The development of hydropower reservoir operation rules is crucial for ensuring their efficient, stable operation and rapid response capabilities. This study proposes an interpretable DDPM-PCCSA-LNN reservoir operation rules framework to address issues such as inadequate scenario representativeness, time-consuming optimization, limited training data, and lack of interpretability in traditional methods. DDPM generates large-scale operation scenarios, PCCSA solves the corresponding optimization problem, and LNN extracts operational rules with SHAP interpreting key factors. The model is applied to the Hongjiadu and Three Gorges hydropower reservoirs and results show that operation rules simulated by proposed model are closer to the actual optimal operation process, and the power generation balances efficiency and sustainability. In the test scenarios of the Hongjiadu hydropower reservoir, compared with traditional model, proposed model achieves 15.3% to 20.3% improvement in SI, while average annual power generation can reach 99.1% to 99.3% of optimal operation model. Based on traditional models, this method adds modules for scenario simulation and interpretability analysis, improves the construction accuracy of operation rules, and provides a valuable technical approach for watershed management.
{"title":"Optimization of interpretable hydropower reservoir operation rules by denoising diffusion probabilistic model, parallel chaotic cooperation search algorithm and liquid neural network","authors":"Yi-fan Xia , Zhong-kai Feng , Tie-sheng Guan , Wen-jing Niu , Xin Yin , Yan-li Zheng","doi":"10.1016/j.energy.2026.140211","DOIUrl":"10.1016/j.energy.2026.140211","url":null,"abstract":"<div><div>The development of hydropower reservoir operation rules is crucial for ensuring their efficient, stable operation and rapid response capabilities. This study proposes an interpretable DDPM-PCCSA-LNN reservoir operation rules framework to address issues such as inadequate scenario representativeness, time-consuming optimization, limited training data, and lack of interpretability in traditional methods. DDPM generates large-scale operation scenarios, PCCSA solves the corresponding optimization problem, and LNN extracts operational rules with SHAP interpreting key factors. The model is applied to the Hongjiadu and Three Gorges hydropower reservoirs and results show that operation rules simulated by proposed model are closer to the actual optimal operation process, and the power generation balances efficiency and sustainability. In the test scenarios of the Hongjiadu hydropower reservoir, compared with traditional model, proposed model achieves 15.3% to 20.3% improvement in SI, while average annual power generation can reach 99.1% to 99.3% of optimal operation model. Based on traditional models, this method adds modules for scenario simulation and interpretability analysis, improves the construction accuracy of operation rules, and provides a valuable technical approach for watershed management.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140211"},"PeriodicalIF":9.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186948","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 : 2026-02-03DOI: 10.1016/j.energy.2026.140314
Jiahao Zou, Zhaocai Wang, Zhaoyang Zhu, Zuowen Tan
Photovoltaic power generation (PVPG) is susceptible to meteorological conditions, exhibiting significant randomness and volatility. Therefore, accurate and reliable PVPG prediction is crucial for enhancing grid stability. However, existing data-driven prediction methods often overlook the system's inherent physical mechanism, which can lead to prediction results that violate actual operating laws. This study presents a physics-constrained hybrid model, integrating Transformer and Long Short-Term Memory (LSTM) networks with a secondary decomposition strategy, for the multi-step short-term forecasting of PVPG. Initially, a Seasonal and Trend Decomposition using Loess (STL) method is utilized to decompose the original dataset. Subsequently, variational mode decomposition (VMD), optimized by an improved Dream Optimization Algorithm (DOA), is utilized to decompose the residual term. Subsequently, the decomposed components and the screened features are fed into a hybrid Transformer-LSTM model, with its hyperparameter optimized by an improved Dream Optimization Algorithm, to complete the final power prediction. To ensure the predictions adhere to the physical principles of photovoltaic power generation, the model utilizes a designed physics-constrained loss function specifically. On the Australian dataset, the proposed model is evaluated and is observed to achieve better performance than other methods in both prediction accuracy and robustness. Specifically, on Site 1, the R-squared and RMSE for the overall prediction performance are 0.9423 and 0.2326, respectively, demonstrating superior prediction performance. Moreover, it also exhibits superior prediction capability across different datasets, seasons, and weather conditions. Finally, explainability analysis was conducted using SHAP method. This multi-step short-term PVPG prediction method has the potential to enhance grid stability and the stable regulation of energy.
