Solar

Photovoltaic module performance is governed by the combined effects of solar-cell architecture, optical packaging, electrical interconnection, irradiance distribution, and reliability. This study develops a comparative module-level framework for PERC, n-TOPCon, heterojunction (HJT), interdigitated-back-contact (IBC), parallel-coupled Si/Si, and III–V/crystalline-silicon configurations. The framework combines experimentally measured solar-cell electrical characteristics and parallel Si/Si performance with a literature-supported assessment of module manufacturing, bifacial operation, qualification, degradation, and diagnostics, together with a quantitative factor-resolved cell-to-module analysis of tandem configurations. At their respective technology-specific maximum-output conditions, the PERC-, TOPCon-, HJT-, and IBC-based parallel Si/Si configurations reached ηtandem values of 28.42%, 31.62%, 32.43%, and 32.29%, respectively, under the adopted effective front-plus-rear irradiance basis. When the complete front-plus-rear incident power was considered, the corresponding ηphysical values were 23.45%, 26.08%, 26.85%, and 26.65%. The ηtandem values represent performance under the adopted rear-side utilization convention and should not be interpreted as complete total-input efficiencies. Factor-resolved CTM analysis yielded module efficiencies of 30.18%, 27.48%, and 31.16% for the III–V Flex/n-TOPCon, GaAs type-1/n-TOPCon, and GaAs type-2/n-TOPCon configurations, corresponding to absolute CTM reductions of 3.56, 3.24, and 3.68 percentage points. The results demonstrate that module performance cannot be inferred from standalone cell efficiency; irradiance normalization, operating-voltage compatibility, optical coupling, inactive area, spectral transmission, and electrical interconnection must be treated consistently.

Battery energy storage systems (BESSs) are increasingly coupled with photovoltaic (PV) generation, yet their deployment is governed by economics rather than by technical feasibility. This review synthesises 122 indexed Q1/Q2 studies (2016–2026) on the financial payback and return on investment of PV-coupled storage, extending the analysis to vehicle-to-grid (V2G) integration. Records retrieved from Scopus, Web of Science, IEEE Xplore, and the MDPI portal were screened to peer-reviewed journals, assigned to ten thematic clusters, appraised against a ten-criterion reporting-transparency rubric, and combined by narrative synthesis. Reported payback periods range from a few years to beyond the asset’s service life, and the levelized cost of storage spans roughly 170–350 USD/MWh. In the reviewed corpus, the retail-to-export price spread, the stacking of self-consumption, arbitrage, grid-service revenues, and degradation-aware operation each move returns in a consistent direction, whereas which of them binds hardest is a property of the case rather than a ranking the evidence supports. V2G is almost always analysed in isolation from stationary storage; an illustrative harmonised comparison indicates that it substitutes for stationary capacity rather than adding to it. The review maps the combined PV+BESS+V2G revenue stack and identifies an integrated, degradation-corrected, policy-sensitive economic model as the principal research gap.

The rapid global expansion of solar photovoltaic (PV) capacity has increased the operational need for accurate generation forecasting to support grid balancing, dispatch, and market participation. Artificial intelligence (AI) and machine learning (ML) methods now dominate this research area, but the resulting literature is large and methodologically fragmented, making it difficult to establish which methods are used, what data they require, and where the principal gaps lie. This paper combines a bibliometric analysis of 3111 records retrieved from the Web of Science Core Collection (2010–2026) with a technical synthesis of 27 highly cited studies published from 2022 onward, combining the most highly cited works with targeted additions from 2024–2025 covering specific methodological gaps. The bibliometric analysis shows exponential growth in annual output, from three publications in 2010 to 609 in 2025, with keyword evolution tracing a clear methodological trajectory from classical and fuzzy-logic approaches, through shallow and deep neural networks, to transformer- and attention-based architectures since 2023. The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer architectures address multi-site and multi-horizon forecasting, respectively. A comparison of reported results shows that absolute error metrics are not directly comparable across studies due to heterogeneous datasets, metrics, temporal resolutions, and climates, although relative improvements within controlled comparisons are directionally consistent. Seven research gaps are identified, including the absence of standardized benchmarks, limited public dataset availability, weak cross-region generalization, and underdeveloped uncertainty quantification.

