Search Results (4,819)

The rapid growth of renewable energy sources, particularly solar and wind power, has increased the demand for efficient and reliable battery energy storage systems (BESSs) to address intermittency, enhance grid stability, and improve energy management. In recent years, artificial intelligence (AI) has emerged as a transformative technology for optimizing the operation, control, monitoring, and maintenance of battery storage systems. This review provides a comprehensive overview of AI-driven BESS technologies for renewable energy applications. The study examines recent advances in machine learning, deep learning, reinforcement learning, and hybrid intelligent algorithms applied to battery state estimation, energy management, fault diagnosis, predictive maintenance, thermal management, and lifetime prediction. Furthermore, the integration of AI-based BESSs with photovoltaic systems, wind farms, microgrids, and smart grids is critically analyzed. The review highlights the advantages of AI techniques in improving system efficiency, reliability, adaptability, and decision-making capabilities under uncertain operating conditions. Current challenges, including data quality, model interpretability, computational requirements, cybersecurity concerns, and real-time implementation issues, are also discussed. Finally, emerging research directions such as digital twins, explainable artificial intelligence, federated learning, and edge intelligence are explored to provide insights into the future development of intelligent battery storage systems. This review aims to serve as a valuable reference for researchers, engineers, and practitioners working at the intersection of artificial intelligence, battery technologies, and renewable energy systems. Full article

Perovskite solar cells (PSCs) have emerged as a research hotspot in photovoltaics owing to their high power conversion efficiency (PCE), low fabrication cost, and simple preparation processes. However, their commercialization remains constrained by interfacial defects, non-radiative recombination, and limited scalability. Self-assembled molecules (SAMs), as a critical interfacial engineering tool, can significantly enhance device performance through defect passivation, energy band alignment, and crystallization regulation. Following the main theme of “molecular design—mechanistic understanding—application expansion,” this review systematically summarizes the structure–property relationships between SAMs architecture (anchoring groups, connecting backbones, and terminal functional groups) and their interfacial regulation mechanisms, with a particular focus on the important role of SAMs uniformity in governing interfacial quality, charge carrier transport, and device stability. The article presents multi-scale characterization techniques for evaluating SAMs interfacial properties, reviews the application progress of SAMs in rigid devices, flexible devices, and large-area modules, and finally discusses their future expansion directions in emerging fields such as tandem solar cells, flexible electronics. Full article

In the present study, the potential applications of Cu thin films and Ag/Cu bilayer thin films obtained by the pulsed laser deposition (PLD) technique are investigated in terms of the physicochemical effects resulting from their interaction with an aqueous solution containing Reactive Blue 21 (RB21) dye and sodium bicarbonate (NaHCO₃). The thin-film deposition process was carried out using a Q-switched Nd:YAG laser system operating at a wavelength of λ = 532 nm, with a pulse duration of τ = 10 ns, a repetition rate of ν = 10 Hz, a pulse energy of E = 180 mJ, a laser spot diameter of d = 336 μm, and an angle of incidence of α = 45°. Two types of thin films were prepared: a Cu thin film and an Ag/Cu bilayer thin film. The thermal effects induced by the interaction of the laser beam with the target materials were investigated by numerical simulations performed in COMSOL, allowing the evaluation of melt-phase formation for each material separately and providing a better understanding of the morphology and topography of the deposited thin films. The simulation results were validated through scanning electron microscopy (SEM) observations and surface roughness analyses. The two thin films were subsequently treated with an aqueous solution containing 10 g/L RB21 dye and 10 g/L NaHCO₃. Physicochemical analyses performed after treatment, including scanning electron microscopy (SEM), optical microscopy (OM), profilometry, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDS), X-ray Photoelectron Spectroscopy (XPS) and UV–Vis spectroscopy, revealed significant degradation of the RB21 dye accompanied by corrosion of the thin films, with the corrosion process being more pronounced in the case of the Cu thin film. The obtained results indicate that the method analyzed in this study may represent an alternative approach for the decomposition of recalcitrant organic dyes using thin Cu films, without relying on conventional photocatalytic processes. Equally important are the potential applications of the RB21/NaHCO₃ solution as an etching and patterning medium for thin Cu layers, while the Ag overlayer may provide a protective effect during such processes. These findings may contribute to the development of novel fabrication techniques for optoelectronic components, including solar cells, photovoltaic windows, and other industrial and laboratory applications. Full article

