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Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article

Photovoltaic (PV)/biomass systems offer stable/continuous power by overcoming solar-energy intermittency with biomass dispatchable energy. Considering gaps in the scientific literature, this article sets out to present information on PV/biomass installations and the eco-profiles of different feedstocks for biogas generation. To this end, this article is split into two parts. The first one outlines some key elements of the literature on PV/biomass systems, highlighting factors that determine feasibility and performance. The second one presents the eco-profiles of three feedstocks. The methodology is based on literature review and Life-Cycle Assessment (LCA). Regarding the first part, the results show that the majority of the prior research placed emphasis on techno-economic analysis and the design/modelling of PV/biomass systems, and there is a dearth of LCA studies on PV/biomass installations. As for the second part, the findings demonstrate that, among the feedstocks examined (manure; waste cooking oil; grass), in most categories, animal waste shows the highest environmental impacts. For instance, considering the total impact of these three feedstocks and based on Environmental Product Declaration (EPD), the results indicate that, in many categories, manure surpasses the percentage values of 40%. Grass exhibits minor percentage shares, with the exception of the “Eutrophication” (54%) and “Acidification” (33%) categories. Full article

Perovskite solar cells (PSCs) have emerged as one of the most rapidly advancing photovoltaic technologies of the past decade, progressing from the initial demonstration of 3.8% power conversion efficiency (PCE) in 2009 to certified single-junction efficiencies exceeding 26% and perovskite–silicon tandem efficiencies exceeding 33.9% as of 2024. Their appeal resides in the combination of a broadly tunable bandgap achieved through compositional engineering of the ABX₃ perovskite crystal structure, compatibility with low-temperature solution processing, and the potential for manufacturing costs substantially below those of silicon photovoltaics. However, the translation of laboratory-scale performance to commercially viable modules at industrial throughput remains the central challenge in the field. This review provides a comprehensive and critically organized account of PSC manufacturing, spanning device architectures and material requirements, scalable deposition and coating technologies, charge transport layer integration and interface engineering, process control and crystallization strategies, post-treatment methods, artificial intelligence and machine learning-assisted manufacturing, module fabrication and encapsulation, advanced tandem and flexible device configurations, green chemistry and circular lifecycle strategies, and the critical barriers to commercialization. The review concludes with a strategic assessment of the technological, regulatory, and economic requirements for PSC technology to transition from pilot-scale demonstration to utility-scale deployment. Full article

Molybdenum carbide (MoC) thin films were deposited by dual-target magnetron co-sputtering and investigated as platinum-free counter electrodes (CEs) for dye-sensitized solar cells (DSSCs). The effects of Mo target power and film thickness on structural evolution, electrical transport properties, and photovoltaic performance were systematically examined. Raman analysis revealed progressive modifications in the carbon bonding structure, accompanied by variations in the G-band position and an overall reduction in the I_D/I_G ratio. These structural changes were correlated with increased hardness, reduced electrical resistivity, and decreased surface wettability, indicating improved structural integrity and electrical transport characteristics of the MoC films. The optimized films exhibited a resistivity as low as 2.04 mΩ·cm and improved charge-transport behavior. DSSCs employing the optimized MoC CEs achieved a maximum power conversion efficiency of 4.13%. The photovoltaic performance trends were consistent with the evolution of the electrical transport properties of the MoC thin films, suggesting a close relationship between electrode structure, charge transport, and device operation. The results demonstrate that sputtered MoC thin films are promising functional materials for Pt-free DSSC CEs and provide insight into structure–transport–performance correlations relevant to sustainable photovoltaic energy-conversion systems. Full article

