Advances in Applied Energy最新文献

{"title":"Enhancing building energy efficiency with thermal mass optimization","authors":"Yichen Han,&nbsp;Zhengyu He,&nbsp;Shuangdui Wu,&nbsp;Yuqiu Liu,&nbsp;Yingkai Lian,&nbsp;Chaohong Wang,&nbsp;Jiajia Feng,&nbsp;Zhengnan Zhou","doi":"10.1016/j.adapen.2025.100224","DOIUrl":"10.1016/j.adapen.2025.100224","url":null,"abstract":"<div><div>High-density urban buildings contain substantial thermal mass, storing significant energy and offering notable potential for heating energy savings. However, effectively harnessing this energy remains challenging due to the spatiotemporal variability of heat storage–release behavior in building components, which often misaligns with building operational demands. This study reveals that thermal mass tends to store heat when it is not needed and release it when buildings do not require it, especially in cities where some buildings are only occupied during the day and others at night. To address these challenges, this study proposes a novel thermal mass arrangement strategy, derived from extensive real-world data analysis. Significant variations in component thermal behavior across different operational schedules were first identified from data collected in 76 rooms. Subsequently, key factors influencing these variations were pinpointed using stepwise linear regression, informing optimization strategies developed through simulations. These strategies were then validated in cold regions using conduction transfer function models (error margin of 3.6 %), which confirmed their year-round effectiveness for both individual buildings with distinct occupancy patterns and groups of buildings. The results demonstrate that optimizing thermal mass arrangements tailored to specific building schedules can significantly enhance energy efficiency. Contrary to prior research advocating for the sole increase in thermal mass, this study indicates that without strategic guidelines, such measures may exacerbate thermal utilization inefficiencies, complementing existing research on thermal storage materials in buildings. Reducing excess heat storage is shown to be beneficial for daytime-use buildings, while nighttime-use buildings benefit from storing heat for evening use. Adjusting the quantity and orientation of thermal mass, alongside optimizing operational schedules, achieves 4–12 % energy savings, with greater benefits in high-solar-radiation areas.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"18 ","pages":"Article 100224"},"PeriodicalIF":13.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global sensitivity analysis towards non-invasive parameterization of the electrochemical-thermal model for lithium-ion batteries","authors":"Jue Chen ,&nbsp;Sven Patrick Mattus ,&nbsp;Wenjiong Cao ,&nbsp;Dirk Uwe Sauer ,&nbsp;Weihan Li","doi":"10.1016/j.adapen.2025.100221","DOIUrl":"10.1016/j.adapen.2025.100221","url":null,"abstract":"<div><div>High-fidelity electrochemical-thermal models are essential for performance improvement, charge/discharge strategy optimization, and the safe operation of lithium-ion batteries. However, model performance significantly relies on the accuracy of parameters, whose measurement is limited by laboratory conditions. Non-invasive methods based on relatively accessible current, voltage, and temperature data combined with artificial intelligence are promising for rapid parameterization of battery models. However, the model’s complexity and the data’s poor quality increase the difficulty of applying the methodology. To design a reasonable identification framework and obtain reliable data, the identifiability of model parameters must be analyzed under different operating conditions. This paper develops an identifiability analysis framework to investigate the impact of model parameters on voltage and temperature outputs and the impact of key operating variables, i.e., current rate and ambient temperature. By adjusting operating conditions, the sensitivity of specific parameters can be improved by two orders of magnitude. The results are discussed in detail concerning the model modeling mechanism and the physical meaning of the parameters, with a focus on improving non-invasive parameterization in terms of experimental design and identification strategy.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"18 ","pages":"Article 100221"},"PeriodicalIF":13.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144098373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current challenges in nano-engineered biomass valorization: A comprehensive review from the whole procedure of biomass fermentation perspective","authors":"Zi-Tong Zhao ,&nbsp;Jie Ding ,&nbsp;Geng Luo ,&nbsp;Bo-Yuan Wang ,&nbsp;Han-Jun Sun ,&nbsp;Bing-Feng Liu ,&nbsp;Guang-Li Cao ,&nbsp;Mei-Yi Bao ,&nbsp;Nan-Qi Ren ,&nbsp;Ji-Wei Pang ,&nbsp;Shan-Shan Yang","doi":"10.1016/j.adapen.2025.100219","DOIUrl":"10.1016/j.adapen.2025.100219","url":null,"abstract":"<div><div>Dark fermentation has been widely regarded and appraised as an efficient and green route for biohydrogen production. Lignocellulosic biomass is a readily available and abundant feedstock that could be used as a sustainable feedstock for biohydrogen generation. However, low yield of biohydrogen is an inherent issue of the bioprocess restricting its further development towards commercial margins. Recently, the supplement of nano-additives has aroused more attention as a process improvement strategy because of their ability to accelerate process performance and their strengths of low energy consumption and easy operation. Nevertheless, the utilization of nanomaterials for biomass fermentation is still