Applied Energy最新文献_第5页

Advanced optimization algorithms for enhanced urban block-scale carbon accounting: A case study from Beijing, China 城市块尺度碳核算的先进优化算法：以中国北京为例

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-23 DOI: 10.1016/j.apenergy.2025.126158

Jing Zhao , Yujie Ren , Xiaolan Tang

{"title":"Advanced optimization algorithms for enhanced urban block-scale carbon accounting: A case study from Beijing, China","authors":"Jing Zhao , Yujie Ren , Xiaolan Tang","doi":"10.1016/j.apenergy.2025.126158","DOIUrl":"10.1016/j.apenergy.2025.126158","url":null,"abstract":"<div><div>Urban carbon emissions have garnered significant attention following the establishment and implementation of global carbon neutrality goals. Accurate carbon accounting in urban areas is crucial for formulating effective emission reduction strategies and assessing their effectiveness. However, the mixed-use nature of urban land presents significant challenges to precise carbon accounting. This study adopts the perspective of urban community living circles and leverages optimization algorithms and the inventory method to propose a novel carbon accounting method that addresses these challenges. Furthermore, this method's reliability was validated through an analysis of eight land parcels with varying spatial configurations in the Beijing area, along with comprehensive sensitivity analyses, while its practical applications were also explored. The findings are as follows: (1) The urban block-scale carbon accounting method optimizes population allocation across industries and incorporates land-use policies into objective function constraints, enhancing both accuracy and applicability. (2) In Beijing's functional core area, annual carbon emissions within the 5–15-min standard-scale living circles were 8.43 ktC, 24.95 ktC, and 151.95 ktC, respectively, with an emission intensity of 810.42 tC/ha in the 10–15-min zone. (3) Urban functional and residential land collectively accounted for approximately 97 % of total emissions, with urban functional land exerting a greater impact. This study presents a reliable, robust, and systematic urban block-scale carbon accounting method that integrates carbon management with land-use policies, demonstrating significant practical value.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126158"},"PeriodicalIF":10.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125148","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}

引用次数: 0

Amplify seasonality, prioritize meteorological: Strengthening seasonal correlation in photovoltaic forecasting with dual-layer hierarchical attention 放大季节性，优先考虑气象：以双层层次关注强化光伏预测的季节相关性

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-23 DOI: 10.1016/j.apenergy.2025.126104

Yunbo Niu , Jianzhou Wang , Ziyuan Zhang , Yisheng Cao , Pengfei Yan , Zhiwu Li

{"title":"Amplify seasonality, prioritize meteorological: Strengthening seasonal correlation in photovoltaic forecasting with dual-layer hierarchical attention","authors":"Yunbo Niu , Jianzhou Wang , Ziyuan Zhang , Yisheng Cao , Pengfei Yan , Zhiwu Li","doi":"10.1016/j.apenergy.2025.126104","DOIUrl":"10.1016/j.apenergy.2025.126104","url":null,"abstract":"<div><div>Overloading beyond the grid’s capacity poses a serious threat to grid security. In 2023, photovoltaic power generation accounted for 75 % of the total increase in renewable energy generation. However, due to the significant fluctuations in photovoltaic power output, forecasting photovoltaic generation has become a crucial tool for ensuring grid security. A key challenge in practical applications remains the deep mining of hidden features in photovoltaic data and their correlation with meteorological data to improve prediction accuracy. To address this, this study proposes a photovoltaic prediction strategy called “Amplify Seasonality, Prioritize Meteorological\". This strategy aims to leverage meteorological information to connect with the seasonal component of photovoltaic power data while preventing meteorological factors from affecting the trend component, thereby effectively reducing the impact of short-term seasonal meteorological fluctuations on the trend component of photovoltaic data. Additionally, this study proposes a seasonal component prediction unit with a dual-layer hierarchical attention mechanism, which enhances the focus on the connections between meteorological features, key time nodes, and the seasonal component. These innovations enable the proposed AspmNet model to achieve superior prediction accuracy. The model was validated using Australian photovoltaic data through experiments with forecast lengths of 1 day, 2 days, and 4 days. In terms of Mean Absolute Error, the model demonstrated over a 10 % improvement compared to other benchmark models.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126104"},"PeriodicalIF":10.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117065","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}

引用次数: 0

Impact of methane leakage rate and carbon capture rate on blue hydrogen sustainability using combined warming index 基于联合变暖指数的甲烷泄漏率和碳捕获率对蓝氢可持续性的影响

