Pub Date : 2026-01-25DOI: 10.1016/j.ecmx.2026.101623
Karib Hassan Khan, Mohammad Mashud
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.
{"title":"Performance and emission analysis of flaxseed biodiesel blends in a direct injection diesel engine","authors":"Karib Hassan Khan, 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 CO<sub>2</sub> and NOx emissions (4.75% and 1.87% lower than diesel respectively), whereas D80F20 produced the lowest CO emissions (21.90% lower) and similar CO<sub>2</sub> 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":"2026-01-25","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}
Pub Date : 2026-01-25DOI: 10.1016/j.ecmx.2026.101615
Reza Hemmati, Hedayat Saboori
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%.
{"title":"Distinguishing noise from low-amplitude false data in cyber-resilient rolling energy management of smart distribution networks","authors":"Reza Hemmati, 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":"2026-01-25","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101587
Abdul Moeed Khan , Ahmad Bala Alhassan , Anvar Kolumbetov , Auwal Haruna , Vijayakumar Gali , Nguyen Gia Minh Thao , Ton Duc Do
Nomadic communities often reside in remote regions requiring extensive transmission infrastructure, which is costly and contributes to higher greenhouse gas emissions. This study proposes a hybrid microgrid (MG) for the Shell Yurt Center, a representative nomadic dwelling in Kazakhstan. The system integrates renewable energy sources (RESs), including photovoltaic (PV), wind turbine (WT), and battery energy storage systems (BESS), to deliver a reliable and cost-effective energy supply. An analysis of a home energy management system (HEMS) is conducted using real-time data of the Yurt to support efficient demand-side management (DSM). The HEMS is designed to enhance energy efficiency and reduce overall energy costs through the smart scheduling of household appliances. Dynamic Programming (DP) and Genetic Algorithm (GA) are applied to manage energy usage under an unscheduled electricity pricing rate of $0.583/kWh as a baseline without using any optimization. Three scenarios are examined: Case 1 (minimal appliances with normal usage), Case 2 (maximum appliances with average usage), and Case 3 (maximum appliances with extreme usage). GA consistently outperforms DP in Case 1, resulting in reduced net present costs (NPC), levelized cost of electricity (LCOE), and lower maintenance costs. In Case 2, DP has a slight edge in NPC and LCOE, but GA maintains favorable maintenance costs. Case 3 shows that GA achieves the lowest NPC ($42,028), LCOE ($0.396/kWh), and maintenance costs ($466/year). Overall, the study establishes an optimal scheduling framework for renewable energy (RE) utilization for nomadic dwellers using a fully functioning MG complex.
{"title":"Optimizing demand-side energy management for stand-alone wind-solar microgrids in rural settlements: A case study for nomadic Yurt in Kazakhstan","authors":"Abdul Moeed Khan , Ahmad Bala Alhassan , Anvar Kolumbetov , Auwal Haruna , Vijayakumar Gali , Nguyen Gia Minh Thao , Ton Duc Do","doi":"10.1016/j.ecmx.2026.101587","DOIUrl":"10.1016/j.ecmx.2026.101587","url":null,"abstract":"<div><div>Nomadic communities often reside in remote regions requiring extensive transmission infrastructure, which is costly and contributes to higher greenhouse gas emissions. This study proposes a hybrid microgrid (MG) for the Shell Yurt Center, a representative nomadic dwelling in Kazakhstan. The system integrates renewable energy sources (RESs), including photovoltaic (PV), wind turbine (WT), and battery energy storage systems (BESS), to deliver a reliable and cost-effective energy supply. An analysis of a home energy management system (HEMS) is conducted using real-time data of the Yurt to support efficient demand-side management (DSM). The HEMS is designed to enhance energy efficiency and reduce overall energy costs through the smart scheduling of household appliances. Dynamic Programming (DP) and Genetic Algorithm (GA) are applied to manage energy usage under an unscheduled electricity pricing rate of $0.583/kWh as a baseline without using any optimization. Three scenarios are examined: Case 1 (minimal appliances with normal usage), Case 2 (maximum appliances with average usage), and Case 3 (maximum appliances with extreme usage). GA consistently outperforms DP in Case 1, resulting in reduced net present costs (NPC), levelized cost of electricity (LCOE), and lower maintenance costs. In Case 2, DP has a slight edge in NPC and LCOE, but GA maintains favorable maintenance costs. Case 3 shows that GA achieves the lowest NPC ($42,028), LCOE ($0.396/kWh), and maintenance costs ($466/year). Overall, the study establishes an optimal scheduling framework for renewable energy (RE) utilization for nomadic dwellers using a fully functioning MG complex.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101587"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080320","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101584
