Search Results (1,767)

Reliable and sustainable electricity supply remains a major challenge for public infrastructure in Sub-Saharan Africa, where frequent grid interruptions and abundant solar resources create favorable conditions for hybrid renewable energy systems. This study presents a comprehensive techno-economic assessment of a hybrid photovoltaic–battery energy storage system–green hydrogen (PV–BESS–H₂) system designed for a public administrative building in Ouagadougou, Burkina Faso. The system was evaluated using an annual building load profile together with locally measured solar irradiance and ambient temperature data. Dynamic simulations were performed in MATLAB/Simulink to assess the technical performance, energy flows, economic viability, and operational behavior of the proposed system. The selected configuration consists of a 77 kWp solar PV array, an 80 kWh lithium-ion battery, a 2 Nm³ h⁻¹ electrolyzer, an 8.4 kg hydrogen storage tank, and a 10.6 kW fuel cell. Annual simulations show that the system generates 140.69 MWh of solar PV electricity and achieves a self-sufficiency ratio of 98.7% while limiting the unmet load to 1.3% of the annual electricity demand. Comparative analysis demonstrates that integrating battery and hydrogen storage substantially reduces the required storage capacities compared with standalone PV–BESS and PV–H₂ configurations. The economic assessment yields a Levelized Cost of Electricity (LCOE) of USD 0.095 kWh⁻¹ and a Levelized Cost of Hydrogen (LCOH) of USD 21.544 kg⁻¹. Sensitivity analysis identifies the discount rate, solar PV investment cost, electrolyzer cost, and project lifetime as the principal drivers of the system’s economic performance. The results demonstrate that the complementary operation of battery and hydrogen storage can enhance the technical and economic performance of renewable energy systems of public buildings in regions with abundant solar resources while providing a practical framework for the design and evaluation of hybrid PV–BESS–H₂ systems. Full article

Accurate modeling of photovoltaic (PV) cell behavior under varying operational conditions is essential for optimizing energy yield and system reliability. This study presents an extended simulation-based methodology for analyzing monocrystalline silicon PV cells using a double-diode model (DDM) with a physics-based, temperature- and irradiance-dependent parameterization. Building on a SPICE-equivalent circuit formulation, the governing implicit DDM equation is solved numerically to regenerate every current–voltage (I–V) and power–voltage (P–V) curve, and all circuit, block and flow diagrams are redrawn as vector-quality figures. Beyond the deterministic analysis, the manuscript introduces two extensions: (i) a quantitative statistical analysis of the influence of temperature (T), irradiance (G) and series resistance $\left(R_s\right)$ on open-circuit voltage, short-circuit current, maximum power and fill factor, using linear/log-linear regression, a multiple linear regression model and a Pearson correlation analysis; and (ii) a machine-learning (ML) validation study in which a random-forest surrogate model is trained on a 600-point physics-consistent synthetic dataset spanning the full $(T, G, R_s)$ operating envelope and evaluated with a held-out test split and 5-fold cross-validation. The surrogate reproduces the DDM outputs with cross-validated coefficients of determination above 0.98 for maximum power, open-circuit voltage, short-circuit current and fill factor, confirming that the DDM response surface is smooth, learnable and suitable for fast surrogate-based design optimization and maximum-power-point-tracking (MPPT) algorithm testing. Simulated outputs at standard test conditions (25 °C, 1000 W/m², AM 1.5) are compared against manufacturer datasheet values, and residual errors are analyzed and attributed to specific modeling assumptions. Full article

Accurate prediction of PV output is critical for optimizing its absorption potential and ensuring the safe, stable, and cost-effective operation of the power grid. Yet, due to the intermittent and stochastic nature of photovoltaic power generation, establishing a highly precise prediction model presents significant difficulties. In this study, a hybrid forecasting framework integrating WCSD, CEEMDAN-FE, IPOA, and Transformer-LSTM is developed to improve PV power forecasting accuracy. Firstly, a new training data sample generation method based on WCSD is developed for determining the historical days having similar meteorological conditions to the predicted day. Secondly, CEEMDAN is employed to decompose original output sequence into an ensemble of components with different amplitudes and frequencies, where they were recombined as new set including a handful of components with low-frequency variation characteristics based on FE index. Thirdly, the IPOA is proposed for the first time, which couples Gaussian mutation and enhanced circle chaotic mapping. Next, the prediction model for each component is formulated by aid of Transformer-LSTM algorithm, the optimal hyperparameter combination of which is determined by IPOA method. Finally, the predicted results are obtained as the sum of individual component predictions. The prediction performance of the designed model is tested and verified via experimental analysis located in Yunnan Province, China and the publicly available Australian DKASC dataset. The empirical findings demonstrate that, in contrast to alternative benchmark models, our developed hybrid prediction model consistently attains superior prediction accuracy. Full article

