Renewable Energy Systems Research Articles

The efficiency of photovoltaic (PV) systems is significantly influenced by the tilt angle of solar panels, especially in regions with varying solar insolation across seasons. This study investigates the optimal tilt angle for a 10 kW solar-powered system installed in the Parkent district of Uzbekistan, a region characterized by a continental climate and high solar irradiance. Based on empirical formulas, the research identifies 33° as the fixed optimal tilt angle for year-round operation. Seasonal adjustments offer marginal gains, with two- and four-season tilt configurations improving performance by up to 4%. The findings highlight the importance of site-specific tilt optimization in maximizing solar energy harvesting, which is particularly relevant for autonomous renewable energy systems used in hydrogen production.

Optimizing machine learning models for cytotoxicity prediction in lanthanide-doped nanomaterials: A data-driven approach for minimizing environmental hazards.

The ever-growing load demand and irregularity in the electricity load profile, especially in residential areas, have led to a surge in electricity prices. Rapid advancements in the electricity market and Renewable Energy Systems (RES) have spurred extensive research into energy management through demand side management (DSM) expedited by Smart Home Energy Management Systems (SHEMS). In countries such as Taiwan, where Real-Time-Pricing (RTP) tariff schemes are used, efficient energy management can be achieved by utilizing optimization algorithms. The focus of this study was to use Genetic Algorithm (GA), a nature-inspired optimization algorithm, to achieve efficient energy management in smart homes via Multi-Objective Optimization (MOO). Three objectives are optimized for the home user: namely electricity cost, user comfort, and peak-to-average ratio (PAR). The scheduling problem not only aims for maximum user satisfaction but also considers two user interruption parameters: with penalty and without penalty. The results have shown a 14.56% cost reduction in scheduling without user interruption, 18.62% cost reduction in scheduling considering user interruption (with penalty), and 15.69% cost reduction in scheduling considering user interruption (without penalty). The maximum user comfort was improved by 67.48% (without user interruption), 62.62% (user interruption with penalty) and 41.65% (user interruption without penalty), and the PAR was reduced by up to 51.53% on average. Despite the stochastic nature of electricity consumers, with an optimization system, the cost and peak demand can be curtailed significantly while still maximizing their comfort level.

Hybrid optimization of EV and renewable energy systems using Hazelnut Tree Algorithm and supervised temporal CNN for grid stability

Optimal operational embedding of renewable energy system in a railway marshaling yard

Techno-economic analysis of renewable energy systems with pumped hydro storage for desalinating water in Saudi Arabia

The cannibalization effect of intermittent renewables: Are wind and solar power in Germany still dependent on policy support?

Deep reinforcement learning approach for hybrid renewable energy systems optimization

A data-driven hierarchical dispatch framework for village renewable energy systems: Integration of user portrait cluster and cross-tier coordination based on ADMM

Multi-criteria decision-making and uncertainty analyses of off-grid hybrid renewable energy systems for an island community

The project “Dynamic Signal System Analysis of Resonant 3-Phase Wireless Energy Transfer” focuses on developing a highly efficient and contactless power transmission system using the principle of resonant inductive coupling. The main aim of this work is to analyze and improve the dynamic behaviour of signals in a three-phase wireless energy transfer network to achieve stable and balanced power delivery. In this system, electrical energy from a three-phase source is transmitted wirelessly through tuned resonant coils that operate at the same frequency, ensuring maximum coupling efficiency. The dynamic signal analysis continuously monitors voltage, current, and phase variations, enabling the system to maintain resonance under different load or distance conditions. This approach reduces power loss, enhances transfer efficiency, and ensures smooth operation without physical connections. The proposed design combines both simulation and hardware implementation to study real-time signal characteristics and performance. The results demonstrate that dynamic analysis improves system reliability, frequency stability, and phase synchronization. This project provides a strong foundation for future applications such as wireless charging of electric vehicles, industrial automation, and renewable energy systems, where efficient and safe wireless power transfer is essential.

