Search Results (5,766)

This study examines the impact of increasing photovoltaic (PV) penetration on the transient stability of the IEEE 9-bus power system. Synchronous machines are modeled with standard subtransient dynamics, while PV units are represented as current-limited grid-following inverters. Transient stability is assessed through the Critical Clearing Time (CCT) and the post-fault dynamic behavior, obtained from time-domain simulations carried out in MATLAB/Simulink^® R2023b. Two permanent three-phase faults are considered: a primary contingency on line 7–5 and a secondary contingency on line 9–6, introduced to assess the robustness of the observed trends across different fault locations. The results show an increase in CCT as PV generation progressively replaces the active power supplied by synchronous machines, whose inertia is therefore maintained: from 210 ms (0% PV) to 440 ms (25%)/1080 ms (40%) at bus 5, 410 ms (25%)/1130 ms (40%) and 290 ms (25%)/650 ms (40%) at buses 6 and 8, respectively, demonstrating that the penetration site is a key factor for system stability. For distributed penetration among the three buses, CCT values of 340 ms (25%) and 1020 ms (40%) highlight the significant influence of PV placement at bus 8. The fault on line 9–6 consistently yields higher CCT values across all scenarios, confirming the robustness of these trends independently of fault location. Although an overall increase in CCT was observed, higher PV penetration also led to more pronounced oscillations and operability issues after the fault. In particular, 75% of the penetration scenarios under the fault on line 9–6 do not meet the active power recovery requirements of IEEE 1547-2018 and IEEE 2800-2022, a result more severe than that observed for the fault on line 7–5. These results underscore that a higher CCT does not guarantee operational compliance, and that stability-oriented control strategies—such as grid-forming operation, fast active power support, and dynamic voltage control—remain essential. They also suggest that planning practices should favor interconnections electrically closer to the slack generator. Overall, a high PV penetration level—modifying only the operating point of synchronous machines—allows longer fault durations to be tolerated; however, appropriate siting of PV units and the adoption of advanced inverter controls could mitigate the observed oscillations and post-fault operability challenges. Full article

As the energy and power sector transitions toward clean and low-carbon development, the installed capacity of renewable energy sources such as wind and photovoltaic power has been rapidly increasing. Wind–solar hydrogen production via water electrolysis can enhance renewable energy utilization and enable the supply of green hydrogen. Meanwhile, the $H_2/C{O}_2$ molar ratio in the syngas produced by conventional biomass gasification generally cannot directly meet the 2:1 stoichiometric requirement for methanol synthesis. To address this issue, this paper proposes an off-grid coordinated system integrating wind–solar hydrogen production and biomass gasification for methanol synthesis. The system incorporates multi-operating-condition constraints of electrolyzers, coordinated regulation between electrochemical energy storage and hydrogen storage, and coordinated matching between biomass gasification and the water–gas shift reaction. Based on the system energy and material balance, a mixed-integer linear programming (MILP) model is formulated with the objective of minimizing the annualized total cost and is solved using the Gurobi solver in the MATLAB environment. To highlight the roles of HES and the WGS reaction, four comparative scenarios are designed for validation. The results show that the system with an annual methanol production capacity of 100,000 tons achieves an annualized total cost of 318 million CNY, with a wind–solar utilization rate of 98.86%. The system is configured with 12 electrolyzers of 5 MW each. The biomass consumption per ton of methanol is 3.06, and the $C{O}_2$ emissions per ton of methanol are 2.37. Finally, a sensitivity analysis of the levelized methanol cost (LCOM) was conducted, providing guidance for cost reduction in green methanol production. Full article

Energy-intensive processes such as flexographic printing, extrusion coating, slitting, compressed air generation, and chilled water production make liquid carton packaging manufacturing a major electricity consumer, increasing the need for cost-effective and sustainable energy solutions. This study evaluates the real-world performance of a 679 kWp grid-tied solar photovoltaic (PV) system integrated at the 11 kV level in a liquid carton packaging factory in Nairobi, Kenya, operating under regulatory export control constraints that require full on-site consumption of PV generation. Using measured operational data from energy monitoring platforms, including Sunny Portal, 1.31.8 Schneider EcoStruxure, and Sphera Cloud 8.17.2, system performance was assessed in accordance with IEC 61724-1, focusing on final yield, capacity utilization factor, grid offset contribution, and carbon emissions reduction. The results show that the system generated 617 MWh over the assessment period, corresponding to an average daily final yield of 2.49 kWh/kWp·day and a capacity utilization factor of 10.38%. On-site PV generation supplied approximately 17% of the plant’s annual electricity demand and avoided about 277.7 t CO₂ emissions. Performance benchmarking against comparable installations in Kenya, Morocco, Malaysia, Senegal, and Uzbekistan indicates that the lower observed yield is primarily driven by curtailment and industrial load-matching limitations rather than inadequate solar resource or component inefficiency. The findings demonstrate that meaningful electricity cost savings and emissions reductions can be achieved in energy-intensive manufacturing environments despite export restrictions while highlighting the importance of improved load alignment and data-driven operational strategies to enhance PV utilization. Full article

