Search Results (150)

To mitigate the impact of photovoltaic (PV) power generation uncertainty on power systems and accurately depict the PV output range, this paper proposes a quantile regression probabilistic prediction model (TCN-QRBiLSTM) integrating a Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM). First, the historical dataset is divided into three weather scenarios (sunny, cloudy, and rainy) to generate training and test samples under the same weather conditions. Second, a TCN is used to extract local temporal features, and BiLSTM captures the bidirectional temporal dependencies between power and meteorological data. To address the non-differentiable issue of traditional interval prediction quantile loss functions, the Huber norm is introduced as an approximate replacement for the original loss function by constructing a differentiable improved Quantile Regression (QR) model to generate confidence intervals. Finally, Kernel Density Estimation (KDE) is integrated to output probability density prediction results. Taking a distributed PV power station in East China as the research object, using data from July to September 2022 (15 min resolution, 4128 samples), comparative verification with TCN-QRLSTM and QRBiLSTM models shows that under a 90% confidence level, the Prediction Interval Coverage Probability (PICP) of the proposed model under sunny/cloudy/rainy weather reaches 0.9901, 0.9553, 0.9674, respectively, which is 0.56–3.85% higher than that of comparative models; the Percentage Interval Normalized Average Width (PINAW) is 0.1432, 0.1364, 0.1246, respectively, which is 1.35–6.49% lower than that of comparative models; the comprehensive interval evaluation index (I) is the smallest; and the Bayesian Information Criterion (BIC) is the lowest under all three weather conditions. The results demonstrate that the model can effectively quantify and mitigate PV power generation uncertainty, verifying its reliability and superiority in short-term PV power probabilistic prediction, and it has practical significance for ensuring the safe and economical operation of power grids with high PV penetration. Full article

As interest in PV installation has spiked in recent years, the need for optimizing several factors of PV performance has become crucial. These are tilt angle and solar cell temperature (taking into account ambient temperature) and their effect on solar insolation for solar photovoltaic (PV) systems. The objective of this study is to achieve the optimal tilt angle and cell temperature accordingly by developing a MATLAB program to reach the target of maximizing the received solar insolation. To achieve this, additional solar angles such as the azimuth, hour, latitude angle, declination angle, hour angle, and azimuth angle need to be calculated. By computing the solar insolation for specific regions of interest, specifically the Gulf Cooperation Council (GCC) countries, the desired results can be obtained. Additionally, the study aims to assess the influence of PV cell temperature on the I–V curves of commercially available PV modules, which will provide insights into the impact of temperature on the performance characteristics of PV cells. By employing a developed model, the study examined the combined collective influences of solar received radiation, tilt angle, and ambient temperature on the output power of PV systems in five different cities. The annual optimal tilt angles were found to be as follows: Mecca (21.4° N)—21.48°, Fujairah (25.13° N)—25.21°, Kuwait (29.3° N)—29.38°, Baghdad (33.3° N)—33.38°, and Mostaganem (35.9° N)—2535.98°. Notably, the estimated yearly optimal tilt angles closely corresponded to the latitudes of the respective cities. Additionally, the study explored the impact of ambient temperature on PV module performance. It was observed that an increase in ambient temperature resulted in a corresponding rise in the temperature of the PV cells, indicating the significant influence of environmental temperature on PV module efficiency. Overall, the findings demonstrate that adjusting the tilt angle of PV modules on a monthly basis led to higher solar power output compared to yearly adjustments. These results underscore the importance of considering both solar radiation and ambient temperature when optimizing PV power generation. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

Accurate parameter estimation of solar cells is critical for early-stage fault diagnosis in photovoltaic (PV) power systems. A physical model based on three-diode configuration has been recently introduced to improve model accuracy. However, nonlinear and recursive relationships between internal parameters and PV output, along with parameter drift and PV degradation due to long-term operation, pose significant challenges. To address these issues, this study proposes a deep learning-based ensemble framework that integrates outputs from multiple optimization algorithms to improve estimation precision and robustness. The proposed method consists of three stages. First, the collected data were preprocessed using some data processing techniques. Second, a PV power generation system is modeled using the three-diode structure. Third, several optimization algorithms with distinct search behaviors are employed to produce diverse estimations. Finally, a hybrid deep learning model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is used to learn from these results. Experimental validation on a 733 kW PV power generation system demonstrates that the proposed method outperforms individual optimization approaches in terms of prediction accuracy and stability. Full article

