Pub Date : 2026-01-23DOI: 10.1016/j.apenergy.2026.127429
Cenxi Li , Shi You , Xiaoti Cui
This review investigates the Carbon-to-Methanol (CTM) pathway for e-methanol production, in which captured CO2 is directly hydrogenated with renewable hydrogen. CTM is gaining attention as a scalable route for converting renewable energy into liquid fuels, yet its system design remains loosely defined. The absence of a clear design framework has led to diverse implementations, each shaped by local energy conditions, carbon sources, and integration strategies. This diversity makes it difficult to evaluate performance or guide replication. To address this, the review systematically analyzes the key design dimensions of CTM systems, including power supply, electrolyzer selection, CO2 sourcing, reactor configuration, and integration strategy. By mapping these components and their associated challenges, the review provides a structured overview of how CTM systems are currently implemented and highlights the diversity of approaches that shape their technical and operational characteristics.
{"title":"From concept to deployment: A review of carbon-to-methanol pathways for e-methanol","authors":"Cenxi Li , Shi You , Xiaoti Cui","doi":"10.1016/j.apenergy.2026.127429","DOIUrl":"10.1016/j.apenergy.2026.127429","url":null,"abstract":"<div><div>This review investigates the Carbon-to-Methanol (CTM) pathway for e-methanol production, in which captured CO<sub>2</sub> is directly hydrogenated with renewable hydrogen. CTM is gaining attention as a scalable route for converting renewable energy into liquid fuels, yet its system design remains loosely defined. The absence of a clear design framework has led to diverse implementations, each shaped by local energy conditions, carbon sources, and integration strategies. This diversity makes it difficult to evaluate performance or guide replication. To address this, the review systematically analyzes the key design dimensions of CTM systems, including power supply, electrolyzer selection, CO<sub>2</sub> sourcing, reactor configuration, and integration strategy. By mapping these components and their associated challenges, the review provides a structured overview of how CTM systems are currently implemented and highlights the diversity of approaches that shape their technical and operational characteristics.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127429"},"PeriodicalIF":11.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","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.apenergy.2026.127417
Pablo Horrillo-Quintero , Pablo García-Triviño , Sérgio F. Santos , David Carrasco-González , Luis M. Fernández-Ramírez , João P.S. Catalão
Energy communities (ECs) offer a significant opportunity for decentralised energy production. However, realising their full potential is hindered by the significant challenge of managing the high volatility of renewable energy technologies (RETs) and dynamic electricity markets. To address this, the present work introduces a novel dynamic adaptive model predictive control (AMPC) framework designed to simultaneously reduce costs, minimise losses, and enhance RET integration in prosumer-based ECs. The methodology is built upon a high-fidelity dynamic model of the EC, operating with a 50 μs time step to accurately capture the switching dynamics of power electronics and ensure a realistic representation of system behaviour. The key innovation lies in the dynamic adaptation of AMPC weights and power constraints, enabling seamless transitions between a self-sufficiency mode during high-price periods and an economically optimised grid-interactive mode during favourable market conditions. The performance of the AMPC is rigorously benchmarked against fixed MPC strategies and the particle swarm optimisation (PSO) algorithm. The results demonstrate the profound superiority of the adaptive approach, showing reductions in operational costs and power losses of 6.13% to 44.92%, without compromising sustainability. The AMPC's average RET utilisation of 79.31% was superior to that of the fixed-MPC strategies, with improvements ranging from 0.45% to 13.34%. Furthermore, it demonstrated a highly efficient balance against the metaheuristic approach, where a minor 2.53% difference in utilisation was exchanged for significant gains in cost and efficiency. Finally, compared with an adaptive PSO strategy, it reduces 120% power losses and increases 28.33% the capacity utilisation. These results demonstrate a superior framework for achieving a cost-effective, efficient, and sustainable operation.