Florian Altmann , Dominik Kuzdas , Dominik Murschenhofer , Johanna Bartlechner , Christoph Hametner , Stefan Jakubek , Stefan Braun
To enhance the durability and performance of proton exchange membrane fuel cells, it is essential to capture both spatial and temporal variations of internal states during dynamic operation. While existing reduced-order models (0D/1D) lack spatial resolution, 3D models are often too computationally expensive for transient simulations. To bridge this gap, we present a quasi-2D, time-dependent multiphase model capable of predicting distributed cell states with high computational efficiency. The model accounts for key transport phenomena, including convection, multicomponent diffusion, capillary effects, and membrane water dynamics via electro-osmotic drag and diffusion. It also includes nitrogen crossover, finite-rate sorption/desorption at membrane interfaces, and heat generation from electrochemical reactions, proton conduction, and phase change. A linearisation scheme combined with Chebyshev collocation ensures low computational cost and near real-time capability. Validation against high-resolution 3D computational fluid dynamics simulations confirms the model’s accuracy in predicting polarisation curves, gas species distributions, liquid water accumulation, and temperature profiles. Dynamic simulations under load transients further demonstrate its ability to capture key physical processes, underpinning the importance of spatially resolved water transport. By enabling fast and accurate simulations of both steady-state and dynamic fuel cell behaviour, the proposed model supports extensive parametric studies, control system development, and predictive diagnostics. Its computational efficiency makes it a valuable tool for improving fuel cell efficiency, longevity, and system-level control strategies.
{"title":"A quasi-2D multiphase flow proton exchange membrane fuel cell model for efficient distributed cell state prediction","authors":"Florian Altmann , Dominik Kuzdas , Dominik Murschenhofer , Johanna Bartlechner , Christoph Hametner , Stefan Jakubek , Stefan Braun","doi":"10.1016/j.ecmx.2026.101584","DOIUrl":"10.1016/j.ecmx.2026.101584","url":null,"abstract":"<div><div>To enhance the durability and performance of proton exchange membrane fuel cells, it is essential to capture both spatial and temporal variations of internal states during dynamic operation. While existing reduced-order models (0D/1D) lack spatial resolution, 3D models are often too computationally expensive for transient simulations. To bridge this gap, we present a quasi-2D, time-dependent multiphase model capable of predicting distributed cell states with high computational efficiency. The model accounts for key transport phenomena, including convection, multicomponent diffusion, capillary effects, and membrane water dynamics via electro-osmotic drag and diffusion. It also includes nitrogen crossover, finite-rate sorption/desorption at membrane interfaces, and heat generation from electrochemical reactions, proton conduction, and phase change. A linearisation scheme combined with Chebyshev collocation ensures low computational cost and near real-time capability. Validation against high-resolution 3D computational fluid dynamics simulations confirms the model’s accuracy in predicting polarisation curves, gas species distributions, liquid water accumulation, and temperature profiles. Dynamic simulations under load transients further demonstrate its ability to capture key physical processes, underpinning the importance of spatially resolved water transport. By enabling fast and accurate simulations of both steady-state and dynamic fuel cell behaviour, the proposed model supports extensive parametric studies, control system development, and predictive diagnostics. Its computational efficiency makes it a valuable tool for improving fuel cell efficiency, longevity, and system-level control strategies.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101584"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080387","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101552
Mohammad Sameti , Tao Fan , Anna Volkova , Zili Li
