Search Results (2,545)

The pressing global transition to sustainable energy has intensified interest in overcoming the efficiency bottlenecks of conventional solar technologies. Hybrid photovoltaic–thermoelectric generator (PV–TEG) systems have recently emerged as a compelling solution, synergistically harvesting both electrical and thermal energy from solar radiation. By converting both sunlight and otherwise wasted heat, these integrated systems can substantially enhance total energy yield and overall conversion efficiency—mitigating the performance limitations of standalone PV panels. This review delivers a comprehensive, systematic assessment of maximum-power-point tracking (MPPT) methodologies specifically tailored for hybrid PV–TEG architectures. MPPT techniques are meticulously categorized and critically analyzed within the following six distinct groups: conventional algorithms, metaheuristic approaches, artificial intelligence (AI)-driven methods, mathematical models, hybrid strategies, and novel emerging solutions. For each category, we examine operational principles, implementation complexity, and adaptability to real-world phenomena such as partial shading and non-uniform temperature distribution. Through thorough comparative evaluation, the review uncovers existing research gaps, highlights ongoing challenges, and identifies promising directions for technological advancement. This work equips researchers and practitioners with an integrated knowledge base, fostering informed development and deployment of next-generation MPPT solutions for high-performance hybrid solar–thermal energy systems. Full article

Bifacial Photovoltaic (bPV) technology is rapidly becoming the standard in the solar photovoltaic (PV) industry due to its ability to capture reflected radiation and generate additional energy. This experimental study analyses the electrical performance of bPV modules under specific installation conditions, including varying heights, module tilt angles (MTA), and surface reflectivity. The methodology combines controlled indoor testing with outdoor experiments that replicate real-world operating environments. The outdoor test setup was carefully designed and included dual data acquisition systems: one with independent sensors and another with wireless telemetry for data transfer from the inverter. A thermal performance model was used to estimate energy output and was benchmarked against experimental measurements. All electrical parameters were obtained in accordance with international standards, including current-voltage characteristic (I–V curve) corrections, using calibrated instruments to monitor irradiance and temperature. Indoor measurements under Standard Test Conditions yielded at bifaciality coefficient $\phi =0.732$, a rear bifacial power gain $BiFi=0.285$, and a relative bifacial gain $BiF{i}_{rel}=9.4\%$. The outdoor configuration employed volcanic red stone (Tezontle) as a reflective surface, simulating a typical mid-latitude installation with modules mounted 1.5 m above ground, tilted from 0° to 90° regarding floor and oriented true south. The study was conducted at a site located at 18.8° N latitude during the early summer season. Results revealed significant non-uniformity in rear-side irradiance, with a 32% variation between the lower edge and the centre of the bPV module. The thermal model used to determine electrical performance provides power values higher than those measured in the time interval between 10 a.m. and 3 p.m. Maximum energy output was observed at a MTA of $0°$, which closely aligns with the optimal summer tilt angle for the site’s latitude. Bifacial energy gain decreased as the MTA increased from $0°$ to $90°$. These findings offer practical, data-driven insights for optimizing bPV installations, particularly in regions between $15°$ and $30°$ north latitude, and emphasize the importance of tailored surface designs to maximize performance. Full article

Current technical approaches find it challenging to reduce hydrogen production costs in combined cooling, heating, and power (CCHP) microgrids integrated with hydrogen refueling stations (HRS). Furthermore, the stability of such systems is significantly impacted by multiple uncertainties inherent on both the source and load sides. Therefore, this paper proposes a two-stage robust optimization for bi-level game-based scheduling of a CCHP microgrid integrated with an HRS. Initially, a bi-level game structure comprising a CCHP microgrid and an HRS is established. The upper layer microgrid can coordinate scheduling and the step carbon trading mechanism, thereby ensuring low-carbon economic operation. In addition, the lower layer hydrogenation station can adjust the hydrogen production plan according to dynamic electricity price information. Subsequently, a two-stage robust optimization model addresses the uncertainty issues associated with wind turbine (WT) power, photovoltaic (PV) power, and multi-load scenarios. Finally, the model’s duality problem and linearization problem are solved by the Karush–Kuhn–Tucker (KKT) condition, Big-M method, strong duality theory, and column and constraint generation (C&CG) algorithm. The simulation results demonstrate that the strategy reduces the cost of both CCHP microgrid and HRS, exhibits strong robustness, reduces carbon emissions, and can provide a useful reference for the coordinated operation of the microgrid. Full article

