Renewable Energy Systems Research Articles

Marine structures used in aquaculture farms and offshore renewable energy systems are constantly exposed to biofouling, the unwanted accumulation of microorganisms, algae, and invertebrates on submerged surfaces, which increases maintenance costs and reduces operational efficiency. This study develops antifouling (AF) materials based on a LLDPE matrix modified with graphite oxide (GrO). GrO is synthesized by treating graphite with a mixture of sulfuric and phosphoric acids, and the product with intermediate oxidation is selected for incorporation into polyethylene matrices at 1, 3, and 5 wt%. Test panels are prepared by molding the composites and subsequently exposed to real marine conditions in two zones—splash (0.1 m) and submerged (0.6 m)—for 12 months. Results show that panels containing 5 wt% GrO present a significant reduction in biofouling biomass compared to the unmodified polyethylene control, particularly under splash conditions where organism colonization is most aggressive. Statistical analysis confirms significant differences between GrO‐modified panels and the control ( p < 0.05), with biofouling reduction reaching 305% for PE‐5GrO. These findings validate the AF efficacy of GrO and demonstrate its applicability in aquaculture and marine renewable energy systems. Further studies should address ecotoxicity, leaching dynamics, and mechanical durability under open‐sea conditions.

This report evaluates the role of Hybrid Renewable Energy Systems (HRESs) in supporting South Africa’s energy transition amidst persistent power shortages, coal dependency, and growing decarbonisation imperatives. Drawing on national policy frameworks including the Integrated Resource Plan (IRP 2019), the Just Energy Transition (JET) strategy, and Net Zero 2050 targets, this study analyses five major HRES configurations: PV–Battery, PV–Diesel–Battery, PV–Wind–Battery, PV–Hydrogen, and Multi-Source EMS. Through technical modelling, lifecycle cost estimation, and trade-off analysis, the report demonstrates how hybrid systems can decentralise energy supply, improve grid resilience, and align with socio-economic development goals. Geographic application, cost-performance metrics, and policy alignment are assessed to inform region-specific deployment strategies. Despite enabling technologies and proven field performance, the scale-up of HRESs is constrained by financial, regulatory, and institutional barriers. The report concludes with targeted policy recommendations to support inclusive and regionally adaptive HRES investment in South Africa.

In this paper, I introduced a new Voltage Stabilization Mechanism of Renewable Energy Systems using Differential Equations by integrating a Hybrid Reinforcement Learning (RL) system with Predictive Modeling. The proposed system uses TensorFlow (with TensorFlow Agents) to do adaptive voltage control and MATLAB to do modeling and simulation. By applying the reinforcement learning, the system will dynamically react to the change in renewable energy production such as wind and solar energy generation, and the predictive modeling component will foresee any changes in voltage and adjust the control strategy. The approach performs better than conventional methods such as PID control, linear state-space control, and fuzzy logic-based systems and attains 95 percent voltage stability and 20 percent power loss reduction. It has demonstrated that renewable energy networks on a large scale can be effectively and reliably voltage stabilized, and has a bright future in model-building, preconditioning its application to practice.

ABSTRACT Three‐level bidirectional buck/boost converters (TLBBBCs) have been widely adopted in renewable energy systems and grid‐tied storage. Still, their efficiency at high switching frequencies remains limited by switching losses. So, this paper introduces a fundamental innovation, which is a novel active auxiliary circuit. The novel active auxiliary circuit enables the generation of bidirectional, discrete triangular‐wave currents. These triangular‐wave currents can create zero‐voltage switching (ZVS) conditions for all main switches, significantly reducing overall switching losses across wide load ranges. First, building upon this core innovation, a family of novel ZVS TLBBBC topologies is systematically derived, so that engineers can choose an optimal one according to actual requirements. Then, Topology 1 and Topology 4 are selected as representative cases. Their operating principles, circuit characteristics, and conditions for maintaining soft‐switching are explored in depth, providing detailed guidance for the design process of practical applications. Finally, to confirm the analytical study's effectiveness, the experimental results of two representative proof‐of‐concept model circuits are presented. Experimental results demonstrate that the topologies proposed in this paper can all maintain an excellent efficiency of over 96% within the load range from 20% to 100% and achieve efficiency improvements of up to 2.96% (Topology 1) and 2.62% (Topology 4) under full‐load operation.