{"title":"Dynamic adaptive model predictive control for prosumers-based energy communities","authors":"Pablo Horrillo-Quintero , Pablo García-Triviño , Sérgio F. Santos , David Carrasco-González , Luis M. Fernández-Ramírez , João P.S. Catalão","doi":"10.1016/j.apenergy.2026.127417","DOIUrl":"10.1016/j.apenergy.2026.127417","url":null,"abstract":"<div><div>Energy communities (ECs) offer a significant opportunity for decentralised energy production. However, realising their full potential is hindered by the significant challenge of managing the high volatility of renewable energy technologies (RETs) and dynamic electricity markets. To address this, the present work introduces a novel dynamic adaptive model predictive control (AMPC) framework designed to simultaneously reduce costs, minimise losses, and enhance RET integration in prosumer-based ECs. The methodology is built upon a high-fidelity dynamic model of the EC, operating with a 50 μs time step to accurately capture the switching dynamics of power electronics and ensure a realistic representation of system behaviour. The key innovation lies in the dynamic adaptation of AMPC weights and power constraints, enabling seamless transitions between a self-sufficiency mode during high-price periods and an economically optimised grid-interactive mode during favourable market conditions. The performance of the AMPC is rigorously benchmarked against fixed MPC strategies and the particle swarm optimisation (PSO) algorithm. The results demonstrate the profound superiority of the adaptive approach, showing reductions in operational costs and power losses of 6.13% to 44.92%, without compromising sustainability. The AMPC's average RET utilisation of 79.31% was superior to that of the fixed-MPC strategies, with improvements ranging from 0.45% to 13.34%. Furthermore, it demonstrated a highly efficient balance against the metaheuristic approach, where a minor 2.53% difference in utilisation was exchanged for significant gains in cost and efficiency. Finally, compared with an adaptive PSO strategy, it reduces 120% power losses and increases 28.33% the capacity utilisation. These results demonstrate a superior framework for achieving a cost-effective, efficient, and sustainable operation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127417"},"PeriodicalIF":11.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","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.apenergy.2026.127391
Hongkai Cheng, Lu Zhang, Zhigang Zhang, Wei Tang
Island distribution networks suffer from weak external support, difficulties in renewable energy integration, high carbon emissions, and vulnerability to extreme disasters. To address these challenges, this study proposes an optimal hydrogen energy storage (HES) configuration method that balances economic performance, resilience, and low-carbon operation. The research develops physics-based models of electrolyzers, hydrogen storage tanks, and fuel cells, and designs operating strategies for both normal and extreme scenarios to capture renewable utilization, emergency supply capability, and Carbon emission reduction capacity. On this basis, a tri-objective optimization framework is constructed using generalized Nash equilibrium (GNE) theory, where device capacities are modeled as independent strategic variables and the three objectives are defined as utility functions of different players. The model is solved using a mathematical program with equilibrium constraints (MPEC), which transforms each objective into KKT conditions and enables stable, interpretable solutions under coupled physical and resource constraints. Case studies on a typical coastal island demonstrate that, compared with electrochemical storage, HES achieves higher renewable absorption and carbon reduction benefits under normal operation, while significantly enhancing resilience during extreme events. Results also reveal nonlinear couplings and high sensitivity among the three objectives, where small capacity adjustments lead to substantial fluctuations in performance. The findings confirm that the proposed GNE-based method effectively captures complex multi-objective interactions, avoids imbalanced configurations caused by traditional weighted or hierarchical approaches, and provides theoretical and practical guidance for resilient and low-carbon development of island distribution networks.
{"title":"Generalized Nash equilibrium-based optimal configuration of hydrogen energy storage in island distribution networks considering economy, resilience, and low-carbon","authors":"Hongkai Cheng, Lu Zhang, Zhigang Zhang, Wei Tang","doi":"10.1016/j.apenergy.2026.127391","DOIUrl":"10.1016/j.apenergy.2026.127391","url":null,"abstract":"<div><div>Island distribution networks suffer from weak external support, difficulties in renewable energy integration, high carbon emissions, and vulnerability to extreme disasters. To address these challenges, this study proposes an optimal hydrogen energy storage (HES) configuration method that balances economic performance, resilience, and low-carbon operation. The research develops physics-based models of electrolyzers, hydrogen storage tanks, and fuel cells, and designs operating strategies for both normal and extreme scenarios to capture renewable utilization, emergency supply capability, and Carbon emission reduction capacity. On this basis, a tri-objective optimization framework is constructed using generalized Nash equilibrium (GNE) theory, where device capacities are modeled as independent strategic variables and the three objectives are defined as utility functions of different players. The model is solved using a mathematical program with equilibrium constraints (MPEC), which transforms each objective into KKT conditions and enables stable, interpretable solutions under coupled physical and resource constraints. Case studies on a typical coastal island demonstrate that, compared with electrochemical storage, HES achieves higher renewable absorption and carbon reduction benefits under normal operation, while significantly enhancing resilience during extreme events. Results also reveal nonlinear couplings and high sensitivity among the three objectives, where small capacity adjustments lead to substantial fluctuations in performance. The findings confirm that the proposed GNE-based method effectively captures complex multi-objective interactions, avoids imbalanced configurations caused by traditional weighted or hierarchical approaches, and provides theoretical and practical guidance for resilient and low-carbon development of island distribution networks.