Applied Energy最新文献

{"title":"A comprehensive review of solid-state batteries","authors":"Aniruddha Joshi ,&nbsp;Dillip Kumar Mishra ,&nbsp;Rajendra Singh ,&nbsp;Jiangfeng Zhang ,&nbsp;Yi Ding","doi":"10.1016/j.apenergy.2025.125546","DOIUrl":"10.1016/j.apenergy.2025.125546","url":null,"abstract":"<div><div>In the era of the 20th century, energy storage technology is essentially as important as the penetration of renewable energy. Although Li-ion battery technology has been investigated for many years, a major breakthrough, the invention of solid-state batteries, has only recently arrived. It offers better safety, higher energy density, and improved cycle life. This paper reviews solid-state battery technology's current advancements and status, emphasizing key materials, battery architectures, and performance characteristics. We analyze various solid electrolyte materials, electrode materials, and interfacial engineering approaches to enhance ion transport and suppress dendrite formation. Furthermore, the critical aspect of battery degradation and its impact on the life cycle through various mechanisms are analyzed. Subsequently, the charging feature of solid-state batteries is explored. Finally, this paper gives the direction of improvements to the challenges threatening solid-state battery commercialization. This comprehensive review study offers valuable insights for regulators, industry professionals, and academics involved in developing a solid-state battery that promises safety, high performance, and sustainability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125546"},"PeriodicalIF":10.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-dimensional building carbon emissions echelon peak target setting in China based on building types, sources, and indicators","authors":"Qiqi Liu ,&nbsp;Yuan Liu ,&nbsp;Weiguang Cai ,&nbsp;Yongjie Du","doi":"10.1016/j.apenergy.2025.125532","DOIUrl":"10.1016/j.apenergy.2025.125532","url":null,"abstract":"<div><div>Building carbon emissions peak is not an independent and single process but the result of the joint action of multiple objects and targets. Therefore, the single target of building carbon emissions peak should also be split into dimensional and phased peak targets with both differentiation and unity to realize carbon peak in the building sector gradually. To achieve this goal, this study constructs a building carbon emissions peak target (CEPT) setting model based on the principle of objective optimization using scenario analysis and Monte Carlo simulation, and applies the model to the Chinese building sector to fully refine the building CEPT in terms of building types, emission sources, and emission indicators, as well as to develop a more detailed carbon peak roadmap. The results show that, except for carbon emissions from commercial buildings (peak time <span><math><msubsup><mi>t</mi><mn>3</mn><mfenced><mn>1</mn></mfenced></msubsup><mo>=</mo><mn>2031</mn><mo>)</mo></math></span>, per capita carbon emissions of buildings (<span><math><msubsup><mi>t</mi><mn>2</mn><mfenced><mn>3</mn></mfenced></msubsup><mo>=</mo><mn>2032</mn><mo>)</mo></math></span>, and electricity carbon emissions (<span><math><msubsup><mi>t</mi><mn>2</mn><mfenced><mn>2</mn></mfenced></msubsup><mo>=</mo><mn>2034</mn><mo>)</mo></math></span>, all other objects can achieve a carbon emissions peak by 2030. Moreover, the building CEPTs under different dimensions follow a pattern of echelon peaking with an apparent order of priority. In addition, under the low-carbon scenario, China will achieve the carbon peak in the building sector by 2029, which suggests that China's building sector should make greater efforts and promptly take more effective measures to ensure the achievement of the 2030 carbon peak target. The building CEPT setting model proposed in this study can provide important support for developing a phased action plan for energy conservation and emission reduction.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125532"},"PeriodicalIF":10.1,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465307","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":"A novel frequency-domain physics-informed neural network for accurate prediction of 3D Spatio-temporal wind fields in wind turbine applications","authors":"Shaopeng Li ,&nbsp;Xin Li ,&nbsp;Yan Jiang ,&nbsp;Qingshan Yang ,&nbsp;Min Lin ,&nbsp;Liuliu Peng ,&nbsp;Jianhan Yu","doi":"10.1016/j.apenergy.2025.125526","DOIUrl":"10.1016/j.apenergy.2025.125526","url":null,"abstract":"<div><div>Wind power is a pivotal clean energy source worldwide. The structural safety and dynamic response analysis of wind turbines is significantly impacted by the availability and precision of wind speed data at their location. However, the sparse distribution of meteorological stations often makes it difficult to obtain high-resolution spatial wind speed data. This necessitates the application of conditional simulation to supplement low-resolution observational data. This study addresses this challenge by developing a frequency-domain physics-informed neural network (FD-PINN) designed to predict three-dimensional (3D) spatio-temporal wind fields for wind turbines by leveraging frequency-domain information. This approach involves constructing a deep neural network and integrating it with key physical models, including wind spectra, wind field coherence functions, and wind profiles. This integration allows the network to accurately predict wind conditions in environments with sparse wind field samples. The efficacy of our proposed methodology is assessed by comparing its predictive performance against traditional neural network approaches and actual observation data. Our findings demonstrate that integrating frequency-domain information significantly enhances the accuracy of spatial wind speed distribution predictions for wind turbines, compared to conventional methods. Additionally, this approach reduces spatial dependency issues with wind speed. Validation against real-world wind fields further confirms the feasibility and precision of this FD-PINN model in predicting 3D spatio-temporal wind fields.