. Evaluations were performed using ray-tracing simulations and experimental tests capturing radiative flux distribution images. The last two versions were used to characterize a densely packed photovoltaic array operated in the solar furnace. Some results of this study show that misaligned mirrors in the furnace were identified as the main problem in achieving a high flux uniformity degree for photovoltaic concentration applications.

Solar doi: 10.3390/solar5010004

Authors: Ghadeer Badran Vlado K. Lazarov

The rapid proliferation of photovoltaic (PV) solar cells as a clean energy source has raised significant concerns regarding their end-of-life (EoL) management, particularly in terms of sustainability and waste reduction. This review comprehensively examines challenges, opportunities, and future directions in the recycling of PV solar cells, focusing on mechanical, thermal, and chemical recycling techniques. It also evaluates the scalability and practicality of these methods to different PV technologies, including crystalline silicon and thin-film modules. It explores the economic and environmental impacts of these processes, highlighting the necessity of developing robust recycling infrastructure and innovative technologies to address the anticipated surge in PV waste. Additionally, this review discusses the critical role of government policies and industry collaboration in overcoming the barriers to effective recycling. Furthermore, the importance of integrating design-for-recyclability principles into PV module development is emphasized, as it can significantly enhance material recovery and process efficiency. By advancing these strategies, the solar industry can achieve greater sustainability, reduce resource depletion, and mitigate environmental risks, thereby ensuring the long-term viability of solar energy as a key component of global renewable energy initiatives.

Solar doi: 10.3390/solar5010003

Authors: Carlos D. Rodríguez-Gallegos Oktoviano Gandhi César A. Rodríguez-Gallegos Manuel S. Alvarez-Alvarado

This study explores the potential for co-locating floating photovoltaics (FPVs) with existing hydropower plants (HPPs) in Ecuador. Ecuador’s heavy reliance on hydropower for electricity generation, combined with recent blackouts caused by prolonged dry seasons, underscores the importance of diversifying energy sources. The integration of FPVs with HPPs offers a promising opportunity to enhance energy security by reducing dependency on a single energy source and improving economic, electrical, and environmental outcomes. In this paper, we assess all HPPs in Ecuador and quantify the potential performance of FPV systems when installed at their sites. Our results show that FPV systems can not only contribute additional electricity to the grid but also improve HPP performance by reducing water evaporation from reservoirs and maintaining generation capacity during dry seasons, when solar irradiation is typically higher. To model the energy production, yield, and performance of the FPV systems, we applied RINA’s methodology to estimate representative weather conditions for each site and simulate FPV performance, accounting for system design loss factors. Additionally, we calculated the water savings resulting from FPV installation. Our findings reveal that, out of approximately 70 HPPs in Ecuador, 11 present favorable conditions for large-scale FPV deployment. Among these, Cumbayá HPP (40 MW) exhibited the most suitable conditions, supporting a maximum FPV capacity of 17 MWp. Marcel Laniado de Wind HPP (213 MW) and Mazar HPP (170 MW) were also identified as optimal candidates, each with potential FPV capacities equal to their installed HPP capacities. While this study primarily aims to provide scientific evidence on the potential of FPV-HPP co-location, the results and methodology can also guide Ecuadorian government authorities and investors in adopting FPV technology to strengthen the country’s energy infrastructure.

Solar doi: 10.3390/solar5010002

Authors: Vincenzo d’Alessandro Santolo Daliento Mahmoud Dhimish Pierluigi Guerriero

In this work, we propose a simulation tool designed for the analysis and optimization of bifacial photovoltaic (PV) modules, which are currently under the spotlight in the renewable energy scenario. The tool is conceived to support researchers and engineers by providing fast and accurate predictions of the PV module yield under various operating and environmental conditions. For a chosen geographical site, the impact of module orientation, tilt, albedo, sky conditions, ambient temperature, and so on can be effortlessly determined. In case of nonuniformity across the cells dictated by localized architectural shading, dirt, bird drops, and defects, a circuit-based cell-level approach can be activated to compute the module production. An extensive simulation campaign is performed by assuming that the panels are installed in Naples without loss of generality. Results are shown to give detailed insights into the performance of bifacial modules, thus providing unambiguous guidelines for their correct installation. Further analyses are conducted to demonstrate the tool capability to quantify the detrimental influence of a poorly-irradiated cell on the backside, as well as of cracked cells.

