Energy最新文献

{"title":"Quantifying the flexibility regulation potential and economic value of pumped storage in extreme scenarios of multi-energy complementary system","authors":"Jingyue Yang ,&nbsp;Hao Zhang ,&nbsp;Chenxi Li ,&nbsp;Pengcheng Guo ,&nbsp;Bo Ming","doi":"10.1016/j.energy.2025.136749","DOIUrl":"10.1016/j.energy.2025.136749","url":null,"abstract":"<div><div>The power system with a high proportion of renewable energy installed capacity requires large-scale power supply adjustment to ensure stable operation. Pumped storage, as a typical large-scale flexible power supply, can effectively stabilize the output fluctuations of renewable energy. This study aims to enhance the flexibility of novel power systems by exploring the regulation potential of pumped storage under extreme scenarios. Extreme scenarios with low, medium, and high renewable penetration rates are generated using Gaussian Mixture Model (GMM) clustering. These scenarios enable an in-depth analysis of flexibility regulation demands and capabilities on both the supply and demand sides. A scheduling optimization model is developed to assess the flexibility regulation capacity and economic benefits of pumped storage. The model quantifies the flexibility regulation capacity and the total daily electricity cost of pumped storage under different scenarios. The results show that in the medium penetration scenario, the total transferable load and its peak periods are reduced. In the high penetration scenario, the flexibility regulation capacity of pumped storage becomes more pronounced. When the ratio of renewable energy, pumped storage, and thermal power is 2:1:1, pumped storage provides enhanced flexibility regulation and minimizes the total electricity cost.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136749"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124699","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":"Hybrid energy system for reverse osmosis desalination: Kalina cycle power from an abandoned oil well with hydrogen and battery backup during geothermal well maintenance","authors":"Siamak Hoseinzadeh ,&nbsp;Mohammad Norouzi ,&nbsp;Kianoosh Rezaie ,&nbsp;M. Hadi Ghasemi ,&nbsp;Davide Astiaso Garcia","doi":"10.1016/j.energy.2025.136712","DOIUrl":"10.1016/j.energy.2025.136712","url":null,"abstract":"<div><div>This study evaluates the integration of geothermal abandoned oil wells for power generation and desalination, addressing the dual challenges of energy and water scarcity. Using advanced simulation tools—Engineering Equation Solver for the Kalina cycle system 11, Wave for the reverse osmosis system, and HOMER for hybrid energy optimization—the research integrates geothermal power, battery storage, and hydrogen storage. The geothermal Kalina cycle system 11 generates efficient power using a water-ammonia mixture, producing 210 kWh at 15 bars and 215 kWh at 20 bars. Exergy analysis showed that higher pressures enhanced system efficiency, compactness, and resilience. The two-stage reverse osmosis system, with a recovery rate of 55 %, produced 805.1 m³/day of potable water at a specific energy consumption of 4.78 kW/m³, confirming its energy-efficient operation. An optimized strategy, developed using HOMER, limited geothermal Kalina cycle system 11 shutdowns from one month a year to a week every three months. During these outages, a hybrid energy solution involving 1146 kg of hydrogen storage and 10,500 12-V batteries ensured reliable operation of the reverse osmosis system, minimizing costs. The hybrid system demonstrated the complementary roles of hydrogen and batteries. While hydrogen storage provided 14,920 kWh/year, the battery system efficiently supplied the reverse osmosis's energy needs for up to seven days. This study validates the technical and economic feasibility of integrating the geothermal Kalina cycle system 11 with hybrid energy storage and desalination technologies, highlighting the importance of simulation tools in optimizing integrated energy-water systems.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"330 ","pages":"Article 136712"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168076","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 end-to-end smart lifetime prediction method for flexible thermal power plants: A case study on main steam pipe","authors":"Wencan Zhang,&nbsp;Lei Pan,&nbsp;Junzheng Zhang,&nbsp;Ming Chen","doi":"10.1016/j.energy.2025.136728","DOIUrl":"10.1016/j.energy.2025.136728","url":null,"abstract":"<div><div>As thermal power plants have transformed in power grids from being the primary power source to being regulatory power source, deep and rapid load changes have become the norm. This frequently subjects thick-walled components under high temperature, such as boiler main steam pipes, to additional creep lifetime damage, posing security issues that cannot be ignored. Therefore, real-time monitoring of creep lifetime during operating conditions has become crucial. Since creep