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125526"},"PeriodicalIF":10.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445927","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":"The user-side energy storage investment under subsidy policy uncertainty","authors":"Manli Zhao ,&nbsp;Xinhua Zhang ,&nbsp;C. James Hueng","doi":"10.1016/j.apenergy.2025.125508","DOIUrl":"10.1016/j.apenergy.2025.125508","url":null,"abstract":"<div><div>We develop a real options model for firms' investments in the user-side energy storage. After the investment, the firms obtain profits through the peak-valley electricity price spreads. They face a choice between making this irreversible investment and holding an option to delay the investment because of the uncertainty in the future price spreads. The government tries to encourage the firms to invest immediately by providing subsidies to this irreversible investment. The subsidy policy, however, can be activated or terminated at an uncertain time and therefore, the firms face additional policy uncertainty when making the decision. We derive the investment thresholds of the market spread that the firms use to make a decision on investing immediately or holding an option. To validate and demonstrate the model, we collect data from China's pilot project for energy storage and use it as an example. This dataset allows us to calibrate the model and numerically demonstrate the impacts of price-spread uncertainty and policy uncertainty on the firms' investment decision at different policy stages. For example, if the Chinese government unexpectedly announces a 30% subsidy and promises no subsidy in the near future, it can lower the spread threshold by 0.3950 RMB/kWh (or 39.8%), thus stimulating more immediate investments. This calibration exercise provides valuable policy measures that a government can use to incentivize an immediate investment in the user-side energy storage elsewhere.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125508"},"PeriodicalIF":10.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445928","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 prediction and evaluation for ionic osmotic energy conversion under concentration and temperature gradients","authors":"Huangyi Zhu,&nbsp;Zhiguo Qu,&nbsp;Ziling Guo,&nbsp;Jianfei Zhang","doi":"10.1016/j.apenergy.2025.125574","DOIUrl":"10.1016/j.apenergy.2025.125574","url":null,"abstract":"<div><div>Ionic osmotic energy conversion under concentration and temperature gradients synergistically utilizes osmotic and thermal energies to drive the directional migration of ions in charged nanochannels for power generation. The current research conducts preliminary experiments and simulations to determine the impact of a single parameter on output performance while lacking prediction models to reflect the link between comprehensive parameters and outputs. The complex partial differential relationship restricts the establishment of prediction models, which can be addressed by combining engineering and data science like transfer learning. This study presents a data-driven insight into ionic osmotic energy conversion to establish a transfer learning-based prediction model for comprehensive parameters using small sample sizes. Based on the trained source task model, the transfer learning-based deep neural network (TL–DNN) model with 17 inputs and 3 outputs is trained by freezing four hidden layers with 600 samples acquired from finite element method (FEM) simulations. The determination coefficients of diffusion potential, maximum power, and energy conversion efficiency are predicted to be 0.97, 0.98, and 0.97, respectively, by the TL–DNN model based on 5-fold cross-validation. Compared with FEM results, the TL–DNN model displays an exceptionally high speedup ratio of 1.37 × 10⁶ with errors less than 4 %. Besides, low concentrations and nanochannel radius exhibit high descriptor importance exceeding 0.70, indicating the dominant influence on performance. The multi-objective optimization is performed by non-dominated sorting genetic algorithm II to obtain 10 sets of parameter combinations with the highest entropy weight scores. This study has provided an alternative prediction model based on transfer learning and promotes theoretical development by applying data science to engineering science.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125574"},"PeriodicalIF":10.