Search Results (1,599)

Hot-spots represent a significant failure mechanism in photovoltaic (PV) modules, typically attributable to electrical mismatching. However, thermo-optical degradation of the encapsulant, including discoloration and delamination, can both trigger and amplify mismatch by inducing localized optical losses and temperature rise. The present paper proposes a compact circuit-level electro-thermal–optical model that explicitly captures the short-term closed-loop interaction between mismatching, cell temperature, and temperature-dependent optical properties. The photogenerated current is formulated as a function of irradiance, cell temperature, and encapsulant degradation, enabling dynamic feedback between heating and optical losses. Numerical simulations are carried out on a commercial 40-cell PV module under four representative operating static scenarios. The results demonstrate that, even in the absence of shading, optical degradation can generate multimodal P–V characteristics, drive cells into reverse bias, and produce hot-spots. When optical degradation coexists with irradiance mismatch, the feedback loop significantly amplifies mismatching and shifts the maximum power point toward thermally unsafe operating conditions. These findings demonstrate that maximizing instantaneous power does not necessarily maximize lifetime energy yield, underscoring the need for thermal-aware MPPT strategies and providing a practical framework for early detection of thermo-optical faults in PV modules. Full article

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs). Full article

As substantial incorporation of variable renewable generation technologies, particularly wind and photovoltaic systems, becomes more common, the complexities of power supply and demand characteristics are increasing, making it essential to conduct a detailed power and energy balance analysis. Aiming at regional power systems with multi-source structures and internal transmission interface constraints, this paper proposes a power and energy balance analysis method that considers demand-side carbon emissions. First, a closed-loop mechanism of “carbon signal–load response–balance optimization” based on nodal carbon potential (NCP) is constructed. In this framework, NCP is utilized to generate carbon signals that guide the active response of flexible loads, which are subsequently integrated into the coordinated optimization of power and energy balance. Second, a power and energy balance optimization model adapted to multi-source structures is established, where transmission power limits between zones are directly embedded into the constraint system, overcoming the defects of traditional heuristic methods that require repeated iterations to correct interfaces. Finally, an improved hybrid solution strategy for large-scale balance analysis is designed, significantly reducing the variable scale through the aggregation of similar units within zones. Case studies show that this method can effectively guide the load to shift toward low-carbon periods and nodes, significantly reducing total system carbon emissions and improving renewable energy consumption while ensuring power and energy balance. Full article

Photovoltaic-storage direct current (DC) flexible (PEDF) systems are susceptible to DC bus voltage disturbances, with the constant power load (CPL) characteristics further exacerbating the risk of system instability. To address these challenges, a collaborative control scheme integrating distributed consensus and demand-side response (DSR) based on a consensus protocol is proposed in this study. A fully distributed control architecture is constructed, wherein the upper layer achieves power coordination through voltage deviation of parallel DC/DC converters and neighborhood interaction, whilst the lower layer dynamically optimizes inter-unit power allocation via the DSR mechanism. Distributed state estimation (DSE) is incorporated to enhance voltage control accuracy. Simulations conducted in the MATLAB (R2022a)/Simulink environment demonstrate that the proposed strategy enables rapid stabilization of bus voltage under load step changes and photovoltaic fluctuation scenarios, with system disturbance rejection capability being effectively enhanced. The effectiveness of the approach in maintaining stable system operation and optimizing power distribution is validated. The results indicate that the voltage deviation of the PEDF system remains below 2% under compound disturbances, with the steady-state error being controlled within 2%. The proposed control strategy, through the integration of the power DSR mechanism, effectively improves the system’s anti-disturbance capability. Compared with conventional droop control methods, which typically result in voltage deviations of 3–5%, the proposed strategy achieves a reduction in voltage deviation of over 50%, demonstrating superior voltage regulation performance. Full article

