Energy Conversion and Management-X最新文献

{"title":"Simultaneous sizing of a photovoltaic system and compressed air energy storage in a microgrid","authors":"Tshilumba Kalala,&nbsp;Mwana Wa Kalaga Mbukani","doi":"10.1016/j.ecmx.2026.101571","DOIUrl":"10.1016/j.ecmx.2026.101571","url":null,"abstract":"<div><div>The integration of Compressed Air Energy Storage (CAES) with photovoltaic (PV) systems, complemented by grid interconnection capabilities and diesel generator backup, represents an advanced approach to sustainable microgrid design for future energy systems. In this study, a multi-objective optimization model for sizing PV-CAES systems is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem with two primary objective functions: (1) minimization of total system investment costs (CAPEX) and operational costs (OPEX), and (2) enhancement of system reliability and maximization of RE penetration. The Augmented <span><math><mi>ϵ</mi></math></span>-constraint method is applied to solve this multi-objective optimization problem by incorporating the reliability and RE penetration objectives as inequality constraints, while maintaining cost minimization as the overall optimization goal. In application to a case study of a South African commercial building, the optimized design saves annual operational costs by 35.2% and achieves 41.5% penetration of RE and 2.4% increase in reliability compared with conventional designs. The results demonstrate the success of the framework in providing economically viable PV-CAES configurations that simultaneously enhance sustainability and system reliability via comprehensive mathematical optimization.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101571"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid energy storage approach for techno-economic optimization of renewable microgrids in SWROD applications","authors":"Md. Asaduz-Zaman ,&nbsp;Makbul A.M. Ramli ,&nbsp;Sultan Alghamdi","doi":"10.1016/j.ecmx.2026.101558","DOIUrl":"10.1016/j.ecmx.2026.101558","url":null,"abstract":"<div><div>The renewable-driven seawater reverse osmosis desalination (SWROD) plant has emerged as a sustainable solution to address the growing freshwater demand worldwide. In such systems, energy storage plays a critical role in coordinating water-energy balance. This study proposes a hybrid battery-tank storage operational strategy for techno-economic optimization of PV/Wind-based microgrids applied to SWROD. The methodology applies genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC) techniques for design comparison and validation. Five decision variables include SWROD capacity, PV panels, wind turbines, battery, and tank storage. The optimization minimizes the levelized cost of water (LCOW) while satisfying the loss of water supply probability (LWSP). Three microgrid configurations are simulated for Yanbu City using MATLAB software. Results indicate that PV/Wind hybrid system yields the lowest LCOW of 1.06657 $/m³ consisting of 2530 kW PV, 5240 kW wind turbine, 8700 kWh battery storage, 7500 m³ tank, and SWROD capacity of 9600 m³/day. Configurations relying solely on PV or Wind exhibit higher costs. ABC algorithm also outperforms the GA and PSO. Sensitivity analysis further reveals that water demand variability imposes greater risks than solar irradiance or wind fluctuations. This storage model offers a promising pathway toward resilient and cost-effective renewable desalination systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101558"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flying green: Life cycle assessment and decomposition of bio-based sustainable aviation fuels production in Australia and global benchmarks","authors":"Xueting Jiang ,&nbsp;Aditi Mankad ,&nbsp;Walter Okelo","doi":"10.1016/j.ecmx.2026.101625","DOIUrl":"10.1016/j.ecmx.2026.101625","url":null,"abstract":"<div><div>Sustainable aviation fuels (SAF) are critical for sustainably transitioning the aviation sector into low-carbon status depending on the type of feedstock and technology. However, studies on the key factors that drive these environmental benefits, and the effect of emerging technologies such as biomanufacturing would have on SAF production in the future are limited. Consequently, we assessed the environmental impact of bio-based SAF production and investigated the key drivers of its carbon footprint (greenhouse gas emissions), focusing on Hydroprocessed Esters and Fatty Acids (HEFA), Alcohol-to-Jet (AtJ), and Fischer-Tropsch (FT) pathways. Using Australia as a case study alongside a global benchmark, this study decomposed the life-cycle carbon footprint of SAF production into carbon intensity, energy efficiency, scalability, cost competitiveness, and industry size factors. Results reveal that the energy efficiency factor significantly reduces the SAF production carbon footprint across all three pathways. The scalability factor was a dominant challenge that greatly influenced the carbon footprint of SAF production across global scenarios, especially for HEFA and AtJ, while for Australia the effects of the scalability factor were smaller though remain a noticeable challenge for AtJ. The decomposition results in Australia resemble mostly the high- and very high- SAF production scenarios globally. Results of a sensitivity analysis suggest that biomanufacturing potentially enhances emission reductions for various SAF feedstocks in both Australia and globally, particularly for oilseed-based pathways in Australia.