Search Results (2,023)

The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently prove ineffective in delivering both robust harmonic mitigation and expeditious dynamic response. To surmount these constraints, the present paper puts forth an intelligent control solution that is predicated on a fractional-order fuzzy logic (FOFL). The FOFL is integrated into a multi-converter HRPS, comprising a photovoltaic generator, a lithium-ion battery power storage system, and a wind turbine equipped with a permanent magnet synchronous generator. A multifunctional voltage source inverter has been developed to control these parts, which are interfaced via a common DC bus. Through the implementation of MATLAB 2021 simulation studies, the efficacy of the suggested algorithm is verified and evaluated in comparison to the FOPI. The findings indicate that the FOFL enhances system efficacy by minimizing harmonic distortion, improving energy quality, and achieving a faster dynamic response under various circumstances. In the context of grid-connected microgrid environments, the FOFL has been demonstrated to offer superior overall energy management, robustness, and adaptability when compared to other evaluated strategies. Full article

Todos Santos Bay, Mexico, features several wave-focusing areas driven by its complex bathymetry, making it an ideal real-world test case for wave energy converter (WEC) park optimization. This study quantifies the influence of submarine cable costs and bathymetry-dependent mooring costs on the proposed park layout (hereafter the star-layout) and the levelized cost of energy (LCOE) of a 10-device WEC park, using a multi-state operational wave climatology of $N=179$ representative sea states from a 2008–2018 SNL-SWAN hindcast (covering 97.20% of the annual time). A binary genetic algorithm combined with K-means clustering analysis was used to minimize LCOE under three cost scenarios: baseline, cable-only, and cable plus bathymetry-dependent mooring. Both infrastructure cost components contribute substantially: cable costs add 52.2% to the baseline LCOE, and bathymetry-dependent mooring costs add a further 16.0% at this site, with cable approximately three times more impactful. These quantitative magnitudes are conditioned on the moderate depth-gradient setting of Todos Santos Bay; the qualitative cost-component hierarchy is expected to generalize, but the relative weights will depend on the bathymetric and wave-climate characteristics of each candidate site. The mooring contribution is nontrivial both economically and spatially (the centroid of the park shifts by approximately 151 m between the cable-only and cable-plus-depth scenarios). K-means clustering identified 2–4 layout families per scenario (K $=4\to 3\to 2$ as cost components are added), indicating that infrastructure constraints reduce the viable solution space. These results support the central hypothesis of this work: WEC park optimization studies that adopt flat-bathymetry simplifications, the prevailing assumption in much of the prior literature, risk substantial underestimation of LCOE at sites with nontrivial depth variation. We recommend that bathymetry-dependent mooring costs be included alongside cable costs in any early-stage techno-economic assessment of WEC parks at sites with complex bathymetry. Full article

Conventional high-voltage direct current (HVDC) systems based on line-commutated converters (LCC) are prone to commutation failures and consume excessive reactive power during AC grid faults. The controllable line-commutated converter (CLCC) was developed to solve these problems. To further investigate CLCC’s practical application in the AC system, this paper proposes a fixed AC voltage control strategy for the inverter-side CLCC. A hybrid LCC-CLCC HVDC transmission system model is built in PSCAD. Simulations are performed under three-phase short-circuit faults and wind power fluctuation scenarios. The results show that, unlike traditional LCC, the CLCC under the proposed control can actively increase its firing angle over 160 degrees during disturbances. This action injects dynamic reactive power into the grid and significantly reduces the AC bus voltage drop. Especially in weak grid conditions, CLCC can greatly reduce reactive power consumption through wide-range active adjustment of the firing angle, thereby improving voltage stability. Full article

Ultra-large-scale offshore photovoltaic (PV) installations require efficient and reliable construction-phase inspection to ensure installation integrity and compliance with engineering specifications. As the deployment scale expands to thousands of platforms and millions of photovoltaic modules, conventional manual inspection becomes labor-intensive, time-consuming, and increasingly prone to omission errors. This study presents an autonomous inspection framework based on AI-driven computer vision for the detection and localization of missing photovoltaic modules in offshore PV systems. The proposed framework integrates high-resolution UAV-acquired RGB imagery, YOLOv8-based object detection, geographic coordinate transformation, spatial deduplication, and deterministic grid-based indexing to convert aerial observations into structured engineering inspection records. Each detected missing module is automatically assigned a unique platform identifier together with row–column coordinates, enabling engineering-level localization while eliminating redundant detections caused by overlapping UAV imagery. The proposed framework was validated using a dataset comprising 2800 annotated UAV images collected from a 1 GW offshore photovoltaic project. The experimental results revealed a recall of 96.15%, an F1-score of 98.04%, and a manual verification consistency of 96.83%. Geographic deduplication eliminated duplicate grid records, while the average processing time of 1.12 s per image demonstrates the computational feasibility of the framework for large-scale offshore deployment. The results confirm that integrating deep learning-based visual detection with geographic spatial mapping enables reliable, scalable, and engineering-oriented verification of missing photovoltaic modules during construction-phase inspection, thereby supporting standardized and data-driven acceptance workflows for large-scale renewable energy infrastructure. Full article