Organic photovoltaic materials and nonlinear optical materials share inherent commonalities in molecular characteristics—such as strong light absorption, high charge carrier mobility, and tunable energy levels. Therefore, this study selects a bithiophene-fused ring system with photovoltaic application potential as the research subject. Using TTTTB6-2CHO (TB1) and IDTTB6-2CHO (TB2) as comparative molecules, their nonlinear optical properties in the near-infrared region were systematically investigated. Transient absorption spectroscopy results demonstrate that TB1 exhibits strong and persistent excited-state absorption within the spectral range of 650–900 nm, endowing it with excellent two-photon absorption performance (a cross-section of up to 5591 GM at 650 nm) and an ultralow optical limiting threshold (0.00147 J/cm² under 800 nm femtosecond laser irradiation). The findings of this study not only confirm the feasibility of developing nonlinear optical materials from photovoltaic candidate molecules but also highlight the effectiveness of the “thiophene-for-benzene substitution” strategy in significantly enhancing optical nonlinearity. These results provide valuable design principles for the development of multifunctional organic optoelectronic materials, particularly for application scenarios such as laser protection. Full article

The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine learning (ML) algorithms for the detection and classification of AC-side faults (inverter and grid faults) in GCPV systems. We utilized three commonly employed algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), and Artificial Neural Networks (ANNs), to develop fault detection models. These models were trained using a monthly electrical dataset obtained from the AYCEM-GES-GCPV power plant in Giresun, Turkiye, and their performance was rigorously evaluated using classification accuracy, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) analyses. The results demonstrate that the algorithms are highly effective in fault detection, with AUC values consistently exceeding the critical threshold. The obtained accuracies for KNN, LR, and ANN were 0.9826, 0.782, and 0.7096, respectively. These findings emphasize the high effectiveness of ML algorithms, with KNN exhibiting the best performance, for identifying AC-side faults in GCPV installations. While the study focused on AC-side fault detection, subsequent work developed a smart card module to identify complex DC side electrical faults and built a PV array for experimental testing. Full article

The large-scale deployment of electric power wireless private networks (EPWPNs) has significantly increased the number of base stations in substations, transmission corridors, and distribution terminals, leading to rapidly rising electricity expenditure for continuous wireless coverage and power-grid monitoring services. However, the increasing number of base stations deployed across substations and distribution networks has led to rising electricity expenditure, making cost-effective energy supply a critical challenge. To reduce the operating costs of base station clusters and enhance the economic efficiency of power supply, this paper proposes a multimodal power consumption optimization method that coordinates wind energy, solar energy, and energy storage based on user interaction behavior. First, considering user interaction characteristics and the complementarity of multiple energy sources, a dual-layer cellular network architecture consisting of macro- and micro-base stations is constructed. This architecture incorporates grid power purchases, wind power generation, and photovoltaic energy. An optimization model is then developed, which includes both equipment operation constraints and energy interaction constraints. Second, the key factors influencing energy consumption are analyzed using operational research methods. The existence of an optimal solution for the energy consumption function is demonstrated based on the Weierstrass optimization theorem. An energy-saving strategy for base stations under user group access is then derived using Karush–Kuhn–Tucker (KKT) conditions. Through spatio-temporal (ST) dynamic analysis, the coupling relationships among wind power, solar energy, energy storage, and grid electricity purchases are quantified. Based on this analysis, a multimodal cost optimization scheme utilizing dynamic bandwidth allocation is proposed. Simulation results demonstrate that, compared with traditional single-source power supply models and representative existing optimization schemes, the proposed multimodal energy scheduling framework can significantly reduce the operating cost of base station clusters while maintaining communication performance. Full article