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101625"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance and emission analysis of flaxseed biodiesel blends in a direct injection diesel engine","authors":"Karib Hassan Khan,&nbsp;Mohammad Mashud","doi":"10.1016/j.ecmx.2026.101623","DOIUrl":"10.1016/j.ecmx.2026.101623","url":null,"abstract":"<div><div>With increasing concerns over environmental sustainability and energy security, biodiesel from renewable sources has emerged as a promising alternative to conventional diesel. This study investigates engine performance and exhaust emissions of diesel–flaxseed biodiesel blends (10%, 20%, and 30% by volume) in a four-stroke direct injection diesel engine, with blends up to 30% selected to avoid excessive viscosity and stability issues. Flaxseed oil was converted to biodiesel via KOH-catalyzed transesterification, yielding 82.5% and meeting ASTM D6751 fuel quality standards. Engine performance results showed that the 20% blend (D80F20) delivered the best overall outcomes: brake power, torque, and mean effective pressure were only slightly lower than diesel (1.23%, 0.51%, and 1.10% respectively), while brake thermal efficiency improved by 7.79% and brake specific fuel consumption decreased by 2.60%. The 30% blend (D70F30) demonstrated the highest volumetric efficiency. Emission analysis revealed that the 10% blend (D90F10) achieved the lowest CO₂ and NOx emissions (4.75% and 1.87% lower than diesel respectively), whereas D80F20 produced the lowest CO emissions (21.90% lower) and similar CO₂ and NOx emissions. Overall, the 20% flaxseed biodiesel blend emerged as the optimal blend. The blends demonstrated comparable or superior performance and emissions to various biodiesel blends and additive-enhanced blends.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101623"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Passively-tuned roll-based wave energy converter for enhanced efficiency and frequency adaptability","authors":"Ruben J. Paredes ,&nbsp;David Plaza ,&nbsp;Raju Datla ,&nbsp;Mijail Arias-Hidalgo ,&nbsp;Paul S. Zambrano ,&nbsp;Jose R. Marin-Lopez ,&nbsp;Jose M. Ahumada ,&nbsp;Ricardo Álvarez-Briceño ,&nbsp;Rafael Soria ,&nbsp;Wilson Guachamin-Acero ,&nbsp;Jesus Portilla-Yandun ,&nbsp;Muhammad R. Hajj","doi":"10.1016/j.ecmx.2026.101575","DOIUrl":"10.1016/j.ecmx.2026.101575","url":null,"abstract":"<div><div>Wave Energy Converters (WECs) typically exhibit natural oscillation frequencies that are significantly higher than the dominant frequencies of ocean waves, limiting their energy capture efficiency. Unlike conventional designs that rely on complex active control systems to address this mismatch, this study investigates a passive alternative based on inverted cone-shaped submerged structures that entrap seawater during upward motion, thereby increasing the effective added mass, lowering the natural frequency, and enabling resonance tuning of a roll-based WEC. Building on previous numerical validation, we present results from tests on a 1:40-scale model in regular and irregular waves. Five configurations with varying cone size and suspension distance were evaluated under regular wave excitation. The configuration achieving the highest performance reached a maximum Capture Width Ratio (CWR) of 52%, exceeding the 20%–40% range typical of conventional WECs. To assess robustness under realistic conditions, that configuration was further tested in irregular wave spectra representative of swell-dominated seas. Even under random excitation, the tuned device maintained efficiencies above 20%, demonstrating robustness against spectral variability. The experimental results show close agreement with predictions from a linear analytical model and confirm that passive tuning via cone-shaped structures effectively broadens the resonance bandwidth of roll-harvesting WECs. By combining high efficiency, robustness, and structural simplicity, this low-cost, scalable approach addresses a long-standing limitation of WECs and provides a viable pathway toward full-scale deployment with integrated power take-off damping and adaptation to diverse wave climates.