The integration of grid-forming energy storage systems (GFM-ESSs) provides essential support for the stable operation of grid-connected converters in renewable energy systems. However, GFM-ESSs may exhibit low-frequency oscillations in response to grid state variations, posing a threat to power system stability. To address this challenge, this paper proposes a fast continuous optimization method for the active power–frequency control loop of multi-VSG-based GFM-ESSs. First, a parameter coupling model for multiple VSGs is established, and an internal parameter decoupling control strategy is proposed. Subsequently, an iterative optimization model based on a gradient-based master–slave game is developed, in which the minimization of converter frequency deviation serves as the leader’s objective, while the minimization of system frequency deviation acts as the follower’s objective. Frequency fluctuations are further mitigated through tracking differentiator-based active power compensation. The effectiveness of the proposed method is validated through simulation with six GFM-ESS units integrated into a modified IEEE 33-node system featuring six renewable energy stations. Simulation results demonstrate that the proposed approach significantly suppresses frequency fluctuations while also reducing the response time and the rate of frequency change under grid disturbance conditions. Full article

When the global energy transition is analyzed through economic lenses, the constraints imposed by the laws of thermodynamics are often overlooked. This study addresses the Latecomer’s Dilemma—the predicament of semi-peripheral nations compelled to decarbonize without the capital stock accumulated following the example of the countries of the Global North during their more than two hundred years of industrial development associated with the saturation of the atmosphere with carbon dioxide. A novel phase space model of the Anthropocene is constructed, synthesizing the political concept of ecological debt with the biophysical reality of entropy debt. The application of the laws of systems ecology and non-equilibrium thermodynamics enables the mapping of national development trajectories against the saturated “atmospheric bathtub”. The analysis identifies a critical Injustice Gap—a region of phase space physically foreclosed by historical emissions. Moreover, it has been demonstrated that a circular economy powered by low-density renewables functions as an entropy trap, converting material debt into radiative debt without achieving a closed loop. Consequently, the Polish correction vector is proposed as a stabilization mechanism. This study’s findings indicate that addressing the emerging phenomenon of adaptation apartheid necessitates the implementation of a high-density energy flux, namely Generation IV nuclear reactors, which would be funded by a retroactive ETS3 mechanism. This approach fulfills the thermodynamic condition for material closure, thereby substantiating the notion that energy justice constitutes a physical necessity for planetary stability. This study quantifies the historical radiative debt of a single early-industrialized hub (Manchester) at approximately 142.8 billion EUR. The novelty lies in the synthesis of biophysical laws and the Latecomer’s Dilemma through the proposed ETS3 mechanism. Full article

The progressive deployment of renewable energy systems has engendered a considerable increase in the generation of glycol-based coolant waste, specifically ethylene glycol (EG) and propylene glycol (PG), thereby raising significant environmental apprehensions. This review analyses the critical environmental challenge and examines the feasibility of microbial degradation as a viable and sustainable alternative to glycol waste treatment, while highlighting significant gaps in current hazardous glycol waste management practices. Present waste management practices are largely founded on incineration or membrane filtration approaches, both of which exhibit significant energy demands and inefficiencies in large-scale waste handling. Reported performance ranges from >99% EG recovery at 10–16 kWh/m³ by electrodialysis and 80–95% recovery at 2–4 MJ/kg by vacuum distillation, to ~17 MJ/kg combustion heat from incineration; biological methods, though promising, currently operate below 10% glycol concentration, an order of magnitude below the 10–100% range in real coolants. We analyze the current understanding of metabolic pathways involved in glycol biodegradation, drawing on the peer-reviewed literature, bioinformatics, and patent databases. Special attention is given to the challenges of high glycol concentrations in industrial coolants and the formation of toxic oxidation products during thermal aging. The review also explores recent advances in genetic engineering approaches to enhance microbial degradation efficiency. Finally, we discuss the potential integration of biological recycling methods into existing waste management systems and future prospects for converting glycol waste into value-added products through microbial biotransformation. Full article

