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Detailed system modeling of a vanadium redox flow battery operating at various geographical locations
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125473
Bence Sziffer, Martin János Mayer, Viktor Józsa
To avoid thermal precipitation, the electrolyte temperature of vanadium redox flow batteries should be within 5–40 °C. Consequently, an online thermal management system is essential, which impacts battery efficiency. A detailed thermal analysis was performed that considered a container, inner thermal radiation, global irradiance, and the thermal relationship between the system and the ambient at eight different weather stations with different climates around the globe. To meet the safe operation threshold criteria, a hybrid thermal management system was used to minimize heating and cooling energy consumption, consisting of control dampers, cooling fans, air conditioners, and heating and cooling electrolyte flows. The simulations were performed during the coldest and hottest 10-day periods of the year to determine the necessary insulation thickness and the energy consumption of cooling and heating; the latter was only required for one location. The presented thermal management system consumes up to 11 % of the total input power in extremely hot weather conditions. The simulation results show that efficiency increases with the decrease in ambient temperature until heating becomes necessary. The presented model helps predict the efficiency at any geographical location before battery installation and evaluates the need for various heating and cooling approaches.
{"title":"Detailed system modeling of a vanadium redox flow battery operating at various geographical locations","authors":"Bence Sziffer,&nbsp;Martin János Mayer,&nbsp;Viktor Józsa","doi":"10.1016/j.apenergy.2025.125473","DOIUrl":"10.1016/j.apenergy.2025.125473","url":null,"abstract":"<div><div>To avoid thermal precipitation, the electrolyte temperature of vanadium redox flow batteries should be within 5–40 °C. Consequently, an online thermal management system is essential, which impacts battery efficiency. A detailed thermal analysis was performed that considered a container, inner thermal radiation, global irradiance, and the thermal relationship between the system and the ambient at eight different weather stations with different climates around the globe. To meet the safe operation threshold criteria, a hybrid thermal management system was used to minimize heating and cooling energy consumption, consisting of control dampers, cooling fans, air conditioners, and heating and cooling electrolyte flows. The simulations were performed during the coldest and hottest 10-day periods of the year to determine the necessary insulation thickness and the energy consumption of cooling and heating; the latter was only required for one location. The presented thermal management system consumes up to 11 % of the total input power in extremely hot weather conditions. The simulation results show that efficiency increases with the decrease in ambient temperature until heating becomes necessary. The presented model helps predict the efficiency at any geographical location before battery installation and evaluates the need for various heating and cooling approaches.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125473"},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349656","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}
引用次数: 0
A physics-based and data-aided transient prediction framework for sustainable operation of pumped-storage hydropower systems
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125470
Weichao Ma , Zhigao Zhao , Chengpeng Liu , Fei Chen , Weijia Yang , Wei Zeng , Elena Vagnoni , Jiandong Yang
Achieving accurate predictions of transient processes for pumped-storage hydropower stations (PSHSs) remains a key challenge due to uncertainties in on-site parameters, particularly the pump-turbine characteristic curves (PTCCs), and limitations of the physics-based models themselves. To address this issue, this study proposes a transient prediction framework for PSHSs, centered on on-site measurements and incorporating both the physics-based model calibration and the data-aided correction. A method for reconstructing PTCCs using point distribution models (PDMs) is proposed, where PDMs act as prior models and are innovatively developed by defining multiple feature points on PTCCs to accommodate potential non-rigid deformations. This approach allows the reconstruction of complete PTCCs using a surface reconstruction algorithm, requiring only limited measured data from steady-state and transient experiments. To further compensate for errors in the physics-based model, a data-aided correction using nonlinear autoregressive with exogenous inputs (NARX) is proposed. The NARX model is optimally tuned by selecting the most sensitive model inputs which have the highest correlations with the predicted error of the physics-based model. Compared with the conventional model, the proposed framework reduces the predicted tendency errors for discharge, pressure at the volute, pressure at the draft tube, and rotational speed by average values of 10.82 %, 13.88 %, 36.67 %, and 7.37 %, respectively, across all experimental cases. The proposed transient prediction framework enables highly accurate predictions for a diverse range of transient processes of PSHSs and serves as a pre-warning basis for real-time monitoring systems, facilitating the sustainable operation of PSHSs.
