Pub Date : 2026-05-01Epub Date: 2026-01-29DOI: 10.1016/j.ecmx.2026.101635
Abdul Rafay , Asadullah , Muhammad Zubair Iftikhar , Syed Ali Abbas Kazmi , Dong Ryeol Shin , Muhammad Waseem
Pakistan’s fossil-heavy power mix drives cost volatility and rising emissions. We assess hybrid renewable energy systems (HRES) for five Special Economic Zones (SEZs), Allama Iqbal Industrial City (AIIC), Bostan, Dhabeji, Mohmand Marble City (MMC), and Rashakai, using RETScreen with policy-aware finance, export-meter accounting, and a combined-margin grid baseline (0.56 tCO2 MWh−1). The preferred portfolio (∼0.53 GW of PV, wind, and canal-drop hydro) exports ≈1.30 TWh yr−1 at ∼$42–49 MWh−1 LCOE while avoiding ≈728 ktCO2 yr−1. Site comparisons show Dhabeji delivers the largest energy export (∼413 GWh yr−1) and highest abatement (∼231 ktCO2 yr−1); Bostan and MMC achieve the fastest paybacks (both 10.3 years); AIIC provides a scalable 200-MW anchor (payback 11.6 years); and Rashakai contributes ∼ 261 GWh yr−1 with 10.6-year payback. Power-to-X analysis of modeled surplus (central case: 10% surplus, PEM 70% efficiency; 47.6 kWh kg−1 SEC) yields ≈2.73 kt H2 yr−1 at ∼$6.10–6.43 kg−1 LCOH. Separately accounting carbon revenues, electricity-displacement and green-hydrogen credits together provide ≈$3.39 M yr−1 at $5 tCO2−1 (net of a 10% MRV haircut), scaling proportionally with price. Overall, the portfolio offers a replicable pathway for SEZ decarbonization, prioritizing Dhabeji for grid impact, Bostan/MMC for rapid cash recovery, and AIIC as a bankable anchor.
{"title":"Techno-economic feasibility of hybrid renewable systems and green hydrogen production in special economic zones (SEZs)","authors":"Abdul Rafay , Asadullah , Muhammad Zubair Iftikhar , Syed Ali Abbas Kazmi , Dong Ryeol Shin , Muhammad Waseem","doi":"10.1016/j.ecmx.2026.101635","DOIUrl":"10.1016/j.ecmx.2026.101635","url":null,"abstract":"<div><div>Pakistan’s fossil-heavy power mix drives cost volatility and rising emissions. We assess hybrid renewable energy systems (HRES) for five Special Economic Zones (SEZs), Allama Iqbal Industrial City (AIIC), Bostan, Dhabeji, Mohmand Marble City (MMC), and Rashakai, using RETScreen with policy-aware finance, export-meter accounting, and a combined-margin grid baseline (0.56 tCO<sub>2</sub> MWh<sup>−1</sup>). The preferred portfolio (∼0.53 GW of PV, wind, and canal-drop hydro) exports ≈1.30 TWh yr<sup>−1</sup> at ∼$42–49 MWh<sup>−1</sup> LCOE while avoiding ≈728 ktCO<sub>2</sub> yr<sup>−1</sup>. Site comparisons show Dhabeji delivers the largest energy export (∼413 GWh yr<sup>−1</sup>) and highest abatement (∼231 ktCO<sub>2</sub> yr<sup>−1</sup>); Bostan and MMC achieve the fastest paybacks (both 10.3 years); AIIC provides a scalable 200-MW anchor (payback 11.6 years); and Rashakai contributes ∼ 261 GWh yr<sup>−1</sup> with 10.6-year payback. Power-to-X analysis of modeled surplus (central case: 10% surplus, PEM 70% efficiency; 47.6 kWh kg<sup>−1</sup> SEC) yields ≈2.73 kt H<sub>2</sub> yr<sup>−1</sup> at ∼$6.10–6.43 kg<sup>−1</sup> LCOH. Separately accounting carbon revenues, electricity-displacement and green-hydrogen credits together provide ≈$3.39 M yr<sup>−1</sup> at $5 tCO<sub>2</sub><sup>−1</sup> (net of a 10% MRV haircut), scaling proportionally with price. Overall, the portfolio offers a replicable pathway for SEZ decarbonization, prioritizing Dhabeji for grid impact, Bostan/MMC for rapid cash recovery, and AIIC as a bankable anchor.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101635"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189900","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}
Hydrogen production plays a key role in the energy transition. However, conventional methods of hydrogen produc’tion, such as steam methane reforming (SMR), are associated with high emissions. To address this issue, carbon capture utilization and storage (CCUS) can be used to convert grey hydrogen into blue hydrogen. However, this process is often inefficient due to its high energy consumption and challenges related to post-combustion carbon capture in conventional configurations, as well as its dependence on fossil fuels. In this research, to enhance the sustainability of blue hydrogen, renewable energy sources, such as solar energy (including photovoltaic system and parabolic trough), are used to power optimized carbon capture plants. Aspen HYSYS v11 and Thermoflex are employed to simulate the production of low-carbon blue hydrogen. By optimizing a standard post-combustion carbon capture configuration and integrating it with a solar plant, a 79% reduction in energy penalties is