Search Results (168)

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

The large-scale integration of renewable energy and electric vehicles introduces grid-connected power fluctuations in microgrids. To address this, this paper proposes a novel two-stage optimization scheduling strategy that balances economic efficiency and grid compatibility. In the first stage, a multi-objective optimization model is formulated to minimize both operating costs and power fluctuations, and the Improved Multi-Objective Grey Wolf Optimization algorithm—incorporating the Bernoulli chaotic map—is employed to solve it efficiently. In the intra-day phase, a rolling tracking strategy based on model predictive control is proposed to address ultra-short-term forecasting errors, and a multi-unit hierarchical error compensation mechanism is designed. This mechanism prioritizes the use of supercapacitors to absorb high-frequency fluctuations, followed by the coordinated use of batteries, electric vehicle clusters, and micro gas turbines to mitigate residual deviations, thereby effectively reducing the operational burden on individual energy storage devices. Finally, a comparative analysis of six simulation cases was conducted using a weighted evaluation metric that integrates average power deviation values and interconnection line power fluctuations. The results confirm that this strategy not only significantly smooths grid-connected power fluctuations but also demonstrates exceptional robustness and adaptability under extreme forecast error scenarios. Full article

In this paper, a new hybrid prediction method is proposed for estimating remaining useful life, emissions, and performance parameters using experimental data obtained from a micro-turbojet engine. Experiments were conducted under various rotational speed conditions, yielding a total of 342 measurement points. Turbine speed, exhaust gas temperature, fuel flow rate, and thrust were considered as input variables in the study. Thermal efficiency, total power, CO₂, and NO₂ were considered as output variables. The experimental findings showed that thermal efficiency varied between 0.49% and 7.1%, total power between 0.266 and 13.94 kW, and CO₂ emissions by volume between 0.317% and 2.183%. The proposed RL-MH-LR-CBR approach combines the advantages of multiple methods. In this method, the interpretable formulation of linear regression serves as the foundation. Additionally, in the adaptive meta-heuristic optimization process, a hyper-heuristic selection mechanism based on the UCB1-based multi-arm bandit approach is used to select the optimal algorithm from among the meta-heuristic methods. Finally, the CatBoost-based residual error learning component aims to capture non-linear patterns that cannot be explained by the linear model. The method was compared with 14 different methods on both the NASA C-MAPSS FD001 dataset and real engine data. The results demonstrate that the proposed framework exhibits more balanced, stable, and higher generalization capabilities compared to classical regression models and powerful AI methods, particularly in non-linear, noisy, and heterogeneous outputs. In the real engine dataset, the proposed method produced R² values of 0.968 for CO₂ and 0.936 for NO₂, while the predictive performance was even stronger for thermal efficiency and total power, with corresponding R² values of 0.998 and 0.995, respectively. Additionally, the method demonstrated a clear advantage in hard-to-model outputs by reducing the error level to 0.061 in NO₂ predictions. These findings demonstrate that the proposed approach is not limited to micro-turbojet-engines. The developed method provides a robust decision support framework that is applicable, scalable, and generalizable to predictive maintenance, emissions monitoring, energy systems, aviation analytics, and other highly dynamic engineering problems. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

A major challenge for aircraft fuel cell propulsion systems is to ensure that the air properties on the cathode side remain within a narrow, suitable envelope throughout the flight. The components must maintain almost constant temperature, pressure and humidity levels under widely varying ambient conditions. The choice of components must take into account the aviation-specific requirements for weight and waste heat. In this numerical study, we investigate a novel cathode air supply system for a hydrogen fuel cell propulsion system which replaces the state-of-the-art electrical components used to drive the compressor in the cathode air supply system with a hydrogen-fuelled micro gas turbine. Previous studies have shown the potential of waste heat and overall cathode gas path size reduction but the off-design performance of such system is yet to be investigated. Hence, based on realistic regional aircraft flight missions and realistic atmospheric conditions, we investigate the off-design performance of the propulsion system. Therefore, a constant mass flow algorithm along cathode and gas turbine gas paths is developed and presented. Next, earth observation data are used to determine realistic boundary conditions and air contamination. Based on these data, the possible contaminant ingestion of the fuel cell is evaluated to allow for future sizing of filters for robust operation. Furthermore, the effects of realistic ambient conditions on the thermodynamic cycle yield important information about necessary revisions of the cycle design point. Full article

