Applied Energy最新文献

{"title":"Multi-output model of medium-temperature chillers for digital twins: A comparative study of steady-state and dynamic modeling approaches","authors":"Zhixuan Fan ,&nbsp;Yanqiang Di ,&nbsp;Yafeng Gao ,&nbsp;Qiulei Zhang ,&nbsp;Lina Jiang ,&nbsp;Shiqian Dong ,&nbsp;Hongbo Chen ,&nbsp;Yuanyang Li ,&nbsp;Mingwen Luo","doi":"10.1016/j.apenergy.2025.126397","DOIUrl":"10.1016/j.apenergy.2025.126397","url":null,"abstract":"<div><div>Chiller modeling is essential for ensuring efficient chiller operation. The existing chiller models are mostly single-output steady-state models that cannot accurately capture the dynamic behavior of chillers and cannot meet the needs of digital twins. In this work, a multi-output model framework was proposed to facilitate the development of a digital twin chiller. Subsequently, three steady-state and three dynamic chiller models were developed based on a medium-temperature case. The hyperparameters of the six candidate models were optimized. To systematically evaluate model suitability, we introduced two novel metrics: the univariate error, which quantifies prediction accuracy for individual variables, and the model overall error, which aggregates errors across all variables to assess comprehensive performance. A comparative analysis was then conducted to contrast the best steady-state and dynamic models, evaluating their overall error and dynamic responsiveness. The study results show that: The chiller power consumption of all models exhibit the lowest prediction accuracy, followed by evaporator outlet water temperature and condenser outlet water temperature. The support vector regression (SVR) model is the best of the steady state models with model overall error of 10.84 %, and the gate recurrent unit (GRU) model is the best of the steady state models with model overall error of 3.67 %. Notably, the GRU model demonstrates superior accuracy in predicting evaporator outlet temperature(<span><math><msub><mi>T</mi><mi>eo</mi></msub></math></span>), condenser outlet temperature(<span><math><msub><mi>T</mi><mi>co</mi></msub><mo>)</mo></math></span> and chiller power consumption(<span><math><mi>P</mi><mo>)</mo></math></span> and better captured transient fluctuations in these variables during chiller start-up and load changes compared with the SVR model. The findings provide a methodological foundation for developing digital twin models and optimizing intelligent operation/maintenance strategies for chillers.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126397"},"PeriodicalIF":10.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522681","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}

{"title":"Photovoltaic fluctuation-adapted dynamic network pruning for low-voltage distribution network edge computing","authors":"Jian Zhao ,&nbsp;Kai Deng ,&nbsp;Xianjun Shao ,&nbsp;Zhibin Zhou ,&nbsp;Fengqian Xu ,&nbsp;Xiaoyu Wang ,&nbsp;Yuan Gao","doi":"10.1016/j.apenergy.2025.126394","DOIUrl":"10.1016/j.apenergy.2025.126394","url":null,"abstract":"<div><div>The inherent volatility of photovoltaic (PV) output necessitates the use of high-complexity deep learning (DL) models for accurate predictions. However, such models operate at full capacity even during stable PV output periods, consuming redundant computational resources and overloading resource-constrained edge devices in low-voltage distribution network (LVDN). To address the above issue, this paper proposes a dynamic network pruning framework that adaptively adjusts DL model complexity based on PV fluctuations. Firstly, a PV fluctuation-sensitive channel importance assessment method is proposed to identify the redundant structures in DL models. Subsequently, a lightweight optimization framework with PV operational constraints is developed to adjusts pruning thresholds based on PV output uncertainty and edge resource availability. Finally, a dynamic network pruning technique is proposed to adaptively balance model accuracy and computational complexity in response to real-time LVDN operation status and PV output volatility, ensuring pruned sub-networks align with the evolving PV data characteristics. The empirical results show that the proposed method can provide a practical solution for deploying lightweight DL models on edge devices. Specifically, our method effectively compresses 72 % FLOPs of the DL model in PV fluctuation challenging environments with slight accuracy degradation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126394"},"PeriodicalIF":10.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522817","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}