This research presents the development of a new Hybrid Operational Strategy model for energy management optimization designed to evaluate the feasibility of implementing hybrid renewable energy modules in ports, aiming to improve their efficiency, sustainability, and innovation. The proposed system integrates photovoltaic, wind, and hydrokinetic energy sources, incorporating electronic components and assessing two energy storage technologies—Pump-as-Turbine (PAT) and battery systems—to determine the most viable solution for practical deployment. The optimization algorithm allows a concurrent refinement process for the power generation data of each renewable source. Four scenarios were analyzed within this optimization framework: two assessing the performance of single modules employing each storage technology individually, and two exploring configurations with multiple modules operating in parallel, either with independent storage units or a single centralized system. Battery storage was identified as the most feasible option based on the optimization outcomes. Considering the demand characteristics and generation capacity of the hybrid module, the configuration yielding the best overall performance consisted of a single module incorporating battery storage, achieving 90% demand coverage and demonstrating economic viability with a Net Present Value (NPV) of 9182.79 € and an Internal Rate of Return (IRR) of 10.88%. Full article

With the increasingly prominent issues of energy shortage and environmental pollution, the development of clean energy materials has become a core topic in the academic community. SnSe, as a material with moderate bandgap, a high light absorption coefficient, and environmental friendliness, has shown broad application prospects in the fields of photovoltaics and thermoelectrics. However, pure SnSe thin films have inherent defects, low carrier concentration, and high recombination rates, which limit their photoelectric conversion efficiency. This article provides a detailed overview of the characteristics of band engineering control technology, defect control technology, and carrier concentration control technology, as well as the improvements in the characteristics of SnSe thin films that they bring. This article systematically reviews the research progress on doping control technology for SnSe thin films characteristics in recent years and analyzes and discusses the differences in typical doping elements on SnSe thin films characteristics, such as optical bandgap and absorption coefficient, and applicable application scenarios, such as photovoltaics, near-infrared/infrared detection, and thermoelectric and flexible optoelectronic devices. Furthermore, the interaction between the doping mechanism of dopants and natural defects, as well as the influence of the structural parameters of doped films on doping efficiency, were analyzed, and a predictive design route for the doping mechanism of SnSe films was proposed. Finally, the influence of different atomic fractions on the characteristics of SnSe thin films was discussed. Low atomic fractions are beneficial for bandgap tuning and absorption enhancement; high atomic fractions can easily introduce phase separation and non-radiative recombination. It is suggested that future researchers can continue to focus on the precise control of atomic fractions, exploration of new element co-doping, and industrial large-scale production applications, providing theoretical guidance for the design and application of SnSe thin films in photothermal devices. Full article

Diamond wire sawing, as the core process for monocrystalline silicon wafering, has gained widespread application in the photovoltaic and microelectronics industries due to its high efficiency and low material loss. This study investigates the cutting mechanism of monocrystalline silicon with (100) crystal orientation under multi-abrasive and multi-scratch conditions using explicit finite element dynamics simulation. It focuses on analyzing the effects of radial spacing and height difference between abrasive grains on surface morphology, cutting force, and residual stress. Based on the Johnson-Holmquist-II (JH-II) constitutive model, a high-precision three-dimensional finite element simulation model was constructed. Simulation results indicate that the spacing and height difference between abrasive grains significantly affect the grain-to-grain coupling, thereby influencing the peak cutting force and the surface damage characteristics of the scratches. To address cutting force and residual stress responses, this study proposes an algorithmic optimization scheme based on a multifactor orthogonal experimental design. The analysis indicates that the optimal parameters—U = 1385 m/min, V = 142°, and W = 6.2 μm—reduce residual stress by 33% and cutting force by 75%. Full article

In order to achieve the goal of carbon neutrality, the installed capacity of photovoltaic (PV) modules has been increasing rapidly. In particular, single-glass PV modules are widely deployed in both utility-scale and distributed PV power generation systems. However, single-glass modules are highly susceptible to internal faults (e.g., direct current arc faults and hotspot faults) and external fire sources (e.g., wildland fires and rooftop fires), which may lead to simultaneous burning of the modules and adjacent combustibles, thereby promoting large-scale fire spread and causing severe economic losses. In this study, a dedicated experimental platform was developed to systematically investigate the fire behavior of single-glass PV modules under exposure to a pool fire. Systematic fire experiments were conducted to investigate the influence of module inclination angle and tempered glass integrity on the burning process, molten dripping flame behavior, and temperature-rise characteristics of single-glass PV modules. The results show that the integrity of the front glass has a pronounced effect on the burning behavior. At the same inclination angle, cracked modules exhibit significantly faster fire growth and higher temperature-rise rates than intact modules, while also being more susceptible to rapid burn-through by the external fire, accompanied by the generation of numerous molten dripping flames. In addition, the module inclination angle has a significant influence on the fire behavior of PV modules. As the inclination angle increases, the fire development rate, temperature-rise rate, and average burning duration of dripping flames all display a non-monotonic trend of first increasing and then decreasing, reaching their maxima at an inclination angle of 15°. These findings provide a theoretical basis for the fire protection design and fire risk assessment of PV power generation systems and are of practical significance for enhancing their operational safety. Full article