Small-scale concentrated solar power (CSP) systems coupled with micro gas turbines (MGTs) offer a promising solution for decentralised and sustainable power generation. However, CSP–MGT systems are subject to pronounced transient behaviour during start-up and operation due to fluctuating solar irradiance, making accurate transient modelling essential. This work introduces a fully coupled transient electro-thermo-mechanical model of a CSP-driven micro gas turbine, explicitly linking thermal transients and heat soakage effects to electrical performance during start-up. Unlike existing models, the proposed approach captures the interaction between turbomachinery thermal inertia, shaft dynamics, and detailed electrical machine and power converter losses under real-world transient operating conditions. The model integrates thermodynamic, mechanical, electrical, and control subsystems within a unified framework using a lumped-volume formulation suitable for real-time-capable simulations. To improve prediction accuracy at low rotational speeds, a dedicated interpolation strategy for turbomachinery performance maps is implemented. The model is validated at both component and system levels using experimental data from a 6 kWe CSP–MGT test facility. The results show good agreement with measurements, with maximum deviations of approximately 8% in receiver outlet temperature and less than 6% in air mass flow rate. The findings demonstrate that accounting for heat soakage is critical for a realistic prediction of thermal and electrical transients, as neglecting thermal inertia leads to an underestimation of the start-up electrical energy consumption by up to 140%, highlighting the dominant role of thermal mass effects in small-scale micro gas turbines compared to larger systems. The proposed model provides a robust tool for analysing start-up behaviour and supports improved control and operational strategy development for CSP–MGT systems under variable solar conditions. Full article

Hydrogen-fueled gas turbines face challenges related to flashback risk, nitrogen oxide (NO_x) emissions, and operational flexibility. In this study, a Center-Graded Spiral Micromixing (CGSM) combustor was designed and experimentally investigated to enhance the robustness of fuel–air mixing under hydrogen-rich conditions. The proposed CGSM concept employs spiral microtubes to induce curvature-driven secondary flows, promoting mixing through airflow-controlled mechanisms rather than relying solely on fuel jet momentum. Numerical simulations were conducted to qualitatively analyze the internal flow and mixing characteristics of the spiral microtubes, followed by pressurized combustor experiments at an inlet pressure of 0.3 MPa and elevated air temperatures. The experimental results demonstrate stable combustion of pure hydrogen under lean conditions, with NO_x emissions being maintained below 25 ppm, corrected to 15% O₂, without observable flashback or combustion oscillations within the designated operating range (from ignition to full load). The combustor further exhibits stable operation with blended hydrogen–methane and hydrogen–ammonia fuels, enabling online fuel switching without hardware modification. Application tests on an 80 kW micro-gas turbine indicate that the CGSM combustor can support stable operation across the full range of load conditions, from ignition to full-load operation, under both simple- and reheat-cycle modes, with performance characteristics that are consistent with established operational standards for micro-gas turbines. These results suggest that the CGSM concept provides a feasible micromixing strategy for hydrogen and hydrogen-rich fuels at a moderate pressure and micro-gas turbine scale. Full article