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101575"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinguishing noise from low-amplitude false data in cyber-resilient rolling energy management of smart distribution networks","authors":"Reza Hemmati,&nbsp;Hedayat Saboori","doi":"10.1016/j.ecmx.2026.101615","DOIUrl":"10.1016/j.ecmx.2026.101615","url":null,"abstract":"<div><div>This paper proposes a real-time energy management optimization model for active distribution networks. In this model, the active distribution network connected to distributed energy resources exchanges data iteratively with a centralized energy management and control system at each time interval. Network-level parameters, including bus voltages and active and reactive power injections, are measured and sent to the central control system, where data are analyzed for variation, validation, noise detection, and cyberattack identification. Based on this analysis, the system performs rolling optimization for upcoming time-intervals and sends updated operational schedules back to the network, ensuring that generation units and controllable loads operate according to the newest optimal plan. As a result, the optimization of grid performance is carried out at every time interval, and the grid along with local generation–consumption resources are scheduled to operate according to the latest changes in grid parameters such as prices and power loads. Such adaptive scheduling guarantees both optimal and robust performance across all upcoming time periods. During data exchange, measurements may be corrupted by noise or falsified by stealthy false data injection (FDI) attacks with amplitudes close to measurement noise (low-magnitude FDI), making them difficult to detect. To address this challenge, several indices are proposed, including the Bus Current Imbalance Index (BCII), the Residual Current Magnitude Index (RCMI), and the Residual Current Angle Index (RCAI), which can effectively distinguish between noisy and falsified data while identifying the location, start time, and duration of cyberattacks. The results indicate that under varying input parameters such as electricity price, solar irradiance, and network load, the rolling optimization updates schedules and provides an optimal plan for upcoming hours. For example, at hour 6, the diesel generator schedule is adjusted for hours 6–24, and at hour 15, a new schedule is set for hours 15–24. Similarly, the battery plan is updated throughout the day; discharging initially scheduled at hours 17 and 19 is shifted to hours 18 and 19. These operational adjustments impacts operational cost. At hour 6 the total cost rises by 153.34%, whereas at hour 20 the total cost drops by 30.26%. The results also show that the model effectively detects small-magnitude FDI attacks under noise, with amplitudes equal to or 1–3 times the noise. Sensitivity analysis confirms that the proposed index consistently detects attacks under noise levels ranging from 1% to 5%.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101615"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging machine learning for advanced flow field design in PEMFCs","authors":"Mehrdad Ghasabehi,&nbsp;Mehrzad Shams","doi":"10.1016/j.ecmx.2026.101586","DOIUrl":"10.1016/j.ecmx.2026.101586","url":null,"abstract":"<div><div>The overall performance of proton exchange membrane fuel cells (PEMFCs) strongly depends on the design of the flow field. This study presents a novel, enhanced tapered parallel flow field featuring sub-channels with widths that vary in a precisely engineered converging–diverging pattern. This innovative design significantly improves oxygen transport in both through-plane and in-plane directions, thereby enhancing water management and ensuring highly consistent reactant delivery to reaction sites. In addition, a machine-learning-based optimisation framework is developed for this flow field. Using a rigorously validated three-dimensional, two-phase CFD model, an extensive dataset of 184 cases is generated to train seven distinct data-driven surrogate models: adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN), response surface methodology (RSM), random forest (RF), CatBoost, XGBoost, and LightGBM. Notably, CatBoost demonstrates superior predictive accuracy for key oxygen mass-transfer metrics and was consequently employed in a sophisticated multi-objective optimization. This process yields an optimal flow-field geometry with a tapering ratio of 3.8, a cycling amplitude of 0.57 mm, and eight cycles at 0.694 V, achieving a high mean oxygen concentration of 0.020 kmol.m⁻³ and an excellent uniformity index of 0.93. This integrated machine learning-accelerated optimization framework enables rapid and reliable flow-field optimisation and provides practical, actionable design guidelines for effectively reducing oxygen starvation in next-generation, high-performance fuel-cell stacks.