Accurate data on solar photovoltaic (PV) generation is essential for the effective prediction of energy production and the effective management of distributed energy resources (DERs). Such data also plays a crucial role in ensuring the operation of DERs within modern power distribution systems is both safe and economical. Missing values, which may be attributed to faults in sensors, communication failures or environmental disturbances, represent a significant challenge for distribution system operators (DSOs) in terms of performing state estimation, optimal dispatch, and voltage regulation. This paper proposes a Pattern-Aware Bidirectional Long Short-Term Memory (PA-BiLSTM) model for solar generation imputation to address this challenge. In contrast to conventional convolution-based approaches such as the Convolutional Autoencoder and U-Net, the proposed framework integrates a 1D convolutional module to capture local temporal patterns with a bidirectional recurrent architecture to model long-term dependencies. The model was evaluated in realistic block–random missing scenarios (1 h, 2 h, 3 h, and 4 h gaps) using 5 min resolution PV data from 50 sites across 11 regions in South Korea. The numerical results show that the PA-BiLSTM model consistently outperforms the baseline methods. For example, with a time gap of one hour, it achieves an MAE of 0.0123, an R² value of 0.98, and an average MSE, with a maximum reduction of around 15%, compared to baseline models. Even under 4 h gaps, the model maintains robust accuracy (MAE = 0.070, R² = 0.66). The results of this study provide robust evidence that accurate, pattern-aware imputation is a significant enabling technology for DER-centric distribution system operations, thereby ensuring more reliable grid monitoring and control. Full article

High penetration of distributed photovoltaics (DPV) in distribution networks can lead to voltage violations, increased network losses, and renewable energy curtailment, posing significant challenges to both economic efficiency and operational stability. To address these issues, this study develops a coordinated planning framework for DPV and energy-storage systems (ESS) that simultaneously achieves cost minimization and operational reliability. The proposed method employs a cluster partitioning strategy that integrates electrical modularity, active and reactive power balance, and node affiliation metrics, enhanced by a net-power-constrained Fast-Newman Algorithm to ensure strong intra-cluster coupling and rational scale distribution. On this basis, a dual layer optimization model is developed, where the upper layer minimizes annualized costs through optimal siting and sizing of DPV and ESS, and the lower layer simultaneously suppresses voltage deviations, reduces network losses, and maximizes PV utilization by employing an adaptive-grid multi-objective particle-swarm optimization approach. The framework is validated on the IEEE 33-node test system using typical PV generation and load profiles. The simulation results indicate that, compared with a hybrid second-order cone programming method, the proposed approach reduces annual costs by 6.6%, decreases peak–valley load difference by 22.6%, and improves PV utilization by 28.9%, while maintaining voltage deviations below 6.3%. These findings demonstrate that the proposed framework offers an efficient and scalable solution for enhancing renewable hosting capacity, and provides both theoretical foundations and practical guidance for the coordinated integration of DPV and ESS in active distribution networks. Full article

The integration of photovoltaic (PV) generation into distribution systems supports decarbonization and cost reduction but introduces challenges for secure and efficient operation due to voltage fluctuations and power flow variability. Traditional centralized optimal power flow (OPF) methods require full system observability and significant computational resources, limiting their real-time applicability in active distribution systems. This paper proposes a deep neural network (DNN)-based OPF control framework designed for active distribution systems with high PV penetration under limited measurement availability. The proposed method leverages offline convex chance-constrained OPF (convex-CCOPF) solutions, generated through iterative simulations across a wide range of PV and load conditions, to train the DNN to approximate optimal control actions, including on-load tap changer (OLTC) positions and inverter reactive power dispatch. To address observability constraints, the DNN is trained using a reduced set of strategically selected measurement points, making it suitable for real-world deployment in distribution systems with sparse sensing infrastructure. The effectiveness of the proposed framework is validated on the IEEE 33-bus test system under varying operating conditions. The simulation results demonstrate that the DNN achieves near-optimal performance with a significantly reduced computation time compared to conventional OPF solvers while maintaining voltage profiles within permissible limits and minimizing power losses. Full article