Group III-nitride semiconductors (GaN, AlN, InN) serve as foundational materials for modern optoelectronics, high-frequency microelectronics, and next-generation energy harvesting devices. However, traditional high-temperature epitaxy (>700 °C) introduces severe thermal stress, high dislocation densities, and fundamental incompatibility with flexible substrates or CMOS back-end-of-line (BEOL) processes. Plasma-enhanced atomic layer deposition (PEALD) provides a disruptive, ultra-low thermal budget (<300 °C) pathway for atomic-scale precision growth and conformal coating. This review systematically summarizes recent frontiers in PEALD-synthesized Group III-nitrides and 2D/3D polar heterostructures. First, we dissect the microscopic nucleation kinetics, surface bond reconstruction, and impurity suppression mechanisms across diverse substrates, including Si, sapphire, quartz, metals, and flexible polymers. Next, we highlight 2D template-assisted van der Waals epitaxy on graphene and MoS2, and elucidate polarization-driven dipole interactions and band alignment engineering at 2D/3D polar interfaces (e.g., α-In₂Se₃, Janus MoSSe). Furthermore, we comprehensively discuss innovative applications in advanced photovoltaics (as electron transport and passivation layers in perovskite and quantum dot-sensitized solar cells), silicon-based microcavity lasers, high-electron-mobility transistors (HEMTs), and flexible multimodal sensors. Finally, key technological challenges—including the low-thermal-budget paradox, wafer-scale uniformity, and deposition throughput—are addressed alongside future perspectives in area-selective ALD and neuromorphic computing, presenting a cohesive blueprint from underlying physics to macroscopic system integration. Full article

Provincial power systems must decarbonize while sustaining rapid demand growth and improving air quality; yet, few integrated assessments separate the contributions of renewable expansion and carbon capture, utilization, and storage (CCUS) for China’s less-developed regions. We apply the Low Emissions Analysis Platform (LEAP) to model Guangxi’s power system to 2060 under four scenarios—Reference (BAS), Renewable-driven (RES), CCUS-intensive (CCS), and an Integrated comprehensive-policy scenario (ICS). Under ICS, solar photovoltaic generation rises from 38 TWh in 2025 to 408 TWh in 2060 (close to half of all generations), non-fossil capacity grows by about 730%, and power-sector CO₂ falls by roughly 95% relative to BAS. A counterfactual decomposition shows that CCUS provides about 75% of the CO₂ reduction along the coal-retaining CCS pathway but only about 5% along the renewables-led ICS pathway and reduces neither SO₂ nor NO_x. The air-quality co-benefits—up to 82%, 78%, and 73% lower SO₂, NO_x, and PM_2.5 than BAS—arise mainly from renewable substitution, which also abates carbon more cheaply (about 120–190 versus 300 CNY t⁻¹ CO₂) while sharply raising flexibility needs. Once avoided fuel and carbon-market costs are included, the renewables-led pathways become net cost-saving on a full-system basis and yield a monetized air-quality health co-benefit roughly twice that of the CCUS-intensive route. The findings give policy-relevant guidance for sustainable low-carbon transitions in Guangxi and comparable emerging regions. Full article

As the most critical component of organic solar cells (OSCs), the morphology of the active layer directly dictates the photovoltaic performance of the devices. Recent studies have demonstrated that tailoring the active layer morphology using solid additives is a facile and effective strategy to boost the performance of OSCs. Herein, we design and synthesize a novel solid additive, 5-bromobenzo[b]thiophene (5-BrBT), by introducing a bromine substituent onto the common benzothiophene unit. It is found that 5-BrBT optimizes the active layer formation process by effectively prolonging the nucleation time, which facilitates more controllable molecular nucleation and subsequent crystal growth during the pre-aggregation stage, leading to a more ideal donor-acceptor phase distribution. Furthermore, the binary PM6:L8-BO organic solar cells, fabricated with the incorporation of 5-BrBT, exhibit superior charge transport properties and exciton-generation efficiency, along with significantly suppressed charge recombination behaviors. Consequently, the 5-BrBT-treated binary PM6:L8-BO-based OSCs achieve an outstanding power conversion efficiency (PCE) of up to 19.44%, accompanied by simultaneous enhancements in short-circuit current density (J_SC) and fill factor (FF). This work provides a promising optimization strategy for achieving ideal nucleation behaviors during the bulk-heterojunction (BHJ) film processing via solid additives, which is expected to promote the development of more efficient OSCs. Full article

