Photovoltaic (PV) solar energy systems are key to achieving sustainable and renewable energy goals, yet their energy conversion efficiency remains constrained by environmental variability and hardware limitations. Recent advances demonstrate that integrating Artificial Intelligence (AI) with PV systems can substantially enhance performance across critical functions such as maximum power point tracking (MPPT), energy forecasting, and real time optimisation. For example, reinforcement learning based dual axis solar tracking has achieved up to 98 % tracking efficiency and increases annual energy yield by approximately 35 % compared to fixed tilt systems. AI enhanced MPPT algorithms have been shown to improve energy generation efficiency by up to 7.5 % over conventional methods in simulation studies, while ANN based predictors can achieve nearly 99 % accuracy in dynamic conditions. These results illustrate that AI driven strategies not only improve power extraction under fluctuating irradiance and temperature but also reduce system downtime through predictive maintenance and advanced control. This paper systematically reviews these AI applications and presents simulation analyses comparing conventional and AI based control methods, concluding that intelligent techniques offer significant gains in PV efficiency, reliability, and adaptability, which are critical for scalable renewable energy deployment.

This paper proposes a simple, cost effective and efficient brushless DC (BLDC) motor drive for solar photovoltaic (SPV) array fed water pumping system. A zeta converter is utilized in order to extract the maximum available power from the SPV array. The integration of solar photovoltaic (PV) systems with Brushless DC (BLDC) motors offers an efficient and ecofriendly solution for applications such as water pumping and electric drives. However, improving dynamic performance parameters like starting response, overshoot, and settling time remains a challenge. This paper presents a performance enhancement strategy for a solar PV-fed BLDC motor drive by employing a Fuzzy Logic Controller (FLC) in conjunction with a Zeta Converter. The Zeta converter provides a continuous input current and voltage regulation, making it suitable for solar energy systems. The proposed FLC replaces the conventional PI controller to enhance the dynamic performance, particularly under varying irradiance conditions. Simulation results show that the FLC achieves smoother motor starting, reduced overshoot, and faster settling time compared to the PI controller, validating its effectiveness in renewable-powered drive systems.The proposed water pumping system is designed and modeled such that the performance is not affected under dynamic conditions. The suitability of proposed system at practical operating conditions is demonstrated through simulation results using MATLAB/ Simulink.

The rapid growth of distributed photovoltaic (PV) systems offers a sustainable solution to rural energydemands. However, integrating PV into rural distribution grids presents challenges related to powerquality and grid reliability. Traditional PV site selection approaches often neglect the impact ondistribution reliability metrics, resulting in suboptimal deployments. This paper proposes RuralGridPVO (Rural Grid Photovoltaic Optimization), an optimization modeling framework for PV gridconnection site selection in rural areas, incorporating distribution grid reliability considerations. TheRuralGrid-PVO method integrates the RTS-GMLC synthetic power system model with solar irradiancedata from the NSRDB to simulate realistic rural deployment scenarios. A multi-objective optimizationmodel combines a Genetic Algorithm (GA) with Deep Neural Networks (DNN) for fast reliabilityassessments, optimizing PV placement based on energy yield, voltage stability, power losses, andreliability indices (SAIDI/SAIFI). GA-DNN framework incorporates SAIDI/SAIFI into optimization,outperforming baseline GA, AHP-GIS, and ReliOpt-Hybrid in energy yield, voltage stability, andreliability improvements, though sensitivity to reliability weights requires further exploration. Thispaper proposes RuralGrid-PVO, a comprehensive six-module optimization pipeline for distributedphotovoltaic (PV) site selection in rural grids with a focus on enhancing grid reliability. The pipelineintegrates data acquisition, candidate site identification, capacity estimation via Random Forestregression (R² = 0.93), reliability-aware grid simulation utilizing a Deep Neural Network (DNN) forSAIDI/SAIFI prediction (MAE = 3.8 min/year), multi-objective optimization with a Genetic Algorithm(GA), and final configuration selection. The methodology operates on a hardware/software stackcomprising Python-based ML frameworks and power system simulation tools to ensure reproducibility.Experimental evaluation demonstrates that RuralGrid-PVO improves voltage profiles by 15.3%,reduces energy losses by 12.8%, decreases SAIFI by up to 9.6%, and lowers SAIDI by 39%, significantlyoutperforming baseline site selection methods. These results validate the framework's effectiveness inachieving reliable, energy-efficient rural PV integration.