The global shift toward sustainable energy highlights the critical role of renewable energy systems (RES) in addressing climate change, energy insecurity, and economic development. This study conducts a techno-economic assessment of solar photovoltaic (PV), wind, and biomass energy systems in Nigeria, examining their potential contributions to the United Nations Sustainable Development Goals (SDGs). Secondary data from international and national energy agencies, complemented by peer-reviewed literature, were analyzed to evaluate technical feasibility, economic viability, and developmental impact. Findings reveal that solar PV holds the greatest promise due to Nigeria’s high solar irradiance and rapidly declining costs, while wind and biomass provide complementary options in specific regions and rural areas. Levelized cost of electricity (LCOE) analysis indicates that renewable energy technologies are increasingly competitive with fossil fuels, with solar PV and wind now achieving cost parity. Beyond economic viability, renewable energy expansion contributes significantly to SDGs, particularly SDG 7 (Affordable and Clean Energy), SDG 8 (Decent Work and Economic Growth), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). The study concludes that renewable energy adoption is both a strategic and practical pathway to sustainable development in Nigeria. However, infrastructural limitations, inconsistent policies, and financing barriers continue to constrain progress. It recommends strengthening regulatory frameworks, promoting innovative financing models, investing in grid and off-grid infrastructure, and fostering public awareness and community participation. This research underscores the urgency of accelerating renewable energy deployment to enhance energy security, stimulate green growth, and position Nigeria to achieve its SDG commitments by 2030.

The global rise in carbon emissions has intensified the urgency of transitioning toward renewable, environmentally friendly and sustainable energy systems, particularly in industrial sectors with high fossil fuel dependency such as oil and gas. Solar panels represent a clean and reliable alternative for electricity generation. This research evaluates the performance of a 119.88 kWp monocrystalline solar panel system integrated with an Internet of Things (IoT)-based on-grid monitoring system at the Grissik Administration Building. Over a 30-day observation period, the solar panels supplied an average of 432 kWh/day, approximately 72.07% of the installed capacity, reducing fuel gas consumption by 0.19 MMSCFD and lowering CO₂ emissions by 10.38 tons. System efficiency exceeded 80% under optimal irradiation conditions. The IoT-based monitoring platform facilitated real-time data and system control, improving operational decision-making and reliability. This research provides novel empirical evidence of field-scale performance of IoT-integrated photovoltaic systems within Indonesia’s oil and gas facilities, demonstrating their significant role in enhancing industrial energy efficiency and supporting the national clean energy transition.

ABSTRACT High-gain DC-DC converters are becoming increasingly common in solar PV systems and renewable energy applications. This article presents a non-isolated, non-coupled inductor-based high-gain DC-DC boost converter that offers high voltage gain at reduced duty ratios while ensuring low voltage stress on controlled power switches. The proposed converter is well-suited for boosting low-input DC voltage from distributed generation sources, such as fuel cells or photovoltaic (PV) systems, to a significantly higher DC voltage. With just two switches controlled by a single PWM signal, the topology simplifies control, reduces weight, minimizes cost, and enhances compactness. To further optimize performance, a machine learning-based predictive algorithm using a paragraph model is integrated into the converter's control system. This model analyzes historical and real-time data to dynamically adjust the duty cycle, improving voltage regulation and efficiency under varying load and input conditions. By leveraging machine learning, the converter can predict optimal switching patterns, reducing losses and enhancing stability in renewable energy applications. A comparative analysis with existing high-gain boost converters demonstrates that the proposed model outperforms previous topologies across multiple performance metrics. A 300 W hardware prototype is developed and tested in a laboratory environment to validate the theoretical claims. The proposed topology achieves a high gain of approximately 12 times the input voltage, with a reduced duty ratio—11.25 at a duty cycle of 0.6 and 17.77 at a duty cycle of 0.7. Efficiency ranges between 92.5% and 94.5%, making it suitable for medium-to-high power applications. The integration of machine learning further enhances system adaptability and operational efficiency, making the converter an ideal solution for next-generation sustainable energy systems requiring high output voltage and improved performance.

