Energies doi: 10.3390/en19122839

Authors: Chidera Nwosu Jude O. Iroh

Microwave-assisted polymerization is a transformative technique for synthesizing conductive polymers such as polypyrrole (PPy). Unlike conventional chemical or electrochemical methods that rely on external heating or electrode mediated oxidation, microwave irradiation induces volumetric and selective heating through dipole orientation and ionic conduction, which leads to faster reaction kinetics, improved uniformity and higher yields. This review highlights the fundamental mechanisms governing microwave polymer interactions, compares conventional and microwave-assisted polymerization routes and traces the evolution of pyrrole polymerization. Special emphasis is placed on the microwave-synthesized PPy composites and their superior electrochemical performance in energy storage, sensing and biomedical applications. Case studies of graphene/PPy, PPy–metal oxide (e.g., SnO2@PPy nanotubes) and magnetic ferrite hybrids (e.g., BaFe12O19/PPy) nanocomposites demonstrate enhanced electrical conductivity, specific capacitance and more uniform nanostructures. Beyond energy storage, microwave polymerization techniques have led to the development of PPy composites that are used for sensing, antimicrobial activity and photothermal cancer therapy, highlighting the technique’s versatility across biomedical sciences. Reactor scale up, temperature and pressure control under sealed conditions, reproducibility and deeper mechanism understanding of how microwave radiation influences nucleation, chain growth, doping and charge transport were identified as the outstanding challenges that must be addressed to transform microwave-assisted synthesis from pilot to industrial scale. Overall, microwave-assisted polymerization is on its way to becoming a mainstream, energy efficient method for manufacturing high performance polymer composite materials.

Energies doi: 10.3390/en19122838

Authors: Piotr Filipowicz Bogdan Saletnik

Biomass pellets and briquettes are commonly treated as compacted solid biofuels, but their potential extends beyond direct combustion and heat generation. This review aims to synthesise current knowledge on pellets and briquettes as both energy carriers and functional materials for agro-environmental, biological, sorption, and material applications. A structured narrative review was conducted using Web of Science, Scopus, and OpenAlex, complemented by targeted searches of standards, life-cycle assessment studies, and recent experimental literature. This review discusses key physicochemical, mechanical, and environmental properties, including density, moisture content, durability, ash content, higher heating value, elemental composition, storage stability, and biodegradability. It also compares major energy pathways, including combustion, combined heat and power, torrefaction, hydrothermal carbonisation, pyrolysis, and gasification, with non-combustion uses such as fertiliser and microbial carriers, sorbents, bedding materials, mushroom substrates, biocomposites, and lightweight building components. Published studies indicate that the environmental performance of densified biomass depends strongly on feedstock origin, drying energy, transport, end-use technology, and system boundaries. The review proposes a quality-to-function framework in which pellet and briquette quality is interpreted in relation to the intended application rather than through a single universal fuel-quality criterion. This approach supports more precise biomass valorisation within circular bioeconomy systems.

Energies doi: 10.3390/en19122837

Authors: Adrian Ilinca

The global energy sector is undergoing one of the most significant transformations in its history [...]

Energies doi: 10.3390/en19122836

Authors: Shanghong Xie Akihisa Kaneko Yutaka Iino Yasuhiro Hayashi Ryohei Momokawa Takahiro Shimoo Shinya Naoi Yoshihiro Ogita

To address the operational challenges posed by the high penetration of photovoltaic systems in distribution networks—such as system congestion, voltage violations, and increased distribution losses—this study proposes a spatiotemporal sparsified dynamic reconfiguration scheduling method considering practical implementation in real distribution system operations. The proposed framework comprises two complementary sparsification mechanisms. Spatial sparsification is achieved by clustering hourly net-load distributions in a high-dimensional net-load space to aggregate characteristic net-load patterns, thereby restricting power flow evaluations and configuration screening to a small set of representative patterns and substantially reducing the computational burden. Temporal sparsification is realized by solving an integer linear programming problem to optimize the reconfiguration schedule under a daily reconfiguration frequency constraint, which optimizes the reconfiguration timing while mitigating excessive switching operations. Numerical experiments under deterministic forecast assumptions demonstrated that the proposed method can effectively eliminate congestion and voltage violations while achieving loss reduction by 4.56% and 27.4% respectively in two scenarios from the conventional method with the computational scalability significantly improved.

Energies doi: 10.3390/en19122835

Authors: So-Hyun Park Siljung Yeo Jae-Hyuk Choi Won-Ju Lee

To address the climate impact of maritime transport, the International Maritime Organization (IMO) has implemented regulations targeting ship emissions, particularly greenhouse gases (GHGs), to achieve net-zero emissions by 2050. Meeting these goals requires accurate estimates of air pollutant emissions and a clear understanding of emission trends. This study estimated air pollutant emissions from ships registered in Republic of Korea between 2021 and 2023 using a bottom-up approach. The methodology incorporates ship specification data, regression models, and correction factors based on actual fuel consumption. For ships lacking engine power data, power was estimated using a regression of gross tonnage by ship type. Annual fuel consumption was calculated using engine power, fuel type, engine configuration, and ship age, and emission factors were applied to estimate CO2, CH4, N2O, and other air pollutants. The results showed that CO2 accounts for over 98% of GHG emissions, while cargo ships, which represent only 10% of the fleet, contribute more than 60% of total GHG emissions. These findings highlight the importance of prioritizing cargo vessels in CO2 reduction strategies. This study provides baseline data to align policy development with IMO regulations and underscores the need for a continuous national framework for estimating ship emissions.

Energies doi: 10.3390/en19122834

Authors: Ebelechukwu Okeke Mehdi Khatamifar Wenxian Lin

Lattice-based heat sinks have attracted increasing attention as volumetric thermal management architectures for forced-convection cooling of high-power electronic systems. In contrast to conventional plate-fin, pin-fin, and straight-channel configurations, lattice geometries promote three-dimensional flow–solid interaction through interconnected ligament networks that modify boundary-layer development, wake formation, and internal heat-spreading pathways. This review synthesizes recent experimental and numerical studies to examine the thermo-fluid mechanisms governing lattice performance, with emphasis on the coupled influence of porosity, ligament dimensions, topology, orientation, and channel confinement on heat-transfer enhancement and hydraulic resistance. The analysis indicates that while lattice structures can increase average Nusselt number and improve temperature uniformity, these gains are intrinsically linked to pressure-drop penalties associated with flow tortuosity and form drag, resulting in regime-dependent thermal-hydraulic behavior. Apparent discrepancies reported across the literature are frequently attributable to differences in geometric definition, Reynolds-number normalization, and boundary-condition specification rather than to inconsistencies in physical mechanisms. By consolidating geometric scaling, performance metrics, manufacturing considerations, and system-level constraints, this review clarifies the conditions under which lattice heat sinks may provide net benefit relative to conventional cooling technologies and identifies key research directions required to support application-relevant design and evaluation.

Energies doi: 10.3390/en19122833

Authors: Giovanni Gaetano Gianetti Nicola Morandi Tommaso Lucchini Matteo Ferrarini Angelo Onorati

Internal combustion engines are a well-established, efficient and dispatchable solution for distributed power generation and they are widely used in various sectors including grid balancing, data centers and combined heat and power systems. Current research efforts focus on further increasing efficiency, enabling decarbonization through renewable fuels and improving responsiveness to electricity demand in the presence of variable renewable energy sources. In this context, the free-piston linear generator (FPLG) stands out as a highly promising technology, as it directly converts piston motion into electricity, offering high efficiency, reduced mechanical complexity and seamless grid integration. Initially explored for its high-efficiency potential with homogeneous charge compression ignition combustion at extreme compression ratios, opposed-piston FPLGs are now commercially available for distributed power generation, delivering global efficiencies exceeding 45%, near-zero emissions and multi-fuel capability. Building on the detailed studies conducted by Svrcek and co-authors, this work investigates the power-generation potential of low-temperature homogeneous combustion using CFD simulations with detailed chemical kinetics. First, rapid compression machine (RCM) experiments with methane were reproduced in simulations to validate the proposed methodology and to consolidate experimental findings on the maximum achievable efficiency. Subsequently, an extensive RCM simulation campaign supported the identification of optimal operating conditions in terms of air–fuel ratio using methane as fuel. The RCM results enabled the definition of a preliminary methane-fueled opposed-piston FPLG configuration. Full-cycle simulations including gas exchange, mixing and combustion demonstrated an indicated efficiency of 58% at an equivalence ratio ϕ=0.5 and a compression ratio of 50. The key novelties of this study are the development of a novel RCM-2 configuration that more closely reproduces the dynamic behavior of an opposed-piston FPLG including air-spring effects and the introduction of a divided intake port strategy to simultaneously reduce fuel slip and mitigate knocking behaviour through charge stratification. The simulation results for the proposed configuration confirm the potential of opposed-piston FPLGs for high-efficiency power generation and highlight key parameters affecting performance and emissions formation.

Energies doi: 10.3390/en19122832

Authors: Parisa Ebrahimi Methene Briones Cutad Anand Kumar Mohammed J. Al-Marri

Tri-reforming of methane (TRM) has emerged as a promising pathway for low-carbon syngas production by integrating steam reforming, dry reforming, and partial oxidation within a single process. This coupling enables simultaneous CH4 utilization and CO2 valorization while enabling internal heat generation and flexible adjustment of the H2/CO ratio for downstream synthesis. However, TRM performance cannot be adequately evaluated using conversion or energy efficiency alone, because the process involves complex interactions among competing reaction pathways, transport phenomena, catalyst stability, and thermodynamic irreversibility. This review provides a multiscale critical assessment of TRM from both first-law energy and second-law exergy perspectives, linking reaction-network fundamentals to reactor-level behavior and system-level performance. The literature evidence shows that although high temperatures and near-autothermal operation can enhance CH4 conversion and reduce external heat demand, these conditions may simultaneously intensify deep oxidation, hotspot formation, carbon-forming tendencies, and exergy destruction. While equilibrium analyses help define feasible operating windows, they are insufficient without kinetic modeling and reactor-scale studies that capture spatial non-uniformities and pathway competition. Across reported TRM systems, exergy destruction is consistently concentrated within the reformer, identifying the reacting core as the dominant thermodynamic bottleneck. Accordingly, the key challenge in TRM is not simply to maximize conversion but to preserve chemical work potential while maintaining syngas quality and operational stability. Viewed from this perspective, TRM is better understood as an irreversibility-aware multiscale design problem in which optimal performance depends on the integrated optimization of catalyst functionality, reactor architecture, heat management, and system-level operation.

Energies doi: 10.3390/en19122831

Authors: Dasen Yang Wei Lu Qihang Jin

To address hydrogen separation from hydrogen-blended natural gas, this work develops a mathematical model for a novel thermal-transpiration-effect-based circulating-flow gas separator according to the Navier–Stokes equations, following the joint modification with velocity-slip and temperature-jump boundary conditions, and a binary gas diffusion model derived from the Maxwell–Stefan equations. The model is then used to investigate the component transport and flow of a CH4-H2 mixture at the slip flow regime. The average hydrogen mole fraction in the component enrichment zone increases monotonically as the temperature difference increases, reaching 0.429 at a hot channel temperature of 400 K. An optimum inlet gas velocity of 0.93 m/s is identified to achieve the maximum average hydrogen mole fraction in the enrichment zone. In addition, decreasing the microchannel diameter enhances the hydrogen enrichment performance, with the average hydrogen mole fraction reaching 0.578 at a microchannel diameter of 1 μm whereas increasing the microchannel diameter improves the product gas flow rate, indicating a trade-off between separation performance and processing capacity. These insights provide guidance for understanding the component transport mechanism and for the preliminary design of this type of gas separator for hydrogen separation applications.

Energies doi: 10.3390/en19122830

Authors: Liang Zhu Aichao Yang Xiaohui You Jingyi Wang Yinxiao Li

Accurate customer baseline load (CBL) estimation is crucial for incentive allocation and flexibility potential assessment in demand response (DR) programs. However, residential electricity consumption is highly stochastic, and long-duration DR events often result in missing critical load segments, making it difficult for traditional regression-based and daily load-profile clustering methods to accurately capture the counterfactual baseline pattern. To address this issue, this paper proposes a CBL estimation method that integrates a physics-/domain-informed response-consistency constraint with a conditional generative adversarial network. In the proposed framework, deep soft clustering is employed to extract weekly scale load modes, while mutual information (MI) and autocorrelation coefficient (ACC) are quantified as user-specific conditioning fingerprints to characterize intrinsic consumption behaviors. Comparative experiments on a publicly available real-world dataset demonstrate that the proposed method provides strong event-period accuracy among the recurrent and attention-based benchmark models considered in the main comparison. Under matched response-consistency budgets, PI-ICGAN achieves the lowest constrained DR-period MAE at the tested NRR targets, and the ablation results show that the attention, fingerprint, response-consistency, and GradNorm components contribute to different aspects of the accuracy–consistency trade-off.

Energies doi: 10.3390/en19122829

Authors: Jiwei Wang Peihao Xu Long Liu Yongjian Feng Qiang Liu Qinglong Zhu Luming Shi Wei Wang

The Chang 8 tight oil reservoir in the Xifeng area of the Ordos Basin is characterized by poor reservoir properties, making conventional water flooding ineffective for efficient reservoir development. CO2 flooding is therefore considered an important approach for enhancing oil recovery in tight reservoirs. However, suitable development strategies for direct CO2 injection in undeveloped reservoir areas remain insufficiently understood. In this study, compositional numerical simulation combined with a single-factor sensitivity analysis was employed to investigate the effects of key parameters, including well pattern configuration, fracturing parameters, injection–production strategy, and gas injection modes. The results indicate that an inverted nine-spot well pattern with vertical well injection and vertical well production, a well spacing of 500 m, and a row spacing of 200 m can achieve relatively favorable areal and vertical sweep performance. A fracture half-length of 80 m, fracture widths of 0.003–0.005 m, and fracturing treatment before initial production help balance early-stage productivity and gas channeling control. Maintaining an injection rate of 0.03–0.04 PV/a, an oil production rate of 2–3 m3/d, and a bottomhole flowing pressure of 13–14 MPa is beneficial for maintaining reservoir energy and stabilizing displacement-front propagation. Based on neighboring field development experience, switching from continuous CO2 injection to water–alternating–gas (WAG) injection during the mid-development stage can improve mobility control and enlarge the CO2 swept volume. Under the current geological model and simulation conditions, the recommended development strategy predicts a recovery factor of 35.43% over a 30-year production period. The results provide reasonable parameter ranges and an engineering reference for direct CO2 flooding development in the Chang 8 tight oil reservoir and similar reservoirs.

Energies doi: 10.3390/en19122828

Authors: Manjoon Han Minjae Ahn Hyunchul Ku

This paper presents a multibeam hybrid beamforming (MHBF) architecture for microwave power transfer (MPT), enabling wireless power delivery to multiple receivers with a reduced number of RF chains. The proposed architecture decouples beam control into the horizontal and vertical dimensions, where horizontal multibeams are generated in the baseband through digital precoding, while the vertical beam direction is controlled by a Butler-matrix-based analog beamformer. In particular, multibeam transmission is achieved using multi-tone signals with distinct phase weights assigned to each tone, enabling beams to be steered toward different directions, while the Butler-matrix-based analog beamformer provides vertical beam-steering capability. Compared with fully digital beamforming (DBF), MHBF enables simultaneous multibeam formation in the horizontal domain with fewer RF chains, thereby reducing hardware overhead and system complexity. To validate the proposed architecture, a 5.8 GHz prototype was designed and fabricated. The experimental results demonstrate three-beam and four-beam operation under a transmit power of 30.57 dBm, while the average received RF power in the single-beam case was 12.11 dBm at a distance of 1 m. In the three-beam and four-beam cases, average received RF power levels of 7.3 dBm and 6.1 dBm per beam were achieved, respectively. RF-to-DC conversion measurements under 430 Ω and 680 Ω load conditions further showed average PCE values of up to 38.77% and 35.05% for the three-beam and four-beam cases, respectively. These results confirm the feasibility of simultaneous multibeam wireless power delivery and its potential as an effective solution for multi-receiver operation with reduced RF-chain requirements.

Energies doi: 10.3390/en19122827

Authors: Victor Pallares-Lopez Juan Jose Gonzalez-de-la-Rosa Agustin Aguera-Perez Rafael Real-Calvo Miguel Gonzalez-Redondo Isabel Santiago-Chiquero Manuel Jesus Espinosa-Gavira Olivia Florencias-Oliveros Jose Maria Sierra-Fernandez Jose Carlos Palomares-Salas Victoria Arenas-Ramos

Currently, there is a trend in the energy sector towards the application of edge computing techniques to facilitate active monitoring of distribution networks. The adoption of this technique is crucial for applications involving distributed monitoring systems that require real-time data processing with low latency. An edge computing environment ensures an adequate response to two time-level response requirements. One for events that could trigger a serious problem in the distribution network, and a less demanding one for the management of energy. This article justifies and analyzes an architecture specifically designed to provide an adequate response to the two levels of time demand that set the procedure followed for the monitoring, storage and local diagnosis of several photovoltaic plants located on the same distribution network, with the aim of studying their joint production. One of the main contributions is related to the expansion of the capabilities of Phasor Measurement Units (PMUs) to monitor solar radiation or energy production perimeters by sector. The second major contribution is to guarantee the quality of the measurements and low latency in communications, using as a reference the IEEE C37.118-2011 synchrophasor standard in cooperation with the Time Sensitive Networking (TSN) synchronization protocol that guarantees simultaneity in distributed measurements. In short, a procedure is sought that allows a real-time response with the use of computing techniques very close to the origin of the measurements, guaranteeing exhaustive control from the moment the capture begins until the parameters are stored in a time series database.

