Search Results (86)

Sources of renewable energy have attracted considerable attention. Their expanded use will have a substantial impact on both the cost of energy production and climate change. Solar energy is one efficient and safe option; however, solar energy harvesting sites, irrespective of the location, can impact the ecosystem. This experimental study explores the energy available inside and outside of novel miniature energy harvesting cages by measuring light intensity and power generated. Varying light intensity outside the cage has been utilized to study the remaining energy inside the cage of a flexible design, where the heights of the harvesting panels are parameters. Cages are built from custom photovoltaic panels arranged in a staircase manner to provide access to growing plants. The balance between power generation and biological development is investigated. Two different structures are presented to explore the variation of illumination intensity inside the cages. The experimental results show a substantial reduction in energy inside the cages. The experimental results showed up to 24% reduction in illumination inside the cages in winter. The reduction is even larger in summer, up to 57%. The results from the models provide a framework to study the possible impact on a biological system residing inside the cages, paving the way for practical farming with sustainable energy harvesting. Full article

Natural disasters in the United States frequently wreak havoc on critical infrastructure, affecting energy, water, transportation, and communication systems. To address these disruptions, the use of mobile power solutions like PowerKiosk trailers is a partial solution during recovery periods. PowerKiosk is a trailer equipped with renewable energy sources such as solar panels and biogas generators, offering a promising strategy for emergency power restoration. With a daily power output of 12.1 kWh, PowerKiosk trailers can support small lift stations or a few homes, providing a temporary solution during emergencies. Their key strength lies in their mobility, allowing them to quickly reach disaster-affected areas and deliver power when and where it is most needed. This flexibility is particularly valuable in regions like Florida, where hurricanes are common, and power outages can cause widespread disruption. Although the PowerKiosk might not be suitable for long-term use because of its limited capacity, it can play a critical role in disaster recovery efforts. In a community-wide power outage, deploying the PowerKiosk to a lift station ensures essential services like wastewater management, benefiting everyone. By using this mobile power solution, community resilience can be enhanced in the face of natural disasters. Full article

Dual Maximum Power Point Tracking (MPPT) inverters are essential in residential and small commercial solar power systems, optimizing power extraction from two independent solar panel arrays to enhance efficiency and energy harvesting. On the other hand, the Three-Level T-Type Voltage Source Inverter (3L T-Type VSI) is known for its reduced switching losses, improved harmonic distortion, and reduced part count in comparison to other three-level topologies. In this paper, a novel architecture is proposed to integrate the dual MPPT structure directly to each DC-side split capacitor of the 3L T-Type VSI, taking advantage of the intrinsic characteristics of the inverter’s topology. Further performance enhancement is achieved by integrating a classical MPPT strategy to the control framework to make it feasible for a real-case grid integration. The combination of these methods ensures faster and stable tracking under dynamic irradiance conditions. Considering that strategies dedicated to balancing the DC-link capacitor’s voltage slightly affect the AC-side current waveform, an enhanced sliding mode control (SMC) strategy tailored for dual MPPT and 3L T-Type VSI is deployed, combining the simplicity of conventional PI controllers used in the independent MPPT-based DC-DC converters with the superior robustness and dynamic performance of SMC. Real-time results obtained using the OPAL-RT Hardware-in-the-Loop platform validated the performance of the proposed control strategy under realistic test scenarios. The current THD was maintained below 4.8% even under highly distorted grid conditions, and the controller achieved a steady state within approximately 15 ms following perturbations in the DC-link voltage, sudden irradiance variations, and voltage sags and swells. Additionally, the power factor remained unitary, enhancing power transfer from the renewable source to the grid. The proposed system was able to achieve efficient power extraction while maintaining high power quality (PQ) standards for the output, positioning it as a practical and flexible solution for advanced solar PV systems. Full article

With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km². The results show that the method has a high accuracy for the identification of the roof area, with a maximum maxF_β of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. Full article