Solar doi: 10.3390/solar5010001

Authors: Maher Maghalseh Nassim Iqteit Haitham Alqadi Salman Ajib

The conventional unidirectional power flow model of centralized energy grids is being revolutionized by integrating renewable energy sources, particularly photovoltaic (PV) systems, to meet the escalating demand for electricity while ensuring sustainability. However, this integration challenges the efficiency and performance of power systems and impacts various parameters, including power quality, voltage profile, power factor, power loss, and load flow. This paper investigates the effects and performance of a grid-tied PV system integrated into the conventional power system, focusing on the Palestine Polytechnic University (PPU) 230 kWp PV plant as a real-world case study. Simulations conducted using ETAP software revealed that integrating the PV system resulted in a slight increase in the voltage level at the main bus of the PPU feeder, with an increase of 0.03% at the medium-voltage level. Additionally, the voltage level at the Point of Common Coupling (PCC) increased by 0.51% with a PV penetration level of only 14.7%, which remains within the acceptable range according to IEEE 1547 standards. These findings underscore the minimal impact of the PV system on the voltage profile and highlight the system’s ability to maintain power quality and efficiency even with the addition of renewable energy sources. The daily load profiles were studied with and without the PV system, providing a comprehensive analysis of its effects on the grid.

Solar doi: 10.3390/solar4040034

Authors: Victoria Lofstad-Lie Bjørn Lupton Aarseth Nathan Roosloot Erik Stensrud Marstein Torbjørn Skauli

The degradation of solar photovoltaic (PV) modules over time, which are aggravated by defects, significantly affects the performance of utility-scale PV parks. This study presents a quantitative assessment of the power loss from module defects and evaluates the cost-effectiveness of replacing defective modules at various stages of degradation. A module test site was established in Norway with six different defects, and continuous thermographic monitoring, combined with light IV measurements and electroluminescence (EL) imaging, provides partial support for further calculations on the long-term effects of the defects. The cumulative module energy loss is calculated over a 25-year park lifespan under both Norwegian and Chilean environmental conditions, with the latter representing higher solar irradiation levels. The energy gain from replacing the defective modules at various stages of degradation is compared to the costs of replacement, both for infant-life failures and mid-life failures. It is likely not beneficial to replace minor infant-life defects of 1% power loss in low-irradiation regions like Norway. For Chilean conditions, it can be cost-effective, but primarily if the module is replaced around mid park life, which gives a larger yield when replaced with a new module. For more severe defects of 10% loss, the replacement gain is above the replacement cost for high-irradiation locations, and replacing the 33% power loss defect is cost-effective for both locations, even when discovered late in the park lifetime. It is primarily beneficial to replace mid-life defects in high-irradiation locations.

Solar doi: 10.3390/solar4040033

Authors: Josephine Nakato Kakande Godiana Hagile Philipo Stefan Krauter

This work analyses load profiles for East African microgrids, and then investigates the integration of electric two-wheelers and portable storage into a solar PV with battery microgrid in Uganda, East Africa. By introducing e-mobility and portable storage, demand side management strategic load growth can thus be achieved and electricity access can be expanded. Battery degradation is also considered. The results showed a 98.5% reduction in PV energy curtailment and a 57% reduction in the levelized cost of energy (LCOE) from 0.808 USD/kWh to 0.350 USD/kWh when the electric two-wheeler and portable storage loads were introduced. Such reductions are important enablers of financial viability and sustainability of microgrids. It is possible to avoid emissions of up to 73.27 tons of CO2/year with the proposed e-bikes, and an average of 160 customers could be served annually as off-microgrid consumers without requiring an investment in additional distribution infrastructure. Annual revenue could be increased by 135% by incorporating the additional loads. Sensitivity analyses were conducted by varying component costs, the battery lifetime, the interest rate, and the priority weighting of the additional loads. The battery costs were found to be a major contributor to lifecycle costs (LCC) and also have a big impact on the LCOE. The interest rate significantly affects the LCC as well.

Solar doi: 10.3390/solar4040032

Authors: Vincent Boitier Kha Bao Khanh Cao Bruno Estibals Vincent Raimbault Maxime Cauchoix Jean-Louis Druilhe Arnaud Elger

The move toward sophisticated sensor networks in ecological applications requires a substantial amount of energy. Energy storage solutions based simply on batteries are often not sufficient to cover the energy needs, so a standalone power supply using solar energy harvesting is generally required. However, designing an appropriate solar power supply without oversizing and avoiding output power disruption all year long is not a trivial task. This paper provides a set of guidelines as well as useful information and advice for environmental researchers and other non-experts to select the right components when designing their own autonomous solar power supply for a range between 10 mW and 10 W. The design steps are compiled into a comprehensive document, free of irrelevant information yet still presenting a general overview of the solar power supply design process, in order to make this task more accessible and understandable for non-experts. The methodology for simple initial dimensioning was carried out and applied to a real-life use case by using the estimated or measured daily consumption combined with free meteorological data of the deployment site provided by various websites. Next, an hourly simulation completed the first sizing. A year of experimental results validated the methodology.