It is well-known nowadays that the usage of conventional opaque photovoltaics in agricultural practices has negative effects on crops growth, mainly due to the shading effect they cause. On the other hand, most of the emerging semi-transparent solar cells do not demonstrate the appropriate optical characteristics and scalability to attain their viable integration in agriculture, undermining their commercialization. This study deals with the development of wavelength-selective semi-transparent dye-sensitized solar cells (DSSCs) using the scalable slot-die deposition method. These devices are designed to provide high transparency in the photosynthetically active radiation (PAR) region and effectively exploit the near-ultraviolet to blue-visible spectrum for power production, simultaneously protecting cultivations from harmful short-wavelength irradiation. To this aim, a new quinoline-based dye and a highly transparent iodine-free electrolyte were employed in DSSCs, giving an external quantum efficiency of 70% for wavelengths up to 500 nm and a PAR transmittance on the level of 50% (55% crop growth factor). Additionally, the light-to-electricity conversion efficiency of these devices is high for both front- and rear-side illumination under all-weather irradiation conditions (up to 94% bifaciality factor). Finally, two new figures-of-merit (greenhouse compatibility factor, agrivoltaic performance factor) are introduced to quantify the balance of photovoltaic performance and agronomic functionality. Full article

The large-scale integration of wind and solar photovoltaic (PV) power is a cornerstone of low-carbon, sustainable energy systems. However, the uncertainty of the output brings great challenges to the operation and dispatching of power systems. To clearly describe the fluctuation characteristics of wind–PV power output and the spatial–temporal coupling relationship, a two-stage wind–PV scenario-generation method is proposed. This method is based on Difference-Constrained Variational Autoencoder and Mixture of Factor Analyzers (DVAE-MFA). In the first stage, a differential constraint term is added to the reconstruction loss of the Variational Autoencoder (VAE) to build the Difference-Constrained Variational Autoencoder (DVAE) model. This helps the model better learn the fluctuation characteristics of output sequences. In the second stage, to solve the problem of the posterior distribution of the DVAE latent variables deviating from the standard normal prior, the Mixture of Factor Analyzers (MFA) model is introduced for secondary probability modeling of the latent space. The simulation experiment results show that the proposed DVAE-MFA model outperforms comparison models in terms of the scenario temporal fluctuation characteristics, spatial–temporal correlations, and statistical distribution similarity. The generated output scenarios can reproduce the features of historical data, providing high-quality data support for the stochastic optimization scheduling of sustainable power systems. Full article

Hydrogen has emerged as a promising energy carrier for sustainable, low-carbon energy systems because of its high energy density and compatibility with fuel cell technologies. This study presents a comprehensive investigation of hydrogen production through the integration of steam methane reforming (SMR), solar photovoltaic (PV) power generation, proton exchange membrane (PEM) electrolysis, hydrogen storage, and PEM fuel cells. A three-dimensional computational fluid dynamics (CFD) model was developed to analyze fluid flow, heat transfer, species transport, and chemical reactions within a catalytic steam methane reformer. The simulation predicted a methane conversion of 94.71%, a hydrogen yield of 3.75 mol H₂/mol CH₄, and an overall efficiency of 63.35%, indicating highly efficient hydrogen production. Sensitivity analyses identify catalyst temperature, inlet temperature, and residence time as the dominant parameters affecting hydrogen yield. Integration with renewable energy systems demonstrated that a hybrid configuration consisting of a 120 kW PV array, a 50 kW PEM electrolyzer, a 6 kW PEM fuel cell, and 6–8 kg hydrogen storage can effectively support sustainable hydrogen production and auxiliary power demands. The proposed framework provides a practical pathway for integrating thermochemical and renewable hydrogen technologies into future energy applications worldwide. Full article