Solar photovoltaic (PV) plays a crucial role in China’s pursuit of carbon neutrality. Assessing the PV power potential over China is essential for future energy planning and policy making. Surface solar radiation and panel tilt angle are critical factors influencing PV power generation. However, existing solar radiation datasets cannot fully meet assessment needs due to insufficient temporal coverage and limited accuracy, and the impact of panel tilt angles on PV potential is largely overlooked. This study developed a PV power estimation framework to assess the long-term (1980–2019) PV power potential at 609 stations across China, based on reconstructed high-quality solar radiation and optimized tilt angles. The validation of PV power estimates using ground measured outputs from four operational PV power stations indicated a correlation coefficient of 0.67 and a root mean square error of 0.07 for estimated daily capacity factor (CF). The assessment results revealed that the multi-year mean CF of China is 0.149 ± 0.031, with higher potentials in northern provinces and lower in southern provinces. The mean annual CF shows a declining trend of −7 × 10⁻⁴ per decade during 1980–2019, with significant decreases primarily in heavily polluted regions. In addition, we propose an optimal tilt angle estimation model based on diffuse fraction, achieving higher accuracy than previously released models. The estimated optimal tilt angle results in an increase in PV energy yield by 14.9 TWh/year for China compared with latitude-based schemes, based on China’s cumulative PV capacity by 2023 (609 GW). Our findings provide valuable insights for the effective implementation of solar PV projects in China. Full article

Transformerless inverters have received significant attention in solar photovoltaic (PV) applications. The absence of low-frequency transformers contributes to improved efficiency and reduced size compared to other topologies; however, there are concerns about leakage currents. The common ground (CG) connection in PV inverters is an attractive solution to this issue, as it generates a constant common-mode voltage and theoretically eliminates the leakage current. In this context, multilevel CG inverters can eliminate the leakage current while achieving high-quality output voltages. Nonetheless, achieving simultaneous control of the grid current and inner capacitor voltages can be challenging. Furthermore, controlling the capacitor voltages in multilevel inverters requires feedback from measurement sensors, which can increase the cost and may affect the overall reliability. To address these issues, this paper proposes a model predictive controller (MPC) for a CG multilevel inverter with a reduced number of sensors. While conventional MPC uses a classical multi-objective technique with a single cost function, the proposed method avoids the use of weighting factors in the cost function. Additionally, a sliding-mode observer is developed to estimate the capacitor voltages, and an incremental conductance-based maximum power point tracking (MPPT) algorithm is used to generate the current reference. Simulation and experimental results confirm the effectiveness of the proposed observer and MPC strategy. Full article

Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decomposition algorithm based on Improved Density Peak Clustering (IDPC) and the Simplified Hidden Markov Model (SHMM) is proposed to decompose PV generation power and load consumption power from net load power data, providing data support for power demand-side management. First, the Improved Density Peak Clustering algorithm is used to adaptively obtain load power templates. Then, historical power data from PV proxy sites are classified based on weather types, while radiation proxies are used to estimate the historical PV power of the target users. These estimated PV power data are combined with historical load information to derive the parameters of the SHMM under different PV output conditions, thereby constructing the load decomposition objective function. Finally, the net load power data are used to achieve non-intrusive load decomposition and photovoltaic power extraction for households with PV systems; the effectiveness of the proposed algorithm is validated using Apmds datasets and Pecans Street datasets. 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

This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs (PanelTemp, Irradiance, AmbientTemp, Humidity), together with physically motivated-derived features (ΔT, IrradianceEff, IrradianceSq, Irradiance × ΔT). Samples acquired under very low irradiance (<50 W m⁻²) were excluded. Predictors were standardized with training-set statistics (z-score), and the target variable was modeled in log space to stabilize variance. A shallow artificial neural network (ANN; single hidden layer, widths {4–32}) was compared with deeper multilayer perceptrons (DNN; stacks {16 8}, {32 16}, {64 32}, {128 64}, {128 64 32}). Hyperparameters were selected with a grid search using validation mean squared error in log space with early stopping; Bayesian optimization was additionally applied to the ANN. Final models were retrained and evaluated on a held-out test set after inverse transformation to watts. Test performance was obtained as MSE, RMSE, MAE, R², and MAPE for the ANN and DNN. Hence, superiority in absolute/squared error and explained variance was exhibited by the ANN, whereas lower relative error was achieved by the DNN with a marginal MAE advantage. Ablation studies showed that moderate depth can be beneficial (e.g., two-layer variants), and a simple bootstrap ensemble improved robustness. In summary, the ANN demonstrated superior performance in terms of absolute-error accuracy, whereas the DNN exhibited better consistency with relative-error accuracy. Full article

This study evaluates a solar-powered electric mobility pilot implemented at the University of Cuenca (Ecuador), combining two electric vans with daytime charging from a 35 kWp PV microgrid. Real-world monitoring with SCADA covered one year of operation, including efficiency tests across urban, highway, and mountainous routes. Over the monitored period, the fleet completed 5256 km in 1384 trips with an average occupancy of approximately 87%. Energy use averaged 0.17 kWh/km, totaling 893.52 kWh, of which about 98.2% came directly from on-site PV generation; only 2.41% of the annual PV output was required for vehicle charging. This avoided 1310.52 kg of CO₂ emissions compared to conventional vehicles. Operating costs were reduced by institutional electricity tariffs (0.065 USD/kWh) and the absence of additional PV investment, with estimated savings of around USD 2432 per vehicle annually. Practical guidance from the pilot includes aligning fleet schedules with peak solar generation, ensuring access to slow daytime charging points, maintaining high occupancy through route management, and using basic monitoring to verify performance. These results confirm the technical feasibility, economic competitiveness, and replicability of solar-electric transport in institutional settings with suitable solar resources and infrastructure. Full article