Energies doi: 10.3390/en19122826

Authors: Zhijie Li Liejun Li Yuchao Wang Jiqing Chen Fengchong Lan

Short circuits and subsequent fires resulting from objects impacting the bottom of vehicle power battery packs considerably jeopardize electric vehicle (EV) operations. This study investigated underbody impacts in EVs and the overall mechanical properties of battery cells. Key features of road debris were extracted and simplified to establish a geometric parameter structure model and determine realistic battery pack responses to debris impact. Quasi-static compression and dynamic impact tests on a prismatic lithium-ion battery (LIB) and power battery pack followed. Macroscopic mechanical responses, deformation failure modes, and internal jellyroll damage of cells and packs were evaluated, and constitutive equations and failure parameters were derived to develop a finite element model, whose effectiveness and reliability were verified by comparing simulation results with experimental data. Finally, a homogenized model of the prismatic LIB and power battery pack was constructed, which effectively predicted the macroscopic mechanical response and internal short-circuit failure under mechanical loading. However, simulation and test results revealed certain deviations in cell indentations under battery pack bottom impacts, presumably because the FEMs neglect the dynamic strain rate effects of electrolyte and cooling liquid. Overall, this study elucidates safety risks to cells and their key components under power battery pack bottom impacts.

Energies doi: 10.3390/en19122825

Authors: Tianxiao Xie Marko Kleissl Mathis Baudonnière Axel Himmelberg Heinz Peter Berg

This paper addresses the current development status of a innovative direct high-pressure electrolyser (DHPEL, operating up to 700 bar) and its integration into a microgrid system in which solar energy constitutes the primary energy source and a hybrid energy storage system, comprising a battery and hydrogen, is employed. The DHPEL under development enables the direct production and storage of hydrogen at high pressures, thereby obviating the need for intermediate mechanical compression. In combination with standardized pressure vessels (300–350 bar) or the increasingly widespread use of CFRP-based high-pressure storage tanks (up to 700 bar), the DHPEL concept represents a technically and economically attractive option for microgrids with hybrid energy storage. The hybrid storage concept is based on functional differentiation between the storage media: the battery is intended to act predominantly as a buffer or short-term storage unit, and the hydrogen is designated for long-term energy storage. In principle, this configuration facilitates an autonomous energy supply relying exclusively on renewable energy sources; this is achieved by enabling the surplus solar energy generated in summer to be converted into hydrogen and subsequently utilized in winter. A rule-based energy-management algorithm is presented, prioritizing hydrogen production from surplus energy during the summer period and aiming to minimize interaction with the public electricity grid. This is particularly relevant for high-latitude regions, such as Germany, where solar irradiation is significantly lower in winter than in summer. A quasi-optimal sizing of all components in the microgrid, along with a realistic techno-economic assessment of the overall system, is performed using an energy-management model implemented in Simulink and utilised with realistic boundary conditions. A case study utilizing realistic solar generation and empirically derived electrical load profiles demonstrates the technical and economic viability of seasonal energy shifting from summer to winter (resulting in an autarky degree exceeding 1) within an economically acceptable cost range.

Energies doi: 10.3390/en19122824

Authors: Songkai Liu Lei Liu Lei Zhang Xiang Xiong Jinbo Liang

In transient stability preventive control of power systems, time-domain simulation is computationally intensive. In addition, the initial operating feature data often contain abundant redundant and irrelevant information. These factors may adversely affect the assessment performance of machine learning models. To address these issues, a transient stability preventive control method based on the sample convolution and interaction network (SCINet) is proposed. First, a feature selection algorithm based on the orthogonal maximal information coefficient and information gain (OMICIG) is developed to extract the key operating features of the system. Second, the SCINet model is employed to learn the nonlinear mapping relationship between the selected key operating features and the transient stability index (TSI). Then, the trained SCINet model is embedded into the transient stability constrained optimal power flow (TSCOPF) model as a surrogate transient stability constraint. In this way, the complicated computation associated with nonlinear differential-algebraic equations (DAE) in the conventional TSCOPF model is avoided. Furthermore, an improved dung beetle optimizer (IDBO) algorithm is used to iteratively solve the resulting model, thereby deriving a preventive control strategy that ensures transient stability while maintaining system operating economy. Finally, simulation studies on the New England 10-machine 39-bus and the IEEE 118-bus system demonstrate the effectiveness of the proposed method.

Energies doi: 10.3390/en19122823

Authors: Wenhua Li Zishuai Wang Chao Pan Qian Zhao Xianchun Meng Chao Liu Zilin Xu

Low-voltage electrical contacts are core components of power distribution systems, renewable energy installations, and industrial automation equipment. The electric arc generated during contact switching is the primary cause of contact erosion, material transfer, and equipment failure, posing significant threats to system reliability and operational safety. The accurate prediction of arc parameters is hindered by two challenges: the high scatter in available data undermines empirical models, and purely data-driven approaches risk physically implausible results. To address this, a Gaussian Mixture-enhanced Bayesian-optimized Physics-Informed Neural Network (GB-PINN) is proposed. Three core contributions are made: (1) High-fidelity MHD simulation foundation: A magnetohydrodynamic (MHD) multi-physics coupling model of the contact arc was constructed and validated against experiments, showing high fidelity with only 1.63% error in arc duration and 1.82% in arc energy. A multivariate simulation dataset was generated by varying key contact parameters based on this validated model. (2) GMM-based data augmentation: The measured and simulated data were modeled and sampled via Gaussian Mixture Model (GMM) to enrich the dataset while preserving physical consistency. (3) BOHB-optimized PINN prediction: The Bayesian Optimization and Hyperband (BOHB) algorithm was employed to optimize the PINN hyperparameters, enhancing training efficiency and predictive accuracy. Experimental results demonstrated that the proposed GB-PINN achieved superior performance in predicting arc duration and energy, with mean absolute errors (MAE) of 0.079 ms and 0.624 mJ, root mean square errors (RMSE) of 0.099 ms and 0.774 mJ, and coefficients of determination (R2) of 0.980 and 0.979, significantly outperforming grey model (GM (1, N)), long short-term memory (LSTM), and Transformer models. As a physics-informed data-driven tool, GB-PINN enables high-precision arc prediction, providing reliable support for electrical contact design.

Energies doi: 10.3390/en19122822

Authors: Yun Zhang Wenjing Gao Siyuan Zhang Xueying Li Jing Ren

The continuous rise in turbine inlet temperatures to maximize engine efficiency makes highly integrated composite cooling schemes essential, but their intricate thermal interactions pose formidable challenges for parameter optimization. In this study, an impingement–pin-fin–film configuration is extracted as a representative composite cooling unit from a double-wall blade and subjected to 3D steady-state RANS simulations under realistic engine conditions. The numerical results are then used to construct quadratic polynomial response surface surrogate models for multi-objective optimization. It is revealed that the blowing ratio dictates overall thermal performance primarily through internal cooling, and excessively high ratios weaken the film coverage. Geometrically, insufficient control over the spanwise ratio disrupts film coverage and breaks the continuity of internal cooling, thereby degrading both cooling effectiveness and structural thermal compatibility. Additionally, a critical region is located upstream of the film hole exit; the combination of an extremely thin solid wall and high heat transfer coefficients creates a localized over-cooled zone, severely constraining temperature uniformity. Ultimately, the optimization framework clarifies the coupled flow and heat transfer behaviors of the double-wall unit. It simultaneously maximizes area-averaged overall cooling effectiveness and temperature uniformity while minimizing coolant mass flow, revealing the key mechanism behind induced thermal stress concentrations.

Energies doi: 10.3390/en19122820

Authors: Muhammad Ahsan Niazi Vikram Kumar Usama Aslam Syed Rizwan Hassan KangYoon Lee Noman Shabbir

Traditional Virtual Power Plant (VPP) dispatch is mainly based on numerical time-series forecasting, which may respond slowly to extreme market events and early-warning signals expressed in unstructured text. This paper proposes a Retrieval-Augmented Generation Virtual Power Plant (RAG-VPP) framework that integrates ISO-style market notices, emergency alerts, weather warnings, and regulatory updates into risk-aware dispatch optimization. The framework includes a semantic perception engine, a hybrid numerical forecasting engine, and a human-in-the-loop dispatch gateway. Unstructured market text is converted into a bounded semantic uncertainty metric and embedded into stochastic MIQP dispatch through semantic-conditioned scenario generation, a semantic exposure penalty, Dynamic Semantic Reserve Margin, and Semantic Demand Response Pre-Activation constraints. The framework is evaluated using a 7-day ERCOT-style controlled stress-test with synthetic ISO-like EEA1/EEA2 alerts and 5 min market resolution. The results show that RAG-VPP achieved a total profit of $285.8 k, representing a 32% improvement over the deterministic baseline. It also improved CVaR by $85.2 k, showed a four-hour semantic lead in the controlled stress-test scenario, achieved 0.92 semantic alignment, and maintained zero reserve-margin violation hours. These results indicate the potential of linguistically informed dispatch for improving VPP resilience under extreme-event conditions.

Energies doi: 10.3390/en19122821

Authors: Jiazheng Chen Xue Li

The location and capacity of electric vehicle charging stations (EVCSs) directly determine the capital invested and construction costs while also affecting the travelling convenience and economy of electric vehicle (EV) users. Furthermore, the siting and sizing of EVCSs has an impact on distribution network flexibility. Therefore, a method for the siting and sizing of EVCSs that takes into account distribution network flexibility is proposed. Firstly, based on the definition of distribution network flexibility, the flexibility deficit is analyzed, and five flexibility assessment indicators are established. Secondly, the travel characteristics of EVs are simulated based on urban road topology and a trip probability matrix, and a model incorporating users’ bounded rationality is adopted to predict the temporal and spatial distribution of EV charging requirements. Furthermore, based on charging requirements and distribution network flexibility deficit, this paper establishes a model for the siting and sizing of EVCSs considering distribution network flexibility. Finally, case studies are conducted with a 29-node transportation network and a 33-node distribution network. The results show that the proposed method can formulate a more reasonable siting and sizing scheme for EVCSs, decrease the flexibility deficit of the distribution network, and reduce the annual comprehensive cost by 11.96%.

Energies doi: 10.3390/en19122819

Authors: Omar Chalak Joy Najem Mickael Mattar Chawki Lahoud Macole Sabat Michel Daaboul

This study evaluates the aerodynamic performance of a modified Archimedes Spiral Wind Turbine (ASWT) using Computational Fluid Dynamics (CFD). A baseline model was compared with different designs, including surface dimples and a trailing-edge flap. Simulations were carried out in SolidWorks Flow Simulation 2025 under a constant inlet velocity of 12 m/s and rotational speeds ranging from 50 to 500 RPM. The performance of the modified ASWTs was evaluated using key parameters, including the power coefficient (Cp), torque, and tip speed ratio (TSR). The obtained results follow the expected Cp−TSR behavior, with a peak of Cp=0.24277 for the smooth blades and Cp=0.2565 for the blades with the flap at TSR=1.63625. While the addition of dimples along the surface of the blades resulted in reduced Cp values, the trailing-edge flap consistently improved performance, yielding increased Cp values in comparison to the baseline configuration. Overall, the flap modification highlighted higher aerodynamic efficiency, recognizing it as the most successful improvement among all the tested configurations. These findings shed light on the relevance of geometry-specific optimization in improving ASWT productivity for small-scale wind energy applications.

Energies doi: 10.3390/en19122818

Authors: Ye Ding Kai Zhou Xiuming He Yuan Sun

Demand response (DR) plays a key role in enhancing power system flexibility under increasing renewable penetration, yet most existing approaches rely on aggregate demand models that fail to capture appliance-level heterogeneity. A bilevel programming framework for DR incentive design incorporating non-intrusive load monitoring (NILM)-based flexibility estimation is proposed. A conditional factorial hidden Markov model (CFHMM) is used to disaggregate smart meter data and recover appliance-level consumption patterns, which are then mapped to willingness-to-accept (WTA) values to construct device-informed DR potential functions. These estimates are embedded in a bilevel optimization model, where a retailer determines optimal incentives while accounting for the endogenous impact of demand response on locational marginal prices through market clearing. The model is reformulated as a single-level mixed-integer linear program using Karush–Kuhn–Tucker (KKT) conditions. Case studies using real-world data and the IEEE test system show that the proposed framework produces more effective incentive strategies than aggregate DR modeling, leading to improved DR utilization and higher retailer profitability.

Energies doi: 10.3390/en19122817

Authors: José Cabrera-Escobar David Vera Lenin Orozco Cantos Francisco Jurado Carlos Mauricio Carrillo Rosero César Hernán Arroba Arroba Santiago Paúl Cabrera Anda Raúl Cabrera-Escobar

The present research, using finite element method simulation, studies the heat dissipation of a fin-type passive cooling system installed on monocrystalline photovoltaic panels under different environmental conditions associated with altitude. For this purpose, three scenarios at different altitudes were analyzed: Manta (14 m.a.s.l.), Puyo (926 m.a.s.l.), and Ambato (2724 m.a.s.l.). A model simulated using the finite element method, validated in a previous investigation, was used to simulate these three cases. The model was meshed, and the boundary conditions used were obtained from meteorological data averaged over one year. The variables used in this stage were irradiance, ambient temperature, and wind speed in the time range from 08:00 to 17:00. The numerical model used in the simulation considered the mechanisms of conduction in the panel layers, mixed convection toward the surrounding air, and thermal radiation from the exposed surfaces. The results show that, in the city of Ambato, the heat sink presents its best thermal performance. Under conditions of minimum ambient temperature and solar irradiance, a maximum percentage reduction of 3.11% in the photovoltaic panel temperature was obtained, while under conditions of maximum ambient temperature and solar irradiance, the reduction reached 11.11%. This reveals that, when higher panel temperatures occur, the heat sink exhibits better performance. In general, the results showed a reduction in temperature when this heat dissipation mechanism was used. It is evident that the effectiveness of these systems depends not only on geometry or materials, but also on the atmospheric conditions associated with altitude. It is concluded that the heat dissipation capacity of passive cooling mechanisms is influenced by the meteorological conditions of the area, such as ambient temperature, solar irradiance, and wind speed, which may vary according to the altitude at which the system is located.

Energies doi: 10.3390/en19122816

Authors: Jerjis Kapra Larry Hughes

Nova Scotia is positioned to become the first Canadian province to develop offshore wind energy. Recently, Nova Scotia announced four Wind Energy Areas (WEAs) selected for bidding following extensive review of ecological and land-use considerations. In selecting these areas, the effect of climate change and extreme winds was neglected. This study looks to assess the impact of climate change, extreme winds, and tropical cyclones on turbine siting across the Scotian Shelf with a focus on the four WEAs. Analysis of historical wind climate using ERA5 reanalysis data and return period methods reveals that extreme winds intensify with distance from shore, with the highest values concentrated near Sable Island and outer shelf regions. Fifty-year return wind speeds across the WEAs range from approximately 40.7 to 45.4 m/s, resulting in IEC Class II designation for Sable Island Bank and Class III for the remaining sites. Projections derived from CMIP6 climate models indicate that future mean wind speed changes are modest across all emission scenarios, always within 4% of the historical baseline. Critically, these projected changes do not alter the IEC turbine class designations for any WEA, suggesting that classifications based on historical data remain valid under the range of climate futures considered. Three recommendations are made to strengthen future assessments: expanding the buoy observation network on the Scotian Shelf; investigating the influence of climate indicators such as sea surface temperatures on extreme winds and tropical cyclone activity; and conducting targeted measurement campaigns within the WEAs to support site-specific analysis and developer confidence.

Energies doi: 10.3390/en19122815

Authors: Juwei Yang Yin Luo Ying Zhao Liangsong Zhou Zheng Yuan

With the high penetration of renewable energy, the demand of the power system for flexible regulation resources is gradually growing. Pumped storage and battery energy storage are the most common storage types in the system, and the former can be further categorized into weekly-regulated (multi-day-regulated) and daily-regulated pumped storage. To fully leverage the regulation characteristics of these resources, this paper proposes a coordinated scheduling strategy for diversified energy storage considering varied regulation time scales. First, the correspondence of the regulation time scale of energy storage and the optimization time scale of scheduling is discussed. A two-stage coordinated scheduling framework for diversified energy storage is proposed. Second, based on models for pumped storage, battery energy storage, and thermal power units, considering deep peak shaving, an optimization model is established. This model achieves the optimal scheduling of regulation resources across day-ahead and intraday horizons. Finally, simulations are conducted on a modified IEEE 30-bus system. The results show that the proposed scheduling strategy reduces the system operating costs by 0.5% in the day-ahead scheduling and 16.1% in the intraday scheduling compared to the traditional strategy. The results verify that the proposed scheduling strategy can fully exploit the regulation characteristics of different types of storage, promote renewable energy accommodation, and ensure power supply in the power system.

Energies doi: 10.3390/en19122813

Authors: Jingxian Liu Weihuang Wu Xiuju Fu Jiaxiang Cai Hongchu Yu

With the global energy system undergoing a transition toward green and low-carbon systems, the scale of liquefied natural gas (LNG) maritime transportation has expanded rapidly. Influenced by a combination of factors including the global economy, geopolitics, energy policies, and environmental conditions, the Liquefied Natural Gas Maritime Transportation Network (LMTN) exhibits a high degree of structural complexity and has gradually emerged as a prominent research focus in the field. This study provides a comprehensive review of the current research progress on LMTN. First, the concept of LMTN is introduced and the major stages of its research development are highlighted. Second, LMTN construction methods are systematically summarized with data sources, theoretical foundations, and application scenarios, thereby establishing a technical framework for global LMTN research. Subsequently, bibliometric analysis is also employed to extract representative publications and reveal the knowledge structure, historical evolution, and emerging research frontiers of the field. Finally, from three technical perspectives—methodology, data, and computational power—this study discusses existing limitations and challenges, and identifies future development trends of LMTN research driven by big data and artificial intelligence. Overall, this study aims to provide scientific guidance for future LMTN research and theoretical support for enhancing the security and resilience of global energy transportation systems.