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127391"},"PeriodicalIF":11.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study develops a multi-scale coupled model including electrochemical, thermodynamic, and kinetic processes to systematically analyze the electro-thermal balance characteristics of ammonia-fed solid oxide fuel cell (SOFC) systems for marine applications. The framework resolves stack temperature fields under various operating parameters and load conditions and evaluates system running boundaries, emissions, and levelized cost of electricity (LCOE) for six electro-thermal balance schemes. Compared with previous SOFC system studies, the model explicitly links ammonia pre-cracking and anode-off gas recirculation strategies to the electro-thermal feasibility envelope and techno-economic and environmental metrics. Comprehensive analysis shows that increasing ammonia pre-cracking ratio significantly reduces stack temperature gradients and enhances thermal safety, while narrowing the operating windows for dead-end anode (DEA) loop and baseline systems and expanding that for the anode-off gas recirculation (AGR) system. The recirculation ratio also strongly influences matching performance. Specifically, the AGR system offers the widest operating range and high efficiency, the DEA system achieves the highest efficiency with zero NOx and N2O emissions, and the baseline system delivers the highest power density but exhibits lower efficiency and higher emissions. According to the entropy-weighted TOPSIS method, the best case is the DEA system, showing an LCOE of 0.652 USD/kWh, zero emissions, a maximum stack temperature gradient of 28.16 K/cm, and a running range of 0.2132. These findings establish quantitative electro-thermal balance strategies for SOFC applications across diverse marine scenarios and provide theoretical support for net-zero, high-efficiency transformation of future ship propulsion systems.
{"title":"Characterizing electro-thermal balance of zero-emission ammonia-fed solid oxide fuel cell systems integrated into hybrid propulsion plant for marine applications","authors":"Jinbo Qu , Yongming Feng , Bin Miao , Siew Hwa Chan , Yuanqing Zhu","doi":"10.1016/j.apenergy.2026.127426","DOIUrl":"10.1016/j.apenergy.2026.127426","url":null,"abstract":"<div><div>This study develops a multi-scale coupled model including electrochemical, thermodynamic, and kinetic processes to systematically analyze the electro-thermal balance characteristics of ammonia-fed solid oxide fuel cell (SOFC) systems for marine applications. The framework resolves stack temperature fields under various operating parameters and load conditions and evaluates system running boundaries, emissions, and levelized cost of electricity (LCOE) for six electro-thermal balance schemes. Compared with previous SOFC system studies, the model explicitly links ammonia pre-cracking and anode-off gas recirculation strategies to the electro-thermal feasibility envelope and techno-economic and environmental metrics. Comprehensive analysis shows that increasing ammonia pre-cracking ratio significantly reduces stack temperature gradients and enhances thermal safety, while narrowing the operating windows for dead-end anode (DEA) loop and baseline systems and expanding that for the anode-off gas recirculation (AGR) system. The recirculation ratio also strongly influences matching performance. Specifically, the AGR system offers the widest operating range and high efficiency, the DEA system achieves the highest efficiency with zero NOx and N<sub>2</sub>O emissions, and the baseline system delivers the highest power density but exhibits lower efficiency and higher emissions. According to the entropy-weighted TOPSIS method, the best case is the DEA system, showing an LCOE of 0.652 USD/kWh, zero emissions, a maximum stack temperature gradient of 28.16 K/cm, and a running range of 0.2132. These findings establish quantitative electro-thermal balance strategies for SOFC applications across diverse marine scenarios and provide theoretical support for net-zero, high-efficiency transformation of future ship propulsion systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127426"},"PeriodicalIF":11.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1016/j.apenergy.2026.127419
Guiyue Duan, Fernando Porté-Agel
This study investigates the potential of the cyclic yaw control (CYC) strategy to enhance the power performance of wind farms. Wind tunnel experiments are conducted to evaluate the effectiveness of CYC (only yawing the first wind turbine) under various inflow conditions and farm configurations. The effects of different turbine numbers, spacing and control parameters (including initial yaw angle, yaw amplitude and Strouhal number) on power efficiency are systematically examined. Results show that CYC significantly improves power production in full wake state, with a maximum gain in an eight-turbine wind farm model under moderate inflow turbulence intensity (). This gain decreases to as turbulence intensity increases to . The control strategy becomes more effective in larger farms with denser turbine arrangements, especially when wake effects are significant. In all scenarios, the maximum power gain is achieved when the yaw Strouhal number falls within and the amplitude is in the range . The optimal yaw Strouhal number decreases with farm length, while the optimal amplitude increases slightly. Inflow conditions and spanwise spacing affect wind farm power production but have negligible effects on the optimal control parameters. Power outputs under CYC exhibit periodic behavior, with fluctuations generally stronger than baseline cases. Interestingly, in the Strouhal number range around , CYC increases the mean power production while reducing power fluctuations of a three-turbine wind farm. Overall, these findings suggest that CYC holds considerable potential to improve wind farm power performance.