{"title":"Explainable and physics-constrained PV power prediction via a hybrid framework Integrating secondary decomposition and improved Transformer-LSTM","authors":"Jiahao Zou, Zhaocai Wang, Zhaoyang Zhu, Zuowen Tan","doi":"10.1016/j.energy.2026.140314","DOIUrl":"10.1016/j.energy.2026.140314","url":null,"abstract":"<div><div>Photovoltaic power generation (PVPG) is susceptible to meteorological conditions, exhibiting significant randomness and volatility. Therefore, accurate and reliable PVPG prediction is crucial for enhancing grid stability. However, existing data-driven prediction methods often overlook the system's inherent physical mechanism, which can lead to prediction results that violate actual operating laws. This study presents a physics-constrained hybrid model, integrating Transformer and Long Short-Term Memory (LSTM) networks with a secondary decomposition strategy, for the multi-step short-term forecasting of PVPG. Initially, a Seasonal and Trend Decomposition using Loess (STL) method is utilized to decompose the original dataset. Subsequently, variational mode decomposition (VMD), optimized by an improved Dream Optimization Algorithm (DOA), is utilized to decompose the residual term. Subsequently, the decomposed components and the screened features are fed into a hybrid Transformer-LSTM model, with its hyperparameter optimized by an improved Dream Optimization Algorithm, to complete the final power prediction. To ensure the predictions adhere to the physical principles of photovoltaic power generation, the model utilizes a designed physics-constrained loss function specifically. On the Australian dataset, the proposed model is evaluated and is observed to achieve better performance than other methods in both prediction accuracy and robustness. Specifically, on Site 1, the R-squared and RMSE for the overall prediction performance are 0.9423 and 0.2326, respectively, demonstrating superior prediction performance. Moreover, it also exhibits superior prediction capability across different datasets, seasons, and weather conditions. Finally, explainability analysis was conducted using SHAP method. This multi-step short-term PVPG prediction method has the potential to enhance grid stability and the stable regulation of energy.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140314"},"PeriodicalIF":9.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187721","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 : 2026-02-03DOI: 10.1016/j.energy.2026.140308
Peihao Chen , Yan Zhang , Saeed Harati , Sara Walker , Karl Dearn
Wave and tidal energy are promising renewable resources for offshore electricity generation, with hydrogen serving as a storable and transportable energy carrier. This study presents an integrated offshore hydrogen production system combining full-scale hybrid wave-tidal energy converters (HWTEC), hybrid supercapacitor-battery energy storage system, proton exchange membrane (PEM) electrolyzers, and subsea underground hydrogen storage (UHS). A system-level co-simulation framework is developed to capture the coupled dynamics of energy conversion, storage, and hydrogen production under stochastic marine conditions. UHS significantly reduces platform space requirements for hydrogen storage, enabling higher on-platform hydrogen capacity. A case study using 2024 UK wave and tidal data evaluates a conceptual platform with six HWTECs and PEM electrolyzers with combined average output of 64.8 kW. Results indicate a representative hydrogen production rate of 1.4 kg/h and an estimated annual yield of 12.4 t, with specific energy consumption of 46.8–55.7 kWh/kgH2 and exergy efficiency of 21.4–25.3%. The system demonstrates enhanced power continuity, efficient conversion of intermittent offshore energy, and feasibility for grid-independent operation. The proposed framework advances beyond previous device-level studies by integrating multiple subsystems with real marine inputs, providing a scalable and practical tool for design, optimization, and performance assessment of offshore hybrid renewable hydrogen platforms.