Energies doi: 10.3390/en19122814

Authors: Junming Zhang Shuaiqi An Junyi Cao Hongye Pan Haonan Zhang Yucheng Zou Guangchun Song Qihui Hu Yuxing Li

With the global advancement of carbon peaking and carbon neutrality goals, the importance of carbon capture, utilization, and storage (CCUS) technologies has become increasingly prominent. As a critical component of CCUS systems, gaseous CO2 pipeline transportation has emerged as a research hotspot due to its efficiency and cost effectiveness. However, there are invariably corrosion problems when it comes to gaseous CO2 pipeline transportation. These issues pose a significant threat to both the safety and economic viability of pipeline operations. Therefore, it is of importance to investigate gaseous CO2 corrosion during pipeline transportation. In this work, based on recent domestic and international research achievements, research progress in the field of gaseous CO2 corrosion during pipeline transportation is systematically reviewed. First, the corrosion mechanisms and corrosion characteristics during gaseous CO2 pipeline transportation are studied, and the synergistic mechanisms by which key parameters such as impurities, temperature, pressure, flow velocity, and water content jointly influence pipeline wall corrosion behavior are elucidated. Then, corrosion products in CO2 transportation pipelines are analyzed, and protective measures applicable to gaseous CO2 pipeline systems are synthesized. Finally, future research goals are proposed to promote research on gaseous CO2 corrosion during pipeline transportation: the impact of interactions among multiple impurities on corrosion behavior should be clarified; the inhibitory effects of the dynamic evolution of product films on mass transfer processes should be considered in corrosion rate calculation models; and more economical and efficient anti-corrosion technologies should be developed to meet diverse operational requirements. This work can provide guidance for the corrosion protection of gaseous CO2 pipeline transportation.

Energies doi: 10.3390/en19122812

Authors: Renjie Wu Jianhua Zhao Zhaowen Wang Kun Yang Lei Zhou Yuwei Zhang Qiguang Wang

Exhaust piping in diesel engines is subject to severe thermal stress arising from high-temperature, high-pressure gas flows, and spray cooling with atomizing nozzles has become a widely adopted method to safeguard structural reliability. However, at present, the understanding of the spray fragmentation mechanism of two-phase flow under low inlet pressure is still not comprehensive. This study establishes a three-dimensional model of a gas–liquid impinging-jet nozzle and applies a coupled Volume-of-Fluid to Discrete-Phase-Model (VOF–DPM) approach to resolve the liquid breakup process in detail. High-speed imaging experiments were carried out to validate the numerical results. Orthogonal tests were conducted at five pressure levels for both gas and water—0.28, 0.24, 0.20, 0.16, and 0.12 MPa—producing 25 data pairs of spray cone angle and Sauter Mean Diameter (SMD). Within the 0–0.3 MPa air inlet pressure range explored here, raising the pressure consistently reduced the SMD and widened the cone angle, although both trends weakened as the pressure increased. Water inlet pressure exhibited a nonlinear influence, with local extrema appearing in the higher-pressure region. The overall SMD reached a minimum of 34.12 μm and a maximum of 149.04 μm. Using these 25 data points, a genetic algorithm was employed to optimize the pressure ratio under the constraint of total hydraulic power, yielding optimization strategies for different power budgets. An additional outcome of the simulation was the identification of a structural weakness: by reshaping the original flat impingement surface into a full conical surface, atomization quality improved by 29.36% under identical boundary conditions. These findings clarify the atomization mechanism of gas–liquid impinging jets under low inlet pressure and offer practical guidance for nozzle optimization.

Energies doi: 10.3390/en19122811

Authors: Wei Wang Hongquan Xu Chao Fang Huibin Jia Yipeng Wu

The escalating frequency of extreme weather events poses severe threats to power system security, often resulting in catastrophic economic and societal consequences. As modern information and communication technologies (ICTs) integrate deeply with power grids, post-disaster communication failures and electrical faults become increasingly interdependent, complicating the restoration of distribution cyber–physical systems (CPSs). To bridge the gap where conventional Unmanned Aerial Vehicle (UAV)-enabled emergency communication ignores coordination with power system restoration, this paper proposes a coordinated recovery method featuring a two-stage UAV deployment strategy. First, a coupled cyber–physical model is established to characterize the cross-layer interaction mechanisms. On this basis, a bi-level optimization framework is developed: the upper level formulates a dynamic two-stage UAV deployment strategy to minimize the mobilization of resources, while the lower level executes network topology reconfiguration to maximize weighted load restoration, constrained by the recovered communication coverage. Simulation results on a modified IEEE 33-bus system demonstrate that the proposed method significantly enhances restoration efficiency. Compared with conventional schemes, the cumulative load loss rate is reduced by 15.75% and 2.42% across different scenarios; the two-stage UAV deployment method achieves a time reduction of 67.23%, 21.40% and 71.56%, validating the superior performance of the coordinated recovery strategy in disaster-stricken CPS.

Energies doi: 10.3390/en19122810

Authors: Tommaso Gallozzi Felipe Micangeli Daniele Bricca Daniele Groppi Davide Astiaso Garcia

The growing adoption of distributed renewable energy systems (DRES) calls for advanced planning methodologies capable of addressing their inherent complexity and multi-dimensional trade-offs. Multi-Criteria Decision-Making (MCDM) frameworks are widely used to balance diverse objectives, but their effectiveness depends heavily on the selection of criteria, weighting techniques, and integration methods. This paper undertakes a systematic review of the existing literature to analyze how MCDM approaches have been applied in the planning and optimization of DRES projects. The review focuses on the criteria considered in MCDM, the techniques used to assign their relative importance, and the methods employed to integrate these weights into multi-objective evaluations. The analysis draws from a diverse set of peer-reviewed papers, examining economic, technical, environmental, and social dimensions, as well as the relationships between project-specific features and the criteria selection process. Results show that social criteria remain underrepresented both in terms of frequency and of relative importance in the evaluation process, while economic criteria are the most used and influential, underlining the need for more balanced, context-sensitive, and socially inclusive MCDM frameworks. Among MCDM methods and weighting methods, TOPSIS and AHP are by far the most common approaches, respectively. This review provides a foundation for future research aimed at improving the adaptability and effectiveness of MCDM frameworks in DRES.

Energies doi: 10.3390/en19122809

Authors: Qinhao Li Suchun Fan Lai Zhou Zhongwen Wang Pan Qi

The integration of renewable energy and rising electricity demand strain system flexibility. While price-based demand response (PBDR) improves flexibility through pricing signals, its efficacy hinges critically on accurate consumer modeling. Recognizing this pivotal role, this paper provides a comprehensive review of consumer models in PBDR and their applications to electric vehicles (EVs). First, a unified conceptual framework is presented, delineating the energy, information and financial flows among the system operator (SO), load aggregators (LAs), and end-users, and highlighting the central position of consumer modeling. Second, existing modeling approaches are systematically classified into four categories, namely rule-based, optimization-based, data-driven, and hybrid, to facilitate the selection of appropriate models by researchers and stakeholders for diverse scenarios. Furthermore, the application and adaptation of these models to EVs are critically analyzed, accounting for unique vehicular constraints. Subsequently, a systematic summary of the key characteristics and existing research gaps is provided. Finally, key directions for future research are proposed accordingly, aimed at incorporating bounded rationality into behavioral models, developing individualized consumer modeling coupled with user-specific dynamic pricing, and extending consumer modeling to residential multi-energy prosumers in integrated energy systems.

Energies doi: 10.3390/en19122808

Authors: Yaocong Fan Binjie Zhang Hsiao Mun Lee Heow Pueh Lee

This study investigates broadband vibration and mechanical shock mitigation for an aluminum (AlSi10Mg) battery pack casing by integrating mechanical metamaterial wall modifications and add-on damping structures. A 12.432 kWh underbody-type casing is designed. Two wall architectures, i.e., the star-triangular honeycomb (STH) and a novel hybrid auxetic (NHA), are implemented on three walls (top, front, and rear) of the battery pack casing. A mechanical damping (DSMS) and three biomimetic damping concepts (BWBIS, BPPIS and BBIGPS) are further compared. All designs are evaluated through simulation using random vibration analysis based on ISO 12405-2 standard, followed by shaker-based shock and random vibration experiments. Simulations show that both modified casings suppress the casing vibration by approximately 102–106 relative to the solid casing, and their dominant peaks shift to above 150 Hz. The NHA casing provides higher overall vibration mitigation than the STH casing (98.07% longitudinal, 95.09% vertical, and 93.60% transverse versus 97.64%, 94.00%, and 91.51%). Thus, the NHA casing is selected for fabrication. In addition, BPPIS and BBIGPS outperform BWBIS and DSMS, and thus, BPPIS is selected for fabrication due to its simpler geometry and lower mass. Experimentally, the solid-BPPIS configuration achieves the most robust random vibration attenuation across all measurement points, with average root mean square (RMS) reductions of 26.82% (vertical), 87.34% (longitudinal), and 83.60% (transverse). Shock tests reveal strong direction dependence; adding damping structures improves longitudinal and transverse shock mitigation, while vertical shock mitigation remains limited. The results provide design-level guidance on selecting wall architectures and damping layouts for practical vibration and shock protection of electric vehicle (EV) battery pack casings.

Energies doi: 10.3390/en19122807

Authors: Limin Cheng Xu Ji

Green ammonia systems provide an important pathway for converting fluctuating renewable electricity into transportable chemical products. To address the coupled challenges of renewable power variability, heterogeneous electrolyzer dynamics, hydrogen storage constraints, and continuous ammonia synthesis under weak-grid conditions, this paper develops a mixed-integer linear programming scheduling model considering the coordination and start–stop characteristics of ALK–PEM hybrid electrolyzers. The model uses a 15 min resolution over a two-day horizon and integrates renewable power supply, grid electricity purchase, electrolysis, hydrogen storage, and flexible ammonia synthesis in a unified framework. The off, hot-standby, and running states of ALK and PEM electrolyzers are explicitly represented. The case results show that, under the high-renewable-resource scenario, ammonia production reaches 494.93 t, with a curtailment ratio of 3.23% and a grid electricity share of 0.68%, indicating strong renewable-energy conversion capability. Under the low-renewable-resource scenario, ammonia production decreases to 180.09 t and the grid electricity share increases to 40%, showing that the operating priority shifts to maintaining continuous production and safe hydrogen inventory. The ALK hydrogen production share decreases from 93.96% in the high-resource scenario to 75.66% in the low-resource scenario, while the PEM share increases from 6.04% to 24.34%. This indicates that ALK mainly supports large-scale base-load hydrogen production under abundant renewable resources, whereas PEM provides fast compensation and marginal regulation when renewable resources are limited and more volatile. The results demonstrate that ALK base-load production, PEM fast regulation, hydrogen storage buffering, and platform-like flexible ammonia operation jointly provide the main flexibility sources in the studied weak-grid green ammonia system.

Energies doi: 10.3390/en19122806

Authors: Mwamba S. Nkwambe Bonginkosi A. Thango

Accurate fault diagnosis of power transformers using Dissolved Gas Analysis (DGA) depends on effective feature extraction to reduce redundancy and improve classification performance. This study compares linear and nonlinear feature extraction methods viz. Principal Component Analysis (PCA) and bottleneck Autoencoders (AE) to determine whether nonlinear representations provide diagnostic advantages for transformer fault classification. A dataset of 595 IEC 60599-labeled DGA samples covering six fault classes (PD, D1, D2, T1, T2, T3) was used. A 15-dimensional feature space was constructed from gas concentrations, total hydrocarbon content, and IEC-aligned gas ratios. PCA and AE were applied for dimensionality reduction across latent dimensions (k = 1–15), followed by an identical Artificial Neural Network (ANN) classifier. Performance was evaluated using test accuracy, cross-validation stability, and per-class F1-scores. The PCA+ANN model achieved a maximum accuracy of 68.9% at k = 11, outperforming AE+ANN, which achieved 66.4% at k = 4. PCA also demonstrated greater cross-validation stability (62 ± 3.5%) compared to AE (62 ± 6.6%). However, AE improved F1-scores for discharge faults (D1 and D2) by enhancing nonlinear separation of overlapping samples. PCA provides superior overall accuracy and stability for transformer fault diagnosis, while AE offers targeted advantages in distinguishing discharge-related faults. These findings establish a consistent benchmark for future studies and highlight the complementary roles of linear and nonlinear feature extraction in DGA-based diagnostic systems.

Energies doi: 10.3390/en19122805

Authors: Changxiong Xia Wei Xie Changfei Deng Changjin Hao

Partial discharge (PD) signals acquired from gas-insulated switchgear (GIS) are often severely contaminated by discrete-spectrum interference and periodic narrowband noise, which impairs the accuracy of subsequent fault diagnosis. This paper proposes a hybrid denoising method that integrates Spearman coefficient-optimized variational mode decomposition (S_VMD), spatially related recursive sample entropy (Sdr_SampEn) for intrinsic mode function (IMF) classification, an improved wavelet threshold function, and Savitzky–Golay (SG) filtering. First, the Spearman correlation coefficient between the original signal and the reconstructed signal is used to adaptively determine the optimal mode number K of VMD, avoiding the over- and under-decomposition problems of conventional VMD. Second, Sdr_SampEn, which characterizes signal irregularity along both the Chebyshev distance and spatial direction of a recurrence plot, is employed to classify the obtained IMFs into noise-dominant and PD-dominant components, with the discrimination threshold calibrated as p = 1.94 at 0 dB. Third, an improved wavelet threshold function—continuous at the threshold and asymptotically unbiased—is applied to the noise-dominant components, while SG filtering is applied to the PD-dominant components, after which the denoised signal is reconstructed. The results demonstrate that the proposed method effectively suppresses both white and narrowband noise while preserving the detailed morphology of PD pulses.

Energies doi: 10.3390/en19122804

Authors: Siyan Qin Weiwei Yang Xiao Wu Yi Cai Bingzhen Wang

Taking a small long-endurance unmanned surface vehicle (USV) with a trapezoidal cross-section deck structure as the research object, this study investigates the power generation characteristics of a heterogeneous photovoltaic (PV) system consisting of two symmetrically arranged PV arrays with different orientations, under various electrical connection schemes, tilt angles, and heading angles. A PV power prediction model that accounts for dynamic USV attitude changes was established, and the simulation model was validated based on a trapezoidal deck test setup with a tilt angle of 26.6°. Using this model, the daily cumulative energy yields of the independent and parallel configurations were simulated and analyzed under different tilt and heading angles, focusing on the power generation efficiency of the heterogeneous PV system under seakeeping hull constraints. The results show that at a tilt angle of 24°, the daily cumulative energy yield of the heterogeneous system is approximately 95% of that of the horizontal layout, indicating that the trapezoidal frame structure maintains high power generation efficiency while improving wave resistance. The heading angle has only a minor effect on the daily cumulative energy yield, suggesting that variations in course during marine navigation have little impact on power generation. Nevertheless, a significant coupling effect exists between heading angle and tilt angle. Taking a tilt angle of 60° as an example, when the heading increases from 0° to 90°, the energy yield deficit increases from 26.5% to 30.5%. The parallel configuration exhibits slightly lower energy loss at non-south headings and offers a simplified system structure, although its absolute energy yield is marginally lower at large tilt angles. These findings provide practical design guidance for heterogeneous PV systems in sustained ocean observation, climate research, and other long-duration marine missions. Future work will focus on sea trials, hybrid energy integration, and durability studies to further validate and extend these findings.

Energies doi: 10.3390/en19122803

Authors: Calliope Panoutsou Francesca Tozzi David Chiaramonti

Intermediate crops, such as Brassica carinata and Camelina sativa, offer a promising pathway for expanding sustainable feedstock supply for advanced biofuels in Europe without competing with food and feed production. This study applies a competitive priority framework to assess the performance of intermediate crops in Italy and Spain, integrating agronomic, environmental, and regulatory dimensions. Using Member State-specific agroecological conditions, cost structures, and land-use profiles, the analysis identifies key challenges across land use and biomass-production stages and links them to measurable indicators and targeted optimisation strategies. Evidence from both experimental studies and modelling indicates that camelina can be seamlessly integrated into existing cropping systems without compromising crop yields or triggering soil carbon losses. These findings highlight the potential of intermediate crops to enhance soil health, to reduce erosion, and to stabilise yields under climate variability. This study also examines the policy conditions required to enable deployment, emphasising the need for region-specific crop calendars, digital traceability systems, and coherent implementation of RED III, CAP, ESCA, and CRCF frameworks. The distinction between volumetric and GHG-based targets is shown to be critical: intermediate crops perform strongly under GHG-based intensity reduction frameworks that reward soil carbon gains and sustainable cultivation. National instruments in Italy and Spain—including the Piano Strategico della PAC, Decreto Biocarburanti, Plan Estratégico de la PAC, and Real Decreto 376/2022—provide mechanisms for operationalising these strategies. Overall, the results demonstrate that intermediate crops can contribute meaningfully to both national and EU renewable energy, soil restoration, and climate mitigation objectives when supported by coherent agronomic and policy frameworks.

Energies doi: 10.3390/en19122802

Authors: Ioannis Nikolaos Charitos Dimitrios Karonis

Recently there has been notable interest in the reduction in emissions of the shipping industry via the substitution of the currently used fossil fuels with alternative green fuels. One such alternative studied presently could be the use of pure vegetable oils, which are cheaper and easier to produce than other proposed fuels. In this study, pure vegetable oils were tested in a constant volume combustion chamber to assess their ignition quality via the measurement of their Estimated Cetane Number (ECN) and to compare it with that of heavy fuel oils (HFOs). Moreover, the effect of vegetable oil composition on ignition quality was investigated. It was found that all the vegetable oils tested possessed significantly higher ignition quality than standard heavy fuel oils. Vegetable oil ignition quality was found to be most impacted by their degree of unsaturation. The results of the present study indicate that from the point of view of ignition quality, vegetable oils are a viable alternative to fossil fuels, being expected to lead to an increase in the ignition quality of standard heavy fuel oils.

