Search Results (2,734)

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article

This study examines the adverse environmental impacts of solar photovoltaic parks located in established protected areas, aiming to determine the level of landscape fragmentation through the calculation of relevant landscape metrics. For this purpose, a case study was carried out in a Mediterranean Natura 2000 Special Protection Area (SPA), and landscape metrics were calculated using Geographic Information System spatial analysis tools. The analysis of metrics showed that the installation of renewable energy parks within the designated protected area negatively affect landscape fragmentation and the absence of carefully defined and evidence-based mitigation measures. The land cover categories that are significantly affected are those considered critical habitats of bird species that have been designated as SPAs. The results of this study highlight the need to integrate, in the National Renewable Energy Spatial Plans, specific biodiversity objectives, such as conservation objectives and the suspension of the installation of photovoltaic parks in certain areas that are important for conservation of biodiversity, in order to ensure the overall sustainability of renewable energy production. Full article

The use of power smoothing for renewable energy resources is attracting increasing attention. One widely used resource that could benefit from this technique is the grid-tied photovoltaic (PV) system. Solar energy production typically follows a Gaussian bell curve, with peaks at midday. This paper confirms this pattern by using the bell curve as a reference; however, climate variations can significantly alter this pattern. Therefore, this study aimed to smooth the power supplied to the grid by a PV system. The proposed controller manages the charge and discharge processes of the energy storage system (ESS) to ensure a smooth Gaussian bell curve output. It adjusts the parameters of this curve to closely match the generated energy, absorbing or supplying fluctuations to maintain the desired profile. This system also aims to provide accurate predictions of the power that should be supplied to the grid by the PV system, based on the capabilities of the ESS and the overall system performance. Although experimental results were not included in this analysis, the system was implemented in SIMULINK using real-world data. The controller utilizes a hybrid ESS comprising a vanadium redox battery (VRB) and supercapacitors (SCs). The design and operation of the controller, including curve tuning and ESS charge–discharge management, are detailed. The simulation results demonstrate excellent performance and are thoroughly discussed. Full article

In recent years, solar energy has emerged as one of the most advanced renewable energy sources, with its production capacity steadily growing. To maximize output and efficiency, choosing the right configuration for a specific location for these installations is crucial. This study uniquely integrates detailed multi-variant fixed-tilt PV system simulations with comprehensive economic evaluation under temperate climate conditions, addressing site-specific spatial constraints and grid integration considerations that have rarely been combined in previous works. In this paper, an energy and economic efficiency analysis for a photovoltaic power plant, located in central Poland, designed in eight variants (10°, 15°, 20°, 25°, 30° PV module inclination angle for a south orientation and 10°, 20°, 30° for an east–west orientation) for a limited building area of approximately 300,000 m² was conducted. In PVSyst computer simulations, PVGIS-SARAH2 solar radiation data were used together with the most common data for describing the Polish local solar climate, called Typical Meteorological Year data (TMY). The most energy-efficient variants were found to be 20° S and 30° S, configurations with the highest surface production coefficient (249.49 and 272.68 kWh/m²) and unit production efficiency values (1123 and 1132 kWh/kW, respectively). These findings highlight potential efficiency gains of up to approximately 9% in surface production coefficient and financial returns exceeding 450% ROI, demonstrating significant economic benefits. In economic terms, the 15° S variant achieved the highest values of financial parameters, such as the return on investment (ROI) (453.2%), the value of the average annual share of profits in total revenues (56.93%), the shortest expected payback period (8.7 years), the value of the levelized cost of energy production (LCOE) (0.1 EUR/kWh), and one of the lowest costs of building 1 MWp of a photovoltaic farm (664,272.7 EUR/MWp). Among the tested variants of photovoltaic farms with an east–west geographical orientation, the most advantageous choice is the 10° EW arrangement. The results provide valuable insights for policymakers and investors aiming to optimize photovoltaic deployment in temperate climates, supporting the broader transition to renewable energy and alignment with national energy policy goals. Full article

This work investigates the application of artificial intelligence techniques for optimizing photovoltaic systems using maximum power point tracking (MPPT) algorithms. Simulation models were developed in MATLAB/Simulink (Version 2024), incorporating conventional and intelligent control strategies such as fuzzy logic, genetic algorithms, neural networks, and Deep Reinforcement Learning. A DC/DC buck converter was designed and tested under various irradiance and temperature profiles, including scenarios with partial shading conditions. The performance of the implemented MPPT algorithms was evaluated using such metrics as Mean Absolute Error (MAE), Integral Absolute Error (IAE), mean squared error (MSE), Integral Squared Error (ISE), efficiency, and convergence time. The results highlight that AI-based methods, particularly neural networks and Deep Q-Network agents, outperform traditional approaches, especially in non-uniform operating conditions. These findings demonstrate the potential of intelligent controllers to enhance the energy harvesting capability of photovoltaic systems. Full article

