Renewable Energy Systems Research Articles (Page 5)

Comparative study of climate impacts on the optimal allocation and dispatch of renewable energy systems under Saudi Arabia’s 2030 vision

Synergies and trade-offs between storage, transmission, and sector coupling in high renewable energy systems

Techno-economic analysis of a hydrogen-based hybrid renewable energy system for off-grid power supply in Kenya's urban area, considering the impact of the hydrogen market participation on the economic viability of the system

Abstract This paper addresses the energy challenges faced by off-grid conservation areas, with a focus on the Kinabalu United Nations Educational, Scientific and Cultural Organization (UNESCO) Global Geopark in Sabah. The primary aim is to enhance the existing hydro-genset system through the integration of solar photovoltaic (PV) technology, forming a hybrid renewable energy system (HRES). A comprehensive load analysis was conducted using simulation modelling to understand the energy consumption patterns in the conservation area. The study utilized HOMER Pro software to simulate and compare the performance and economic viability of different configurations, including the existing hydro-genset set system, a full hydropower system, and hybrid hydro-PV systems with 2 kW PV capacities. The optimal design identified as the hydro-PV system was determined based on its ability to operate with a stream flow as low as 2.6 L/s. Under optimal conditions, this configuration produces surplus energy nearly equivalent to the daily load demand and achieves a levelized cost of energy (LCOE) of USD 0.31/kWh. This finding shows that the optimal design has a 17.6% lower LCOE compared to the existing hydro-genset system and a 66% lower LCOE than the full hydropower system.

Efficient thermal energy storage is crucial for sustainable technologies, including solar energy harvesting, electronic device cooling, and battery thermal management. This study investigates the thermal and entropy behavior within a magnetohydrodynamic natural convection environment filled with nano-encapsulated phase change materials (NEPCMs) in a wavy porous triangular enclosure containing a centrally embedded cold cylinder. The main objective is to optimize heat transfer performance and energy storage capabilities through geometric and thermophysical enhancements, while also minimizing irreversibility. The finite element method (FEM) is employed for numerical simulation, while an artificial neural network (ANN), trained using the Levenberg–Marquardt algorithm, provides high-accuracy predictive modeling. Results reveal that increasing Rayleigh number, wall undulations, and NEPCM volume fraction significantly enhance the Nusselt number, indicating improved convective heat transfer. Entropy generation analysis shows that optimal Stefan number and fusion temperature minimize irreversibility. The ANN model achieves near-perfect agreement with FEM data [regression (R) = 0.999 99; mean square error ≈ 0.0014], offering a reliable predictive framework. This integrated computational intelligent approach presents a novel pathway for designing high-efficiency latent heat thermal energy storage systems. The findings hold promise for advanced applications in smart renewable energy systems, electronic cooling devices, and battery management technologies.

Techno-economic evaluation of hybrid renewable energy system integrated with battery energy storage and EV charging through pufferfish optimization algorithm–spiking deep residual network

MG-OPT: intelligent multi-objective Pareto-based optimization framework and transactive energy for Hybrid Renewable Energy Systems with hydrogen integration

Optimal sizing of hybrid renewable energy system with bi-dimensional energy management strategy

Design and optimization of graphene-based two-diamond-shaped solar absorber using Zr-GaSb-Fe3O4 materials for industrial heating renewable energy system with machine learning

Optimizing the design of stand-alone hybrid renewable energy systems with storage using genetic algorithms: Analysis of the impact of temporal complementarity of wind and solar sources

A novel Buck-Boost Modified Series Forward (BBMSF) converter for enhanced efficiency in hybrid renewable energy systems

Advanced hybrid control strategy for a 19-level asymmetrical multilevel inverter in grid-connected hybrid renewable energy systems

Hybrid renewable energy systems for seawater-based green hydrogen in Egyptian coastal zones: A case study