{"title":"A physics-based and data-aided transient prediction framework for sustainable operation of pumped-storage hydropower systems","authors":"Weichao Ma ,&nbsp;Zhigao Zhao ,&nbsp;Chengpeng Liu ,&nbsp;Fei Chen ,&nbsp;Weijia Yang ,&nbsp;Wei Zeng ,&nbsp;Elena Vagnoni ,&nbsp;Jiandong Yang","doi":"10.1016/j.apenergy.2025.125470","DOIUrl":"10.1016/j.apenergy.2025.125470","url":null,"abstract":"<div><div>Achieving accurate predictions of transient processes for pumped-storage hydropower stations (PSHSs) remains a key challenge due to uncertainties in on-site parameters, particularly the pump-turbine characteristic curves (PTCCs), and limitations of the physics-based models themselves. To address this issue, this study proposes a transient prediction framework for PSHSs, centered on on-site measurements and incorporating both the physics-based model calibration and the data-aided correction. A method for reconstructing PTCCs using point distribution models (PDMs) is proposed, where PDMs act as prior models and are innovatively developed by defining multiple feature points on PTCCs to accommodate potential non-rigid deformations. This approach allows the reconstruction of complete PTCCs using a surface reconstruction algorithm, requiring only limited measured data from steady-state and transient experiments. To further compensate for errors in the physics-based model, a data-aided correction using nonlinear autoregressive with exogenous inputs (NARX) is proposed. The NARX model is optimally tuned by selecting the most sensitive model inputs which have the highest correlations with the predicted error of the physics-based model. Compared with the conventional model, the proposed framework reduces the predicted tendency errors for discharge, pressure at the volute, pressure at the draft tube, and rotational speed by average values of 10.82 %, 13.88 %, 36.67 %, and 7.37 %, respectively, across all experimental cases. The proposed transient prediction framework enables highly accurate predictions for a diverse range of transient processes of PSHSs and serves as a pre-warning basis for real-time monitoring systems, facilitating the sustainable operation of PSHSs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125470"},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349657","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
Collaborative optimization of vehicle and charging scheduling for mixed bus systems considering charging load balance
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125457
Guang-Jing Zhou, Xiao-Mei Zhao, Xiang-Yuan Zhu, Dong-Fan Xie
With the widespread development of electric buses, the impact of charging scheduling on peak grid loads and fluctuations has become increasingly significant. Existing studies primarily focus on dynamically adjustable charging power to mitigate charging load peak or fluctuations. However, these strategies gradually adjust the charging power too frequently and are based on predetermined vehicle scheduling, resulting in poor applicability. To address this issue, a segmented adjustable charging power strategy (SACP) in charging scheduling is proposed that simultaneously considers reducing the fluctuations and the peak of the charging load. Meanwhile, this study proposes a collaborative optimization model for both vehicle and charging scheduling of a mixed bus system that comprising electric human-driven buses and electric autonomous modular buses. The objective is to minimize peak loads and fluctuations on the grid, while also reducing operating costs for bus enterprises. An improved NSGA-II algorithm is developed to solve the collaborative optimization model, incorporating an objective-oriented strategy in the initial solution to enhance search efficiency and solution quality. Case studies demonstrate that the SACP strategy significantly reduces peak grid loads and fluctuation costs compared with a fixed charging power scenario, thereby achieving balanced charging loads. Furthermore, compared to the charging scheduling strategy alone, the SACP strategy exhibits a significant reduction in fluctuation cost of charging load by 50% and the peak cost of charging load by 21.6%, thereby ensuring the stability of charging load for both the system and charging events.