Energies doi: 10.3390/en19122801

Authors: Zhenchao Liu Bing Liang Zizhen Xie Wei Hu Baijun Liu

Polymer electrolyte membranes serving in proton exchange membrane fuel cells (PEMFCs) and direct methanol fuel cells (DMFCs) must possess sufficient mechanical–dimensional stability and excellent proton conducting capacity. Derived from the successful syntheses of two different sulfonated poly(aryl ether ketone)s bearing functional amine groups, two series of novel epoxy-crosslinked and silane-crosslinked sulfonated poly(aryl ether ketone) electrolyte networks are constructed for highly conductive and mechanically stable proton exchange membranes. The designed multi-component architecture, which integrates a moderate-ion-exchange-capacity sulfonated poly(aryl ether ketone) (moderate-IEC SPAEK), a high-IEC SPAEK, and a tailored crosslinker (epoxy or silane), enables a breakthrough in decoupling the traditional trade-off between conductivity and stability. The resulting membranes exhibit an outstanding combination of properties: exceptional proton conductivity exceeding 0.18 S cm−1 at 100 °C, tensile strength above 28.80 MPa, and enhanced chemical resistance, thermo-oxidative stability, and competitive direct methanol fuel cell performance. This work establishes a rational design strategy for crosslinked multi-component membranes as a promising platform for next-generation high-performance fuel cells.

Energies doi: 10.3390/en19122800

Authors: Xijie Song Zhengwei Wang Huili Bi Lianheng Guo Yongxin Liu

Hydropower is a crucial component of renewable energy, and sediment erosion is a key factor affecting the operation of impulse turbines, with erosion inside the nozzle being particularly prominent and leading to reduced unit efficiency. This paper investigates the distribution patterns of energy dissipation and erosion locations inside the nozzle under varying particle sizes, based on numerical simulation and entropy production theory. The results indicate that small particle sizes (0.02 mm) exhibit good fluidity, uniform flow velocity distribution, and a small high-entropy-production region. As particle size increases (0.1 mm, 0.3 mm), fluidity gradually deteriorates, the flow field becomes more turbulent, and the high-entropy-production region expands. When the turbulent kinetic energy exceeds 10 m2/s2, the entropy production rate increases sharply. A significant negative correlation is observed between entropy production rate and erosion rate; smaller particle sizes correspond to more severe erosion. Erosion on the needle is primarily due to friction, while erosion on the nozzle is primarily due to impact. High erosion levels on both the nozzle and needle are concentrated within a particle velocity range of [80, 100], and the erosion rate within this speed range shows a sharp upward trend.

Energies doi: 10.3390/en19122799

Authors: Peng Chang Liangliang Song Zhaokun Zhou Xinrui Zuo Hanli Weng Zhenxing Li

Power system parameters are susceptible to multiple influencing factors such as environmental conditions and load current, with line parameters being notably affected. This compromises the accuracy of power flow calculation and fault analysis, and can significantly undermine the reliability of protection schemes. To address these limitations, this study proposes an integrated correction method for power system line parameters via a framework that combines soil resistivity inversion and multi-factor sag calculation. First, based on fault-recording data from external line faults, sequence impedance parameters are calculated using a two-terminal impedance difference subtraction strategy, followed by the inversion of soil resistivity along the transmission corridor. Second, considering the spatial inhomogeneity of the transmission corridor, a sliding-window statistical method is applied to segment the line, and a piecewise series model is employed to correct the zero-sequence impedance parameter. Finally, a conductor temperature and sag model based on the heat balance equation is established. By coupling ambient temperature, wind speed, solar radiation, and mechanical load, the ground capacitance and susceptance parameters are dynamically corrected. Simulation results demonstrate that the proposed framework can systematically achieve dynamic correction of power system line parameters and significantly reduce calculation errors. The developed method provides an effective technical pathway for enhancing the accuracy of power system simulation and improving the reliability of protection schemes.

Energies doi: 10.3390/en19122798

Authors: Jinxiu Ding Qingfen Liao Fei Tang Bincheng Li Yixin Yu Tingyu Zhou

In the global transition toward low-carbon power systems with high renewable energy penetration, pumped storage has emerged as a strategic cornerstone for modern power grids. However, the collaborative planning of pumped storage and backbone-grids faces critical challenges, including the lack of explicit quantification of the resilience value of pumped storage and the coarse treatment of N-1 connectivity constraints. This paper proposes a bi-objective resilient backbone-grid planning approach that integrates the pumped-storage hub effect, aiming to minimize total life-cycle costs and the system resilience mismatch index. The proposed framework incorporates network connectivity, N-1 connectivity (edge connectivity ≥ 2), and dual-scenario power flow security as rigid constraints. Furthermore, a three-stage constrained evolutionary algorithm TER-NSGA-II is developed. During the N-1 connectivity reinforcement phase, the max-flow min-cut theorem is employed to achieve precise validation and guidance for edge-connectivity enhancement. Case studies on the IEEE 118-bus system, together with extended validation on the IEEE 300-bus system, show that the proposed method can explicitly quantify the resilience value of pumped storage, obtain Pareto solutions that balance economy and resilience under strict edge-connectivity constraints, and demonstrate competitive overall performance in terms of solution-set quality, feasible-domain search stability, and scalability compared with NSGA-II and the more recent NSGA-III/NG benchmark.

Energies doi: 10.3390/en19122796

Authors: Francesco Porta Antonio Pucciarelli Sergio Lavagnoli

To overcome the computational bottlenecks of iterative Computational Fluid Dynamics (CFD) in turbomachinery design, this study introduces a real-time, data-driven inverse design framework for 2D uncooled, high-Reynolds turbine blades. The novelty of this work lies in the application of Kolmogorov–Arnold Networks (KAN), a distinct deep-learning architecture, to predict blade geometry and performance metrics from aerodynamic loading inputs. The foundation of the model is a comprehensive database of approximately 30,000 blade profiles, generated through an automated optimization pipeline coupled with the MISES solver. This dataset explores an extensive design space, covering inlet flow angles from −50∘ to 0∘ and outlet angles from 50∘ to 75∘, with flow turning up to 125∘. A rigorous benchmarking campaign compares KAN against Multi-Layer Perceptrons (MLPs) and Gaussian Process Regression (GPR), highlighting KAN’s capability to overcome the scalability bottlenecks of Gaussian Process Regression to enable real-time performance while achieving MLP-level accuracy with significantly fewer parameters. A further analysis regarding the trade-off between database size and filtration of unfeasible designs indicates that an optimal data filtration threshold exists, balancing noise reduction with model robustness. The final KAN tool achieves real-time inference speeds (∼0.1 s), reducing the design cycle by four orders of magnitude compared to traditional solvers, while maintaining high accuracy (mean outlet angle error of 0.086∘ and Mach profile RMS error of 0.004). Furthermore, the model’s predicted RMS error is exploited as a quantitative proxy for aerodynamic feasibility, identifying ill-posed inverse problems where the target loading cannot be physically realized. This metric enables the generation of comprehensive maps that rigorously delineate the boundaries of the viable design space across arbitrary aerodynamic loading styles, providing physics-aware guidelines for preliminary design.

Energies doi: 10.3390/en19122797

Authors: Dany Hachem Roger Bonnecaze Quoc P. Nguyen

This work evaluated the effect of enriched gas flood maturity and mobile water on transient foam behavior and oil recovery under high-pressure (2000 psi), moderate-temperature (38 °C) and salinity (20,000 ppm NaCl) conditions in high-permeability Bentheimer sandstone. A synthetic gas mixture containing relatively high contents of CO2 (20%) and propane (26%) was used to simulate the enriched field gas. Screening of foaming surfactants including alpha olefin sulfonates and a betaine for good foamability and stability as well as low adsorption on the sandstone indicates that the alpha olefin sulfonate with a longer chain length was the best candidate for foaming the enriched gas in the presence of oil. Core flooding experiments conducted with this surfactant showed a strong impact of gas flood maturity and injection foam quality on both the transient foam behavior and oil displacement efficiency. Foam injection at residual oil saturation (about 14%) to a gas–brine flood exhibited robust foam propagation. The presence of mobile oil before foam injection due to the immaturity of the gas–brine flood (e.g., oil saturations above 50%) posed a detrimental effect on the rate of foam viscosity buildup. However, water injection during the pre-foam flood strongly supported foam generation even at relatively high oil saturations. A further evaluation of water contribution to enhancing foam propagation by adjusting foam quality showed that the water injection strategy before and during foam flooding should be optimized to improve both transient foam behavior and gas–oil contact for enhanced oil sweep efficiency.

Energies doi: 10.3390/en19122794

Authors: Ran Sang Yifei Li Qianpeng Yang Yan Han

To address heat accumulation, localized hot spots, and non-uniform temperature distribution in large-capacity lithium iron phosphate energy storage battery modules under high ambient temperature and high-rate charge/discharge conditions, this study proposes a fin-enhanced phase change material (PCM)-air hybrid thermal management structure for a 100 Ah prismatic lithium iron phosphate battery and a 2P18S energy storage battery module. First, the battery thermal model is validated using single-cell experimental data reported in the literature. Subsequently, a three-dimensional transient fluid–solid coupled heat transfer model is established by considering transient battery heat generation, PCM solid–liquid phase change, air-side flow and heat transfer, and temperature-dependent thermophysical properties. User-defined functions are employed to implement the transient heat source and temperature-dependent material properties. Under identical boundary conditions, the thermal management performances of three configurations, namely Fin-Air, PCM-Air, and Fin-PCM-Air, are compared. The effects of ambient temperature (20 °C, 25 °C, and 30 °C) and inlet air velocity (1 m/s, 2 m/s, and 3 m/s) on the maximum module temperature, temperature uniformity, PCM liquid fraction evolution, and flow field distribution are quantitatively analyzed. The results show that, compared with the Fin–Air system without PCM and the PCM-Air system without fins, the Fin-PCM-Air configuration reduces the maximum module temperature by 1.57% and 0.25%, respectively, at an ambient temperature of 30 °C and an inlet air velocity of 3 m/s. After four charge–discharge cycles, the peak maximum temperature of the module is approximately 38.56 °C, and the peak maximum temperature difference remains below 3.6 K, indicating good temperature uniformity and latent heat buffering capability. In addition, the air velocity trade-off analysis indicates that increasing the inlet air velocity can improve cooling performance but also increases the air-channel pressure drop and fan power consumption. Therefore, the Fin-PCM-Air structure is more suitable for high-thermal-load conditions, and its practical application should comprehensively consider cooling benefits, additional mass, manufacturing cost, and long-term reliability. This study provides a reference for the design and engineering application of hybrid thermal management structures for large-capacity energy storage battery modules.

Energies doi: 10.3390/en19122795

Authors: Luis Salazar Fonseca Josua Oña Aráuz José Oscullo Lala Nathaly Orozco Garzón Henry Carvajal Mora José Vega-Sánchez Takaaki Ohishi

Hybrid transmission corridors combining overhead lines and underground cables introduce impedance discontinuities that significantly modify electromagnetic transient behavior. These discontinuities generate traveling-wave reflections, waveform distortions, and high-frequency components at relay measurement locations during the first microseconds following disturbance inception. This paper presents a waveform-level electromagnetic transient (EMT) analysis of overhead–cable transition effects using detailed EMTP-RV simulations including frequency-dependent line and cable models, tower representations, grounding systems, and instrument transformers within a differential protection measurement framework. The results show that overhead–cable transitions produce transient waveform modifications characterized by reflections, attenuation, dispersion, and temporary current imbalance mechanisms associated with traveling-wave propagation and cable capacitive effects. The analysis also demonstrates the transient evolution of instantaneous waveform-derived (EMT-derived) differential and restraining current quantities, defined as combinations of terminal current signals obtained directly from EMT waveforms. These quantities do not represent final phasor-domain operating values of practical numerical relays, but provide insight into the transient electromagnetic environment preceding conventional filtering and phasor estimation. The study contributes to a clearer physical interpretation of transient phenomena in hybrid transmission systems and supports EMT-based evaluation of signals relevant to differential protection applications.

Energies doi: 10.3390/en19122793

Authors: Weiyi Tang Yi Li Kefeng Hu Jin Li

The dual-active-bridge series resonant converter (DBSRC) is attractive for bidirectional DC conversion, but its output voltage may respond slowly and exhibit overshoot during start-up, load-step, and reference-step transients when conventional controllers are designed mainly from steady-state or small-signal models. This paper addresses the problem of improving the large-signal transient regulation of a DBSRC while avoiding undesired charging and discharging of the switching capacitor and output capacitor. A finite-state-machine-based state-trajectory control method is proposed. Thus, the converter consists of two full-bridge circuits, each with four switches. The proposed technique enhances the dynamic response of output voltage regulation. By examining the system dynamics in two state-plane domains, the switching behavior of the converter can be clearly characterized, enabling an accurate geometric representation of its operating mechanism. Consequently, a finite-state machine controller is designed based on state-trajectory planning. Switching conditions are utilized to achieve fast start-up and step-load transient responses. Finally, experiments are conducted to validate the effectiveness of the proposed control method.

Energies doi: 10.3390/en19122792

Authors: Iván Ramírez Pazmiño Kevin Pantaleón Alexander Aguila Téllez

This study proposes a rigorous methodology for the optimal placement of voltage regulators in the Guayacanes feeder of the Buena Fe substation, Ecuador, by integrating electrical feeder modeling, exhaustive search, and multi-criteria decision-making. The feeder was modeled in detail by incorporating its radial topology, nodal electrical parameters, and representative operating conditions under minimum- and maximum-load scenarios. Based on this model, a set of technical evaluation criteria was established to quantify the impact of regulator installation, including active power losses, reactive power losses, global voltage deviation, average voltage variation, and voltage imbalance. An exhaustive search strategy was then implemented to evaluate all feasible regulator-location alternatives over the candidate nodes, thereby ensuring a complete exploration of the solution space. The resulting alternatives were ranked using the Weighted Sum Method (WSM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), allowing the comparison of candidate locations from a multi-criteria perspective. The results indicate that node MTS 108932 provides the most technically favorable overall solution, achieving the greatest improvement in voltage profile quality and the most significant reduction in electrical losses. In addition, a sensitivity analysis was conducted by varying the weighting structure of the decision criteria, confirming the robustness of the selected alternative under different decision-maker preference scenarios. The proposed framework provides a technically sound decision-support methodology for voltage regulation planning in real radial distribution systems.

Energies doi: 10.3390/en19122791

Authors: Montri Ngao-det Teerasak Somsak Jutturit Thongpron Anon Namin Nopporn Patcharaprakiti Naris Khampangkaew Kittinun Srasuay Nattawat Panlawan Kan Nakaiam Satean Tunyasrirut Worrajak Muangjai

This study presents a field-validated, scenario-based two-layer dispatch framework for sustainable rural electrification, demonstrated at the Khlong Ruea hybrid microgrid (50 kW micro-hydro, 20 kWp PV, 48 kWh LiFePO4 BESS, 48 kW diesel) in Chumphon Province, southern Thailand. The framework combines an offline mixed-integer linear program (MILP) with scenario-based uncertainty handling (k-medoid clustering, N = 8; CVaR penalty at α = 0.9) and an operator-assisted execution layer implementing source transitions via manual changeover switches. A Fluke 435 IEC 61000-4-30 Class-A field campaign with stationary block-bootstrap inference (B = 2000 resamples, 10 min blocks) documented substantial power quality improvements under BESS supply: the three-phase average THD-V reduced from 5.4% to 2.9% with 95% confidence intervals that do not overlap between the two supply modes; the THD-I dropped from 55.8% to 4.9% (Phase A; 91.2% reduction; three-phase average 64.0% → 7.8%); the voltage unbalance fell from 0.86% to 0.03%; and the displacement power factor improved from 0.92 to 0.95. IEEE Std 1459-2010 decomposition reveals that 93% of the non-fundamental apparent power under diesel supply is attributable to current-distortion volt-amperes (Dᵚ = 4737 VA vs. 283 VA under BESS). A composite power quality index confirms that diesel operation fails the IEEE 519-2022 current-distortion limits while BESS supply satisfies all EN 50160 and IEEE 519-2022 thresholds (PQI: 0.75 vs. 3.89). A 365-day closed-loop simulation confirmed an 18.4% reduction in annual operating cost and a 27.6% reduction in diesel runtime relative to a rule-based baseline, while maintaining LPSP at or below 0.53%. Techno-economic projection from field-verified HOMER inputs reduced the levelized cost of electricity from approximately 0.69 USD/kWh (diesel-only) to 0.36 USD/kWh for the proposed PV + BESS + Hydro + Diesel configuration, which retains diesel as a low-utilization backup at a near-100% renewable energy share. The same configuration delivered a 47.9% net present cost advantage over diesel-only operation and a 12.8 t (82%) annual CO2 reduction. Manual source-transfer interruptions of 1–3 min are fully characterized, and a cost-estimated ATS + SCADA upgrade roadmap is defined.

Energies doi: 10.3390/en19122789

Authors: Justinas Medzevičius Stasys Slavinskas

This two-year study proposes the application of industrial computed tomography (CT) as a complementary technique to conventional capacity and internal resistance measurements for evaluating not only the state of health (SOH) of different lithium-ion battery types used in electric vehicles, but also to predict its past. While commonly used assessment methods primarily focus on electrical properties of batteries, industrial CT allows non-destructive, three-dimensional visualization and systematic evaluation of internal structural changes within individual battery cells and allows to compare different lithium battery type internal structure changes. The study investigates two lithium-ion battery chemistries: lithium iron phosphate (LFP) and nickel manganese cobalt oxide (NMC). The effects of different discharge rates (1C, 2C, and 3C) on battery degradation were analyzed by comparing CT scan data obtained for the cells in their initial (new) condition and after reaching 60% SOH following cycling-induced aging. The findings provide improved understanding of the physical processes associated with battery aging under varying discharge conditions, enabling a more complete evaluation of battery health.

