Energy最新文献

{"title":"Utilizing supercritical carbon dioxide/propane mixture for efficient heat extraction from salinity gradient solar ponds","authors":"Morteza Khoshvaght-Aliabadi ,&nbsp;Fatemeh Hojjati ,&nbsp;Yong Tae Kang","doi":"10.1016/j.energy.2025.138525","DOIUrl":"10.1016/j.energy.2025.138525","url":null,"abstract":"<div><div>Efficient heat extraction from salinity gradient solar ponds remains a critical challenge for their practical application and widespread adoption. This study addresses this challenge by investigating the use of supercritical carbon dioxide (sCO₂)/propane binary mixtures as high-performance heat transfer fluids in the internal heat exchanger of solar ponds, which represents a novel approach in this field. A comprehensive three-dimensional numerical analysis is conducted, and the system is optimized using response surface methodology with a Box-Behnken design and Analysis of Variance to systematically assess the influence of design and operational parameters on key performance indicators, including heat extraction rate, outlet fluid temperature, and pumping power. The results reveal that introducing propane modifies the flow dynamics of sCO₂ by altering the balance between centrifugal and buoyancy forces, which reduces the Richardson number and significantly affects the thermal and hydraulic behavior. Notably, higher mass fluxes amplify thermal variations, while increased propane content stabilizes them. Pumping power initially decreases with propane addition up to a 60 % mass fraction, followed by a slight increase. Optimization demonstrates that mass flux predominantly governs heat extraction, whereas propane mass fraction is more influential in increasing the outlet temperature. The best-performing configurations achieve a 110.6 % increase in heat extraction rate and a 4.8 % increase in outlet temperature compared to the central point, highlighting the potential of sCO₂/propane mixtures for efficient solar pond thermal management.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138525"},"PeriodicalIF":9.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156952","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 deposition–removal-informed hybrid temporal model for online fouling estimation of industrial heat exchangers under parameter variability and nonstationarity","authors":"Chao Ren ,&nbsp;Jie Han ,&nbsp;Lin Sun ,&nbsp;Chunhua Yang","doi":"10.1016/j.energy.2025.138367","DOIUrl":"10.1016/j.energy.2025.138367","url":null,"abstract":"<div><div>Fouling-induced efficiency degradation in industrial heat exchangers poses a critical challenge to energy sustainability in process industries. This study proposes a physics-informed hybrid temporal model (PI-HTM) for online estimation of fouling resistance. The proposed model combines a physics-based deposition–removal mechanism (DRM) to represent fouling dynamics with a deep temporal neural network. The network architecture integrates temporal convolutional networks (TCN) and bidirectional gated recurrent units (BiGRU) to effectively capture multi-scale temporal dependencies. An adaptive online learning framework is introduced to improve the model’s adaptability to variations in intrinsic fouling parameters, which are driven by fluctuations in fluid composition and operating conditions. This approach mitigates the limitations of conventional methods in handling such dynamic environments. Model parameters are updated in real time using the state transition algorithm (STA) based on recent operational trajectories. Additionally, fouling discontinuities induced by cleaning actions are incorporated into the improved DRM, enabling accurate tracking of abrupt process nonstationarities. Furthermore, a monotonicity constraint is incorporated into the physics-informed component to embed prior knowledge of the progressive nature of fouling accumulation. The proposed method is evaluated on three real-world fouling datasets, encompassing both crude oil and crystalline fouling. With only 15% of the training data, it achieves <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of 0.959, 0.989, and 0.957, demonstrating high predictive accuracy, strong generalization capability, and adherence to the underlying physical mechanisms.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138367"},"PeriodicalIF":9.