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454932","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":"Enhancing PV power forecasting accuracy through nonlinear weather correction based on multi-task learning","authors":"Zhirui Tian ,&nbsp;Yujie Chen ,&nbsp;Guangyu Wang","doi":"10.1016/j.apenergy.2025.125525","DOIUrl":"10.1016/j.apenergy.2025.125525","url":null,"abstract":"<div><div>Accurate short-term photovoltaic (PV) power forecasting is critical for optimizing energy management and maintaining grid stability within the rapidly growing renewable energy sector. However, the inherent high sensitivity of PV systems to varying weather conditions poses significant challenges to achieving reliable predictions. Existing research endeavours to enhance short-term forecasting accuracy through two primary approaches. On the one hand, some studies incorporate weather variables as input features to improve prediction precision, yet this method often falls short of fully capturing the intricate and dynamic interactions between diverse weather factors and PV output. On the other hand, most correction methods utilize error correction (EC) techniques that adjust initial PV forecasts based on predicted errors. Nonetheless, the highly volatile nature of error sequences substantially restricts the effectiveness of EC, as these unpredictable errors compromise the reliability of the corrective adjustments. To this end, we propose a novel two-stage framework that leverages weather information from multiple perspectives to enhance short-term PV power forecasting accuracy. In the first stage, a customized multi-task learning (MTL) framework employs a task interaction matrix to differentiate between task-specific and shared features, thereby facilitating meaningful interactions between PV output and weather variables while providing interpretability. Additionally, a dynamic loss weighting mechanism ensures balanced training across tasks. In the second stage, we implement a nonlinear weather correction (WC) module using neural networks, which refines the initial PV predictions by effectively incorporating the predicted weather variables, thereby enhancing both accuracy and robustness. Experimental validation using real PV data from the Northern Territory, Australia, demonstrates that our framework consistently outperforms baseline models across various seasons and confirms the effectiveness of each component within the framework through ablative experiments.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125525"},"PeriodicalIF":10.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454929","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":"Risk-based optimal network planning considering resources remuneration and daily uncertainty","authors":"Fábio Castro ,&nbsp;Bruno Canizes ,&nbsp;João Soares ,&nbsp;José Almeida ,&nbsp;Bruno Francois ,&nbsp;Zita Vale","doi":"10.1016/j.apenergy.2025.125531","DOIUrl":"10.1016/j.apenergy.2025.125531","url":null,"abstract":"<div><div>The integration of renewable energy into power networks introduces challenges due to intermittency and unpredictability, making precise expansion planning essential. This research introduces a novel two-stage stochastic approach for distribution network expansion planning in smart grids with high renewable energy penetration, addressing uncertainty, risk, and distributed generators' remuneration. Key contributions include: the incorporation of third-party generation owners' economic remuneration into a risk-based stochastic model; the use of conditional value-at-risk to manage uncertainty and extreme events, with a detailed analysis of cost evolution for various confidence levels and risk aversion parameters; the optimization of energy storage systems sizing and placement, alongside the location and type of new power lines and substation transformers, ensuring a reliable and radial network topology; and the integration of multiple factors, including uncertainty, risk aversion, ESS allocation, remuneration, and reliability, into a unified model that ensures optimal network design under technical constraints. Tested on a 180-bus network in Leiria, Portugal and on a 13-bus smart city mockup from Salamanca, Spain, the approach proved economically viable, reducing extreme scenario costs by up to 34 % through CVaR-based risk management, and demonstrating its potential for sustainable, risk-averse network expansion.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"386 ","pages":"Article 125531"},"PeriodicalIF":10.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance and stability of renewable fuel production via H2O electrolysis and H2O–CO2 co-electrolysis using proton-conducting solid oxide electrolysis cells","authors":"Zehua Pan ,&nbsp;Jingyi Wang ,&nbsp;Liangzhu Zhu ,&nbsp;Chuancheng Duan ,&nbsp;Zhenjun Jiao ,&nbsp;Zheng Zhong ,&nbsp;Ryan O'Hayre ,&nbsp;Neal P. Sullivan","doi":"10.1016/j.apenergy.2025.125571","DOIUrl":"10.1016/j.apenergy.2025.125571","url":null,"abstract":"<div><div>Electrochemical production of commodity chemicals via H₂O electrolysis or H₂O–CO₂ co-electrolysis using solid oxide electrolysis cells (SOECs) offers a way to utilize excess renewables to address hard-to-decarbonize industrial sectors. Recently, proton-conducting SOECs (PCECs) have emerged as a promising type of SOEC in such applications, due to their unique properties of lower operating temperatures and flexible coupling with other chemical processes. However, the Faradaic efficiency (FE), i.e., the ratio of the experimentally produced H₂ to that which could be theoretically generated, of PCECs is less than 100 % and their stability, particularly under co-electrolysis operation, has yet to be verified. In this work, a systematic investigation of the variation of FE under different operating conditions and the stability of PCECs in both H₂O electrolysis and H₂O–CO₂ co-electrolysis is conducted. It is shown that the operating parameters have a significant effect on the apparent FE. During short-term stability testing, H₂O–CO₂ co-electrolysis mode presents much less favorable operating characteristics than H₂O electrolysis or H₂–CO₂ thermochemical conversion, with both FE and the catalytic activity of the negatrode (Ni-based fuel electrode) degrading gradually. Opportunities are identified to optimize operating parameters to maximize effectiveness and minimize degradation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"385 ","pages":"Article 125571"},"PeriodicalIF":10.