Conventional small-scale district heating (DH) systems that serve a limited number of buildings or neighbourhoods often exhibit higher specific capital costs and lower long-term efficiency compared to large-scale 4th or 5th generation DH networks. This is mainly because conventional systems typically operate at higher temperature levels and with less flexibility to integrate multiple renewable and waste heat sources, leading to greater distribution losses and reduced system synergy. In this study, underground metro space is utilized to integrate several small DHC systems into a single large-scale network, and a thermo-economic model is proposed. The metro helps lower the cost of distributing heat and makes it easier to integrate different heat sources. The cost reduction achieved in large-scale DHC systems is attributed to the use of existing conduits, reduced thermal losses, and enhanced heat exchange among the interconnected small DHC units. Additionally, this large-scale DHC network can easily accommodate future growth of consumers along it without extending the Metro and piping infrastructure and with upgrading the pumping capacity. A case study of the Dublin MetroLink, incorporating current 16 and future 26 small DHs, is analyzed to demonstrate the effectiveness of the proposed model. The primary heat sources considered are data centers and underground water from the Dublin Port Tunnel which also functions as the primary heat-transport medium. Each smaller DH along the underground route would utilize its own large-scale heat pump to extract heat from the supply line in the underground space and inject their extra/unused heat back to cover the peak demand in another smaller DHs. He case study showed 17% reduction in annualized cost over its lifetime.
{"title":"Large-scale climate-neutral district heating and cooling: Integration of local microgrids for thermal distribution","authors":"Mohammad Sameti , Tao Fan , Anna Volkova , Zili Li","doi":"10.1016/j.ecmx.2026.101552","DOIUrl":"10.1016/j.ecmx.2026.101552","url":null,"abstract":"<div><div>Conventional small-scale district heating (DH) systems that serve a limited number of buildings or neighbourhoods often exhibit higher specific capital costs and lower long-term efficiency compared to large-scale 4th or 5th generation DH networks. This is mainly because conventional systems typically operate at higher temperature levels and with less flexibility to integrate multiple renewable and waste heat sources, leading to greater distribution losses and reduced system synergy. In this study, underground metro space is utilized to integrate several small DHC systems into a single large-scale network, and a thermo-economic model is proposed. The metro helps lower the cost of distributing heat and makes it easier to integrate different heat sources. The cost reduction achieved in large-scale DHC systems is attributed to the use of existing conduits, reduced thermal losses, and enhanced heat exchange among the interconnected small DHC units. Additionally, this large-scale DHC network can easily accommodate future growth of consumers along it without extending the Metro and piping infrastructure and with upgrading the pumping capacity. A case study of the Dublin MetroLink, incorporating current 16 and future 26 small DHs, is analyzed to demonstrate the effectiveness of the proposed model. The primary heat sources considered are data centers and underground water from the Dublin Port Tunnel which also functions as the primary heat-transport medium. Each smaller DH along the underground route would utilize its own large-scale heat pump to extract heat from the supply line in the underground space and inject their extra/unused heat back to cover the peak demand in another smaller DHs. He case study showed 17% reduction in annualized cost over its lifetime.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101552"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080322","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101627
Aditya Dinakar, D. Cenitta, R. Vijaya Arjunan, Venkatesh Bhandage, Krishnaraj Chadaga
Photovoltaic (PV) systems are responsible for the conversion of solar energy into electricity and with the rising usage of renewable energy, solar energy has emerged as one of the leading contributors. However, solar energy is dependent on various environmental conditions which raises the need for forecasting of the electricity produced. With the rise in the usage of machine learning (ML) there have been attempts to forecast the solar energy harvested by PV systems. In this study a robust framework is used to predict the current and voltage generated by a PV system. This study employs the use of feature selection using BorutaSHAP and Variance Inflation Factor (VIF) to train various ML models consisting of Linear Regression, tree-based models, TabNet and transformer-based models. These models were later interpreted using Explainable Artificial Intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), Local Interpretable Model-agnostic Explanations (LIME) and Diverse Counterfactual Explanations (DiCE). The best performing model was TabPFN, a transformer-based model and it achieved an R-squared of 0.998 and 0.934 for current and voltage respectively. This study shows a strong performing and interpretable framework to predict the current and voltage of a PV system.