prediction for main steam pipes involves microscopic-scale calculations and is challenging to implement online, this paper proposes an online lifetime prediction method for main steam pipes that combines mechanism-based and data-driven modeling. Firstly, a finite element method is used to establish a mechanism-based model for pipe creep life. Secondly, a rapid creep lifetime deep learning prediction model based on stress prediction is introduced, where training data is generated from the mechanism-based model. This achieves an end-to-end intelligent prediction from operational data to real-time creep lifetime variations. The proposed method yields a root mean square error of 2.5 × 10⁻⁷ and an R-squared score of 0.925 on the test set, demonstrating good prediction accuracy.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136728"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138804","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":"Synergistic effects on biohydrogen production from vinasse and molasses co-digestion: Influence of mixture composition on process stability","authors":"Taciana Carneiro Chaves ,&nbsp;Fernanda Santana Peiter ,&nbsp;Georgia Nayane Silva Belo Gois ,&nbsp;Nadjane Leite dos Santos Telles ,&nbsp;Renata Maria Rosas Garcia Almeida ,&nbsp;Eduardo Lucena Cavalcante de Amorim","doi":"10.1016/j.energy.2025.136677","DOIUrl":"10.1016/j.energy.2025.136677","url":null,"abstract":"<div><div>Organic raw materials processed through anaerobic digestion are increasingly recognized as valuable renewable energy sources. Agroindustrial biomasses like vinasse and sugarcane molasses, rich in carbohydrates, can produce hydrogen via dark fermentation. This study evaluated the co-fermentation of vinasse and molasses for hydrogen production using the Simplex Lattice mixture design. Batch tests were conducted to analyze the interactive effects between vinasse (V) and molasses (M) and to determine mixture compositions for achieving maximum volumetric hydrogen yield (<em>VHY</em>_{<em>CODappl</em>}) and production rate (<em>VHPR</em>_{<em>CODappl</em>}) per applied load in terms of chemical oxygen demand (<em>COD</em>_{<em>appl</em>}). The tested conditions were 100 % vinasse (V100/M0), 75 % vinasse + 25 % molasses (V75/M25), 50 % vinasse + 50 % molasses (V50/M50), 25 % vinasse + 75 % molasses (V25/M75), and 100 % molasses (V0/M100). Results indicated synergistic interactions, though mixtures with ≥75 % vinasse caused process instability. Reactors achieved carbohydrate removal efficiencies of 46.49–74.75 %, COD removal of 13.49–26.53 %, and volatile solids reduction of 41.58–50.93 %. The V50/M50 condition yielded the highest production potential (3113.27 mL-H₂) and production rate (10.07 mL-H₂/h), with maximum <em>VHY</em>_{<em>CODappl</em>} of 595.63 mL-H₂/g-COD_appl and <em>VHPRCOD</em>_{<em>appl</em>} of 50.63 mL-H₂/g-COD_appl/d, statistically similar to V25/M75 and V0/M100. Significant quadratic models (<em>p</em> ≤ 0.05) with strong fits (R² = 0.98 for H₂ yield and R² = 0.92 for H₂ production rate) were observed. Butyric acid was the primary metabolite, supporting hydrogen production through this route.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136677"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal-electrical analysis of a novel interconnection for hybrid busbars in electric vehicle batteries","authors":"D.P.M. da Costa ,&nbsp;M.M. Kasaei ,&nbsp;R.J.C. Carbas ,&nbsp;E.A.S. Marques ,&nbsp;R.F.V. Sampaio ,&nbsp;I.M.F. Bragança ,&nbsp;L.F.M. da Silva","doi":"10.1016/j.energy.2025.136629","DOIUrl":"10.1016/j.energy.2025.136629","url":null,"abstract":"<div><div>Reliable and efficient busbar connections are critical for electric vehicle battery performance, yet conventional joining methods struggle with joining dissimilar materials such as copper and aluminum. This paper investigates a novel solution for joining hybrid copper-aluminum busbars using a technique called hole hemming, which eliminates the need for heating or additional elements. The focus is placed on the thermal-electrical performance of hole-hemming joints. Two configurations are studied: joints with and without branches. Numerical models analyse how sheet thickness affects temperature, electric current density, electric potential, and resistance, including models with cantered holes to study hole inclusion effects. Experimental tests are conducted on material strips and unit cells to assess electrical resistance changes with temperature and the effect of Joule heating on joint configurations. Compression using a hydraulic press is applied to improve contact, leading to significant electrical resistance improvements (78 % reduction for branched joints and 36 % for branchless ones). Mechanical shear tests before and after compression show a peak shear load of 4.54 kN and 13.84 mm displacement for branched joints, with slightly lower values for branchless joints. Despite a minor decrease in mechanical performance after compression, the improved thermal-electrical performance of the joints outweighs this. The findings highlight the promising potential of hole-hemmed joints for enhancing hybrid busbar connections.