{"title":"From ocean motion to green fuel: Integration of hybrid wave-tidal energy and offshore hydrogen production","authors":"Peihao Chen , Yan Zhang , Saeed Harati , Sara Walker , Karl Dearn","doi":"10.1016/j.energy.2026.140308","DOIUrl":"10.1016/j.energy.2026.140308","url":null,"abstract":"<div><div>Wave and tidal energy are promising renewable resources for offshore electricity generation, with hydrogen serving as a storable and transportable energy carrier. This study presents an integrated offshore hydrogen production system combining full-scale hybrid wave-tidal energy converters (HWTEC), hybrid supercapacitor-battery energy storage system, proton exchange membrane (PEM) electrolyzers, and subsea underground hydrogen storage (UHS). A system-level co-simulation framework is developed to capture the coupled dynamics of energy conversion, storage, and hydrogen production under stochastic marine conditions. UHS significantly reduces platform space requirements for hydrogen storage, enabling higher on-platform hydrogen capacity. A case study using 2024 UK wave and tidal data evaluates a conceptual platform with six HWTECs and PEM electrolyzers with combined average output of 64.8 kW. Results indicate a representative hydrogen production rate of 1.4 kg/h and an estimated annual yield of 12.4 t, with specific energy consumption of 46.8–55.7 kWh/kgH<sub>2</sub> and exergy efficiency of 21.4–25.3%. The system demonstrates enhanced power continuity, efficient conversion of intermittent offshore energy, and feasibility for grid-independent operation. The proposed framework advances beyond previous device-level studies by integrating multiple subsystems with real marine inputs, providing a scalable and practical tool for design, optimization, and performance assessment of offshore hybrid renewable hydrogen platforms.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140308"},"PeriodicalIF":9.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187595","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 : 2026-02-03DOI: 10.1016/j.energy.2026.140313
Jeremias E. Castro , Andreas V. Olympios , Asmaa A. Harraz , Bryce S. Richards , Jingyuan Xu
The growing adoption of solar energy in the residential sector plays a pivotal role in advancing sustainable energy practices, reducing carbon dioxide emissions, and enhancing energy independence. This study examines a solar combined cooling, heating, and power (S-CCHP) system incorporating photovoltaic–thermal (PVT) technology and assesses its performance alongside alternative photovoltaic (PV) and solar thermal (ST) configurations. A transient model is developed, together with economic and environmental assessments, to simulate overall energy performance, including the use of thermal energy from the PVT system to support summer cooling via a diffusion absorption refrigeration (DAR) cycle without using electricity during summer months. All system configurations are analysed under different layouts, both with and without battery storage. As a case study, the system is designed for application in Berlin, Germany, and the results show that the PVT-based system can supply 68% of domestic hot water demand and 48% of appliance electricity use, but only 12% of space heating due to the limited temperature output of the PVT collectors. Importantly, while the DAR system achieves full coverage of space cooling demand in summer, it relies heavily on auxiliary thermal energy input, underscoring a key area for system improvement. The economic analysis indicates net present values of approximately €7800 for PVT, €11,300 for ST, and €23,600 for PV, with corresponding payback periods of 21.0, 16.5, and 6.9 years. In terms of environmental performance, the PVT-based system achieves the highest carbon dioxide emission reduction at 2658 kg/year, followed by the PV (1904 kg/year) and ST (1781 kg/year) systems. The sensitivity analysis highlights the critical role of battery integration, especially under high grid electricity prices. In conclusion, the PVT-based S-CCHP system demonstrates strong economic and environmental potential in urban environments, while the DAR integration offers a compelling pathway for electricity-free cooling, revealing significant opportunities for optimisation and future development.
