Pub Date : 2025-03-04DOI: 10.1016/j.apenergy.2025.125485
Xiao Liu, Sinan Li, Cuo Zhang, Meng Liu, Jianguo Zhu
In contrast to the traditional peer-to-grid (P2G) market, the emerging decentralized peer-to-peer-to-grid (P2P2G) trading can generate enormous potential to reduce the overall operational costs of microgrids (MGs) further. However, it is challenging to incorporate this decentralized market framework directly into the distribution network (DN) trading framework to account for mutual benefits holistically, impeding progress toward future smart electricity markets. This paper proposes an online non-iterative method based on data-driven multi-agent deep reinforcement learning. The decentralized P2P2G trading framework is formulated as partially observable Markov games (POMGs) to consider mutual benefits efficiently and make it compatible for DN operations. It is further integrated with a novel adaptive margin update (AMU) method to protect DN's topology information and return differential rewards to improve training efficiency and operation safety. Comprehensive numerical simulations on a modified IEEE test system demonstrate the superiority of the proposed method, outperforming other data-driven algorithms and a model-based optimization approach in smart electricity market applications.
{"title":"Holistic mutual benefits aware P2P2G market among microgrids in a distribution network: A decentralized data-driven approach","authors":"Xiao Liu, Sinan Li, Cuo Zhang, Meng Liu, Jianguo Zhu","doi":"10.1016/j.apenergy.2025.125485","DOIUrl":"10.1016/j.apenergy.2025.125485","url":null,"abstract":"<div><div>In contrast to the traditional peer-to-grid (P2G) market, the emerging decentralized peer-to-peer-to-grid (P2P2G) trading can generate enormous potential to reduce the overall operational costs of microgrids (MGs) further. However, it is challenging to incorporate this decentralized market framework directly into the distribution network (DN) trading framework to account for mutual benefits holistically, impeding progress toward future smart electricity markets. This paper proposes an online non-iterative method based on data-driven multi-agent deep reinforcement learning. The decentralized P2P2G trading framework is formulated as partially observable Markov games (POMGs) to consider mutual benefits efficiently and make it compatible for DN operations. It is further integrated with a novel adaptive margin update (AMU) method to protect DN's topology information and return differential rewards to improve training efficiency and operation safety. Comprehensive numerical simulations on a modified IEEE test system demonstrate the superiority of the proposed method, outperforming other data-driven algorithms and a model-based optimization approach in smart electricity market applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125485"},"PeriodicalIF":10.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1016/j.apenergy.2025.125568
Yi Zhang , Yanji Hao , Yu Fu , Yijing Feng , Yeqing Li , Xiaonan Wang , Junting Pan , Yongming Han , Chunming Xu
Data-driven machine learning (ML) has the potential to improve biomass energy production methods such as incineration, composting, pyrolysis, and anaerobic digestion. However, due to the scarcity and variability of data in the field, there is currently no universal model that excels across all production technique domains. To address these challenges, this study combines Model-Agnostic Meta-Learning (MAML) with Generative Adversarial Networks (GANs) to improve ML generalization in complex biomass conversion scenarios. Compared to the best ML models, the GAN-MAML models demonstrated superior performance in various domains and scales. During the testing phase, the GAN-MAML models mitigated the limitations associated with data scarcity and variability, improving performance by up to 33.1 % over the best ML models. This represents a significant improvement over the initial increase of up to 28.2 % for the MAML models. Subsequently, models trained on literature data were deployed in a real energy production factory and predicted samples they had never seen before. The results showed that the GAN-MAML models outperformed the best ML models, with the highest improvement being 28.6 %. This is a significant improvement over traditional ML and offers a flexible framework for research and practice in biomass energy production, promoting sustainable environmental solutions.