The use of buck converters as DC step-down voltage regulators is increasingly important in various power electronics applications. However, the quality of the output voltage is often disturbed by the presence of ripple, which is influenced by variations in the duty cycle. This study aims to analyze the effect of duty cycle variations on the output voltage and ripple of a buck converter using MATLAB/Simulink simulation. The method used is quantitative simulation by varying the duty cycle from 10% to 90% in a buck converter circuit with fixed parameters: input voltage 30 V, switching frequency 40 kHz, inductor 176.25 μH, and capacitor 44.33 μF. The simulation results show that the output voltage is proportional to the duty cycle, increasing from 3.245 V at D=10% to 26.82 V at D=90%. The highest ripple occurred at D=40% with a value of 0.07 V, while the lowest ripple was at D=50% with a value of 0.0003 V. These findings indicate the existence of an optimal operating point where the system works most stably. This study provides practical guidance in designing efficient and stable buck converters for applications such as battery charging and renewable energy systems.

Abstract The escalating global pursuit of environmentally benign energy alternatives has spurred intensive investigations into sustainable hydrogen generation technologies. Although hydrogen energy can be produced via multiple approaches, the integration of nanotechnology materials in its generation results in its production improvements and efficiency of the production methods. Nanotechnology, with its astonishing ability to control materials at the atomic and molecular scale, has emerged as a vital technology for improving the efficiency and affordability of hydrogen production from renewable energy sources. This technology provides a unique platform for creating materials with specific properties for energy conversion and storage. Nanotechnology is accelerating the transition to a hydrogen economy by boosting hydrogen production efficiency and storage. Its applications span from enhancing water-splitting catalysts to developing advanced membranes and photocatalysts. These nanomaterial-based innovations are crucial for producing clean hydrogen and its effective storage. Nevertheless, nanotechnology highlights the significant role of nanomaterials in overcoming the kinetic challenges associated with hydrogen evolution reactions, which can be attained through several features like increased surface area, enhanced catalytic activity, and improved charge transfer. Therefore, this study explores the latest advancements in nanomaterials and their catalytic impact on hydrogen generation, particularly in photocatalysis, electrocatalysis, and photoelectrochemical systems. The study has examined the nanomaterials' production, characterization, and performance, their integration into renewable energy systems, and their potential for widespread commercial use.

Renewable energy adoption (RNE) has become a worldwide concern owing to its fundamental role in achieving environmental goals. The literature has suggested diverse factors that can influence RNE. However, the role of artificial intelligence (AI) and climate finance in shaping RNE has received little attention. This research examines the role of AI and climate finance in shaping renewable energy, utilizing panel data from 29 high-income countries from 2000 to 2020. The empirical analysis is conducted using panel data estimators such as fixed and effects models and the system generalized method of moments. Moreover, the method of moments quantile regression is used to assess the nonlinear effects of AI on RNE. The results are estimated using Stata software. The empirical outcomes indicate that AI exerts a positive influence on renewable energy. This finding implies that AI initiatives can trigger efforts toward the renewable energy transition. Moreover, the results demonstrate that the marginal effects of AI on RNE vary across different levels of AI. Similarly, climate finance also positively and significantly contributes to renewable energy. Finally, the empirical outcomes demonstrate that climate finance moderates the role of AI in RNE. Policymakers need to focus on AI integration in renewable energy systems by prioritizing climate finance availability in AI applications that support renewable energy development.

As Trinidad and Tobago (TT) navigates the complexities of transitioning its energy mix, the interplay between global climate commitments outlined in the Paris Agreement, combined with efforts to achieve its Nationally Determined Contributions (NDCs) and reduce Greenhouse Gas (GHG) emissions associated with the electricity sector has become increasingly crucial. This case study evaluates the role of Renewable Energy (RE) resources in reducing GHGs in the residential electricity sector of TT. Energy data was utilised to create a simulated load profile of a base model using Hybrid Optimisation Model for Multiple Energy Resources (HOMER) Pro. The base model conducted comparative load analyses simulating various grid-tied and stand-alone RE systems and examined economic and environmental impacts via sensitivity analyses. The simulated 94.9% RE hybrid grid-tied system with a 3 kW wind turbine, 0.96 kW photovoltaics (PV) and 0.69 kW converter performed the best, with the potential to reduce GHGs by 50% (670.535 kg CO2 per year). Simulations incorporating grid sellback and unsubsidised grid purchase prices required larger PV capacity (12 kW) and reduced wind capacity (3 kW), resulting in net CO2 emissions of 682.536 kg CO2 per year). Simulated stand-alone systems require significantly higher RE capacities coupled with energy storage and thus are not financially viable for TT. Lastly, the Levelised Cost of Electricity (LCOE) analysis showed that wind turbines have the greatest impact on GHG savings. These findings are crucial and highlight the potential of RE to reduce GHGs, achieve TT’s NDCs and enhance energy independence, and play a vital role in informing policy.