Solar doi: 10.3390/solar4040031

Authors: Vincenzo d’Alessandro Santolo Daliento Mahmoud Dhimish Pierluigi Guerriero

This paper focuses on the analytical modeling of albedo reflection in bifacial photovoltaic modules, with particular emphasis on the backside. First, we critically examine the approaches proposed in the literature, presenting them with a tutorial style and a uniform nomenclature. These approaches are demonstrated to yield physically meaningless results, as they erroneously assume that the ground area shaded by the module acts as a source of reflected irradiance independent of the portion of sky dome visible to such an area. Then we introduce a correction based on the view factor between the shaded area and the sky. The result is a comprehensive and accurate analytical model that also describes the case of suspended panels and can be easily implemented into PV plant simulators.

Solar doi: 10.3390/solar4040030

Authors: Lotfi Aktouf Yathin Shivanna Mahmoud Dhimish

This study presents an advanced defect detection approach for solar cells using the YOLOv10 deep learning model. Leveraging a comprehensive dataset of 10,500 solar cell images annotated with 12 distinct defect types, our model integrates Compact Inverted Blocks (CIBs) and Partial Self-Attention (PSA) modules to enhance feature extraction and classification accuracy. Training on the Viking cluster with state-of-the-art GPUs, our model achieved remarkable results, including a mean Average Precision (mAP@0.5) of 98.5%. Detailed analysis of the model’s performance revealed exceptional precision and recall rates for most defect classes, notably achieving 100% accuracy in detecting black core, corner, fragment, scratch, and short circuit defects. Even for challenging defect types such as a thick line and star crack, the model maintained high performance, with accuracies of 94% and 96%, respectively. The Recall–Confidence and Precision–Recall curves further demonstrate the model’s robustness and reliability across varying confidence thresholds. This research not only advances the state of automated defect detection in photovoltaic manufacturing but also underscores the potential of YOLOv10 for real-time applications. Our findings suggest significant implications for improving the quality control process in solar cell production. Although the model demonstrates high accuracy across most defect types, certain subtle defects, such as thick lines and star cracks, remain challenging, indicating potential areas for further optimization in future work.

Solar doi: 10.3390/solar4040029

Authors: Francisca Muriel Daniel-Durandt Arnold Johan Rix

A novel spectral-corrected Performance Ratio calculation method that aligns the short-term Performance Ratio calculation to the annual calculated Performance Ratio is presented in this work. The spectral-corrected Performance Ratio allows short-term measurements to reasonably estimate the annual Performance Ratio, which decreases the need for long-term measures and data storage and assists with routine maintenance checkups. The piece-wise empirical model incorporates two spectral variables, a geographical location-based variable, the air mass, a PV-technology-based variable, and a newly defined spectral correction factor that results in a universal application. The spectral corrections show significant improvements, resulting in errors across different air mass and clearness index ranges, as well as temporal resolutions. The results indicate that a spectral correction methodology is possible and a viable solution to estimate the annual Performance Ratio. The results further indicate that by correcting the spectrum, short-term measurements can be used to predict the annual Performance Ratio with superior performance compared to the well-known normal and weather-corrected PR calculation methods. This approach is the first documented effort to address the spectrum’s influence on the utility-scale Performance Ratio calculation from hourly measurements. The empirical formula suggested for the Performance Ratio calculation can be of extreme value for the real-time monitoring of PV systems and enhancing PV power forecasting accuracy when the spectrum is considered instead of its usual omission. The model can be universally applicable, as it incorporates location and technology, marking a groundbreaking start to comprehending and incorporating the spectral influence in utility-scale PV systems. The novel calculation has widespread application in the PV industry, performance modelling, monitoring, and forecasting.

Solar doi: 10.3390/solar4040028

Authors: Abdulhamid Atia Fatih Anayi Ali Bahr Gao Min

This paper reports on the correlation between broken contact fingers and the shape of the current–voltage (I–V) curve of a photovoltaic (PV) module. It was found that the broken contact fingers of a solar cell in the PV module cause a noticeable change in the I–V curve of the PV module when the solar cell was partially shaded. The broken contact fingers were inspected by microscopic imaging and electroluminescence (EL) imaging, and a further investigation was carried out using a single solar cell. The results show that the fill factor of the cell decreased from 0.75 of full contact to 0.47 after 16 contact fingers were broken, confirming the correlation between the I–V curve shape and broken contact fingers. This result reveals that the shape of the I–V curve of a PV module under individual-cell partial shading may be used as an indicator of broken contact fingers, which offers an alternative approach to EL imaging for detecting broken contact fingers in PV modules in daylight.