Transparent conductive aluminum-doped zinc oxide (AZO) films were investigated as potential recombination layers for perovskite/silicon tandem solar cells, comparing the results of atomic layer deposition (ALD) and magnetron sputtering (MS) on vertically aligned silicon nanowire (SiNW) scaffolds. Conformality and thickness control were examined by cross-sectional SEM/TEM and profilometry, revealing fully conformal ALD coatings with tunable thicknesses (40–120 nm) versus tip-capped, semi-uniform MS films (100–120 nm). Optical transmission measurements on glass substrates showed that both 120 nm ALD and MS layers exhibit interference maxima near 450–500 nm and 72–89% transmission across 800–1200 nm; the thinnest ALD films reached up to 86% near-IR transparency. Four-point probe analysis demonstrated that ALD reduces surface resistance from 1150 Ω/□ at 40 nm to 245 Ω/□ at 120 nm, while MS layers achieved 317 Ω/□ at 120 nm. These results delineate the balance between conformality, transparency, and conductivity, providing design guidelines for AZO recombination interfaces in next-generation tandem photovoltaics. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Photovoltaic–thermal (PVT) technology has attracted considerable attention for its ability to significantly improve solar energy conversion efficiency by simultaneously providing electricity and heat during the day. PVT technology serves a purpose in condensers and subcoolers for passive cooling in refrigeration systems at night. In our previous work, we proposed a switchable film-insulated photovoltaic–thermal–passive cooling (PVT-PC) module to address the structural incompatibility between diurnal and nocturnal modes. However, the performance of the proposed module strongly depends on two key design parameters: the structural height and the vacuum level of the air cushion. In this study, a numerical model of the proposed module is developed to examine the impact of design and meteorological parameters on its all-day performance. The results show that diurnal performance remains stable across different structural heights, while nocturnal passive cooling power shows strong dependence on vacuum level and structural height, achieving up to 103.73 W/m² at 10 mm height and 1500 Pa vacuum, which is comparable to unglazed PVT modules. Convective heat transfer enhancement, induced by changes in air cushion shape, is identified as the primary contributor to improved nocturnal cooling performance. Wind speed has minimal impact on electrical output but significantly enhances thermal efficiency and nocturnal convective cooling power, with a passive cooling power increase of up to 31.61%. In contrast, higher sky temperatures degrade nocturnal cooling performance due to diminished radiative exchange, despite improving diurnal thermal efficiency. These findings provide fundamental insights for optimizing the structural design and operational strategies of PVT-PC systems under varying environmental conditions. Full article

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article

Meta-heuristic algorithms are the dominant techniques for parameter estimating for solar photovoltaic (PV) models. Current algorithms are primarily designed with a focus on search performance and convergence speed, but they fail to account for the significant difference in the lengths of the feasible regions for each decision variable in the solar parameter estimation problem. The consideration of variable length difference in algorithm design may be beneficial to the efficiency for solving this problem. A gray predictive evolutionary algorithm with adaptive threshold adjustment strategy (GPEat) is proposed in this paper to estimate the parameters of several solar photovoltaic models. Unlike original GPEs and their existing variants with fixed thresholds, GPEat designs an adaptive threshold adjustment strategy (ATS), which adaptively adjusts the threshold parameter of GPE to be proportional to the length of each dimensional variable of the PV problem. The adaptive change of the threshold helps GPEat to select suitable operators for different dimensions of the PV problem. Several sets of experiments are conducted based on single-, double-, and triple-diode models and PV panel models. The experimental results indicate the highly competitive in parameter estimation for solar PV models of the proposed algorithm. Full article

Dynamic and Adaptive solar systems demonstrate a greater potential to enhance the satisfaction of occupants, in terms of indoor environment quality and the energy efficiency of the buildings, than conventional shading solutions. This study has evaluated Dynamic and Adaptive Photovoltaic Shading Systems (DAPVSSs) through a comprehensive analysis of six shading designs in which their energy production and the comfort of occupants were considered. Energy generation, thermal comfort, daylight, and glare control have been assessed in this study, considering multiple orientations throughout the seasons, and a variety of tools, such as Rhino 6.0, Grasshopper, ClimateStudio 2.1, and Ladybug, have been exploited for these purposes. The results showed that the prototypes that were geometrically more complex, designs 5 and 6 in particular, had approximately 485 kWh higher energy production and energy savings for cooling and 48% better glare control than the other simplified configurations while maintaining the minimum daylight as the threshold (min DF: 2%) due to adaptive and control methodologies. Design 6 demonstrated optimal balanced performance for all the aforementioned criteria, achieving 587 kWh/year energy production while maintaining the daylight factor within the 2.1–2.9% optimal range and ensuring visual comfort compliance during 94% of occupied hours. This research has established a framework that can be used to make well-informed design decisions that could balance energy production, occupants’ wellbeing, and architectural integration, while advancing sustainable building envelope technologies. Full article

This study proposes a novel metaheuristic algorithm, Cosmic Evolution Optimization (CEO), for numerical optimization and engineering design. Inspired by the cosmic evolution process, CEO simulates physical phenomena including cosmic expansion, universal gravitation, stellar system interactions, and celestial orbital resonance.The algorithm introduces a multi-stellar framework system, which incorporates search agents into distinct subsystems to perform simultaneous exploration or exploitation behaviors, thereby enhancing diversity and parallel exploration capabilities. Specifically, the CEO algorithm was compared against ten state-of-the-art metaheuristic algorithms on 29 typical unconstrained benchmark problems from CEC2017 across different dimensions and 13 constrained real-world optimization problems from CEC2020. Statistical validations through the Friedman test, the Wilcoxon rank-sum test, and other statistical methods have confirmed the competitiveness and effectiveness of the CEO algorithm. Notably, it achieved a comprehensive Friedman rank of 1.28/11, and the winning rate in the Wilcoxon rank-sum tests exceeded 80% in CEC2017. Furthermore, CEO demonstrated outstanding performance in practical engineering applications such as robot path planning and photovoltaic system parameter extraction, further verifying its efficiency and broad application potential in solving real-world engineering challenges. Full article