achieved. This optimization leads to an estimated reduction of approximately 310 tonnes of CO2 per day for the blue hydrogen plant, which has a total production capacity of 214.2 tonnes per day. Feasibility, exergy, and exergoeconomic analyses reveal the following efficiency metrics: exergy efficiency for SMR, PCC (Post-combustion Carbon Capture), and the solar plant is 95.5%, 82.3%, and 15%, respectively, while exergoeconomic efficiency is 30%, 20.8%, and 28.45%. The levelized cost of hydrogen (LCOH) was compared across different technologies, showing that grey hydrogen costs approximately $1.53 per kg. Incorporating carbon capture technology increases the cost to $2.01 per kg while enhancing sustainability. However, optimizing the carbon capture process and integrating solar energy can reduce the cost to $1.74 per kg.
{"title":"Solar-integrated blue hydrogen production with optimized post-combustion carbon capture: A techno-economic and exergoeconomic assessment","authors":"Farzin Hosseinifard , Mohsen Salimi , Milad Hosseinpour , Majid Amidpour","doi":"10.1016/j.ecmx.2026.101528","DOIUrl":"10.1016/j.ecmx.2026.101528","url":null,"abstract":"<div><div>Hydrogen production plays a key role in the energy transition. However, conventional methods of hydrogen produc’tion, such as steam methane reforming (SMR), are associated with high emissions. To address this issue, carbon capture utilization and storage (CCUS) can be used to convert grey hydrogen into blue hydrogen. However, this process is often inefficient due to its high energy consumption and challenges related to post-combustion carbon capture in conventional configurations, as well as its dependence on fossil fuels. In this research, to enhance the sustainability of blue hydrogen, renewable energy sources, such as solar energy (including photovoltaic system and parabolic trough), are used to power optimized carbon capture plants. Aspen HYSYS v11 and Thermoflex are employed to simulate the production of low-carbon blue hydrogen. By optimizing a standard post-combustion carbon capture configuration and integrating it with a solar plant, a 79% reduction in energy penalties is achieved. This optimization leads to an estimated reduction of approximately 310 tonnes of CO<sub>2</sub> per day for the blue hydrogen plant, which has a total production capacity of 214.2 tonnes per day. Feasibility, exergy, and exergoeconomic analyses reveal the following efficiency metrics: exergy efficiency for SMR, PCC (Post-combustion Carbon Capture), and the solar plant is 95.5%, 82.3%, and 15%, respectively, while exergoeconomic efficiency is 30%, 20.8%, and 28.45%. The levelized cost of hydrogen (LCOH) was compared across different technologies, showing that grey hydrogen costs approximately $1.53 per kg. Incorporating carbon capture technology increases the cost to $2.01 per kg while enhancing sustainability. However, optimizing the carbon capture process and integrating solar energy can reduce the cost to $1.74 per kg.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101528"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189851","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}
The electrochemical reduction of CO2 (CO2RR) to multi-carbon products such as ethanol (C2H5OH) and ethylene (C2H4) is a promising strategy for mitigating CO2 emissions and producing valuable chemicals. In this study, we investigate the role of ZnO in enhancing the performance of Cu-based catalysts for CO2RR. Using both experimental and theoretical approaches, we demonstrate that ZnO incorporation significantly improves the catalytic efficiency of Cu by modifying its electronic structure, stabilizing key intermediates, and facilitating C–C coupling. DFT calculations show that ZnO stabilizes intermediates such as *CO and *HCOH, promoting their hydrogenation and enhancing C2 product formation. The presence of oxygen vacancies (OVs) on the Cu-ZnO interface is found to facilitate proton-coupled electron transfer (PCET) and H-spillover, leading to improved catalytic performance. XPS and UV–Vis DRS analyses confirm that ZnO modifies the Cu surface, increasing the Cu0/Cu+ species and narrowing the band gap, which enhances charge transfer and intermediate stabilization. The CZ catalyst exhibits significantly higher Faradaic efficiency for C2 products compared to the Cu catalyst, as confirmed by experimental data. These findings highlight the importance of defect engineering in the design of more efficient catalysts for CO2 reduction. This study provides valuable insights into optimizing Cu-based catalysts for sustainable CO2 utilization and C2 product formation.