{"title":"Explainable machine learning models for predicting current and voltage in photovoltaic systems","authors":"Aditya Dinakar, D. Cenitta, R. Vijaya Arjunan, Venkatesh Bhandage, Krishnaraj Chadaga","doi":"10.1016/j.ecmx.2026.101627","DOIUrl":"10.1016/j.ecmx.2026.101627","url":null,"abstract":"<div><div>Photovoltaic (PV) systems are responsible for the conversion of solar energy into electricity and with the rising usage of renewable energy, solar energy has emerged as one of the leading contributors. However, solar energy is dependent on various environmental conditions which raises the need for forecasting of the electricity produced. With the rise in the usage of machine learning (ML) there have been attempts to forecast the solar energy harvested by PV systems. In this study a robust framework is used to predict the current and voltage generated by a PV system. This study employs the use of feature selection using BorutaSHAP and Variance Inflation Factor (VIF) to train various ML models consisting of Linear Regression, tree-based models, TabNet and transformer-based models. These models were later interpreted using Explainable Artificial Intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), Local Interpretable Model-agnostic Explanations (LIME) and Diverse Counterfactual Explanations (DiCE). The best performing model was TabPFN, a transformer-based model and it achieved an R-squared of 0.998 and 0.934 for current and voltage respectively. This study shows a strong performing and interpretable framework to predict the current and voltage of a PV system.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101627"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080262","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101626
Ricardo González-Cárabes , Luis Bernardo López-Sosa , Janneth López-Mercado , José Guadalupe Rutiaga Quiñones , Francisco Javier Reynoso Marín , Luis Fernando Pintor-Ibarra , Luis Ángel Ascencio de la Cruz , Mario Morales Máximo , Arturo Aguilera Mandujano , Saúl Leonardo Hernández-Trujillo
This research presents an analysis of the energy potential of 5 agricultural crop residues in the state of Michoacán, Mexico, considering their possible use as solid biofuels. This study consists of five phases: (a) Identification of agricultural areas and collection of residues of each of the crops, Persea americana Mill. (avocado), Saccharum officinarum L. (sugarcane), Lens culinaris Medik. (lentil), Zea mays L. (corn) and Mangifera indica L (mango); (b) processing of the residues for characterization; (c) physicochemical characterization of the collected residues using characterization techniques such as CHONS, polymeric compound composition, FTIR, ash microanalysis and calorific value, in addition to the proximate analysis of the residues by obtaining the moisture, ash, volatiles and fixed carbon contents; (d) determination of the energy potential (TJ/year); (e) dissemination of results. The results of this research show values for the crops analyzed in terms of ash contents lower than 10%, percentages of volatile matter higher than 70%, while fixed carbon values were lower than 21%, elemental analysis showed results for carbon higher than 40%, lower than 7% for hydrogen, higher than 47% for oxygen and for nitrogen lower than 2%, in terms of polymeric compounds showed values higher than 12% for cellulose, values higher than 8% for hemicellulose, and regarding lignin, values above 5% were reported. The calorific value values were estimated between 15. MJ/kg and 19.8 MJ/kg, with energy potential values that could, in their minimum production, eventually satisfy the energy demand for cooking of 30% of the rural sector of the state.