The rapid expansion of solar photovoltaics has intensified land-use competition between renewable energy and agriculture, particularly in regions where food security and clean-energy transitions must progress together. Semi-transparent photovoltaic technologies may provide a land-use-compatible pathway for potential agrivoltaic applications by allowing partial light transmission while generating electricity. This study evaluates Dye-Sensitized Solar Cells (DSSCs) as a semi-transparent photovoltaic option and investigates the use of DSSC-generated electricity to power Direct Air Capture (DAC). The scope is limited to photovoltaic-system performance and economics and does not include Photosynthetically Active Radiation (PAR) transmission, crop growth, crop yield, or microclimate analysis. A techno-economic assessment was performed using the System Advisor Model for two contrasting regions, Karachi, Pakistan, and Davis, USA. DSSC and monocrystalline silicon systems were compared over the same land area. Due to their lower installed capacity, the DSSC systems generated less annual electricity. However, their lower assumed capital expenditure resulted in a Levelised Cost of Electricity (LCOE) that was 10% lower in Davis, at US Dollars (USD) 0.29/kWh, and 0.7% lower in Karachi, at USD 0.25/kWh. Based on literature-reported DAC energy requirements, the DSSC systems could support annual Carbon Dioxide (CO2) removal of 331–9707 tonnes in Karachi and 334–9783 tonnes in Davis, depending on the selected DAC pathway. These results indicate that semi-transparent DSSCs may provide a lower-capital, land-use-compatible photovoltaic pathway combined with renewable-electricity-driven carbon removal. Their suitability for practical agrivoltaic deployment requires future PAR-transmission measurements and crop-specific experimental validation.

The nature of solar radiation and the high penetration of photovoltaic (PV) systems in the smart electrical grid necessitate the development of models driven by historical operational data capable of precisely estimating the performance of PV systems. In this work, six proposed models are applied to predict the conversion efficiency of a 7.98 kWp rooftop on-grid PV system in Jordan. The dataset comprises 179 daily samples obtained during a single spring–summer period (17 March–24 September 2014). The efficiency modeled is the combined efficiency of the modules and inverter as a system. The proposed models are Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Process Regression (GPR), and Elastic Net (EN). The effectiveness of these proposed models is assessed by calculating four performance metrics, namely, the Mean Square Error, prediction accuracy, Coefficient of Determination (R2), and adjusted R2, and benchmarking the results with those of six prediction models discussed in our previous work. The results from the unweighted Decision-Making Matrix show that RF showed the best overall performance among the proposed and benchmark models considered. By contrast, the SVM, DT, and GB models exhibited moderate predictive behavior. However, Elastic Net is the worst-performing model among the 12 proposed and benchmark models discussed in this work. Moreover, the RF model’s consistently low prediction error supports its practical utility for PV system performance estimation, despite a slightly higher training cost than simpler models.

Organic solar cells are widely recognized for their flexibility, light weight and semitransparency, all relevant for niche applications. A significant challenge in their development lies in the accurate prediction of their power conversion efficiency depending on the combination of the donor and acceptor selected in the bulk heterojunction. To address this issue, we developed a robust machine learning (ML) framework designed to establish correlations between molecular structure and device performance. A feature selection strategy, incorporating SHapley Additive exPlanations and Boruta algorithms, was employed to extract the most informative descriptors. Among the regression models that were systematically evaluated on a curated dataset comprising 1575 experimentally characterized donor–acceptor pairs, histogram-based gradient boosting demonstrated superior predictive performance, giving an R2 score of 0.79, with a low root mean square error of 2.16. Subsequently, the optimized model was used to predict new donor–acceptor pairs with PCEs above 20% and identify prospective candidates for further experimental validation.