The growing adoption of photovoltaic (PV) distributed generation (DG) in Nigerian distribution networks introduces operational uncertainty arising from variable solar irradiance and fluctuating load demand. This work develops a probabilistic assessment framework that combines the Three-Point Estimation Method (3-PEM) with a Genetic Algorithm (GA) to evaluate and optimize PV integration in a radial distribution network. Load and PV uncertainties are incorporated into probabilistic load flow analysis to estimate expected voltage profiles and voltage violation probabilities. The GA is employed to identify optimal PV locations and capacities that enhance voltage stability and reduce network stress. The proposed approach was implemented on the Nigerian 11 kV Ayepe-34 bus radial feeder using MATLAB R2022a. Three PV-DG of size 300kW each was used in the simulation. Gaussian normal distribution was used for the stochastic load variation. The results show that Fixed PV locations were buses 14, 24 and 30. The optimal PV buses using GA were buses 17, 18 and 34. Results indicate that GA-optimized PV placement significantly improves voltage performance and lowers the probability of voltage violations compared to fixed and no-PV scenarios. The framework provides an efficient and practical planning tool for renewable energy deployment in Nigerian distribution systems.

Low-cost and high-efficiency innovative technology for solar photovoltaic power generation

Comparative analysis of shallow and hybrid deep learning models for predicting the cooling efficiency of nanofluid-cooled photovoltaic panel across multiple materials.

Abstract Cu 2 MnSnS 4 (CMTS) is regarded as an emerging absorber for thin-film photovoltaic (PV) devices. In this work, CMTS thin films prepared using a low-cost, straightforward solution-based technique were extensively characterized, and the results were correlated with the preparation conditions and corresponding PV performances. X-ray diffraction techniques have been used to study crystallographic structure and microstructural parameters, while energetic band positions were depicted exploiting photoelectron yield spectroscopy and Kelvin probe measurements. The importance of fine-tuning the composition of the starting solution and the beneficial effect of post-deposition treatments were consequently highlighted. A strategy of slow cooling after annealing was studied, resulting in a new record for wet-prepared CMTS, with a champion device yielding 0.97% efficiency 5 months after the first PV measurement. Factors responsible for the typical modest efficiencies of CMTS have been investigated: photoluminescence revealed the presence of intra-gap defects, magnetic characterization revealed room-temperature short-range magnetic ordering, and electrical transport and thermal measurements highlighted a semimetallic-like character, all of which are barriers to achieving high PV performance in CMTS-based devices. Moreover, magnetometry indicates a weakly ordered spin-glass-like state, providing insight into the electronic correlations underlying the observed transport behavior.

This paper proposes an enhanced Finite Control Set Model Predictive Control (FCS-MPC) approach to optimize the performance and efficiency of grid-connected photovoltaic (PV) systems. The novelty of this study lies in applying a two-step forward prediction scheme within the FCS-MPC framework, coupled with optimized cost functions, to improve control accuracy, harmonic reduction, and transient response. A 1MW industrial-scale PV system model, based on the Oued El Kebrit power plant in Algeria, is simulated to evaluate the controller under realistic grid disturbances. Simulation results demonstrate that the proposed strategy improves efficiency from 97.63% to 97.73%, reduces voltage Total Harmonic Distortion (THDv) to 2.08%, and shortens the voltage stabilization time from 0.25s to 0.165s. Furthermore, the method ensures consistent performance during grid faults such as voltage sags and maintains grid code compliance. The proposed FCS-MPC method outperforms conventional strategies, offering a scalable and robust solution for enhancing the energy conversion and stability of large-scale PV systems.

