Applied Energy最新文献

{"title":"Electricity-hydrogen-heat coupled sharing for multi-energy buildings: A game theoretic approach","authors":"Jie Wang ,&nbsp;Xiaolong Jin ,&nbsp;Hongjie Jia ,&nbsp;Da Xu ,&nbsp;Yunfei Mu ,&nbsp;Xiaodan Yu ,&nbsp;Kai Hou","doi":"10.1016/j.apenergy.2025.126350","DOIUrl":"10.1016/j.apenergy.2025.126350","url":null,"abstract":"<div><div>The accelerating integration of renewable energy sources (RESs) has introduced significant challenges to the coordination and low-carbon operation of multi-energy building systems. To further facilitate the renewable energy accommodation and achieve carbon emissions reduction, this paper proposes an electricity‑hydrogen-heat coupled sharing framework for multi-energy buildings based on coalitional game theory. In this framework, a novel concept of combined heat and hydrogen (CHH) is introduced in an aggregator of prosumers (AOP). The CHH acts as a flexible heat source, hydrogen producer, and controllable electric load, with the AOP providing both electricity and heat to the multiple buildings. To enhance the operational flexibility of hydrogen-blended combined heat and power (CHP), hybrid operation mode is empowered to break its inherent operational limitation and expand its feasible region. In order to achieve the proactive and fair electricity‑hydrogen-heat coupled sharing, a coalition game model with a two-step reward allocation scheme is proposed for multi-energy buildings and AOP. A distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) is further developed to guarantee the privacy and security of individual users through limited information exchange. Finally, the case study involving six building users is provided to demonstrate the effectiveness of the proposed framework in improving renewable energy utilization, facilitating energy sharing, and reducing carbon emissions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126350"},"PeriodicalIF":10.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549046","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":"How do companies manage energy? A systematic literature review on energy cost accounting, energy efficiency, and energy management","authors":"Philip Dickemann","doi":"10.1016/j.apenergy.2025.126372","DOIUrl":"10.1016/j.apenergy.2025.126372","url":null,"abstract":"<div><div>It is imperative to recognize the indispensable role energy plays in sustaining business operations. Nevertheless, energy and topics associated with energy are often neglected, despite the crucial nature of information on energy consumption for the needs of managers in decision-making and the act of managing a company. As energy and its application are interdisciplinary topics, information is scattered throughout the literature. Consequently, the absence of a comprehensive study systematically collecting, describing and analyzing the current state of knowledge is striking. To address this deficiency, this systematic literature review synthesizes information from 39 papers published in 19 journals. The extracted contents of the papers were categorized, summarized, and analyzed according to their contribution toward first and second stage allocation of energy costs, energy measurement, energy efficiency, energy strategy, and energy-related investment decisions. The results of the study indicate a significant research gap in empirical investigations. The interdisciplinarity of the investigated topics leads to scattered information and frameworks for a structured analysis of corporate energy cost practices are lacking. To address this gap, this paper develops a framework for the collection of information on corporate energy practices based on the findings. This article puts forth the proposition that empirical investigations of corporate energy practices warrant scholarly attention. Such empirical studies need to find out what practice is doing, as the depth of the current state of literature is inadequate.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126372"},"PeriodicalIF":10.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535077","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":"Non-contact intelligent diagnosis method for key components in energy equipment based on acoustic signal and deep learning","authors":"Junming Yao ,&nbsp;Wei Liang ,&nbsp;Lixiang Duan ,&nbsp;Yilei Ouyang ,&nbsp;Zheng Wang ,&nbsp;Biao Wei","doi":"10.1016/j.apenergy.2025.126380","DOIUrl":"10.1016/j.apenergy.2025.126380","url":null,"abstract":"<div><div>With the development of clean energy, the deployment of energy equipment such as natural gas compressors and wind turbines has been steadily increasing. Non-contact monitoring and fault diagnosis of key components in energy systems are critical for preventing operational failures and ensuring safety. Acoustic signals, as a prominent non-contact sensing modality, are highly sensitive to fault features. This paper conducted corresponding research and proposed a novel MF-IDCL (2D Feature Mapping - Improved Deep Convolution Lightweight Framework) method based on noisy acoustic signals. Non-contact acoustic monitoring experiments were designed and conducted on energy equipment gearboxes, and the collecting data were used to perform a comparative validation between the proposed framework and conventional methods. Furthermore, cross-domain validation was conducted to analyze the method's generalization capability. The results demonstrate that the proposed 2D feature mapping mechanism exhibits advantages in accuracy, computational efficiency, and adaptability over traditional feature extraction methods. The MF-IDCL framework achieves superior performance, with an accuracy of 87.9 % at SNR5 and 85.2 % at SNR0, while maintaining lower training costs and memory consumption. In cross-domain validation, the framework also showed promising applicability to non-acoustic data such as vibration signals. It is of positive significance in ensuring safety and stability of energy equipment.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126380"},"PeriodicalIF":10.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549003","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":"Hollow thermoelectric legs with extremely low power-generation cost compatible with scalable manufacturing","authors":"Yousung Choi ,&nbsp;Sungjin Park ,&nbsp;Woochul Kim","doi":"10.1016/j.apenergy.2025.126358","DOIUrl":"10.1016/j.apenergy.2025.126358","url":null,"abstract":"<div><div>Although thermoelectric systems offer advantages such as compactness, silent operation, absence of moving parts, and long-term reliability, their applicability is hindered by high power-generation costs ($/W). This study introduces hollow thermoelectric legs that achieve extremely low $/W while being compatible with existing scalable manufacturing processes. A sodium chloride rod was sintered together with thermoelectric materials and then dissolved to obtain the hollow structure. This unique structure enables reduced material consumption by 60 % (low $) as well as 230 % enhancement in power output (high W) leading to 83 % reduction in $/W over a conventional thermoelectric device with fully filled legs. The scalability of the manufacturing process for the proposed device was also verified by fabricating a thermoelectric module and evaluating its performance. The results achieved with the proposed device architecture highlight the potential for the commercialization of thermoelectric generators.