Solar doi: 10.3390/solar4040027

Authors: Robert Sørensen Lucian Mihet-Popa

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in the MATLAB/Simulink software package evaluated these algorithms’ performances under various shading scenarios. The results indicate that, while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. This study concludes that integrating Artificial Intelligence (AI) and Machine Learning (ML) into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for a broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and supporting green energy transition.

Solar doi: 10.3390/solar4040026

Authors: Diogo Chambel Lopes Isabel Nogueira

One important aspect to consider when buying a house or apartment is adequate solar exposure. The same applies to the evaluation of the shadowing effects of existing buildings on prospective construction sites and vice versa. In different climates and seasons, it is not always easy to assess if there will be an excess or lack of sunlight, and both can lead to discomfort and excessive energy consumption. The aim of our project is to design a method to quantify the availability of direct sunlight to answer these questions. We developed a tool in Octave to calculate representative parameters, such as sunlight hours per day over a year and the times of day for which sunlight is present, considering the surrounding objects. The apparent sun position over time is obtained from an existing algorithm and the surrounding objects are surveyed using a picture taken with a 360° camera from a window or other sunlight entry area. The sky regions in the picture are detected and all other regions correspond to obstructions to direct sunlight. The sky detection is not fully automatic, but the sky swap tool in the camera software could be adapted by the manufacturer for this purpose. We present the results for six representative test cases.

Solar doi: 10.3390/solar4040025

Authors: Carmine Cancro Valeria Palladino Aniello Borriello Antonio Romano Luigi Mongibello

Concentrated photovoltaic (CPV) technology is based on the principle of concentrating direct sunlight onto small but very efficient photovoltaic (PV) cells. This approach allows the realization of PV modules with conversion efficiencies exceeding 30%, which is significantly higher than that of the flat panels. However, to achieve optimal performance, these modules must always be perpendicular to solar radiation; hence, they are mounted on high-precision solar trackers. This requirement has led to the predominant use of CPV technology in the construction of solar power plants in open and large fields for utility scale applications. In this paper, the authors present a novel approach allowing the use of this technology for residential installations, mounting the system both on flat and sloped roofs. Therefore, the main components of cell and primary lens have been chosen to contain the dimensions and, in particular, the thickness of the module. This paper describes the main design steps: thermal analysis allowed the housing construction material to be defined to contain cell working temperature, while with deep optical studies, experimentally validated main geometrical and functional characteristics of the CPV have been identified. The design of a whole CPV system includes thermal storage for domestic hot water and a 1 kWh electrical battery. The main design results indicate an estimated electrical conversion efficiency of 30%, based on a cell efficiency of approximately 42% under operational conditions and a measured optical efficiency of 74%. The CPV system has a nominal electric output of 550 Wp and can simultaneously generate 630 W of thermal power, resulting in an overall system efficiency of 65.5%. The system also boasts high optical acceptance angles (±0.6°) and broad assembly tolerances (±1 mm). Cost analysis reveals higher unit costs compared to conventional PV and CPV systems, but these become competitive when considering the benefit of excess thermal energy recovery and use by the end user.

Solar doi: 10.3390/solar4030024

Authors: Andrii Cheilytko Spiros Alexopoulos Andriy Pozhuyev Oliver Kaufhold

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown.

Solar doi: 10.3390/solar4030023

Authors: Shakil Mirza Arif Hussain

Grid-connected PV inverters require sophisticated control procedures for smooth integration with the modern electrical grid. The ability of FCS-MPC to manage the discrete character of power electronic devices is highly acknowledged, since it enables direct manipulation of switching states without requiring modulation techniques. This review discusses the latest approaches in FCS-MPC methods for PV-based grid-connected inverter systems. It also classifies these methods according to control objectives, such as active and reactive power control, harmonic suppression, and voltage regulation. The application of FCS-MPC particularly emphasizing its benefits, including quick response times, resistance to changes in parameters, and the capacity to manage restrictions and nonlinearities in the system without the requirement for modulators, has been investigated in this review. Recent developments in robust and adaptive MPC strategies, which enhance system performance despite distorted grid settings and parametric uncertainties, are emphasized. This analysis classifies FCS-MPC techniques based on their control goals, optimal parameters and cost function, this paper also identifies drawbacks in these existing control methods and provide recommendation for future research in FCS-MPC for grid-connected PV-inverter systems.