This paper presents an experimental investigation of hydrogen enrichment effects on combustion behavior and exhaust emissions in a self-developed micro gas turbine fueled with a propane–butane mixture. Hydrogen was blended with the base fuel in volume fractions of 0–30%, and combustion was examined under unloaded operating conditions at three global equivalence ratios (ϕ = 0.7, 1.1, and 1.3). The global equivalence ratio (ϕ) is defined as the ratio of the actual fuel–air ratio to the corresponding stoichiometric fuel–air ratio, with ϕ < 1 representing lean, ϕ = 1 stoichiometric, and ϕ > 1 fuel-rich operating conditions. The micro gas turbine is based on an automotive turbocharger coupled with a custom-designed counterflow combustion chamber developed specifically for alternative gaseous fuel research. Exhaust gas emissions of CO, CO₂, and NO_x were measured using a laboratory-grade FTIR analyzer (Horiba Mexa FTIR Horiba Ltd., Kyoto, Japan), while combustion chamber temperature was monitored with thermocouples. The results show that hydrogen addition significantly influences flame stability, combustion temperature, and emission characteristics. Increasing the hydrogen fraction led to a pronounced reduction in CO emissions across all equivalence ratios, indicating enhanced oxidation kinetics and improved combustion completeness. CO₂ concentrations decreased monotonically with hydrogen enrichment due to the reduced carbon content of the blended fuel and the shift of combustion products toward higher H₂O fractions. In contrast, NO_x emissions increased with increasing hydrogen content for all tested equivalence ratios, which is attributed to elevated local flame temperatures, enhanced reaction rates, and the formation of locally near-stoichiometric zones in the compact combustor. A slight reduction in NO_x at low hydrogen fractions was observed under near-stoichiometric conditions, suggesting a temporary shift toward a more distributed combustion regime. Overall, the findings demonstrate that hydrogen–propane–butane blends can be stably combusted in a micro gas turbine without major operational issues under unloaded conditions. While hydrogen addition offers clear benefits in terms of CO reduction and carbon-related emissions, effective NO_x mitigation strategies will be essential for future high-hydrogen microturbine applications. Full article

The frictional resistance of impeller machinery blades such as aircraft engines, gas turbines, and wind turbines has a decisive impact on their efficiency and energy consumption. Inspired by the micro-tooth structure on the surface of shark skin, microstructural drag reduction technology has become a cutting-edge research direction for improving aerodynamic performance and a continuous focus of researchers over the past 20 years. However, the significant difficulty in fabricating microstructures on three-dimensional curved surfaces has led to the limited widespread application of this technology in engineering. Addressing the issue of drag reduction and efficiency improvement for small axial flow fans (local Reynolds number range: (36,327–40,330), this paper employs Design of Experiments (DOE) combined with high-precision numerical simulation to clarify the drag reduction law of bionic microgroove surfaces and determine the dimensions of bionic microstructures on fan blade surfaces. The steady-state calculation uses the standard k-ω model and simpleFoam solver, while the unsteady Large Eddy Simulation (LES) employs the pimpleFoam solver and WALE subgrid-scale model. The dimensionless height (h⁺) and width (s⁺) of microgrooves are in the range of 8.50–29.75, and the micro-grooved structure achieves effective drag reduction. The microstructured surface is fabricated on the suction surface of the blade via a spray coating process, and the dimensions of the microstructures are determined according to the drag reduction law of grooved flat plates. Aerodynamic performance tests indicate that the shaft power consumed by the bionic fan blades during the tests is significantly reduced. The maximum static pressure efficiency of the bionic fan with micro-dimples is increased by 2.33%, while that of the bionic fan with micro-grooves is increased by 3.46%. The fabrication method of the bionic microstructured surface proposed in this paper is expected to promote the engineering application of bionic drag reduction technology. Full article

Parabolic dish collectors (PDCs) focus solar radiation onto a small area, minimizing the heat-loss area of the solar receiver and improving the heating of the working fluid. This fluid usually drives a Stirling-like or micro-gas turbine (Brayton-like) power generator. PDCs, initially intended for small-capacity applications, are well-suited for electricity and heat generation in remote rural areas, working alone and/or as parabolic dish arrays. PDCs have received considerable attention among solar thermal collectors due to their high concentration ratios and the high temperatures they achieve. However, nowadays, they are the least developed and least commissioned among concentrated solar power configurations, lacking a well-established technology. This review aims to compile the evolution of research on PDCs over recent years from a global perspective and is mainly focused on the subsystems constituting a PDC plant, their integration, and overall system optimisation, thereby addressing a gap in the current literature. Methodological tools used in the field are comprehensively revised, and recent related projects are summarized. Some innovative and promising applications are also highlighted. Full article