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.125888

Daniel Davids , Neil Grant , Shivika Mittal , Adam Hawkes , Gbemi Oluleye

{"title":"Impact of methane leakage rate and carbon capture rate on blue hydrogen sustainability using combined warming index","authors":"Daniel Davids , Neil Grant , Shivika Mittal , Adam Hawkes , Gbemi Oluleye","doi":"10.1016/j.apenergy.2025.125888","DOIUrl":"10.1016/j.apenergy.2025.125888","url":null,"abstract":"<div><div>Blue hydrogen may become important to achieve decarbonisation targets. Yet, the uncertainty and aggregated impact of methane leakage rate and carbon dioxide capture rate on the value of blue hydrogen from a whole systems perspective has not been investigated. Our study focuses on the impact of the dual influence of these variables in an energy system model of the United Kingdom (UK). We incorporate practical ranges for methane leakage rate and carbon capture rate and analyse their impact by formulating a novel parameter, termed the Combined Warming Index (CWI). The CWI can be used to assess decarbonisation scenario outputs from energy system models giving insights into their effects on the dynamics of energy system and decarbonisation parameters. Our results suggest that sustainable deployment of blue hydrogen becomes threatened at a carbon capture rate of 85 % and across the range of methane leakage rates of 0.125 %, 0.5 %, 1 %, 1.5 % and 2.5 %. At a carbon capture rate of 90 %, and methane leakage rates at 1 %, 1.5 % and 2.5 %, blue hydrogen is not significantly deployed to 2050. Methane leakage rate and carbon capture rate are key parameters for the success of blue hydrogen as a low-carbon hydrogen option, and although carbon capture rate is the more critical parameter, methane leakage rate is also important but becomes a secondary concern in natural gas supply chains with low fugitive emissions. The outcome of our research can contribute to framing relevant policy for the application of CCS technology as society seeks to attain low-carbon economy aims.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 125888"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117062","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}

引用次数: 0

Resilience-oriented soft open point deployment with topology-variable-based frequency stability constraints for distribution networks 基于拓扑变量的配电网频率稳定性约束的弹性软开放点部署

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126120

Pengyi Fan, Tao Yu, Ziyao Wang, Zhenning Pan, Yufeng Wu

{"title":"Resilience-oriented soft open point deployment with topology-variable-based frequency stability constraints for distribution networks","authors":"Pengyi Fan, Tao Yu, Ziyao Wang, Zhenning Pan, Yufeng Wu","doi":"10.1016/j.apenergy.2025.126120","DOIUrl":"10.1016/j.apenergy.2025.126120","url":null,"abstract":"<div><div>The flexible multi-microgrid cluster offers a promising solution by integrating distributed resources to enhance the resilience of modern distribution networks. Soft open points are crucial for forming flexible multi-microgrid clusters, yet most existing soft open point deployment models neglect transient frequency stability in islanded operations under diverse post-fault scenarios, causing the suboptimal in the solution. To this end, this paper proposes a novel resilience-oriented soft open point deployment model for distribution networks, where the topology-variable-based frequency stability constraints based on the virtual flow method are incorporated. Compared to traditional formulation of frequency constraints that are topologically parameterized, the topology-variable-based frequency stability constraints can be embedded into optimization models containing topological variables, such as network planning or network reconfiguration. This ensures optimality of the deployment model. Case studies on a dual feeder distribution network highlight the importance of considering frequency stability in soft open point deployment and provide a robust framework for enhancing the resilience of modern distribution networks.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126120"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107926","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}

引用次数: 0

Prospects and challenges of application of structural battery in vehicles 结构电池在汽车上应用的前景与挑战

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126161

Shuanglong Geng, Kai Zhang, Bailin Zheng

{"title":"Prospects and challenges of application of structural battery in vehicles","authors":"Shuanglong Geng, Kai Zhang, Bailin Zheng","doi":"10.1016/j.apenergy.2025.126161","DOIUrl":"10.1016/j.apenergy.2025.126161","url":null,"abstract":"<div><div>Structural batteries offer a novel solution to the issue of battery mass as a “weight burden” and can also alleviate the problem of “range anxiety” in electric vehicles (EVs). This paper reviews structural battery design methods. By comparing the advantages of decoupled structural battery design and coupled structural battery design in relation to vehicle structural characteristics, it is found that vehicles incorporating decoupled structural battery designs may reach the market faster than those with coupled designs. The impact of structural battery applications on improving vehicle range is analyzed using the vehicle energy equation, along with an exploration of the boundary effects of increasing battery mass. Additionally, the paper discusses the challenges faced by structural batteries, particularly in real-world vehicle application scenarios. Finally, the paper discusses the potential applications of structural batteries in other fields and explores their potential in low-altitude aerial vehicles. Through a review and discussion of current research in the field of structural batteries, the goal is to encourage more scholars to focus on this technology and to promote its application not only in the vehicle industry but across a wider range of fields.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126161"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117061","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}