The rapid development of renewable energy systems has created an unprecedented demand for critical minerals, which places the United States at a crossroads of energy security, environmental sustainability and social responsibility. Traditionally, mining has focused on production efficiency, often leading to extensive environmental damage, displacement of communities and worker safety concerns, thereby undermining long-term sustainability goals. This paper introduces a multi-objective optimization model that combines Artificial Intelligence with ethical considerations and sustainable development goals, which offers a rigorous approach to responsible critical mineral mining across the United States. The paper conducts a systematic literature review to examine five key areas: 1) the vulnerabilities of the critical mineral supply chain and its geopolitical implications; 2) the environmental and social impacts of conventional mining; 3) the role of Artificial Intelligence in the mining industry; 4) ethical principles guiding responsible AI management; and 5) multi-objective optimization of decision-support systems. Synthesis of recent empirical research shows that AI technologies improve ore-grade prediction accuracy by about 30 percent, however, geopolitical risks significantly influence mineral price volatility and supply stability. The analysis reveals that 54% of global mining operations are located on Indigenous land, often without permission and that by 2035, automation could displace 30-45% of the mining workforce. The proposed framework addresses the complex trade-offs among production efficiency, environmental protection, social equity and economic viability, using advanced optimization algorithms. These algorithms incorporate environmental monitoring, community impact considerations and regulatory compliance, which ensures comprehensive decision-making.

Heat sinks play a vital role in maintaining the safe operation and reliability of electronic and mechanical systems by preventing thermal failure. Their efficiency directly determines device performance, energy consumption, and lifespan. However, conventional heat sinks are often limited by geometric constraints and the thermal capacity of base fluids. The present study addresses these challenges by introducing a hybrid approach that combines optimized V-shaped wing geometries (passive enhancement) with radiative nanofluids (active enhancement), offering a practical pathway to significantly improve heat dissipation in compact, high-performance thermal systems. A novel hybrid cooling strategy is proposed that integrates V-shaped lateral wings (passive technique) with radiative graphene-based nanofluids (active technique) under magnetic field effects to enhance natural convection in finned heat sinks. The numerical investigation is realized using COMSOL Multiphysics based on the finite element method. The enhancement process begins by equipping Conventional Fins (CF) with Classic Wings (FCW). In a Further step, V-Shaped Wings (FVSW) replace the standard wings to improve natural convection. The influence of the opening angle of the V-shaped wings ([Formula: see text]) is also explored to optimize thermal management. Key parameters studied include buoyancy forces ([Formula: see text] Ra [Formula: see text], thermal radiation (0 [Formula: see text] 1), magnetic field strength (0 [Formula: see text] Ha [Formula: see text] 50), and the concentration of graphene nanoparticles (0[Formula: see text]4[Formula: see text]. The findings reveal that adding classic wings (FCW) to fins greatly increases heat sink performance through convective heat transfer, with a 25.03% improvement over conventional fins (CF), while adopting V-shaped wings further improves efficiency by 16.43%. Optimization of the V-wing opening angle yields an additional 13.10% enhancement, and radiative nanofluids raise overall efficiency by up to 27.76%. These results demonstrate, for the first time, the synergistic effect of passive — active enhancement methods in improving heat sink performance, providing a new pathway for advanced thermal management in next-generation electronic cooling systems. The proposed design is particularly relevant for real-world applications in advanced thermal management, including compact electronic devices (CPUs, GPUs, LEDs), electric vehicles, and renewable energy systems, where efficient, space-saving, and reliable cooling strategies are essential.