{"title":"Synergistic effects of ZnO in Cu-based catalysts for CO2 reduction: mechanistic insights into enhanced C2 product formation","authors":"Masoud Safari Yazd , Mohammadreza Omidkhah , Mahmoud Moharrami , Farshid Sobhani Bazghaleh , Hamidreza Rahmani , Azam Akbari","doi":"10.1016/j.ecmx.2026.101647","DOIUrl":"10.1016/j.ecmx.2026.101647","url":null,"abstract":"<div><div>The electrochemical reduction of CO<sub>2</sub> (CO<sub>2</sub>RR) to multi-carbon products such as ethanol (C<sub>2</sub>H<sub>5</sub>OH) and ethylene (C<sub>2</sub>H<sub>4</sub>) is a promising strategy for mitigating CO<sub>2</sub> emissions and producing valuable chemicals. In this study, we investigate the role of ZnO in enhancing the performance of Cu-based catalysts for CO<sub>2</sub>RR. Using both experimental and theoretical approaches, we demonstrate that ZnO incorporation significantly improves the catalytic efficiency of Cu by modifying its electronic structure, stabilizing key intermediates, and facilitating C–C coupling. DFT calculations show that ZnO stabilizes intermediates such as *CO and *HCOH, promoting their hydrogenation and enhancing C<sub>2</sub> product formation. The presence of oxygen vacancies (OVs) on the Cu-ZnO interface is found to facilitate proton-coupled electron transfer (PCET) and H-spillover, leading to improved catalytic performance. XPS and UV–Vis DRS analyses confirm that ZnO modifies the Cu surface, increasing the Cu<sup>0</sup>/Cu<sup>+</sup> species and narrowing the band gap, which enhances charge transfer and intermediate stabilization. The CZ catalyst exhibits significantly higher Faradaic efficiency for C<sub>2</sub> products compared to the Cu catalyst, as confirmed by experimental data. These findings highlight the importance of defect engineering in the design of more efficient catalysts for CO<sub>2</sub> reduction. This study provides valuable insights into optimizing Cu-based catalysts for sustainable CO<sub>2</sub> utilization and C<sub>2</sub> product formation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101647"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189853","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 : 2026-05-01Epub Date: 2026-02-11DOI: 10.1016/j.ecmx.2026.101665
T. Michael-Ahile , J.A. Samuels , M.J. Booysen
The growing adoption of decentralised renewable energy, particularly solar photovoltaic (PV) systems, presents opportunities to advance low-carbon development and circular economy objectives. However, unequal policy frameworks, infrastructure availability, and socio-economic conditions continue to limit equitable outcomes, especially in under-resourced communities. This study presents a simulation-based proof-of-concept evaluation of a Community-Based Energy Trading (CBET) framework designed to operationalise energy circularity within low-income communities through local energy redistribution. The proposed CBET model centres on a public school equipped with solar PV generation acting as a single prosumer that redistributes surplus electricity to nearby households using rule-based energy allocation rather than optimisation-based control. Using empirical electricity demand data for one school and five households in Cape Town, South Africa, two system configurations are simulated: CBET without household battery storage and CBET with battery-enabled households. Performance is evaluated in terms of local renewable energy utilisation, household electricity cost reductions, and peak-period grid demand. Results indicate that CBET increases local solar energy reuse efficiency to approximately 90% and reduces household reliance on grid electricity by up to 16% when battery storage is included. In addition, peak-hour demand is reduced by 13%, contributing to improved grid stability, energy equity, waste minimisation, and community-level resilience. These findings demonstrate that meaningful techno-economic benefits can be achieved through simplified community-level energy sharing arrangements without complex market mechanisms. However, the results are contingent on the assumed demand profiles and the single case configuration analysed. The study positions CBET as a feasible proof-of-concept for community-scale circular energy sharing systems operating under local capacity and policy constraints in the Global South.