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445948","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":"Process analyses on sorption-enhanced electrified steam methane reforming for near-zero emission hydrogen production with CO2 capture by calcium looping thermochemical reaction","authors":"Huchao Song,&nbsp;Xinyue Zhang,&nbsp;Xiaolong Lin,&nbsp;Hao Bian,&nbsp;Yinhe Liu","doi":"10.1016/j.apenergy.2025.125537","DOIUrl":"10.1016/j.apenergy.2025.125537","url":null,"abstract":"<div><div>The predominant hydrogen production method is steam methane reforming, which generates substantial CO₂ emissions from both the reforming reaction and the combustion required to drive the process. Sorption-enhanced steam methane reforming (SESMR) enables the in-situ CO₂ removal during the reforming reaction, enhancing hydrogen yield. Replacing combustion with renewable electricity for reforming reaction can eliminate combustion-related emissions and flue gas heat loss. However, the intrinsic randomness, intermittency, and instability of renewable electricity present significant challenges to maintaining continuous and stable operation. This study introduces a process that innovatively leverages the mass and energy flow matching characteristics of the calcium looping with renewable electricity driving calcination, thermochemical energy storage, and sorption-enhanced steam methane reforming, thereby establishing the sorption-enhanced electrified steam methane reforming (SEESMR) process. The SEESMR exploits the dual functions of sorbent as both an energy carrier and a CO₂ carrier. Renewable electricity facilitates calcination in the regenerator, storing energy in the form of chemical energy and sensible heat. This stored energy is subsequently utilized in the reformer during CO₂ adsorption, providing the requisite energy to drive methane reforming and produce hydrogen. First and second law analyses indicate that SEESMR demonstrates a 7.90 percentage point improvement in thermal efficiency compared to SESMR through the substitution of combustion heating with electric heating and a high-efficiency heat recovery. Additionally, this modification reduces exergy losses by up to 53.2 % through elimination of combustion in SESMR. Furthermore, SEESMR enables continuous hydrogen production from renewable electricity, achieves a 35 % cost reduction within the current fuel pricing framework in China compared to direct electric heating methane reforming. This study may provide a stable, efficient and economical approach to zero carbon hydrogen production and large-scale renewable energy accommodation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"385 ","pages":"Article 125537"},"PeriodicalIF":10.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445950","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":"The benefits and challenges of soft-linking investment and operational energy system models","authors":"M. Rosendal ,&nbsp;J. Janin ,&nbsp;T. Heggarty ,&nbsp;D. Pisinger ,&nbsp;R. Bramstoft ,&nbsp;M. Münster","doi":"10.1016/j.apenergy.2025.125512","DOIUrl":"10.1016/j.apenergy.2025.125512","url":null,"abstract":"<div><div>Large-scale energy system modelling is often applied to inform decision-making in the green transition. Energy system models tend to increase in complexity at the expense of increased computation time. Soft-linking of energy systems models is frequently applied to increase the modelling scope and thereby answer complex research questions while maintaining tractability. However, evaluations of the soft-linking strategy itself are rarely investigated or documented. We, therefore, explore the benefits and challenges of soft-linking through assessment of a bi-directional soft-linking framework for an integrated, pan-European electricity and hydrogen system. The frameworks Balmorel and Antares are chosen as components in the bi-directional soft-linking framework, with harmonised input data and spatial resolutions. Conversion, storage and transmission investments are computed in Balmorel based on one representative weather year and aggregated timeslices. These investments are subsequently evaluated over 31 historical weather years at full hourly resolution in Antares. Different strategies for increasing the potentially inadequate investments by Balmorel are analysed. This includes previously applied methods based on so-called capacity credits and profit signals. We also introduce a novel ’fictive demand’ approach, and the results and computation times are discussed using a low loss of load expectancy (LOLE) in electricity and hydrogen as a key performance indicator. The fictive demand method proved stable across multiple iterations and successfully reduced LOLE, while the capacity credit showed some promise. Finally, recommendations for soft-linking studies are formulated, and we argue that soft-linking enables large-scale problems to be investigated, but the necessary model harmonisations, choice of specific soft-linking strategy, and tuning of it present significant challenges.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"385 ","pages":"Article 125512"},"PeriodicalIF":10.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}