Hydrogen's extreme flammability and propensity for undetected leaks pose critical safety hazards in renewable energy and industrial systems, yet noble-metal-free sensors face intrinsic limitations in response kinetics and stability. Herein, we report a noble-metal-free hydrogen-sensitive SnO2@WO3 hexagonal nanosheets synthesized via a cluster-nucleus coassembly strategy. The bottom-up coassembly approach directs the interfacial self-assembly of WO3 clusters and SnO2 nuclei, enabling atomic-level coupling at the heterointerface. The SnO2@WO3 heterointerface modulates the W coordination environment, amplifying oxygen vacancy (Ov) density compared to that of pristine SnO2. Remarkably, the sensor based on SnO2@WO3 exhibited unique H2 gas sensing properties in the absence of catalytic sensitization of noble metals, including a high response value (Ra/Rg = 12.06 for 1000 ppm of H2), rapid response time (8 s), excellent selectivity, and long-term stability and durability. The synergy of the two-dimensional nanosheet morphology and interfacial Ov-rich heterojunction facilitates efficient gas diffusion, charge transfer, and dissociation. The H2 adsorption (-1.367 eV) and O2 dissociation (-0.767 eV) at interfacial Ov sites explain the performance enhancement. Furthermore, we present a fully integrated wireless sensor module for real-time H2 monitoring with smartphone visualization via Bluetooth. In addition, we also demonstrated how a sensor-integrated smart car can dynamically inspect hydrogen leaks. This work introduces a new paradigm for designing high-performance tunable heterostructures for next-generation gas detection.

Abstract The sustainability of the electricity market relies on enhanced forecasting of renewable energy sources combined with energy storage arbitrage. This paper introduces a bi-level approach with a novel contribution to wind farms and energy storage to generate maximum revenue in the day-ahead (DA) electricity market. In the first stage, the maximum revenue of the wind farm is estimated by forecasting wind power and market prices. The second stage models the optimal energy arbitrage strategy for storage devices. To solve the first problem, a well-organized Long-Short-Term-Memory (LSTM) combined with recurrent neural network (RNN)—Adam optimizer model is used, and for the second level by using Monte-Carlo optimization, the superior frontier of the revenue is estimated with locational based marginal prices (LBMPs). The solution to the second-level problem delivers a solitary value of the foremost boundary of the revenue and the equivalent charging/discharging schedule. An effective and feasible solution for improving the financial performance of renewable energy and storage systems in the DA market is offered by the suggested framework, which presents a novel approach to incorporating analytical prediction and optimization methodologies.

A hybrid renewable energy system utilizes an SI engine fueled by a syngas–biogas–hydrogen mixture with a wide range of compositional variations. Due to the significantly different stoichiometric air/fuel (A/F) ratios of these fuels, conventional fuel supply systems cannot meet the engine's operational requirements. Simulation results indicate that a 4 mm injector is suitable for biogas/hydrogen but not for syngas, whereas a 9 mm injector is appropriate for syngas but unsuitable for the other fuels. To handle the large variations in composition, a twinning injector system, consisting of two injectors, has been proposed. When the syngas content is low, only one injector operates; as the syngas proportion increases, the second injector is activated. This paper presents the simulation results of the ECU-controlled twinning injector system, along with experimental validation on a Honda GX390 engine.