{"title":"A simulation-based assessment of Community-Based Energy Trading for circular energy sharing in low-income communities","authors":"T. Michael-Ahile , J.A. Samuels , M.J. Booysen","doi":"10.1016/j.ecmx.2026.101665","DOIUrl":"10.1016/j.ecmx.2026.101665","url":null,"abstract":"<div><div>The growing adoption of decentralised renewable energy, particularly solar photovoltaic (PV) systems, presents opportunities to advance low-carbon development and circular economy objectives. However, unequal policy frameworks, infrastructure availability, and socio-economic conditions continue to limit equitable outcomes, especially in under-resourced communities. This study presents a simulation-based proof-of-concept evaluation of a Community-Based Energy Trading (CBET) framework designed to operationalise energy circularity within low-income communities through local energy redistribution. The proposed CBET model centres on a public school equipped with solar PV generation acting as a single prosumer that redistributes surplus electricity to nearby households using rule-based energy allocation rather than optimisation-based control. Using empirical electricity demand data for one school and five households in Cape Town, South Africa, two system configurations are simulated: CBET without household battery storage and CBET with battery-enabled households. Performance is evaluated in terms of local renewable energy utilisation, household electricity cost reductions, and peak-period grid demand. Results indicate that CBET increases local solar energy reuse efficiency to approximately 90% and reduces household reliance on grid electricity by up to 16% when battery storage is included. In addition, peak-hour demand is reduced by 13%, contributing to improved grid stability, energy equity, waste minimisation, and community-level resilience. These findings demonstrate that meaningful techno-economic benefits can be achieved through simplified community-level energy sharing arrangements without complex market mechanisms. However, the results are contingent on the assumed demand profiles and the single case configuration analysed. The study positions CBET as a feasible proof-of-concept for community-scale circular energy sharing systems operating under local capacity and policy constraints in the Global South.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101665"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190006","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}
Accurate characterization of electrochemical parameters is critical for interpreting the time- and frequency-domain responses of energy conversion devices and for enabling precise, efficient, and controllable management. In the context of lithium-ion batteries, this study introduces a deep learning (DL) framework using electrochemically derived simulation data to predict discharge curves and to distinguish the dominant internal electrochemical parameters for each of the three constant-current stages. Extracting attention weights from the transformer encoder helps identify the most influential parameters at each stage. Our study reveals subtle variations in the key electrochemical parameters that control discharge across different time-evolution stages. During the early stage of discharge, the behavior is mainly governed by positive-electrode parameters, such as the volume fraction of active material and the maximum concentration in the positive electrode. As the discharge progresses, however, negative-electrode parameters—particularly, the volume fraction of active material in the negative electrode, become increasingly influential. These outcomes are further verified through two additional operations: Sobol-based global sensitivity analysis and Shapley additive explanations. This DL framework reproduces the time-dependent battery behavior during a single discharge while elucidating the relationship between electrochemical parameters and battery response, thereby enabling efficient parameter assessment or identification and rational voltage-window selection for battery applications.
{"title":"On the critical battery electrochemical parameters across different phases of a single discharge process using a transformer framework","authors":"Chi-Jyun Ko, Cheng-Hsi Tien, Kuo-Ching Chen, Chih-Hung Chen","doi":"10.1016/j.ecmx.2026.101629","DOIUrl":"10.1016/j.ecmx.2026.101629","url":null,"abstract":"<div><div>Accurate characterization of electrochemical parameters is critical for interpreting the time- and frequency-domain responses of energy conversion devices and for enabling precise, efficient, and controllable management. In the context of lithium-ion batteries, this study introduces a deep learning (DL) framework using electrochemically derived simulation data to predict discharge curves and to distinguish the dominant internal electrochemical parameters for each of the three constant-current stages. Extracting attention weights from the transformer encoder helps identify the most influential parameters at each stage. Our study reveals subtle variations in the key electrochemical parameters that control discharge across different time-evolution stages. During the early stage of discharge, the behavior is mainly governed by positive-electrode parameters, such as the volume fraction of active material and the maximum concentration in the positive electrode. As the discharge progresses, however, negative-electrode parameters—particularly, the volume fraction of active material in the negative electrode, become increasingly influential. These outcomes are further verified through two additional operations: Sobol-based global sensitivity analysis and Shapley additive explanations. This DL framework reproduces the time-dependent battery behavior during a single discharge while elucidating the relationship between electrochemical parameters and battery response, thereby enabling efficient parameter assessment or identification and rational voltage-window selection for battery applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101629"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190171","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 : 2026-05-01Epub Date: 2026-01-22DOI: 10.1016/j.ecmx.2026.101613