Digital ceramic printing enables visually versatile, coloured building-integrated photovoltaics (BIPV) for architecturally sensitive contexts. However, printed frontglass patterns attenuate incident light and introduce local shading, increasing hotspot risk and efficiency losses. Designers currently lack integrated methods to evaluate these trade-offs prior to module fabrication. To bridge this gap, we present an end-to-end workflow that predicts the relative efficiency (RE), layer composition, and effective colour of printed PV modules directly from printer-ready RIP files. A physically motivated multilayer printing simulation determines the ink layer surface coverage and resulting RE. Preliminary tests show low bias, with simulated RE deviations within ca. ±5% (mean 0.28%, $\sigma$ = 3.26%). Inverting the simulated RE through multivariate regression yields the layer compositions for a target RE. The effective colour is predicted in $L^{*}{a}^{*}{b}^{*}$ colourspace by a trained neural network with a mean CIE difference $\Delta E_{2000}$ of 1.86 ($\sigma$ = 0.92). Effective colours may be grouped into equificiency classes with an RE difference $\Delta RE$ of 5–10% to support flexible design exploration within target efficiency margins and hotspot mitigation. Three case studies demonstrate the workflow’s applicability across educational, design, and built domains, including a full-scale façade installation. Full article

High levels of photovoltaic (PV) generation in distribution networks create substantial uncertainty and voltage variability, which limits the effectiveness of conventional deterministic distribution network reconfiguration (DNR) strategies. In PV-dominated feeders, rare but severe operating conditions may considerably influence active power losses and voltage stability. To address this challenge, this paper proposes a risk-informed optimization framework for DNR that combines reinforcement learning with probabilistic performance assessment. A Deep Q-Network (DQN) agent is designed to support the selection of feasible radial switching configurations by interacting with the distribution network environment. Throughout the learning process, candidate network topologies are evaluated through radial load flow calculations, while a composite objective function incorporating active power losses and voltage deviation steers the agent toward improved configurations. The training stage is based on deterministic performance indices; however, the final reconfiguration solution is assessed under uncertainty to examine its operational robustness. For this purpose, extensive Monte Carlo simulations are performed to capture the stochastic behavior of PV generation and load demand. Tail-based risk metrics, including Value at Risk (VaR) and Conditional Value at Risk (CVaR), are computed for both loss and voltage deviation indices, providing insight into the performance of the selected configuration under unfavorable operating scenarios. The proposed framework is first validated on the IEEE 33-bus distribution system and then further investigated on the IEEE 69-bus network. The obtained results demonstrate that the proposed DQN-based reconfiguration approach can enhance voltage profiles and reduce power losses under high PV penetration. In addition, the probabilistic analysis identifies meaningful trade-offs between efficiency and voltage robustness, highlighting the importance of considering uncertainty-driven risk assessment in computational decision-making for modern active distribution networks. Full article

This research presents a straightforward and detailed method for calculating the cost of recycling solar panels and the associated economic benefits. The contribution of this research is to estimate the impact of the recycling process on the cost of energy and the payback period. The Full Recovery End-of-Life Photovoltaic (FRELP) method was utilized to assess the PV recycling process. Calculations were made for every 1000 kg of solar panels and converted to calculate the cost and revenue per square meter of panels. Calculations showed that the cost of recycling in Saudi Arabia reached 9.46 $/m² based on the geographical environment, fuel prices, and the various materials used in recycling processes, while the revenue was approximately 24.6 $/m² according to the current prices of materials resulting from the recycling process, especially the price of silver. The study results were applied to a 400 MW solar power plant to determine the feasibility of recycling the energy price and the payback period. The solar power plant was designed using variable-sized solar panels with capacities of 255, 330, and 580 watts. The recycling revenue for the plant with the smaller panels was the highest, being $2.6 M as an annual rate. Full article