{"title":"GAN-MAML strategy for biomass energy production: Overcoming small dataset limitations","authors":"Yi Zhang , Yanji Hao , Yu Fu , Yijing Feng , Yeqing Li , Xiaonan Wang , Junting Pan , Yongming Han , Chunming Xu","doi":"10.1016/j.apenergy.2025.125568","DOIUrl":"10.1016/j.apenergy.2025.125568","url":null,"abstract":"<div><div>Data-driven machine learning (ML) has the potential to improve biomass energy production methods such as incineration, composting, pyrolysis, and anaerobic digestion. However, due to the scarcity and variability of data in the field, there is currently no universal model that excels across all production technique domains. To address these challenges, this study combines Model-Agnostic Meta-Learning (MAML) with Generative Adversarial Networks (GANs) to improve ML generalization in complex biomass conversion scenarios. Compared to the best ML models, the GAN-MAML models demonstrated superior performance in various domains and scales. During the testing phase, the GAN-MAML models mitigated the limitations associated with data scarcity and variability, improving performance by up to 33.1 % over the best ML models. This represents a significant improvement over the initial increase of up to 28.2 % for the MAML models. Subsequently, models trained on literature data were deployed in a real energy production factory and predicted samples they had never seen before. The results showed that the GAN-MAML models outperformed the best ML models, with the highest improvement being 28.6 %. This is a significant improvement over traditional ML and offers a flexible framework for research and practice in biomass energy production, promoting sustainable environmental solutions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125568"},"PeriodicalIF":10.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550365","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 : 2025-03-04DOI: 10.1016/j.apenergy.2025.125600
Chengliang Xu , Shiao Chen , Haoshan Ren , Chen Xu , Guannan Li , Tao Li , Yongjun Sun
Accurately assessing building solar potential is becoming increasingly important for sustainable urban development. However, evaluating the solar energy potential of building facades in urban areas poses significant challenges due to complex shading from surrounding structures and a lack of detailed facade information. This study proposes a comprehensive framework for assessing the solar PV potential of urban facades by integrating deep learning and geographic information systems (GIS). GIS was used to extract information about the layouts and heights of buildings, while a deep learning-based approach was developed to identify the window-to-wall ratio (WWR) of various building facades from street view images. To validate the proposed methodology, a region in Wuhan with a diverse range of architectural features was selected. The solar energy potential was estimated by combining facade information with shadow analysis. Additionally, a solar irradiance measurement experiment was conducted to verify the findings. The results revealed that a lack of WWR information for building facades can lead to significant overestimations of their solar energy potential, with errors ranging from 15 % to 50 %. Moreover, using standardized WWRs in the assessment can still result in errors between 3 % and 20 %. These discrepancies primarily stem from differences between actual and assumed WWRs used in the calculations. Further analysis shows that accurately assessing the solar energy potential of facades in various orientations requires considering both WWR data and shading effects. This comprehensive approach can be employed to more accurately characterize the solar energy potential of building facades in urban areas, facilitating the broader adoption of solar energy at the city scale.
{"title":"A novel deep learning and GIS integrated method for accurate city-scale assessment of building facade solar energy potential","authors":"Chengliang Xu , Shiao Chen , Haoshan Ren , Chen Xu , Guannan Li , Tao Li , Yongjun Sun","doi":"10.1016/j.apenergy.2025.125600","DOIUrl":"10.1016/j.apenergy.2025.125600","url":null,"abstract":"<div><div>Accurately assessing building solar potential is becoming increasingly important for sustainable urban development. However, evaluating the solar energy potential of building facades in urban areas poses significant challenges due to complex shading from surrounding structures and a lack of detailed facade information. This study proposes a comprehensive framework for assessing the solar PV potential of urban facades by integrating deep learning and geographic information systems (GIS). GIS was used to extract information about the layouts and heights of buildings, while a deep learning-based approach was developed to identify the window-to-wall ratio (WWR) of various building facades from street view images. To validate the proposed methodology, a region in Wuhan with a diverse range of architectural features was selected. The solar energy potential was estimated by combining facade information with shadow analysis. Additionally, a solar irradiance measurement experiment was conducted to verify the findings. The results revealed that a lack of WWR information for building facades can lead to significant overestimations of their solar energy potential, with errors ranging from 15 % to 50 %. Moreover, using standardized WWRs in the assessment can still result in errors between 3 % and 20 %. These discrepancies primarily stem from differences between actual and assumed WWRs used in the calculations. Further analysis shows that accurately assessing the solar energy potential of facades in various orientations requires considering both WWR data and shading effects. This comprehensive approach can be employed to more accurately characterize the solar energy potential of building facades in urban areas, facilitating the broader adoption of solar energy at the city scale.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125600"},"PeriodicalIF":10.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-04DOI: 10.1016/j.apenergy.2025.125609
Tong Ye , Yuping Huang , Weijia Yang , Guotian Cai , Yuyao Yang , Feng Pan
Due to fundamental differences in operational entities between distribution networks and microgrids, the equitable allocation of carbon responsibilities remains challenging. Furthermore, achieving real-time, efficient, and secure low-carbon economic dispatch in decentralized multi-entities continues to face obstacles. Therefore, we propose a co-optimization framework for Active Distribution Networks (ADNs) and multi-Microgrids (MMGs) to improve operational efficiency and reduce carbon emissions through adaptive coordination and decision-making. To facilitate decentralized low-carbon decision-making, we introduce the Spatiotemporal Carbon Intensity Equalization Method (STCIEM). This method ensures privacy and fairness by processing local data and equitably distributing carbon responsibilities. Additionally, we propose a non-cooperative optimization strategy that enables entities to optimize their operations independently while considering both economic and environmental interests. To address the challenges of real-time decision-making and the non-convex nature of low-carbon optimization inherent in traditional approaches, we have developed the Enhanced Action Projection Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (EAP-MATD3) algorithm. This algorithm enhances the actor's objective to address the actor-critic mismatch problem, thereby outperforming conventional safe multi-agent deep reinforcement learning methods by generating optimized actions that adhere to physical system constraints. Experiments conducted on the modified IEEE 33-bus network and IEEE 123-bus network demonstrate the superiority of our approach in effectively balancing economic and environmental objectives within complex energy systems.