{"title":"Techno-economic-environmental analysis of a PVT-based solar combined cooling, heating, and power system","authors":"Jeremias E. Castro , Andreas V. Olympios , Asmaa A. Harraz , Bryce S. Richards , Jingyuan Xu","doi":"10.1016/j.energy.2026.140313","DOIUrl":"10.1016/j.energy.2026.140313","url":null,"abstract":"<div><div>The growing adoption of solar energy in the residential sector plays a pivotal role in advancing sustainable energy practices, reducing carbon dioxide emissions, and enhancing energy independence. This study examines a solar combined cooling, heating, and power (S-CCHP) system incorporating photovoltaic–thermal (PVT) technology and assesses its performance alongside alternative photovoltaic (PV) and solar thermal (ST) configurations. A transient model is developed, together with economic and environmental assessments, to simulate overall energy performance, including the use of thermal energy from the PVT system to support summer cooling via a diffusion absorption refrigeration (DAR) cycle without using electricity during summer months. All system configurations are analysed under different layouts, both with and without battery storage. As a case study, the system is designed for application in Berlin, Germany, and the results show that the PVT-based system can supply 68% of domestic hot water demand and 48% of appliance electricity use, but only 12% of space heating due to the limited temperature output of the PVT collectors. Importantly, while the DAR system achieves full coverage of space cooling demand in summer, it relies heavily on auxiliary thermal energy input, underscoring a key area for system improvement. The economic analysis indicates net present values of approximately €7800 for PVT, €11,300 for ST, and €23,600 for PV, with corresponding payback periods of 21.0, 16.5, and 6.9 years. In terms of environmental performance, the PVT-based system achieves the highest carbon dioxide emission reduction at 2658 kg/year, followed by the PV (1904 kg/year) and ST (1781 kg/year) systems. The sensitivity analysis highlights the critical role of battery integration, especially under high grid electricity prices. In conclusion, the PVT-based S-CCHP system demonstrates strong economic and environmental potential in urban environments, while the DAR integration offers a compelling pathway for electricity-free cooling, revealing significant opportunities for optimisation and future development.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140313"},"PeriodicalIF":9.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187606","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 : 2026-02-03DOI: 10.1016/j.energy.2026.140310
Xinyu Zhuang , Yuliang Su , Wendong Wang , Bo Zhang
CO2 geological sequestration in saline aquifers shows significant potential for carbon reduction. However, predicting wellbore behavior during CO2 injection or leakage is complex due to flow and heat transfer mechanisms with dual temporal characteristics, both spatial distribution across depth and temporal evolution. In addition, current prediction approaches typically exhibit a singular focus on isolated sequence data, failing to account for the fundamental physical continuity inherent in depth and time series relationships. This study presents a hybrid deep learning framework, the Dual-Temporal Dual Attention Network, to predict wellbore transient temperature, pressure, and CO2 phase behavior during injection and leakage processes. A mathematical model incorporating phase transitions and thermal-hydraulic coupling mechanisms was established to characterize multiphase flow and heat transfer within wellbores. To address the dual temporal characteristics, the framework uses masked self-attention for depth sequences and temporal pattern attention for time series to capture local features, while Temporal Fusion Transformer extracts global dependencies. Model validation across representative injection and leakage scenarios substantiates exceptional predictive performance, with maximum relative errors rigorously maintained within 8% and 6% for pressure predictions, and 3% and 4% for temperature predictions, respectively. Result reveals that phase state is initially governed by axial pressure, later shifting to temperature dependence. Throughout leakage events, CO2 undergoes complex phase transitions which can amplify leakage rates through volumetric expansion. The framework provides theoretical support for CO2 sequestration risk assessment and safety management through efficient wellbore condition prediction across diverse scenarios.
{"title":"Dual-temporal prediction of wellbore condition and phase behavior during transient CO2 injection and leakage in saline aquifers","authors":"Xinyu Zhuang , Yuliang Su , Wendong Wang , Bo Zhang","doi":"10.1016/j.energy.2026.140310","DOIUrl":"10.1016/j.energy.2026.140310","url":null,"abstract":"<div><div>CO<sub>2</sub> geological sequestration in saline aquifers shows significant potential for carbon reduction. However, predicting wellbore behavior during CO<sub>2</sub> injection or leakage is complex due to flow and heat transfer mechanisms with dual temporal characteristics, both spatial distribution across depth and temporal evolution. In addition, current prediction approaches typically exhibit a singular focus on isolated sequence data, failing to account for the fundamental physical continuity inherent in depth and time series relationships. This study presents a hybrid deep learning framework, the Dual-Temporal Dual Attention Network, to predict wellbore transient temperature, pressure, and CO<sub>2</sub> phase behavior during injection and leakage processes. A mathematical model incorporating phase transitions and thermal-hydraulic coupling mechanisms was established to characterize multiphase flow and heat transfer within wellbores. To address the dual temporal characteristics, the framework uses masked self-attention for depth sequences and temporal pattern attention for time series to capture local features, while Temporal Fusion Transformer extracts global dependencies. Model validation across representative injection and leakage scenarios substantiates exceptional predictive performance, with maximum relative errors rigorously maintained within 8% and 6% for pressure predictions, and 3% and 4% for temperature predictions, respectively. Result reveals that phase state is initially governed by axial pressure, later shifting to temperature dependence. Throughout leakage events, CO<sub>2</sub> undergoes complex phase transitions which can amplify leakage rates through volumetric expansion. The framework provides theoretical support for CO<sub>2</sub> sequestration risk assessment and safety management through efficient wellbore condition prediction across diverse scenarios.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140310"},"PeriodicalIF":9.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187718","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 : 2026-02-02DOI: 10.1016/j.energy.2026.140176
