Applied Energy最新文献

{"title":"Distributed robust cooperative optimization of multi-integrated energy systems based on variational inequality-driven non-cooperative game theory","authors":"Zongnan Zhang ,&nbsp;Kudashev Sergey Fedorovich","doi":"10.1016/j.apenergy.2025.126696","DOIUrl":"10.1016/j.apenergy.2025.126696","url":null,"abstract":"<div><div>In integrated energy system (IES), multiple uncertainties exist regarding renewable energy output and electricity prices, while traditional electric hydrogen module model fail to accurately characterize the operational states of electrolyzer. Additionally, complex coupling and competitive interests among multi-integrated energy system (MIES) pose significant challenges for effective management. To address these challenges, this paper integrates non-cooperative game theory with distributionally robust optimization (DRO) to propose an efficient and robust operational strategy for MIES. Firstly, a DRO model for IES is established using Wasserstein distance, allowing for a refined modeling of the electric hydrogen module. Subsequently, a non-cooperative game equilibrium model based on variational inequalities (VI) is constructed to describe the interactions among MIES, leading to the development of a VI-driven equilibrium model for non-cooperative games in MIES under distributionally robust optimization. To achieve efficient equilibrium while safeguarding the privacy of each IES, an AOP-BL-ADMM algorithm is developed. Finally, the effectiveness of the proposed method is validated through case analysis.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126696"},"PeriodicalIF":11.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262740","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":"Falling costs for battery storages make facade photovoltaic systems more attractive for prosumers","authors":"Dennis Bredemeier ,&nbsp;Alexander Mahner ,&nbsp;Tobias Wietler ,&nbsp;Raphael Niepelt ,&nbsp;Rolf Brendel","doi":"10.1016/j.apenergy.2025.126871","DOIUrl":"10.1016/j.apenergy.2025.126871","url":null,"abstract":"<div><div>Photovoltaics (PV) is a key technology in the transformation of the energy system with a large share being installed on rooftops. However, suitable roof space is becoming increasingly scarce. PV systems on facades partially remove these space limitations. Facades offer significant additional potential, but their widespread use is hampered by higher costs and lower annual yields. We model the energy system of free-standing buildings in Europe and optimize the dimensioning of system components. We show that the inclusion of a battery storage results in a pronounced increase of the economic value of PV on south-facing facade surfaces. This is because the battery's short-term storage and the seasonally favorable generation profile of facade PV systems complement each other. If there is no south facing roof available for PV, we find facade PV shares greater than 80 % for large parts of Europe to be cost optimal with techno-economic assumptions for 2030. The share of PV on the facades increases further when an additional load for covering the heating and cooling demand is added to the building. PV systems on facades can thus play an important role in the future energy system, particularly in view of falling costs for both PV and battery storage systems. Importantly, we find that with the cost assumptions for 2030 the inclusion of a battery storage lowers the total system costs for all locations across Europe. This decrease in total system costs is particularly pronounced in Southern Europe, reaching up to 44 %.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126871"},"PeriodicalIF":11.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262576","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":"Superconducting hydrogen-electricity multi-energy system for transportation hubs: Modeling, technical study and economic-environmental assessment","authors":"Yu Chen ,&nbsp;Xiaoyuan Chen ,&nbsp;Lin Fu ,&nbsp;Shan Jiang ,&nbsp;Boyang Shen","doi":"10.1016/j.apenergy.2025.126823","DOIUrl":"10.1016/j.apenergy.2025.126823","url":null,"abstract":"<div><div>This article presents a novel hydrogen-electricity multi-energy system for transportation hubs. A new model has been established to simulate the entire process of renewable energy production, storage, transmission, to utilization, which can efficiently coordinate renewable energy and dynamic demands for electricity, cooling, heating and hydrogen energy. The system can fully utilize renewable energy, produce LH₂ by surplus renewable energy. Superconducting hydrogen-electricity multi-energy pipeline is introduced to increase the transmission capacity and reduce transmission loss. Economic analysis shows that with a discount rate of 6 %, the system would be profitable after 17 years. Environmental assessment shows that the CO₂ emission of the hydrogen-electricity multi-energy system can be only 5 % of that of the conventional system if the renewable energy is sufficient. This study shows great potential of using hydrogen-electricity multi-energy system, offering directions of fully utilizing renewable energy and cooling/heating loads for future transportation hubs and other high-dense power sectors.