This study presents a short-term outdoor comparison of mirror-glass and AISI 304 stainless-steel reflectors in parabolic trough collectors equipped with identical helical copper absorbers. Both configurations were operated simultaneously using the same collector geometry, fixed inclination angle, water-supply arrangement, measurement schedule, and instrumentation. Solar irradiance, inlet and outlet water temperatures, absorber temperature, and reflector temperature were recorded over three consecutive experimental days, namely 24–26 October 2025. The results were evaluated using temperature rise and time-dependent temperature output because the gravity-assisted system was not equipped with a flow meter or active flow-control device, preventing reliable calculation of useful heat gain and thermal efficiency. The descriptive results showed that the mirror-glass configuration produced a modestly higher overall temperature response and lower variation among the three daily mean values, although it did not outperform stainless steel at every measurement time or in every daily average. The observed difference is interpreted primarily in terms of the expected higher specular reflectivity and lower optical scattering of mirror glass, which can increase the solar radiation intercepted by the absorber. However, the conclusions are limited by the three-day testing period, absence of verified mass-flow data, lack of direct reflectivity measurements, and unquantified cosine losses associated with fixed operation without automatic tracking. The findings therefore provide configuration-specific guidance for reflector selection rather than a generalized ranking of collector performance.

We report a two-layer computational workflow for designed heterocyclic polymer dimers as candidates for organic solar cell (OSC) materials. The workflow integrates geometry-optimized B3LYP/6-31G(d) quantum-chemical descriptors (HOMO, LUMO, gap, dipole moment) computed for five fully disclosed monomer–dimer pairs (1m–5m; 1d–5d), with a verified, literature-curated 17-entry OSC dataset (PCE 3.6–19.9%, years 2016–2024) modeled by a non-tautological ridge regression baseline (Model A; predictors Year + source_block + log10 hole mobility). All five dimers were computed under uniform neutral closed-shell conditions. Pareto-front analysis in the gap–dipole descriptor space identifies dimer 2d (difluorinated thiophene–diazine D-A dimer; gap 1.74 eV, dipole 16.59 D) as the Tier I lead candidate, with 3d (bis(thiophene–triazine) dimer) and 1d (bis-thiophene–thiazole dimer) as additional Tier I candidates. Model A yields R2(LOOCV) = 0.660, MAE = 2.36%, and RMSE = 3.67%, surviving a 500-shuffle permutation null at empirical p < 0.001. A descriptor-augmented Model B (Eg + HOMO added) demonstrates that the present literature dataset cannot support a deployable molecular-descriptor regression without expansion. The combined DFT–regression workflow provides a transparent screening framework that identifies 2d as the priority synthesis target.

Deep networks dominate photovoltaic (PV) thermographic fault detection but need large annotated datasets and resist interpretation. We present a lightweight, interpretable infrared workflow combining support-vector-machine (SVM) module/background segmentation from four handcrafted features with an adaptive grid analysis labelling regions as nominal-intensity, high-intensity anomaly or low-intensity anomaly relative to a module-internal reference; the anomaly classes are inspection candidates, not confirmed faults. Evaluation used 21 close-range images of one 20 W module—recorded with the camera’s visible-light edge fusion active, so they are fused infrared/visible frames—and all 596 of a public five-sector UAV dataset. Segmentation against manual masks reached a mean intersection-over-union of 0.64; a feature ablation shows intensity statistics dominate, and an end-to-end Otsu pipeline gives almost the same high-intensity share (4.54% versus 4.50%): the SVM contributes reproducibility—removing the manual segmentation threshold, though not the empirical +48/−60 offsets—not accuracy. High-intensity regions concentrated in the module’s lower half, co-locating with a bus-bar defect known from hardware inspection—suggestive, not validated. The single-module, image-level close-range evaluation is optimistic, and the UAV shares, from a separately trained SVM, illustrate cross-domain application only. Segmentation runs at about 15 images per second on CPU. The method is a relative-intensity thermal screening workflow, not a validated defect-diagnosis or plant-health assessment method, and applies only where acquisition is controlled and the offsets are recalibrated for the target camera and palette.

