Pub Date : 2025-12-05DOI: 10.1016/j.renene.2025.124961
Pengcheng Li , Chengxing Shu , Jing Li , Zhenyu Ma , Chenhan Jin , Yiran Cheng , Sifan Nie , Desuan Jie , Gang Pei
Pumped thermal energy storage (PTES) is an emerging scheme for low-cost, site-independent, and environmentally friendly electricity storage. However, it faces critical technical challenges of low round-trip efficiency (generally<60 %) and significant irreversible loss during heat transfer process. This paper proposes an innovative high-temperature PTES coupling a transcritical CO2 (TCO2) heat pump cycle with a transcritical steam Rankine cycle (TSRC). It originally employs dual-storage fluids of molten salts and water with a four-tank structure, covering a wide temperature range from about 33 °C to 560 °C. Water is both low-temperature storage fluid and TSRC working fluid, thereby eliminating a secondary water-water heat transfer. In the charging process, CO2 at the compressor outlet releases heat to the molten salts and then splits into two streams. One stream increases water storage temperature, and the other preheats CO2 from the evaporator. Fundamentals of the PTES are illustrated, and mathematical models are built. The results show that the cascade sensible storage configuration can tackle the challenge of large throttling irreversibility and a high round-trip efficiency of 60.21 % can be achieved.
{"title":"An innovative high-temperature pumped thermal energy storage driven by transcritical CO2 heat pump and steam Rankine cycles","authors":"Pengcheng Li , Chengxing Shu , Jing Li , Zhenyu Ma , Chenhan Jin , Yiran Cheng , Sifan Nie , Desuan Jie , Gang Pei","doi":"10.1016/j.renene.2025.124961","DOIUrl":"10.1016/j.renene.2025.124961","url":null,"abstract":"<div><div>Pumped thermal energy storage (PTES) is an emerging scheme for low-cost, site-independent, and environmentally friendly electricity storage. However, it faces critical technical challenges of low round-trip efficiency (generally<60 %) and significant irreversible loss during heat transfer process. This paper proposes an innovative high-temperature PTES coupling a transcritical CO<sub>2</sub> (TCO<sub>2</sub>) heat pump cycle with a transcritical steam Rankine cycle (TSRC). It originally employs dual-storage fluids of molten salts and water with a four-tank structure, covering a wide temperature range from about 33 °C to 560 °C. Water is both low-temperature storage fluid and TSRC working fluid, thereby eliminating a secondary water-water heat transfer. In the charging process, CO<sub>2</sub> at the compressor outlet releases heat to the molten salts and then splits into two streams. One stream increases water storage temperature, and the other preheats CO<sub>2</sub> from the evaporator. Fundamentals of the PTES are illustrated, and mathematical models are built. The results show that the cascade sensible storage configuration can tackle the challenge of large throttling irreversibility and a high round-trip efficiency of 60.21 % can be achieved.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124961"},"PeriodicalIF":9.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734823","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}
Pub Date : 2025-12-05DOI: 10.1016/j.renene.2025.124967
Peng Zou , Hongjian Lin , Yongling Zou , Xianjie Zhou , Zhan Liu , Xiongbo Duan , Yangyang Li , Jianqiang Chen , Yingying Song
Achieving stable and efficient hydrogen production via large-scale alkaline electrolysis under fluctuating renewable energy remains a major challenge, primarily due to overlooked electrolyzer startup thresholds and runtime imbalance among stacks in conventional scheduling strategies. These issues often lead to energy curtailment, equipment degradation, and operational risks. The authors’ previous work combined segmented power allocation with periodic cycling, improving performance but lacking real-time adaptability and optimal coordination between hydrogen production and load balancing. Building on this, this study proposes a dynamic scheduling strategy (CS4) that integrates a segmented dispatch algorithm—enhancing energy capture by aligning power input with electrolyzer startup thresholds—with a simulated annealing (SA)-based optimization approach. A binary-variable, state-aware model represents electrolyzer transitions among production, standby, and shutdown states, enabling responsive control under variable wind input. CS4 establishes a closed-loop framework to balance load, extend equipment life, and improve efficiency. Simulations over 250 days using real wind data show that CS4 increases hydrogen output by 5.36%, reduces runtime deviation by 99.82%, and improves lifecycle total profit by 10.48% compared to baseline strategies. Its verification of the robustness and scalability across system sizes confirms CS4 as a robust, adaptive, and lifecycle-conscious solution for intelligent hydrogen production under intermittent renewables.