Efficient BLDC motor control using hybrid crayfish optimization and sine cosine algorithm in renewable energy systems

A Review of Machine Learning Approaches for Optimizing Hybrid Renewable Energy Systems (HRES) in Decentralized Smart Grids: Enhancing Energy Efficiency and Grid Stability

This study proposes a novel Fermatean fuzzy (FF) multi-criteria decision-making framework to evaluate renewable energy systems under uncertainty, focusing on sustainable development and efficient energy utilisation. The novelty lies in developing a new scoring function for FF sets that enhances the differentiation of closely ranked alternatives and in extending step-wise weight assessment ratio analysis and logarithmic percentage change-driven objective weighting methods into the FF domain for computing subjective and objective weights, respectively. The integrated framework employs the distance-based evaluation from average solution method to rank five renewable sources, solar, wind, hydro, biomass, and geothermal across four sustainability main criteria and eighteen sub-criteria. Data are collected through structured expert surveys involving professionals from the renewable energy sector. The analysis identifies policy alignment (weight: 0.09985) and emission reduction potential (weight: 0.07838) as the most influential criteria. Solar energy ranks highest with a performance score of 0.83208, followed by wind (0.65355), while biomass scores lowest (0.24781). Sensitivity and comparative analyses validate the robustness and consistency of the proposed model. The results suggest prioritising solar and wind energy and offer actionable insights for policymakers to develop resilient and uncertainty-aware renewable energy strategies.

Abstract The growing urban population and increasing climate anomalies pose persistent challenges to urban resilience by threatening food, water, and energy security and intensifying land-use competition. Utilizing urban rooftops for gardening, rainwater harvesting, and renewable energy systems offers a sustainable pathway to mitigate these pressures. This study develops a geospatial framework to evaluate the sustainable use of built-up areas in Coimbatore, India, using open-access geospatial datasets. Spatial and economic assessments were conducted to estimate the feasibility and revenue potential of rooftop gardening, rainwater harvesting, and photovoltaic installations. Our results reveal the economic potential of 368,748 rooftops in Coimbatore, with the projected revenue. Photovoltaic systems could generate ₹ 28.58 billion, while rooftop gardens and rainwater harvesting contribute ₹ 15.79 billion and ₹ 0.34 billion, respectively. Crop-specific analysis identified chillies as the most profitable rooftop crop, with a potential revenue of ₹ 38.51 billion, whereas coriander showed the lowest at ₹ 4.57 billion. These findings highlight the economic and environmental opportunities associated with rooftop agriculture and renewable energy systems, emphasizing their role in sustainable urban planning Open-access satellite imagery proved to be an invaluable tool in assessing the potential of rooftop spaces, offering valuable insights for urban planners and policymakers.

Sindh’s clean energy transition reflects a complex interplay of social readiness, technical capacity, and institutional coherence. Despite Sindh’s strong renewable potential, averaging 5.8 kWh/m²/day of solar irradiance, progress remains constrained by governance failures, financial limitations, and coordination gaps. This study evaluates the drivers and barriers shaping clean energy initiatives (CEIs) in Sindh using an integrated Force Field Analysis (FFA) and Multi-Level Perspective (MLP). Primary data were collected from 35 semi-structured stakeholder interviews conducted in Hala (Matiari) and Khairpur Districts, supported by provincial policy documents and secondary literature. The results identify 30 major forces: 11 enablers and 19 barriers operating across social (niche), technical (regime), and political-economic (landscape) levels. Social participation and women-led enterprises emerged as key enablers, whereas low technical literacy, fragmented institutions, and weak financing systems persisted as dominant constraints. The force field analysis score (ΔF = -67.1) indicates a negative transition momentum, suggesting that Sindh’s clean-energy transformation is socially emergent but structurally constrained. Theoretically, the study advances transition research by operationalising the MLP through quantitative force weighting, offering a diagnostic framework to assess transition fragility in developing-country contexts. Practically, it underscores the need for governance coherence, financial innovation, and community-based energy literacy to transform scattered pilot projects into resilient, inclusive renewable energy systems.