{"title":"Cyclic yaw control for wind farm power optimization: The role of strouhal number, farm configuration, and turbulence intensity","authors":"Guiyue Duan, Fernando Porté-Agel","doi":"10.1016/j.apenergy.2026.127419","DOIUrl":"10.1016/j.apenergy.2026.127419","url":null,"abstract":"<div><div>This study investigates the potential of the cyclic yaw control (CYC) strategy to enhance the power performance of wind farms. Wind tunnel experiments are conducted to evaluate the effectiveness of CYC (only yawing the first wind turbine) under various inflow conditions and farm configurations. The effects of different turbine numbers, spacing and control parameters (including initial yaw angle, yaw amplitude and Strouhal number) on power efficiency are systematically examined. Results show that CYC significantly improves power production in full wake state, with a <span><math><mn>32</mn><mi>%</mi></math></span> maximum gain in an eight-turbine wind farm model under moderate inflow turbulence intensity (<span><math><mn>0.07</mn></math></span>). This gain decreases to <span><math><mn>17</mn><mi>%</mi></math></span> as turbulence intensity increases to <span><math><mn>0.12</mn></math></span>. The control strategy becomes more effective in larger farms with denser turbine arrangements, especially when wake effects are significant. In all scenarios, the maximum power gain is achieved when the yaw Strouhal number falls within <span><math><mo>[</mo><mn>0.10</mn><mo>,</mo><mn>0.25</mn><mo>]</mo></math></span> and the amplitude is in the range <span><math><mo>[</mo><msup><mn>20</mn><mrow><mo>∘</mo></mrow></msup><mo>,</mo><msup><mn>30</mn><mrow><mo>∘</mo></mrow></msup><mo>]</mo></math></span>. The optimal yaw Strouhal number decreases with farm length, while the optimal amplitude increases slightly. Inflow conditions and spanwise spacing affect wind farm power production but have negligible effects on the optimal control parameters. Power outputs under CYC exhibit periodic behavior, with fluctuations generally stronger than baseline cases. Interestingly, in the Strouhal number range around <span><math><mn>0.24</mn><mo>−</mo><mn>0.44</mn></math></span>, CYC increases the mean power production while reducing power fluctuations of a three-turbine wind farm. Overall, these findings suggest that CYC holds considerable potential to improve wind farm power performance.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127419"},"PeriodicalIF":11.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.apenergy.2025.127265
Zongnan Zhang , Xiaojun Shen , Xubin Liu , Mingjiang Chang , Zhuocheng Dai
The coordinated operation of integrated energy system (IES) and hydrogen refueling station (HRS) is crucial for advancing the hydrogen vehicle industry and achieving dual carbon goals. However, competition between their interests, along with uncertainties in renewable energy generation and hydrogen demand, significantly compromises system economics, stability, and hydrogen storage safety. Traditional scheduling models inadequately address the thermal and electrical demands of carbon capture system (CCS) and struggle to balance robustness in handling uncertainty with computational efficiency, thereby limiting their engineering applicability. This study proposes a bi-level distributionally robust coordination mechanism based on stackelberg game theory, developing a refined model for the coordinated operation of combined heat and power (CHP), CCS, and power-to-gas (P2G) technologies. An equivalent reformulation strategy is designed to transition from two-stage distributionally robust optimization (TSDRO) to two-stage robust optimization (TSRO), incorporating distributionally robust chance constraint (DRCC) for the safe capacity of hydrogen storage tank. The proposed sequential bound tightening nested improved constraint & column generation (SBT-nested-IC&CG) algorithm, offers efficient computational solutions. Simulation results indicate that the method effectively balances the interests of both parties, enhances robustness against multiple uncertainties, maintains reasonable hydrogen storage safety levels, and reduces computation time by approximately 50 % compared to traditional nested column & constraint generation (NC&CG) algorithm, thus providing a feasible engineering solution for the coordinated scheduling of IES and HRS