{"title":"Collaborative optimization of vehicle and charging scheduling for mixed bus systems considering charging load balance","authors":"Guang-Jing Zhou,&nbsp;Xiao-Mei Zhao,&nbsp;Xiang-Yuan Zhu,&nbsp;Dong-Fan Xie","doi":"10.1016/j.apenergy.2025.125457","DOIUrl":"10.1016/j.apenergy.2025.125457","url":null,"abstract":"<div><div>With the widespread development of electric buses, the impact of charging scheduling on peak grid loads and fluctuations has become increasingly significant. Existing studies primarily focus on dynamically adjustable charging power to mitigate charging load peak or fluctuations. However, these strategies gradually adjust the charging power too frequently and are based on predetermined vehicle scheduling, resulting in poor applicability. To address this issue, a segmented adjustable charging power strategy (SACP) in charging scheduling is proposed that simultaneously considers reducing the fluctuations and the peak of the charging load. Meanwhile, this study proposes a collaborative optimization model for both vehicle and charging scheduling of a mixed bus system that comprising electric human-driven buses and electric autonomous modular buses. The objective is to minimize peak loads and fluctuations on the grid, while also reducing operating costs for bus enterprises. An improved NSGA-II algorithm is developed to solve the collaborative optimization model, incorporating an objective-oriented strategy in the initial solution to enhance search efficiency and solution quality. Case studies demonstrate that the SACP strategy significantly reduces peak grid loads and fluctuation costs compared with a fixed charging power scenario, thereby achieving balanced charging loads. Furthermore, compared to the charging scheduling strategy alone, the SACP strategy exhibits a significant reduction in fluctuation cost of charging load by 50% and the peak cost of charging load by 21.6%, thereby ensuring the stability of charging load for both the system and charging events.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348208","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
Distributive energy justice in local electricity markets: Assessing the performance of fairness indicators
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125463
Marthe Fogstad Dynge , Umit Cali
Local electricity markets are increasingly seen as a key factor in democratising the energy system. While there has been a significant amount of research on the technical and economic aspects of these markets, it is still not clear how well they align with the social acceptance of potential participants. The concept of fairness in these markets has been gaining attention, but there has not been a clear discussion or definition of what constitutes fairness in a local electricity market context. Furthermore, some fairness indicators have become popular in the literature on local electricity markets, but have yet to be fully analysed on how well they capture fairness in these markets. This study aims to address these issues by formally defining distributive energy justice in the context of local electricity markets and evaluating how well popular fairness indicators perform based on this definition. The indicators are tested on a simulated local electricity market using real consumption data from Norwegian households. The results of this extensive evaluation lead to proposed adjustments to the indicators to make them more suitable for local electricity markets, and a discussion on future research directions for fairness in local electricity markets. When indicators are appropriately aligned with local electricity markets and their definition of fairness, they can trigger changes to market designs that improve distributive energy justice and avoid bias against market participants.
{"title":"Distributive energy justice in local electricity markets: Assessing the performance of fairness indicators","authors":"Marthe Fogstad Dynge ,&nbsp;Umit Cali","doi":"10.1016/j.apenergy.2025.125463","DOIUrl":"10.1016/j.apenergy.2025.125463","url":null,"abstract":"<div><div>Local electricity markets are increasingly seen as a key factor in democratising the energy system. While there has been a significant amount of research on the technical and economic aspects of these markets, it is still not clear how well they align with the social acceptance of potential participants. The concept of fairness in these markets has been gaining attention, but there has not been a clear discussion or definition of what constitutes fairness in a local electricity market context. Furthermore, some fairness indicators have become popular in the literature on local electricity markets, but have yet to be fully analysed on how well they capture fairness in these markets. This study aims to address these issues by formally defining distributive energy justice in the context of local electricity markets and evaluating how well popular fairness indicators perform based on this definition. The indicators are tested on a simulated local electricity market using real consumption data from Norwegian households. The results of this extensive evaluation lead to proposed adjustments to the indicators to make them more suitable for local electricity markets, and a discussion on future research directions for fairness in local electricity markets. When indicators are appropriately aligned with local electricity markets and their definition of fairness, they can trigger changes to market designs that improve distributive energy justice and avoid bias against market participants.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125463"},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395271","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}
引用次数: 0
Machine learning assisted health status analysis and degradation prediction of aging proton exchange membrane fuel cells
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125483
Fan Zhang , Meng Ni , Shupeng Tai , Bingfeng Zu , Fuqiang Xi , Yangyang Shen , Bowen Wang , Zhikun Qin , Rongxuan Wang , Ting Guo , Kui Jiao
Proton exchange membrane fuel cells (PEMFCs) represent a significant application scenario for hydrogen energy and an important sector in achieving net-zero carbon emission. Prognostics and health management are crucial for enhancing their durability and reducing maintenance costs. This study proposes a framework for health status analysis and degradation prediction of aging PEMFCs, addressing the challenge of accurately identifying internal parameter states faced by current life prediction methods. Six aging factors are incorporated into the developed PEMFC mechanism model to characterize its intricate degradation process. The variations in these factors over a 3750-h experimental period are then estimated using the Particle Filtering method. Results demonstrate a notable reduction in the electrochemical surface area, decreasing from 5.76 m2 to 4.08 m2, accompanied by a significant increase in leakage current to nearly 6 A m−2. These findings indicate substantial degradation of both the catalyst layer and membrane. Furthermore, ionic and contact resistances have increased as a result of reduced membrane conductivity and bipolar plate corrosion, respectively. The mass transport capacity has diminished, leading to an elevated concentration loss within the cell. Subsequently, the Transformer model is employed to forecast future changes in the aging factors and realize the degradation prediction over the next 1000 h. The effectiveness of the proposed method is fully validated under various conditions, with the average prediction error less than 4 %, which demonstrates higher long-term prediction accuracy compared to previous studies. This study provides an effective framework for the health management of PEMFCs and facilitates their widespread commercialization.