{"title":"Safe multi-agent deep reinforcement learning for decentralized low-carbon operation in active distribution networks and multi-microgrids","authors":"Tong Ye , Yuping Huang , Weijia Yang , Guotian Cai , Yuyao Yang , Feng Pan","doi":"10.1016/j.apenergy.2025.125609","DOIUrl":"10.1016/j.apenergy.2025.125609","url":null,"abstract":"<div><div>Due to fundamental differences in operational entities between distribution networks and microgrids, the equitable allocation of carbon responsibilities remains challenging. Furthermore, achieving real-time, efficient, and secure low-carbon economic dispatch in decentralized multi-entities continues to face obstacles. Therefore, we propose a co-optimization framework for Active Distribution Networks (ADNs) and multi-Microgrids (MMGs) to improve operational efficiency and reduce carbon emissions through adaptive coordination and decision-making. To facilitate decentralized low-carbon decision-making, we introduce the Spatiotemporal Carbon Intensity Equalization Method (STCIEM). This method ensures privacy and fairness by processing local data and equitably distributing carbon responsibilities. Additionally, we propose a non-cooperative optimization strategy that enables entities to optimize their operations independently while considering both economic and environmental interests. To address the challenges of real-time decision-making and the non-convex nature of low-carbon optimization inherent in traditional approaches, we have developed the Enhanced Action Projection Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (EAP-MATD3) algorithm. This algorithm enhances the actor's objective to address the actor-critic mismatch problem, thereby outperforming conventional safe multi-agent deep reinforcement learning methods by generating optimized actions that adhere to physical system constraints. Experiments conducted on the modified IEEE 33-bus network and IEEE 123-bus network demonstrate the superiority of our approach in effectively balancing economic and environmental objectives within complex energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125609"},"PeriodicalIF":10.1,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550366","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 : 2025-03-03DOI: 10.1016/j.apenergy.2025.125554
Bennett Platt, Derek Young, Todd Bandhauer
Thermally activated chillers, like absorption and organic Rankine vapor compression (ORVC) systems, are solutions to improve efficiency and meet decarbonization goals in the heating, ventilation, and air-conditioning (HVAC) industry. However, technical limitations prevent these chillers from providing steady cooling power under variable operating parameters. This work evaluated an electrified ORVC system that can address the limitations of thermally activated chillers, by utilizing both thermal and electric input. Three different configurations (one with parallel compressors and two with series compressors) were evaluated using coupled thermodynamic and turbomachinery analysis. The highest performing configuration (series with the thermal compressor first) was simulated at 175 kW scale under industry standard operating conditions, and across a range of parameter studies to characterize off design performance. Simulation results indicated efficient performance, with compression load being shifted between the thermally and electrically driven compressors. With the compression load balanced, the thermal COP was 0.69 and the electric COP was 10.1 at design conditions. Simulations showed a wide operating range, with acceptable heat input ranging from 100 kW – 327 kW in hybrid operation, in addition to purely electric or thermal operation. Parametric results also indicated large operating ranges for heat supply inlet temperature (85 °C – 117 °C), chilled water delivery temperature (2.1 °C – 10.7 °C), and heat rejection inlet temperature (26.6 °C – 30.9 °C). Turbomachinery analysis indicated a mismatch between the thermal and electric devices, which impacted the performance of the system. Simulations with a properly sized electric device increased the capacity to 268.3 kW, highlighting the importance of turbomachinery analysis for this technology.