Scaling up solar photovoltaics (PV) is essential for global decarbonization, particularly in China-the world's largest greenhouse gas (GHG) emitter. Despite leading in PV installations, China has yet to widely adopt the more efficient tracking technologies for capturing solar radiation (12% adoption rate), in stark contrast to the United States (90%). To examine the rationale behind this divergence and its consequences, we develop a spatially explicit, integrated model to evaluate and compare tracking and fixed-tilt systems in China-comparing power generation, land use, cost, sustainability, and policy resilience. We find that although single-axis tracking provides electricity gains and appears technically more cost-effective, rising land prices in China could offset its benefits. Land costs increase the levelized cost of electricity by 20% for tracking systems, compared to 8% for fixed-tilt, making the latter cheaper in real-world conditions. Consequently, land-efficient fixed-tilt systems are favored, despite requiring 18 to 26% more panels for the same output-intensifying material demands. Under a 6 PWh target in 2060, current land policies would drive 59% of electricity toward fixed-tilt. Reducing soft land costs could increase the adoption of tracking systems to 63% and reduce installed capacity by up to 8% (219 GW) under the same electricity output, compared with an increasing costs scenario, but would expand land use by 35% or 12.9 thousand km2. Our findings underscore how land economics and policy shape renewable technology deployment. They highlight critical trade-offs between energy yield, land use, and material demand, offering insights for designing more balanced and resilient decarbonization strategies.

Multi-dimensional ecological impacts of utility-scale solar and wind energy across terrestrial ecosystems: A systematic review.

ABSTRACT Photovoltaic‐thermal (PVT) solar collectors offer a promising solution for the co‐generation of electricity and heat. Here, we investigate a spectral‐splitting PVT collector that integrates a selectively‐absorptive hybrid liquid‐solid optical filter (LSOF). The LSOF offers a stable and efficient alternative to conventional nanofluid‐based optical filters for spectral‐splitting PVT collectors. Two photovoltaic (PV) configurations are examined—a silicon (Si) solar cell operated under non‐concentrated sunlight, and a gallium arsenide (GaAs) solar cell operated under concentrated sunlight. A Fresnel lens with a geometric concentration ratio of 100 is employed to focus sunlight onto the LSOF, which selectively absorbs ultraviolet and sub‐bandgap infrared radiation for heat generation. The remaining solar spectrum is transmitted to the PV cells for electricity generation. This configuration enables fluid temperatures of up to 86.8°C, while maintaining the PV cell temperature as low as 38.2°C, demonstrating effective thermal decoupling between the PV and solar thermal absorber. The PV cells have electrical efficiencies of 7.9% for the Si cell and 5.7% for the GaAs cell. Although the efficiency and output heat temperature of the current LSOF‐based PVT collectors remain modest owing to optical losses and elevated temperatures, the system demonstrates the potential of hybrid optical filtering for solar co‐generation.

Abstract Photovoltaic (PV) panels in tropical areas like Bhimavaram, Andhra Pradesh, encounter major efficiency issues due to dust buildup and high temperatures, impacting energy generation. This study examines experimental data from a 975 Wp multicrystalline PV array at SRKR Engineering College’s EEE Department (16.543658°N, 81.495646°E), collected from February 17 to March 13, 2025, under an average irradiance of 800 W/m², with peaks reaching 903 W/m². Results show a 25.44% performance drop from dust and a 3.51% decline at 62°C, equating to a 0.4% loss per degree Celsius above 25°C. To counter this, a low-cost automated sprinkler system is recommended, priced at ₹2000–₹3000, featuring a waterproof temperature sensor, a relay-operated pump, and well-placed sprinklers. Integrated with a rainwater collection and reuse system, it aims to boost performance by 3-5% (potentially up to 15%) while supporting eco-friendly building standards. This approach aids India’s 500 GW renewable energy goal by 2030, providing a scalable solution for tropical regions. Long-term field trials are advised to confirm its effectiveness.