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156980","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":"Performance enhancement of thermally regenerative flow battery by a novel design coupling Venturi-effect-inducing structure with nature-inspired flow distributors","authors":"Jiebo Yang ,&nbsp;Qinghua Yu ,&nbsp;Sheng Chen ,&nbsp;Fuwu Yan ,&nbsp;Yongcheng Jin","doi":"10.1016/j.energy.2025.138597","DOIUrl":"10.1016/j.energy.2025.138597","url":null,"abstract":"<div><div>The symmetric sinusoidal flow channel (SSFC), which can induce the Venturi effect, has been found to hold significant potential for enhancing the output performance and low-grade waste heat recovery efficiency of the thermally regenerative ammonia-based flow battery (TRAFB). However, this flow channel suffers from substantial mass transfer dead zones between adjacent constrictions. Inspired by the streamlined profile of water droplets in nature, four water droplet-like flow distributors (WFD Ⅰ-Ⅳ) are designed by this study and ingeniously coupled with the flow channel to address this issue and to achieve a higher performance thermally regenerative ammonia-based flow battery. The results indicate that the incorporation of these flow distributors not only significantly reduces the mass transfer dead zones but also further amplifies the Venturi effect, thereby diminishing reactant-starved regions and enhancing uniformity. The coupling of the symmetric sinusoidal flow channel with each of the four flow distributors enables the battery to achieve higher net power, electrical capacity, and thermoelectric conversion efficiency, as well as lower overpotential. Among these, the flow channel design scheme coupling symmetric sinusoidal structure with the flow distributor Ⅳ performs the best, realizing a peak net power increase of approximately 17.68 %, an ultimate electrical capacity increase of approximately 26.35 %, and a thermoelectric conversion efficiency increase of approximately 38.26 % compared to the original symmetric sinusoidal flow channel. To further evaluate the scalability and application feasibility of this scheme, it is applied to modify the structures of the three most commonly used flow fields. The modified flow fields all demonstrate better mass transfer, larger energy storage scale and higher efficiency, with the improvement becoming more pronounced at higher discharge currents. The highest Carnot-relative efficiency and net power are achieved by the modified serpentine flow field, reaching approximately 32.05 % and 317.14 W m⁻², respectively. However, the flow field with the most significant performance enhancement is the parallel flow field, which realizes a peak net power increase of approximately 27.47 % and a Carnot-relative efficiency increase of approximately 7.14 percentage points. Overall, the improvement effects are ranked as follows: parallel flow field &gt; interdigital flow field &gt; serpentine flow field.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138597"},"PeriodicalIF":9.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156778","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":"High-pressure pyrolysis mechanism of tar-rich coal in Taiyuan Formation, Ordos Basin","authors":"Han Tian ,&nbsp;Wei Guo ,&nbsp;Qiang Li ,&nbsp;Sunhua Deng ,&nbsp;Fengtian Bai ,&nbsp;Yanwei Li ,&nbsp;Yijian Zeng ,&nbsp;Chaofan Zhu","doi":"10.1016/j.energy.2025.138675","DOIUrl":"10.1016/j.energy.2025.138675","url":null,"abstract":"<div><div>Poor fluidity and difficult extraction of coal tar pose critical challenges for in-situ tar-rich coal development. However, the influence of temperature-pressure variations on pyrolysis products remains unclear. This study explores pressure's dual regulatory mechanisms on pyrolysis kinetics and product distribution via pressurized thermogravimetric and pyrolysis experiments. Thermogravimetric analysis shows 24.1 % thermal weight loss at atmospheric pressure; at 8 MPa, this decreases by 6.97 %, with decomposition activation energy increasing by 20.9 % due to enhanced organic matter interactions promoting small-molecule and coke formation. At 8 MPa, 500–700 °C condensation converts high-viscosity heavy tar to lighter tar and residual carbon. Coal tar analysis reveals pressure boosts light hydrocarbons (max. 61.6 %). High-pressure environments promote the cleavage of macromolecular Cal-Cal and Car-CH₃ bonds, thereby facilitating the generation of CH₄: 550 °C/8 MPa yields 33.45 % CH₄ (five times atmospheric), favoring methane-rich syngas. At 550 °C, kerogen fully cracks, with residues (TOC &gt;40 %) providing energy via oxidation-coupled exothermic reactions. This work demonstrates improved tar fluidity, supporting optimized in-situ conversion sweet spot theory.