{"title":"Thermodynamic and turbomachinery analysis of a hybrid electric organic Rankine vapor compression system","authors":"Bennett Platt, Derek Young, Todd Bandhauer","doi":"10.1016/j.apenergy.2025.125554","DOIUrl":"10.1016/j.apenergy.2025.125554","url":null,"abstract":"<div><div>Thermally activated chillers, like absorption and organic Rankine vapor compression (ORVC) systems, are solutions to improve efficiency and meet decarbonization goals in the heating, ventilation, and air-conditioning (HVAC) industry. However, technical limitations prevent these chillers from providing steady cooling power under variable operating parameters. This work evaluated an electrified ORVC system that can address the limitations of thermally activated chillers, by utilizing both thermal and electric input. Three different configurations (one with parallel compressors and two with series compressors) were evaluated using coupled thermodynamic and turbomachinery analysis. The highest performing configuration (series with the thermal compressor first) was simulated at 175 kW scale under industry standard operating conditions, and across a range of parameter studies to characterize off design performance. Simulation results indicated efficient performance, with compression load being shifted between the thermally and electrically driven compressors. With the compression load balanced, the thermal COP was 0.69 and the electric COP was 10.1 at design conditions. Simulations showed a wide operating range, with acceptable heat input ranging from 100 kW – 327 kW in hybrid operation, in addition to purely electric or thermal operation. Parametric results also indicated large operating ranges for heat supply inlet temperature (85 °C – 117 °C), chilled water delivery temperature (2.1 °C – 10.7 °C), and heat rejection inlet temperature (26.6 °C – 30.9 °C). Turbomachinery analysis indicated a mismatch between the thermal and electric devices, which impacted the performance of the system. Simulations with a properly sized electric device increased the capacity to 268.3 kW, highlighting the importance of turbomachinery analysis for this technology.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125554"},"PeriodicalIF":10.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529106","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 : 2025-03-03DOI: 10.1016/j.apenergy.2025.125624
Zhaoming Yang , Zhiwei Zhao , Qi Xiang , Zixin Li , Jingjing Hu , Shiliang Peng , Dingyu Jiao , Yiwei Xie , Huai Su , Enrico Zio , Michael H. Faber , Laibin Zhang , Jinjun Zhang
The natural gas pipeline system (NGPS) is a crucial component of the natural gas supply chain. Due to the complexity of its external environment, disturbances, topological and hydraulic characteristics, it is essential to study NGPS disaster mechanisms from a systems science perspective. Self-organized criticality (SOC), a widely used theory in systems science, has been applied to many network systems, but its application to NGPS is still in the early stages. One challenge is the lack of sufficient data on disturbance effects, which limits the study of SOC in NGPS. To address this, we propose a data augmentation model based on the Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to overcome the shortage of gas transportation data. By augmenting practical data from open datasets in various ways, the results show that the CTGAN-based approach can capture key characteristics of gas transportation data, and the data size is from 333 to 18,545. Additionally, the augmented data reveal an atypical form of SOC in NGPS. The results show that there is a local range of gas transportation parameters where the NGPS has SOC characteristics, and the mean value of R2 is 0.92. However, the NGPS do not fit SOC characteristics in the whole range of gas transportation, where the mean value of R2 is 0.45. To further understand this atypical SOC, graph theory and network analysis are applied to divide the affected areas of NGPS, quantifying the disaster mechanism by measuring the propagation scale of disturbances. The results of this study offer a novel perspective by shifting the focus from the global system to specific affected areas, which is particularly useful for analyzing NGPS vulnerability, reliability, and resilience.