Population growth, industrialization and climate change have placed increasing stress on natural freshwater reserves, making conventional water sources inadequate. Coupled with rising energy constraints and environmental concerns, interest in desalination technologies that can operate more sustainably and efficiently has intensified. Among the available approaches, membrane desalination has gained particular importance because of its modularity, relatively low energy demand, and compatibility with decentralized water treatment. In parallel, thermoelectric devices have emerged as promising components for hybrid desalination systems due to their ability to convert temperature gradients into electricity or provide localized heating and cooling for process enhancement. This article presents a narrative review of thermoelectric integration in desalination systems, with particular emphasis on membrane desalination and membrane-hybrid water treatment configurations powered by renewable-energy or low-grade heat sources. The review examines the role of thermoelectric devices in relation to key membrane-based and hybrid desalination processes, including reverse osmosis, membrane distillation, electrodialysis, nanofiltration, forward osmosis, and selected hybrid systems. Particular attention is given to system configurations, renewable energy coupling pathways, functional roles of thermoelectric devices, water productivity, module output, desalination efficiency, water quality, and economic performance. The reviewed literature indicates that thermoelectric integration can provide meaningful benefits in hybrid desalination, particularly through improved thermal management, enhanced utilization of low-grade heat, and supplementary energy recovery. These opportunities appear especially relevant for thermally driven membrane systems such as membrane distillation and for membrane-hybrid configurations intended for decentralized or renewable-powered applications. However, the available evidence remains highly heterogeneous, with substantial variation in system scale, operating conditions, reporting metrics, and cost assumptions, which limits direct cross-study comparison and broad generalization of performance claims. This review highlights the technical challenges, reporting inconsistencies, and research gaps that currently constrain the practical development of thermoelectric-assisted membrane desalination and outlines future directions for membrane-aligned hybrid desalination research. Full article

The increased penetration of renewable energy sources (RES) in the electrical grid has necessitated the concept of a Virtual Synchronous Generator (VSG) control which is used to make grid-connected power electronic converters behave as synchronous generators. While VSG controls are suitable for supporting the inertia of a microgrid, their use leads to grid instability in the event of a disturbance. This research addresses this limitation by integrating a fully connected Feedforward Neural Network (FCNN) into a VSG control to dynamically adjust the damping coefficient and inertia constant in real time. This approach could enhance system stability by reducing frequency and active power oscillations during grid disturbances, particularly during partial load rejection. To evaluate the effectiveness of the proposed method, a supervised learning-based FCNN was trained on VSG damping behavior under various grid disturbances. The trained model was then implemented in a simulation environment to regulate the VSG parameters dynamically. Simulation results show the neural network-based approach reduces high overshoots at the point of disturbance in active power and frequency oscillations; however, the VSG signal settles faster after the grid disturbance. These findings highlight the potential of machine learning in enhancing the stability of VSG-based microgrids, offering a computationally efficient solution for improving transient response and power-sharing performance. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

The continuous increase in Renewable Energy Sources (RES) in Greece’s electricity system has led to growing energy curtailment due to limited grid capacity, especially in high-production regions. According to recent data, more than 200 GWh of clean energy was curtailed in a single quarter in 2024, highlighting the urgent need for effective storage solutions. Curtailment represents a growing system level challenge, but it also creates an opportunity to convert surplus renewable electricity into green hydrogen through electrolysis. This study quantifies the hydrogen production potential of curtailed RES electricity in four Greek regions, Peloponnese, Crete, Thrace, and Western Macedonia, and evaluates alternative storage pathways under harmonized techno-economic assumptions. A scenario-based framework is developed using regional RES capacity, curtailment estimates, electrolyzer efficiency, hydrogen conversion factors, and indicative storage cost ranges. The analysis compares pressurized tank storage, underground storage, and hybrid configurations, while also estimating avoided CO₂ emissions from the substitution of grey hydrogen. The results indicate substantial regional variation. The Peloponnese exhibits the highest annual hydrogen potential, followed by Crete, Thrace, and Western Macedonia, while each region presents different infrastructure constraints and deployment roles. Mainland regions with access to geological storage show lower indicative hydrogen costs than island systems, where storage and export constraints increase costs. The findings show that curtailed renewable electricity can function as a low-carbon feedstock for hydrogen production in Greece, supporting grid flexibility, regional decarbonization, and the gradual development of hydrogen hubs under differentiated regional strategies. Full article