Asmail A.M. Abdalkarem , Ahmad Fazlizan , Najm Addin Al-Khawlani , Wan Khairul Muzammil , Zambri Harun , Adnan Ibrahim
Vertical-axis wind turbines (VAWTs), such as the Darrieus configuration, offer a clean renewable energy source that can reduce reliance on fossil fuels. Compared with horizontal-axis wind turbines (HAWTs), VAWTs present several advantages. However, their performance is constrained by inherent limitations, including dynamic stall, wake rotation effects, and self-starting difficulties, which hinder their commercial viability. Passive flow control techniques, such as adding a wedge flap (WF) to the trailing edge of rotor blades, offer a potential solution. This study examines the performance of straight-bladed VAWTs (SB-VAWTs) with and without optimized WFs. Rotor blades were designed, fabricated, and tested in a wind tunnel under varying wind speeds and loading conditions. Results showed that adding a WF significantly enhances the power coefficient (Cp) across different wind speeds. Maximum Cp and electrical power output increased by 11% at 5 m/s and up to 20% at 13 m/s compared to clean VAWTs. Furthermore, Cp-TSR curves became flatter, indicating improved stability and reduced sensitivity to sudden wind speed changes. The WF demonstrates potential as a passive flow control device, enhancing VAWT performance while maintaining adaptability. With proper dimensioning, WFs could be integrated into new turbines or retrofitted onto existing ones, making them a promising option for renewable energy systems.
{"title":"Experimental investigation of the potential of wedge flaps for improving the aerodynamic performance of a straight-bladed vertical axis wind turbine","authors":"Asmail A.M. Abdalkarem , Ahmad Fazlizan , Najm Addin Al-Khawlani , Wan Khairul Muzammil , Zambri Harun , Adnan Ibrahim","doi":"10.1016/j.ecmx.2026.101613","DOIUrl":"10.1016/j.ecmx.2026.101613","url":null,"abstract":"<div><div>Vertical-axis wind turbines (VAWTs), such as the Darrieus configuration, offer a clean renewable energy source that can reduce reliance on fossil fuels. Compared with horizontal-axis wind turbines (HAWTs), VAWTs present several advantages. However, their performance is constrained by inherent limitations, including dynamic stall, wake rotation effects, and self-starting difficulties, which hinder their commercial viability. Passive flow control techniques, such as adding a wedge flap (WF) to the trailing edge of rotor blades, offer a potential solution. This study examines the performance of straight-bladed VAWTs (SB-VAWTs) with and without optimized WFs. Rotor blades were designed, fabricated, and tested in a wind tunnel under varying wind speeds and loading conditions. Results showed that adding a WF significantly enhances the power coefficient (<em>C<sub>p</sub></em>) across different wind speeds. Maximum <em>C<sub>p</sub></em> and electrical power output increased by 11% at 5 m/s and up to 20% at 13 m/s compared to clean VAWTs. Furthermore, <em>C<sub>p</sub></em>-TSR curves became flatter, indicating improved stability and reduced sensitivity to sudden wind speed changes. The WF demonstrates potential as a passive flow control device, enhancing VAWT performance while maintaining adaptability. With proper dimensioning, WFs could be integrated into new turbines or retrofitted onto existing ones, making them a promising option for renewable energy systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101613"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080264","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 : 2026-05-01Epub Date: 2026-01-13DOI: 10.1016/j.ecmx.2026.101547
Ali Balal , Mohsen Ghorbian
The increasing global population and escalating clean and renewable energy sources must be widely used in order to reduce greenhouse gas emissions. In this context, photovoltaic (PV) systems have gained significant prominence worldwide. Modern PV panels are increasingly utilized in both industrial and residential applications as a sustainable and cost-effective method for generating electricity and heat. This study investigates the impact of absorber plate cooling methods on the electrical and thermal performance of a solar photovoltaic-thermal (PV/T) co-generation system. A novel hybrid cooling approach, employing simultaneous water and air cooling, was implemented in the present system. The performance of this hybrid-cooled system was then compared against a system without cooling. Experiments were conducted during the summer season (June-July-August 2025) at the University of Kashan’s Energy Research Institute. The implementation of the novel hybrid cooling method resulted in approximate increases of 40%, 53%, and 93% in electrical, thermal, and overall efficiencies, respectively. The findings indicate that water cooling significantly improved electrical and thermal efficiencies by up to 50% and 130%, respectively, compared to air cooling. Furthermore, the electrical efficiency of the water-cooled system exhibited a relative improvement of up to 100% compared to the uncooled reference case, particularly under high operating temperature conditions. Notably, the highest overall electrical and thermal efficiency, approximately 93%, was achieved with the novel hybrid cooling method (simultaneous Air cooling in the interior channel and water cooling of the panel’s front and back surfaces at the same time). Additionally, the hybrid’s thermal efficiency cooling method demonstrated rises of approximately 200% and 75% when compared to air and water cooling, respectively.