in its infancy. Here we review and evaluate the feasibility of nanotechnology in each procedure of biomass to biohydrogen to improve the economic feasibility of the process. Numerous aspects such as the possibility of utilizing nanomaterials as an alternative to chemical pretreatment techniques have been highlighted in this review. Additionally, the effect of these nanostructured materials (e.g., metal-based nanoparticles, nanocomposites, and graphene-based nanomaterials) on biohydrogen fermentation and the potential functional mechanisms were also analyzed in detail. Moreover, the assessment on how the immobilized nanoparticles affect enzymatic efficiency and how well they can block inhibitory chemicals were elaborated. Further, the sustainability of biomass fermentation was assessed in terms of science economics as well as carbon neutrality to improve the overall benefits of the process. Finally, the review suggests ways in which the nano-engineered bioprocesses might be improved, as well as suggested avenues for further research.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"18 ","pages":"Article 100219"},"PeriodicalIF":13.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrifying aviation: Innovations and challenges in airport electrification for sustainable flight","authors":"Martin Lindberg,&nbsp;Jennifer Leijon","doi":"10.1016/j.adapen.2025.100222","DOIUrl":"10.1016/j.adapen.2025.100222","url":null,"abstract":"<div><div>The aviation sector is investigating opportunities to reduce pollution and to limit the dependence on fossil fuels. The design of new electric and hybrid aircraft requires airport developments to meet the need for charging. This review article provides an overview of recent developments and the latest research and innovation on electrification at and around airports. The paper describes technical innovations in electrified aviation, sustainable aviation fuels, and hydrogen, and the infrastructure needed at airports to meet the future electricity demand of electric aircraft charging. This study finds that plug-in charging of future electric aircraft will lead to elevated fluctuations in electric power demand at airports, while battery swapping has a more constant electricity demand. The review reveals a significant interest in energy storage and renewable energy systems to supply electricity and mitigate peak power at airports, suggesting high potential for batteries and solar power. Hydrogen for airport energy storage could support electric aircraft charging and be used as a fuel for hydrogen-powered aircraft. More research is needed regarding the optimal configuration of airport infrastructure to support electric aircraft development.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"18 ","pages":"Article 100222"},"PeriodicalIF":13.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of scalable and privacy-preserving multi-agent frameworks for distributed energy resources","authors":"Xiang Huo ,&nbsp;Hao Huang ,&nbsp;Katherine R. Davis ,&nbsp;H. Vincent Poor ,&nbsp;Mingxi Liu","doi":"10.1016/j.adapen.2024.100205","DOIUrl":"10.1016/j.adapen.2024.100205","url":null,"abstract":"<div><div>Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental question restrains the management of numerous DERs in large-scale power systems, “<em>How should DER data be securely processed and DER operations be efficiently optimized?</em>” To address this question, this paper considers two critical issues, namely <em>privacy</em> for <em>processing DER data</em> and <em>scalability</em> in <em>optimizing DER operations</em>, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber–physical systems.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100205"},"PeriodicalIF":13.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic reliability assessment framework for integrated energy systems: A new methodology to address cascading failures","authors":"Lidian Niu ,&nbsp;Zeyan Zhao ,&nbsp;Jiawei Tan ,&nbsp;Tao Liang ,&nbsp;Fuzheng Zhang ,&nbsp;Ning Xiao ,&nbsp;Yi He ,&nbsp;Shan Xie ,&nbsp;Rui Jing ,&nbsp;Jian Lin ,&nbsp;Feng Wang ,&nbsp;Yingru Zhao","doi":"10.1016/j.adapen.2024.100203","DOIUrl":"10.1016/j.adapen.2024.100203","url":null,"abstract":"<div><div>As the energy internet and integrated energy systems develop, the interconnections among different systems increase operational risks, highlighting the need for urgent reliability research. Recent large-scale blackouts, often caused by cascading failures, reveal that current reliability assessments frequently overlook dynamic equipment conditions and the risk of such failures. Traditional model-driven methods for single energy systems are becoming inadequate due to rapid operational changes. To address these challenges, this study proposes a reliability assessment method for integrated energy systems that considers equipment operational states and cascading failures. It introduces an equipment reliability model for simulating cascading failures due to equipment overloads after initial failures. A hybrid data-model driven approach is proposed to improve the efficiency of load reduction calculations. Then the reliability evaluation is realized by combining the analysis of system energy flow state and index calculation. The modified model simulates more failure events than conventional model and the reliability level reflected by the calculated index is lower than that of the conventional model assessment by 25.39 % to 179.13 %. Evaluation time is reduced by 98.10 % while maintaining an average relative error within 6 %. The subsystem reliability level increases by 69.72 % and decreases by 2.25 % depending on the coupling degree. Failures of less than 20 % of all fault types contributed 43.34 % to 69.59 % of the load reduction. In summary, this model effectively simulates cascading failures from changes in operating states and provides a rapid, accurate reflection of system reliability.Based on this method, the reliability influencing factors can be analyzed and the weak link can be identified.