Reliable rooftop photovoltaic planning requires design-stage energy predictions to be verified against actual system performance. The novelty of this study is the integration of a multi-year assessment of agreement with PV*SOL design estimates with an independent assessment of normalized productivity, interannual variability, seasonality, and meteorological effects for several rooftop systems operating under the same regional conditions. PV*SOL, a commercial photovoltaic simulation software used to estimate system energy production during the design stage, was evaluated using three years (2023–2025) of monitored data from three rooftop photovoltaic (PV) systems (17.60–75.40 kWp) in Rzeszów, south-eastern Poland. The analysis comprised 108 installation-month observations and included final yield, capacity factor, annual prediction errors, seasonal variability, Pearson correlations, and hierarchical regression. Mean annual final yield ranged from 907.4 to 966.2 kWh/kWp, while annual deviations from PV*SOL design estimates ranged from −0.80% to +7.41%. Monthly final yield was strongly associated with solar irradiation, and the final hierarchical regression model explained 96.4% of its variability. The results indicate that PV*SOL provides a useful annual design reference, but operational monitoring and local benchmark data remain essential for reliable performance assessment. The study supports United Nations Sustainable Development Goal 7 (Affordable and Clean Energy) by improving the evidence base for rooftop photovoltaic planning and monitoring. Full article

Building facades and construction materials are continuously exposed to airborne particulate matter, organic pollutants, and microbial colonization, which cause progressive soiling, aesthetic degradation, and structural deterioration while imposing high maintenance and energy burdens. Photocatalytic titanium dioxide (TiO₂) has emerged as the most widely studied material for imparting self-cleaning functionality to building surfaces, owing to its ability to mineralize adsorbed contaminants under solar irradiation and to modulate surface wettability. This narrative review provides a structured account of TiO₂-based self-cleaning coatings for building materials, organized around three complementary themes: surface mechanisms, performance metrics, and outdoor durability. We first rationalize the two intertwined self-cleaning mechanisms—photocatalytic oxidative degradation and photoinduced superhydrophilicity—and their combination with physically repellent (superhydrophobic/superamphiphobic) wetting states. We then survey the principal coating-design strategies, including morphology and facet engineering, SiO₂-TiO₂ composites, metal/non-metal doping and heterojunction construction for visible-light activation, and dual-functional photocatalytic–superhydrophobic systems, and their integration into cementitious substrates, natural stone and cultural heritage, and transparent glass/photovoltaic surfaces. The quantitative metrics used to benchmark self-cleaning performance—water contact angle, dye photodegradation, NO_x and VOC abatement, and antimicrobial activity—are critically discussed together with the limitations of standardized laboratory tests. Finally, we analyze the weathering-induced deactivation pathways (photocatalyst leaching, surface contamination by soluble salts, and UV aging of organic matrices) and the emerging strategies for durable coatings, including inorganic binders, light-driven hydration, and defect- and heterojunction-engineered photocatalysts. The review concludes with an outlook on the open challenges that must be addressed to translate these coatings from laboratory demonstrations to long-lived, large-scale building applications. Full article

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation. Full article

Hydrogen is acknowledged as a clean and sustainable energy source due to the increasing demand for renewable energy sources. Photoelectrochemical (PEC) water splitting presents a viable approach for directly producing hydrogen from solar energy with negligible implications for the environment. However, regardless of the intensive studies over several years, a major hurdle to translating impressive laboratory-scale efficiency into robust, dependable, large-scale production of hydrogen is increasing competition from quickly advancing photovoltaic (PV)–based electrolysis technology. In parallel, Z-scheme or S-scheme artificial leaf catalyst systems mimicking photosynthesis are gaining ground in the research community. The performance and reliability of photo-electrocatalytic large-scale hydrogen production should be evaluated via pilot-scale and field studies, along with life cycle and economic studies. In the present manuscript, a comprehensive overview of technologies related to scalability is presented, with a focus on semiconductor materials and reactor design. In conclusion, problems and opportunities for future research on large-scale production technologies are presented. Full article