Solar doi: 10.3390/solar4030022

Authors: Mehdi Aghaei Meybodi Andrew C. Beath

Australia aims to achieve net zero emissions by 2050, with an interim target of reducing emissions to 43% below 2005 levels by 2030. Electrification of industry processes currently reliant on fossil fuels is a necessary step to achieve these emission reduction goals. This study investigates electrification of steam generation relevant to major industrial operations in the southwest of Western Australia using different renewable energy input levels. The designed system incorporates thermal storage to ensure continuous steam generation. The optimized technology mix, including wind, PV, and concentrated solar thermal (CST) systems for each renewable energy input target, is presented. The optimization process also identifies optimal locations for new renewable energy plants. In summary, the optimization tends towards favouring the development of large CST plants near a demand point. This avoids the use of the transmission network by direct use of the CST system for heating of the storage media, to address the costs and efficiency reductions arising from electrical heating, but the scope of CST use is expected to be limited by site constraints. The levelized cost of heat (LCOH) for the studied renewable energy input targets (i.e., 30–90%) ranges from 15.34 to 36.92 AUD/GJ. This is promising for the 30% renewable energy target, as future natural gas prices in Western Australia are likely to match or exceed the expected LCOH. Cost reductions for renewable generation and storage technologies with further implementation at a large scale in the future may result in more competitive LCOH at higher decarbonization levels, but it is likely that additional technologies will be required for cost competitiveness at very high decarbonization levels.

Solar doi: 10.3390/solar4030021

Authors: Tovondahiniriko Fanjirindratovo Didier Calogine Oanh Chau Olga Ramiarinjanahary

The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, one of these methods will be optimized to effectively fit tropical sites. Five methods in the literature (Step algorithm, Carpentras Algorithm, Slob and Monna Algorithm, Slob and Monna 2 Algorithm, and linear algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, with regard to its original form, the step algorithm is in the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical sites in the southern hemisphere.

Solar doi: 10.3390/solar4030020

Authors: Talha Batuhan Korkut Ahmed Rachid

This study presents a compact educational photovoltaic/thermal (PV/T) system designed for thorough performance assessment under simulated weather conditions. As an affordable educational tool, the system offers significant pedagogical value. The PV/T system features two photovoltaic modules: a thermally enhanced module and a standard one. The thermally enhanced module uses water as a coolant, which transfers heat from the PV cells to a fan-operated heat exchanger, with the coolant being recirculated to maintain optimal conditions. A halogen lamp, placed between the modules, simulates solar radiation to ensure effective electrical current generation. The system’s remote-control capabilities, managed via the Message Queuing Telemetry Transport (MQTT) protocol, enable real-time adjustments to the coolant flow rate, heat exchanger efficiency, and lamp brightness, as well as monitoring of electrical parameters. Experimental findings indicate that the PV/T module achieves a 7.71% increase in power output compared to the standard PV module and offers a 17.41% improvement in cooling efficiency over scenarios without cooling. Additionally, the numerical methods used in the study show a maximum deviation of 4.29% from the experimental results, which is considered acceptable. This study showcases a best practice model for solar training, applicable from elementary to university levels, and suggests innovative approaches to enhancing solar energy education.

Solar doi: 10.3390/solar4030019

Authors: Christopher Pike Daniel Riley Henry Toal Laurie Burnham

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Solar doi: 10.3390/solar4030018

Authors: Neri Banti Cecilia Ciacci Frida Bazzocchi Vincenzo Di Naso

The Italian industrial sector contains 22% of the final energy demand due to the poor energy performance of manufacturing buildings. This proposed study aimed to evaluate retrofit interventions for existing industrial buildings integrating photovoltaic solutions into the external envelope to improve both the environmental sustainability and the facade performance. The methodology is based on an innovative procedure including BIM and generative design tools. Starting from the Revit model of a representative case study, interoperability with energy analysis plugins via Grasshopper were exploited to optimize the differently oriented facade layout of photovoltaic modules to maximize the electricity production. In the case of comparable facade sizes, the building orientation had a minor impact on the results, although a southern exposure was preferable. The optimized configuration involved the installation of PV panels with a tilt angle ranging from −35° to −75°. The best compromise solution between the panel surface area and energy production during the summer solstice involves 466 m2 of PV modules. The design-optioneering approach was used to define possible alternatives to be explored for the possible installation of solar shading systems on existing windows. In this case, the impact on visual comfort within the working environment was chosen as a reference parameter, along with the value of the indoor air temperature. A decrease in this parameter equal to 0.46 was registered for the solution with horizontal (or nearly horizontal) shaders and a spacing ranging between 0.2 and 0.4. The integration of the BIM environment with generative design tools effectively assists decision-making processes for the selection of technological solutions in the building sector.