{"title":"Exploring the energy potential of agricultural and agroindustrial residues in michoacán: characterization to determine the feasibility of solid biofuels","authors":"Ricardo González-Cárabes , Luis Bernardo López-Sosa , Janneth López-Mercado , José Guadalupe Rutiaga Quiñones , Francisco Javier Reynoso Marín , Luis Fernando Pintor-Ibarra , Luis Ángel Ascencio de la Cruz , Mario Morales Máximo , Arturo Aguilera Mandujano , Saúl Leonardo Hernández-Trujillo","doi":"10.1016/j.ecmx.2026.101626","DOIUrl":"10.1016/j.ecmx.2026.101626","url":null,"abstract":"<div><div>This research presents an analysis of the energy potential of 5 agricultural crop residues in the state of Michoacán, Mexico, considering their possible use as solid biofuels. This study consists of five phases: (a) Identification of agricultural areas and collection of residues of each of the crops, <em>Persea americana Mill.</em> (avocado)<em>, Saccharum officinarum</em> L<em>.</em> (sugarcane)<em>,</em> Lens culinaris <em>Medik.</em> (lentil)<em>, Zea mays</em> L<em>.</em> (corn) and <em>Mangifera indica</em> L (mango); (b) processing of the residues for characterization; (c) physicochemical characterization of the collected residues using characterization techniques such as CHONS, polymeric compound composition, FTIR, ash microanalysis and calorific value, in addition to the proximate analysis of the residues by obtaining the moisture, ash, volatiles and fixed carbon contents; (d) determination of the energy potential (TJ/year); (e) dissemination of results. The results of this research show values for the crops analyzed in terms of ash contents lower than 10%, percentages of volatile matter higher than 70%, while fixed carbon values were lower than 21%, elemental analysis showed results for carbon higher than 40%, lower than 7% for hydrogen, higher than 47% for oxygen and for nitrogen lower than 2%, in terms of polymeric compounds showed values higher than 12% for cellulose, values higher than 8% for hemicellulose, and regarding lignin, values above 5% were reported. The calorific value values were estimated between 15. MJ/kg and 19.8 MJ/kg, with energy potential values that could, in their minimum production, eventually satisfy the energy demand for cooking of 30% of the rural sector of the state.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101626"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080263","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101606
Ramin Mehdipour, Zahra Baniamerian, Seamus Garvey
Given the urgent need to transition from fossil fuels, this study investigates aqua-ammonia as an alternative to natural gas for space heating and local energy supply. The research evaluates the feasibility of transporting aqua-ammonia through existing natural gas pipelines, including the necessary adaptations. It compares the performance and economics of three alternative fuels—hydrogen, ammonia, and aqua-ammonia—with natural gas. Key quantitative findings are: for 15 wt% aqua-ammonia at typical urban pressures (0.2–13 bar) the pipeline energy transfer is 1.5–2.8 × that of natural gas. The required distribution network capacity for aqua-ammonia, depending on ammonia concentration, is 2.2–6.6 × smaller than comparable municipal water networks and can be 2–8 × smaller than current gas mains for the same delivered energy; ∼130 L of 15 wt% aqua-ammonia can meet the estimated daily heating energy of a typical UK household; and optimal aqua-ammonia concentrations for residential heating fall in the 10–15 wt% NH3 range (while 18–25% suits work/industrial applications). By contrast, hydrogen transport faces material and compression penalties (compressor energy can be ≈4 × that required for natural gas in comparable scenarios) and pure ammonia requires higher pressures (phase change issues above ≈8 bar). These quantitative results indicate that aqua-ammonia offers practical advantages in transportation efficiency and system design simplicity compared with gaseous alternatives that merit experimental follow-up.