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138675"},"PeriodicalIF":9.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156689","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":"Declared strategy of risk-constrained wind power participating in the power markets considering multiple uncertainties","authors":"Mengchao Xu,&nbsp;Xiyun Yang,&nbsp;Shengwei Huang,&nbsp;Zihao Luo","doi":"10.1016/j.energy.2025.138613","DOIUrl":"10.1016/j.energy.2025.138613","url":null,"abstract":"<div><div>The large-scale integration of wind power has significantly increased the demand for frequency regulation in power grids, making the declaration strategy of wind power producers increasingly crucial. However, existing wind power declaration strategy often lack practicality, as they typically fail to fully account for uncertainties in wind power output, electricity price fluctuations, and revenue risks. To address this issue, this paper proposes a multi-market bidding strategy for wind power that incorporates interval probabilistic forecasting and risk-coordinated constraints to account for multiple uncertainties in wind power generation. First, a cooperative trading mechanism for wind farms in the electricity market is established. Then, a risk-constrained wind power joint declaration decision model (RCWP) is developed. The model employs Interval Stochastic Constrained Optimization-Long Short-Term Memory (ISCO-LSTM) to address uncertainties in market prices and wind power output. Additionally, the LOF-Interpolation-Joint Adaptive Noise Reduction and Reconstruction (LOFI-JANRR) method is integrated to enhance the quality of input data used for forecasting. Finally, the model is solved using an Enhanced Population-Based Beluga Whale Optimization (EPBWO) algorithm. A comprehensive evaluation of real-world case studies demonstrates that the proposed RCWP model enhances wind farm revenues while effectively mitigating the probability of low returns under extreme scenarios. Moreover, compared to other algorithms, the proposed approach exhibits superior optimal performance and forecasting accuracy.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138613"},"PeriodicalIF":9.4,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156951","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":"Material and exergy-driven comparative assessment of hydrogen-rich fuels injection in blast furnaces: Feasibility envelope and carbon reduction","authors":"Xiaohui Zhang ,&nbsp;Nan Wang ,&nbsp;Wenjun Duan ,&nbsp;Haifeng Li ,&nbsp;Min Chen","doi":"10.1016/j.energy.2025.138659","DOIUrl":"10.1016/j.energy.2025.138659","url":null,"abstract":"<div><div>To address the urgent need for decarbonization in ironmaking, this study investigates the sustainable co-injection of hydrogen-rich fuels, including hydrogen (H₂), coke oven gas (COG), and natural gas (NG) with pulverized coal into blast furnaces. Based on a comprehensive mass-energy-exergy balance model incorporating the Rist operation line, a systematic evaluation model has been developed. This model enables synergistic assessment of technical indicators, CO₂ emission reduction, and optimization of the matching relationship among hydrogen-rich fuels, pulverized coal, and oxygen enrichment ratio at a critical raceway adiabatic flame temperature (RAFT) of 2050 °C and reasonable top gas temperature. Under optimal operating conditions, the maximum injection rates for H₂, COG, and NG are determined to be 165, 97, and 85 m³/THM, respectively, while the maximum carbon reduction amount with pulverized coal and H₂ co-injection reaches 9.03 %. In comparison to a traditional blast furnace, the input exergy of pulverized coal for H₂, COG, and NG co-injection decreases by 7.11, 8.98 and 12.47 %, whereas the output exergy of blast furnace gas (BFG) increases by 20.29, 17.28 and 24.41 %, respectively. Furthermore, hydrogen-rich blast furnaces exhibit favorable performance in terms of energy efficiency and thermodynamic perfection degree. However, maintaining a constant RAFT in hydrogen-rich blast furnaces requires higher exergy input. This study provides a quantitative framework for optimizing the hydrogen-rich fuels injection in blast furnaces, establishing decarbonization roadmaps for achieving low-carbon ironmaking.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138659"},"PeriodicalIF":9.