Zhenyang Guo , Xiaxia Xiang , Yeming Lu , Xiaofang Wang , Weijun Wang
The CAP1400 passive pressurized water reactor unit stands as the largest third-generation nuclear power facility of its kind developed in China. To elucidate the influence of piping and key equipment within the nuclear island's primary loop system on the operational characteristics of the CAP1400 reactor coolant pump (RCP), this research established a three-dimensional numerical simulation approach for the nuclear island's primary loop, based on the source term methodology. By introducing source terms and porous media models to simplify the steam generator and reactor pressure vessel, the computational bottleneck associated with simulating large-scale nuclear island full loops was effectively mitigated, thereby achieving a compromise between computational efficiency and precision. The study contrasted the hydraulic performance, pressure fluctuation, hydraulic excitation forces, entropy production distribution, and SPOD modal information of key RCP components under two operational scenarios: independent operation (RCP-I) and operation within the piping configuration system (RCP-C). The findings indicate that: (1) The simulation of the nuclear island primary-side full loop has been successfully implemented employing the source term and porous medium approach, yielding high accuracy and aligning well with experimental data. (2) Compared to the independent operation condition, the RCP within the piping configuration system exhibits a slight head increase of 0.21 m; however, its efficiency declines markedly by 2.64%, accompanied by an escalation in internal energy loss. (3) Entropy production analysis reveals that the significant increase in energy loss predominantly originates from the vane region and the inlet region, with augmentations of 77.0% and 55.4%, respectively. (4) Further examination of the SPOD modes indicates that the piping configuration prematurely induces flow separation at the impeller leading edge and exacerbates backflow in the vane outlet region, which constitutes the primary cause of the pump's overall performance deterioration and the rise in internal energy loss. This research is anticipated to offer technical support for the simulation and assessment of large-scale nuclear islands.
{"title":"Quantitative analysis of piping configuration effects on the hydraulic performance of the CAP1400 reactor coolant pump's core components via source term and modal decomposition methods","authors":"Zhenyang Guo , Xiaxia Xiang , Yeming Lu , Xiaofang Wang , Weijun Wang","doi":"10.1016/j.energy.2026.140176","DOIUrl":"10.1016/j.energy.2026.140176","url":null,"abstract":"<div><div>The CAP1400 passive pressurized water reactor unit stands as the largest third-generation nuclear power facility of its kind developed in China. To elucidate the influence of piping and key equipment within the nuclear island's primary loop system on the operational characteristics of the CAP1400 reactor coolant pump (RCP), this research established a three-dimensional numerical simulation approach for the nuclear island's primary loop, based on the source term methodology. By introducing source terms and porous media models to simplify the steam generator and reactor pressure vessel, the computational bottleneck associated with simulating large-scale nuclear island full loops was effectively mitigated, thereby achieving a compromise between computational efficiency and precision. The study contrasted the hydraulic performance, pressure fluctuation, hydraulic excitation forces, entropy production distribution, and SPOD modal information of key RCP components under two operational scenarios: independent operation (RCP-I) and operation within the piping configuration system (RCP-C). The findings indicate that: (1) The simulation of the nuclear island primary-side full loop has been successfully implemented employing the source term and porous medium approach, yielding high accuracy and aligning well with experimental data. (2) Compared to the independent operation condition, the RCP within the piping configuration system exhibits a slight head increase of 0.21 m; however, its efficiency declines markedly by 2.64%, accompanied by an escalation in internal energy loss. (3) Entropy production analysis reveals that the significant increase in energy loss predominantly originates from the vane region and the inlet region, with augmentations of 77.0% and 55.4%, respectively. (4) Further examination of the SPOD modes indicates that the piping configuration prematurely induces flow separation at the impeller leading edge and exacerbates backflow in the vane outlet region, which constitutes the primary cause of the pump's overall performance deterioration and the rise in internal energy loss. This research is anticipated to offer technical support for the simulation and assessment of large-scale nuclear islands.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140176"},"PeriodicalIF":9.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187593","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}
Lithium-ion battery (LIB) fires in branched tunnels present complex safety challenges due to confined geometry and limited ventilation, yet their thermal runaway (TR) behaviour remains insufficiently understood. This study investigates the influence of state of charge (SOC) and trigger cell position on TR characteristics of lithium iron phosphate (LFP) cells and battery modules under tunnel-like conditions. A series of controlled experiments was conducted in a scaled branched tunnel using single LFP cell and battery module arranged in 3 × 3 cells. TR was initiated at different SOC levels and module positions (#5, #7, #8), while ceiling temperature and radiation heat flux were monitored using thermocouples and thermal radiometer.