{"title":"Aqua-ammonia; an alternative fuel to natural gas for space Heating: Fuel transmission and comparative analysis","authors":"Ramin Mehdipour, Zahra Baniamerian, Seamus Garvey","doi":"10.1016/j.ecmx.2026.101606","DOIUrl":"10.1016/j.ecmx.2026.101606","url":null,"abstract":"<div><div>Given the urgent need to transition from fossil fuels, this study investigates aqua-ammonia as an alternative to natural gas for space heating and local energy supply. The research evaluates the feasibility of transporting aqua-ammonia through existing natural gas pipelines, including the necessary adaptations. It compares the performance and economics of three alternative fuels—hydrogen, ammonia, and aqua-ammonia—with natural gas. Key quantitative findings are: for 15 wt% aqua-ammonia at typical urban pressures (0.2–13 bar) the pipeline energy transfer is 1.5–2.8 × that of natural gas. The required distribution network capacity for aqua-ammonia, depending on ammonia concentration, is 2.2–6.6 × smaller than comparable municipal water networks and can be 2–8 × smaller than current gas mains for the same delivered energy; ∼130 L of 15 wt% aqua-ammonia can meet the estimated daily heating energy of a typical UK household; and optimal aqua-ammonia concentrations for residential heating fall in the 10–15 wt% NH<sub>3</sub> range (while 18–25% suits work/industrial applications). By contrast, hydrogen transport faces material and compression penalties (compressor energy can be ≈4 × that required for natural gas in comparable scenarios) and pure ammonia requires higher pressures (phase change issues above ≈8 bar). These quantitative results indicate that aqua-ammonia offers practical advantages in transportation efficiency and system design simplicity compared with gaseous alternatives that merit experimental follow-up.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101606"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080383","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}
Pub Date : 2026-01-24DOI: 10.1016/j.ecmx.2026.101622
Trevor J. Kramer, David Schafer, Griffin Layhew, Daniel Cannon, Sam Chumney, Rory Roberts
The need for rapid and accurate performance estimations for solid oxide fuel cells (SOFCs) under wide ranges of operating conditions grows as more SOFC hybrid power plants gain traction as possible players in the future power generation landscape. Typical one-dimensional, steady-state SOFC modeling requires numerically solving differential equations which can impose added difficulties to lower fidelity, higher level power generation system models. The handling of the SOFC polarization behavior and how it changes due to variation in operating conditions can be captured through multiple normalization techniques. It was found from a literature survey that the general polarization behavior of SOFCs remains relatively constant, and independent of specific measured performance and testing conditions. Polarization curve normalization utilizing peak power conditions can be implemented seamlessly with SOFC reduced order modeling performance predictions. The relative changes in peak power due to variation in operating conditions can be captured with regression based reduced order models allowing for an infinite number of SOFC performances to be represented through the normalized reduced order SOFC model discussed in this work.
{"title":"SOFC polarization curve normalization and reduced order model generation for rapid and accurate performance prediction","authors":"Trevor J. Kramer, David Schafer, Griffin Layhew, Daniel Cannon, Sam Chumney, Rory Roberts","doi":"10.1016/j.ecmx.2026.101622","DOIUrl":"10.1016/j.ecmx.2026.101622","url":null,"abstract":"<div><div>The need for rapid and accurate performance estimations for solid oxide fuel cells (SOFCs) under wide ranges of operating conditions grows as more SOFC hybrid power plants gain traction as possible players in the future power generation landscape. Typical one-dimensional, steady-state SOFC modeling requires numerically solving differential equations which can impose added difficulties to lower fidelity, higher level power generation system models. The handling of the SOFC polarization behavior and how it changes due to variation in operating conditions can be captured through multiple normalization techniques. It was found from a literature survey that the general polarization behavior of SOFCs remains relatively constant, and independent of specific measured performance and testing conditions. Polarization curve normalization utilizing peak power conditions can be implemented seamlessly with SOFC reduced order modeling performance predictions. The relative changes in peak power due to variation in operating conditions can be captured with regression based reduced order models allowing for an infinite number of SOFC performances to be represented through the normalized reduced order SOFC model discussed in this work.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101622"},"PeriodicalIF":7.6,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080325","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}