Convolutional Neural Networks (CNNs) are fundamental tools for image analysis and recognition in monitoring systems, particularly in photovoltaic (PV) installations, where visual inspection and thermal imaging play crucial roles in anomaly detection and energy optimization. This publication presents a Hybrid Fuzzy Convolutional Neural Network (HFCNN) that integrates a fuzzy dense layer utilizing Ordered Fuzzy Numbers (OFNs) into the CNN architecture. The architecture is additionally validated on the public ELPV benchmark of 2624 electroluminescence images of photovoltaic cells, where the HFCNN with Mean of Maxima defuzzification attains classification quality statistically indistinguishable from a CNN baseline while using a four times smaller dense layer and training two to three times faster. The methodology combines the feature extraction capabilities of traditional CNNs with the uncertainty handling properties of fuzzy logic. Experiments using the MNIST dataset demonstrate that HFCNN with Mean of Maxima (MOM) defuzzification achieves comparable accuracy to standard CNNs while using significantly fewer parameters (75% reduction in the dense layer). This efficiency gain is advantageous for deployment on edge computing devices. This work constitutes a methodological contribution—establishing, for the first time, the feasibility of integrating Ordered Fuzzy Numbers into CNN architectures without requiring expert membership function design. While the current study validates this approach on MNIST, actual photovoltaic applications require dedicated future research on real PV thermal imagery. Nevertheless, the proposed HFCNN framework could potentially support practical photovoltaic energy system applications in detecting panel degradation, performance anomalies, and autonomous decision-making in large-scale PV installations. Full article

Photovoltaic converters operate under varying climatic conditions and non-ideal grid regimes, but factor importance is often assessed either through isolated local metrics or through pooled operating data that hide regime shifts and interaction effects. This study develops a hierarchical framework for sensitivity analysis of operating profiles of grid-connected PV converters under climatic and grid uncertainty. A compact operating-profile formulation is introduced that relates solar radiation, cell and ambient temperature, grid voltage, load, and selected design/control parameters to active power, efficiency, power factor, harmonic distortion, DC bus ripple, clipping behavior, and thermal headroom. The proposed workflow combines local normalized sensitivities for fast ranking around nominal conditions, Morris screening for factor reduction, and Sobol/Saltelli variance-based indices for global prioritization under uncertainty. The framework is demonstrated on a 100 kW synthetic reduced-order benchmark representing a three-phase two-level grid-connected PV inverter with an LCL filter. To clarify the scope of validity, the reduced-order model is cross-checked against switching-level simulations for representative nominal, clipping-prone, high-temperature and grid-stress operating windows. The results show that factor importance is not universal, but depends on the selected KPI, operating regime and uncertainty scenario. In the considered benchmark, grid voltage, cell temperature and equivalent thermal resistance are the dominant total-effect contributors, while the strongest second-order contribution appears between grid voltage and filter inductance under grid-stress conditions. The proposed framework is therefore intended as a reproducible, regime-aware sensitivity workflow rather than as a universal ranking of PV converter parameters. Full article

This paper presents an optimized control strategy that enables a grid-connected photovoltaic (PV) system to operate as a static synchronous compensator (PV-STATCOM) to damp power oscillations in the transmission system, using an arithmetic optimization algorithm (AOA). The key contribution of this work is a synchronized, AOA-optimized multi-mode switching approach that includes standard PV operation, Full STATCOM, and Partial STATCOM with ramp-rate recovery, rather than relying solely on PI-gain adjustment. This is accomplished across the complete pre-fault, fault, and post-fault cycle. Under the proposed strategy, the PV system temporarily curtails its real power output when power oscillations arise following a system disturbance, thereby releasing the full inverter capacity for STATCOM operation and, hence, for oscillation damping. Once the oscillations are damped, the PV system ramps its real power back to the pre-disturbance level; at night, the inverter’s full capacity remains available for damping oscillations. The control scheme is implemented with a set of proportional–integral (PI) controllers whose parameters are tuned with the AOA, and its performance is benchmarked against tuning with the cuckoo search (CS) algorithm. Simulation results demonstrate that the AOA-tuned PV-STATCOM significantly improves damping, reduces oscillation amplitudes, maintains the point-of-common-coupling voltage within the low-voltage ride-through envelope, and keeps the system frequency within grid-code limits, thereby ensuring stable grid operation. Compared to a CS-tuned benchmark, the AOA-tuned design keeps the frequency continuously within the grid code band, settles at nominal 50 Hz, and reduces the maximum voltage overshoot from 20% to 15%. Full article