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156871","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":"Parallel TCN-BiGRU architecture with dynamic attention for ship energy consumption prediction under variable navigation conditions","authors":"Enzhe Song ,&nbsp;Xinyue Zhang ,&nbsp;Yuwei Ge ,&nbsp;Chong Yao ,&nbsp;Bo Wang","doi":"10.1016/j.energy.2025.138601","DOIUrl":"10.1016/j.energy.2025.138601","url":null,"abstract":"<div><div>Accurate prediction of ship energy consumption is essential for improving operational efficiency and reducing emissions. However, existing models often fail to capture complex spatiotemporal dependencies inherent in dynamic maritime environments. This study presents a parallel hybrid deep learning framework (TSBG-Para), which integrates Temporal Convolutional Networks (TCN), Bidirectional Gated Recurrent Units (BiGRU), and two attention mechanisms: Squeeze-and-Excitation (SE) and Global Attention (GA). Unlike conventional serial models, TSBG-Para adopts dual parallel branches for spatial and temporal feature extraction, followed by attention-based feature fusion. Experiments on real-word voyage data show that TSBG-Para outperforms 20 benchmark models, achieving up to 46.3 % reduction in MSE under stable operating conditions. It also maintains robustness under dynamic conditions, with a MSE of 0.0719. Compared to serial counterparts, the parallel architecture reduces MSE and RMSE by 28.3 % and 15.1 %, respectively. Ablation studies further demonstrate that the SE and GA modules jointly enhance feature discrimination and improve prediction stability. These results underscore the effectiveness of parallel, attention-enhanced architectures for ship energy prediction and provide a scalable foundation for intelligent maritime energy management.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138601"},"PeriodicalIF":9.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157333","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 deep learning framework for global transportation energy carbon emission forecasting: integrating generative pre-trained transformer with multi-scale feature analysis","authors":"Wenyang Wang ,&nbsp;Yuping Luo ,&nbsp;Zihan Jiang ,&nbsp;Jibin Zhou ,&nbsp;Peng Jia","doi":"10.1016/j.energy.2025.138586","DOIUrl":"10.1016/j.energy.2025.138586","url":null,"abstract":"<div><div>The transportation sector contributes approximately 20% of global carbon dioxide emissions, posing significant challenges for energy transition and decarbonization efforts. We proposed TransCarbon-GPT, an advanced deep learning framework based on a generative pre-trained transformer architecture, designed to forecast transportation-related carbon emissions across 22 major economies. This framework integrates a multimodal dataset encompassing 33 domains and over 29000 feature variables, including energy price indices, fossil fuel consumption patterns, and energy policy indicators. Leveraging transfer learning techniques built upon the open-source LLaMA3 model, TransCarbon-GPT achieves state-of-the-art predictive performance, with SMAPE values ranging from 0.3782% to 5.7329%, significantly surpassing conventional forecasting approaches. The framework employs SHapley Additive exPlanations (SHAP) to identify key drivers of carbon emissions at both global and national scales to enhance interpretability. Our findings highlight energy price volatility, economic policy uncertainties surrounding energy transitions, and geopolitical risks as dominant factors influencing transportation emissions, with distinct impacts observed between developed and developing nations. Notably, natural gas prices influence more than crude oil prices in economies with diversified energy portfolios. Ablation studies reveal that incorporating patching reduces RMSE and MAE by 23.09% and 19.23%, respectively, while channel independence achieves reductions of 20.48% and 17.92%. Combining both components, the hybrid architecture delivers the most substantial improvements, reducing RMSE and MAE by 68.45% and 72.01%, respectively. TransCarbon-GPT provides actionable insights for policymakers to design targeted carbon reduction strategies, supports transportation enterprises in optimizing energy consumption, and facilitates the development of cleaner energy pathways, advancing the transition toward energy-efficient transportation systems.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"338 ","pages":"Article 138586"},"PeriodicalIF":9.