{"title":"Bi-level distributionally robust coordinated optimization of integrateed energy system and hydrogen refueling station based on stackelberg game","authors":"Zongnan Zhang , Xiaojun Shen , Xubin Liu , Mingjiang Chang , Zhuocheng Dai","doi":"10.1016/j.apenergy.2025.127265","DOIUrl":"10.1016/j.apenergy.2025.127265","url":null,"abstract":"<div><div>The coordinated operation of integrated energy system (IES) and hydrogen refueling station (HRS) is crucial for advancing the hydrogen vehicle industry and achieving dual carbon goals. However, competition between their interests, along with uncertainties in renewable energy generation and hydrogen demand, significantly compromises system economics, stability, and hydrogen storage safety. Traditional scheduling models inadequately address the thermal and electrical demands of carbon capture system (CCS) and struggle to balance robustness in handling uncertainty with computational efficiency, thereby limiting their engineering applicability. This study proposes a bi-level distributionally robust coordination mechanism based on stackelberg game theory, developing a refined model for the coordinated operation of combined heat and power (CHP), CCS, and power-to-gas (P2G) technologies. An equivalent reformulation strategy is designed to transition from two-stage distributionally robust optimization (TSDRO) to two-stage robust optimization (TSRO), incorporating distributionally robust chance constraint (DRCC) for the safe capacity of hydrogen storage tank. The proposed sequential bound tightening nested improved constraint & column generation (SBT-nested-IC&CG) algorithm, offers efficient computational solutions. Simulation results indicate that the method effectively balances the interests of both parties, enhances robustness against multiple uncertainties, maintains reasonable hydrogen storage safety levels, and reduces computation time by approximately 50 % compared to traditional nested column & constraint generation (NC&CG) algorithm, thus providing a feasible engineering solution for the coordinated scheduling of IES and HRS</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127265"},"PeriodicalIF":11.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.apenergy.2025.127345
Jing Dang , Meng Qi , Jonggeol Na , Zijian Deng , Chi-Min Xu , Wei Feng , Yi Liu
As a pivotal technology for clean hydrogen production, proton exchange membrane electrolysis cells (PEMEC) have garnered widespread industrial adoption. The implementation of high-pressure operation in PEMEC systems enhances hydrogen output pressure, thereby reducing compression demands and associated costs in downstream transportation. However, this strategy potentially introduces critical safety challenges associated with gas crossover, particularly hydrogen-in‑oxygen (HTO) enrichment. To systematically address the inherent trade-offs between economic performance and safety risks, this study employs a novel proposed multi-objective optimization framework based on Bayesian optimization. Our methodology is designed to achieve explicit system-level objectives, maintaining the HTO concentration constraint below the safety threshold of 2%, while concurrently minimizing the levelized cost of hydrogen (LCOH) and the inherent safety index (ISI). First, we established an integrated process model by combining first-principles unit models with process simulation to assess system performance and safety. Second, we implemented Bayesian optimization to navigate the multi-objective trade-offs. This method is well-suited for this problem due to its ability to efficiently handle high-dimensional, non-linear constrained optimization. Within this framework, the multi-objective optimization process simultaneously quantifies gas crossover dynamics for each set of evaluated parameters. The optimization results identify an optimal operational window, an operating pressure of 25–50 bar, a current density of 1.5–2.0 A/cm2, and a membrane thickness below 100 μm. This window provides a scientifically rigorous foundation for balancing economic and safety objectives in industrial-scale PEMEC applications.