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136629"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131383","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":"Car-following speed collaborative energy management strategy for connected PHEV","authors":"Feng Wang ,&nbsp;Yihang Chen ,&nbsp;Yuanjian Zhang ,&nbsp;Xiaoyuan Zhu ,&nbsp;Yi-Qing Ni","doi":"10.1016/j.energy.2025.136717","DOIUrl":"10.1016/j.energy.2025.136717","url":null,"abstract":"<div><div>Sequential vehicle speed planning and energy management design is widely employed in plug-in hybrid electric vehicle (PHEV). However, this hierarchical strategy is difficult to achieve comprehensive performance optimization, as vehicle speed and energy management are inherently coupled. This paper proposes a new real-time optimization car-following speed collaborative energy management strategy (RTO-SC-EMS) for connected PHEV in the context of V2X environment, Firstly, a gated recurrent unit neural network is utilized to predict the short-term speed of the lead-vehicle based on collected representative urban driving cycles incorporating traffic information. Then, the solution section of real-time optimization car-following speed collaborative energy management strategy is used to generate host-vehicle's optimal acceleration and corresponding control sequence. Finally, the effectiveness of the proposed RTO-SC-EMS was validated by Hardware-in-the-Loop (HIL) platform.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136717"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138885","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":"Towards sustainable and intelligent urban transportation: A novel deep transfer reinforcement learning framework for eco-driving of fuel cell buses","authors":"Ruchen Huang ,&nbsp;Hongwen He ,&nbsp;Qicong Su ,&nbsp;Jingda Wu","doi":"10.1016/j.energy.2025.136730","DOIUrl":"10.1016/j.energy.2025.136730","url":null,"abstract":"<div><div>Eco-driving is a sustainable technology that optimizes both energy management and speed planning for electrified vehicles. Particularly when combined with emerging deep reinforcement learning (DRL) techniques, eco-driving strategies (EDSs) can be more intelligent. However, current research on eco-driving, namely the holistic solution, lags behind the advancements in its sub-problem namely energy management, and the development of DRL-based EDSs remains time-consuming. Since energy management is a sub-task of eco-driving, it offers a potential way to rapidly develop EDSs by reusing pre-trained energy management strategies (EMSs). Based on this, this paper proposes an expedited method for developing soft actor-critic (SAC) based EDSs for fuel cell buses (FCBs) in the vehicle-following scenario. To ensure that SAC-based EMSs can be effectively transferred to EDSs, an innovative heterogeneous deep transfer reinforcement learning framework is designed. Within this framework, all the knowledge learned in the source EMS can be transferred and reused by the target EDS. More importantly, the transferability of heterogeneous deep neural networks and heterogeneous experience replay buffers is particularly verified. Simulation results show that the proposed framework provides a 71.01 % acceleration in convergence speed and a 7.30 % improvement in fuel economy. This article contributes to correlating different optimization tasks of FCBs through advanced artificial intelligence technologies.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"330 ","pages":"Article 136730"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168222","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":"Investigation of Savonius hydrokinetic turbine’s 3D structural characteristics affecting cluster performance","authors":"Yunrui Chen,&nbsp;Lezhi Xia,&nbsp;Yonghui Wu,&nbsp;Penghua Guo,&nbsp;Jingyin Li","doi":"10.1016/j.energy.2025.136702","DOIUrl":"10.1016/j.energy.2025.136702","url":null,"abstract":"<div><div>S-VAHT clusters have become a promising approach for marine current energy utilization. However, most research has focused on cluster layout optimization, with limited attention to how 3D structural aspects like aspect ratio (<em>AR</em>) and twist angle (<em>TA</em>) affect the cluster's performance. This study fills that gap using 3-D simulations to explore the effects of <em>AR</em> and <em>TA</em> on both single turbines and clusters. Results show that the influence of <em>AR</em> on turbine performance is mainly related to the local flow near the end-plates, which affects the pressure distribution along the blade height. A larger <em>AR</em> reduces the impact of these local effects, leading to more consistent performance. In clusters, a larger <em>AR</em> enhances the blockage effect on the incoming flow, thereby improving overall array performance. <em>TA</em> has a substantial effect on the performance of both single turbines and clusters across all operating conditions, with increasing <em>TA</em> widening the performance gap between turbines. Furthermore, the interaction between <em>TA</em> and the relative azimuth angle is crucial for cluster coupling gains. Finally, the study demonstrates that <em>TA</em> improves the self-starting capability of turbines at specific azimuth angles. These findings provide a theoretical foundation for optimizing S-VAHT cluster design and offer innovative strategies to improve marine current energy efficiency.