This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, and current gaps in simultaneous wind + PV co-generation on a single moving structure are highlighted. Key performance indicators such as power coefficient (C_p), DC ripple, cell temperature difference (ΔT), and levelised cost of energy (LCOE) are defined, and an integrated assessment methodology is proposed based on blade element momentum (BEM) and computational fluid dynamics (CFD) modelling, dynamic current–voltage (I–V) testing, and failure modes and effects analysis (FMEA) to evaluate system performance and reliability. Preliminary results point to moderate aerodynamic penalties (ΔC_p ≈ 5–8%), PV output during rotation equal to 15–25% of the nominal PV power (P_PV), and an estimated 70–75% reduction in blade–root bending moment when the peripheral ring converts each blade from a cantilever to a simply supported member, resulting in increased blade stiffness. Major challenges include the collective pitch mechanism, dynamic shading, and wear of rotating components (slip rings); however, the suggested technical measures—maximum power point tracking (MPPT), string segmentation, and redundant braking—keep performance within acceptable limits. This study concludes that the concept shows promise for distributed microgeneration, provided extensive experimental validation and IEC 61400-2-compliant standardisation are pursued. This paper has a dual scope: (i) a concise literature review relevant to low-Re flat-blade aerodynamics and ring-stiffened rotor structures and (ii) a multi-fidelity aero-structural study that culminates in a 3 m prototype proposal. We present the first evaluation of a hybrid wind–PV rotor employing untwisted flat-plate blades stiffened by a peripheral ring. Using low-Re BEM for preliminary loading, steady-state RANS-CFD (k-ω SST) for validation, and elastic FEM for sizing, we assemble a coherent load/performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm), and spokes (Ø 40 mm), von Mises stresses remain < 25% of the 6061-T6 yield limit and tip deflection ≤ 0.5%·R acrosscut-in (3 m s⁻¹), nominal (5 m s⁻¹), and extreme (25 m s⁻¹) cases. CFD confirms a broad efficiency plateau at λ = 2.4–2.8 for β ≈ 10° and near-zero shaft torque at β = 90°, supporting a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). Cross-model deviations for C_p, torque, and pressure/force distributions remain within ± 10%. This study addresses only the rotor; off-the-shelf generator, brake, screw-pitch, and azimuth/tilt drives are intended for later integration. The results provide a low-cost manufacturable architecture and a validated baseline for full-scale testing and future transient CFD/FEM iterations. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

There is an obvious correlation between the photovoltaic (PV) output of different physical levels; that is, the overall power change trend of large-scale regional (high-level) stations can provide a reference for the prediction of the output of sub-regional (low-level) stations. The current PV prediction methods have not deeply explored the multi-level PV power generation elements and have not considered the correlation between different levels, resulting in the inability to obtain potential information on PV power generation. Moreover, traditional probabilistic prediction models lack adaptability, which can lead to a decrease in prediction performance under different PV prediction scenarios. Therefore, a probabilistic prediction method for short-term PV power based on multi-level adaptive dynamic integration is proposed in this paper. Firstly, an analysis is conducted on the multi-level PV power stations together with the influence of the trend of high-level PV power generation on the forecast of low-level power generation. Then, the PV data are decomposed into multiple layers using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and analyzed by combining fuzzy entropy (FE) and mutual information (MI). After that, a new multi-level model prediction method, namely, the improved dual dynamic adaptive stacked generalization (I-Stacking) ensemble learning model, is proposed to construct short-term PV power generation prediction models. Finally, an improved dynamic adaptive kernel density estimation (KDE) method for prediction errors is proposed, which optimizes the performance of the prediction intervals (PIs) through variable bandwidth. Through comparative experiments and analysis using traditional methods, the effectiveness of the proposed method is verified. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

In this paper, a robust optimized controller is implemented in the photovoltaic generator system (PVGS). The PVGS is composed of individual photovoltaic (PV) cells, which convert solar energy to electrical energy. To optimize the efficiency of the PVGS under variable solar irradiance and temperatures, a maximum power point tracking (MPPT) controller is necessary. Additionally, the PVGS output voltage is typically low for many applications. To achieve the MPPT and to gain the output voltage, an increasing boost converter (IBC) is employed. Then, two issues should be considered in MPPT. At first, a smooth control signal for adjusting the duty cycle of the IBC is important. Another critical issue is the PVGS and IBC unknown sections, i.e., the total system uncertainty. Therefore, to address the system uncertainties and to regulate the smooth duty cycle of the converter, a robust dynamic sliding mode control (DSMC) is proposed. In DSMC, a low-pass integrator is placed before the system to suppress chattering and to produce a smooth actuator signal. However, this integrator increases the system states, and hence, a sliding mode observer (SMO) is proposed to estimate this additional state. The stability of the proposed control scheme is demonstrated using the Lyapunov theory. Finally, to demonstrate the effectiveness of the proposed method and provide a reliable comparison, conventional sliding mode control (CSMC) with the same proposed SMO is also implemented. Full article