{"title":"Machine learning assisted health status analysis and degradation prediction of aging proton exchange membrane fuel cells","authors":"Fan Zhang ,&nbsp;Meng Ni ,&nbsp;Shupeng Tai ,&nbsp;Bingfeng Zu ,&nbsp;Fuqiang Xi ,&nbsp;Yangyang Shen ,&nbsp;Bowen Wang ,&nbsp;Zhikun Qin ,&nbsp;Rongxuan Wang ,&nbsp;Ting Guo ,&nbsp;Kui Jiao","doi":"10.1016/j.apenergy.2025.125483","DOIUrl":"10.1016/j.apenergy.2025.125483","url":null,"abstract":"<div><div>Proton exchange membrane fuel cells (PEMFCs) represent a significant application scenario for hydrogen energy and an important sector in achieving net-zero carbon emission. Prognostics and health management are crucial for enhancing their durability and reducing maintenance costs. This study proposes a framework for health status analysis and degradation prediction of aging PEMFCs, addressing the challenge of accurately identifying internal parameter states faced by current life prediction methods. Six aging factors are incorporated into the developed PEMFC mechanism model to characterize its intricate degradation process. The variations in these factors over a 3750-h experimental period are then estimated using the Particle Filtering method. Results demonstrate a notable reduction in the electrochemical surface area, decreasing from 5.76 m<sup>2</sup> to 4.08 m<sup>2</sup>, accompanied by a significant increase in leakage current to nearly 6 A m<sup>−2</sup>. These findings indicate substantial degradation of both the catalyst layer and membrane. Furthermore, ionic and contact resistances have increased as a result of reduced membrane conductivity and bipolar plate corrosion, respectively. The mass transport capacity has diminished, leading to an elevated concentration loss within the cell. Subsequently, the Transformer model is employed to forecast future changes in the aging factors and realize the degradation prediction over the next 1000 h. The effectiveness of the proposed method is fully validated under various conditions, with the average prediction error less than 4 %, which demonstrates higher long-term prediction accuracy compared to previous studies. This study provides an effective framework for the health management of PEMFCs and facilitates their widespread commercialization.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348498","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
Energy-efficient driving for distributed electric vehicles considering wheel loss energy: A distributed strategy based on multi-agent architecture
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125462
Yufu Liang, Wanzhong Zhao, Jinwei Wu, Kunhao Xu, Xiaochuan Zhou, Zhongkai Luan, Chunyan Wang
Distributed electric vehicles equipped with four-wheel independent drive (4WID) and four-wheel independent steering (4WIS) systems offer trajectory tracking performance and energy-saving potential. However, the challenge remains in how to coordinate the steering angles and torques of the four wheels to balance both tracking accuracy and energy efficiency. Distributed control, which trades design complexity for control flexibility, is able to differentiate the control of different wheels according to the vehicle's driving state to reduce wheel loss energy, providing a new perspective for improving the energy-efficient potential of vehicles. In this paper, a physical-data-driven distributed predictive control strategy is proposed within a distributed control framework, and multi-agent vehicle and wheel energy consumption models are constructed. To address the increased energy consumption and reduced trajectory tracking accuracy caused by model mismatches, a novel physical-data-driven predictive model-building approach is introduced, with real-time updates facilitated by the Givens Rotation and forgetting mechanism. The weights of the optimization objective function are dynamically adjusted according to changes in the wheel states to achieve comprehensive optimization of trajectory tracking and energy efficiency. Experimental results demonstrate that the proposed control strategy significantly reduces driving energy consumption while improving trajectory tracking performance. Under the CLTC-P cycle condition, energy loss is reduced by 11.5 %; under S-curve and double lane change steering conditions, energy losses are reduced by 15.0 % and 16.6 %, respectively. These results fully validate the effectiveness and superiority of the proposed strategy in practical applications.