This study addresses the challenge of effectively modeling uncertainty and hesitation in complex decision-making environments, where traditional fuzzy and vague set models often fall short. To overcome these limitations, we propose the Fermatean neutrosophic vague soft set (FNVSS), an advanced extension that integrates the concepts of neutrosophic sets with Fermatean membership functions into the framework of vague sets. The FNVSS model enhances the representation of truth, indeterminacy, and falsity degrees, providing greater flexibility and resilience in capturing ambiguous and imprecise information. We systematically develop new operations for the FNVSS, including union, intersection, complementation, the Fermatean neutrosophic vague normalized weighted average (FNVNWA) operator, the generalized Fermatean neutrosophic vague normalized weighted average (GFNVNWA) operator, and an adapted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. To demonstrate the practicality of the proposed methodology, we apply it to a solar panel selection problem, where managing uncertainty is crucial. Comparative results indicate that the FNVSS significantly outperforms traditional fuzzy and vague set approaches, leading to more reliable and accurate decision outcomes. This work contributes to the advancement of predictive decision-making systems, particularly in fields requiring high precision, adaptability, and robust uncertainty modeling. Full article

The major challenges in designing a Hybrid Renewable Energy System (HRES) include selecting appropriate renewable energy sources and storage systems, accurately sizing each component, and defining suitable optimization criteria. This study addresses these challenges by employing Particle Swarm Optimization (PSO) within a multi-criteria optimization framework to design an HRES in Kern County, USA. The proposed system integrates wind turbines (WTS), photovoltaic (PV) panels, Biomass Gasifiers (BMGs), batteries, electrolyzers (ELs), and fuel cells (FCs), aiming to minimize Annual System Cost (ASC), minimize Loss of Power Supply Probability (LPSP), and maximize renewable energy fraction (REF). Results demonstrate that the PSO-optimized system achieves an ASC of USD6,336,303, an LPSP of 0.01%, and a REF of 90.01%, all of which are reached after 25 iterations. When compared to the Genetic Algorithm (GA) and hybrid GA-PSO, PSO improved cost-effectiveness by 3.4% over GA and reduced ASC by 1.09% compared to GAPSO. In terms of REF, PSO outperformed GA by 1.22% and GAPSO by 0.99%. The PSO-optimized configuration includes WT (4669 kW), solar PV (10,623 kW), BMG (2174 kW), battery (8000 kWh), FC (2305 kW), and EL (6806 kW). Sensitivity analysis highlights the flexibility of the optimization framework under varying weight distributions. These results highlight the dependability, cost-effectiveness, and sustainability for the proposed system, offering valuable insights for policymakers and practitioners transitioning to renewable energy systems. Full article

The global energy landscape is experiencing growing challenges, with energy crises in regions such as South Africa underscoring the drive to accelerate the shift toward renewable energy solutions. This paper presents an approach for improving solar energy planning, specifically focusing on leveraging the capabilities of the ATlite software in conjunction with custom data. Using mathematical models, ATlite (which was initially developed by the Renewable Energy Group at the Frankfurt Institute for Advances Studies) is a Python software package that converts historical weather data into power generation potentials and time series for renewable energy technologies such as solar photovoltaic (PV) panels and wind turbines. The software efficiently combines atmospheric and terrain data from large regions using user-defined weights based on land use or energy yield. In this study, European Centre for Medium-Range Weather Forecasts reanalysis data (ERA5) data was modified using Kriging to enhance the resolution of each data field. This refined data was applied in ATlite, instead of utilizing the standard built-in data download and processing tools, to generate solar capacity factor maps and solar generation time series. This was utilized to identify specific PV technologies as well as optimal sites for solar power. Thereafter, a simulated power generation time series was compared with measured solar generation data, resulting in a root mean square error (RMSE) of 19.6 kW for a 250 kWp installation. This approach’s flexibility and versatility in the inclusion of custom data, led to the conclusion that it could be a suitable option for renewable energy planning and decision making in South Africa and globally, providing value to solar installers and planners. Full article