Abstract This study addresses the effects of uneven temperature distribution on the performance of photovoltaic (PV) modules in series, parallel, series-parallel, and parallel-series electrical configurations. This comprehensive and extensive work presents a new mathematical approach that models thermal gradients caused on by environmental or structural factors. In comparison to pure series or parallel connections, the results indicate that hybrid configurations, in particular, series-parallel and parallel-series, show a higher tolerance to temperature mismatches. In large-scale PV installations, where perfect thermal uniformity is uncommon, the results emphasize the significance of thermal management and configuration selection in reducing power loss from localized heating. Experimenatl validation is carried out showing a good agrremeent with the current simulation model.

Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module.

Machine vision based on artificial intelligence technology is being actively utilized to reduce defect rates in the photovoltaic module production process. This study aims to propose a machine vision approach using EfficientNet-B0 for defective photovoltaic module detection. In particular, the proposed approach is applied to the electroluminescence (EL) operation, which identifies microcracks in PV modules by using polarization current. The proposed approach extracts low-level structures and local brightness variations, such as busbars, fingers, and cell boundaries, from a single convolutional block. Furthermore, the mobile inverted bottleneck convolution (MBConv) block progressively transforms defect patterns—such as microcracks and dark spots—that appear at various shooting angles into high-level feature representations. The converted image is then processed using global average pooling (GAP), Dropout, and a final fully connected layer (Dense) to calculate the probability of a defective module. A sigmoid activation function is then used to determine whether a PV module is defective. Experiments show that the proposed Efficient-B0-based methodology can stably achieve defect detection accuracy comparable to AlexNet and GoogLeNet, despite its relatively small number of parameters and fast processing speed. Therefore, this study will contribute to increasing the efficiency of EL operation in industrial fields and improving the productivity of PV modules.

This study introduces a $k$ -step look-ahead active concurrent learning-based dual control of exploration and exploitation (KSLCL-DCEE) framework designed to address the challenges of auto-optimization in systems with unknown references and environments, inherently balancing parameter estimation and optimal reference tracking. The KSLCL-DCEE algorithm incorporates two loops that employ future gradients of the cost function to generate the subsequent control command by looking ahead $k$ -steps: the inner loop generates $k$ -step look-ahead gradients (i.e., estimated reference trajectory), while the outer loop utilizes the gradient at the $k$ th step to generate the dual control commands which act on a general linear system. Active concurrent learning with a modified learning rate in the initial period is introduced to relax the reliance on the condition of persistent excitation and achieve faster convergence. A comprehensive stability analysis of KSLCL-DCEE is provided. The effectiveness and performance of KSLCL-DCEE are demonstrated through numerical studies and applications on photovoltaic (PV) arrays.

Wide-bandgap mixed-halide perovskite photovoltaic modules show strong potential for portable chargers, building-integrated photovoltaics, agrivoltaics, and tandem systems, but large-area processing exacerbates crystallization heterogeneity, surface defects, and halide phase segregation. Conventional spin-coating passivation fails to deliver uniform interfacial control at scale. Here, an industrially inspired solution-soaking quenching technique is introduced, in which hot blade-coated wide-bandgap perovskite films ( ~ 30 cm2) are immersed in cold SrI2/isopropanol. It enables rapid surface reconstruction and uniform surface passivation, enhances photoluminescence uniformity, improves crystallinity, reduces roughness, and stabilizes halides via gradient Sr2+ incorporation. These effects mitigate tensile stress, optimize energy-level alignment, and suppress light-induced phase separation. Methylammonium-free wide-bandgap small-area (0.04 cm2) devices achieve efficiencies up to 22.03%, while a 10.13 cm2 module delivers 20.32% efficiency with excellent operational stability. The method is versatile across wide-bandgap perovskite compositions and enables practical applications including portable chargers, semitransparent modules (18.41% bifacial equivalent efficiency), and >27% efficient all-perovskite tandem windows.