The integration of renewable energy into smart grids is beneficial for a sustainable future and the environment. Still, it has challenges such as energy optimisation, environmental conditions and power quality degradation. Existing Maximum Power Point Tracking (MPPT) techniques often focus on tracking efficiency under steady-state conditions, ignoring the impact of real-time variation in environmental conditions and load. The predictive power flow control (PPFC) algorithm is available with one or more fixed MPPT algorithms. No studies have reported on how the choice of MPPT affects PPFC harmonic mitigation. This paper addresses both concerns through a systematic comparative analysis of MPPT techniques integrated with a PPFC method to mitigate harmonics in renewable-integrated smart grid systems. To address this research gap, a comprehensive comparative analysis of various MPPT techniques, such as Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic Control (FLC), and hybrid Machine Learning (ML) techniques, integrated with PPFC to achieve effective harmonic mitigation in a smart grid environment is conducted. A 3 MW solar farm integrated with a battery storage system is modelled in MTALB/Simulink 2025b under real-time varying conditions, such as environmental and load variations over time in Auckland, New Zealand. The study focuses on key performance parameters such as total harmonic distortion (THD), power loss, stability and efficiency. The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller, integrated with forecast-based power flow control, achieved overall performance by providing higher efficiency (97.5%), effective harmonic mitigation, and enhanced system stability under the nonlinear behaviour of the photovoltaic system. The proposed ANFIS-based system ensured a stable and smooth power output under varying environmental conditions, outperforming conventional and other intelligent MPPT techniques.

Night-time low temperature remains a major constraint on thermal stability, crop safety and energy-efficient operation in winter solar greenhouses, especially when heat release and auxiliary heating are triggered only after the indoor temperature has approached a low temperature threshold. This study developed a temperature prediction-based active–passive heat storage and release system integrating Internet of Things monitoring, liquid neural network (LNN)-based multi-horizon temperature forecasting, heat storage and release circulation, and decision-making control. The LNN achieved the best forecasting performance among the tested models, with MAE/RMSE values of 0.620/0.775, 0.683/0.854 and 0.758/0.948 °C for 12 h, 24 h and 48 h forecasts, respectively, and was embedded into the system for prediction-assisted operation. A continuous 30-day winter test was conducted in two consecutive stages: heat storage and release without predictive control (HS-NPC, days 1–15) and with prediction-assisted operation (HS-PC, days 16–30). During the consecutive-stage winter test, HS-PC showed higher daily minimum indoor temperature and night-time mean temperature than HS-NPC by 1.92 °C and 4.29 °C, respectively, while reducing daily exposure below 13 °C and 10 °C by 55.0% and 98.2%. Stage-based equivalent input-energy evaluation indicated reductions of 17.1% and 34.3% for HS-NPC and HS-PC relative to the corresponding TG reference periods, respectively. Because HS-NPC and HS-PC were tested in consecutive weather windows rather than in fully synchronized parallel experiments, these improvements should be interpreted as stage-based operational benefits supported by the TG reference and outdoor environmental statistics, rather than as completely weather-independent causal effects. These results indicate that integrating temperature forecasting with heat storage and release regulation can improve low-temperature buffering and energy-saving operation in winter solar greenhouses, while further synchronized or weather-normalized validation is still needed.