The optoelectronic properties of the lead-free perovskite CsSn_0.5Ge_0.5I₃ are investigated by first-principles calculations and numerical simulations using SCAPS-1D. The energy-level alignment between transport layers and the perovskite layer is evaluated, resulting in the establishment of the PCBM/CsSn_0.5Ge_0.5I₃/PEDOT:PSS structure. Key parameters, including bulk defect density, layer thickness, and electrode materials, are optimised, and the effects of resistance, illumination intensity, thermal stability, and carrier generation-recombination rates on device performance are analysed. The optimal device structure FTO/PCBM/CsSn_0.5Ge_0.5I₃/PEDOT:PSS/C achieves a power conversion efficiency (PCE) of 24.50% and a fill factor (FF) of 80.01%. Machine learning (ML) algorithms are applied to predict photovoltaic parameters, with Random Forest (RF) exhibiting the highest accuracy. SHAP analysis identifies absorber layer thickness as the dominant factor influencing efficiency, providing guidance for experimental optimisation. This integrated approach offers a practical pathway for designing high-performance, stable, and environmentally sustainable perovskite solar cells (PSCs). Full article

This investigation evaluated the suitability of solar waste sand (SWS), derived from recycled photovoltaic panels, as a raw material in clinker production. Raw materials were prepared, analysed, and used to design reference and SWS raw mix kneaded balls. The balls were subjected to sintering tests with varying peak temperatures; the clinker mineralogy was analysed using X-ray diffraction (XRD). The SWS raw mix contained 13.14% SWS with 78.31% and 8.55% of limestone and clay, respectively. The SWS clinker had 55.64 ± 2.26% C₃S, 18.67 ± 1.27% β-C₂S, 1.57 ± 0.35% CaO_f, and 3.70 ± 1.41% MgO_f at 1350 °C, all within recommended ranges. Minor oxides, including Na₂O, K₂O, and MgO, reduced the required sintering temperature relative to the typical 1450 °C, as expected. The presence of an amorphous layer suggested the optimal temperature may be around 1350 °C. Furthermore, the high Na₂O content in the SWS clinker is believed to have helped stabilise β-C₂S. Low C₄AF and the absence of C₃A might indicate its suitability as a clinker for sulfate-resistant cements. Incorporating SWS directly into the raw mix for clinker production is an innovative approach to both diverting solar waste from landfills and partially supplementing sand and limestone. Full article

To address the renewable energy hosting capacity problem in regional power grids under security, stability, and power supply adequacy constraints, this paper proposes an energy storage configuration method that coordinates demand–response, synchronous condensers, grid-forming renewable energy retrofits, and storage. First, wind and photovoltaic output scenarios are generated by clustering historical data while considering wind–solar correlations, and a time-of-use-price-based demand–response model is established to improve the net-load profile. Second, a three-layer optimization framework is constructed. The upper layer determines the rated power and energy capacity of the storage system and minimizes storage investment and operation and maintenance costs. The middle layer performs source–grid–load–storage coordinated dispatch under the storage boundary provided by the upper layer and minimizes the total operating costs while embedding node voltage and short-circuit ratio security checks. The lower layer maximizes renewable energy hosting capacity through incremental capacity iteration and determines the maximum admissible renewable capacity subject to reliability, security, and stability constraints. A normalized normal constraint (NNC) method is adopted to solve the multi-objective three-layer model. Case studies based on an improved IEEE 118-bus system show that the coordinated measures can significantly increase hosting capacity, reduce storage demand, and improve voltage and frequency security. Full article