Energies doi: 10.3390/en19122790

Authors: Jin Liang Xiaojin Liu Weiye Ma Zhiyi Pan Zhiqiang Liu Yuxin Liang Wence Xu Shengli Zhu Zhonghui Gao

N-doped Li2ZrCl6−3xNx chloride solid electrolytes were synthesized via a mechanochemical method, and the effects of N incorporation on crystal structure, Li local environment, and Li+ transport were systematically investigated. X-ray diffraction suggested that the main Li2ZrCl6-related diffraction features were largely retained, while N introduction induced partial structural evolution toward C2/m-related features. 7Li MAS NMR revealed that N incorporation sharpened Li resonance peaks. Among the series, Li2ZrCl5.7N0.1 exhibited the highest room-temperature ionic conductivity of 1.15 mS cm−1, with the lowest activation energy of 0.237 eV, demonstrating a reduced Li+ migration barrier. All-solid-state batteries incorporating Li2ZrCl5.7N0.1 showed stable rate capability and long-term cycling, retaining 85.9% capacity after 500 cycles at 1C and 77.4% after 3000 cycles at 3C. These results suggest that appropriate N modification can tune the Li2ZrCl6-based structure and Li local environment, thereby improving Li+ transport in all-solid-state lithium batteries. This work provides a feasible strategy for improving chloride-based solid electrolytes for next-generation energy storage.

Energies doi: 10.3390/en19122788

Authors: Piotr Bogusław Jasiński Piotr Szymczak Krzysztof Kantyka

This article presents the results of a numerical CFD study of heat exchanger channels with passive heat transfer enhancement methods. Two types of channel geometry were analyzed with different flow turbulization methods. In case I, internal micro-fins were applied to the tube wall, which disturbed the flow directly in the boundary layer; the investigated relative fin heights ranged from 0.01 h/D to 0.08 h/D, and the dimensionless longitudinal spacing varied from 0.92 L/D to 3.27 L/D. In case II, an insert with repeating drop-shaped elements was used, causing fluid turbulization in the tube core; the relative droplet diameter ranged from 0.38 d/D to 0.73 d/D, with the same longitudinal spacing as for the fins. The influence of the geometry and longitudinal spacing of the disturbance elements on the thermal–flow characteristics of such channels, namely, the friction factor, Nusselt number, and thermal efficiency evaluated using the PEC, was investigated over a Reynolds number range of 5000 to 400,000. The results show that the insert produces a larger increase in the Nusselt number, whereas the micro-finned tube generally achieves higher PEC values due to lower hydraulic losses. The results clearly indicate that, in most cases, the PEC is higher for the finned tube, particularly at low Reynolds numbers not exceeding 50,000. In turn, for the insert, the longitudinal distance between the elements, L, has a significant influence on the PEC; as L increases, the PEC also increase, reaching its maximum value for the largest L.

Energies doi: 10.3390/en19122787

Authors: Xuyan Xu Tao Zhang Dongming Li Wanchun Sun Zhijiang Wu Yansheng Xu

This study presents a novel investigation into the coupled effects of ambient temperature and compressor frequency on frosting behavior and thermal performance of inverter-driven air-source heat pumps (ASHPs) under low-temperature, high-humidity conditions. Unlike previous studies that focused on single environmental parameters, this work systematically explores temperature–frequency coupling. Experiments were conducted on a 3-HP DC inverter low-ambient-temperature ASHP unit using a multi-climate simulated enthalpy difference test bench. Single-factor analysis shows that frosting is most severe at 0 °C, where the frost growth rate peaks. Regarding compressor frequency, the coefficient of performance (COP) initially increases and then decreases with frequency. The maximum COP occurs near 45 Hz, representing the optimal energy efficiency balance in this experimental system. Sensitivity analysis demonstrates that relative humidity contributes less than 5% to performance degradation at the critical 10% COP reduction point. Thus, ambient temperature and compressor frequency are the core determinants of defrosting timing. A dual-factor prediction model for the critical defrosting air-to-coil temperature difference (∆T) is developed using temperature (t) and frequency (f) as independent variables. Validation confirms that the model maintains prediction error within 10% under both single-factor and multi-factor coupling conditions. Collectively, this research quantifies the coupled effects of ambient temperature and compressor frequency on frosting performance and provides a novel theoretical framework for precise defrosting control in inverter ASHPs based on performance attenuation.

Energies doi: 10.3390/en19122784

Authors: Jiacheng Liu Yihang Lu Guoping Zou

Short-term wind power forecasting is a key enabling technology for wind farm operation optimization, power grid dispatch, and electricity market decision-making. However, existing studies often lack unified standards in data partitioning, input feature construction, and hyperparameter tuning, making fair and reproducible comparisons across models difficult to achieve. To address this issue, this study focuses on day-ahead wind power forecasting for a single wind farm and establishes a benchmarking framework with strict chronological splitting, a shared feature information set, and a consistent hyperparameter tuning budget. Within this framework, six representative models, namely Ridge, XGBoost, LightGBM, DLinear, Transformer, and PatchTST, are systematically evaluated. A two-level evaluation protocol combining a fixed hold-out split and expanding-window rolling validation is adopted to compare model performance from multiple perspectives, including overall accuracy, sensitivity to hyperparameter tuning, robustness across rolling windows, and performance under typical operating scenarios. The results show that model rankings are not fully consistent between the hold-out evaluation and the rolling-validation setting. Under the fixed hold-out split, LightGBM achieved the lowest NRMSE of 10.2326%, while Transformer obtained the lowest NMAE of 6.9944%. In contrast, under the 8-fold expanding-window rolling validation, Transformer achieved the lowest average NRMSE of 8.1684%, followed by LightGBM with 8.7344%. These results indicate that the best performance on a single test split does not necessarily imply the strongest robustness across multiple time windows. In addition, strong tree-based models remain highly competitive in this single-wind-farm forecasting task, whereas more complex deep temporal models do not always deliver stable advantages. Meanwhile, the performance gains brought by hyperparameter optimization vary substantially across models, suggesting that conclusions drawn from default-parameter comparisons are of limited reliability. These findings demonstrate that systematic benchmarking under strict temporal constraints and fair tuning conditions is essential for clarifying the comparative performance, robustness, and engineering applicability of different model families. The study can therefore provide practical guidance for model selection and deployment in short-term wind power forecasting for single wind farms.

Energies doi: 10.3390/en19122785

Authors: Shunyu Zhu Zhao Xu Jian Zhang Hongyi Ye

This paper proposes a composite carbon emission factor (CCEF) demand response framework to address the limitations of single-factor carbon accounting and achieve economic–environmental synergy. The CCEF mechanism integrates the dynamic carbon emission factor (DCEF) and marginal carbon emission factor (MCEF) through an adaptive weight allocation based on the real-time generation mix. To ensure practical scheduling, the load shifting process is embedded in a co-optimization model that minimizes system generation costs under demand-side physical constraints and network security limits. This mechanism guides spatiotemporal load shifting from thermal-dominated evening peaks to high-renewable midday periods based on carbon potential gradients. Simulations on a modified IEEE 39-bus system show that the CCEF framework achieves a unit emission reduction efficiency of 0.5024 tCO2/MW and a total reduction of 462.03 tCO2. These results outperform individual DCEF and MCEF strategies, demonstrating feasible scheduling and an effective balance between carbon reduction and operational costs.

Energies doi: 10.3390/en19122786

Authors: Xinping Yang Dongsheng Yao Jianwei Wang Guanxing Luo Teng Zhao Hong Pan Chuanchuan Qian Lifeng Zhang Li Wang Wenying Wu Yi Wang Tongjing Liu

In response to the problems of deteriorating quality of newly added reserves of deep and ultra-deep shale oil, significant differences in well efficiency among similar sweet spots, and difficulty in converting resources and production capacity, a deep and ultra-deep shale oil resource conversion evaluation method is proposed based on the entire process of shale oil development. Based on the production dynamics during the initial stage of production, we establish oil well classification standards for deep and ultra-deep shale oil and obtain two types of shale oil development unit classification standards. We construct well efficiency response indicators for development units, well efficiency index and well efficiency index, quantitatively characterizing the comprehensive well efficiency response within the development unit. We construct a production capacity evaluation system for the development of sweet spots that integrates resource utilization, technological economy, and business management. We use the fuzzy comprehensive evaluation method to obtain sweet spot evaluation scores and grades and quantitatively characterize the differences in production capacity construction for the development of sweet spots. On this basis, the “Resource–Productivity Matching Five Step Method” is proposed, which couples the sweet spot evaluation results with the well efficiency index to obtain the final score and evaluation results of resource conversion. The instance application of the M development unit shows that its overall resource conversion efficiency is not high, with only one type of area being a good match and the other areas being basic matches. The evaluation results are relative. This method achieves the integration of “sweet spot advantage (comprehensive score)—well efficiency response (well efficiency index)—conversion effect (final score)” and can identify false matches, improve the objectivity and reliability of evaluation results, and provide a scientific basis and technical support for shale oil block screening, well location deployment, construction planning and other research.

Energies doi: 10.3390/en19122783

Authors: Yaya Li Qing Hu Xingyue Liu Yu Jiang Xuanqi Liao Kaibo Shi

In modern power-system load frequency control (LFC), proportional–integral–derivative (PID) controllers are widely used because of their simple structure and ease of implementation. However, the combined effects of communication delay and nonlinear constraints can degrade control performance. To address this issue, this paper proposes a model-constraint-aware optimal PID tuning method based on a Hybrid Differential Evolution–Chaotic Grey Wolf Optimizer (HDE-CGWO). First, a nonlinear LFC model incorporating data sampling, communication delay, governor deadband (GDB), and generation rate constraint (GRC) is established, and a PID-based LFC model is formulated. Next, an objective function based on the integral of time-weighted absolute area control error (ACE), namely ACE-based integral of time-weighted absolute error (ITAE), is constructed. Accordingly, quasi-opposition-based learning (QOBL), chaotic warm-up, Lévy flight, and differential evolution (DE) are incorporated into the standard Grey Wolf Optimizer (GWO) to develop an HDE-CGWO-based PID design scheme for LFC under sampled-data delay and nonlinear unit constraints. Finally, simulation studies are carried out on a multi-area LFC system. The resulting time-domain responses and statistical results show that, compared with standard GWO in the single-area test, HDE-CGWO reduces the ACE-based ITAE by about 43.3%. In the three-area system, the ACE-based ITAE is reduced by about 3.0% under step disturbances and about 1.4% under random disturbances compared with the warm-up Grey Wolf Optimizer (WGWO), indicating that the proposed method can reduce frequency deviations, attenuate post-disturbance oscillations, and accelerate the dynamic recovery process under the considered disturbance conditions.

Energies doi: 10.3390/en19122782

Authors: Feng Wu Gaojian Xiao Xiao Yin Jinsong Zhou Jun Cao

The pore-throat structure is a key factor in the exploration and development of tight sandstone reservoirs. In the present study, 14 tight sandstone samples from the Chang 8 member of the Ordos Basin were analyzed using high-pressure mercury intrusion, cast thin section analysis, scanning electron microscopy and cathodoluminescence imaging techniques. Fractal dimensions, obtained from the slopes of log(SW) versus log(Pc) double-logarithmic plots, were applied to quantitatively characterize pore-throat structures and classify reservoirs through multifractal analysis, and discuss the diagenetic controlling factors affecting the pore-throat structure of different reservoir types. The results showed that the Chang 14 tight sandstones are characterized as two segments fractal features, which indicated that these samples have complex pore-throat structure and consist of two types of spaces: mesopore-throat spaces and micropore-throat spaces. The mesopore-throat system shows a higher fractal dimension (D1: 2.74–2.99), indicating greater heterogeneity and irregularity, while the micropore-throat system exhibits a lower dimension (D2: 2.28–2.61). D1 exhibits a negative correlation with the porosity and permeability of mesopores, while D2 shows a weak positive correlation with the properties of micropores. The total fractal dimension (D) is weakly correlated with overall reservoir properties, confirming that reservoir storage and flow capacity are primarily governed by the mesopore system rather than the micropore system. By analyzing the contribution of pore throats to sample physical properties, the results indicate that the 14 samples can be classified into two types based on 35% porosity contribution and 60% permeability contribution thresholds. Type 1, reservoirs dominated by microporous throat space (D values ranging from 2.603 to 2.644); Type 2, reservoirs dominated by mesoporous throat space (D values ranging from 2.544 to 2.598). Type 1 is characterized by primary intergranular pores, residual intergranular pores and intergranular dissolution pores, which enhance connectivity and reduce network complexity, thereby improving fluid permeability. In contrast, Type 2 consists mainly of intragranular dissolution pores, intergranular gap pores and micro-dissolution pores in clay minerals, which significantly inhibit fluid mobility. Diagenesis, including compaction, dissolution and cementation, exerts a significant control on the fractal characteristics and pore-throat structure evolution. The fractal characteristics exhibited in the pore-throat structure could provide a desirable analytical method, distinguishing from classification based on scale or size, for the evaluation and classification of tight sandstone reservoirs.

Energies doi: 10.3390/en19122781

Authors: Michal Cichowicz Marcin Wardach Pawel Wojciech Herbin

Nowadays, the market for wearable devices and biorobotics is growing rapidly. In active prostheses and exoskeletons, joints are typically driven by electric machines. The critical challenge is balancing the generated torque with the size and mass of the motor, ensuring the overall weight does not hinder the user’s mobility. This paper presents a comprehensive review of high-torque electric machines based on an analysis of 162 publications, primarily from the last decade. The study systematically compares geometric, electrical, and efficiency parameters across various electromechanical converters to identify the optimal limits for bionic applications. Data suggests that an electric machine aiming to align with typical biorobotic requirements would likely fall within an outer diameter of 150 mm, an axial length of 85 mm, and a mass of 1200 g. Furthermore, the required parameters for advanced applications include an efficiency above 95%, a safe nominal voltage of up to 48 V, and the ability to generate torques up to 65 Nm. The analysis highlights that while conventional motors (such as BLDC and PMSM) dominate the market, achieving a torque density exceeding 35–45 Nm/kg—necessary to approach biological muscle capabilities—often requires adopting emerging topologies, such as magnetic gears or Vernier machines. This review provides clear quantitative guidelines for engineers designing optimal drive systems for biorobotics and wearable devices.

Energies doi: 10.3390/en19122780

Authors: Noura Ben Mbarek

Oil-price volatility represents a major challenge for hydrocarbon-dependent economies pursuing renewable-energy transition. In GCC countries, fluctuations in global oil markets may influence renewable-energy deployment through their effects on fiscal revenues, investment conditions, and long-term energy planning. While previous studies have largely examined the direct effects of oil prices, renewable energy, and financial development separately, limited evidence exists on whether financial development can mitigate the adverse implications of oil-market uncertainty for renewable-energy transition in GCC economies. Using annual data for six GCC countries over the period 1990–2024, this study investigates the links among oil-price volatility, financial development, and renewable-energy transition within a second-generation panel econometric framework that accounts for cross-sectional dependence and heterogeneity. The analysis employs Pesaran cross-sectional dependence tests, CIPS unit-root tests, Westerlund cointegration, common correlated effects mean group (CCE-MG), augmented mean group (AMG), and error-correction modeling. The results support the existence of a stable long-run relationship among the variables. Oil-price volatility is negatively associated with renewable-energy consumption, with a long-run coefficient of approximately −0.21. Financial development exhibits a positive association with renewable-energy transition, while the interaction between oil-price volatility and financial development remains positive and statistically significant. This finding suggests that stronger financial systems may partially reduce the adverse effects of oil-market instability. The short-run estimates also support the presence of a stable adjustment process toward long-run equilibrium. Robustness checks based on alternative financial-development proxies, lagged regressors, Driscoll–Kraay estimations, leave-one-out country analysis, and alternative volatility measures confirm the stability of the main findings. The findings suggest that financial development may strengthen the resilience of renewable-energy transition strategies in GCC economies exposed to volatile energy-market conditions.

Energies doi: 10.3390/en19122779

Authors: Zaid Salah Al-Haydri Konstantin V. Osintsev Sergei V. Aliukov Pavel A. Drogovoz Evgeny V. Solomin Nikita A. Pshenisnov Elena N. Fedorenko

This study presents a comparative sensitivity analysis of the Hartmann number (Ha) and Brinkman number (Br) on magnetohydrodynamic (MHD) flow in rectangular channels and circular pipes. Normalized sensitivity coefficients quantify the response of key metrics, including velocity, wall shear stress, temperature, and convective heat transfer, with validation against recent experimental and numerical studies. The system equations were solved through a coupled analytical–numerical method coded in Python 3.14; velocity field was solved analytically whereas temperature field was discretized using a finite differences scheme and solved numerically using the Thomas algorithm. The entire code was written by the authors. The results show that Ha predominantly governs hydrodynamics, inducing velocity suppression, flow flattening, and enhanced wall shear stress. Rectangular channels experience stronger Hartmann layer effects, while circular pipes exhibit smoother velocity profiles. Conversely, Br primarily controls thermal behavior, with higher values intensifying internal heat generation and elevating centerline temperature, potentially attenuating the average Nusselt number at high Br levels. Nonlinear Ha–Br interactions define distinct operational regimes, from heat transfer enhancement to thermal degradation. Optimal performance windows are identified: Ha ≈ 8–12 and Br ≈ 0.05–0.3 for channels, and Ha ≈ 10–15 and Br ≈ 0.1–0.4 for pipes, balancing thermal and hydraulic efficiency. Deviations from benchmark studies remain within ±5%, confirming predictive reliability. This work provides practical design guidance for advanced MHD thermal systems and establishes a foundation for future studies on temperature-dependent properties, three-dimensional effects, and complex flow regimes.