The rapid expansion of residential photovoltaics (PV) increases the need for long-term evidence on system performance under real operating conditions. This study compared four grid-connected rooftop PV microinstallations (4.69–5.04 kWp) located in the Sandomierz Basin, southeastern Poland, over 2022–2025. Monthly alternating-current production from SolarEdge monitoring was combined with regional sunshine duration and mean air temperature from the IMGW Sandomierz station, providing 192 installation–month observations. Specific yield, capacity factor, a model-based estimate of the performance ratio (PR_AP), Pearson correlations, ordinary least-squares regression, and sensitivity analysis of a documented failure were applied. Mean annual specific yields were 1077.7, 1061.7, 908.6, and 779.6 kWh/kWp for PV-I–PV-IV, respectively, while mean PR_AP estimates were 82.2%, 82.5%, 70.6%, and 65.5%. Sunshine duration was strongly correlated with monthly specific yield (r = 0.830–0.983; p < 0.001), and the combined model explained 88.8% of its variability. Excluding the zero-output failure month of PV-IV increased R² for the sunshine–yield relationship from 0.689 to 0.812 and improved the combined-model fit from 0.888 to 0.921. Greater nominal capacity did not guarantee higher normalized productivity. Regional solar-resource information should therefore be complemented by monitored operational data to support design, benchmarking, fault detection, and local distributed-energy planning. The findings also support SDG 7 by providing evidence for more reliable, locally adapted planning and operation of household photovoltaic systems. Full article

Currently, insulation faults in the DC system of photovoltaic (PV) power stations are handled by a full shutdown strategy of inverters, and fault branch localization relies on manual inspection, resulting in low efficiency and poor accuracy, leading to prolonged unplanned outages and substantial power generation losses. This paper proposes an integrated solution combining high-precision insulation monitoring and intelligent fault line selection, which ensures the reliability of line selection criteria through improved measurement accuracy and achieves automatic fault isolation via optimized line selection strategies. The paper analyzes the mathematical essence of the ill-conditioned measurement equations of the traditional bridge method under severe single-pole grounding faults, establishes a dual-channel heteroscedastic noise model, and utilizes the inherent physical constraint that the sum of the positive and negative pole-to-ground voltages always equals the bus voltage to transform the ill-posed inverse problem into an equality-constrained optimal estimation problem, deriving an analytical solution in the sense of constrained least squares. A collaborative monitoring strategy of “balanced bridge monitoring first, unbalanced bridge precision measurement afterward” is proposed. An automatic fault line selection and isolation algorithm based on sequential branch switching is designed, which leverages the operational characteristic that PV systems allow short-term branch interruption, enabling automatic identification and isolation of faulty branches and automatic restoration of non-faulty branches without installing any leakage current sensors. Experimental results show that under severe fault conditions with a single-pole insulation resistance as low as 22 kΩ, the proposed method limits the error to within 5%; the proposed line selection strategy can complete identification and isolation of all faulty branches within at most two rounds of switching. Full article