Results reveal distinct TR dynamics that the single-cell TR exhibits a single peak temperature stage, whereas module TR produces multiple peaks accompanied by intermittent jet flames, significantly prolonging event duration. Maximum ceiling temperature for modules exceeds that of single cells, with deeper trigger positions amplifying thermal severity. SOC strongly influences thermal response, with ceiling temperature growth rates reaching 94% for single-cell TR and 44% for module TR. Radiation heat flux increases with SOC and is highest when the trigger cell is located deeper within the module. A predictive model for maximum ceiling temperature and longitudinal temperature decay is proposed for both single-cell and module TR scenarios. These findings enhance understanding of LIB fire behaviour in complex tunnel environments and provide actionable insights for tunnel ventilation design, emergency response planning, and battery safety standards.
{"title":"Effect of trigger cell position on thermal runaway propagation in lithium-ion battery modules within branched tunnels","authors":"Youbo Huang , Chao Xiang , Fei Tang , Bingyan Dong , Xiaolin Yao , Hua Zhong","doi":"10.1016/j.energy.2026.140185","DOIUrl":"10.1016/j.energy.2026.140185","url":null,"abstract":"<div><div>Lithium-ion battery (LIB) fires in branched tunnels present complex safety challenges due to confined geometry and limited ventilation, yet their thermal runaway (TR) behaviour remains insufficiently understood. This study investigates the influence of state of charge (SOC) and trigger cell position on TR characteristics of lithium iron phosphate (LFP) cells and battery modules under tunnel-like conditions. A series of controlled experiments was conducted in a scaled branched tunnel using single LFP cell and battery module arranged in 3 × 3 cells. TR was initiated at different SOC levels and module positions (#5, #7, #8), while ceiling temperature and radiation heat flux were monitored using thermocouples and thermal radiometer.</div><div>Results reveal distinct TR dynamics that the single-cell TR exhibits a single peak temperature stage, whereas module TR produces multiple peaks accompanied by intermittent jet flames, significantly prolonging event duration. Maximum ceiling temperature for modules exceeds that of single cells, with deeper trigger positions amplifying thermal severity. SOC strongly influences thermal response, with ceiling temperature growth rates reaching 94% for single-cell TR and 44% for module TR. Radiation heat flux increases with SOC and is highest when the trigger cell is located deeper within the module. A predictive model for maximum ceiling temperature and longitudinal temperature decay is proposed for both single-cell and module TR scenarios. These findings enhance understanding of LIB fire behaviour in complex tunnel environments and provide actionable insights for tunnel ventilation design, emergency response planning, and battery safety standards.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140185"},"PeriodicalIF":9.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187729","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 : 2026-02-02DOI: 10.1016/j.energy.2026.140299
Santi Bardeeniz , Chayanit Chuay-ock , David Shan-Hill Wong , Yuan Yao , Jia-Lin Kang , Chanin Panjapornpon
Effective steam management supports cost control and carbon abatement in industrial processes. However, steam monitoring in industrial records often exhibits mixed sampling intervals. The mismatch in time interval creates a limited-data problem that conventional energy models often struggle to handle. Therefore, a model-agnostic meta-learning framework integrated with an attention-based long short-term memory network is proposed for steam-consumption prediction under limited-data conditions. Meta-training on related high-frequency source units learns shared attention parameters and enables rapid adaptation to a low-frequency target unit without requiring synthetic data generation. The performance of steam consumption prediction is validated using a large-scale case study of the crude glycerin purification process. The results demonstrate that the attention-based long short-term memory model outperforms traditional models with the highest coefficient of determination value (R2) of 0.772. The incorporation of meta-learning further enhances the prediction performance of the model, with a decrease in the prediction error from 168.891 to 123.777 kg/h and an improvement in R2 of 0.847. Furthermore, the energy-saving analysis indicates the reduction in annual steam consumption and greenhouse gas emissions of 4372.304 (11.63% reduction) and 613.815 tons, respectively.