Energies doi: 10.3390/en19122778

Authors: Chaojie Li Shisong Li Siran Peng Peng Pei

To address the limitations in the accuracy of existing methods for calculating greenhouse gas emission intensity from underground coal mining, this study develops a more precise model for estimating methane emissions. The model is grounded in the methane release mechanism of coal, and incorporates field-measured original gas content, residual gas content after extraction, and retained gas content following ventilation. The model defines the computational scope based on different mining methods (with and without coal pillars) and incorporates potential direct emission reduction measures applicable at various stages of the mining process. Case studies of both a high-gas mine and a low-gas mine reveal that, while the pillarless mining method increases total methane emissions, emission intensity is reduced. Furthermore, the study demonstrates that preventing the direct release of low-concentration methane from ventilation systems is critical for further emission reductions. Compared to existing methods, the proposed framework adopts a computational approach that reduces operational complexity while maintaining accuracy through the use of readily available field-measured data. These findings offer a scientific basis for formulating tailored emission reduction strategies in the coal mining sector.

Energies doi: 10.3390/en19122773

Authors: Sahendara Kumar Sajid Kamal Avneet Kumar Xuewei Pan

Decoupled maximum power point tracking control and output voltage control can be accomplished simultaneously using dual-duty cycle control. However, developed triple switch triple mode (TSTM) exhibits absence of the common ground between the solar panel and output load therefore causing the leakage current to flow which creates safety concern especially for household electrification. In addition to having a negative effect on the solar panel, leakage current increases power losses. Thus, this work proposes a unique TSTM dc-dc converter. The suggested converter has the following advantages: (1) The presence of a common ground between the output load and the solar panel eliminates the leakage current. (2) Reduced electromagnetic interference issues present due to leakage current. (3) Enhanced voltage gain over wider duty cycle. (4) Enables simultaneous decoupled control of MPPT and output voltage. (5) Absence of voltage oscillation across the switches. The proposed TSTM converter is an unique combination of switched inductor and switched capacitor. Both inductor and capacitors are connected in order to boost the level of voltage at the output terminal. The operating principle, design equations and device stress are analyzed in detail for the proposed TSTM. The comparison over existing converter in terms of voltage gain and switch stresses are highlighted in details. Lastly, a laboratory prototype (40/400 V) for 400 W is created and thoroughly tested in order to validate mathematical calculations.

Energies doi: 10.3390/en19122777

Authors: Julio Guerra Gustavo Recalde Jean Gavilanez Dirley Cuenca

High renewable penetration introduces stochastic variability in distribution-network operation, requiring probabilistic AC power-flow tools that remain accurate in the tails while avoiding the computational burden of large Monte Carlo simulation. This paper presents a fully reproducible non-intrusive polynomial chaos expansion (PCE) framework for uncertainty propagation through nonlinear Newton–Raphson AC power flow. The method uses sparse-grid quadrature to train PCE surrogates from deterministic power-flow evaluations and is benchmarked against high-fidelity Monte Carlo simulations. In the validation, the IEEE 33-bus feeder is evaluated using up to 50,000 Monte Carlo samples, 95% bootstrap confidence intervals, PCE orders 2–5, correlated uncertainty scenarios, realistic thermal-loading recalibration, reactive-power sensitivity of renewable injections, multi-feeder testing on IEEE 33-bus, CIGRE MV, CIGRE LV, and IEEE 118-bus networks, and a 365-snapshot full-year daily screening. For the base IEEE 33-bus case, third-order PCE required only 494 deterministic power-flow evaluations and reproduced the 50,000-sample Monte Carlo benchmark with relative mean errors of 0.014% for minimum voltage, 0.119% for active losses, and 0.113% for substation import. The corresponding wall-clock speed-up was 13.29×, while reducing deterministic evaluations by approximately 101×. Correlated load–PV uncertainty increased the upper tail of substation import from 6.06 MW to 6.30 MW, and realistic thermal recalibration revealed line-loading p99 values above 100% for the 60% target case, demonstrating the operational value of physically meaningful ampacity settings. The proposed workflow provides an open, scalable, and tail-aware basis for uncertainty-informed distribution-network planning under renewable variability.

Energies doi: 10.3390/en19122776

Authors: Billal Belfegas Aissa Laouissi Vasanth Swaminathan Yacine Karmi Raouache Elhadj Mourad Nouioua

Solar chimneys represent an effective passive ventilation technology capable of improving indoor thermal comfort while reducing building energy consumption. In this study, the thermal and fluid dynamic performance of a solar chimney integrated into a residential building located in Bordj Bou Arréridj (Eastern Algeria) was investigated through a comprehensive numerical, predictive, and optimization framework. A transient mathematical model was developed to evaluate the influence of key geometric parameters, including chimney width and inlet opening width, as well as environmental factors such as solar radiation intensity and wind speed, on the system performance. The generated simulation database was subsequently employed to develop and compare four machine learning models, namely, Artificial Neural Networks with Bayesian Regularization (ANN-BR), Deep Neural Networks optimized by Improved Grey Wolf Optimization (DNN-IGWO), k-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGBoost), for predicting eight output parameters including glazing temperature, fluid temperature, absorber temperature, outlet temperature, thermal efficiency, air change rate (ACH), mass flow rate, and outlet velocity. The results demonstrated that increasing chimney and inlet widths significantly enhances ventilation performance by increasing airflow rate and ACH. Weather conditions and wind speed were also found to strongly affect thermal efficiency and buoyancy-driven airflow. Among the predictive models, XGBoost and DNN-IGWO exhibited the highest predictive accuracy, achieving coefficients of determination (R2) close to unity and very low prediction errors for all output variables, confirming their robustness and generalization capability. The proposed methodology provides a reliable tool for rapid performance prediction and design optimization of solar chimney systems under different climatic and operating conditions, thereby supporting the development of energy-efficient passive ventilation strategies for residential buildings.

Energies doi: 10.3390/en19122775

Authors: Afraz Ahmad Akanksha Akanksha Prarthana Pillai Ilamparithi Thirumarai Chelvan Balakumar Balasingam

This paper presents a primary side model predictive control (MPC) strategy for wireless power transfer (WPT) based charging of light electric vehicle (LEVs). A battery simulator develops a model to accurately reproduce constant-current (CC) charging profile from Open Ciruit Voltage (OCV) and State of Charge (SoC) parameters of the battery. This model forms the foundation of the predictive control design, allowing accurate prediction of the charging trajectory while avoiding reliance on secondary-side feedback signals. The WPT system employs a phase-shifted full-bridge (PSFB) inverter with S-S compensation, where the primary-side controller regulates the secondary-side charging current using only primary-side current measurements. In contrast to conventional secondary side control, which is tuned around nominal coupling, requires explicit feedback, and degrades under coil misalignment and parameter variations, the proposed MPC leverages integrated system and battery models to predict future states and optimally adjust the phase shift for robust charging operation. Simulation and experimental validation on a real-time LEV charging prototype under aligned, lateral, and angular misalignment conditions demonstrate significant reduction in current-settling time compared to fixed-gain proportional-integral (PI) and known adaptive feedback controllers for same system, with lower RMS current and reduced current spikes at the battery. On the embedded controller, the proposed MPC executes within approximately 1 µs per 85 kHz PWM cycle, corresponding to less than 10% CPU utilization, confirming its practical real-time feasibility.

Energies doi: 10.3390/en19122774

Authors: Siqiang Liu Wei Ye Yongfei Wu Ze Ye

The inequitable redistribution of electricity price cross-subsidies constitutes a critical issue, as it compromises the implementation efficiency of tiered electricity pricing (TEP) policies and impedes the equalization of basic public services in the power sector. Drawing on residential TEP data from Hebei Province spanning 2016 to 2020, this paper employs the Gini coefficient method and reveals that high-income residential users receive substantially larger electricity price cross-subsidies than their low-income counterparts. Overall, the degree of such inequality has been rising annually. Furthermore, both high-income and low-income groups exhibit greater inequity in subsidy allocation relative to the middle-income group. Against this backdrop, this paper proposes a more rational tiering framework for TEP by adopting the rank-sum ratio (RSR) method, thereby identifying a viable pathway for residential users across all income brackets to share electricity costs equitably. This research contributes to the sound management of electricity price cross-subsidies, mitigates the inequity in subsidy distribution, and guides residents toward rational electricity consumption behaviors.

Energies doi: 10.3390/en19122772

Authors: John Sessions Rene Zamora-Cristales Robert J. Macias Andres Susaeta Francisca Marrs Belart

Forest bioenergy holds significant potential for North American decarbonization and energy security, yet persistently high logistics costs, feedstock quality variability, and geographic dispersion of biomass resources continue to constrain commercial viability. This review asks what it will take for forest bioenergy supply chains to achieve economic and operational lift-off, identifying key bottlenecks and the most promising pathways to scale. We systematically review 237 peer-reviewed studies and technical reports with the majority published between 2000 and 2025, covering feedstock types ranging from logging residues and woody biomass to short rotation woody crops, and end-products spanning solid biofuels, heat and power, thermochemical products, and sustainable aviation fuel. The literature consistently identifies delivered cost, feedstock quality control, and the geographic mismatch between biomass supply and conversion facility location as the three primary barriers to sector viability. Depot-based preprocessing, cascading utilization strategies, and participatory landowner contracting emerge as the most effective near-term solutions for improving supply chain economics and mobilizing economically recoverable biomass. At the frontier, AI-enabled optimization, digital twin modeling, and integrated biorefinery configurations show strong potential to manage spatial variability and unlock the scale economies on which commercial viability depends. Translating these advances into practice will require stable, long-term policy signals and coordinated investment across the full supply chain.

Energies doi: 10.3390/en19122771

Authors: Long Jin Zhongqing Li Jiangchun Liu Yixiao Luo

Conventional model predictive control (MPC) is highly vulnerable to motor parameter variations. Meanwhile, existing parameter-based MPC schemes are often constrained by the accuracy of model reconstruction. To overcome these limitations, this article proposes a model-free predictive control (MFPC) strategy based on a fuzzy approximation method for a permanent magnet synchronous motor (PMSM). Leveraging the exceptional nonlinear mapping capability of fuzzy approximation, the proposed strategy approximates the autoregressive term within a structurally simple first-order autoregressive model with exogenous input (ARX). This significantly enhances model reconstruction accuracy. Furthermore, discrete-time Lyapunov stability analysis rigorously demonstrates that the estimation errors of the internal states under the proposed control scheme are uniformly ultimately bounded (UUB). Finally, experimental results reveal that the proposed MFPC strategy achieves superior steady-state current quality while ensuring excellent dynamic performance, effectively validating the advantages of the proposed method.

Energies doi: 10.3390/en19122770

Authors: Sara El Afia Francisco Jurado R. Mazuir Raja Ahsan Shah Antonio Cano Ortega

The rapid growth in the electric vehicle sector has increased demand for advanced battery thermal management systems (BTMSs) with high heat-dissipation capacity and temperature uniformity. Immersion cooling using dielectric fluids has recently been recognized as a promising alternative technology to conventional indirect liquid cooling methods. This study investigates the thermal and hydrodynamic behaviour of a sixteen-lithium-ion cell battery (LIB) module immersed in low-viscosity dielectric fluids using three-dimensional computational fluid dynamics simulations. In this context, a total of twenty dielectric fluids are evaluated using the ANSYS Fluent solver, with particular emphasis on the effects of key thermophysical properties, including viscosity, density, specific heat capacity, and thermal conductivity. The simulation findings reveal that mineral oil and PAO4 yield the lowest maximum LIB cell temperatures, with a reduction of approximately 4 K compared to the least effective dielectric fluids, such as undecane and cumene. Moreover, in terms of temperature uniformity, mineral oil, Novec 7000, and PAO4 exhibit the most homogeneous temperature distributions among the twenty dielectric fluids. In addition, they show an improvement in the temperature uniformity index of approximately 32.4% compared with the least effective dielectric fluid, cumene. On the other hand, mineral oil and PAO4 generate significantly higher pressure drops because of their relatively high viscosities, which increases hydraulic resistance and pumping power requirements. These findings demonstrate that excellent thermal performance does not necessarily correspond to optimal overall thermo-hydraulic behaviour. Overall, the results confirm that immersion-BTMS performance is governed by a complex interaction between dielectric fluid thermophysical properties and flow behaviour, highlighting the importance of coupled thermo-hydraulic optimization in the selection of dielectric fluids for next-generation immersion-cooled battery systems.

Energies doi: 10.3390/en19122769

Authors: Wiktor Halecki Anna Młyńska Michał Gąsiorek Karolina Jóźwiakowska Agnieszka Petryk Krzysztof Chmielowski

This study assessed the long-term energy self-sufficiency and operational dynamics of a full-scale wastewater treatment plant over the period 2015–2023, with particular emphasis on biogas-driven energy recovery and time-dependent process interactions. The relationship between biogas production and electricity and heat generation was evaluated alongside the influence of different sludge streams on system performance using cross-correlation analysis. The results demonstrated a high level of energy recovery, with biogas-derived electricity covering, on average, 60% of the plant’s demand and reaching a maximum of 74% annually. A very strong correlation was observed between annual biogas production and electricity generation (r = 0.94), confirming the direct energetic coupling of both processes. Monthly analyses further indicated strong consistency between biogas yield and both electricity and heat production (r = 0.55–0.91 and r = 0.86, respectively). Cross-correlation analysis identified Thickened Waste Activated Sludge and Primary Sludge as important process drivers, with statistically significant delayed effects at 10–20 days. In contrast, recirculation-related streams exhibited negligible influence on system dynamics. Statistical analysis revealed that most heavy metals, including Cd, Cr, Ni, and Hg, exhibited high variability (Coefficient Variability > 40%), which can directly impact the stability of methane production. These results indicate that wastewater treatment plants’ energy performance is governed by delayed process responses linked to sludge residence time, highlighting the need for predictive models incorporating at least two weeks of historical operational data. In addition, physicochemical analysis of sewage sludge confirmed generally stable nutrient content, despite variability in biological parameters and heavy metal concentrations. Overall, the study demonstrates that integrating long-term operational datasets with time-lag analysis provides valuable insights for optimizing energy recovery and supporting circular economy strategies in wastewater treatment plants.

Energies doi: 10.3390/en19122768

Authors: Raveen Appuhamy Faraz Alderson Stephen A. Gadsden

Hydrogen peroxide fuel cells have emerged as a promising class of electrochemical energy conversion device owing to the dual redox character of H2O2, its liquid-phase storage, and its ability to operate in air-free environments. In this work, a dual-compartment direct H2O2 fuel cell using a Prussian Blue cathode and a tantalum anode, separated by a Nafion 115 proton exchange membrane, was systematically characterized and optimized with respect to electrolyte pH and ionic composition. The influence of pH on OCV was investigated independently in each compartment across the range of pH 2 to 12. In the tantalum compartment, OCV increased non-linearly with pH from 573 mV to 808 mV, driven by the enhanced electrochemical reactivity of the system under alkaline conditions. In the Prussian Blue compartment, OCV decreased from 676 mV to 199 mV with increasing pH, reflecting the instability of the material in alkaline conditions. The effect of the electrolyte ionic composition on average current density was subsequently investigated by varying the concentrations of NaCl and Dy(NO3)3. Increasing NaCl from 0 to 2.5 M produced an increase in current density from 0.414 mA/cm2 to 0.973 mA/cm2, consistent with ohmic resistance reduction through improved ionic conductivity. The addition of Dy(NO3)3 produced a positive response with an optimal concentration of 0.05 M, at which current density reached 1.08 mA/cm2, before declining sharply. Under the fully optimized conditions, pH 12 in the tantalum compartment, pH 2 in the Prussian Blue compartment, 0.3 M H2O2, 2.0 M NaCl, and 0.05 M Dy(NO3)3, the cell produced an OCV of 724 mV and a peak power density of 0.283 mW/cm2 at a current density of 0.8 mA/cm2. These results demonstrate that meaningful electrochemical performance can be achieved in a dual-compartment H2O2 fuel cell without the use of precious metal catalysts and highlight electrolyte engineering as an effective strategy for improving cell output in this class of device.

Energies doi: 10.3390/en19122767

Authors: Aqib Mashood Khan Umayar Ahmed MD Rahatuzzaman Rahat Muhammad Umar Muhammad Asad Ali Malaika Bushra Samina Yasmeen

Energy consumption and resource utilization have become critical challenges in modern machining due to increasing manufacturing costs, stringent environmental regulations, and global carbon-reduction targets. While sustainable machining strategies such as dry machining, minimum quantity lubrication (MQL), and cryogenic cooling have been widely investigated, recent years have witnessed the rapid development of advanced assisted and hybrid machining processes aimed at further reducing energy demand and material waste. However, existing review studies largely focus on individual techniques or lubrication approaches, lacking a systematic perspective on the combined energy–resource saving mechanisms in advanced sustainable machining. This review presents a comprehensive and up-to-date analysis of energy consumption characteristics and resource-saving strategies in advanced sustainable machining processes. Particular attention is given to emerging and hybrid technologies, including ultrasonic-assisted machining, ultrasonic-assisted MQL, electrostatic MQL (eMQL), multi-nozzle MQL systems, nanofluid-based MQL, laser-assisted machining, vortex tube-assisted cooling, dry ice machining, and hybrid cryogenic–MQL strategies such as LN2-MQL and CO2-MQL. The review systematically discusses how these techniques influence energy flow, tool–workpiece interactions, lubrication efficiency, and thermal behavior during machining. Furthermore, this paper highlights the synergistic effects of combining multiple assistance methods, emphasizing their role in achieving simultaneous improvements in productivity, tool life, surface integrity, and sustainability performance. Energy-based metrics, resource efficiency indicators, and carbon emission considerations reported in the literature are critically evaluated to identify current limitations and inconsistencies. Finally, key research gaps and future directions are outlined, including the need for standardized sustainability assessment frameworks, data-driven energy optimization, and intelligent hybrid machining systems. This review aims to provide a valuable reference for researchers and practitioners seeking to design next-generation sustainable machining processes with enhanced energy efficiency and reduced environmental impact.