This work proposes a simplified HTL-free PSC structure based on Cs₂AgBiBr₆ doped with praseodymium (Pr³⁺). By reducing interfacial layers, this design reduces defect-induced recombination and increases the resistance of the device under environmental stresses (such as temperature, oxygen, and humidity). The performance of the Pr³⁺-doped PSC (Cs₂Ag_0.95Pr_0.05BiBr₆) with both FTO and Tb-FTO as front contacts were investigated using solar capacitance simulation software (SCAPS-1D) version 3.3.10. The FTO-based reference device showed photovoltaic parameters of V_oc = 0.86 V, J_sc = 12.52 mA/cm², FF = 70.13%, and PCE = 7.51%, while the Tb-doped FTO device showed V_oc = 0.86 V, J_sc = 12.67 mA/cm², FF = 72.98%, and PCE = 7.91%. The performance of the device was analyzed based on variation in absorber thickness and defect density, ETL thickness and dopant concentration, band gap, and ETL/absorber interface defect density in the Tb-FTO/TiO₂/Cs₂Ag_0.95Pr_0.05BiBr₆/C to obtain optimal values of 1.0 μm, 10¹³ cm⁻², 0.09 μm, 10²¹ cm⁻², 1.5 eV, and 10⁸ cm⁻³. Utilizing these optimized values, the final device predicted a PCE of 19.93%, FF of 84.51%, J_sc of 27.73 mA/cm², and V_oc of 0.85 V. The device was also found to be sensitive to variations in the back-contact work function, ambient temperature, series and shunt resistances. A PCE of ~27.65% was achieved at higher metal work function (e.g., W_F = 5.9 eV for Se), with a corresponding FF of ~82.69%, J_sc of ~27.78 mA/cm², and V_oc of 1.20 V. Therefore, while direct experimental validation for the proposed HTL-free structure is not yet available, the comparison with experimentally demonstrated HTL-containing counterparts provides confidence in the predictive capability of our model. We expect that the present work will serve as a theoretical foundation and motivation for future experimental fabrication and characterization of HTL-free devices. Full article

Autonomous robotic systems designed for extraterrestrial in situ resource utilization (ISRU) will play a central role in enabling a sustained human presence on the Moon and Mars. These robots are expected to perform tasks such as regolith excavation, water extraction, oxygen production, and propellant generation under extremely harsh environmental conditions, including large temperature variations, abrasive dust, high radiation levels, and significant communication delays with Earth. Consequently, their onboard electrical systems must operate with high reliability, autonomy, and fault tolerance. A critical enabling technology for these systems is the isolated power conversion architecture, which distributes energy from primary power sources to multiple robotic subsystems, including mobility actuators, drilling systems, sensors, computing units, and thermal management modules. Future lunar and Martian missions are expected to rely on a combination of alternative energy sources, including solar photovoltaic arrays with energy storage, fuel cells, radioisotope power systems, and nuclear surface power reactors, which can provide continuous and high-density energy independent of sunlight availability. These diverse power sources require flexible and highly efficient isolated DC–DC power conversion architectures capable of managing wide input voltage ranges while ensuring electrical isolation, safety, and system stability across distributed robotic platforms. This literature review surveys recent developments in autonomous isolated power conversion architectures suitable for lunar and Martian resource extraction robots. The review examines advanced converter topologies such as resonant converters, phase-shifted full-bridge converters, dual-active bridge converters, and modular multiport power converters designed for high efficiency, high power density, and scalable power distribution. Emphasis is placed on converter architectures capable of interfacing with nuclear-powered systems and other high-energy-density sources while supporting distributed loads in robotic mining and processing systems. In addition, the paper reviews emerging autonomous control strategies, including adaptive digital control, intelligent power management, fault detection and self-recovery mechanisms, and distributed power architectures capable of maintaining stable operation under dynamic load conditions. The role of wide-bandgap semiconductor technologies, including silicon carbide (SiC) and gallium nitride (GaN), is also examined, highlighting their potential to enable higher switching frequencies, improved efficiency, reduced system mass, and enhanced thermal performance in vacuum environments. Finally, system-level considerations for integrating isolated power conversion within robotic ISRU platforms are discussed, including redundancy strategies, power bus architectures, electromagnetic compatibility, thermal management, and long-duration reliability requirements. By consolidating advances across power electronics, autonomous control, and space power systems, this review identifies key research gaps and outlines design directions for next-generation autonomous power conversion systems capable of supporting scalable lunar and Martian resource extraction infrastructures powered by both renewable and nuclear energy sources. Full article