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126823"},"PeriodicalIF":11.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262732","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":"Transfer learning-based multi-energy load forecasting method for integrated energy system with zero-shot","authors":"Ke Li ,&nbsp;Zheng Qin ,&nbsp;Yuchen Mu ,&nbsp;Haiyang Wang ,&nbsp;Qingfeng Bie ,&nbsp;Xianxin Yin ,&nbsp;Yi Yan","doi":"10.1016/j.apenergy.2025.126769","DOIUrl":"10.1016/j.apenergy.2025.126769","url":null,"abstract":"<div><div>In the planning and capacity design of integrated energy system (IES), the critical reliance on multi-energy load data faces a paradoxical dilemma: a scarcity or complete absence of historical operating data. This “fundamental demand vs. data scarcity” contradiction challenges optimal design. This paper systematically proposes a transfer learning (TL)-based forecasting framework designed for zero-shot scenarios, which addresses this challenge through a three-stage innovative approach: First, a novel algorithm, Tnet, is designed based on probabilistic generalization assessment. By decomposing temporal features and incorporating weighted mutual information entropy, a source domain selection paradigm guided by probabilistic judgment is constructed. This paradigm identifies source domain groups from multiple candidates with the highest generalization value for a given target domain. Second, an improved meta-learning strategy, Metas, is developed to optimize cross-domain parameter transfer by adapting task weights dynamically, significantly enhancing the modeling accuracy of temporal features. Third, an encoder-decoder model integrated with a multi-head attention mechanism is constructed to enable the coordinated forecasting of electricity, heating, gas, and cooling loads. Experimental results show that under zero-shot conditions, the proposed method reduces mean absolute percentage error by more than 42 % compared to benchmark models while improving the coefficient of determination by over 50 %. Further validation through few-shot fine-tuning (FSFT) demonstrates that when the target domain gradually acquires a small amount of real data, the model can achieve rapid correction within a few iterations and maintain high forecasting robustness. Its performance in the “cold-start” phase, where data is scarce, far exceeds that of direct training. This highlights the core role of the FSFT strategy in bridging the performance gap during the critical transition from zero-shot scenarios to those with sufficient data. It provides a complete, feasible, and efficient forecasting paradigm for IES that have not yet been commissioned or lack comprehensive historical data. This paradigm covers the entire process from a zero-shot start-up to few-shot optimization, offering valuable insights for energy planning and operational scheduling in real-world applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126769"},"PeriodicalIF":11.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262508","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":"Public transit electrification planning with energy storage via second-life batteries – a stochastic programming approach","authors":"Sergio Edgar Franco,&nbsp;Jing Wang,&nbsp;Majid Gholami Shirkoohi,&nbsp;Qiao Chen,&nbsp;Walter Mérida","doi":"10.1016/j.apenergy.2025.126868","DOIUrl":"10.1016/j.apenergy.2025.126868","url":null,"abstract":"<div><div>As part of decarbonization strategies, public transit systems are aiming to electrify their fleets in response to climate targets and net-zero goals. However, the resulting increase in electricity demand may lead to energy stress on the electrical grid. Second-life batteries (SLBs) offer a potential solution, yet their financial, energy, and environmental impacts remain underexplored, as does the long-term planning for their integration. This study proposes a strategic planning model for transitioning a public transit fleet to battery electric buses (BEBs), incorporating the deployment of SLBs as a battery energy storage system (BESS). The model jointly optimizes decisions on asset procurement, replacement, route-level fleet assignments, the integration of SLBs as BESS, and the installation of a supporting renewable energy system (RES). A multi-period stochastic programming framework is employed to optimize planning under uncertainties, such as vehicle and battery costs, and the model is formulated as a mixed-integer linear program. A case study of Metro Vancouver's transit system is conducted to evaluate three electrification pathways. Results show that SLBs can meet up to 84 % of the fleet's recharging energy demand, reduce annual operating costs by up to $107 million, and lower total system costs by $78 million. A sensitivity analysis of battery and electricity prices provides insights into the integration of SLBs under different market and policy conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126868"},"PeriodicalIF":11.