Solar

Hydropower-dependent electricity systems, such as Ecuador’s, face critical supply disruptions during droughts: a vulnerability exemplified by the 2024 power outages. This study assesses the technical, economic and environmental feasibility of a 200.84 MWp grid-connected solar photovoltaic (PV) plant proposed for the Pacific Refinery site in Manabi, Ecuador, as a strategy to diversify the energy matrix and reduce hydrological risk. Using site-specific solar resource data (4.65 kWh/m²/day) and PVSyst simulations, the plant achieves an annual energy production of 295 GWh with a performance ratio (PR) of 85.3%. A discounted cash flow analysis over 25 years, assuming a 7% discount rate and an electricity price of 60 USD/MWh, yields a net present value (NPV) of 104.9 MUSD, an internal rate of return (IRR) of 62.2%, and a levelized cost of energy (LCOE) of 14.5 USD/MWh, well below current industrial tariffs in Ecuador. Sensitivity analysis confirms project viability under ±15% variations in investment cost, energy price, and solar resource. Over its lifetime, the plant avoids 1.83 Mt of CO₂ emissions, supporting national decarbonization goals. The results demonstrate that large-scale PV deployment in high-radiation, low-latitude regions can be highly profitable and contribute to energy sovereignty in hydropower-dependent systems. Furthermore, this study provides a replicable model for repurposing unused industrial land for renewable energy generation, offering actionable insights for policymakers and investors in developing economies. Full article

The increased deployment of photovoltaic (PV) technology has led to an increased demand for grid-balancing capacity owing to growing short-term variability and forecast uncertainty. Simultaneously, higher PV penetration can lead to daytime energy market oversupply, pushing day-ahead prices toward zero and undermining PV revenues. Against this backdrop, this study investigated a market participation paradigm in which PV power plants supply reserve power themselves while actively absorbing their own uncertainty, rather than merely relying on balancing the services provided by external resources. We propose a risk-aware framework that classifies solar irradiance prediction errors into four risk categories using GPV-GSM numerical weather forecast data, translating the inferred risk level into practical bidding rules for balancing market participation. We adopted a hierarchical classification pipeline consisting of sign determination (stage 1, under- vs. overprediction), followed by degree determination (Stages 2 and 3), implemented with a multi-layer perceptron. To enhance class separability and reduce features, we introduced a stage-wise area under the curve (AUC)-based feature selection and compared AUC-selected and all-features settings under identical training conditions. The proposed strategies substantially reduce shortage events compared with directly using the original predictions as bids, although they increase surplus energy. The AUC-based model achieves comparable imbalance evaluation results, indicating that the selected features are sufficient for practical bidding support. Full article

Grid-connected photovoltaic–energy storage systems (PV-ESSs) enhance electricity reliability and lower energy costs in emerging markets. However, their commercial viability under multi-objective optimization remains under-quantified. This study offers a techno-economic and financial analysis of PV-ESS setups optimized with the Non-Dominated Sorting Genetic Algorithm II across Nigeria, South Africa, and India. The best systems feature 1.3–1.5 MW of solar capacity and 2.5–2.9 MWh of lithium-ion batteries. Results show unsubsidized levelized energy costs of USD 0.061–USD 0.064/kWh, achieving 27–35% savings compared to grid tariffs. Battery storage accounts for 67–76% of total capital costs, making battery expenses the key economic factor. Financial analysis reports net present values of USD 238,000–USD 522,000, internal rates of return of 13.7–15.8%, and discounted payback periods of 7.9–9.2 years. Monte Carlo simulations indicate an 83.4–100% probability of a positive net present value. Sensitivity analysis highlights grid tariffs and battery costs as major influences. Revenue diversification through grid services, capacity credits, and demand response can boost net present value by up to 35%. Overall, optimized PV-ESS projects can be commercially viable in emerging markets with suitable tariffs, financing, and revenue strategies. Full article