With the growing severity of water pollution, conventional treatment technologies are increasingly unable to satisfy the demand for deep purification. Catalytic wastewater treatment has emerged as an effective strategy for degrading refractory pollutants because of its high efficiency, mild operating conditions, and environmentally friendly nature. This review systematically summarizes recent progress in catalytic materials for wastewater treatment, covering four major categories: metal-based materials, carbon-based materials, multicomponent composites, and photo/electrocatalytic systems. Particular attention is given to their design strategies, structural characteristics, and performance advantages. On this basis, the full mechanistic chain is discussed, from interfacial adsorption and activation to reactive-species generation, including both radical and non-radical pathways, intermediate transformation, and macroscopic reaction kinetics. The review also highlights representative applications in practical wastewater streams, including textile dyeing and pharmaceutical, chemical, landfill leachate, and municipal tailwater treatment, thereby demonstrating the engineering potential of catalytic technologies. At the same time, several critical challenges remain, including insufficient long-term material stability, incomplete mechanistic understanding in complex water matrices, limited adaptability to real wastewater, and the high cost of large-scale preparation. Future research should therefore focus on the development of highly stable, low-cost, and interference-resistant catalytic materials, deeper mechanistic elucidation through in situ characterization and theoretical calculations, stronger integration with membrane separation, biological treatment, photovoltaic or electrochemical processes, and the establishment of standardized evaluation protocols and life-cycle assessment frameworks. These efforts will accelerate the transition of catalytic wastewater treatment toward greener, smarter, and more practical engineering applications. Full article

The rapid advancement of photovoltaic (PV) technologies has transformed solar energy systems into intelligent, high-efficiency platforms. This review systematically examines next-generation PV materials, hybrid system architectures, and intelligent control strategies. Key technologies include perovskite-based tandem cells, N-type TOPCon, bifacial, heterojunction (HJT), and photovoltaic-thermal (PVT) systems. These innovations overcome the intrinsic limitations of conventional P-type silicon panels by reducing recombination losses, mitigating light- and temperature-induced degradation, and enhancing energy yield under real-world operating conditions. At the system level, AI-enabled inverters, adaptive maximum power point tracking (MPPT), predictive maintenance, and real-time grid interaction enable dynamic optimization under variable irradiance, thermal stress, and load fluctuations. A critical comparison across diverse deployment environments highlights current challenges, including manufacturing complexity, material stability, and AI data-quality limitations. Despite higher upfront costs and system complexity, these advanced PV systems offer superior long-term performance, improved reliability, and reduced levelized cost of electricity through lower degradation rates and enhanced operational resilience. Collectively, intelligent, material-optimized PV technologies represent a scalable, sustainable, and grid-compatible solution for solar energy deployment across diverse climates, supporting the global transition toward low-carbon energy infrastructures. Full article

This paper investigates solar energy production forecasting at a monthly temporal resolution using a pooled neural network framework applied to the Chikalov photovoltaic systems in southwestern Bulgaria. The study considers several related PV installations with unequal time-series lengths and formulates the forecasting task as one-step-ahead prediction of the next monthly total energy yield, measured in kWh, in a global pooled setting. Two complementary neural architectures are compared: a multilayer perceptron (MLP), which serves as a nonlinear feed-forward benchmark based on lagged observations and seasonal descriptors, and a gated recurrent unit (GRU), which explicitly models sequential temporal dependence. In both cases, seasonality is represented through cyclical calendar encodings, while model selection is performed by chronological hyperparameter search using a separate validation block. Forecast accuracy is assessed by RMSE, MAE, coefficient of determination (R²), MAPE, and sMAPE, and uncertainty is quantified through validation residual prediction intervals. The results show that the MLP achieves stronger validation performance, whereas the GRU provides better final out-of-sample generalization after refitting on the combined training and validation data. For both architectures, the best configurations are obtained with a 12-month input horizon, indicating that one full annual cycle contains the most informative memory for forecasting monthly aggregated photovoltaic energy yield in the considered dataset. After refitting on the combined training and validation data, the GRU achieved the best final out-of-sample performance, with RMSE = 296.38 kWh, MAE = 213.16 kWh, R² = 0.9231, MAPE = 7.52%, and sMAPE = 7.49%. Overall, the findings demonstrate that pooled neural modeling is an effective framework for monthly PV production forecasting and can provide practically useful support for sustainable energy planning, monitoring, and optimization. Full article