Pub Date : 2026-01-23DOI: 10.1016/j.ecmx.2026.101618
Qing Li , Tianjiao Ma , Shumao Zheng , Yihui Lu , Zhaoxiang Deng , Fu Shen
The increasing penetration of photovoltaic (PV) power presents severe challenges to power system operation due to its inherent output uncertainty. To accurately quantify the uncertainty of regional PV generation, this paper proposes a novel integrated framework for direct multi-step probabilistic forecasting of PV cluster power. First, to address temporal misalignments in PV series caused by cloud movement, a differentiable soft Dynamic Time Warping (softDTW) method is introduced, enabling the joint and adaptive selection of the most representative station and key meteorological features, thereby ensuring the physical interpretability and representativeness of model inputs. Second, to overcome the limitations of single clustering methods in disentangling complex weather patterns, an improved hybrid clustering strategy that combines ClusterGAN and KShape is proposed. This strategy synergizes deep feature learning with shape-sensitive clustering to construct a condition-specific, highly discriminative weather-pattern dataset. Furthermore, an Attention-enhanced MQ-WaveNet (AMQWaveNet) probabilistic forecasting model is developed, where a multi-head attention (MHA) mechanism focuses on critical spatiotemporal information, and a residual-connected WaveNet encoder extracts multi-scale deep features, culminating in a dual-MLP decoder that directly outputs multi-step quantile forecasts. An empirical evaluation on 14 neighboring PV stations in a large-scale base in Xinjiang, China, demonstrates that: a) Under various weather conditions (sunny, cloudy, overcast/rainy), the proposed model reduces RMSE by an average of 15–25% compared to state-of-the-art benchmarks (e.g., TFT, DeepAR); b) Its Winkler Score is significantly lower than those of competing models under complex weather, proving superior uncertainty quantification; c) The method requires only key features from one representative station to achieve high-accuracy cluster forecasting, substantially reducing data dependency and model complexity, showing strong potential for practical deployment.
{"title":"A direct upscaling probabilistic forecasting model for PV cluster power generation based on softDTW-(ClusterGAN-KShape)-AMQWavenet","authors":"Qing Li , Tianjiao Ma , Shumao Zheng , Yihui Lu , Zhaoxiang Deng , Fu Shen","doi":"10.1016/j.ecmx.2026.101618","DOIUrl":"10.1016/j.ecmx.2026.101618","url":null,"abstract":"<div><div>The increasing penetration of photovoltaic (PV) power presents severe challenges to power system operation due to its inherent output uncertainty. To accurately quantify the uncertainty of regional PV generation, this paper proposes a novel integrated framework for direct multi-step probabilistic forecasting of PV cluster power. First, to address temporal misalignments in PV series caused by cloud movement, a differentiable soft Dynamic Time Warping (softDTW) method is introduced, enabling the joint and adaptive selection of the most representative station and key meteorological features, thereby ensuring the physical interpretability and representativeness of model inputs. Second, to overcome the limitations of single clustering methods in disentangling complex weather patterns, an improved hybrid clustering strategy that combines ClusterGAN and KShape is proposed. This strategy synergizes deep feature learning with shape-sensitive clustering to construct a condition-specific, highly discriminative weather-pattern dataset. Furthermore, an Attention-enhanced MQ-WaveNet (AMQWaveNet) probabilistic forecasting model is developed, where a multi-head attention (MHA) mechanism focuses on critical spatiotemporal information, and a residual-connected WaveNet encoder extracts multi-scale deep features, culminating in a dual-MLP decoder that directly outputs multi-step quantile forecasts. An empirical evaluation on 14 neighboring PV stations in a large-scale base in Xinjiang, China, demonstrates that: a) Under various weather conditions (sunny, cloudy, overcast/rainy), the proposed model reduces RMSE by an average of 15–25% compared to state-of-the-art benchmarks (e.g., TFT, DeepAR); b) Its Winkler Score is significantly lower than those of competing models under complex weather, proving superior uncertainty quantification; c) The method requires only key features from one representative station to achieve high-accuracy cluster forecasting, substantially reducing data dependency and model complexity, showing strong potential for practical deployment.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101618"},"PeriodicalIF":7.6,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080388","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}
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