Lhasa combines abundant solar resources with a high heating demand and low winter solar altitude, making residential morphology simultaneously important to operational energy use and roof–façade photovoltaic (PV) generation. Based on a residential-area inventory covering more than 80 residential areas and 15 typical slab-type blocks, this study developed locally constrained parametric prototypes and generated 2186 valid physics-based samples. Thirteen morphological predictors were retained for surrogate modelling, followed by sunlight-constrained bi-objective optimization at floor area ratio (FAR) = 1.5, 1.8, and 2.2. On the independent test set, the selected energy and PV surrogate models achieved R² values of 0.999 and 0.989, respectively. The Pareto fronts at FAR = 1.5 and 1.8 included near-zero annual net-energy solutions, whereas FAR = 2.2 retained a minimum deficit of 0.682 × 10⁶ kWh. Low-energy solutions generally favoured a lower site coverage, continuous elongated slabs, and a greater mean height, whereas high-generation solutions favoured larger building footprints, a lower height, and controlled shading. As FAR increased, façade PV made a larger contribution, but this was insufficient to offset the decline in the rooftop supply and the rise in total energy demand. Energy-balance-oriented solutions generally concentrated the building orientation within 5–15° and the building depth within 14.0–15.5 m, with the density and height requiring a joint adjustment across FAR scenarios. The results provide an interpretable basis for the early-stage morphology screening and planning control of slab-type residential development in Lhasa. Full article

This research presents an integrated techno-energetic framework to decouple electric vehicle (EV) fleet growth from urban grid instability, using Bogotá, Colombia, as a case study. As emerging megacities confront rising charging demands, conventional reactive grid reinforcements are becoming technically and economically constrained. To address this, we develop a multi-layered optimization model that transforms urban Voronoi polygons into operational energy catchment units. Utilizing a Monte Carlo Tree Search (MCTS) algorithm, the framework determines infrastructure deployment sequences under two operational thresholds: a target high-resilience EV-to-charger ratio and a conservative scenario. Each localized node is technically dimensioned as a Representative Charging Station (RCS) equipped with a photovoltaic array and a Battery Energy Storage System (BESS). The results reveal spatial heterogeneity; high-density polygons in specific commercial and residential districts exhibit elevated infrastructure utilization alongside a stable solar resource baseline. Furthermore, the model demonstrates that this distributed architecture alleviates transformer thermal stress during the peak nocturnal charging period, mitigating localized overload risks and supporting distribution grid operational reliability. This study provides a scalable decision-making tool that bridges geospatial urban planning with renewable energy engineering to support the transition of constrained electrical networks. Full article

This study characterizes the photovoltaic potential of three urban areas in Cuenca, Ecuador, located in the San Sebastián, Totoracocha, and Yanuncay parishes, through twelve technical, energy-related, socioeconomic, and environmental indicators. The analysis was complemented with grid simulations in CYME 9.2 to assess photovoltaic integration capacity in the distribution network and with a multicriteria synthesis using PROMETHEE II. The variables considered included solar irradiation, usable rooftop area, electricity consumption, population density, socioeconomic level, and the operating characteristics of distribution transformers. The results show that photovoltaic potential varies according to urban morphology, electricity demand, and the operating capacity of the grid. Totoracocha exhibited the highest theoretical potential and a self-sufficiency level of 99.1%, but it also recorded the lowest technical utilization factor, at 15.6%, because of transformer constraints. Yanuncay achieved the highest estimated technically available surplus under the evaluated operating scenarios, at 452,737 kWh/year, whereas San Sebastián exhibited the highest technical utilization factor, at 27.2%. The simulations confirmed that transformers constitute the main technical constraint on photovoltaic integration, while medium-voltage lines and voltage levels remained within their operating limits. The technically usable surplus energy could be allocated to induction cooking, electric mobility, or green hydrogen production as alternative, non-simultaneous scenarios. The energy allocated to self-sufficiency could avoid approximately 1022.5 tCO₂/year. Orientation analysis showed statistically significant but limited differences among the evaluated configurations, indicating that orientation should be considered together with other rooftop and grid-related factors rather than as a standalone feasibility criterion. Overall, the proposed integrated framework provides a comparative decision-support basis for sustainable urban photovoltaic planning by jointly considering rooftop potential, electricity demand, grid constraints, socioeconomic conditions, and environmental benefits. Full article