A prototype of a mobile electric charging station was developed to simulate the energy supply to a rural medical post. A 20 m² medical post module was built, divided into two rooms (medical staff room and patient room) and a heater, a freezer, a refrigerator, lights and a personal computer were added inside. The mobile electric charging station was made up of an array of 2.88 kW flexible photovoltaic panels, a 48 V and 19.2 kW·h LiFePO₄ battery bank, a charger inverter with a total capacity of 5 kW and a 4 kW electric generator. All of this equipment was placed in an all-terrain pickup truck. Temperature sensors and electrical sensors were installed to evaluate the performance of the prototype in charging and discharging scenarios. Results were obtained according to the operation over 10 months in the city of Arequipa, Peru. The results indicate an indefinite autonomy on clear days, the autonomy varying between 7 and 10 days for a climate with medium cloudiness, and with very cloudy conditions (i.e., with rain), the autonomy is 2 to 3 days. In circumstances of low solar irradiance, the generator had to supply the energy, thereby improving energy autonomy. Full article

To achieve the low-carbon transition in polysilicon production, this study proposes and validates a low-carbon economic dispatch strategy for a renewable hydrogen production and storage system in polysilicon parks based by TD3 algorithm. The study uses XGBoost to construct a surrogate model that reflects the nonlinear physical characteristics of the electrolyzer. Through a comparative analysis of operating strategies in five scenarios and sensitivity assessments of key parameters, complemented by comparisons with dispatch results from the DDPG and DQN algorithms, the effectiveness of the coupled operating strategy for electrolyzers, energy storage, and hydrogen storage devices is fully validated. This highlights the critical role of the TD3 algorithm in strengthening the robustness of the energy system under double-end source-load uncertainties. The results show that batteries flexibly adjust to the time-of-use electricity price, and the coordinated operation of the hydrogen storage devices as well as electrolyzers stabilize the electrolyzer efficiency, reducing the total system cost by 0.027% compared to fixed condition equipment models. The TD3 algorithm shows significant advantages in optimized dispatch, reducing the average daily operating cost by 0.6% and 1.2%, respectively, compared to the DDPG and DQN algorithms, and reducing the carbon emission cost by 2.0% and 12.0%, respectively. A comprehensive analysis shows that the proposed model reduces daily carbon emissions by 29.3% compared to the original system, but also introduces cost pressure, mainly due to the high operating costs of renewable energy equipment such as solar panels. This study provides a practical solution for renewable energy management. Full article

In the space environment, the elastic vibrations of satellite solar panels are caused by various factors that disturb satellite missions. Therefore, we propose a multi-layered high-damping yoke structure based on a passive control method. To optimize the proposed yoke structure, we performed a free vibration test on various multi-layered blade specimens and designed a yoke structure with the maximum damping performance based on the test results. This high-damping yoke structure was mounted on a dummy solar panel with flexible mode (0.79 Hz) and basic characteristic tests were performed to validate the effectiveness of the solar panel vibration suppression. The test results demonstrated that the proposed multi-layered high-damping yoke is effective in suppressing the vibrations of the first and second modes. In addition, a thermal vacuum test was performed to investigate the delamination between multi-layered structures, and the test results proved the applicability of the proposed yoke structure in an actual space environment. Full article

Due to the supply problems of fossil-based energy sources, the tendency towards alternative energy sources is relatively high. For this reason, the use of solar energy systems is increasing today. This study combines experimental data and machine learning algorithms to evaluate the energy performance of four different photovoltaic (PV) panel designs (monocrystalline, polycrystalline, flexible, and transparent) under harsh environmental conditions on Horseshoe Island (Antarctica). In this research, the effects of environmental factors, such as solar radiation, temperature, humidity, and wind speed, on the panels were analyzed. Electrical power output of the PV panels are analyzed using six machine learning models. Random forest (RF) and CatBoost (CB) models showed the highest accuracy and reliability among these models. According to the experimental results, Monocrystalline PV provided the highest electrical power (20.5 Watts on average), and Flexible PV provided the highest energy efficiency (19.67%). However, Flexible PV was observed to have higher surface temperatures compared to the other panel types. Furthermore, using Monocrystalline PV resulted in an average reduction of 4.1 tons of CO₂ emissions per year, demonstrating the positive environmental impact of renewable energy systems. Thanks to this study, renewable energy research for temporary stations in Antarctica will focus on explainable and interpretable artificial intelligence models that will provide an understanding of the factors affecting the energy performance of PV panels. The research results will be an important guide for optimizing energy consumption, management, and demand forecasting in temporary research stations in Antarctica. Full article