{"title":"Energy-efficient driving for distributed electric vehicles considering wheel loss energy: A distributed strategy based on multi-agent architecture","authors":"Yufu Liang,&nbsp;Wanzhong Zhao,&nbsp;Jinwei Wu,&nbsp;Kunhao Xu,&nbsp;Xiaochuan Zhou,&nbsp;Zhongkai Luan,&nbsp;Chunyan Wang","doi":"10.1016/j.apenergy.2025.125462","DOIUrl":"10.1016/j.apenergy.2025.125462","url":null,"abstract":"<div><div>Distributed electric vehicles equipped with four-wheel independent drive (4WID) and four-wheel independent steering (4WIS) systems offer trajectory tracking performance and energy-saving potential. However, the challenge remains in how to coordinate the steering angles and torques of the four wheels to balance both tracking accuracy and energy efficiency. Distributed control, which trades design complexity for control flexibility, is able to differentiate the control of different wheels according to the vehicle's driving state to reduce wheel loss energy, providing a new perspective for improving the energy-efficient potential of vehicles. In this paper, a physical-data-driven distributed predictive control strategy is proposed within a distributed control framework, and multi-agent vehicle and wheel energy consumption models are constructed. To address the increased energy consumption and reduced trajectory tracking accuracy caused by model mismatches, a novel physical-data-driven predictive model-building approach is introduced, with real-time updates facilitated by the Givens Rotation and forgetting mechanism. The weights of the optimization objective function are dynamically adjusted according to changes in the wheel states to achieve comprehensive optimization of trajectory tracking and energy efficiency. Experimental results demonstrate that the proposed control strategy significantly reduces driving energy consumption while improving trajectory tracking performance. Under the CLTC-P cycle condition, energy loss is reduced by 11.5 %; under S-curve and double lane change steering conditions, energy losses are reduced by 15.0 % and 16.6 %, respectively. These results fully validate the effectiveness and superiority of the proposed strategy in practical applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125462"},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372647","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
Flattening the peak demand curve through energy efficient buildings: A holistic approach towards net-zero carbon
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125421
Yerbol Akhmetov , Ekaterina Fedotova , Martha Maria Frysztacki
This study employs a sector-coupled energy system model to co-optimise investments in the supply side, demand side, and efficiency improvements. Beginning with a novel validation exercise of 2023, we demonstrate that the model can accurately reproduce the energy mix with an error of less than 5%. This approach incorporates often-neglected energy carriers, such as coal, gas, and nuclear, providing a holistic view of the current energy landscape. The analysis focuses on the impact of energy efficiency measures and building renovations on seasonal peak heating demand in Europe, featuring a pathway study that examines carbon emission targets for 2030, 2040, and 2050, while incorporating a new focus on efficiency improvements and demand-side response for the heating sector. Results indicate that reducing peak heating demand by up to 49% is cost-optimal and can facilitate annual reductions of 0.2 billion tons of greenhouse gas emissions by 2030, exceeding current emissions targets by 10%. Additionally, the findings suggest potential savings of €44.2 billion in distribution grid investments and a 75% decrease in transmission grid congestion. The study highlights that lowering peak demand could alleviate the need for significant investments in renewable energy infrastructure, potentially eliminating the requirement for 600 GW of onshore wind and 872 GW of solar PV capacity. Furthermore, optimising transmission and supply investments could lead to lower electricity prices, improving equity in pricing across European countries and significantly reducing energy bills for households and industries. Overall, the research underscores the critical role of energy efficiency and flexibility measures in achieving Europe’s decarbonisation goals while ensuring affordable energy access.