The careful identification of modelling parameters particular to the device under study is necessary for the realistic simulation of a photovoltaic solar cell. Five parameters , , , , and must be established in the case of the single diode model. In general, these values can be determined using analytical or numerical approaches. Although analytical methods are quick and easy to use, the assumptions and simplifications they incorporate to deal with a solar cell's non-linear properties could lead to inaccurate modelling. This paper describes two numerical strategies for solving an equation for a PV cell with a single diode utilizing two appropriate approximations: the Newton Raphson (NRM) and Householder's (SET) algorithms. Two rounds of the nonlinear function are required in the new suggested technique. The suggested algorithm's progression is based on NRM. The major goal of this project is to create a circuit-based simulation model of a photovoltaic (PV) cell that can be used to predict how the electrical behavior of a real cell would change as a function of variables including open circuit voltage, load resistance, and short current. According to the findings, the output is nonlinear in nature and demonstrates that the proposed technique NRM is more convenient to use, efficient, and accurate than the previous numerical approach provided SET. In parallel with numerical optimization techniques, recent advances in nanotechnology have played a significant role in improving photovoltaic (PV) cell performance. Nanostructured materials, such as quantum dots, nanowires, and thin-film nanolayers, enhance light absorption, charge transport, and overall conversion efficiency. The accurate numerical modeling of PV cells, as presented in this study, is essential for understanding and optimizing the electrical behavior of nanotechnology-enhanced solar cells, thereby supporting the development of high-efficiency next-generation photovoltaic systems.

Ag-Sn-S/Se semiconductors, particularly Ag8SnS6 and Ag8SnSe6, have emerged as promising thermoelectric (TE) materials due to their intrinsically low lattice thermal conductivity and favorable electronic transport properties. Owing to their direct and super-narrow bandgaps, these semiconductors also hold significant potential for photovoltaic (PV) applications, especially in near-infrared (NIR) energy harvesting and tandem architecture. This review provides a detailed analysis of the synthesis strategies, crystallographic evolution, phase transition mechanisms, and bandgap modulation in Ag-Sn-S/Se semiconductors. Particular focus is given to the structural adaptability of argyrodite-type compounds, where intrinsic cationic disorder and halogen-assisted anion substitution collectively enable the fine-tuning of electronic transport and lattice dynamics. TE performance is evaluated in terms of carrier mobility and thermal conductivity, highlighting a significant improvement in figure of merit. The review further explores the potential of Ag-Sn-S/Se semiconductors in energy conversion PVs, particularly as photoabsorber layers and counter electrode materials. Despite initial demonstrations, systematic studies on device integration remain limited, highlighting substantial opportunities for future research aimed at optimizing their optoelectronic interfaces and overall PV performance. This review ultimately discusses the potential of Ag-Sn-S/Se semiconductors, emphasizing their tunable properties as being key to next-generation PV and thermoelectric technologies. It highlights the current achievements and unresolved challenges, outlining strategic pathways for future research and device integration.

This paper presents robust energy-demand and renewable power forecasts for the microgrid using deep learning-based forecasting and a metaheuristic-based optimization model. A Long Short-Term Memory (LSTM) is used to model the temporal nonlinear dynamics of the energy datasets. A new Improved Dynamic Arithmetic Optimization Algorithm (IDAOA) is developed to fine-tune LSTM parameters, incorporating inertial weights, a mutation factor, and the triangle mutation operator to balance exploration and exploitation. The model's performance is verified on various datasets, including wind turbines (WT), photovoltaic (PV) systems, load demands, and day-ahead electricity pricing. This work shows that the IDAOA-LSTM model outperforms other strategies. Practically, the Root Mean Squared Error (RMSE) was 0.021 in the forecast of WT power and 0.031 in the case of PV power. The model performs well in predictions, with high coefficient of determination (R²) values (R² ≥ 0.98) throughout all tasks. These findings strengthen the applicability of the proposed method to enhance energy-saving measures while preserving the stable operation of those microgrid (MG) systems.