Distributed energy systems have gained increasing popularity due to their plethora of benefits. However, their evaluation in terms of reliability mostly concerns the time frequency domain, and, thus, merits associated with the spatial scale are often overlooked. A recent study highlighted the benefits of distributed production over centralized one by establishing a spatial reliability framework and stress-testing it for decentralized solar photovoltaic (PV) generation. This work extends and verifies this approach to wind energy systems while also highlighting additional challenges for implementation. These are due to the complexities of the non-linear nature of wind-to-power conversion, as well as to wind turbine siting, and turbine model and hub height selection issues, with the last ones strongly depending on local conditions. Leveraging probabilistic modeling techniques, such as Monte Carlo, this study quantifies the aggregated reliability of distributed wind power systems, facilitated through the capacity factor, using Greece as an example. The results underscore the influence of spatial complementarity and technical configuration on generation adequacy, offering a more robust basis for planning and optimizing future wind energy deployments, which is especially relevant in the context of increasing global deployment. Full article

This paper proposes a nonlinear control strategy for a microgrid, comprising a PV generator, wind turbine, battery, solid oxide fuel cell (SOFC), electrolyzer, and a three-phase four-leg voltage source inverter (VSI) with an LC filter. The microgrid is designed to supply unbalanced AC loads while maintaining high power quality. To address chattering and enhance control precision, a super-twisting algorithm (STA) is integrated, outperforming traditional PI, IP, and classical SMC methods. The four-leg VSI enables independent control of each phase using a dual-loop strategy (inner voltage, outer current loop). Stability is ensured through Lyapunov-based analysis. Scalar PWM is used for inverter switching. The battery, SOFC, and electrolyzer are controlled using integral backstepping, while the SOFC and electrolyzer also use Lyapunov-based voltage control. A hybrid integral backstepping–STA strategy enhances PV performance; the wind turbine is managed via integral backstepping for power tracking. The system achieves voltage and current THD below 0.40%. An energy management algorithm maintains power balance under variable generation and load conditions. Simulation results confirm the control scheme’s robustness, stability, and dynamic performance. Full article

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. Full article

The growing deployment of photovoltaic (PV) power plants has made reliable fault detection and classification a critical challenge for ensuring operational efficiency, safety, and economic viability. Faults on the direct current (DC) side, especially during the commissioning phase, can significantly affect power output and maintenance costs. This paper proposes a fault classification methodology for the direct current (DC) side of PV power plants, using the MATLAB/Simulink 2023b simulation environment for system modeling and dataset generation. The method accounts for different environmental and operational conditions—including irradiance and temperature variations—to enhance fault identification robustness. The main electrical faults—such as open circuit (OC), short circuit (SC), connector faults, and partial shading—are analyzed based on features extracted from current–voltage (I–V) and power–voltage (P–V) curves. The proposed classification system achieved 100% accuracy by applying the One-Versus-One (OVO) and One-Versus-Rest (OVR) techniques, using a dataset with 704 samples for one string and 2480 samples for three strings. The lowest accuracies were observed with the OVO technique: 99.03% for 1024 samples with one string, and 97.35% for 880 samples with three strings. The study also highlights the performance of multiclass machine learning techniques across different dataset sizes. The results reinforce the relevance of using machine learning integrated into the commissioning phase of PV systems, with the potential to improve reliability, reduce losses, and optimize the operational costs of solar plants. Future work should explore the application of this method to real-world data, as well as its deployment in the field to support companies and professionals in the sector. Full article