{"title":"Flattening the peak demand curve through energy efficient buildings: A holistic approach towards net-zero carbon","authors":"Yerbol Akhmetov ,&nbsp;Ekaterina Fedotova ,&nbsp;Martha Maria Frysztacki","doi":"10.1016/j.apenergy.2025.125421","DOIUrl":"10.1016/j.apenergy.2025.125421","url":null,"abstract":"<div><div>This study employs a sector-coupled energy system model to co-optimise investments in the supply side, demand side, and efficiency improvements. Beginning with a novel validation exercise of 2023, we demonstrate that the model can accurately reproduce the energy mix with an error of less than 5%. This approach incorporates often-neglected energy carriers, such as coal, gas, and nuclear, providing a holistic view of the current energy landscape. The analysis focuses on the impact of energy efficiency measures and building renovations on seasonal peak heating demand in Europe, featuring a pathway study that examines carbon emission targets for 2030, 2040, and 2050, while incorporating a new focus on efficiency improvements and demand-side response for the heating sector. Results indicate that reducing peak heating demand by up to 49% is cost-optimal and can facilitate annual reductions of 0.2 billion tons of greenhouse gas emissions by 2030, exceeding current emissions targets by 10%. Additionally, the findings suggest potential savings of €44.2 billion in distribution grid investments and a 75% decrease in transmission grid congestion. The study highlights that lowering peak demand could alleviate the need for significant investments in renewable energy infrastructure, potentially eliminating the requirement for 600 GW of onshore wind and 872 GW of solar PV capacity. Furthermore, optimising transmission and supply investments could lead to lower electricity prices, improving equity in pricing across European countries and significantly reducing energy bills for households and industries. Overall, the research underscores the critical role of energy efficiency and flexibility measures in achieving Europe’s decarbonisation goals while ensuring affordable energy access.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":""},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348497","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
Energy management of electric vehicles based on improved long short term memory network and data-enabled predictive control
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-08 DOI: 10.1016/j.apenergy.2025.125456
Bin Chen , Guo He , Lin Hu , Heng Li , Miaoben Wang , Rui Zhang , Kai Gao
As a popular energy management strategy (EMS) in electric vehicles with hybrid energy storage systems (HESS), model predictive control (MPC) is vulnerable to model accuracy and parameter sensitivity effects with existing parametric modeling methods. This paper proposes a novel EMS based on hierarchical data-driven predictive control. The upper layer utilizes an optimized long short-term memory (LSTM) network for trajectory prediction, enabling the acquisition of cost-effective load power demands for the lower layer. In the lower layer, a data-enabled predictive control (DeePC) is proposed for the HESS to achieve optimal power distribution between the battery and supercapacitor while minimizing battery capacity loss. Unlike conventional MPC, DeePC is based on a non-parametric model built solely from input–output data of the HESS, enabling agile handling of diverse nonlinearities and uncertainties across different tasks and environments. Comparison with nonlinear model predictive control shows that DeePC reduces the total operating cost by 22.68%, with optimization results closer to offline dynamic programming results. Furthermore, the effectiveness of the proposed DeePC method is validated through hardware-in-the-loop (HIL).
{"title":"Energy management of electric vehicles based on improved long short term memory network and data-enabled predictive control","authors":"Bin Chen ,&nbsp;Guo He ,&nbsp;Lin Hu ,&nbsp;Heng Li ,&nbsp;Miaoben Wang ,&nbsp;Rui Zhang ,&nbsp;Kai Gao","doi":"10.1016/j.apenergy.2025.125456","DOIUrl":"10.1016/j.apenergy.2025.125456","url":null,"abstract":"<div><div>As a popular energy management strategy (EMS) in electric vehicles with hybrid energy storage systems (HESS), model predictive control (MPC) is vulnerable to model accuracy and parameter sensitivity effects with existing parametric modeling methods. This paper proposes a novel EMS based on hierarchical data-driven predictive control. The upper layer utilizes an optimized long short-term memory (LSTM) network for trajectory prediction, enabling the acquisition of cost-effective load power demands for the lower layer. In the lower layer, a data-enabled predictive control (DeePC) is proposed for the HESS to achieve optimal power distribution between the battery and supercapacitor while minimizing battery capacity loss. Unlike conventional MPC, DeePC is based on a non-parametric model built solely from input–output data of the HESS, enabling agile handling of diverse nonlinearities and uncertainties across different tasks and environments. Comparison with nonlinear model predictive control shows that DeePC reduces the total operating cost by 22.68%, with optimization results closer to offline dynamic programming results. Furthermore, the effectiveness of the proposed DeePC method is validated through hardware-in-the-loop (HIL).</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125456"},"PeriodicalIF":10.1,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348986","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
How heat waves and urban microclimates affect building cooling energy demand? Insights from fifteen eastern Chinese cities