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100203"},"PeriodicalIF":13.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy","authors":"Kechuan Dong ,&nbsp;Qing Yu ,&nbsp;Zhiling Guo ,&nbsp;Jian Xu ,&nbsp;Hongjun Tan ,&nbsp;Haoran Zhang ,&nbsp;Jinyue Yan","doi":"10.1016/j.adapen.2025.100212","DOIUrl":"10.1016/j.adapen.2025.100212","url":null,"abstract":"<div><div>The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100212"},"PeriodicalIF":13.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic viability and CO2 emissions of hydrogen production for ammonia synthesis: A comparative analysis across Europe","authors":"Alessandro Magnino,&nbsp;Paolo Marocco,&nbsp;Massimo Santarelli,&nbsp;Marta Gandiglio","doi":"10.1016/j.adapen.2024.100204","DOIUrl":"10.1016/j.adapen.2024.100204","url":null,"abstract":"<div><div>Ammonia production accounts for 15–20% of greenhouse gas emissions from the chemical sector. Traditionally, ammonia is produced via Steam Methane Reforming (SMR) for hydrogen production, coupled with the Haber-Bosch process. This study compares the SMR-based configuration with emerging alternatives based on water electrolysis – Proton Exchange Membrane Electrolyser Cell (PEMEC) and Solid Oxide Electrolyser Cell (SOEC) – from both economic and CO₂ emissions perspective. Process models for the three plant layouts are developed, incorporating heat integration between different components. The economic results are presented in terms of the levelised cost of ammonia, which accounts for both capital and operating expenses over the plant's lifetime. Sensitivity analyses on electricity and methane prices are conducted to assess the cost-competitiveness of each technology across various scenarios. The outcomes reveal that the optimal technology is highly dependent on electricity prices. PEMEC systems are the most cost-effective option at very low electricity prices (approximately 0.02 €/kWh_e), while SOEC systems become more competitive as prices rise due to their higher efficiency. Above 0.08 €/kWh_e, SMR emerges as the most viable option. Special attention is given to the CO₂ emissions from both SMR and electrolyser systems, also considering the carbon intensity of the electricity used. While electrolysis is often assumed to be carbon-free, this research shows that electrolysers can produce more emissions than SMR, depending on the electricity carbon intensity: when carbon intensity exceeds about 260 gCO₂/kWh_e, SMR results in lower emissions than the electrolyser-based pathways. Finally, future projections suggest that SOEC technology will become highly cost-competitive by 2030–2040.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100204"},"PeriodicalIF":13.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements and future outlook of Artificial Intelligence in energy and climate change modeling","authors":"Mobolaji Shobanke,&nbsp;Mehul Bhatt,&nbsp;Ekundayo Shittu","doi":"10.1016/j.adapen.2025.100211","DOIUrl":"10.1016/j.adapen.2025.100211","url":null,"abstract":"<div><div>This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, <em>i.e.</em>, through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100211"},"PeriodicalIF":13.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal load shifting for truly clean computing","authors":"Iegor Riepin ,&nbsp;Tom Brown ,&nbsp;Victor M. Zavala","doi":"10.1016/j.adapen.2024.100202","DOIUrl":"10.1016/j.adapen.2024.100202","url":null,"abstract":"<div><div>Companies operating datacenters are increasingly committed to procuring renewable energy to reduce their carbon footprint, with a growing emphasis on achieving 24/7 Carbon-Free Energy (CFE) matching—eliminating carbon emissions from electricity use on an hourly basis. However, variability in renewable energy resources poses significant challenges to achieving this goal. This study investigates how shifting computing workloads and associated power loads across time and location supports 24/7 CFE matching. We develop an optimization model to simulate a network of geographically distributed datacenters managed by a company leveraging spatio-temporal load flexibility to achieve 24/7 CFE matching. We isolate three signals relevant for informed use of load flexibility: (1) varying average quality of renewable energy resources, (2) low correlation between wind power generation over long distances due to different weather conditions, and (3) lags in solar radiation peak due to Earth’s rotation. Our analysis reveals that datacenter location and time of year influence which signal drives an effective load-shaping strategy. By leveraging these signals for coordinated energy procurement and load-shifting decisions, clean computing becomes both more resource-efficient and cost-effective—the costs of 24/7 CFE are reduced by 1.29 ± 0.07 €/MWh for every additional percentage of flexible load. This study provides practical guidelines for datacenter companies to harness spatio-temporal load flexibility for clean computing. Our results and the open-source optimization model offer insights applicable to a broader range of industries aiming to eliminate their carbon footprints.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"17 ","pages":"Article 100202"},"PeriodicalIF":13.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}