Indonesia has significant fisheries potential; however, limited cold chain infrastructure at small-scale fishing ports contributes to post-harvest losses and quality degradation of fishery products. This study presents the design and techno-economic assessment of a modular portable cold storage unit for tuna fisheries integrated with renewable energy systems. The system (7 × 3 × 5 m) uses polyurethane sandwich panels and requires a maximum cooling load of 6.14 kW with peak power consumption of 7.93 kW. The estimated capital cost is approximately USD 34,100, while the operational cost is about USD 198 per cycle. A comparative analysis using the Levelized Cost of Energy (LCOE) method indicates that diesel generators provide the lowest cost at approximately USD 0.56/kWh, whereas standalone photovoltaic (PV) systems exhibit the highest cost at around USD 0.89/kWh. However, hybrid PV systems offer the best balance between cost efficiency and environmental performance by reducing carbon emissions. The results demonstrate that integrating hybrid renewable energy into modular cold storage enhances cold chain reliability, reduces fish losses, and supports sustainable coastal development. This approach contributes to low-carbon fisheries infrastructure and aligns with global sustainability and renewable energy transition goals. Full article

Against the background of green campus development and building decarbonization, photovoltaic (PV) systems in university research buildings involve not only energy performance but also campus character, architectural form, and spatial experience. To address the limited attention paid to such multidimensional adaptability, this study derives candidate indicators through a literature review and practical project analysis, refines them through two rounds of Delphi expert consultation, and determines their relative weights using the analytic hierarchy process (AHP). The resulting evaluation framework comprises five criteria and eighteen indicators covering planning pattern coordination, architectural form integration, building physical environmental performance, landscape character integration, and technical performance and innovation. Architectural form integration received the highest criterion weight (0.240), while power generation efficiency had the highest comprehensive indicator weight (0.142). Applied to the research building clusters at Zhejiang University Zijingang Campus, the framework yielded a comprehensive adaptability score of 87.3/100, corresponding to the excellent level. By translating multidimensional considerations into a unified quantitative result, the framework provides a structured basis for scheme assessment and adaptability classification at the design stage. Full article

Against the backdrop of the dual-carbon goals, a new power system is undergoing rapid construction, and distributed renewable energy is being connected at high density to regional ring networks. This study deeply explores the impact of photovoltaic (PV) power station connection positions on energy distribution and flow, as well as the voltage and energy loss of ring networks. Firstly, it takes the actual 220 kV/500 kV ring network in a certain city in Yunnan Province as the research object, and constructs a high-precision ETAP simulation model. It then systematically explores the mechanism of how PV power connection positions and grid connection penetration rates affect node voltage and line loss and derives the energy loss calculation formula. On this basis, two differentiated operation scenes corresponding to renewable energy output peaks and valleys are established. Through comparative analysis of multiple sets of simulation data, this study reveals the coupling laws among PV output fluctuation, bidirectional reverse power flow, system energy loss, and voltage over-limit. Finally, combined with the operational pain points of existing ring network and distribution network connection modes, this study proposes diversified loss reduction optimization strategies, including coordinated optimization of active and reactive power, and coordinated regulation of PV power and energy storage. The relevant research conclusions and optimization methods can improve the line loss analysis theory for ring networks integrated with PV power, and provide important engineering references for renewable energy planning and design, operation regulation, and loss management for similar mountain power grids. Full article

Lithuania is a success case in the prosumer-driven deployment of distributed solar photovoltaic (PV) systems. Supported by generous investment subsidies and a net-metering scheme, behind-the-meter solar PV capacity expanded at an exponential rate. This study examined how the rapid growth of prosumer-based solar PV generation has affected the broader electricity market, prosumers, and electricity suppliers. Using electricity market data, the research evaluated impacts on electricity market price formation, electricity suppliers’ profits, prosumer costs, and the prosumers’ investment decisions. The results indicate that, during the early stages of deployment, distributed solar PV systems improved market outcomes by reducing average electricity market prices and enhancing consumer welfare. However, as the installed solar PV capacity increased rapidly, structural imbalances emerged. High solar output during low-demand periods contributed to increased electricity market price volatility, including frequent close-to-zero and even negative price episodes, while periods of low solar availability—particularly during windless winter days—were associated with sharp price spikes. These dynamics generated negative revenue streams for electricity suppliers and weakened investment signals for market-based generation projects. While prosumers, who are predominantly middle- and upper-income households, benefited substantially from reduced electricity costs and stable returns, the findings suggest that non-participating consumers, including vulnerable households, may have faced higher electricity costs. The study highlights the need for adaptive support mechanisms and market design reforms to ensure an equitable and investment-friendly energy transition. Full article