The growing need for fast, efficient, and durable energy storage, especially in electric vehicles and renewable energy systems, has made supercapacitors (SCs) increasingly important. Their ability to charge rapidly, deliver high power, and withstand thousands of cycles makes them a strong alternative to traditional batteries. In this study, we present a simple and effective method for the in situ polymerization of polymerize polypyrrole (PPy) on copper (Cu) foam using femtosecond (Fs) laser pulses. This single-step process enables direct polymerization of the pyrrole monomer on the Cu foam surface, creating a uniform, binder-free and strongly adhered PPy layer. Electrochemical testing reveals that the fabricated electrode (Femto-Cu PPy) exhibits significantly enhanced capacitance (148.5 mF cm-2 at 0.5 mA cm-2), stable performance at various current densities, and retains approximately 78.6% of its capacity after 10 000 charge-discharge cycles. Furthermore, well-established models such as Lindström's, Trasatti's, and Dunn's methods were employed to analyze and quantify the charge storage mechanism. Overall, this approach offers a practical and scalable method for producing high-performance SC electrodes by integrating laser processing with conductive polymer chemistry.

Energy poverty—defined by inadequate access to reliable and affordable energy services—continues to pose a major barrier to economic development, agricultural productivity, and environmental sustainability in Sub-Saharan Africa (SSA). This study explores the complex interrelationship between energy poverty, environmental degradation, and agricultural productivity in SSA, with the aim of extracting policy insights relevant to Vietnam, a developing country facing similar rural energy access challenges. Drawing on a comprehensive literature review and empirical evidence from SSA, the study identifies that rural electrification significantly boosts agricultural productivity, while environmental degradation exerts a detrimental effect. The impact of renewable energy adoption is found to be context-dependent, with both enabling and constraining factors. Using qualitative synthesis and comparative case study analysis, the research contextualizes these findings within Vietnam’s rural development landscape. The results suggest that Vietnam can strengthen energy access and agricultural outcomes by expanding rural electrification programs, investing in decentralized renewable energy systems, and enforcing environmental regulations to prevent resource degradation. Policy recommendations include promoting public-private partnerships, supporting digital innovations for precision agriculture, and tailoring energy strategies to local socio-economic conditions. By leveraging lessons from SSA’s experiences, Vietnam can make strategic progress toward achieving energy equity and sustainable development in its rural sectors.

In the context of harmonizing environmental protection and pollution control requirements, conventional power grids necessitate increased energy and resource consumption, which imposes higher demands on energy utilization. Consequently, flexible interconnection technologies for grid integration of new energy systems have become increasingly imperative. However, in the flexible interconnection new energy grid-connection structure system, the local control of the flexible interconnection device relies on rapid response logic, and the coordination between new energy and energy storage systems focuses on smoothing output fluctuations. Combined with long-term output prediction and optimized dispatching strategies, the power of new energy can be more stably integrated into the power grid. This paper conducts a comprehensive analysis of flexible interconnected power control strategies, categorizing application scenarios for hybrid distributed and centralized renewable energy systems. It adopts a hierarchical power allocation framework and an energy management strategy centered on optimizing energy utilization. While prioritizing the consumption of renewable energy generation and ensuring system stability, the study elucidates the synergistic logic among various components. This provides a practical approach for the efficient grid integration of diverse renewable energy sources, offering substantial significance for enhancing system stability and renewable energy utilization efficiency.

Hybrid energy systems (HESs) have garnered significant interest in recent years because they combine many energy sources to enhance efficiency and dependability. This review article thoroughly examines the most effective design approaches and tactics for improving performance in hybrid energy systems through efficient energy management. The problem encompasses multiple aspects of HES design optimization, such as identifying the most efficient component sizes, choosing the most appropriate technology, and setting up the system. Furthermore, it involves implementing an energy management system (EMS) to optimize the system’s overall efficiency. Moreover, this article examines difficulties, current progress, and potential research prospects. A hybrid system, which integrates renewable sources with backup units, provides a cost-efficient, eco-friendly, and dependable energy supply and outperforms single-source systems in satisfying diverse load requirements. An essential factor in these hybrid systems is the precise evaluation of the ideal dimensions of the components to ensure that they sufficiently meet all the load requirements while minimizing both the initial investment and ongoing operating expenses. This study extensively examines suitable methods for determining the proper sizes, as the current body of literature describes. These methods can significantly enhance renewable energy systems’ economic feasibility and practicality, promoting their wider adoption.