作为清洁制氢的关键技术，质子交换膜电解电池（PEMEC）已经得到了广泛的工业应用。在PEMEC系统中实施高压操作可以提高氢气输出压力，从而减少下游运输的压缩需求和相关成本。然而，这种策略可能会带来与气体交叉相关的关键安全问题，特别是氢-氧（HTO）富集。为了系统地解决经济绩效和安全风险之间的内在权衡，本研究采用了一种新的基于贝叶斯优化的多目标优化框架。我们的方法旨在实现明确的系统级目标，保持HTO浓度约束低于2%的安全阈值，同时最小化氢的平准化成本（LCOH）和固有安全指数（ISI）。首先，我们将第一性原理单元模型与过程仿真相结合，建立了一个集成过程模型，以评估系统的性能和安全性。其次，我们实现了贝叶斯优化来导航多目标权衡。由于该方法能够有效地处理高维、非线性约束优化，因此非常适合于该问题。在此框架内，多目标优化过程同时量化了每组评估参数的气体交叉动力学。优化结果确定了最佳操作窗口，操作压力为25-50 bar，电流密度为1.5-2.0 a /cm2，膜厚度小于100 μm。这个窗口为平衡工业规模PEMEC应用的经济和安全目标提供了科学严谨的基础。
{"title":"Insight into safety and economic trade-offs for high-pressure PEMEC operation using Bayesian optimization","authors":"Jing Dang , Meng Qi , Jonggeol Na , Zijian Deng , Chi-Min Xu , Wei Feng , Yi Liu","doi":"10.1016/j.apenergy.2025.127345","DOIUrl":"10.1016/j.apenergy.2025.127345","url":null,"abstract":"<div><div>As a pivotal technology for clean hydrogen production, proton exchange membrane electrolysis cells (PEMEC) have garnered widespread industrial adoption. The implementation of high-pressure operation in PEMEC systems enhances hydrogen output pressure, thereby reducing compression demands and associated costs in downstream transportation. However, this strategy potentially introduces critical safety challenges associated with gas crossover, particularly hydrogen-in‑oxygen (HTO) enrichment. To systematically address the inherent trade-offs between economic performance and safety risks, this study employs a novel proposed multi-objective optimization framework based on Bayesian optimization. Our methodology is designed to achieve explicit system-level objectives, maintaining the HTO concentration constraint below the safety threshold of 2%, while concurrently minimizing the levelized cost of hydrogen (LCOH) and the inherent safety index (ISI). First, we established an integrated process model by combining first-principles unit models with process simulation to assess system performance and safety. Second, we implemented Bayesian optimization to navigate the multi-objective trade-offs. This method is well-suited for this problem due to its ability to efficiently handle high-dimensional, non-linear constrained optimization. Within this framework, the multi-objective optimization process simultaneously quantifies gas crossover dynamics for each set of evaluated parameters. The optimization results identify an optimal operational window, an operating pressure of 25–50 bar, a current density of 1.5–2.0 A/cm<sup>2</sup>, and a membrane thickness below 100 μm. This window provides a scientifically rigorous foundation for balancing economic and safety objectives in industrial-scale PEMEC applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127345"},"PeriodicalIF":11.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.apenergy.2026.127400
Ashwin Jacob , L. Jino , A. Anderson , V. Praveen , R. Sriabisha , J. Aravind Kumar
Amid dwindling fossil fuel reserves and an escalating climate crisis, the transition towards sustainable transportation has intensified, positioning electric vehicles as a viable alternative to internal combustion engine vehicles. However, the environmental benefits of EVs are contingent on the use of renewable energy for charging. While solar-powered EV charging presents a promising solution, existing research has largely overlooked the impact of solar panel technology selection on system efficiency, energy yield, and economic performance, particularly in the Indian context. This study addresses this gap by evaluating and comparing the performance of CIGS and CdTe thin-film solar panels for EV charging across six climatically diverse Indian cities. The study also involves simulating off-grid PV systems using PVsyst software to determine optimal configuration for these panels, considering their suitability for India's climatic conditions. Key performance indicators such as performance ratio (PR), panel sizing, annual and monthly energy generation, unused energy, investment cost, and CO2 emission reductions were assessed. Results show that 8.1 kWp systems for both technologies achieved peak PR with minimal energy wastage. CIGS systems produced 13,168 kWh annually, enabling the charging of 438 EVs and reducing CO2 emissions by 8150 kg per year, 2.7% more than CdTe. Economically, CIGS offer greater cost efficiency at ₹3.95 per kilometer, translating to saving of 3.8% and 7.7% over CdTe and gasoline-based transportation, respectively. This study underscores the critical role of solar module selection in optimizing EV infrastructure, contributing to both economic viability and environmental sustainability in alignment with Sustainable development goals 7 and 13.