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136702"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124803","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":"Temperature-dependent multi-physical modeling strategy and safety optimization of lithium-ion battery under mechanical abuses","authors":"Lingling Hu ,&nbsp;Jinyu Yan ,&nbsp;Mingzhe Zhou ,&nbsp;Heguang Wei","doi":"10.1016/j.energy.2025.136682","DOIUrl":"10.1016/j.energy.2025.136682","url":null,"abstract":"<div><div>Mechanical abuse risks remain a major concern in the widespread use of lithium-ion batteries (LIBs) in electric transportation. Extreme ambient temperatures in different service environments significantly affect the mechanical properties and failure behaviors of LIBs, posing safety challenges. This study investigates the failure process of lithium-ion pouch batteries under mechanical indentation leading to internal short circuit (ISC) at both low and high temperatures using a coupled mechanical-electrical-thermal model. By incorporating a temperature-dependent, strain-based failure criterion for the separator and heat generation models, the proposed framework accurately reproduces two experimentally observed failure behaviors across different temperatures. At low temperatures, electrical failure is characterized by minor voltage drops and limited heat generation due to localized separator cracking. In contrast, at higher temperatures, extensive internal fractures result in sharp voltage drops and significant heat buildup. The indentation-induced failure mechanisms, integrating mechanical deformation and ISC characteristics, are discussed. Additionally, parametric studies on the jellyroll and shell casing reveal that optimizing the yield strength, elastic modulus, and geometric thickness enhances LIB safety under mechanical indentation. However, a balance must be maintained among failure displacement, load-bearing capacity, and ISC severity at the onset of electrical failure. This modeling strategy offers a multi-physical approach to predicting and optimizing battery safety, providing valuable insights for improving LIB design across diverse environmental conditions.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136682"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124595","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":"Interpretable machine learning model for bio-oil property prediction from hydrothermal liquefaction of biomass via graph neural networks-based molecular structure","authors":"Yue Tian ,&nbsp;Junghwan Kim ,&nbsp;Yi Liu","doi":"10.1016/j.energy.2025.136708","DOIUrl":"10.1016/j.energy.2025.136708","url":null,"abstract":"<div><div>Hydrothermal bio-oil is a promising fuel source to effectively alleviate the energy crisis and contribute to carbon neutrality. However, the application performance of hydrothermal bio-oil is primarily affected by its yield, compositions (C, H, O, N, and S contents), and higher heating value (HHV). Hence, we proposed an interpretable machine learning model for hydrothermal bio-oil property prediction. Graph neural network (GNN)-based molecular structure was utilized to enhance datasets. Single- and multi-task prediction models were established by extreme gradient boosting (XGB), random forest (RF), gradient boosting decision tree (GBDT), and deep neural network (DNN). Shapely values and partial dependence were utilized to interpret the impact of input features on output responses. Results indicated that XGB model (average train R² = 0.95, RMSE = 1.29, MAE = 0.69, average test R² = 0.91, RMSE = 1.30, MAE = 0.93) performed the most optimally after applying GNN, with an average performance improvement of 6.74–7.95 %. The model demonstrated outstanding performance on unknown data, achieving an average R² of 0.916. Temperature and biomass ultimate analysis emerged as pivotal features influencing output. Triglycerides exhibited a stronger influence than fatty acids owing to their higher carbon content. A combination of high temperature (&gt;350 °C) and elevated triglyceride content (&gt;30 %) increased bio-oil yield (∼40 wt%) and HHV (∼35 MJ/kg).</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"329 ","pages":"Article 136708"},"PeriodicalIF":9.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134535","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}