This research numerically investigates the cooling performance of Diamond-type triply periodic minimal surface (TPMS) networks as a gas turbine effusion cooling layer, augmented with various jet impingement configurations. The study analyzes the internal and external flow characteristics, pressure loss, and overall cooling effectiveness using conjugate heat transfer simulations. The Diamond design is compared to conventional film cooling and micro-hole models within a blowing ratio range of 0.5 to 2.0. The jet hole diameter and jet-to-plate distance are varied to identify an optimal double-wall cooling configuration. The results reveal that the Diamond hole mitigates the strong discharge of coolant, resulting in a more adherent cooling film, which provides excellent surface coverage. While jet impingement enhances internal heat transfer, its contribution to cooling effectiveness is minor compared to the benefit of film coverage. At an equivalent total pressure loss coefficient, the Diamond with impinging jets demonstrates 101% higher cooling effectiveness than the film hole. The thermal-mechanical analysis indicates that the Diamond model exhibits a more uniform distribution of thermal stress and displacement. The average stress is reduced by 44.7% compared to the film hole. This work confirms the TPMS-based effusion as an advanced cooling solution for next-generation gas turbines. Full article

This study investigates the performance, emissions, and physicochemical characteristics of a small-scale gas turbine fueled with Jet A and camelina biodiesel blends (B10, B20, and B30). The blends were characterized by slightly higher density (up to +3%), viscosity (+12–18%), and lower heating value (−7–9%) compared to Jet A. These fuel properties influenced the combustion behavior and overall turbine response. Experimental results showed that exhaust gas temperature decreased by 40–60 °C and specific fuel consumption (SFC) increased by 5–8% at idle, while thrust variation remained below 2% across all operating regimes. Fuel flow was reduced by 4–9% depending on the blend ratio, confirming efficient atomization despite the higher viscosity. Emission measurements indicated a 20–30% reduction in SO₂ and a 10–35% increase in CO at low load, mainly due to the sulfur-free composition and lower combustion temperature of biodiesel. Transient response analysis revealed that biodiesel blends mitigated overshoot and undershoot amplitudes during load changes, improving combustion stability. Overall, the results demonstrate that camelina biodiesel–Jet A blends up to 30% ensure stable turbine operation with quantifiable environmental benefits and minimal performance penalties, confirming their suitability as sustainable aviation fuels (SAFs). Full article

In micro gas turbines, electrical power from the high-speed generator is delivered to the grid through a converter that influences overall efficiency and energy quality. This subsystem is often overlooked in efforts to improve turbine performance, which have traditionally focused on combustors and turbomachinery. This study investigates how replacing conventional silicon switching devices in the converter with silicon carbide technology can directly reduce greenhouse gas emissions from micro gas turbines. Although silicon carbide is widely used in electric vehicles and distributed energy systems, its emission reduction impact has not been assessed in micro gas turbines. A MATLAB-based model of a 100 kW Ansaldo Energia micro gas turbine was used to compare the performance of silicon and silicon carbide converters across the 20–100 kW operating range. Silicon carbide reduced total converter losses from 4.316 kW to 3.426 kW at full load, a decrease of 0.889 kW. This improvement lowered carbon dioxide emissions by 5.7 g/kWh and increased net electrical efficiency from 30.03% to 30.29%. Each turbine can therefore avoid about 1.53 tonnes of carbon dioxide annually, or 11.61 tonnes over a 50,000 h service life, without altering turbine design, combustor geometry, or fuel composition. This work establishes the first quantitative link between wide-bandgap semiconductor performance and direct greenhouse gas mitigation in micro gas turbines, demonstrating that upgrading converter technology from silicon to silicon carbide offers a deployable pathway to reduce emissions from micro gas turbines and, by extension, lower the carbon intensity of distributed generation systems. Full article

From advanced research platforms to Unmanned Aerial Vehicles (UAVs), Micro Turbojet Engines (MTEs) have grown to be essential parts in many different kinds of applications. Extensive testing, encompassing analysis, alternative fuel suitability, performance characterization, and control system validation, supports their development. This survey examines the current state of MTE experimentation, along with information on experimental methods, sensor technologies, and new directions in corresponding research. Measurement techniques, alternative fuel effects, characterization of gaseous and particle emissions, and the utilization of experimental data to validate control systems and sophisticated engine models are among the recent advances in test bed design discussed here. Presenting an overall view, the aim is to highlight current challenges and inspire more MTE technology advancement. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article