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126358"},"PeriodicalIF":10.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549047","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":"Distributed multi-objective African vulture accelerated optimization intelligent algorithm for multi-objective economic dispatch of power systems","authors":"Linfei Yin,&nbsp;Yongzi Ye","doi":"10.1016/j.apenergy.2025.126377","DOIUrl":"10.1016/j.apenergy.2025.126377","url":null,"abstract":"<div><div>The context of expanding power system scale and rapid development of the power market brings many challenges to economic dispatch. Although distributed multi-objective optimization methods are more convenient in solving large-scale systems, distributed multi-objective optimization methods still have shortcomings such as inefficient economic dispatch of the system and long computation time. This study combines the acceleration of deep neural networks with distributed multi-objective optimization, constructs a novel FlattenSelf-attentionNet structure, and proposes a distributed multi-objective African vulture accelerated optimization algorithm (DMOAVAOA) to enhance computational efficiency and reduce the dispatch time of the whole system. Experimental results in the American midwestern 118-bus and the 1790-bus system indicate that: Qu et al. (2018) (1) in the American midwestern 118-bus system, compared with distributed optimization, distributed accelerated optimization reduced carbon emissions by 9.95 %, cost by 2.16 %, and total operating time by 12.38 %; Qu et al. (2019) (2) in a 1790-bus system, the distributed accelerated optimization reduced carbon emissions by 3.5 %, cost spend by 4.8 %, and total system operating time by 43.25 % compared to distributed optimization; Chen et al. (2019) (3) the DMOAVAOA proposed in this study outperforms the distributed optimization method in the evaluation of performance metrics.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126377"},"PeriodicalIF":10.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549004","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":"Optimal charging schedules for EV charging stations considering hybrid smart and uncontrolled charging: A scalable framework","authors":"Xizhen Zhou ,&nbsp;Qiang Meng ,&nbsp;Yanjie Ji","doi":"10.1016/j.apenergy.2025.126366","DOIUrl":"10.1016/j.apenergy.2025.126366","url":null,"abstract":"<div><div>The randomness, temporal variability, and extended idle connection time of electric vehicle (EV) charging behavior impose significant load pressure and regulatory challenges on the grid and charging facility operations. Most studies have focused exclusively on smart charging, often overlooking the impact of uncontrolled charging. This singular focus has created a discrepancy between charging scheduling strategies and real-world conditions. To address these issues, this study investigated the factors influencing smart charging choices through a survey conducted in Jiangsu, China, and developed a smart charging choice model. Based on this model, a charging schedule method that integrates both smart and uncontrolled charging modes at stations was proposed. An energy boundary model and a relaxation mechanism for hybrid charging were employed to ensure alignment with charging demands. The charging process was modeled as a markov decision process, and a decentralized framework was proposed to provide charging power to each EV, using deep deterministic policy gradient reinforcement learning algorithms to determine charging strategies for multiple heterogeneous EVs. Numerical experiments confirm that the proposed method effectively reduces charging costs and peak loads at charging stations, and manages both homogeneous and heterogeneous charging demands. Additionally, centralized training of the decentralized framework demonstrates performance consistency across multiple charging units while consuming fewer training resources, thereby enhancing scalability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126366"},"PeriodicalIF":10.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535627","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":"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":"Exploring bidirectional charging strategies for an electric vehicle population","authors":"Soomin Woo ,&nbsp;Leo Strobel ,&nbsp;Yuhao Yuan ,&nbsp;Marco Pruckner ,&nbsp;Timothy E. Lipman","doi":"10.1016/j.apenergy.2025.126361","DOIUrl":"10.1016/j.apenergy.2025.126361","url":null,"abstract":"<div><div>Vehicle-grid integration (VGI) technologies control the energy exchange of electric vehicles (EVs) with power grids for economic and environmental benefits. Despite early investigations, it is still unclear how VGI operations should be designed to balance the goals of mobility needs and electrical grid operational costs. In this paper, our objectives are to examine VGI strategies including bidirectional or vehicle-to-grid (V2G) concepts reflecting realistic operation scenarios, evaluate the performance of the proposed strategies using actual EV charging data and future concepts for hourly varying electricity rates, and identify critical service parameters that impact V2G benefits. The most aggressive V2G control scenarios produce estimated revenues of 2397 USD/veh/year, with a synergistic effect of reducing <span><math><msub><mtext>CO</mtext><mn>2</mn></msub></math></span> emissions to 669 lb/veh/year from the baseline with 1438 lb/veh/year. Other strategies with more constraints and in different settings indicate somewhat to significantly lower revenues and higher emissions. Sensitivity analysis shows that charging and discharging efficiency, potential fees charged by aggregators for discharging energy, and degradation impacts on battery health can critically affect V2G revenues. Also, enhancing vehicle battery capacities and charging and discharging powers can significantly enhance the V2G revenues, while the emission varies only slightly.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"397 ","pages":"Article 126361"},"PeriodicalIF":10.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535216","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}