Photovoltaic (PV) systems in arid regions experience substantial energy losses from dust accumulation, but automated cleaning can introduce risk when damaged modules are visually misclassified as dirty. Manual inspection is also impractical at utility scale. This study presents an edge-based cleaning-decision architecture in which vision proposes a cleaning request and independent electrical checks determine whether actuation is permitted. The prototype combines a YOLOv8n detector, INA219 voltage/current sensing, photodiode-based shading context, a Raspberry Pi 4, and an Arduino co-processor. Cleaning is allowed only when three conditions are satisfied: persistent dust detection, measured panel power below a predefined threshold, and the absence of an electrical structural-fault signature based on rolling-window voltage depression and instability. The detector was trained using 950 field-recorded frames containing 2850 annotated panel instances and achieved 77.3% mAP@0.5 on the validation set; across five retraining seeds, mAP@0.5 was $77.1\pm 1.1\%$. In a retrospective ablation of the recorded test campaign, the two verification gates reduced false or unsafe cleaning activations from five to one, while both unsafe activations involving the tested cracked module were vetoed. A 13.5 h three-day campaign recorded seven persistent visual dust requests; the power gate rejected six requests that did not justify cleaning, and the remaining request triggered a successful cleaning cycle. In that field event, panel power increased from 0.33 W to 7.48 W, corresponding to a 22.9-fold recovery. An assumption-based sensitivity analysis indicates potential water-cost savings from condition-based cleaning. An offline, template-constrained language model is used only for report generation and has no connection to actuation. Full article

Offshore floating photovoltaics (OFPVs) can expand solar generation without terrestrial land-use conflicts, but marine structures, installations, and operations remain cost-intensive. This study develops a reproducible deterministic and probabilistic levelized cost of electricity (LCOE) framework for the modular 0.5 MW PV-BOS platform. The methodological contribution is the consistent integration of component-level CAPEX and OPEX ranges, route-specific logistics evidence, lifetime degradation, financing uncertainty, and scenario-dependent cost modes within one model. The deterministic calculations were reproduced using initial CAPEX at year 0 and annual energy degradation expressed as (1 − d)^(t−1). For a 30-year long-term scenario, the optimized deterministic LCOE is 85.56 USD/MWh. A 100,000-trial Monte Carlo analysis gives a mean of 119.42 USD/MWh and P10/P50/P90 values of 91.64/117.11/150.23 USD/MWh under the explicitly defined baseline distributions. The route-specific Vigo–Valencia comparison shows a 58.7% reduction for the transport-and-port-assembly subtotal, but this ratio is not interpreted as a universal full T&I saving. The capacity factor is the dominant LCOE driver, followed by CAPEX. The results support modular logistics as a potentially important cost-reduction mechanism while showing that bankability depends on site-specific energy-yield, metocean design, availability, financing, and O&M validation. Full article