One of the most important applications of solar energy is electricity generation using photovoltaic (PV) panels. Yet, as the temperature of PV modules rises, both their efficiency and service life decline. A common approach to mitigate this issue is cooling with fins, a design that is now widely adopted. However, traditional fin-based cooling systems often fail to deliver adequate performance in hot regions with strong solar radiation. In particular, passive cooling alone shows limited effectiveness under conditions of high ambient temperatures and intense sunlight, such as those typical in Malaysia. To address this limitation, hybrid cooling strategies, especially those integrating both air and water, have emerged as promising solutions for enhancing PV performance. In this study, an experimental and economic investigations were carried out on a PV cooling system combining copper tubes and aluminium fins, tested under Malaysian climatic conditions. The economic feasibility was evaluated using the Simple Payback Period (SPP) method. An outdoor test was conducted over four consecutive days (10–13 June 2024), comparing a conventional PV module with one fitted with the hybrid cooling system (active and passive). The cooled module achieved noticeable surface temperature reductions of 2.56 °C, 2.15 °C, 2.08 °C, and 2.58 °C across the four days. The system also delivered a peak power gain of 66.85 W, corresponding to a 2.82% efficiency improvement. Economic analysis showed that the system’s payback period is 4.52 years, with the total energy value increasing by USD 477.88, representing about a 2.81% improvement compared to the reference panel. In summary, the hybrid cooling method demonstrates clear advantages in lowering panel temperature, enhancing electrical output, and ensuring favorable economic performance. Full article

This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs. Full article

Hybrid Renewable Energy Systems (HRESs) can be an effective and sustainable way to provide electricity for remote and rural villages in Morocco; however, the design and optimization of such systems can be a challenging and difficult task. In this context, the objective of this research is to design and optimize different (HRESs) that incorporate various renewable energy technologies, namely Photovoltaics (PVs), wind turbines, and Concentrating Solar Power (CSP), whereas biomass generators and batteries are used as a storage medium. Overall, 15 scenarios based on different HRES configurations were designed, simulated, and optimized by the HOMER software for the site of Ain Beni Mathar, located in eastern Morocco. Furthermore, the potential CO₂ emissions reduction from the different scenarios was estimated as well. The results show that the scenario including PVs and batteries is most cost-effective due to favorable climatic conditions and low costs. In fact, the most optimal HRES from a technical and economic standpoint is composed of a 48.8 kW PV plant, 213 batteries, a converter capacity of 43.8 kW, and an annual production of 117.5 MWh with only 8.8% excess energy, leading to an LCOE of 0.184 USD/kWh with a CO₂ emissions reduction of 81.7 tons per year, whereas scenarios with wind turbines, CSP, and biomass exhibit a higher LCOE in the range of 0.472–1.15 USD/kWh. This study’s findings confirm the technical and economic viability of HRESs to supply 100% of the electricity demand for rural Moroccan communities, through a proper HRES design. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

As the core component of photovoltaic (PV) power generation systems, PV cells are susceptible to subtle surface defects, including thick lines, cracks, and finger interruptions, primarily caused by stress and material brittleness during the manufacturing process. These defects substantially degrade energy conversion efficiency by inducing both optical and electrical losses, yet existing detection methods struggle to precisely identify and localize them. In addition, the complexity of background noise and other factors further increases the challenge of detecting these subtle defects. To address these challenges, we propose a novel PV Cell Surface Defect Detector (PSDD) that extracts subtle defects both within the backbone network and during feature fusion. In particular, we propose a plug-and-play Subtle Feature Refinement Module (SFRM) that integrates into the backbone to enhance fine-grained feature representation by rearranging local spatial features to the channel dimension, mitigating the loss of detail caused by downsampling. SFRM further employs a general attention mechanism to adaptively enhance key channels associated with subtle defects, improving the representation of fine defect features. In addition, we propose a Background Noise Suppression Block (BNSB) as a key component of the feature aggregation stage, which employs a dual-path strategy to fuse multiscale features, reducing background interference and improving defect saliency. Specifically, the first path uses a Background-Aware Module (BAM) to adaptively suppress noise and emphasize relevant features, while the second path adopts a residual structure to retain the original input features and prevent the loss of critical details. Experiments show that PSDD outperforms other methods, achieving the highest $mA{P}_{50}$ scores of 93.6% on the PVEL-AD. Full article