Energies doi: 10.3390/en19122766

Authors: Naief Almalki

The performance of photovoltaic (PV) generation systems is widely evaluated using physics-based simulation. However, this often provides limited insight into the interaction between the operating parameters that fundamentally govern energy outputs. In response to this limitation, this study presents an explainable machine learning framework that uses a normalized efficiency target to recover physically meaningful sensitivity coefficients directly from system-level data. The presented framework is validated on the System Advisor Model (SAM) simulated dataset for four mounting configurations: fixed-tilt, horizontal single-axis tracking (HSAT), tilted single-axis tracking (TSAT), and dual-axis tracking. The same system design parameters and loss assumptions are retained across all configurations to ensure the difference reflected in the generated dataset is due to the tracking modes. To capture the nonlinear input–output relationships, an XGBoost surrogate model is trained, and SHapley Additive exPlanations (SHAP) are subsequently applied to quantify the global importance of individual parameters. To investigate the interaction between the irradiance gain and temperature-induced efficiency losses at the system level induced by PV tracking, two complementary prediction targets are employed: raw system power output and a normalized efficiency-like metric. The results demonstrate that plane-of-array irradiance dominates PV power generation across all tracking configurations, while module temperature governs variations in normalized performance. Thermal sensitivity analysis under high-irradiance conditions reveals a weakly configuration-dependent slope of approximately −5.63 × 10−4 to −5.85 × 10−4 °C−1 (R2 ≈ 0.99). However, the relative spread among the slopes is only approximately 3.6%, showing that tracking systems increase energy yield primarily through enhanced irradiance capture while the temperature-induced efficiency penalty remains similar in engineering magnitude across configurations. The proposed framework extends the role of machine learning from prediction to physically meaningful interpretation and increased transparency.

Energies doi: 10.3390/en19122765

Authors: Pavol Knut František Vranay Zuzana Vranayova Maria Kocurkova

Photovoltaic (PV) systems integrated with green roofs have attracted increasing research interest due to their potential influence on rooftop microclimatic conditions and photovoltaic operating performance. This study experimentally investigated the thermal and electrical behavior of two identical PV modules installed above green and asphalt roof surfaces under real operating conditions in a Central European climate. Rear-side module temperatures and meteorological parameters were monitored, while electrical performance was evaluated using on-site I–V curve measurements. The observed rear-side temperature differences ranged from 0.01 °C to 0.86 °C during the monitored short-term summer periods. A representative I–V measurement indicated approximately 13% higher instantaneous maximum power output for the PV module installed above the green roof configuration under comparable operating conditions. However, the electrical results should be interpreted cautiously due to short-term environmental variability and irradiance-related uncertainty during consecutive field measurements. The presented results correspond to a short-term summer field-monitoring study and should not be generalized to annual photovoltaic performance without extended long-term multi-season experimental validation. The scientific contribution of this study lies in the synchronized side-by-side evaluation of identical PV modules using combined rear-side thermal monitoring and in-situ electrical characterization under real operating conditions.

Energies doi: 10.3390/en19122764

Authors: K. B. Tynyshtykbayev D. V. Bouhvalov N. A. Chuchvaga E. A. Dmitrieva B. Zhumabay P. Kousherova B. Rakymetov A. Serikbekov A. S. Serikkanov A. Ainabayev

This study presents experimental investigations of a hydrogen power unit operating on hydrogen–oxygen gas mixtures produced by water electrolysis (HHO gas). The system was operated with additional molecular hydrogen enrichment in order to investigate the influence of hydrogen concentration on combustion characteristics and system performance. The flame temperature of the HHO + H2 mixture was measured as a function of hydrogen concentration. The results show that the flame temperature increases nonlinearly with hydrogen content, approaching ~2800 °C under the investigated conditions at about 30 vol.% hydrogen in the enriched mixture. Despite the increase in flame temperature, the effective calorific value of the HHO + H2 mixture remains significantly lower than that of pure hydrogen because the electrolysis-derived gas contains oxygen and excess water vapor. The presence of water vapor acts as a thermal diluent, influencing combustion behavior and suppressing autoignition under the investigated operating conditions. Optimal operating parameters for the hydrogen power unit were determined from experimental measurements. The results indicate that hydrogen-enriched HHO mixtures can be operated safely under controlled conditions and may represent a potential working medium for hydrogen-based energy conversion systems.

Energies doi: 10.3390/en19122763

Authors: Joseph Sinchitullo Gregory Zuñiga Mao Romero Cesar Celis

Low-salinity waterflooding (LSWF) represents a cost-effective enhanced oil recovery (EOR) strategy for mature sandstone reservoirs. However, its success strongly depends on pore-scale transport and wettability mechanisms that conventional reservoir simulators cannot accurately capture. This study implements a pore-network modeling (PNM) framework to evaluate and optimize LSWF performance in sandstone systems. A representative pore network was calibrated to match core-scale petrophysical properties—porosity, permeability, and pore-throat size distributions. The LSWF process was simulated using a coupled advective–diffusive salinity transport model integrated with salinity-dependent wettability alteration, expressed through variations in contact angle and interfacial tension. From the multiphase invasion and flow simulations, macroscopic constitutive relationships were derived, including capillary pressure, relative permeability, and fractional flow curves for different injection salinities. Sensitivity analyses indicate that wettability alteration induced by salinity reduction is the dominant mechanism enhancing oil recovery, as reflected in measurable shifts in the relative permeability endpoints and capillary pressure curves. The model predicts an optimal injection salinity window between 2000 and 4500 ppm, yielding up to 7.2% incremental oil recovery, while extremely low salinities produce non-monotonic trends due to competing interfacial tension effects. Overall, the proposed PNM workflow demonstrates a robust approach for (i) translating pore-scale phenomena into reservoir-scale constitutive laws, (ii) identifying salinity ranges for pilot testing, and (iii) reducing uncertainty in field-scale LSWF simulations.

Energies doi: 10.3390/en19122762

Authors: Daoyi Zhu

With the gradual depletion of global fossil resources, optimizing the development of mature oilfields and improving the efficiency of unconventional oil and gas reservoirs have become key priorities for the oil and gas industry [...]

Energies doi: 10.3390/en19122761

Authors: Stanisław Duer Konrad Zajkowski Marek Woźniak Tomasz Klimczak Atif Iqbal Jacek Paś Marek Stawowy Krzysztof Leonowicz

Medium-voltage distribution networks are an important element of the global power system, being responsible for the distribution of electrical energy from transformer stations to local points of delivery and to transformer stations of lower voltage levels. The reliability of the operation of these networks has a direct impact on the continuity of energy supply and the level of unmet energy demand in the power system. The article presents a reliability analysis of a selected segment of a medium-voltage distribution network located in northern Poland. In this study, a probabilistic model of the operation process based on an eight-state graph describing successive levels of technical degradation of the analyzed network was applied. Transitions between the states of the model were described by failure intensities and restoration intensities of the system elements. On the basis of the Kolmogorov–Chapman state equations, the probabilities of the system being in particular operational states were determined. The results obtained were then used to assess the energy-related consequences of failures by linking state probabilities with the share of unmet energy demand. The analysis conducted enabled the identification of the most critical elements of the analyzed network structure and the determination of their impact on the energy supply capability of the distribution network. The obtained results may constitute a basis for planning operational activities, maintenance strategies, and modernization processes of medium-voltage distribution networks.

Energies doi: 10.3390/en19122760

Authors: Haval Kukha Hawez Shaee Radha Omar Layla Lateef Alwan

Hydrogen (H2) is becoming a meaningful way to store energy for long-term use and support thorough decarbonization in systems that use renewable energy. Underground hydrogen storage (UHS) has strategic benefits over above-ground systems because it can hold large volumes, is contained by geology, and is cheap to operate in cycles. This review compares four key geological formations for underground hydrogen storage (UHS): salt caverns, lined rock caverns, depleted hydrocarbon reservoirs, and saline aquifers. Each system is evaluated based on storage mechanisms, efficiency, safety, technological maturity, and economic feasibility. This review also introduces a unified cross-media evaluation framework, a TRL-risk matrix, a technology development roadmap, and novel insights into AI-based monitoring, offering prescriptive guidance for large-scale UHS implementation. Salt caverns have high injectivity, maintain their purity, and undergo 6 to 12 cycles per year at pressures of 60 to 180 bar; however, they are only found in certain places. Lined rock caverns can be built anywhere, but sealing and economic issues make them difficult to use. Depleted hydrocarbon reservoirs with TWh-scale capacity and already built infrastructure. Saline aquifers, on the other hand, have the most potential in the world but need enhanced management of microbiological responses and cushion gas optimization. A synthesis of current studies highlights key research gaps in cyclic geomechanics, hydrogen–rock–microbe interactions, and liner performance for high-pressure storage. The review concludes with techno-economic and safety considerations and identifies future directions for deploying geological UHS as a critical component of a net-zero hydrogen economy.

Energies doi: 10.3390/en19122759

Authors: Daniela Marasová Karolína Hrešková Peter Koščák Martina Koščáková

This review paper presents a comprehensive synthesis of current scientific knowledge on the integration of low-emission technologies into airport operational models. Attention is also given to the role of artificial intelligence techniques in predicting environmental risks, optimizing energy system design, and enhancing operational safety. The primary objective of the study is to evaluate the synergy between renewable energy sources (solar and wind energy) and emerging propulsion technologies in aviation (hydrogen and electrification) from the perspective of safety and operational stability. The methodology is based on a systematic review of 78 scientific studies identified in the Scopus and Web of Science databases. The analysis identifies critical technical and operational barriers, including electromagnetic interference caused by wind turbines, optical hazards associated with photovoltaic systems, and stability challenges in airport microgrids under peak loads resulting from the charging of electric aircraft. Particular attention is given to the safety of hydrogen infrastructure, where findings from the literature indicate the need to revise separation distances and highlight the potential reduction of airport stand capacity by 5% to 16%. The study synthesizes these findings into a strategic framework for “Smart Green Airports”, proposing solutions such as adaptive infrastructure design, the deployment of predictive models based on artificial intelligence, and the implementation of inherently safe energy storage systems. The paper concludes that achieving airport energy self-sufficiency while maintaining the integrity of flight operations is feasible only through the holistic integration of technical measures, simulation-based planning, and strict compliance with updated safety regulations.

Energies doi: 10.3390/en19122758

Authors: Shuangyin He Jianli Zhao Chunxu Qin Guowei Cui Bing Han

To address the difficulty of directly measuring the internal conductor temperature and the complex influence of external environmental factors on gas-insulated switchgear (GIS), a three-dimensional thermal–fluid multiphysics coupling model was developed for a 110 kV three-phase common-enclosure GIS disconnect switch. The model incorporates contact resistance heating, natural convection of SF6 gas, wind speed, and solar radiation. The effects of contact resistance and environmental factors on the temperature field distribution were systematically investigated. The results show that an increase in contact resistance significantly raises the conductor temperature, while higher wind speeds effectively reduce the temperature rise of the equipment. Solar radiation substantially increases the enclosure temperature, whereas ambient temperature has little influence on temperature rise. Based on the enclosure temperature rise, a conductor temperature-rise prediction model and a multi-factor correction model were established. Validation results indicate that all models achieved coefficients of determination greater than 0.98, with prediction errors controlled within ±2 °C. The proposed method enables the accurate prediction of conductor temperature under complex environmental conditions and provides technical support for condition monitoring and overheating fault diagnosis of GIS equipment.

Energies doi: 10.3390/en19122757

Authors: Oksana Liashenko Ihor Turskyy Tomasz Wołowiec Marcin Gąsior Sylwester Bogacki Oleksandr Dluhopolskyi

The European 2035 decarbonisation framework rests on a conditional premise—that higher renewable-electricity penetration accelerates battery electric vehicle (BEV) adoption—yet it has not been tested at the panel level. The question is timely: the December 2025 Automotive Package would soften the 2035 target from 100 to 90 percent CO2 reduction and permit continued production of plug-in hybrids beyond 2035, while the Alternative Fuels Infrastructure Regulation (AFIR) imposes binding charging-coverage targets from 2025 onwards. We assemble an annual panel of 31 European economies over 2015–2024 (310 country-year observations) and combine a two-way fixed-effects baseline on five disaggregated powertrain shares, an interaction model with public charging coverage as a moderator, and a Hansen-style threshold panel. The within-country BEV-share coefficient on renewable-electricity penetration is statistically null (β = +0.18, p = 0.247), rejecting the linear premise. The plug-in hybrid share, by contrast, responds positively and unconditionally (β = +0.36, p = 0.001)—a “PHEV paradox” of compositional response. The BEV channel, by contrast, is conditional on infrastructure: its marginal effect rises with public charging coverage and is positive only in the upper part of the charging distribution (interaction β3 = +0.13, p = 0.027). A formal Hansen-style threshold test in the renewable share does not reject the linear specification (sup-F = 0.73, bootstrap p = 0.97), so the BEV conditionality is identified through the charging-coverage interaction. The findings characterise a two-speed European transition. The first channel reflects compliance-led PHEV hedging; the second reflects BEV charging network complementarity enabled by AFIR-mandated coverage. Subsidy rebalancing away from PHEV eligibility, strict AFIR enforcement, and PHEV utility-factor reform are necessary policy levers for the 2035 framework to deliver full electrification rather than the partial electrification that current incentives yield.

Energies doi: 10.3390/en19122756

Authors: Songwei Li Bo Zhu Hao Zhou Xiangjie Ma

In this paper, the influence of voltage change rate on the process of steep pulse air discharge is studied under an environment of 7000 m atmospheric pressure. Six sets of nanosecond pulses with different voltage change rates are used, and the initial and breakdown gaps of the streamer are analyzed by numerical simulation and ICCD imaging. The results show that when the voltage change rate is large, the electric field develops rapidly, which can promote the early formation of the streamer. However, if the effective duration of the pulse is too short and the voltage duration is insufficient, the streamer cannot develop further, and partial breakdown occurs. As the voltage change rate decreases and the pulse width increases, the streamer is more likely to form a through channel, and the discharge penetration time decreases first and then increases. The experimental and simulation results are consistent. In the low-pressure environment, the pulse leading edge variation characteristics are more sensitive to the formation of streamers, which has a reference value for the gap insulation and pulse withstand voltage design of high-altitude electrical equipment.

Energies doi: 10.3390/en19122755

Authors: Elman Iskandarov Inglab Aliyev Auyelkhan Yergali

Hydrate formation and erosion-related pipe wear are critical operational challenges in natural gas pipelines. This study evaluates a pyrocondensate-based liquid composition modified with fine clay particles as a dual-function formulation for apparent hydrate-onset control and erosion-wear mitigation. The liquid phase contains pyrocondensate solvent, heavy gasoline fraction, and white oil. Laboratory screening was performed for composition ranges including 60/20/20 and 65/15/20 with formulations at reported dosages of 20–25 g/1000 m3. Under the applied procedure, visible hydrate formation for model gas samples was suppressed down to −24 °C with the 60/20/20 formulation. For a field gas sample from well No. 422, the 65/15/20 formulation shifted the observed apparent hydrate-onset temperature from +6 °C to approximately −20 °C at a dosage of 20 g/1000 m3. To mitigate erosion-wear, fine clay particles were added at 10 wt.% of the liquid composition. Laboratory tests demonstrated increased visual erosion-onset time in gas–liquid–solid flows. A preliminary four-month field application on well No. 422 recorded no hydrate formation or visible erosion-related complications. The results demonstrate the empirical potential of this dual-function composition under the investigated conditions.

Energies doi: 10.3390/en19122754

Authors: Nikolay Hinov

The increasing integration of distributed energy resources (DERs), including photovoltaic systems, battery energy storage systems, electric vehicles, and flexible loads, is transforming modern power systems and creating new challenges for coordination, control, and cybersecurity. Conventional Virtual Power Plant (VPP) architectures typically rely on centralized energy management systems, which may face scalability limitations, communication bottlenecks, cybersecurity risks, and reduced resilience to failures. This paper presents a blockchain-enabled decentralized Virtual Power Plant framework for secure and autonomous coordination of distributed energy resources. The proposed architecture combines blockchain technology, smart contracts, IoT-based communication infrastructure, and decentralized energy management functions within a unified multi-layer coordination framework. Smart contracts automate energy scheduling, peer-to-peer transaction validation, and settlement processes, reducing dependence on centralized control entities. Lightweight blockchain consensus mechanisms are employed to improve scalability while limiting computational overhead. The effectiveness of the proposed framework is evaluated through simulation-based case studies involving decentralized DER coordination, peer-to-peer energy trading, and resilience assessment under node-failure conditions. Its performance is compared with that of a conventional centralized VPP architecture in terms of scalability, coordination reliability, communication overhead, transaction transparency, and fault tolerance. The results indicate that the decentralized framework improves operational resilience, coordination transparency, and scalability under increasing DER participation while maintaining satisfactory energy balancing performance. Although blockchain-based coordination introduces additional transaction latency, the proposed approach enhances cybersecurity, reduces dependence on centralized control structures, and provides a flexible foundation for future intelligent smart-grid applications.

Energies doi: 10.3390/en19122753

Authors: Aleksander Krótki Tomasz Spietz Joanna Bigda Agata Czardybon Karina Ignasiak

This study experimentally evaluates hydrogen recovery from synthetic syngas and water–gas shift (WGS) syngas using a laboratory-scale pressure swing adsorption (PSA) unit equipped with layered activated carbon/zeolite 5A beds. Breakthrough tests were first performed to determine adsorption-time limits and identify the critical impurity controlling product quality. Continuous PSA experiments were then carried out using two cycle configurations: a two-bed Berlin-type cycle and a four-bed Linde-type cycle. CO was the first impurity breakthrough experimentally detected and it therefore defined the practical adsorption-time cut-off, whereas CO2 exhibited the strongest retention, especially in beds with an increased activated-carbon fraction. The results showed a clear trade-off between purity and recovery. The four-bed Linde-type cycle provided a wider operating window than the two-bed Berlin-type cycle, owing to pressure equalization and product-purge steps. The best overall performance was obtained for WGS syngas with the 1.6:1 AC:zeolite bed, reaching 99.5 vol.% H2 at 84% recovery and maintaining 99.2 vol.% H2 at 86% recovery. The tail gas was enriched in CO2 up to approximately 72 vol.%, indicating potential for integration with downstream CO2 management.