As global efforts to decarbonize the transportation sector intensify, integrating renewable energy sources into electric vehicle (EV) infrastructure has become a critical challenge, particularly under strong temporal mismatches between generation and demand. This study evaluates the potential of urban rooftop photovoltaic (PV) systems in Israel to support full electrification of the private vehicle fleet using a planning-oriented modeling framework that links energy supply, transport demand, and seasonal variability. Current annual fleet demand is estimated at 14 TWh, based on both internal combustion vehicle replacement and EV-specific consumption. A three-stage modeling framework is applied. First, national vehicle data are used to estimate total electricity demand. Second, rooftop PV generation potential is calculated using a monthly irradiance model, rooftop availability data, and system-level efficiency factors. Under these assumptions, residential rooftop PV could generate up to 81 TWh per year, corresponding to approximately 44 km² of usable rooftop area. Third, temporal matching between supply and demand is evaluated, with explicit focus on intra-annual variability rather than only annual energy balance. Winter irradiance declines to approximately 45% of summer levels, while maintaining continuous charging requires approximately 38 GWh of energy storage. These results show that system feasibility is constrained by winter minimum generation rather than annual energy balance. The findings highlight that large-scale rooftop PV-based electrification is primarily limited by a temporal mismatch between generation and demand. This shifts the evaluation of PV-EV integration from a static annual energy perspective to a temporal system-design problem. This underscores the importance of integrating storage, grid flexibility, and system-level planning when evaluating the role of distributed PV in supporting electrified transport. Full article

This paper presents an adaptive nonlinear control and state observation framework for energy management in standalone photovoltaic (PV) systems integrated with battery energy storage. A unified nonlinear dynamic model is developed to describe the interactions between the PV generator, the DC/DC buck converter, and the lithium-ion battery. Based on this model, a multi-mode control strategy is designed to ensure efficient and safe operation under varying environmental and loading conditions. The proposed scheme incorporates maximum power point tracking (MPPT) to maximize photovoltaic energy extraction, along with constant current (CC) and constant voltage (CV) charging modes to guarantee battery safety and longevity. To address uncertainties and unmeasured states, an adaptive nonlinear observer is developed for real-time estimation of the battery open-circuit voltage and state of charge. The observer design is supported by Lyapunov-based stability analysis, ensuring boundedness and convergence of the estimation error in the presence of modeling uncertainties and external disturbances. An energy management algorithm is further introduced to coordinate the transition between operating modes according to the estimated system states and battery constraints. The effectiveness and robustness of the proposed control and observation strategy are validated through detailed simulations in MATLAB/Simulink under varying solar irradiance conditions. The results demonstrate accurate maximum power tracking, reliable state estimation, and safe battery charging performance, highlighting the potential of the proposed approach for advanced autonomous PV–battery systems. Full article

The growing integration of solar, wind and battery energy storage (BES) of the microgrids (MGs) has increased the necessity of real-time energy management, especially in the multi-microgrid (multi-MG) setting, where the generation and the load change stochastically. This paper presents a Java Agent DEvelopment (JADE)-based Multi-Agent System (MAS) for real-time energy management of a low-voltage hybrid multi-MG system incorporating solar photovoltaic (PV), wind generation, and battery energy storage (BES). The proposed framework’s novelty lies in its physical campus-scale hardware deployment—validated across four operating scenarios (single MG off-grid, single MG on-grid, dual MG off-grid, and dual MG on-grid)—combined with autonomous inter-MG power sharing, which distinguishes it from existing simulation-only MAS-based microgrid studies. The suggested framework facilitates decentralized communication between interconnected MGs and the utility AC grid to facilitate the proper management of power flow, its exchange, and the reliability of the system. The intelligent agents are used to coordinate solar, wind, BES, and load changes in order to adjust to changing demand conditions. The system is physically implemented on a campus rooftop with two 1 kW solar PV arrays and two 1.5 kW wind turbine generators, each paired with a 24 V, 150 Ah battery bank, operating on a 24 V DC bus. Results across 24 h real operational profiles demonstrate effective power balance maintenance, renewable energy maximization, and constraint-compliant battery operation (SOC is bounded within 20–90%). A direct comparison with a conventional centralized JavaScript-based EMS confirms equivalent dispatch accuracy while demonstrating superior scalability, fault tolerance, and modularity of the proposed JADE MAS architecture. Full article