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101586"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust framework for simultaneous optimization of performance and stability in active free-piston stirling engines","authors":"Hadi Sazgar,&nbsp;Shahryar Zare","doi":"10.1016/j.ecmx.2026.101664","DOIUrl":"10.1016/j.ecmx.2026.101664","url":null,"abstract":"<div><div>Free-piston Stirling Engines (FPSEs) are highly promising for solar-to-electric conversion; however, passive designs lack adaptability and robustness. This paper introduces a pioneering unified framework based on a robust technique for optimizing the dynamics of active FPSEs. The framework simultaneously addresses three critical objectives: (1) quantifying the output power generation, (2) guaranteeing the existence of stable oscillations (the sufficient condition), and (3) precisely identifying the optimal operating resonance frequency. A robust control method, formulated using Lagrangian mechanics, regulates the displacer piston and significantly enhances the power piston’s amplitude. The core innovation lies in the methodology’s ability to effectively enhance system robustness against dynamic disturbances, prevent unwanted stabilization, and ensure optimal power extraction. Simulation results validated against the B10-B engine data reveal a crucial design insight: for each spring stiffness value, there exists a unique optimal operating frequency that maximizes performance. For instance, a stiffness of 1000 <!--> <!-->N/m yields a peak output power of 80.47 <!--> <!-->W at 90 <!--> <!-->rad/s. This synthesis of dynamic stability assurance and precise performance maximization via a robust methodology marks a significant step forward in the design of highly reliable and computationally efficient active FPSE systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101664"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid textile nanogenerators for wearable energy harvesting: synergistic mechanisms, challenges, and future directions","authors":"Bekinew Kitaw Dejene ,&nbsp;Misganaw Engdasew Woldeab","doi":"10.1016/j.ecmx.2026.101589","DOIUrl":"10.1016/j.ecmx.2026.101589","url":null,"abstract":"<div><div>The rapid expansion of wearable electronics and the Internet of Things (IoT) has intensified the demand for sustainable, lightweight, and flexible power solutions beyond conventional batteries, which suffer from short lifespans, bulkiness, and environmental concerns. Hybrid textile nanogenerators (HTNGs) offer a sustainable and transformative alternative by integrating multiple energy conversion mechanisms, such as piezoelectric, triboelectric, thermoelectric, solar, enzymatic biofuel cell, and electromagnetic effects, into flexible textile platforms. By exploiting the synergistic interactions between different nanogenerator modes, HTNGs achieve superior energy output, multifunctionality, and system stability compared to single-mode devices. This review first introduces the fundamental principles and classifications of HTNGs in textile systems, followed by a discussion of the materials and fabrication strategies that enable seamless integration into fabrics while preserving softness, comfort, and breathability. Recent hybridization strategies, performance metrics, and design innovations are critically evaluated, with attention to durability, washability, and large-scale manufacturability, which are crucial for practical applications. Applications in wearable health monitoring, self-powered sensing, smart garments, and the IoT are examined alongside key challenges such as scalability and user comfort. Finally, future perspectives are outlined, emphasizing cross-disciplinary opportunities, including eco-friendly materials, scalable manufacturing, and intelligent energy management, such as AI-assisted optimization, to accelerate the transition of HTNGs from laboratory prototypes to commercially viable, self-sustaining wearable systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101589"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical model for calculating pressure development in vented explosions of methane-air mixture","authors":"Xingxing Liang ,&nbsp;Junjie Cheng ,&nbsp;Zhongqi Wang ,&nbsp;Yaling Liao ,&nbsp;Huajiao Zeng","doi":"10.1016/j.ecmx.2026.101581","DOIUrl":"10.1016/j.ecmx.2026.101581","url":null,"abstract":"<div><div>A mathematical model was proposed to predict pressure development in vented explosions of methane-air mixture, considering the effect of secondary explosion indoors and external explosion on pressure development in chamber. Validation against experimental data demonstrates strong predictive accuracy, with model predictions for peak overpressure falling within ±10 % of measured values under lean mixture conditions (φ = 0.6–1.5). The model shows that indoor secondary explosions occur only when the residual gas concentration remains within the explosive limits (5–15 % vol for methane), a condition influenced by the initial equivalence ratio, the chemical reaction process variables and the gas venting ratio. Higher venting pressures (0.3–25 kPa) amplify indoor secondary explosion peaks, whereas excessively rich mixtures (Φ &gt; 1.5) or elevated initial temperatures (&gt;140 °C) suppress indoor secondary explosion. The proposed model offers a robust tool for designing venting systems by accurately capturing multi-peak pressure profiles and coupling residual gas concentration with criteria for secondary explosions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101581"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}