引用次数: 0

Enhanced precision data center server power consumption model with temperature estimation based on CPU operating statues 基于CPU运行状态的温度估计的增强精度数据中心服务器功耗模型

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126097

Lujie Yu , Donghao Liu , Jiebei Zhu , Huan Zhou , Yunfeng Li , Yongzhen Wang , Hongjie Jia

{"title":"Enhanced precision data center server power consumption model with temperature estimation based on CPU operating statues","authors":"Lujie Yu , Donghao Liu , Jiebei Zhu , Huan Zhou , Yunfeng Li , Yongzhen Wang , Hongjie Jia","doi":"10.1016/j.apenergy.2025.126097","DOIUrl":"10.1016/j.apenergy.2025.126097","url":null,"abstract":"<div><div>To mitigate energy consumptions in data centers, the accurate establishment of a server power consumption model is imperative. Traditional server power consumption model, which rely solely on CPU utilization, often overlook the CPU temperature and operating statues inherent characteristics, resulting in substantial forecast errors. In response to this gap, a novel enhanced precision Power consumption Model based on Temperature estimation considering CPU working state (PMTC) model, is proposed based on the identification of CPU operating statuses. By incorporating temperature variables at the initial model construction phase, the PMTC model effectively captures the delayed dynamic characteristics of server power consumption changes that are influenced by the lagging adjustments in CPU core temperatures, thereby eliminating temperature-related modeling inaccuracies. In the subsequent power forecasting stage, the PMTC model accurately identifies specific CPU operating statues, which facilitate precise estimations of the CPU core temperature, thus circumventing the implementation challenges associated with additional measurements of temperature variables. To validate the efficacy of the proposed PMTC model against traditional server power consumption models, a dedicated server power consumption testbed was established. The results demonstrate that the PMTC model, by incorporating the temperature-related delayed dynamic characteristics of server power consumption change without augmenting the dimensions of input data, significantly reduces modeling calculation errors.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126097"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107987","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}

引用次数: 0

Online incremental probability power prediction for distributed PVs in heterogeneous and dynamic data environments 异构动态数据环境下分布式光伏发电在线增量概率功率预测

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126110

Le Zhang, Ziyu Chen, Jizhong Zhu, Kaixin Lin, Linying Huang

{"title":"Online incremental probability power prediction for distributed PVs in heterogeneous and dynamic data environments","authors":"Le Zhang, Ziyu Chen, Jizhong Zhu, Kaixin Lin, Linying Huang","doi":"10.1016/j.apenergy.2025.126110","DOIUrl":"10.1016/j.apenergy.2025.126110","url":null,"abstract":"<div><div>Data sharing is a standard solution to improve the prediction accuracy of data-driven models for distributed photovoltaic (PV) power with small samples. Unfortunately, in practice, due to decentralized ownership and diverse, dynamic external environments, this solution suffers from challenges in data privacy, heterogeneity, and dynamic data learning. To handle these challenges, this paper proposes an incremental probabilistic prediction method based on a Bayesian stochastic configuration network (BSCN) and personalized federated learning (PFL). Concretely, a stochastic configuration network, an emerging neural network with a single hidden layer and no iteration, is used to quickly build the power predictor. Aiming to obtain the posterior distribution and determine the probabilistic output, Bayesian inference is used to evaluate the output parameters of SCN. Faced with the performance degradation caused by small samples and heterogeneous data, a novel PFL framework is designed to improve the prediction accuracy while protecting privacy. Technically, the server acts as a bridge for information sharing and aggregates local posterior distributions in a personalized manner, guided by Wasserstein distance to integrate similar features as much as possible. With the personalized posterior from the server as the prior, each client performs personalized retraining locally to mitigate the adverse effects of the data heterogeneity while learning shared information from other clients. Moreover, an incremental learning strategy is proposed and seamlessly embedded into the PFL framework to continuously learn new modes without forgetting in dynamic environments. Extensive experiment results using public datasets demonstrate that the proposed method exhibits competitive probabilistic prediction performance compared to several state-of-the-art solutions for distributed PVs in the presence of small-sample, heterogeneous, and dynamic data.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126110"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117060","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}