The substrate plays a critical yet often underappreciated role in determining the performance, stability and manufacturability of photovoltaic devices. While conventional glass and polymer films have enabled the rapid development of solar technologies, emerging applications such as building-integrated photovoltaics, wearable systems and large-area conformal devices demand the use of non-conventional substrates, including ceramics, metals, paper, textiles and elastomeric materials. This review provides a comprehensive analysis of the current state of the art of non-conventional substrates for photovoltaic technologies, with particular emphasis on the interplay between material properties, surface chemistry and deposition processes. These substrates introduce distinct mechanical, thermal and interfacial constraints that fundamentally alter thin-film growth, defect formation and device reliability. Key challenges such as porosity, roughness, thermal transport limitations and outgassing are discussed in relation to nucleation, film continuity and interfacial stability. The role of substrate-dependent effects in both chemical and physical deposition techniques is critically examined, highlighting cases where conventional processing approaches are insufficient. Representative device demonstrations are analyzed to illustrate how substrate selection influences performance and integration strategies across different photovoltaic platforms. Finally, common limitations and emerging opportunities are identified, emphasizing the need for the co-design of substrates, materials and processing routes. This work establishes a unified framework to guide the development of next-generation photovoltaic devices on unconventional substrates. Full article

Crop disease diagnosis can be degraded by structured cast shadows, including panel-like strip shadows that motivate applications in agrivoltaic-style farming. This paper presents ShadowFormer-AV, a transformer-based framework that adapts general shadow-robust visual learning to crop disease classification by separating disease evidence from illumination interference. The proposed approach combines a soft shadow-prior extractor, an illumination-decoupled dual-stream token encoder, lesion-preserving adaptive attention, and a cross-view consistency objective between original and shadow-perturbed images. The method uses only standard RGB inputs and does not require shadow-free reference images, multispectral sensing, or pixel-level shadow annotation. We evaluated the framework on publicly available plant disease datasets using calibrated panel-like synthetic shadows and a naturally shadowed PlantDoc subset. Because no on-site agrivoltaic disease dataset was used, the conclusions were limited to shadow robustness under these simulated and naturally shadowed test conditions rather than verified performance under real photovoltaic-panel shadows. Within this validation boundary, ShadowFormer-AV improved accuracy, Macro-F1, and calibration over representative convolutional and transformer baselines, suggesting that illumination-aware token learning is useful for crop disease recognition under structured shadow interference. Full article

Buildings account for a substantial proportion of global energy consumption and greenhouse-gas emissions, largely due to the widespread use of conventional heating, ventilation, and air-conditioning (HVAC) systems. Hybrid systems that integrate passive and active technologies have emerged as a promising strategy for reducing energy demand while maintaining adequate indoor environmental conditions. This study evaluates the thermal and airflow performance of a hybrid air-conditioning system (HACS) that combines transparent thermal insulation (TTI) filled with R-410A refrigerant and a pig-fat-based organic phase-change material (PCM). Experimental measurements of heat flux, temperature, airflow velocity, and CO₂ concentration were conducted in a controlled prototype system. In parallel, computational simulations were performed using computational fluid dynamics (CFD) and multizone airflow modeling. The hybrid system incorporates a TTI container acting as a solar absorber and a galvanized-steel PCM container filled with 10 kg of pig fat used as latent heat storage. Heat-flux measurements were obtained using an HFS-5 sensor connected to a data acquisition system, while airflow velocity and temperature were monitored with analog data loggers. Indoor CO₂ concentrations were recorded using a dedicated CO₂ meter and simulated using CONTAMW software version 3.4.0.8. The experimental results show that the TTI and PCM containers reached average heat-flux values of 77.04 W/m² and 55.31 W/m², respectively. Airflow within the system is induced by buoyancy forces arising from temperature gradients generated by heat transfer processes at the surfaces of the TTI and PCM, resulting in a mixed air stream with an average temperature of 37.54 °C during winter operation. Recorded CO₂ concentrations remained between 290 and 413 ppm, indicating high indoor air quality levels. The overall experimental campaign spanned 6 years and 3 months. CFD simulations confirmed the airflow patterns and heat-transfer behavior observed experimentally. The findings demonstrate that hybrid air-conditioning systems combining refrigerant-filled transparent insulation with bio-based phase-change materials can effectively enhance passive thermal performance while maintaining acceptable indoor air quality. The integration of photovoltaic-powered ventilation systems could further the operational autonomy and overall energy efficiency of such hybrid systems. Full article