{"title":"An experimental investigation of the effects of absorber plate cooling methods on the efficiency of a solar cogeneration system","authors":"Ali Balal , Mohsen Ghorbian","doi":"10.1016/j.ecmx.2026.101547","DOIUrl":"10.1016/j.ecmx.2026.101547","url":null,"abstract":"<div><div>The increasing global population and escalating clean and renewable energy sources must be widely used in order to reduce greenhouse gas emissions. In this context, photovoltaic (PV) systems have gained significant prominence worldwide. Modern PV panels are increasingly utilized in both industrial and residential applications as a sustainable and cost-effective method for generating electricity and heat. This study investigates the impact of absorber plate cooling methods on the electrical and thermal performance of a solar photovoltaic-thermal (PV/T) co-generation system. A novel hybrid cooling approach, employing simultaneous water and air cooling, was implemented in the present system. The performance of this hybrid-cooled system was then compared against a system without cooling. Experiments were conducted during the summer season (June-July-August 2025) at the University of Kashan’s Energy Research Institute. The implementation of the novel hybrid cooling method resulted in approximate increases of 40%, 53%, and 93% in electrical, thermal, and overall efficiencies, respectively. The findings indicate that water cooling significantly improved electrical and thermal efficiencies by up to 50% and 130%, respectively, compared to air cooling. Furthermore, the electrical efficiency of the water-cooled system exhibited a relative improvement of up to 100% compared to the uncooled reference case, particularly under high operating temperature conditions. Notably, the highest overall electrical and thermal efficiency, approximately 93%, was achieved with the novel hybrid cooling method (simultaneous Air cooling in the interior channel and water cooling of the panel’s front and back surfaces at the same time). Additionally, the hybrid’s thermal efficiency cooling method demonstrated rises of approximately 200% and 75% when compared to air and water cooling, respectively.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101547"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080385","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}
Effective thermal management of lithium-ion (Li-ion) batteries in electric vehicles (EVs) is essential for ensuring safety, extending battery life, and maintaining performance under varying operating conditions. This study presents a novel battery thermal management system (BTMS) that integrates thermoelectric cooling with dielectric immersion cooling, and evaluates its performance through both simulation and experimentation. A relatively new 26650 LiFePO4 battery model, characterized by high capacity and high discharge capability was selected due to its elevated heat generation. The proposed BTMS was first analyzed numerically using computational fluid dynamics (CFD) to assess temperature distribution and cooling effectiveness. Subsequent experimental testing was performed with a physical battery cell simulator, and the measured data were compared with CFD predictions. In all cases, the experiments yielded slightly higher temperature values than those predicted by simulation. At the maximum coolant flow rate of 1.96 L/min, the BTMS reduced the temperature rise of the battery cell simulator by 28.78 %, 41.52 %, and 46.54 % at discharge rates of 5.8 C, 7.7 C, and 9.6 C, respectively, compared to operation without any BTMS. Under the highest discharge rate (9.6 C), where heat generation was greatest, temperature reductions of 9.71 K, 12.57 K, and 16.57 K were achieved over 375 s for coolant flow rates of 0.58 L/min, 1.08 L/min, and 1.96 L/min, respectively. Overall, the developed BTMS proved highly effective in controlling the temperature of the Li-ion battery cell simulator. The findings offer valuable guidance for designing and implementing thermoelectric–dielectric immersion cooling technologies, particularly for high-performance EV applications.