Energy system optimization seeks to utilize multiple energy sources efficiently under technical, economic, and environmental constraints. The increasing integration of renewable energy and the need for sustainable operation have made the optimal planning and management of hybrid energy systems crucial. Classical optimization methods, including Linear Programming, Nonlinear Programming, and simulation-based models, often face limitations when addressing high-dimensional and nonlinear problems. This study introduces a deep learning–based surrogate modeling framework for sizing the components of hybrid renewable energy systems. Initially, Particle Swarm Optimization (PSO) is employed to determine the optimal component sizes for a large number of synthetically generated hourly solar irradiance and load profiles. These optimal solutions are then used as target labels. The associated annual time-series data are transformed into multi-channel Data Map (DMAP) images, which serve as inputs for convolutional neural networks (CNNs). After training, the CNN models are capable of directly estimating the required number of photovoltaic (PV) panels, inverter capacity, and battery units from the DMAP images, eliminating the need to perform the iterative PSO optimization during the prediction stage. Various convolutional neural network architectures, including ResNet, DenseNet121, RegNet, ConvNeXt, EfficientNet, SqueezeNet, MobileNet, and InceptionV3, were evaluated for this multi-output regression task. The results indicate that ResNet and DenseNet121 achieve the best performance, while ConvNeXt provides strong results with a modern architectural design. Among the evaluated models, DenseNet121 achieved coefficients of determination (R2) of 0.934, 0.988, and 0.947 for predicting the sizes of the PV array, inverter, and battery bank, respectively. These results correspond to an average prediction accuracy of approximately 90.6%. ResNet produced similar performance, with its highest R2 value reaching 0.983 for inverter sizing. Lightweight networks such as SqueezeNet and MobileNet demonstrate notable effectiveness for resource-constrained systems, whereas InceptionV3 underperforms in leveraging its multi-scale architecture. These results demonstrate that, once the models have been trained, deep learning–based surrogate models can generate sizing decisions comparable to those obtained using PSO with only a fraction of the computational effort. As a result, they provide a fast and practical alternative to conventional iterative optimization methods for component sizing in smart grid and sustainable energy planning applications.

By the end of 2024, global photovoltaic (PV) capacity exceeded 2.2 TW, shifting planning from feasibility demonstration toward site–technology co-selection under energy, technical, economic, environmental, territorial, and socio-regulatory constraints. The existing multicriteria literature treats site and technology selection as independent problems under an implicit infinite-grid assumption, which is untenable in markets such as Chile and Peru. This study develops and validates an integrated Delphi–AHP framework with six criteria and eighteen subcriteria calibrated by twenty-eight experts from six Latin American countries. The framework underwent Delphi binary validation, AHP consistency control (CRagg between 0.0013 and 0.0247; discard rate 2.6%), geometric-mean aggregation, deterministic sensitivity analysis, Monte Carlo simulation (10,000 iterations), rank-reversal testing, and nonparametric subgroup analysis. The dominant pair {I2,Ec2}, consisting of grid hosting capacity and LCOE, appears as Top-2 in 84.77% of Monte Carlo iterations and is preserved across 15 of 16 leave-one-out scenarios. Grid hosting capacity surpasses useful solar resource by a factor of 3.41. A demonstrative application to 18 site–technology alternatives confirms the ranking, with an objective-weighting benchmark (entropy, CRITIC) yielding concordant results (Spearman ρ≥0.89). The findings formalize a shift in the PV planning bottleneck from solar resource to grid capacity.

Space missions working under harsh heliocentric conditions demand more efficient photovoltaics operating under high solar concentration, high temperatures, and harsh radiation conditions. Although most simulation work has been conducted using the terrestrial AM1.5 spectrum, AM0 high concentrators are of great importance to realistic satellite missions. Though III–V multijunction solar cells are currently the norm in space applications, their efficiency under extremely high solar concentration ratios is not yet optimized to support future space missions. This work designs and numerically optimizes a GaAs VTJ solar cell using SILVACO ATLAS software (5.40.0.R). In the optimization, the thickness of the front and back layers, as well as the doping profile within the emitter, base, and tunnel junction regions, were adjusted. The important PV semiconductor attributes, including the short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), and efficiency (η), were examined over a concentration factor ranging between 1 and 10,000 suns. The efficiency of the optimized VTJ solar cell increased from 20.4% at 1 sun to 26.0% at 10,000 suns. This is mainly due to the near-linear increase in Jsc and the stable FF, which remains between 87% and 89%. In addition, the solar cell shows a steady increase in Voc between 1.85 V and 2.33 V. An optimized GaAs VTJ solar cell design is a promising component in future space missions, which require high power density and are suited to operating under high heliocentric orbits, such as in the Parker Solar Probe and solar-electric propulsion systems.