Despite the rapid expansion of photovoltaic (PV) installations over the past decade, challenges such as curtailments of renewable energy sources (RESs) and grid constraints continue to limit the capacity of Cyprus’ power system to accommodate higher solar penetration. In this context, grid reliability, defined as the ability to maintain stable operation by balancing supply and demand, minimizing curtailment, and reducing stress on the island network, has emerged as a critical concern. The deployment of PV-plus-storage systems offers a viable solution to enhance grid reliability while alleviating operational constraints. This paper presents a real-world case study of the first commercially deployed grid-connected PV-powered, battery-integrated electric vehicle (EV) charging station in Cyprus. Commissioned in May 2025, the system integrates a 60.32 kW_p rooftop PV array, a 100 kW/97 kWh battery energy storage system (BESS), and a 160 kW DC fast charger. A custom cloud-based energy management platform enables real-time monitoring, forecasting, and optimization under a zero-export scheme. High-resolution operational and weather data were collected between 15 May and 30 November 2025. Over this period, the integrated PV-battery system supplied 29% of the site’s total energy demand (self-sufficiency rate of 28.97%) and achieved a self-consumption rate of 98.69%. Such rates would not have been attainable with a pure PV system, given the depot’s evening-concentrated EV charging demand profile, which requires the BESS to time-shift daytime solar generation. The system reduced depot electricity costs by approximately 29%, generating €16,010 in savings and avoiding 26.47 tonnes of carbon dioxide (CO₂) emissions compared to a grid-only baseline. Beyond site-level performance, the system contributed to grid stress reduction by absorbing excess PV generation that would otherwise have been curtailed/wasted. Operational insights indicate minimal temperature-related issues, highlight the importance of automated fault detection and alerting to minimize downtime, and demonstrate how periodic operation strategies can optimize system performance and mitigate curtailment in Cyprus’s isolated grid. Full article

The integration of large-scale electric vehicle (EV) fleets into vehicle-to-grid (V2G) systems offers significant potential for enhancing the operation of renewable-based smart grids. However, stochastic uncertainties in photovoltaic (PV) generation, vehicle availability, and load demand—coupled with battery degradation and life-cycle assessment (LCA) carbon emissions—pose major challenges to optimal scheduling. This paper proposes a scenario-based multi-objective MILP framework for a 500-EV fleet aggregator. The model incorporates Monte Carlo simulations for multi-source uncertainty quantification (±25% PV forecast errors, ±40% availability), LCA penalties (45 kgCO₂eq/kWh), and ancillary service revenues (25 USD/MW-h). Long-term state-of-health (SOH) projections, including a 1-year fade to 96.5%, are also integrated. Comparative analysis of V2X scenarios shows that the V2G Hybrid strategy reduces daily costs by 34.6% (from ~11,000 USD in the uncontrolled case to 7741 USD when reserve revenues are included), achieves over 50% peak shaving, and maintains voltage stability within 0.994–1.008 pu. The stochastic Pareto frontier identifies knee-point solutions that lower normalized expected costs to 134.61 while achieving 1–2% lower expected emissions compared to deterministic baselines. These results demonstrate a comprehensive framework, uncertainty-aware framework that balances economic viability, grid resilience, and environmental sustainability, offering actionable insights for fleet aggregators and policymakers working toward net-zero energy systems. Full article

The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind generation, a microturbine, a fuel cell, an energy storage system, and utility-grid exchange. The proposed model was implemented and simulated in a MATLAB (2024b) environment. The Birds of Prey-Based Optimization algorithm is applied to determine the optimal 24 h dispatch schedule by minimizing a weighted objective function that combines operating and emission costs. Uncertainties in solar irradiance, wind speed, electrical load, ambient temperature, and electricity prices are modeled using probabilistic distributions and Monte Carlo simulations. To improve computational efficiency, 1000 generated scenarios are reduced to 10 representative scenarios using Fast Forward Selection based on Kantorovich distance. Seasonal case studies for winter, spring, summer, and autumn are used to evaluate the proposed method. Compared with five metaheuristic algorithms, the proposed approach achieves the lowest fitness value in all seasons, with reductions of 15.2%, 26.5%, 6.8%, and 23.9%, respectively. The results confirm improved economic and environmental microgrid operation under seasonal renewable uncertainty. Full article