{"title":"Learning Li-ion battery health and degradation modes from data with aging-aware circuit models","authors":"Zihao Zhou ,&nbsp;Antti Aitio ,&nbsp;David Howey","doi":"10.1016/j.apenergy.2025.126375","DOIUrl":"10.1016/j.apenergy.2025.126375","url":null,"abstract":"<div><div>Non-invasive estimation of Li-ion battery state-of-health from operational data is valuable for battery applications, but remains challenging. Pure model-based methods may suffer from inaccuracy and long-term instability of parameter estimates, whereas pure data-driven methods rely heavily on training data quality and quantity, causing lack of generality when extrapolating to unseen cases. We apply an aging-aware equivalent circuit model for health estimation, combining the flexibility of data-driven techniques within a model-based approach. A simplified electrical model with voltage source and resistor incorporates Gaussian process regression to learn capacity fade over time and also the dependence of resistance on operating conditions and time. The approach was validated against two datasets and shown to give accurate performance with less than 1 % relative root mean square error (RMSE) in capacity and less than 2 % mean absolute percentage error (MAPE). Critically, we show that changes from the open circuit voltage versus state-of-charge function will strongly influence the learnt resistance. We use this feature to further estimate <em>in operando</em> differential voltage curves from operational data.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126375"},"PeriodicalIF":10.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522680","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}

{"title":"Using Mobile soft open points to enhance power balance capability between LVDNs through mobility multi-stage optimization","authors":"Xuehan Chen ,&nbsp;Lu Zhang ,&nbsp;Yue Zhou ,&nbsp;Yuqi Ji ,&nbsp;Bo Zhang ,&nbsp;Wei Tang","doi":"10.1016/j.apenergy.2025.126381","DOIUrl":"10.1016/j.apenergy.2025.126381","url":null,"abstract":"<div><div>The flexible AC/DC interconnection of low-voltage distribution networks (LVDNs) using soft open points (SOPs) enhances load-carrying capacity and distributed generation (DG) accommodation capability through optimal power shifting considering the power complementarity between LVDNs. However, the degree of power complementarity of interconnected LVDNs is usually affected by the periodic change of load power, and the current fixed flexible AC/DC interconnection scheme plays a significant role only during some periods, resulting in a low utilization rate of flexible interconnection equipment. To tackle this issue, mobile soft open points (MSOP) are proposed to be used, which are vehicle-mounted SOPs with lightweight and can be installed/removed between LVDNs to achieve flexible interconnection. This paper proposes a multi-stage optimization method for scheduling MSOP. Firstly, the complementary characteristics and benefits of different LVDNs at various time scales in mobile flexible interconnection scenarios are analyzed quantitatively. Secondly, a nonlinear model is developed to assess the comprehensive moving cost of MSOP, taking into account the processes of removal, transportation and installation. Then, the mobility multi-stage optimal scheduling method for MSOP is proposed to enhance the intermittent power complementary capability and utilization capability of SOPs. Finally, the simulation results show that the proposed method enables intermittent power exchange between multi-zone LVDNs through the mobile and flexible interconnection of MSOP to overcome the spatial interconnection limitations of fixed SOPs. It effectively addresses the short-term overloading issues in low-voltage distribution networks caused by the temporal and spatial mismatch between generation and load, enhances the power balancing capability between LVDNs, and simultaneously improves the economic efficiency of LVDNs and the utilization rate of SOPs, laying a foundation for future smart grid interconnection strategies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126381"},"PeriodicalIF":10.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517597","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}

{"title":"Diagnostics methodology based on differential mechanical measurements for lithium-ion batteries","authors":"Francesca Pistorio,&nbsp;Davide Clerici,&nbsp;Aurelio Somà","doi":"10.1016/j.apenergy.2025.126156","DOIUrl":"10.1016/j.apenergy.2025.126156","url":null,"abstract":"<div><div>One of the main challenges in lithium-ion battery diagnostics is the absence of sensors directly measuring the health of the battery during operation. This parameter can be estimated from voltage and current measurements, employing differential voltage or incremental capacity analyses. However, this estimation is often challenging in real-world applications because the shape of the differential voltage and incremental capacity curves changes with increasing current rates, and some key features in the curves vanish, affecting the applicability of the method.</div><div>In this work, the potential of differential mechanical measurements (second derivative of expansion and incremental expansion) for battery diagnostics is revealed, explaining for the first time the correlation between mechanical and voltage responses of commercial active materials. A procedure for estimating stoichiometric limits and electrode capacities using differential measurements is proposed. This procedure is particularly suitable to assess battery degradation in real-world applications, requiring monitoring the variation of the electrode parameters at high current rates and without performing complete charge/discharge cycles.</div><div>Mechanical and voltage differential curves of LCO-graphite, LFP-graphite and NMC111-graphite batteries are performed and compared, showing a strong correspondence and highlighting the close correlation between mechanics and electrochemistry in lithium-ion batteries.</div><div>Consequently, this work demonstrates that mechanical differential curves can be used similarly to differential voltage and incremental capacity curves, but with the significant advantage that the key features of the curve do not vanish at higher current rates. This makes mechanical measurements a promising alternative tool for battery diagnostics, particularly in real-world scenarios.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126156"},"PeriodicalIF":10.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522816","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}