{"title":"Assessment of CIGS and CdTe thin-walled solar photovoltaics as potential energy capture Systems for Electric-Vehicle Charging Stations: A comparative case study in India","authors":"Ashwin Jacob , L. Jino , A. Anderson , V. Praveen , R. Sriabisha , J. Aravind Kumar","doi":"10.1016/j.apenergy.2026.127400","DOIUrl":"10.1016/j.apenergy.2026.127400","url":null,"abstract":"<div><div>Amid dwindling fossil fuel reserves and an escalating climate crisis, the transition towards sustainable transportation has intensified, positioning electric vehicles as a viable alternative to internal combustion engine vehicles. However, the environmental benefits of EVs are contingent on the use of renewable energy for charging. While solar-powered EV charging presents a promising solution, existing research has largely overlooked the impact of solar panel technology selection on system efficiency, energy yield, and economic performance, particularly in the Indian context. This study addresses this gap by evaluating and comparing the performance of CIGS and CdTe thin-film solar panels for EV charging across six climatically diverse Indian cities. The study also involves simulating off-grid PV systems using PVsyst software to determine optimal configuration for these panels, considering their suitability for India's climatic conditions. Key performance indicators such as performance ratio (PR), panel sizing, annual and monthly energy generation, unused energy, investment cost, and CO<sub>2</sub> emission reductions were assessed. Results show that 8.1 kWp systems for both technologies achieved peak PR with minimal energy wastage. CIGS systems produced 13,168 kWh annually, enabling the charging of 438 EVs and reducing CO<sub>2</sub> emissions by 8150 kg per year, 2.7% more than CdTe. Economically, CIGS offer greater cost efficiency at ₹3.95 per kilometer, translating to saving of 3.8% and 7.7% over CdTe and gasoline-based transportation, respectively. This study underscores the critical role of solar module selection in optimizing EV infrastructure, contributing to both economic viability and environmental sustainability in alignment with Sustainable development goals 7 and 13.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127400"},"PeriodicalIF":11.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.apenergy.2026.127360
Yiyang Peng, Zhuowei Wang, Anthony Chen
Solar electric buses (SEBs), which install rooftop solar panels on battery electric buses (BEBs), harness photovoltaic (PV) electricity generated from solar radiation during in-motion and parking periods. The onboard PV system provides auxiliary power for SEBs in addition to the primary on-site charging. Particularly, varying PV gains greatly affect Li-ion battery consumption (LBC), requiring a schedule to avoid running out of Li-ion batteries while covering trips. Meanwhile, the schedule also affects PV gains in terms of parking periods and itinerary-specific deductions during in-motion processes. This study contributes to the vehicle scheduling problem (VSP) of SEBs, and a SEB-VSP model is developed on the basis of an underlying network, while quantitatively examining the operational benefits of installing solar panels. This model endogenously involves vehicle-trip assignment, varying PV gains, and on-site charging for Li-ion batteries. The branch-and-price algorithm is employed to solve this problem, wherein the pricing problem can be solved using a customized labeling algorithm. Heuristic approaches are applied over the branch-and-bound (B&B) tree to rapidly find high-quality integer solutions. The methodology is tested using real-world information on bus routes and solar radiation metrics in Hong Kong. The results show that more LBC is needed during the initial and end of operation periods. To collect more PV gains, SEBs favor serving trips with longer layovers in the scheduling phase. Additionally, the comparative findings underscore the operational benefits of adopting SEBs, mainly attributed to reduced charging detours.