{"title":"Mixed-interval steam consumption modeling for industrial energy optimization via meta-learning through shared attention","authors":"Santi Bardeeniz , Chayanit Chuay-ock , David Shan-Hill Wong , Yuan Yao , Jia-Lin Kang , Chanin Panjapornpon","doi":"10.1016/j.energy.2026.140299","DOIUrl":"10.1016/j.energy.2026.140299","url":null,"abstract":"<div><div>Effective steam management supports cost control and carbon abatement in industrial processes. However, steam monitoring in industrial records often exhibits mixed sampling intervals. The mismatch in time interval creates a limited-data problem that conventional energy models often struggle to handle. Therefore, a model-agnostic meta-learning framework integrated with an attention-based long short-term memory network is proposed for steam-consumption prediction under limited-data conditions. Meta-training on related high-frequency source units learns shared attention parameters and enables rapid adaptation to a low-frequency target unit without requiring synthetic data generation. The performance of steam consumption prediction is validated using a large-scale case study of the crude glycerin purification process. The results demonstrate that the attention-based long short-term memory model outperforms traditional models with the highest coefficient of determination value (R<sup>2</sup>) of 0.772. The incorporation of meta-learning further enhances the prediction performance of the model, with a decrease in the prediction error from 168.891 to 123.777 kg/h and an improvement in R<sup>2</sup> of 0.847. Furthermore, the energy-saving analysis indicates the reduction in annual steam consumption and greenhouse gas emissions of 4372.304 (11.63% reduction) and 613.815 tons, respectively.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140299"},"PeriodicalIF":9.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187248","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 : 2026-02-02DOI: 10.1016/j.energy.2025.139800
Yan Cao , Zongyou Zhang , Yilei Chen , Sheng Cheng
Climate-related physical and transition risks have become key forces shaping cross-market linkages and systemic vulnerabilities during the global shift from fossil to clean energy. This study examines how the two types of climate risk, both independently and jointly, drive multi-moment spillovers between fossil and clean energy markets and amplify systemic risk. Using returns, volatility, skewness, and kurtosis extracted from the GJRSK model, a higher-order moment connectedness network is constructed within a TVP-VAR framework, and spillovers are decomposed into within- and cross-moment as well as within- and cross-category dimensions. Nonlinear causality tests, together with wavelet and partial wavelet coherence analyses, are further employed to assess the time-frequency effects of physical and transition risks, while allowing for their interaction. The results show that systemic risk diffusion is primarily driven by cross-moment spillovers, with kurtosis and volatility playing a key role. Clean energy markets exhibit stronger internal connectedness than fossil energy markets, while cross-category spillovers primarily flow from fossil to clean energy. Physical and transition risks interact significantly, and transition risk persistently strengthens cross-moment and cross-category connectedness at longer horizons. In contrast, the independent effect of physical risk becomes weak once transition risk is controlled for. These findings disentangle the cross-moment and cross-category mechanisms through which PRI-TRI interactions reshape energy-market spillovers and amplify systemic fragility during the transition.