The sizing of rooftop solar energy systems is commonly based on the most visible load or on generic end-use allocations, leading to an inadequate distribution of the limited rooftop area between heat and electricity. This study formalizes the energy audit within a three-level deterministic framework that selects and sizes solar technologies by energy vector: demand is first decomposed by vector; the technology for the thermal vector is then selected through a levelized cost of heat selection ratio ψ, while the photovoltaic system of the electrical vector is sized for self-consumption; and the rooftop area is finally allocated among vectors according to marginal value per unit area. In a 75-bed university residence in Cienfuegos, Cuba, air conditioning is the dominant energy end-use in terms of installed power (accounting for 77% of the connected load), whereas the thermal vector dominates annual energy consumption (domestic hot water: 127,440 versus 76,818 kWh/year for electricity; thermal-to-electric ratio 1.66). Solar thermal technology has been selected for the thermal vector (0.018 versus 0.088 USD/kWhth; ψ=0.21, a robust value according to the sensitivity analysis), and the marginal value (≈111 versus ≈32 USD/(m2·year)) allocates 104 m2 to solar thermal collectors and 134 m2 to photovoltaic energy, thereby reversing the original design that prioritized photovoltaic energy. The resulting portfolio achieves an annual solar fraction close to 100% in both vectors on an energy balance basis, avoids 86.5 t of operational CO2 emissions per year, and combines a simple payback of 1.1 years (solar thermal) with a net present value of 55,327 USD and an internal rate of return of 28% (photovoltaics). The sizing decision is shown to be robust to the choice of statistical design criterion (median, mean, P90, maximum), and none of the three framework decisions is reversed under ±30% parameter variations. By replacing the subjective weightings of multi-criteria methods with observable economic criteria, the framework provides a replicable and auditable design protocol.

Studying photovoltaics in engineering and science curricula is a time-consuming and expensive activity. This is why simulations are used, which, however, do not allow direct observation of the physical phenomena occurring in the devices. Based on an array of LEDs (light-emitting diodes) in photodetection mode, a low-cost educational platform for simulating photovoltaic systems including bypass and blocking diodes was developed. This allowed for the experimental characterization of the system’s I-V and P-V characteristics, obtained with a variable-load method under controlled lighting, as well as the qualitative reproduction of key photovoltaic phenomena such as mismatch and bypass diode activation. Additionally, the system allows for quantitative analyses starting from a reference value of 25 μW, obtained under full illumination conditions. This value will inevitably decrease as the platform’s operating conditions worsen, intentionally generated to study the behavior of the platform. Although the method does not provide a representation of the real photovoltaic field, it provides a simple and low-cost tool for the experimental study of photovoltaic behavior. The paper has been conceived for educational purposes, oriented towards laboratory teaching activities.

This study experimentally compares the thermal performance of two vertically oriented passive solar water heating systems under arid outdoor conditions in Gabes, Tunisia: a thermosiphon solar water heater (TSWH) and an integrated solar water heater (ISWH). Both prototypes were installed side by side, facing south, with identical collector areas and the same climatic exposure. Experiments were conducted over two consecutive clear-sky days under no-load and load conditions. The systems were evaluated in terms of water-temperature evolution, thermal stratification, thermal efficiency, heat-retention behaviour, overall heat-loss coefficient, and useful hot-water production. The results showed that the ISWH achieved higher peak water temperatures, reaching 59.8 °C and 57.5 °C during the two test days, compared with 48.55 °C and 53.65 °C for the TSWH. The ISWH also showed higher peak thermal efficiencies of approximately 49% and 48%, while the TSWH reached approximately 41% and 40%. Under hot-water extraction conditions, the ISWH delivered about 25 L of usable hot water at 45 °C, compared with 19 L for the TSWH. However, the TSWH exhibited better thermal retention, with a lower overall heat-loss coefficient of 1.763 W/m2K compared with 2.38 W/m2K for the ISWH. These findings demonstrate a clear trade-off between rapid daytime heat capture and non-solar heat preservation. The ISWH is more suitable for applications requiring higher daytime hot-water production, whereas the TSWH is preferable when improved heat retention after solar input decreases is required.

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.

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.

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.