{"title":"Benefiting from solar: Optimal scheduling for solar electric buses with onboard PV auxiliary power","authors":"Yiyang Peng, Zhuowei Wang, Anthony Chen","doi":"10.1016/j.apenergy.2026.127360","DOIUrl":"10.1016/j.apenergy.2026.127360","url":null,"abstract":"<div><div>Solar electric buses (SEBs), which install rooftop solar panels on battery electric buses (BEBs), harness photovoltaic (PV) electricity generated from solar radiation during in-motion and parking periods. The onboard PV system provides auxiliary power for SEBs in addition to the primary on-site charging. Particularly, varying PV gains greatly affect Li-ion battery consumption (LBC), requiring a schedule to avoid running out of Li-ion batteries while covering trips. Meanwhile, the schedule also affects PV gains in terms of parking periods and itinerary-specific deductions during in-motion processes. This study contributes to the vehicle scheduling problem (VSP) of SEBs, and a SEB-VSP model is developed on the basis of an underlying network, while quantitatively examining the operational benefits of installing solar panels. This model endogenously involves vehicle-trip assignment, varying PV gains, and on-site charging for Li-ion batteries. The branch-and-price algorithm is employed to solve this problem, wherein the pricing problem can be solved using a customized labeling algorithm. Heuristic approaches are applied over the branch-and-bound (B&B) tree to rapidly find high-quality integer solutions. The methodology is tested using real-world information on bus routes and solar radiation metrics in Hong Kong. The results show that more LBC is needed during the initial and end of operation periods. To collect more PV gains, SEBs favor serving trips with longer layovers in the scheduling phase. Additionally, the comparative findings underscore the operational benefits of adopting SEBs, mainly attributed to reduced charging detours.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127360"},"PeriodicalIF":11.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-20DOI: 10.1016/j.apenergy.2026.127427
Haonan Liu , Jiangong Zhu , Bin Shen , Wenyuan Weng , Wentao Xu , Yang Wang , Huapeng Lu , Wuliyasu He , Xiuwu Wang , Xuezhe Wei , Haifeng Dai
The detrimental lithium (Li) plating is widely recognized as a primary cause of capacity degradation and safety hazards in lithium-ion batteries (LIBs). However, inducing Li-plating under practical conditions is often unstable and poorly reproducible, making it difficult to systematically investigate the Li-plating mechanism. In this study, a controllable and reproducible Li-plating platform is established by designing LIBs with a controlled N/P ratio through adjusting the areal loading of graphite anodes, thereby enabling stable triggering of Li-plating under defined charge protocols. A Li4Ti5O12 (LTO) reference electrode is implanted to decouple the electrode voltages and directly interrogate the anode behavior associated with Li-plating. The N/P ratio controlled LIBs are evaluated under different charge rates and post-mortem analysis is conducted to further verify the associated degradation mechanism. The study examines the charge rate dependent evolution of Li-plating morphology and clarifies how the balance between reversible Li-stripping and irreversible Li accumulation shifts with current rate. It is found that a low current rate under overcharging conditions demonstrates high reversibility of Li-plating in the initial stages, but structural damage accumulates and capacity degradation becomes significant in the subsequent stages of cycling. Conversely, a high current rate under overcharging conditions leads to a rapid accumulation of “dead Li” and the simultaneous loss of active anode material from the early cycling, which accelerates the battery degradation. This study establishes a robust experimental framework for the controlled induction of Li-plating and provides valuable insights for the design of high-safety and durable LIBs.
{"title":"Investigation of lithium plating mechanism based on N/P ratio controlled lithium-ion batteries","authors":"Haonan Liu , Jiangong Zhu , Bin Shen , Wenyuan Weng , Wentao Xu , Yang Wang , Huapeng Lu , Wuliyasu He , Xiuwu Wang , Xuezhe Wei , Haifeng Dai","doi":"10.1016/j.apenergy.2026.127427","DOIUrl":"10.1016/j.apenergy.2026.127427","url":null,"abstract":"<div><div>The detrimental lithium (Li) plating is widely recognized as a primary cause of capacity degradation and safety hazards in lithium-ion batteries (LIBs). However, inducing Li-plating under practical conditions is often unstable and poorly reproducible, making it difficult to systematically investigate the Li-plating mechanism. In this study, a controllable and reproducible Li-plating platform is established by designing LIBs with a controlled N/P ratio through adjusting the areal loading of graphite anodes, thereby enabling stable triggering of Li-plating under defined charge protocols. A Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub> (LTO) reference electrode is implanted to decouple the electrode voltages and directly interrogate the anode behavior associated with Li-plating. The N/P ratio controlled LIBs are evaluated under different charge rates and post-mortem analysis is conducted to further verify the associated degradation mechanism. The study examines the charge rate dependent evolution of Li-plating morphology and clarifies how the balance between reversible Li-stripping and irreversible Li accumulation shifts with current rate. It is found that a low current rate under overcharging conditions demonstrates high reversibility of Li-plating in the initial stages, but structural damage accumulates and capacity degradation becomes significant in the subsequent stages of cycling. Conversely, a high current rate under overcharging conditions leads to a rapid accumulation of “dead Li” and the simultaneous loss of active anode material from the early cycling, which accelerates the battery degradation. This study establishes a robust experimental framework for the controlled induction of Li-plating and provides valuable insights for the design of high-safety and durable LIBs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"408 ","pages":"Article 127427"},"PeriodicalIF":11.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号