The intermittent nature of photovoltaic power generation makes short-term forecasting critical for grid management. In this study, a hybrid model combining a one-dimensional convolutional neural network (1D-CNN), Long Short-Term Memory (LSTM), and multi-head attention (MHA) mechanism was developed and evaluated on a 300-day dataset at 15 min (H1) and 60 min (H4) forecast horizons. The model generates prediction intervals with 80% nominal coverage using a quantile regression approach trained with the pinball loss function. The hyperparameters were determined through Optuna-based Tree-structured Parzen Estimator (TPE) optimisation. The LSTM, 1D-CNN-LSTM, LSTM-MHA, and 1D-CNN-LSTM-MHA models were compared under the same experimental setting. The 1D-CNN-LSTM-MHA model achieved the best deterministic performance at both forecast horizons. At the H1 horizon, R² = 0.9370 and nRMSE = 7.13% were obtained, whereas at the H4 horizon, R² = 0.9327 and nRMSE = 7.37% were achieved. In the probabilistic evaluation, this model produced the lowest PINAW and Winkler score values. In the statistical comparison, the performance differences in the 1D-CNN-LSTM-MHA model relative to the LSTM reference model were statistically significant in the DM-MAE, DM-RMSE, Clark–West, and Fisher-Z tests at both horizons. The statistical results indicate that the contribution of the attention mechanism becomes more evident when combined with the convolutional component, whereas adding attention alone to the LSTM did not produce a statistically significant improvement. This study provides a comparative evaluation framework for short-term PV active power forecasting by combining probabilistic forecasting through quantile regression with statistical model comparison. Full article

Against the backdrop of the global energy transition and China’s dual-carbon strategy, the photovoltaic (PV) industry is entering a new stage of large-scale, intensive development, where efficiency improvement and cost control in module encapsulation have become the core of industrial competition. To address the drawbacks of traditional silicone plate laminators—frequent consumable replacement, high maintenance costs, and poor adaptability to dual-glass module encapsulation—this paper proposes a flat-plate laminator technical scheme. By replacing flexible silicone plates with rigid pressure plates and optimizing pressure transmission paths and sealing structures, we achieved efficient, low-cost lamination. We first compared the working principles of flat-plate and silicone plate laminators, completed the structural design of five core modules with an optimized rigid platen and annular silicone sealing system, developed a modular retrofitting scheme for existing equipment, and verified performance via engineering tests. Tests show that the retrofitted equipment achieves a module thickness deviation ≤ ±0.06 mm, a product yield of 99.88%, annual cost savings of USD 342,000 per unit, and a 0.61-year investment payback period. This work provides theoretical support and an engineering reference for technical innovation in PV module encapsulation equipment, with significant promotion and application value. Full article

The substrate plays a critical yet often underappreciated role in determining the performance, stability and manufacturability of photovoltaic devices. While conventional glass and polymer films have enabled the rapid development of solar technologies, emerging applications such as building-integrated photovoltaics, wearable systems and large-area conformal devices demand the use of non-conventional substrates, including ceramics, metals, paper, textiles and elastomeric materials. This review provides a comprehensive analysis of the current state of the art of non-conventional substrates for photovoltaic technologies, with particular emphasis on the interplay between material properties, surface chemistry and deposition processes. These substrates introduce distinct mechanical, thermal and interfacial constraints that fundamentally alter thin-film growth, defect formation and device reliability. Key challenges such as porosity, roughness, thermal transport limitations and outgassing are discussed in relation to nucleation, film continuity and interfacial stability. The role of substrate-dependent effects in both chemical and physical deposition techniques is critically examined, highlighting cases where conventional processing approaches are insufficient. Representative device demonstrations are analyzed to illustrate how substrate selection influences performance and integration strategies across different photovoltaic platforms. Finally, common limitations and emerging opportunities are identified, emphasizing the need for the co-design of substrates, materials and processing routes. This work establishes a unified framework to guide the development of next-generation photovoltaic devices on unconventional substrates. Full article