{"title":"Self-organized criticality study in natural gas pipeline systems: A system & data science approach","authors":"Zhaoming Yang , Zhiwei Zhao , Qi Xiang , Zixin Li , Jingjing Hu , Shiliang Peng , Dingyu Jiao , Yiwei Xie , Huai Su , Enrico Zio , Michael H. Faber , Laibin Zhang , Jinjun Zhang","doi":"10.1016/j.apenergy.2025.125624","DOIUrl":"10.1016/j.apenergy.2025.125624","url":null,"abstract":"<div><div>The natural gas pipeline system (NGPS) is a crucial component of the natural gas supply chain. Due to the complexity of its external environment, disturbances, topological and hydraulic characteristics, it is essential to study NGPS disaster mechanisms from a systems science perspective. Self-organized criticality (SOC), a widely used theory in systems science, has been applied to many network systems, but its application to NGPS is still in the early stages. One challenge is the lack of sufficient data on disturbance effects, which limits the study of SOC in NGPS. To address this, we propose a data augmentation model based on the Conditional Tabular Generative Adversarial Network (CTGAN) algorithm to overcome the shortage of gas transportation data. By augmenting practical data from open datasets in various ways, the results show that the CTGAN-based approach can capture key characteristics of gas transportation data, and the data size is from 333 to 18,545. Additionally, the augmented data reveal an atypical form of SOC in NGPS. The results show that there is a local range of gas transportation parameters where the NGPS has SOC characteristics, and the mean value of <em>R</em><sup>2</sup> is 0.92. However, the NGPS do not fit SOC characteristics in the whole range of gas transportation, where the mean value of <em>R</em><sup>2</sup> is 0.45. To further understand this atypical SOC, graph theory and network analysis are applied to divide the affected areas of NGPS, quantifying the disaster mechanism by measuring the propagation scale of disturbances. The results of this study offer a novel perspective by shifting the focus from the global system to specific affected areas, which is particularly useful for analyzing NGPS vulnerability, reliability, and resilience.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125624"},"PeriodicalIF":10.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550360","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 : 2025-03-03DOI: 10.1016/j.apenergy.2025.125636
Xin Fan , Y. Frank Cheng
Pipelines represent the most economical and efficient means for transporting hydrogen in large volumes across vast distances, contributing to accelerated realization of hydrogen economy. Nowadays, the development of hydrogen pipeline projects, including repurposing existing pipelines for hydrogen service, has become a global interest, especially in those major energy-producing and energy-consuming countries. However, steel pipelines are susceptible to hydrogen embrittlement (HE) in high-pressure hydrogen gas environments, potentially leading to pipeline failures. In this review, we establish a comprehensive knowledge base for comprehending, testing, and evaluating the gaseous HE in pipelines by a thorough examination of relevant research work. In addition to an overview of some major hydrogen pipeline projects in the world, the article consists of four integral parts essential to gaseous HE studies, namely, methods for exposure of steels to high-pressure hydrogen gas; measurements of the quantity of H atoms inside the steels; stress-strain behavior of pipeline steels under high-pressure hydrogen gas exposure; and fracture and fatigue testing of pre-cracked steels within gaseous environments. Further research into gaseous HE in pipelines focuses on developing standardized, quantitative, and consistent methods to assess and define the susceptibility of pipelines to gaseous HE.
{"title":"Hydrogen pipelines and embrittlement in gaseous environments: An up-to-date review","authors":"Xin Fan , Y. Frank Cheng","doi":"10.1016/j.apenergy.2025.125636","DOIUrl":"10.1016/j.apenergy.2025.125636","url":null,"abstract":"<div><div>Pipelines represent the most economical and efficient means for transporting hydrogen in large volumes across vast distances, contributing to accelerated realization of hydrogen economy. Nowadays, the development of hydrogen pipeline projects, including repurposing existing pipelines for hydrogen service, has become a global interest, especially in those major energy-producing and energy-consuming countries. However, steel pipelines are susceptible to hydrogen embrittlement (HE) in high-pressure hydrogen gas environments, potentially leading to pipeline failures. In this review, we establish a comprehensive knowledge base for comprehending, testing, and evaluating the gaseous HE in pipelines by a thorough examination of relevant research work. In addition to an overview of some major hydrogen pipeline projects in the world, the article consists of four integral parts essential to gaseous HE studies, namely, methods for exposure of steels to high-pressure hydrogen gas; measurements of the quantity of H atoms inside the steels; stress-strain behavior of pipeline steels under high-pressure hydrogen gas exposure; and fracture and fatigue testing of pre-cracked steels within gaseous environments. Further research into gaseous HE in pipelines focuses on developing standardized, quantitative, and consistent methods to assess and define the susceptibility of pipelines to gaseous HE.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125636"},"PeriodicalIF":10.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.apenergy.2025.125616