Research in renewable energy sources and microgrid systems is critical for the evolving power industry. This paper examines the operational behavior of both pico- and nano-grids during transitions between grid-connected and islanded modes. Simulation results demonstrate that both grids effectively balance the power flow, regulate the state of charge (SOC), and stabilize the voltage during dynamic operational changes. Specific scenarios, including grid disconnection, load sharing, and weather-based energy fluctuations, were tested and validated. This paper models both pico-grids and nano-grids at the Singapore Institute of Technology Punggol Campus, incorporating solar PVs, energy storage systems (ESSs), power electronic converters, and both DC and AC loads, along with utility grid connections. The pico-grid includes a battery storage system, a single-phase inverter linked to a single-phase grid, and DC and AC loads. The nano-grid comprises solar PV panels, a boost converter, a battery storage system, a three-phase inverter connected to a three-phase grid, and AC loads. Both the pico-grid and nano-grid are configurable in standalone or grid-connected modes. This configuration flexibility allows for a detailed operational analysis under various conditions. This study conducted subsystem-level modelling before integrating all components into a simulation environment. MATLAB/Simulink version R2024b was utilized to model, simulate, and analyze the power flow in both the pico-grid and nano-grid under different operating conditions. Full article

In this article, a novel electromagnetic diaphragm pump design controlled by an Arduino NANO microcontroller is proposed to pump liquid inside the pumping chamber completely separated from mechanical and transmission parts. The prototype is primarily based on alternating the polarity of two electromagnets that attract or repel a permanent magnet located on a flexible diaphragm. The system hardware layout is completed by electronic components:. an Arduino NANO microcontroller created by Atmel, Headquarters San Jose, California. and display within the cabinet to control the polarization of the electromagnets and exhibit the temperature inside the pump. The electromagnetic pump and control system consist of innovative approaches as a solution for the treatment of unclean water and integration with solar panel systems. In addition, the measurement tests of the electromagnetic pump, including the temperatures of electromagnets and the quantity of the pumped liquid within the chamber, indicate a dependence on the selected speed of the electromagnet’s polarization. The electromagnetic pump achieves high efficiency as a combination of the temperature and the amount of liquid that can be regulated and controlled by the switching speed of the electromagnet’s polarization. Full article

Owing to their physical and chemical properties and stimuli-responsive nature, gels and hydrogels play vital roles in diverse application fields. The three-dimensional polymeric network structure of hydrogels is considered an alternative to many materials, such as conductors, ordinary films, constituent components of machines and robots, etc. The most recent applications of gels are in different devices like sensors, actuators, flexible screens, touch panels, flexible storage, solar cells, batteries, and electronic skin. This review article addresses the devices where gels are used, the progress of research, the working mechanisms of hydrogels in those devices, and future prospects. Preparation methods are also important for obtaining a suitable hydrogel. This review discusses different methods of hydrogel preparation from the respective raw materials. Moreover, the mechanism by which gels act as a part of electronic devices is described. Full article

When spacecraft execute missions in space, their solar panels—crucial components—often need to be folded, unfolded, and adjusted at an angle. These operations can induce numerous detrimental nonlinear vibrations. This paper addresses the issues of nonlinear and thermal-coupled vibration control within the context of space-based flexible solar panel systems. Utilizing piezoelectric smart hybrid vibration control technology, this study focuses on a flexible plate augmented with an active constrained layer damping. The solar panel, under thermal field conditions, is modeled and simulated using the commercial finite element simulation software ABAQUS. The research examines variations in the modal frequencies and damping properties of the model in response to changes in the coverage location of the piezoelectric patches, their coverage rate, rotational angular velocity, and the thickness of the damping layer. Simulation results indicate that structural damping is more effective when the patches are closer to the rotation axis, the coverage area of the patches is larger, the rotational speed is lower, and the damping layer is thicker. Additionally, the effectiveness of vibration suppression is influenced by the interplay between the material shear modulus, loss factor, and specific working temperature ranges. The selection of appropriate parameters can significantly alter the system’s vibrational characteristics. This work provides necessary technical references for the analysis of thermally induced vibrations in flexible solar sails under complex space conditions. Full article