Chalcopyrite-based thin-film solar cells have great potential for various applications, such as top or bottom cells in tandem devices, in addition to their use as standard single-junction modules due to their tuneable bandgap energy. A bandgap energy E_g > 1.5 eV should be targeted to realize a wide-bandgap top cell, e.g., by increasing the [Ga]/([Ga] + [In]) (GGI) ratio in Cu(In,Ga)Se₂ (CIGS) cells to the range of 0.7–1. A second approach is targeting the second theoretical efficiency maximum at a little lower E_g = 1.34 eV with a GGI around 0.6 for high-efficiency single-junction applications with reduced electrical losses. An industry-relevant (Ag,Cu)(In,Ga)Se₂ (ACIGS) co-evaporation process for wide-bandgap cells fabricated with GGI ratios above 0.6, with moderate [Ag]/([Ag] + [Cu]) (AAC) ratios < 0.1 and in-line RbF-PDT, was established on molybdenum-coated soda-lime glass substrates. Both measures, Ag alloying and RbF-PDT, can increase power conversion efficiency (PCE) mainly due to improved open-circuit voltage (V_OC). In addition, Ag addition can increase fill factor (FF)_, leading to an increase in the PCE for cells with GGI > 0.6 compared to Ag-free reference cells. (Zn,Mg)O, either with a [Mg]/([Mg] + [Zn]) ratio of 0.15 or 0.25, is a good option as high-resistive layer replacing the commonly used i-ZnO in combination with a CdS buffer. Our best ACIGS wide-bandgap solar cells with RbF-PDT and Zn_0.85Mg_0.15O (without anti-reflective coating (ARC)) from various experimental campaigns show a PCE of 12.7% (E_g = 1.50 eV), and with a slightly reduced E_g of 1.45 eV a PCE of 15.5%, with V_OC of 933 mV (V_OC deficit of 517 mV), and a good FF of 73.2%. In the case when the bandgap is significantly lowered to 1.34 eV (GGI = 0.61), to the second theoretical efficiency maximum, we achieved a PCE of 18.2% with ARC for an Ag-free CIGS cell with RbF-PDT. For this cell with a CdS/i-ZnO buffer system the V_OC deficit is 480 mV, and the FF is 78.1%. Full article

The rapid electrification of road transport has increased interest in distributed energy strategies that reduce grid demand and support decarbonization. Vehicle-integrated photovoltaics (VIPV), including vehicle-applied photovoltaic configurations (VAPV), can generate electricity directly on the vehicle. This systematic review examines peer-reviewed VIPV literature published between 2015 and 2026, focusing on the distinction between theoretical photovoltaic generation and practically usable energy. A Scopus search conducted on 2 May 2026 identified 196 records, of which 88 studies were included after screening against predefined criteria. Due to heterogeneity in vehicle types, climates, technologies, modeling assumptions, and reported metrics, no meta-analysis was performed. Instead, the review applies a multi-layered framework covering climate, geometry, thermal effects, electrical mismatch, battery state-of-charge interactions, fleet-scale modeling, economics, and life-cycle implications. The evidence shows that VIPV is technically feasible and can deliver measurable energy yields, especially in high-irradiance regions and vehicles with favorable daytime parking exposure. However, useful contribution depends strongly on curvature losses, dynamic shading, electrical configuration, SOC limits, charging behavior, seasonality, and vehicle energy demand. Therefore, VIPV is best understood as a context-dependent supplementary energy strategy rather than a transformative standalone solution. Its strongest value lies in specific vehicle classes, climates, and usage patterns where on-board generation can reduce charging demand, support operational resilience, or improve distributed self-consumption. The review also proposes minimum reporting requirements for future studies, including annual energy yield, Wh/km contribution, PV area or capacity, mileage assumptions, SOC modeling, and curtailment treatment. The review was not formally registered, and no formal risk-of-bias or certainty assessment was applied. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships. Full article

Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool benefits from a refined numerical evaluation of ground-reflected irradiance performed through a view-factor-based cell-level approach within a realistic three-dimensional (3D) Sun-module-shadow geometry. This allows capturing both vertical and lateral nonuniformities in the irradiance distributions over the module surfaces, which are neglected in conventional module-level models. The irradiances incident on the cells are subsequently supplied to a circuit-based block, operating with a cell-level granularity as well, which computes the I–V characteristics and the maximum power point (MPP) at selected time instants. Simulations performed on a simplified tool variant assuming uniform albedo irradiance show that this approximation leads to a non-negligible overestimation of power output. An extensive comparison against state-of-the-art tools, including the previous version of our framework, allows us to conclude that the proposed method is especially advantageous for standalone modules or short-row configurations under medium-to-high albedo conditions. Moreover—like its previous version—the tool can handle a large variety of detrimental effects, namely, partial architectural shading, localized snow coverage, bird droppings, and faulty cells. Additionally, a non-zero elevation from the ground can be effectively described. It is also found that south-oriented 30°-tilted bifacial modules suffer from appreciable albedo-induced mismatch losses on the rear surface during summer under medium-albedo conditions, whereas vertically-mounted West- and East-oriented configurations are less affected by such losses. Experimental validation confirms the accuracy of the proposed framework. Full article