This study aims to demonstrate that, in the context of the energy transition and with solar generation playing a dominant role in the energy mix, key technical parameters of energy infrastructure should be re-evaluated and taken into account, as changes in these parameters affect operational processes and energy-loss calculation mechanisms. Data from newly installed battery energy storage systems (BESSs) connected to the power grid and photovoltaic power plants (PVPPs) in Bulgaria are used to establish a portfolio-level baseline for installed capacity, storage duration, geographic distribution, and preliminary economic indicators. By analyzing annual consumption and generation for 2024, 2025, and the first three months of 2026, the seasonal and cyclical nature of these variables and their dynamics of change are observed. The monthly data distribution allows for an in-depth study of load dynamics and the identification of key factors. The analysis identifies three typical seasonal cycles—winter, summer, and transitional—each of them differing in terms of specific consumption and generation profiles; the operation of PVPPs, BESS, and power transformers exhibits specific characteristics. The results show how changes in power quality indicators, specific technical parameters, and operational values directly or indirectly affect losses during power generation and distribution, and consequently also impact operating, maintenance, and repair costs. Full article

The intermittent nature of solar energy requires precise capacity and battery sizing for reliable microgrid design. Extending beyond traditional annual photovoltaic maps, we introduce a methodology for high-resolution capacity sizing surfaces that integrate hourly solar irradiance and battery storage estimation from averaged meteorological data, demonstrated here for Winnipeg, Canada. Leveraging pvlib Python 3.13 library, we simulate performance across all module orientations using photovoltaic modules with 22.5% efficiency and lithium-ion batteries assuming 92% round-trip efficiency, defining core metrics—Capacity ratio, Storage ratio, Excess ratio, and Battery cycling ratio, including Battery charging and discharging C-rates—while incorporating system losses. The analysis further embeds hourly unmet load and average battery state of charge. Unlike conventional PV-yield tools and single-configuration microgrid studies, the present method combines site-specific hourly meteorological data, full tilt–azimuth evaluation, battery dispatch, and explicit compliance-linked PV and storage sizing. These intuitive capacity sizing surfaces reveal how hourly modeling resolves diurnal and seasonal structure that monthly-average tools omit; a sensitivity analysis bounds the residual effect of inter-annual variability removed by climatological averaging, enhancing reliability and battery longevity under variable target standard deviation of compliance ranging from $\sigma =5/8$ to $\sigma =3$. Open-access capacity sizing surfaces for Canada-wide at 564 stations and an open-source Python library that reproduces the methodology for any hourly meteorological dataset world-wide are provided to easily size solar PV and battery systems. Full article

Accurate parameter estimation of photovoltaic (PV) models is essential for performance evaluation, efficiency enhancement, and reliable energy forecasting in solar energy systems. However, the nonlinear, multimodal, and implicit nature of the current–voltage (I–V) relationship makes this task challenging for conventional optimization methods, which often suffer from premature convergence and sensitivity to initial conditions. In this study, a modified variant of the Artificial Circulatory System Algorithm, termed Dynamic Elite Cooperative ACSA (DEC-ACSA), is proposed for estimating the unknown parameters of the Single-Diode Model (SDM). The proposed approach extends the original ACSA by incorporating dynamic population grouping, elite-guided cooperative interaction, and directional elite refinement, thereby aiming to improve convergence stability and the utilization of high-quality population information. The objective is to minimize the residual root mean square error (RMSE) of the implicit SDM equation using measured I–V data from four established benchmarks: the RTC France solar cell and the PWP201, STM6-40/36, and STP6-120/36 PV modules. The performance of DEC-ACSA is evaluated against the original ACSA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Henry Gas Solubility Optimization (HGSO) over 30 independent runs under an equal budget of 50,100 function evaluations. The DEC-ACSA configuration selected on RTC France was retained unchanged for the three additional module benchmarks. DEC-ACSA achieved mean residual RMSE values of 1.2514 × 10⁻³, 2.656 × 10⁻³, 2.647 × 10⁻³, and 1.8108 × 10⁻² for RTC France, PWP201, STM6-40/36, and STP6-120/36, respectively, while consistently reducing run-to-run variability relative to ACSA. Holm-corrected tests showed no significant difference from PSO on RTC France and PWP201, whereas significant differences from all comparison algorithms were observed on STM6-40/36 and STP6-120/36. I–V reconstruction further confirmed close agreement with the measured data across all four PV systems. Full article