{"title":"Synchronizing flexible loads with wind energy via Stackelberg game for renewable integration and economic efficiency","authors":"Samar Fatima ,&nbsp;Arslan Ahmad Bashir ,&nbsp;Ilkka Jokinen ,&nbsp;Matti Lehtonen ,&nbsp;Mahdi Pourakbari-Kasmaei","doi":"10.1016/j.apenergy.2025.126398","DOIUrl":"10.1016/j.apenergy.2025.126398","url":null,"abstract":"<div><div>The modern electric grid enables prosumers’ participation in energy management by integrating flexible loads like electric vehicles (EVs) and battery energy storage systems (BESS) via demand response (DR). However, the main challenge lies in motivating EV owners to adjust their low-cost charging plans to align with distributed generation, e.g., wind and photovoltaic (PV) power. This requires a reward system with more attractive financial incentives than the customers’ initially planned savings. From the mathematical standpoint, the primary challenge lies in finding the dual of the problem due to the presence of several disjoint or overlapping time-based exceptions in the modeling of EVs and BESS. This study aims to optimize the synergy between PV, wind, and flexible EV loads via an incentive-based DR framework. A bi-level model is proposed and solved using a Stackelberg game between a wind-farm aggregator and customers, reducing customer costs and improving wind-load matching. The proposed bi-level model is transformed into a single-level equivalent through a dual formulation, addressing the complexities of overlapping time-based exceptions causing redundancies. Validated across seasons, the results highlight the DR’s success in enhancing aggregator and customer outcomes. In an example case, customer energy costs dropped by up to 146.56 €/day, and the aggregator reduced the wind energy-load mismatch by up to 356 kWh in one day. Results show that greater load flexibility and wind capacity enhance economic and energy management outcomes, highlighting the impact of scalable DR opportunities.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126398"},"PeriodicalIF":10.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517411","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}

{"title":"Sensors-enabled approach for real-time quantification of lithium plating under extreme environments","authors":"Rui Xiong ,&nbsp;Xinjie Sun ,&nbsp;Ruixin Yang ,&nbsp;Weixiang Shen ,&nbsp;Hongwen He ,&nbsp;Fengchun Sun","doi":"10.1016/j.apenergy.2025.126370","DOIUrl":"10.1016/j.apenergy.2025.126370","url":null,"abstract":"<div><div>Ensuring safe and efficient fast charging of lithium-ion batteries (LiBs) in low-temperature environments remains challenging due to lithium plating on the anode under extreme conditions, which compromises battery safety and longevity. In this paper, we introduce advanced sensors into a LiB that integrates state-of-the-art multi-dimensional sensing technologies for real-time, in-situ detection and quantification of lithium plating. This innovation achieves unparalleled functionality without altering battery's physical dimensions. It provides dynamic, high-resolution insights into internal pressure, temperature, and anode potential, enabling the extraction of multi-dimensional features closely linked to lithium plating. By leveraging advanced statistical approaches, including correlation analysis and least absolute shrinkage and selection operator regression, the critical features are identified and ranked. These features are further integrated using a cutting-edge machine learning framework combining feature distance-based analysis with Adaboost. Only six features during battery charging are required as input, the model achieves remarkable lithium plating quantification accuracy of 93.3 % at a single temperature and 88.5 % at different temperatures. This sensors-enable approach to lithium plating quantification offers a promising pathway toward enhancing the functionality and intelligence of next-generation battery management systems for electric vehicles and portable electronic devices.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126370"},"PeriodicalIF":10.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517412","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}