Purpose In renewable energy systems, supraharmonics as a new type of power quality problem generated by power electronic devices have received extensive attention. Taking three-phase grid-connected inverters with multiple different switching frequencies for this study, a supraharmonic coupling analysis method is proposed, and a supraharmonic coupling model is established to solve the frequency coupling phenomenon caused by supraharmonic propagation among multiple inverters in the renewable energy system. The purpose of this study is to analyze the coupling characteristics of supraharmonics during the propagation process among multiple grid-connected inverters with different switching frequencies. Design/methodology/approach By studying the propagated supraharmonics during parallel operation of inverters with different switching frequencies and analyzing the coupling mechanism of supraharmonics in the inverter A/D sampling and sinusoidal pulse width modulation (SPWM) process, a coupling analysis model of supraharmonics, including the control system, is established to reveal the frequency coupling characteristics of supraharmonics in multiple inverters. Findings The supraharmonic coupling among multiple inverters is mainly affected by the A/D sampling and SPWM modulation process of the inverter control loop, and supraharmonics will generate additional sideband supraharmonic frequency components in the control loop. Originality/value In this paper, a supraharmonic coupling model is established to solve the frequency coupling phenomenon caused by parallel operation of inverters with different switching frequencies in renewable energy systems. The model can accurately describe the coupling characteristics of supraharmonics among inverters and the simulation cases and experimental results show that the accuracy of the established model is about 90%, which can provide theoretical guidance for improving the safety of new energy grid-connected electronic devices.

Investigation of thermal behaviour of tetra nanofluids is a rich research direction for enhanced thermal efficiency, optimizing the renewable energy systems by improving the fluid properties, and in heat exchangers for reducing energy consumption and operational costs. Additionally, the physical effects like magnetic field, combined convection, dissipation, Joule heating, porous matrix, and heating source are also help to optimize the performance. Thus, the study is performed for tetra nanofluid through an operational system of vertical thin needle under mentioned effects for assisting (:lambda:>0), opposing (:lambda:<0) and forced convection (:lambda:=0) cases. The problem formation process completed with the help of similarity transforms and enhanced properties of tetra nanofluid and then bvp4c methodology exercised for the results. The study reveals that the velocity ratio and needle thickness minimize the temperature. Thus, designing of the device (:c=text{0.1,0.2,0.3,0.4}) would help to maintain the temperature. Further, heat dissipation, heating source and magnetic field optimizing the efficiency at appropriate ranges. The transfer of heat at the needle’s surface minimizes for opposing than forced convection and aiding cases. The shear drag can be minimized by solidification of :M,:{F}_{r} and :{D}_{a} in the range of :text{0.0,0.1,0.2,0.3,0.4}. The opposing cases provided optimum decline in the shear drag as compared to forced and assisting cases, respectively. Further, thermal conductivity varies from 1.0032 to 1.00379, from 1.00148 to 1.00177, from 1.00079 to 1.00094, from 1.00003 to 1.00018, under :varphi:=0.001% to :varphi:=0.006%. Further, the study would help to explore eco-friendly and stable nanofluid composites to enhance sustainability and the applications in industries, manufacturing and energy efficiency should be prioritized to accelerate theoretical outcomes into the real-world heat transfer issues.

In recent years, the global energy crisis, concerns about energy security and grid parity, and the pressure to develop policies for reducing the environmental impact of anthropogenic activities have accelerated investments in renewable energy. A growing body of literature applies the real options approach (ROA) to renewable energy projects, recognizing its value in capturing irreversibility and flexibility under uncertainty. The present work provides a detailed state-of-the-art analysis on the adoption of real options to evaluate mixes of energy technologies for power generation, with a special emphasis on investments in hydropower and solar photovoltaics. The objective is to assess current applications, identify knowledge gaps, and outline priorities for advancing decision-making tools in this domain. We performed a systematic literature review following the PRISMA protocol, identifying 38 papers from the Scopus database up to February 2024. Eligible studies were peer-reviewed articles in English applying the ROA to power generation, following a technology selection process; policy evaluation or research and development studies were excluded. The selected papers were analyzed to identify trends over time and space, adopted energy technology, types of real options with valuation methods, and sources of uncertainty. The present paper also discusses the main findings and emerging gaps, providing an overview of hybrid renewable energy systems. Our analysis suggests that, despite the significant advances achieved in this area, further research is needed to exploit the potential of the ROA in investment decisions for combined renewable energy technologies, especially in cases where internal uncertainty and community perspectives need to be explicitly considered. By linking the ROA to the challenges of mixed renewable energy projects, this study enhances understanding of investment decision-making under uncertainty and identifies pathways toward more robust and adaptive project evaluation.