Power electronics is a cornerstone of modern electrical engineering, underpinning technologies from renewable energy systems to electric vehicles. Traditional lecture-based methods often emphasise rote learning and procedural skills but provide limited opportunities for higher-order thinking or experiential practice. To meet the needs of Generation Z learners and align with industry expectations, new pedagogical frameworks are required that combine cognitive rigour with authentic, technology-enhanced learning. This study introduces a Higher-Order Thinking Skills with Real-Time Simulation pedagogical framework to enhance learning outcomes in diploma-level power electronics. A quasi-experimental mixed-methods design was applied with 40 students divided into control and experimental groups. The control group received lectures, while the experimental group engaged with the HOTS–RTS framework across four topics: rectifiers, converters, inverters, and applications. Pre- and post-tests, Likert-scale surveys, reflections, and instructor observations provided data for both quantitative (t-tests, effect sizes) and qualitative thematic analysis. The experimental group achieved higher post-test gains (20.1 vs 9.5 points), with a large effect size (d = 1.9). Surveys revealed that 65 per cent of respondents rated RTS as highly effective, and Likert scores improved by 1 or more points in HOTS-related skills. Reflections emphasised clarity, confidence, and collaboration. HOTS–RTS effectively integrates cognitive rigour with real-time practice, aligning with STREAMS principles and equipping learners with next-generation industry competencies. Full article

To address the power quality deterioration caused by the large-scale integration of grid-following (GFL) electric vehicle charging stations, this paper proposes a comprehensive assessment method based on relative entropy distance fusion weighting and a dynamic ideal solution VIKOR algorithm. First, a multi-dimensional power quality evaluation system is constructed, focusing on key indicators such as voltage deviation, frequency deviation, three-phase imbalance, and harmonic distortion, to accommodate the operational characteristics of vehicle-to-grid (V2G) under grid-following and grid-forming (GFM) interaction scenarios. Building on this, the three-scale analytic hierarchy process (AHP) is employed to determine subjective weights, while the divergence-maximized entropy weight method is used to derive objective weights. The relative entropy distance model is then applied to achieve adaptive fusion of subjective and objective weights, resulting in an optimal combined weighting. Subsequently, a dynamic ideal solution mechanism is introduced into the VIKOR algorithm, where the range of the ideal solution is adjusted based on the indicator weights to enhance the discrimination of key indicators. By comprehensively calculating the group utility value, individual regret value, and compromise evaluation index, accurate ranking and performance assessment of different mitigation schemes are achieved. Using measured data from a vehicle-grid interaction demonstration base for analysis, the results demonstrate that the proposed method can effectively quantify the actual effects of various mitigation schemes, providing decision-making support for power grid safety and stability under high penetration of renewable energy and converter-interfaced generation. Full article

Biomass represents a vital and sustainable resource for developing renewable materials with the potential to replace petroleum-based chemicals. Paulownia wood has high cellulose content and a loose wood structure, giving it natural advantages as a biomass material. Therefore, in this study, Paulownia wood was selected as a lignocellulosic feedstock. An integrated pretreatment process combining a deep eutectic solvent (DES) with an organic solvent was employed to efficiently remove lignin and hemicellulose, yielding cellulose-enriched residues. Subsequently, high-intensity ultrasonication was applied to convert the residues into cellulose nanofibers and nanocrystals. Using the extracted cellulose and nanocellulose, a dual-crosslinked network composite hydrogel was fabricated. The structural, mechanical, thermal, swelling, and conductive properties of the hydrogel were systematically investigated. The results show that, compared with the blank group hydrogel, the addition of nanocellulose increased the maximum tensile strength and tensile strain of the composite hydrogel by approximately 113% and 81%, respectively; meanwhile, the compressive strengths of the nanocellulose-based hydrogels (0.04575–0.09060 MPa) are higher than that of the blank group hydrogel (0.04235 MPa), confirming that the incorporation of nanocellulose significantly enhances the mechanical strength and elasticity of the hydrogel. The introduction of an AlCl₃/ZnCl₂ solvent system imparts appreciable electrical conductivity. Furthermore, the composite hydrogel maintains structural integrity after full swelling, indicating good dimensional stability and reusability. This work not only presents a green and efficient strategy for valorizing Paulownia biomass but also offers a novel design route for high-performance conductive hydrogel materials, highlighting their potential application in areas such as flexible electronics and energy storage. Full article

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper presents a structured review of ML strategies for power-grid resilience applications using a four-phase resilience lens (Prevention and Improvement, Control and Mitigation, Restoration, and Cyber Resilience). The literature is organized through a functional taxonomy that includes fault diagnosis, event prediction, control and stability support, restoration, and cyber resilience. In addition to the qualitative synthesis, a quantitative analysis of a dataset of 13,647 peer-reviewed publications (2015–2026) is conducted to characterize research activity across resilience functions and implementation contexts. This analysis is used to illustrate the increasing adoption of machine learning approaches and to distinguish between simulation-based and real-world applications. The results indicate a methodological shift toward Deep Learning and Reinforcement Learning for complex tasks, while federated and edge-based approaches are gaining attention for privacy preserving and real-time applications. These findings provide a structured view of current research directions and support the growing relevance of machine learning in resilience-oriented power system applications, offering a foundation for the development of intelligent and scalable cyber–physical energy systems. Full article