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262574","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":"Internal short circuit diagnosis of lithium-ion battery packs considering incomplete charging under cell inconsistencies","authors":"Dongxu Shen ,&nbsp;Chao Lyu ,&nbsp;Dazhi Yang ,&nbsp;Gareth Hinds ,&nbsp;Shaochun Xu ,&nbsp;Miao Bai ,&nbsp;Jin Qiu","doi":"10.1016/j.apenergy.2025.126797","DOIUrl":"10.1016/j.apenergy.2025.126797","url":null,"abstract":"<div><div>Internal short circuit (ISC) diagnosis is a major means to prevent thermal runaway in lithium-ion battery packs. During practical operation, the initial state of charge of a lithium-ion battery pack during charging may not be 0 %, and inherent inconsistencies exist among individual cells. Existing ISC diagnosis methods heavily rely on complete charging curves while neglecting the impact of cell inconsistencies, which can lead to misdiagnosing inconsistent cells as ISC cells and vice versa. This work presents an ISC diagnosis method that accounts for cell inconsistencies and is applicable under incomplete charging conditions. Partial incremental capacity curves are extracted from incomplete charging voltage curves to characterize the abnormal state of the battery. During the offline training phase, kernel density estimation is used to obtain the probability density functions of partial incremental capacity curves under different states. During online monitoring, Jensen–Shannon divergence is employed to quantify the similarity between different probability density functions, enabling the detection and differentiation of normal, inconsistent, and ISC cells within the lithium-ion battery pack. For the detected ISC cells, a state-space model is established with short-circuit current as one of the state variables. The short-circuit resistance is estimated using a dual adaptive extended Kalman filter. Experimental results show a diagnosis accuracy of 96.88 % with a false alarm rate of 0. The maximum root mean square error in short-circuit resistance estimation is only <span><math><mn>2.28</mn><mspace></mspace><mi>Ω</mi></math></span>, which empirically validates the proposal.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126797"},"PeriodicalIF":11.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262733","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":"Thermal-overdischarge coupling induced aging mechanisms in LiFePO4 batteries: insights from multi-scale electrochemical and material diagnostics","authors":"Rui Tang ,&nbsp;Yuelei Xu ,&nbsp;Jinyang Dong ,&nbsp;Qi Shi ,&nbsp;Kang Yan ,&nbsp;Yibiao Guan ,&nbsp;Yun Lu ,&nbsp;Yu Su ,&nbsp;Jinzhong Liu ,&nbsp;Fangze Zhao ,&nbsp;Yi Jin ,&nbsp;Ning Li ,&nbsp;Yuefeng Su ,&nbsp;Feng Wu ,&nbsp;Lai Chen","doi":"10.1016/j.apenergy.2025.126870","DOIUrl":"10.1016/j.apenergy.2025.126870","url":null,"abstract":"<div><div>This study deciphers aging mechanisms in lithium iron phosphate (LiFePO₄) batteries under coupled thermal-electrochemical stresses using a multi-scale approach. Industrial-grade LiFePO₄/graphite pouch cells underwent accelerated aging across orthogonal temperature (25/45/65 °C) and discharge cut-off voltage (2.5/1.0/0.5 V) conditions. Integrating electrochemical diagnostics (DCIR, EIS, IC/DV) with post-mortem characterization (SEM/TEM/XRD/ToF-SIMS/μXRF) reveals that: (1) Elevated temperatures amplify lithium loss and SEI instability; (2) Deep over-discharge (≤1.0 V) triggers anode degradation via graphite structural collapse, copper dissolution, and SEI rupture; (3) Coupled stresses induce synergistic failure—manifested by DCIR transitions from linear to exponential growth—correlated microscopically with dead lithium accumulation, metal deposition percolation, and interfacial collapse; (4) Extreme conditions (65 °C/0.5 V) provoke atypical mechanisms including cathode FePO₄ segregation and copper migration. The work establishes DCIR inflection as a non-destructive marker for failure-stage transition and provides fundamental insights into stress-coupled degradation pathways, advancing predictive models for LiFePO₄ battery durability in energy storage systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126870"},"PeriodicalIF":11.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262573","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":"Safe reinforcement learning-based prescriptive maintenance of distribution grid using Bi-Mamba+ and constrained policy optimization: A Danish grid case study","authors":"Iason Gram,&nbsp;Hamid Mirshekali,&nbsp;Hamid Reza Shaker","doi":"10.1016/j.apenergy.2025.126803","DOIUrl":"10.1016/j.apenergy.2025.126803","url":null,"abstract":"<div><div>As Denmark pursues ambitious climate goals—aiming to reduce CO₂ emissions by 70 % relative to 1990 levels by 2030 and achieve climate neutrality by 2050—electrification is accelerating across sectors. This transition places increasing strain on the country’s aging distribution grids, driven by rising electricity consumption from electric vehicles and more. Traditional grid expansion, while necessary, is subject to interruptions in supply chains and does not directly reduce emissions. As a result, alternative strategies that enhance operational efficiency and grid resilience are essential. This article presents a prescriptive framework that combines predictive load forecasting and real-time control to facilitate prescriptive maintenance and proactively mitigate some of these challenges. The framework is demonstrated using real data from a Danish grid. The