{"title":"Global warming impacts of the transition from fossil fuel conversion and infrastructure to hydrogen","authors":"Sajjad Rezaei ,&nbsp;Alejandra Hormaza Mejia ,&nbsp;Yanchen Wu ,&nbsp;Jeffrey Reed ,&nbsp;Jack Brouwer","doi":"10.1016/j.apenergy.2025.126363","DOIUrl":"10.1016/j.apenergy.2025.126363","url":null,"abstract":"<div><div>Emissions from fossil fuel extraction, conveyance, and combustion are among the most significant causes of air pollution and climate change, leading to arguably the most acute crises mankind has ever faced. The transition from fossil fuel-based energy systems to hydrogen is essential for meeting a portion of global decarbonization goals. Hydrogen offers certain features, such as high gravimetric energy density that is required for heavy-duty shipping and freight applications, and chemical properties, such as high temperature combustion and reducing capabilities that are required for steel, chemicals, and fertilizer industries. However, hydrogen that leaks has indirect climate implications, stemming from atmospheric interactions, that are emerging as a critical area of research. This study reviews recent literature on hydrogen's global warming potential (GWP), highlighting its indirect contributions to radiative forcing via methane's extended atmospheric lifetime, tropospheric ozone formation, and stratospheric water vapor formation. The 100-year GWP (GWP₁₀₀) of hydrogen, estimated to range between 8 and 12.8, underscores the need to minimize leakage throughout the hydrogen supply chain to maximize the climate benefits of using hydrogen instead of fossil fuels. Comparisons with methane reveal hydrogen's shorter atmospheric lifetime and reduced long-term warming effects, establishing it as a viable substitute for fossil fuels in sectors, such as steel, cement, and heavy-duty transport. The analysis emphasizes the importance of accurate leakage assessments, robust policy frameworks, and advanced infrastructure to ensure hydrogen realizes its potential as a sustainable energy carrier that displaces the use of fossil fuels. Future research is recommended to refine climate models, better understand atmospheric sinks and hydrogen leakage phenomena, and develop effective strategies to minimize hydrogen emissions, paving the way for environmentally sound use of hydrogen.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126363"},"PeriodicalIF":10.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517594","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}

{"title":"Allocation of national energy use to the industrial sector","authors":"Joseph J Saffer ,&nbsp;Natanael Bolson ,&nbsp;Jonathan Cullen","doi":"10.1016/j.apenergy.2025.126388","DOIUrl":"10.1016/j.apenergy.2025.126388","url":null,"abstract":"<div><div>Reducing energy demand is one of the most cost-effective strategies for lowering carbon emissions and mitigating climate change. This study presents a novel methodology to allocate national energy use to specific industrial conversion devices, thus enhancing the precision of energy efficiency analyses. By conducting a comprehensive analysis of current industrial energy-use data, the study identifies the United States as the source of the most robust publicly available industrial energy end-use data. We validate the use of US data as a proxy for other countries, uncovering discrepancies and necessary adjustments for accurate global application. Using detailed Sankey diagrams, we visualise industrial energy flows and pinpoint key areas for efficiency improvements. Recognising inherent limitations in current data collection methods, we propose a standardised protocol for future industrial energy surveys. This protocol aims to ensure data accuracy, consistency, and comparability across nations, facilitating the generation of tailored allocation matrices and insightful Sankey diagrams. Ultimately, this research informs targeted efficiency improvements and contributes to sustainable industrial practices worldwide.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126388"},"PeriodicalIF":10.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513914","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}

{"title":"Advanced graph-based machine learning reveals cross-sector drivers of decarbonization in the United States and China","authors":"Amir Soltani","doi":"10.1016/j.apenergy.2025.126368","DOIUrl":"10.1016/j.apenergy.2025.126368","url":null,"abstract":"<div><div>The United States and China, as the world's two largest carbon emitters, play a critical role in global efforts to mitigate climate change. However, there is a notable lack of comprehensive comparative analyses evaluating their decarbonization trajectories across multiple sectors. This study aims to fill this gap by employing advanced machine learning models to analyze and compare how renewable energy adoption, technological advancements, and policy measures have influenced carbon emissions and energy consumption in the United States and China. The nexus of technological innovation and strategic policy implementation is explored to generate actionable insights into the key drivers of power sector decarbonization and the broader clean energy transition. Utilizing a comprehensive dataset covering the power, industry, buildings, and transport sectors, our analysis leverages the strengths of GCN and GAT in capturing complex interdependencies within the data. The findings highlight the pivotal role of innovation and targeted policies in driving significant CO₂ emissions reductions, offering deeper insights into pathways toward net-zero emissions for both countries. This research contributes to the literature by integrating graph-based machine learning approaches to provide a nuanced understanding of feature interactions, which traditional models may overlook, and offers practical recommendations for policymakers and stakeholders engaged in global climate change mitigation efforts. These insights directly inform Article 4 of the Paris Agreement and subsequent Glasgow and Sharm el-Sheikh commitments by quantifying how technology–policy interactions accelerate national emission targets. The graph-based approach also highlights renewable-energy patents and battery breakthroughs as decisive levers, pointing policymakers toward innovation-led decarbonization pathways.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126368"},"PeriodicalIF":10.1,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517593","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}