引用次数: 0

Deep learning-based distributionally robust joint chance constrained distribution networks PV hosting capacity assessment 基于深度学习的分布式鲁棒联合机会约束配电网光伏托管容量评估

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126130

Zihui Wang , Yanbing Jia , Xiaoqing Han , Peng Wang , Jiajie Liu

{"title":"Deep learning-based distributionally robust joint chance constrained distribution networks PV hosting capacity assessment","authors":"Zihui Wang , Yanbing Jia , Xiaoqing Han , Peng Wang , Jiajie Liu","doi":"10.1016/j.apenergy.2025.126130","DOIUrl":"10.1016/j.apenergy.2025.126130","url":null,"abstract":"<div><div>As distributed photovoltaic (PV) penetration in distribution networks (DNs) is increasing, it is essential to assess the PV hosting capacity (PVHC) to ensure the safe operation of DNs. This paper proposes a data-driven distributionally robust joint chance constrained (DRJCC) distribution networks PVHC assessment framework. Firstly, the spatiotemporal attention, projection, supervision, and Transformer architecture-based generative adversarial blocks are introduced to develop an augmented time series generative adversarial network (ATS-GAN), which, by integrating both supervised and unsupervised learning during the joint training process, better captures the spatiotemporal characteristics of PV and load power. Subsequently, leveraging the ATS-GAN, a Wasserstein metrics-based ambiguity set of PV and load power probability distributions is constructed, centered on the distributions induced by the generator neural network. Secondly, the DRJCC PVHC assessment model is proposed. A combination of the Bonferroni inequality and conditional value-at-risk approximation is adopted to transform the multivariate DRJCC model into a tractable conic formulation for efficient computation. Numerical results demonstrate that the proposed method effectively captures the spatiotemporal characteristics and uncertainties of multivariate distributions under multiple constraints, significantly reducing the conservatism typically associated with distributionally robust individual chance constraints.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126130"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144117063","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}

引用次数: 0

Perceived complexity and effectiveness of dynamic electricity rate designs for smart markets 智能市场动态电价设计的感知复杂性和有效性

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126042

Philipp Staudt , David Dann

引用次数: 0

Improving cross-temporal forecasts reconciliation accuracy and utility in energy market 提高能源市场跨时间预测对账准确性和实用性

IF 10.1 1区工程技术

Applied Energy Pub Date : 2025-05-22 DOI: 10.1016/j.apenergy.2025.126053

Mahdi Abolghasemi , Daniele Girolimetto , Tommaso Di Fonzo

{"title":"Improving cross-temporal forecasts reconciliation accuracy and utility in energy market","authors":"Mahdi Abolghasemi , Daniele Girolimetto , Tommaso Di Fonzo","doi":"10.1016/j.apenergy.2025.126053","DOIUrl":"10.1016/j.apenergy.2025.126053","url":null,"abstract":"<div><div>Wind power forecasting is essential for managing daily operations at wind farms and enabling market operators to manage power uncertainty effectively in demand planning. Traditional reconciliation methods rely on in-sample errors for forecast reconciliation, which may not generalize well to future performance. Additionally, conventional aggregation structures do not always align with the decision-making requirements in practice, and evaluation metrics often neglect the economic impact of forecast errors. To address these challenges, this paper explores advanced cross-temporal forecasting models and their potential to enhance forecasting accuracy and decisions. First, we propose a novel approach that leverages validation errors, rather than traditional in-sample errors, for covariance matrix estimation and forecast reconciliation. Second, we introduce decision-based aggregation levels for forecasting and reconciliation, where certain horizons are tailored to the specific decisions required in operational settings. Third, we assess model performance not only by traditional accuracy metrics but also by their ability to reduce decision costs, such as penalties in ancillary services. Our results show that using validation errors improves the accuracy by more than 7 % across different temporal levels. We also demonstrate that statistical-based hierarchies tend to adopt less conservative forecasts and reduce revenue losses. On the other hand, decision-based reconciliation offers a more balanced compromise between accuracy and decision cost, while saving computational time by 2 %–3 % for simpler models and up to 93 % for more advanced models, making them attractive for practical use.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"394 ","pages":"Article 126053"},"PeriodicalIF":10.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107927","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}

引用次数: 0