Energy is one of the fundamental requirements of human society. With the ever-increasing demand for energy, renewable energy sources have emerged as a promising solution to address environmental concerns and ensure sustainable development. The Internet of Things (IoT) has revolutionized various industries, and its integration with renewable energy systems offers significant potential for enhancing efficiency, reliability, and cost-effectiveness. This comprehensive review provides an in-depth analysis of the role of IoT in different renewable energy sectors, including solar, wind, hydroelectric, tidal, biogas, and geothermal energy. We explore how IoT-enabled technologies, such as sensors, actuators, and data analytics, are transforming the renewable energy landscape. Furthermore, we discuss the benefits and challenges associated with IoT integration and identify future trends and research directions. By leveraging the power of IoT, we can unlock the full potential of renewable energy and move towards a sustainable and environmentally friendly energy future.

Offshore Carbon Capture, Utilization, and Storage (CCUS) is emerging as a critical strategy for achieving net-zero emissions, offering significant storage potential in depleted hydrocarbon reservoirs and deep saline aquifers while leveraging existing offshore infrastructure. This review summarizes recent advances in capture, transport, utilization, and storage technologies in the offshore industry. Case studies including Sleipner, Gorgon, and Northern Lights illustrate both the technical feasibility and the operational, economic, and regulatory challenges associated with large-scale deployment. While post-combustion capture and pipeline transport remain the most technologically mature approaches, significant uncertainties continue to exist regarding the logistics of marine transportation, reservoir integrity, and the robustness of monitoring frameworks. Policy and regulatory complexity, coupled with high capital costs and public acceptance issues, continue to constrain commercial viability. This review highlights that offshore CCUS holds significant promise but requires advances in monitoring technologies, cost reduction strategies, and harmonized international governance. Future research should focus on integrating CCUS with hydrogen production and renewable energy systems to accelerate large-scale deployment.

Access to reliable and affordable electricity remains a major challenge for rural communities in Nigeria, particularly in Kwara State, where weak grid infrastructure and reliance on costly fossil fuels constrain socio-economic development. Despite abundant renewable resources such as solar, wind, hydro, and biomass, these remain underutilized. This study designed and optimized Hybrid Renewable Energy Systems (HRES) for three off-grid communities—Owode, Abe-Eya, and Ila-Oja Aboto—using a mixed-method approach that combined household energy surveys, field-based resource assessments, and techno-economic simulations. HOMER Pro (v3.14.2) was used to develop optimal hybrid configurations integrating solar photovoltaic (PV), wind, and biomass systems, while RETScreen Expert evaluated financial and environmental performance. Results showed strong solar irradiance (~5.85 kWh/m²/day), moderate wind speeds (3.8–4.2 m/s) in Abe-Eya, and abundant biomass availability (1.5–2.2 tons per household per year) in Owode and Ila-Oja Aboto. The optimal configuration included 54 × 215 W PV modules, 2 × 1 kW wind turbines, 4 × 1 kW batteries, and 34 kVA converters, with installation costs of ₦22.32 million (Owode), ₦24.48 million (Abe-Eya), and ₦24.96 million (Ila-Oja Aboto), generating 25,754–31,643 kWh annually. Economic analysis indicated strong viability with NPV &gt; ₦550 million, IRR of 142–158%, payback periods of 3.2–3.5 years, and benefit–cost ratios of 1.15–1.17. Greenhouse gas emissions decreased by 63–70%, from 6,000–7,500 kg CO₂/year to 613–959 kg CO₂/year. Sensitivity analysis confirmed robustness under ±25% cost variation. The study demonstrates that community-specific solar-biomass and solar-wind hybrid systems provide reliable, cost-effective, and sustainable electrification pathways for rural Nigeria, advancing the national energy transition and Sustainable Development Goal 7.

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions.