Hongjun Tan , Zhiling Guo , Yuntian Chen , Haoran Zhang , Chenchen Song , Mingkun Jiang , Jinyue Yan
Urban land utilization for commerce, residence, grassland, and other administrative subdivisions will affect the available area for renewable infrastructure setup, such as photovoltaic (PV) panels. Incorporating land use types into PV potential assessments is essential for optimizing space allocation, aligning with energy demand centers, and enhancing efficiency. To address the limitations of previous studies that overlook urban land use, this study introduces a framework leveraging remote sensing data and deep learning methods to achieve eight fine-grained and three coarse-grained land use classifications. The framework calculates the PV installation area for each land use type and evaluates their power generation potential based on the yearly average solar irradiance in 2023. Case studies demonstrate that Germany Heilbronn land is suitable for ground PV installations, with a power generation of 5333.85 GWh/year, and rooftop PV installations are the most productive for electricity generation in New Zealand Christchurch, with 3290.08 GWh/year. Unutilized land in Heilbronn and Commercial land in Christchurch is estimated to be the most productive per unit area. Finally, the uncertainty of the PV installation ratio by adopting and the confidence interval of potential estimation is discussed. This work experiments with the framework successfully and highlights the effects of the PV installation ratio on the power generation of each land use, providing valuable instructions for urban land utilization and PV installation.
{"title":"PV potential analysis through deep learning and remote sensing-based urban land classification","authors":"Hongjun Tan , Zhiling Guo , Yuntian Chen , Haoran Zhang , Chenchen Song , Mingkun Jiang , Jinyue Yan","doi":"10.1016/j.apenergy.2025.125616","DOIUrl":"10.1016/j.apenergy.2025.125616","url":null,"abstract":"<div><div>Urban land utilization for commerce, residence, grassland, and other administrative subdivisions will affect the available area for renewable infrastructure setup, such as photovoltaic (PV) panels. Incorporating land use types into PV potential assessments is essential for optimizing space allocation, aligning with energy demand centers, and enhancing efficiency. To address the limitations of previous studies that overlook urban land use, this study introduces a framework leveraging remote sensing data and deep learning methods to achieve eight fine-grained and three coarse-grained land use classifications. The framework calculates the PV installation area for each land use type and evaluates their power generation potential based on the yearly average solar irradiance in 2023. Case studies demonstrate that Germany Heilbronn land is suitable for ground PV installations, with a power generation of 5333.85 GWh/year, and rooftop PV installations are the most productive for electricity generation in New Zealand Christchurch, with 3290.08 GWh/year. Unutilized land in Heilbronn and Commercial land in Christchurch is estimated to be the most productive per unit area. Finally, the uncertainty of the PV installation ratio by adopting <span><math><msub><mi>σ</mi><mi>i</mi></msub></math></span> and the confidence interval of potential estimation is discussed. This work experiments with the framework successfully and highlights the effects of the PV installation ratio on the power generation of each land use, providing valuable instructions for urban land utilization and PV installation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125616"},"PeriodicalIF":10.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.apenergy.2025.125576
Nelson James, Ransisi Huang, Jason Woods, Eric Kozubal
Low-cost energy storage will play an important role in supporting the decarbonization of the energy sector. A novel approach to thermal energy storage for buildings is proposed, in which a tank of antifreeze liquid can be used to heat outdoor air in mechanical ventilation systems. If heated to a high enough temperature, the fluid can potentially undergo temperature swings of nearly 100 °C during discharge in cold climates. Because the energy density of sensible storage systems scales proportionally to the system temperature change, this concept has the potential to offer higher energy densities than other liquid-based sensible storage devices used in building applications. A one-dimensional numerical model of the storage system was developed and experimentally validated using 30 wt% (wt%) potassium acetate as an aqueous antifreeze solution. Using a commercial hot water tank, energy densities of 47.0 kW-hour per cubic meter (kWh/m3) were demonstrated in a laboratory setting. Material energy densities of approximately 74.7 (kWh/m3) were measured. Design improvements may boost this energy storage density even further. Because of the system's relative simplicity, ventilation-coupled sensible storage has the potential to be an easily deployable, low-cost energy storage solution for building systems.