Energies doi: 10.3390/en19122752

Authors: Chenzhi Fang Zhishuai Hu Bin He Yongfeng Ren Zhenzhou Zhao

In receiving-end modular multilevel converter (MMC)-based flexible high-voltage direct current (HVDC) grid-connected systems, high-frequency oscillation can significantly increase the peak values of the point of common coupling (PCC) voltage and grid current. To address this issue, this paper proposes a coordinated suppression strategy for high-frequency oscillation in receiving-end MMC grid-connected systems. First, an MMC impedance model is established based on harmonic linearization, and its frequency-domain interaction with the grid impedance is analyzed to clarify the formation mechanism of high-frequency oscillation and its main influencing factors. Then, considering the different roles of the voltage feedforward and current feedback channels in the target frequency band, a coordinated suppression strategy combining band-stop filtering in the voltage feedforward path with low-pass filtering and lead compensation in the current feedback path is designed. Hardware-in-the-loop experimental results show that the proposed method effectively identifies and suppresses high-frequency oscillation. Under the validated operating condition, the oscillation-induced peak increases in the PCC voltage and grid current are limited to within 20% and 12.5%, respectively, thereby suppressing further oscillation growth and reducing the risk of approaching the overvoltage and overcurrent protection thresholds.

Energies doi: 10.3390/en19122751

Authors: Gautier George Yao Quenum Myriam Ertz

Renewable energy sources, such as solar thermal and photovoltaic, geothermal, biomass, hydropower, and wind, offer significant sustainability advantages. Yet the sector still faces difficulties in several areas that tend to reduce the efficiency of these new energy forms. Some of these challenges include inconsistent electricity supply, the diffuse nature of renewable energy sources, which makes them difficult to exploit, and the inconsistent and unpredictable nature of electricity supply, which has repercussions for renewable energy markets. Although Industry 4.0 is inherently energy-intensive, its positive contribution to renewable energy systems may outweigh its costs. Consequently, this study conducts a scoping review on the role of digital technologies in renewable energy systems. It focuses on open-access conference papers, journal articles, and book chapters published between 2020 and 2026, selected from scientific platforms and databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Scopus. A multi-stage screening process and a summary sheet for a set of 89 selected articles were produced to extract the necessary information. The results show that Industry 4.0 influences renewable energy systems at the design and installation stage in predictive maintenance, efficient management, and energy security. Meanwhile, Industry 4.0 in renewable energy systems still faces negative externalities that can be categorised as political, financial, infrastructural, environmental, human, security, and technological. To address these challenges, which tend to become entangled in cycles of negative reinforcement, the paper suggests defining standardised, clear, strict, and stable frameworks at the political, legal, regulatory, and environmental levels to overcome most challenges associated with the digital transformation of renewable energy. The study also recommends flexible, inclusive strategic planning that accounts for the digital maturity of the renewable energy system. From these perspectives, the study contributes to the literature by addressing the role of Industry 4.0 technologies in renewable energy systems from a strategic and coordinated perspective, from both human and technological standpoints. It also offers managerial and policy implications by supporting innovation in renewable energy systems on the one hand and contributing to policy and regulatory decision-making that favour their growth on the other.

Energies doi: 10.3390/en19122750

Authors: Cheng Yang Zepeng Liu Chao Jiang Liang Xue Haoyang Cui

Remaining useful life (RUL) prediction for power semiconductor devices such as insulated-gate bipolar transistors (IGBTs) is central to reliable power-electronics operation, yet remains challenging because degradation is non-stationary and electro-thermal precursors are strongly coupled. Here, we propose a physics-informed incremental learning framework (PIILF), which models aging as a latent incremental state-evolution process rather than static trajectory fitting. PIILF integrates an incremental state evolution network (ISEN) for state-wise degradation updates, task-adaptive parameter sharing (TAPS) for mitigating cross-task interference among coupled precursors, and a physics-informed observation decoder (PIOD) that reconstructs observables through electro-thermal coupling relations. On the NASA IGBT accelerated aging dataset, evaluated over 100 random seeds, PIILF achieves lower RMSE and MAE than TimesNet, TimeXer, and DeepHPM, while retaining competitive MAPE, a slightly better R2, and higher parameter efficiency. When the training data are reduced to 50% and 25%, PIILF exhibits smaller error increases than the baselines, indicating greater robustness in data-scarce settings. These findings suggest that embedding physical consistency directly into incremental representation learning provides an effective and efficient route to robust semiconductor RUL prediction.

Energies doi: 10.3390/en19122749

Authors: Wanlu Li Ya Xu Daming Sun Qie Shen

Traditional cryogenic air separation units are unsuitable for distributed, small-scale liquid oxygen production. Cryocooler-based liquefaction technology offers an alternative solution, featuring a large cooling capacity, high efficiency, a compact structure, and rapid start–stop capability. In this paper, an oxygen liquefaction system based on a high-capacity Stirling cryocooler was developed. Because the heat transfer performance of cryocoolers varies significantly across different temperature ranges, heat exchanger designs must be tailored to specific operating conditions. However, research on cold-end heat exchangers for large-capacity cryocoolers used in liquefaction systems remains limited. In the liquid oxygen temperature range, factors such as liquid film formation and incomplete condensation severely affect heat transfer performance and must be considered. In this paper, numerical simulations were performed to analyze the condensation behavior of oxygen, with particular attention paid to the matching between the heat exchange structure and the cooling capacity. Subsequently, a small-scale experimental system was constructed and tested. The successful operation of the experimental system validated the feasibility of the proposed heat exchanger design. Under the conditions of 300 K and an oxygen inlet gauge pressure of 0.45 MPa, the system achieved a liquefaction capacity of 7.4 L/h, corresponding to a cooling capacity of 787 W. The specific power consumption was 0.89 kW·h/kg, with a coefficient of performance (COP) of 0.116. This performance is competitive among small-scale cryocooler-based oxygen liquefaction systems. This study provides both theoretical and experimental support for further performance optimization and engineering application of such cryocoolers in liquid oxygen production.

Energies doi: 10.3390/en19122748

Authors: Nagwa Amin Abdelkawy Abdullah Sultan Al Shammre Hazem Alshaikhmubarak Taiba Sulaiman Al Fawzan Saleh A. Aljamaan

Following the 2022 disruption of Russian pipeline gas, European countries shifted toward liquefied natural gas (LNG) at markedly different speeds; yet, the drivers of this variation remain poorly understood. This study asks what explains these differences. Using a balanced panel of eight major European gas importers over 2015–2024 (80 observations), the study models the share of LNG in total gas imports as the dependent variable, reversing the conventional approach that treats LNG as an explanatory variable for gas prices. The interaction between the post-2022 structural break and storage fill levels is negative and statistically significant (β = −0.006, p = 0.019 clustered; p = 0.002 Driscoll-Kraay), suggesting that countries with lower storage reserves tended to increase their LNG dependence more strongly. This result is robust across seven of eight specifications and survives time-trend controls and leave-one-country-out analysis. Marginal effects reveal that the storage–LNG relationship was absent before the shock and emerged only after the disruption. Renewable energy penetration emerges as a significant positive predictor.

Energies doi: 10.3390/en19122747

Authors: Xin Yang Wenlong Liao Rui Liu Songhai Fan Yun Feng Yu Zhang Yueping Yang Zhenyu Wang Zhou Mu

Ultra-high-voltage (UHV) converter transformer equipment is critical for UHVDC transmission systems. This paper proposes a Cross-modal Transformer framework for fault diagnosis by fusing dissolved gas analysis (DGA) and infrared (IR) thermography data. The framework encodes DGA measurements into temporal tokens and processes IR images through a ResNet-18 backbone to generate spatial tokens. A Cross-modal Transformer module enables deep semantic interaction via bidirectional cross-attention, allowing DGA tokens to attend to relevant IR regions and vice versa. A modality-gating mechanism adaptively reweights the two modalities under measurement degradation, including partial and fully missing-modality scenarios. The novelty lies in adapting these components into a leakage-controlled DGA-IR diagnostic framework for UHV converter transformers, with explicit interaction between gas-evolution tokens and spatial thermal tokens. Evaluation is performed under a leakage-controlled grouped chronological split that isolates equipment units, converter stations, and fault episodes across train, validation, and test partitions. Labels are drawn exclusively from maintenance inspection and operational records, independent of the IEC 60599 ratio features seen by the model. Under this protocol, the proposed framework consistently improves accuracy and macro-F1 over encoder-matched simple-fusion baselines (Transformer-DGA + ResNet-18 with concatenation, late fusion, and gated averaging). Additional missing-modality, noise, and ablation experiments indicate that the gains come from bidirectional cross-attention and adaptive gating rather than from stronger unimodal encoders alone.

Energies doi: 10.3390/en19122746

Authors: Penka Zlateva Mariana Murzova Angel Terziev Krastin Yordanov Nevena M. Mileva

Hydrogen production from waste plastics is emerging as a potential low-carbon pathway that integrates waste management with energy production. This study develops an integrated socio-technical framework combining a comparative assessment of thermochemical conversion pathways with market acceptance analysis based on survey data (n = 162). The results show that acceptance is mainly driven by trust (β = 0.47) and environmental perception (β = 0.32), while price sensitivity has a negative effect (β = −0.21). Awareness does not significantly affect acceptance (β = 0.08). The model explains 48% of the variance (R2 = 0.48), and a strong correlation is observed between trust and acceptance (r = 0.68). These results show that technological performance alone is insufficient; consumer perception and economic factors play an equally important role, highlighting the need for integrated socio-technical approaches in low-carbon energy systems.

Energies doi: 10.3390/en19122745

Authors: Yang Liu Yuanyuan Zhu Hang Li Shaodong Li Yanxiang Xie

Against the backdrop of frequent adjustments and iterations in global climate policies, the issue of policy uncertainty surrounding corporate energy structure upgrades has become increasingly prominent. A key concern for achieving global green sustainable development is how to efficiently advance corporate low-carbon transition. In view of this, we construct the energy structure low-carbon transition at the enterprise level, and explore the influence and mechanism of climate policy uncertainty on the energy structure low-carbon transition of enterprises from the perspective of enterprise willingness and ability. The research findings indicate: (1) Corporate energy structure low-carbon transition is substantially impeded by climate policy uncertainty, and this conclusion is upheld by a battery of robustness and endogeneity analyses. (2) Climate policy uncertainty inhibits corporate energy structure low-carbon transition by reducing management’s long-term behavior, lowering green technology innovation levels, and weakening effective investment. (3) According to heterogeneity analysis, non-state-owned businesses, areas with lax environmental regulations, and businesses with poor climate risk awareness are more affected by the inhibiting impact caused by climate policy uncertainty. In addition to offering theoretical underpinnings and helpful advice for governments looking to create stable climate policies and enhance climate governance systems, this paper gives fresh perspectives on the fundamental reasoning behind corporate energy structure decarbonization.

Energies doi: 10.3390/en19122744

Authors: Ying Wu Ran Shi Changyang Peng Jianguo Yan Huanyu Zhao Lei Wang Xiaotao Bi

Combining oxygen-enriched combustion CO2 capture technology and CO2 hydrogenation with methanol technology, a new power–chemicals cogeneration (PCC) design is proposed for thermal power stations with CO2 capture and utilization under the power-to-liquid concept. For material integration, CO2 from an oxygen-enriched thermal power station and H2 from water electrolysis using renewable power serve as raw materials for the methanol production process. O2 from water electrolysis using renewable power is supplied to the oxygen-enriched thermal power station; thus, electricity can be saved and investment in an air separation unit can be beneficial. For energy integration, power for gas compression and heat for methanol rectification in the methanol production process are supplied by an oxygen-enriched thermal power station. The energy released from the methanol production process is fully recovered for extra power generation. Energy analysis results show that a high CO2 capture and utilization ratio, which is defined as the ratio of the captured and utilized CO2 to the total CO2 generation, of 78.1% could be achieved. By integrating the system in a 600 MW thermal power station, the net power generation and methanol production of the proposed design reaches 473.1 MW and 56.1 kg/s, respectively. Economic analysis results show that the power cost is estimated to be 62.8 $/MWh, which has great market competitiveness compared to the conventional thermal power station with CO2 capture. Due to the saved material expense and power and heat expense, the methanol cost is reduced from 1.33 $/kg to 1.20 $/kg. The H2 expense by water electrolysis using renewable power has a decisive influence on the methanol cost.

Energies doi: 10.3390/en19122743

Authors: Chen Peng Shuai Peng Dimas Krissyda Ci Song Khalil AL-Bukhaiti Anping Wan

This study introduces a greentelligent scheduling approach to enhance energy efficiency in the aluminum extrusions casting workshop (ACW), addressing the high energy consumption and low efficiency inherent in these processes. Global energy consumption is significantly attributed to the manufacturing sector, with aluminum extrusions being one of the most common products, particularly in energy-intensive casting workshops. Given the considerable demand and potential for energy savings in aluminum extrusions manufacturing (AEM), this study proposes an intelligent scheduling approach to minimize non-processing energy consumption (NPE) while also reducing average completion time (ACT). Utilizing industrial internet of things (IIoT) technologies, practical production data is acquired to support a bi-objective scheduling model. An empirical knowledge-based evolution algorithm (EBA) with an improvement strategy (SO-EBA) is developed to efficiently solve this complex, NP-hard problem. A production case in an ACW demonstrates the effectiveness of the SO-EBA. Compared to benchmark algorithms, the SO-EBA achieves significant reductions in optimal NPE by more than 39.41%, while maintaining production efficiency. This work advances greentelligent manufacturing by integrating IIoT and intelligent algorithms, offering a scalable solution for sustainable production in energy-intensive industries.

Energies doi: 10.3390/en19122742

Authors: Sumera Ahmad Ammar Rashid Ahmed Bilal Awan Usman Javed Butt

The global renewable energy sector now represents the world’s fastest-growing sector, with growth projected to more than double by 2030 and expected to exceed 4600 GW between 2025 and 2030. This is driven by falling costs, increasing consumer awareness, sustainable energy production models, and national and international climate commitments. This review study aims to discuss the transformation initiatives in the renewable energy sector with net-zero targets. A total of 89 studies published between 2020 and 2026 were identified for this literature review. The results indicate that digital transformation has the potential to significantly optimize the performance of the renewable energy sector by resolving its sustainability issues. This study discusses the waste types and waste management strategies in the renewable energy sector. It also highlights the indicators, barriers, and drivers of sustainable performance in the renewable energy sector by integrating advanced technological solutions in manufacturing, supply chain management, maintenance, monitoring, and the management of renewable energy equipment. The study findings demand global commitment and policy coordination in achieving the goals of decarbonization. The literature insights highlight future core research fields and can guide international organizations, industrial policymakers, and academic scholars towards a better and more sustainable future.

Energies doi: 10.3390/en19122741

Authors: Šime Grbin Dinko Vukadinović

This paper proposes an online method for efficiency optimization of a switched reluctance generator (SRG) operating in single-pulse mode and connected to an asymmetric bridge converter. The optimal angles are defined as those that minimize total SRG loss while ensuring accurate tracking of the terminal voltage reference. The Pearson correlation coefficient between SRG loss and selected SRG variables was calculated, with the highest correlation found for the average value of all phase currents. Therefore, the average phase current was selected as the variable to be minimized in a perturb-and-observe (P&O) method used to determine the optimal turn-on angle at a given operating point. The turn-off angle was calculated to maintain the terminal voltage at its reference value. The method was validated using both a conventional SRG simulation model and an advanced model that accounts for mutual coupling, iron losses, and remanent magnetism, and was further verified experimentally on an 8/6 SRG rated at 1.1 kW under various load conditions, terminal voltages, and rotor speeds.

Energies doi: 10.3390/en19122740

Authors: Michalis Sourgoutsidis Leonidas Zouloumis Vasileios Kilis Effrosyni Giama Andreas P. Vouros Manolis Souliotis Nikolaos Ploskas Giorgos Panaras

Accurate design and performance assessment of solar thermal domestic hot water systems coupled with a heat pump auxiliary typically requires transient simulation, as the system’s behavior depends on multiple interactions among collector characteristics, storage stratification, control logic, weather, and draw-off timing. Monthly methods such as the f-chart are useful for first-pass estimates, but they do not resolve stratification, thermostat operation, or demand timing, and they may become inaccurate for stratified thermostat-controlled systems. Direct comparisons of locally inspectable symbolic and black-box surrogate families for this system class remain limited. A 10,982-case development dataset was generated from minute-resolved annual MATLAB simulations, parameterized by collector area, optical efficiency, and first- and second-order loss coefficients. Three surrogate families were benchmarked under a unified protocol, random forest-assisted shape-constrained symbolic regression (SR), feed-forward artificial neural network (ANN) models, and Automatic Learning of Algebraic Models for Optimization (ALAMO), with the f-chart used as a monthly reference method. The targets were the 12 monthly solar fractions under the direct solar heat definition and the corresponding annual mean solar fraction, evaluated on the same independent 991-case test set. SR achieved the lowest average error (mean absolute percentage error, MAPE = 0.82%; root mean square error, RMSE = 0.006), followed by the ANN (MAPE = 2.07%, RMSE = 0.028) and ALAMO (MAPE = 3.67%, RMSE = 0.060), with Nash–Sutcliffe efficiency (NSE) values above 0.98 for all models. Evaluation times were 0.0026–0.124 s per target, compared with about 1000 s for one full-year simulation. These results define the study as a common protocol benchmark within the studied simulator-defined envelope. SR gives the strongest accuracy with local symbolic inspectability, the ANN remains the flexible retrainable option, and ALAMO provides compact algebraic evaluation with the shortest learned model runtime.