{"title":"Systemic risk spillovers between fossil and clean energy under climate risks: New evidence from a multi-moment connectedness network","authors":"Yan Cao , Zongyou Zhang , Yilei Chen , Sheng Cheng","doi":"10.1016/j.energy.2025.139800","DOIUrl":"10.1016/j.energy.2025.139800","url":null,"abstract":"<div><div>Climate-related physical and transition risks have become key forces shaping cross-market linkages and systemic vulnerabilities during the global shift from fossil to clean energy. This study examines how the two types of climate risk, both independently and jointly, drive multi-moment spillovers between fossil and clean energy markets and amplify systemic risk. Using returns, volatility, skewness, and kurtosis extracted from the GJRSK model, a higher-order moment connectedness network is constructed within a TVP-VAR framework, and spillovers are decomposed into within- and cross-moment as well as within- and cross-category dimensions. Nonlinear causality tests, together with wavelet and partial wavelet coherence analyses, are further employed to assess the time-frequency effects of physical and transition risks, while allowing for their interaction. The results show that systemic risk diffusion is primarily driven by cross-moment spillovers, with kurtosis and volatility playing a key role. Clean energy markets exhibit stronger internal connectedness than fossil energy markets, while cross-category spillovers primarily flow from fossil to clean energy. Physical and transition risks interact significantly, and transition risk persistently strengthens cross-moment and cross-category connectedness at longer horizons. In contrast, the independent effect of physical risk becomes weak once transition risk is controlled for. These findings disentangle the cross-moment and cross-category mechanisms through which PRI-TRI interactions reshape energy-market spillovers and amplify systemic fragility during the transition.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 139800"},"PeriodicalIF":9.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187284","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 : 2026-02-02DOI: 10.1016/j.energy.2026.140071
Na Lu , Jinyu Wei , Yaoxi Liu , Xin Yang
Renewable energy substitution, along with carbon capture and utilization, represents crucial strategies for China in its pursuit of achieving carbon peaking and carbon neutrality goals. In this context, this paper contracts an evolutionary game framework including the government (GOVT), coal-fired power plants (CPPs) and renewable energy power plants (REPPs) with the Hotelling model, to elucidate the influence of critical factors on the low-carbon transition of power plants. The results are as follows: (1) carbon tax phase-in policy would be better than a static carbon tax policy; (2) The government should set an appropriate subsidy phase-out rate; (3) For the government and CPPs, when the lifetime of coal-fired generating units is in a high level, the carbon tax and the proportion of CPPs’ carbon tax payment are in a low level, the evolution trajectory of the government and CPPs will be in a more stable situation; REPPs can evolve to an energy storage strategy more rapidly in a low level subsidy phase-out rate, at the same time, the price of hydrogen of the REPPs sold to the outside enterprises should be in a low level, which can prevent the system from evolving to the ideal state.
{"title":"An evolutionary game analysis about the low-carbon transition of power plants considering the carbon tax","authors":"Na Lu , Jinyu Wei , Yaoxi Liu , Xin Yang","doi":"10.1016/j.energy.2026.140071","DOIUrl":"10.1016/j.energy.2026.140071","url":null,"abstract":"<div><div>Renewable energy substitution, along with carbon capture and utilization, represents crucial strategies for China in its pursuit of achieving carbon peaking and carbon neutrality goals. In this context, this paper contracts an evolutionary game framework including the government (GOVT), coal-fired power plants (CPPs) and renewable energy power plants (REPPs) with the Hotelling model, to elucidate the influence of critical factors on the low-carbon transition of power plants. The results are as follows: (1) carbon tax phase-in policy would be better than a static carbon tax policy; (2) The government should set an appropriate subsidy phase-out rate; (3) For the government and CPPs, when the lifetime of coal-fired generating units is in a high level, the carbon tax and the proportion of CPPs’ carbon tax payment are in a low level, the evolution trajectory of the government and CPPs will be in a more stable situation; REPPs can evolve to an energy storage strategy more rapidly in a low level subsidy phase-out rate, at the same time, the price of hydrogen of the REPPs sold to the outside enterprises should be in a low level, which can prevent the system from evolving to the ideal state.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"347 ","pages":"Article 140071"},"PeriodicalIF":9.4,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186949","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}
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