Meeting the growing global electricity demand in remote and off-grid regions requires cost-effective and reliable power solutions that overcome the intermittency of renewable energy sources. This paper presents a comprehensive techno-economic optimization framework for the design and operation of off-grid hybrid renewable energy systems (HRES) integrating photovoltaic (PV), wind turbine, biomass generator, diesel backup, and a dual-chemistry hybrid battery energy storage system (HBESS) combining lithium-ion and nickel-iron batteries. A detailed mathematical modeling approach is employed to capture the nonlinear dynamics, stochastic renewable behavior, battery degradation, and temperature-adjusted component efficiencies. The system is formulated as a multi-objective mixed-integer nonlinear programming problem targeting the minimization of life cycle cost (LCC), levelized cost of energy (LCOE), and CO 2 emissions while satisfying reliability constraints such as loss of power supply probability (LPSP &lt; 0.01). To solve the optimization problem, advanced metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Grey Wolf Optimizer (GWO), and Differential Evolution (DE), and Salp Swarm Algorithm (SSA)—and a Deep Q-Network (DQN)-based reinforcement learning energy management strategy are implemented and benchmarked. The proposed DQN-based controller demonstrates superior performance over conventional rule-based and static dispatch methods by maintaining more stable battery state-of-charge (SOC) profiles, reducing degradation, and enabling intelligent real-time decision-making. Simulation results based on realistic meteorological and demand profiles reveal that the integrated DQN and HBESS strategy reduces total LCC by over 20%, CO 2 emissions by up to 30%, and battery degradation costs by over 10% compared to baseline systems. The Salp Swarm Algorithm (SSA) achieves the fastest convergence and the highest-quality Pareto-optimal solutions among all metaheuristics evaluated. Sensitivity analysis identifies diesel price and interest rate as the most influential parameters on LCOE, while load shifting through aggressive demand-side management further minimizes battery usage, operating costs, and emissions. The proposed framework not only addresses key challenges in off-grid microgrid design but also provides a scalable and robust pathway for sustainable rural electrification using hybrid storage and intelligent control.

The development of solar power plant (SPP) technology presents an effective solution for providing electrical energy in remote or off-grid areas, particularly through the use of portable solar systems. This study discusses the design and implementation of a portable solar power generation system utilizing solar panels, a solar charge controller (SCC), a battery (accumulator), an inverter, and the integration of an ESP32 microcontroller with a PZEM-004T sensor module for real-time monitoring via the Blynk IoT platform. The system converts solar energy into electrical energy, stores it in a battery, and supplies it to AC loads through an inverter. The test was conducted to evaluate the accuracy of voltage and current measurements using a multimeter as a standard. The voltage sensor showed a slight deviation from multimeter readings with error percentages ranging from -0.0013% to -0.0057%. The current measurements remained stable with a consistent error of -0.66%. These results indicate that the sensor modules provide reliable data for monitoring purposes, validating their application in portable and renewable energy systems. The integration of IoT-based monitoring enhances usability and system reliability, especially for outdoor or mobile applications such as camping or emergency backup power.

Green IoT (Internet of Things) refers to the integration of environmentally friendly practices and technologies in the development and implementation of IoT systems. The main goal of Green IoT is to minimize the environmental impact of IoT devices and networks, both during their production and throughout their lifecycle. This includes reducing energy consumption, minimizing electronic waste, and optimizing resource usage. The technical aspects of Green IoT involve the integration of IoT devices and sensors with renewable energy systems, energy storage systems, and energy management systems. These systems are connected to the internet, allowing for real-time monitoring, analysis, and control of energy usage. Green IoT also known as sustainable IoT, it is the marriage of internet of things technology and environmentally friendly practices. It aims to minimize the ecological impact of technology while enhancing resource management and efficiency.

This research optimizes solar, fuel cell, and battery systems for near-fault current, efficiency, and low-transient charging and discharging to extend battery life. Replicating these energy sources on the grid requires MATLAB Simulink assessment and coordination. Goals include assessing PV, Fuel cell, and battery dependability, maintaining load demand, and controlling power generation to reduce battery stress. Battery power management can improve fuel cell longevity and efficiency, and optimizing peak loads can reduce big spikes. Connecting the PV, fuel cell, and battery systems in MATLAB Simulink will simulate load demand and share electricity proportionally. To balance power output, load fulfilment ratios will be based on source capacity and efficiency. This is 2kW from the photovoltaic system, 6kW from the fuel cell system, and 10 kWh from the battery storage system to supply 100 kW. It charges in 1.5&amp;ndash;2 seconds and starts working in 0.5&amp;ndash;1.5 seconds with PV and fuel cells. In an ideal world, the energy management system would use PV and fuel cells and the batteries first. By synchronizing PVs, fuel cells, and batteries, efficiency and battery life will improve. Thus, optimization and monitoring will focus on battery burden control, transient charging and discharging control, and system efficiency to extend battery life. Battery will also determine fuel cell power responses. This project uses MATLAB Simulink to analyses power source capacities, synchronize power production, and share load to create a dependable and accurate hybrid power system.