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-06 DOI: 10.1016/j.apenergy.2025.125424
Xiaoshan Yang , Lingye Yao , Mingcai Li , Jingfu Cao , Qing Zhong , Weidong Peng , Wenkai Wu , Jing Zhou
Heat waves (HW), characterized by prolonged period of excessively high temperatures on a regional scale, are becoming increasingly frequent due to climate change. Concurrently, the urban heat island (UHI) effect—a localized climate phenomenon resulting from urbanization—affects cities worldwide. The interaction between HW and UHI exacerbates urban overheating, posing significant threats to human health, ecological stability, and energy consumption. A critical consequence of this synergy is the heightened demand for cooling energy in urban buildings. However, research examining the combined effects of HWs and urban microclimates (UMs)—particularly concerning both air temperature and humidity—remains limited. The present study utilized three years of hourly meteorological data from 15 cities in eastern China to explore the impacts of HWs and UMs on the cooling energy performance of a typical residential building. Key findings include: (1) During HW days, both air temperature (Ta) and dew-point temperature (Tdew) were significantly elevated compared to normal hot summer days. (2) The UHI effects led to increases in sensible cooling load, whereas the urban dry island (UDI) effects resulted in decreases in latent cooling load. (3) The combined impacts of HWs and UMs contributed to a 65% to 115% rise in sensible cooling energy demand, a 20% to 106% increase in latent cooling energy demand, and a 42% to 103% growth in total cooling energy demand. (4) Daily peak cooling loads for urban buildings during HWs increased by 21% to 62%. (5) Strong correlations were found between daily sensible cooling energy demand and daily mean Ta (R2 = 0.94), as well as between daily latent cooling energy demand and daily mean Tdew (R2 = 0.94). This study leverages long-term meteorological observations from multiple cities to provide a thorough understanding of how HWs and UMs impact building cooling energy performance. It underscores the necessity of considering the combined effects of HWs and UMs, as well as the roles of air temperature and humidity, when evaluating urban cooling energy needs. The findings offer valuable insights for planning energy infrastructure, designing effective cooling systems, improving energy management strategies, and enhancing grid resilience.
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引用次数: 0
Personalized federated learning for household electricity load prediction with imbalanced historical data
IF 10.1 1区 工程技术 Q1 ENERGY & FUELS Pub Date : 2025-02-06 DOI: 10.1016/j.apenergy.2025.125419
Shibo Zhu , Xiaodan Shi , Huan Zhao , Yuntian Chen , Haoran Zhang , Xuan Song , Tianhao Wu , Jinyue Yan
Household consumption accounts for about one-third of global electricity. Accurate results of household load prediction would help in energy management at both the building and the grid levels. Data-driven household load prediction methods have shown great advantages and potential in terms of accuracy. However, these methods still face challenges such as limited data for individual households, diversified electricity consumption behaviors, and data privacy concerns. To solve these problems, this paper proposes a personalized federated learning household load prediction framework (PF-HoLo), which allows personal models to learn collectively, leverages multisource data to capture diverse consumption behaviors, and ensures data privacy. In addition, the global encoder model and mutual learning are proposed to enhance the performance of the PF-HoLo framework considering imbalanced residential historical data. Ablation experiments results prove that the PF-HoLo framework could achieve significant improvements, with 13.41% Mean Square Error and 11.33% Mean Absolute Error, compared to traditional federated learning methods.
{"title":"Personalized federated learning for household electricity load prediction with imbalanced historical data","authors":"Shibo Zhu ,&nbsp;Xiaodan Shi ,&nbsp;Huan Zhao ,&nbsp;Yuntian Chen ,&nbsp;Haoran Zhang ,&nbsp;Xuan Song ,&nbsp;Tianhao Wu ,&nbsp;Jinyue Yan","doi":"10.1016/j.apenergy.2025.125419","DOIUrl":"10.1016/j.apenergy.2025.125419","url":null,"abstract":"<div><div>Household consumption accounts for about one-third of global electricity. Accurate results of household load prediction would help in energy management at both the building and the grid levels. Data-driven household load prediction methods have shown great advantages and potential in terms of accuracy. However, these methods still face challenges such as limited data for individual households, diversified electricity consumption behaviors, and data privacy concerns. To solve these problems, this paper proposes a personalized federated learning household load prediction framework (PF-HoLo), which allows personal models to learn collectively, leverages multisource data to capture diverse consumption behaviors, and ensures data privacy. In addition, the global encoder model and mutual learning are proposed to enhance the performance of the PF-HoLo framework considering imbalanced residential historical data. Ablation experiments results prove that the PF-HoLo framework could achieve significant improvements, with 13.41% Mean Square Error and 11.33% Mean Absolute Error, compared to traditional federated learning methods.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"384 ","pages":"Article 125419"},"PeriodicalIF":10.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143332393","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}
引用次数: 0
期刊
Applied Energy
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