{"title":"Performance analysis of a novel battery thermal management system integrating thermoelectric and dielectric immersion cooling in EVs","authors":"Md Ahnaf Adit, Samiul Hasan, Nirendra Nath Mustafi","doi":"10.1016/j.ecmx.2026.101550","DOIUrl":"10.1016/j.ecmx.2026.101550","url":null,"abstract":"<div><div>Effective thermal management of lithium-ion (Li-ion) batteries in electric vehicles (EVs) is essential for ensuring safety, extending battery life, and maintaining performance under varying operating conditions. This study presents a novel battery thermal management system (BTMS) that integrates thermoelectric cooling with dielectric immersion cooling, and evaluates its performance through both simulation and experimentation. A relatively new 26650 LiFePO<sub>4</sub> battery model, characterized by high capacity and high discharge capability was selected due to its elevated heat generation. The proposed BTMS was first analyzed numerically using computational fluid dynamics (CFD) to assess temperature distribution and cooling effectiveness. Subsequent experimental testing was performed with a physical battery cell simulator, and the measured data were compared with CFD predictions. In all cases, the experiments yielded slightly higher temperature values than those predicted by simulation. At the maximum coolant flow rate of 1.96 L/min, the BTMS reduced the temperature rise of the battery cell simulator by 28.78 %, 41.52 %, and 46.54 % at discharge rates of 5.8 C, 7.7 C, and 9.6 C, respectively, compared to operation without any BTMS. Under the highest discharge rate (9.6 C), where heat generation was greatest, temperature reductions of 9.71 K, 12.57 K, and 16.57 K were achieved over 375 s for coolant flow rates of 0.58 L/min, 1.08 L/min, and 1.96 L/min, respectively. Overall, the developed BTMS proved highly effective in controlling the temperature of the Li-ion battery cell simulator. The findings offer valuable guidance for designing and implementing thermoelectric–dielectric immersion cooling technologies, particularly for high-performance EV applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101550"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039633","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 : 2026-05-01Epub Date: 2026-01-20DOI: 10.1016/j.ecmx.2026.101597
Zaid Allal , Hassan N. Noura , Flavien Vernier , Ola Salman , Khaled Chahine
Accurate prediction of the Remaining Useful Life (RUL) of fuel cell (FC) systems is essential to ensure operational reliability, optimize maintenance strategies, and extend system lifetime in safety-critical hydrogen applications. As FC degradation is governed by complex, nonlinear, and stochastic mechanisms, machine learning (ML) has emerged as a powerful paradigm for data-driven prognostics. This paper presents a structured and comprehensive review of recent ML-based approaches for FC RUL estimation, encompassing supervised, unsupervised, and hybrid methodologies, including regression techniques, support vector machines, ensemble models, neural networks, and advanced deep learning architectures. Despite notable progress, our analysis reveals persistent limitations in the current literature, particularly the widespread neglect of underlying electrochemical and physical degradation laws, as well as the scarcity and ambiguity of explicit RUL and End-of-Life (EoL) labels in publicly available datasets. These challenges significantly constrain model generalization, interpretability, and real-world applicability. To address these gaps, we conduct a comparative analysis of more than 20 recent state-of-the-art studies and propose a unified and generalizable RUL estimation pipeline. This framework integrates data acquisition, preprocessing, feature engineering, model design, and validation, while explicitly accounting for physical consistency and operational constraints. In addition, the paper formulates practical, multi-level recommendations, including first-order guidelines for data modeling and learning strategies, second-order recommendations targeting validation protocols and real-world deployment, and the systematic integration of uncertainty quantification (UQ) techniques to enhance robustness, interpretability, and trustworthiness. By consolidating methodological insights, emerging paradigms, and deployment-oriented considerations, this review provides a comprehensive reference and a forward-looking roadmap for the development of reliable, physics-consistent, and scalable RUL prognostic frameworks for fuel cell systems.