In recent years, the installed capacity of renewable energy sources, such as wind power and photovoltaic generation, has been steadily increasing in power systems. However, the inherent randomness and volatility of renewable energy generation pose greater challenges to grid frequency stability. To address this issue, this paper first introduces the Minkowski sum algorithm to map the feasible regions of dispersed individual units into a high-dimensional hypercube space, achieving efficient aggregation of large-scale schedulable capacity. Compared with conventional geometric or convex-hull aggregation methods, the proposed approach better captures spatio-temporal coupling characteristics and reduces computational complexity while preserving accuracy. Subsequently, aiming at the coordination challenge between day-ahead planning and real-time dispatch, a “hierarchical coordination and dynamic optimization” control framework is proposed. This three-layer architecture, comprising “day-ahead pre-dispatch, intraday rolling optimization, and terminal execution,” combined with PID feedback correction technology, stabilizes the output deviation within ±15%. This performance is significantly superior to the market assessment threshold. The research results provide theoretical support and practical reference for the engineering promotion of vehicle–grid interaction technology and the construction of new power systems. Full article

To address the issues of insulation layer damage and conductor exposure in offshore floating photovoltaic systems occurring in shallow marine regions characterized by significant tidal ranges under multi-field coupling effects, an overhead cable laying scheme based on the hybrid pile–floater structure is proposed, while its mechanical response is investigated in this paper. The motion response model of the floating platform, considering wind load, wave load, current load, and mooring load, as well as the equivalent density and mathematical model of the overhead cable are established. The mechanical response characteristics of the overhead cable are analyzed through finite element analysis software. The results indicate that the overhead cable’s mechanical response is influenced by the span length and coupled wind–ice loads. Specifically, the tension exhibits a nonlinear increasing trend, while the deflection shows differential variations driven by the antagonistic interaction between wind and ice loads. The influence of ice loads on the configuration of overhead cables is significantly weaker than that of wind loads. This study provides crucial theoretical support for enhancing the lifespan of the overhead cable. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

To improve DC-bus voltage regulation of bidirectional DC/DC converters in photovoltaic–energy storage DC microgrids, this paper proposes an improved linear active disturbance rejection control (LADRC) strategy based on observation error reconstruction. In conventional LADRC, the linear extended state observer (LESO) is driven solely by the output tracking error, which may lead to weakened disturbance excitation after rapid error convergence and thus degraded disturbance estimation performance. To address this limitation, an observation error reconstruction mechanism is introduced, in which a reconstructed error variable incorporating higher-order estimation deviation information is used to redesign the LESO update law. This modification fundamentally enhances the disturbance-driving mechanism without excessively increasing observer bandwidth, resulting in improved mid- and high-frequency disturbance estimation capability. The proposed method is analyzed in terms of disturbance estimation characteristics, frequency-domain behavior, and closed-loop stability. Comparative simulations and hardware-in-the-loop experiments under typical load and photovoltaic power step variations within the safe operating range demonstrate that the proposed LADRC–PI significantly outperforms conventional PI and LADRC–PI control. Experimental results show that the maximum DC-bus voltage fluctuation is reduced by over 60%, and the voltage recovery time is shortened by approximately 40–50% under the tested operating conditions. Full article