{"title":"Investigation of ventilation-coupled high energy density sensible thermal energy storage","authors":"Nelson James, Ransisi Huang, Jason Woods, Eric Kozubal","doi":"10.1016/j.apenergy.2025.125576","DOIUrl":"10.1016/j.apenergy.2025.125576","url":null,"abstract":"<div><div>Low-cost energy storage will play an important role in supporting the decarbonization of the energy sector. A novel approach to thermal energy storage for buildings is proposed, in which a tank of antifreeze liquid can be used to heat outdoor air in mechanical ventilation systems. If heated to a high enough temperature, the fluid can potentially undergo temperature swings of nearly 100 °C during discharge in cold climates. Because the energy density of sensible storage systems scales proportionally to the system temperature change, this concept has the potential to offer higher energy densities than other liquid-based sensible storage devices used in building applications. A one-dimensional numerical model of the storage system was developed and experimentally validated using 30 wt% (wt%) potassium acetate as an aqueous antifreeze solution. Using a commercial hot water tank, energy densities of 47.0 kW-hour per cubic meter (kWh/m<sup>3</sup>) were demonstrated in a laboratory setting. Material energy densities of approximately 74.7 (kWh/m<sup>3</sup>) were measured. Design improvements may boost this energy storage density even further. Because of the system's relative simplicity, ventilation-coupled sensible storage has the potential to be an easily deployable, low-cost energy storage solution for building systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125576"},"PeriodicalIF":10.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521244","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 : 2025-03-01DOI: 10.1016/j.apenergy.2025.125592
Mingda Sun, Cheng Bao, Xingyu Lu
Mixed ionic electronic conductors (MIEC) ceramic solid electrolytes have been widely used in solid oxide fuel cells (SOFCs) and electrolysis cells (SOECs). Existing full-cell models are typically difficult to capture the oxygen chemical potential (OCP) profile in electrodes and have poor generality. However, the OCP transition by overpotential is closely related to both degradation near the electrodes of SOECs and the drop in open-circuit voltage (OCV) of SOFCs. Based on phenomenological equations, we develop a unified mathematical framework for charge transport in solid oxide cells (SOCs). Our model quantifies the OCP in electrodes by directly relating the local OCP to the electrode overpotential, thus providing insights into the relationship between overpotential and OCP. Our results show that there is a ubiquitous OCP transition in the electrodes and that is extremely sensitive to electrode overpotentials. The oxygen potential peaks and valleys found in the Yttria-stabilized Zirconia (YSZ) of the multilayer electrolyte SOEC provide new opportunities to understand degradation phenomena in the relevant literature. In addition, our predictions of leakage current and OCV illustrate the generality of the model for cell performance pre diction. The results may offer new methods and insights for predicting the performance and understanding the degradation mechanisms of SOCs with MIEC electrolytes or multilayer electrolytes.
{"title":"Modeling of solid oxide cells with mixed ionic electronic conductor electrolytes using a unified electrochemical potential model","authors":"Mingda Sun, Cheng Bao, Xingyu Lu","doi":"10.1016/j.apenergy.2025.125592","DOIUrl":"10.1016/j.apenergy.2025.125592","url":null,"abstract":"<div><div>Mixed ionic electronic conductors (MIEC) ceramic solid electrolytes have been widely used in solid oxide fuel cells (SOFCs) and electrolysis cells (SOECs). Existing full-cell models are typically difficult to capture the oxygen chemical potential (OCP) profile in electrodes and have poor generality. However, the OCP transition by overpotential is closely related to both degradation near the electrodes of SOECs and the drop in open-circuit voltage (OCV) of SOFCs. Based on phenomenological equations, we develop a unified mathematical framework for charge transport in solid oxide cells (SOCs). Our model quantifies the OCP in electrodes by directly relating the local OCP to the electrode overpotential, thus providing insights into the relationship between overpotential and OCP. Our results show that there is a ubiquitous OCP transition in the electrodes and that is extremely sensitive to electrode overpotentials. The oxygen potential peaks and valleys found in the Yttria-stabilized Zirconia (YSZ) of the multilayer electrolyte SOEC provide new opportunities to understand degradation phenomena in the relevant literature. In addition, our predictions of leakage current and OCV illustrate the generality of the model for cell performance pre diction. The results may offer new methods and insights for predicting the performance and understanding the degradation mechanisms of SOCs with MIEC electrolytes or multilayer electrolytes.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"387 ","pages":"Article 125592"},"PeriodicalIF":10.1,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521245","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