{"title":"Machine learning for fuel cell remaining useful life prediction: A review","authors":"Zaid Allal , Hassan N. Noura , Flavien Vernier , Ola Salman , Khaled Chahine","doi":"10.1016/j.ecmx.2026.101597","DOIUrl":"10.1016/j.ecmx.2026.101597","url":null,"abstract":"<div><div>Accurate prediction of the Remaining Useful Life (RUL) of fuel cell (FC) systems is essential to ensure operational reliability, optimize maintenance strategies, and extend system lifetime in safety-critical hydrogen applications. As FC degradation is governed by complex, nonlinear, and stochastic mechanisms, machine learning (ML) has emerged as a powerful paradigm for data-driven prognostics. This paper presents a structured and comprehensive review of recent ML-based approaches for FC RUL estimation, encompassing supervised, unsupervised, and hybrid methodologies, including regression techniques, support vector machines, ensemble models, neural networks, and advanced deep learning architectures. Despite notable progress, our analysis reveals persistent limitations in the current literature, particularly the widespread neglect of underlying electrochemical and physical degradation laws, as well as the scarcity and ambiguity of explicit RUL and End-of-Life (EoL) labels in publicly available datasets. These challenges significantly constrain model generalization, interpretability, and real-world applicability. To address these gaps, we conduct a comparative analysis of more than 20 recent state-of-the-art studies and propose a unified and generalizable RUL estimation pipeline. This framework integrates data acquisition, preprocessing, feature engineering, model design, and validation, while explicitly accounting for physical consistency and operational constraints. In addition, the paper formulates practical, multi-level recommendations, including first-order guidelines for data modeling and learning strategies, second-order recommendations targeting validation protocols and real-world deployment, and the systematic integration of uncertainty quantification (UQ) techniques to enhance robustness, interpretability, and trustworthiness. By consolidating methodological insights, emerging paradigms, and deployment-oriented considerations, this review provides a comprehensive reference and a forward-looking roadmap for the development of reliable, physics-consistent, and scalable RUL prognostic frameworks for fuel cell systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101597"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039787","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 : 2026-05-01Epub Date: 2026-01-13DOI: 10.1016/j.ecmx.2026.101556
Ziyou Liu, Manojkumar Gudala, Klemens Katterbauer, Bicheng Yan
In geothermal recovery, the reservoir production temperature decline can affect the power plant’s efficiency and electricity output. Therefore, the coupling between the reservoir and the power plant is crucial for accurate estimation of the power plant’s performance. Current studies couple the numerical simulation models of the reservoir and the power plant developed based on physics. However, such simulations are usually computationally inefficient when performing predictions, thus becoming bottleneck in both forward and inverse modeling tasks. Therefore, we aim to accelerate the forward and inverse modeling of the coupled model by replacing the numerical simulation models with deep learning-based surrogate models. In this study, we first develop surrogate models of the geothermal reservoir and power plant, and further couple them into as an integrated forward model through heat source conditions. Further, a multi-objective optimizer combining the forward model is applied to optimize the coupled system. Surrogate models of the reservoir and power plant can predict the wellhead production temperature and electricity with mean relative errors of 0.49% and 1.67% while achieving CPU speedup at and times compared to physics simulators, respectively. Besides, the surrogate-based optimization is times faster than the simulation-based one. The results demonstrate much higher computational efficiency of our coupled model in both the forward and inverse modeling with negligible trade-off in accuracy, as compared to the current physics-based coupled simulation models. This workflow significantly accelerates the procedures of feasibility assessments of geothermal projects as well as the decision making of the geothermal reservoir and the power plant.
{"title":"Robust optimization of fully coupled geothermal reservoir and power plant system based on deep learning","authors":"Ziyou Liu, Manojkumar Gudala, Klemens Katterbauer, Bicheng Yan","doi":"10.1016/j.ecmx.2026.101556","DOIUrl":"10.1016/j.ecmx.2026.101556","url":null,"abstract":"<div><div>In geothermal recovery, the reservoir production temperature decline can affect the power plant’s efficiency and electricity output. Therefore, the coupling between the reservoir and the power plant is crucial for accurate estimation of the power plant’s performance. Current studies couple the numerical simulation models of the reservoir and the power plant developed based on physics. However, such simulations are usually computationally inefficient when performing predictions, thus becoming bottleneck in both forward and inverse modeling tasks. Therefore, we aim to accelerate the forward and inverse modeling of the coupled model by replacing the numerical simulation models with deep learning-based surrogate models. In this study, we first develop surrogate models of the geothermal reservoir and power plant, and further couple them into as an integrated forward model through heat source conditions. Further, a multi-objective optimizer combining the forward model is applied to optimize the coupled system. Surrogate models of the reservoir and power plant can predict the wellhead production temperature and electricity with mean relative errors of 0.49% and 1.67% while achieving CPU speedup at <span><math><mrow><mn>6</mn><mo>.</mo><mn>92</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>4</mn></mrow></msup></mrow></math></span> and <span><math><mrow><mn>1</mn><mo>.</mo><mn>77</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> times compared to physics simulators, respectively. Besides, the surrogate-based optimization is <span><math><mrow><mn>6</mn><mo>.</mo><mn>05</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> times faster than the simulation-based one. The results demonstrate much higher computational efficiency of our coupled model in both the forward and inverse modeling with negligible trade-off in accuracy, as compared to the current physics-based coupled simulation models. This workflow significantly accelerates the procedures of feasibility assessments of geothermal projects as well as the decision making of the geothermal reservoir and the power plant.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101556"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039767","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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