first stage utilizes the Bi-Mamba+ model for forecasting consumption and production at the transformer level, achieving high accuracy and demonstrating the scalability of a single, well-engineered model across the grid. The second stage applies neural network-based real-time control trained via Constrained Policy Optimization (CPO), effectively reducing grid alarms by dynamically controlling storage units, though sensitive to forecast accuracy and system complexity. Finally, the two components are combined via a transformation layer to ensure compatibility between the two components. Together, these components form a forward-looking solution that supports grid stability and flexibility, reduces reliance on infrastructure expansion, and paves the way for smarter grid management. The results underline the potential of integrating optimization, forecasting, and control in distribution system operations to take advantage of the potential that prescriptive maintenance offers.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126803"},"PeriodicalIF":11.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262632","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":"Load forecasting under distribution shift: An online quantile ensembling approach","authors":"Dalin Qin ,&nbsp;Xian Wu ,&nbsp;Dayan Sun ,&nbsp;Zhifeng Liang ,&nbsp;Ning Zhang","doi":"10.1016/j.apenergy.2025.126812","DOIUrl":"10.1016/j.apenergy.2025.126812","url":null,"abstract":"<div><div>Reliable load forecasting is crucial for power system operations but remains challenging under frequent distribution shifts caused by evolving consumption patterns and external disruptions. While deterministic methods (DLF) generate point predictions and probabilistic methods (PLF) capture uncertainty, existing approaches fail to bridge these paradigms to utilize PLF’s distribution insights for improving DLF accuracy under shifting conditions. To address this gap, we propose <em>Adaptive Online Quantile Ensembling</em>, a novel framework that integrates probabilistic insights into deterministic forecasting for robust online adaptation. Our method features dynamic quantile ensembling with long-term and short-term weight decomposition for balancing stability and responsiveness, as well as a detect-then-adapt strategy for adaptive fast-and-slow learning based on real-time error monitoring. Extensive experiments on post-COVID load datasets demonstrate significant improvements in accuracy and responsiveness over baselines, particularly during abrupt and gradual distribution shifts. This work establishes an effective approach to leverage probabilistic information for accurate load forecasting in dynamic, non-stationary environments.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126812"},"PeriodicalIF":11.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262633","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":"Hydrogen production plant via an intensified plasma-based technology","authors":"Shayan S. Niknezhad ,&nbsp;David Staack ,&nbsp;Efstratios N. Pistikopoulos","doi":"10.1016/j.apenergy.2025.126833","DOIUrl":"10.1016/j.apenergy.2025.126833","url":null,"abstract":"<div><div>Developing cleaner processes via newer technologies will accelerate advancement toward more sustainable energy systems. Hydrogen is an energy carrier and an intermediate molecule in chemical processes. This research investigates an innovative hydrogen production process utilizing a non-thermal Cold Atmospheric Pressure Plasma-based Reformer (CAPR). Exploring environmentally friendly and economically viable pathways for hydrogen production is crucial for addressing climate change and reducing the carbon footprint of industrial processes. The study investigates the conversion of natural gas to hydrogen at ambient temperature and pressure, highlighting the ability of plasma-based technology to operate without direct CO₂ emissions.</div><div>Initially, through experimental studies, natural gas was passed through the CAPR, where the plasma's energetic discharges initiate the reforming process. Subsequently, the produced hydrogen, along with other light hydrocarbons, enters the separation system for producing purified hydrogen. The research focuses on techno-economic analyses and sensitivity assessments to determine the levelized cost of producing hydrogen via a nanosecond plasma-based refining plant and benchmark technologies. Sensitivity analyses identify two primary factors that significantly affect the levelized cost of hydrogen production in a plasma-based reforming system.</div><div>The research suggests that instead of producing carbon dioxide and capturing the emitted CO₂, utilize processes that do not emit direct CO₂. CAPR shows potential for cost competitiveness with conventional hydrogen production methods, including steam methane reforming (SMR) and electrolysis. The findings underscore the need for further research to optimize the system's performance and cost-effectiveness, positioning CAPR as a potentially transformative technology for the chemical process industry.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126833"},"PeriodicalIF":11.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145262742","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}