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270450","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":"Intelligent bidirectional control of combustion phase in dual-fuel engines","authors":"Junyang Xie ,&nbsp;Chong Yao ,&nbsp;Keshuai Sun ,&nbsp;Bo Wang ,&nbsp;Enzhe Song","doi":"10.1016/j.energy.2025.138661","DOIUrl":"10.1016/j.energy.2025.138661","url":null,"abstract":"<div><div>The development of intelligent ships imposes increasingly stringent requirements on engine combustion control, a domain in which traditional methods exhibit significant limitations. This study focuses on natural gas engines ignited by a diesel micro-pilot injection strategy and proposes an intelligent, bidirectional combustion phase control framework to achieve end-to-end optimization via a perception-decision-execution pipeline. First, the Gram-Schmidt orthogonalization method is employed to decouple control parameters and quantify their individual contributions to the combustion phase (CA50). This facilitates a three-tiered control strategy: Level 1 establishes baselines for pre-injection and main injection timing; Level 2 compensates for environmental disturbances through intake flow adjustment; and Level 3 optimizes the operating boundaries of fuel parameters. Second, a hybrid GS-DResNet-Boost model, which integrates deep residual networks with gradient boosting, is developed to achieve high-precision CA50 prediction, yielding a mean absolute error (MAE) of 0.099°CA. Furthermore, a reverse parameter recommendation system based on the Optuna framework is designed to further investigate parameter independence and coupling mechanisms. This system inversely derives optimal control parameters from a target CA50 value through a multi-objective search, ensuring stable combustion under dynamic conditions with a recommendation error of ≤0.05°CA. Experimental results demonstrate the model's superior performance across diverse operating conditions and reveal intrinsic relationships between CA50, combustion efficiency, and heat release characteristics. This study provides new methodologies for real-time optimization of intelligent ship power systems and offers theoretical foundations for dual-fuel engine combustion control strategies.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138661"},"PeriodicalIF":9.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157303","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":"Dynamic behavior and control efficacy in carnot battery systems: A comparative study of feedforward, PID, and combined control methods","authors":"Yong-qiang Feng ,&nbsp;Yu-zhe Wu ,&nbsp;Yong-zhen Wang ,&nbsp;Zhi-nan Liu ,&nbsp;Xing-xing Wang ,&nbsp;Shi-long Tian ,&nbsp;Zhi-xia He ,&nbsp;Qian Wang","doi":"10.1016/j.energy.2025.138653","DOIUrl":"10.1016/j.energy.2025.138653","url":null,"abstract":"<div><div>This study establishes a dynamic model of a Carnot Battery (CB) system to analyze the response of key parameters (evaporating pressure, superheat degree, and mass flow rate) to step and periodic heat source fluctuations under two control strategies (compressor input work control and ORC superheat degree control). Three control methods (feedforward, PID, and combined control) are comparatively evaluated in terms of system performance, response time, and stability. Results show that without control, the superheat degree is most sensitive to fluctuations, while mass flow rate is least affected. When the temperature step ratio is 6 %, under ORC superheat control, the superheat and mass flow rate of the ORC system increase by 198 % and 646 % respectively, thereby increasing the net output power by 880 %. Meanwhile, the HP system shows a 35 % and 28 % increase in evaporating pressure and superheat degree under compressor input control. The combined control method delivers the fastest dynamic response (11.53 s for HP, 33.06 s for ORC), outperforming PID by over 55 % and 30 %, respectively. Periodic fluctuation tests reveal increasing evaporating pressure overshoot in the HP system and decreasing overshoot in the ORC system with longer cycles. The superheat degree control strategy reduces levelized cost of storage (LCOS) by 0.0083 $/kWh and increases energy storage capacity (ESC) by 0.112 kWh/t, while enhances the available energy ratio (AER) by 49.7 t. These findings highlight the superheat degree control strategy, especially for combined control, as the most effective approach for enhancing the dynamic, economic, and environmental performance of CB systems.</div></div>","PeriodicalId":11647,"journal":{"name":"Energy","volume":"337 ","pages":"Article 138653"},"PeriodicalIF":9.4,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218426","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}