{"title":"Dynamic scheduling of renewable-powered multi-electrolyzer systems for hydrogen production with simulated annealing optimization: a load balancing and efficiency enhancement approach","authors":"Peng Zou , Hongjian Lin , Yongling Zou , Xianjie Zhou , Zhan Liu , Xiongbo Duan , Yangyang Li , Jianqiang Chen , Yingying Song","doi":"10.1016/j.renene.2025.124967","DOIUrl":"10.1016/j.renene.2025.124967","url":null,"abstract":"<div><div>Achieving stable and efficient hydrogen production via large-scale alkaline electrolysis under fluctuating renewable energy remains a major challenge, primarily due to overlooked electrolyzer startup thresholds and runtime imbalance among stacks in conventional scheduling strategies. These issues often lead to energy curtailment, equipment degradation, and operational risks. The authors’ previous work combined segmented power allocation with periodic cycling, improving performance but lacking real-time adaptability and optimal coordination between hydrogen production and load balancing. Building on this, this study proposes a dynamic scheduling strategy (CS4) that integrates a segmented dispatch algorithm—enhancing energy capture by aligning power input with electrolyzer startup thresholds—with a simulated annealing (SA)-based optimization approach. A binary-variable, state-aware model represents electrolyzer transitions among production, standby, and shutdown states, enabling responsive control under variable wind input. CS4 establishes a closed-loop framework to balance load, extend equipment life, and improve efficiency. Simulations over 250 days using real wind data show that CS4 increases hydrogen output by 5.36%, reduces runtime deviation by 99.82%, and improves lifecycle total profit by 10.48% compared to baseline strategies. Its verification of the robustness and scalability across system sizes confirms CS4 as a robust, adaptive, and lifecycle-conscious solution for intelligent hydrogen production under intermittent renewables.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124967"},"PeriodicalIF":9.1,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684021","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124959
Harini Saravanan , Kiran Babu Uppuluri
The growing global demand for renewable and sustainable energy sources has intensified interest in lignocellulosic biomass as an alternative to fossil fuels. However, the recalcitrant structure of lignocellulose and the reliance on harsh chemical pretreatments remain major barriers to efficient bioethanol production. The present study addresses the challenge of efficiently converting lignocellulosic biomass into bioethanol without relying on harsh chemicals, focusing on Cynodon dactylon (Bermuda grass), an underutilized and abundant feedstock. A novel microwave-assisted deep eutectic solvent pretreatment system, composed of potassium carbonate and glycerol, was developed and applied to the biomass. Subsequently, simultaneous saccharification and co-fermentation (SSCF) using Saccharomyces cerevisiae NCIM 3219 and Kluyveromyces marxianus MTCC 1389 facilitated effective conversion of both C5 and C6 sugars into ethanol. Process optimization was conducted using response surface methodology (RSM) and a hybrid artificial neural network-genetic algorithm (ANN-GA) modeling approach. The ANN-GA model outperformed RSM in predictive capability, achieving a maximum ethanol of 23.84 ± 0.45 g/L compared to 14.85 ± 0.32 g/L with RSM. Overall, this work presents a novel, chemical-free valorization route for Bermuda grass and offers a promising framework for optimizing lignocellulosic bioethanol production through advanced modeling, contributing to Sustainable Development Goals on clean energy and responsible resource utilization.
{"title":"Chemical-free processing of Bermuda grass for bioethanol production: Hybrid optimization of simultaneous C5/C6 sugar utilization using response surface methodology, genetic algorithm, and artificial neural network","authors":"Harini Saravanan , Kiran Babu Uppuluri","doi":"10.1016/j.renene.2025.124959","DOIUrl":"10.1016/j.renene.2025.124959","url":null,"abstract":"<div><div>The growing global demand for renewable and sustainable energy sources has intensified interest in lignocellulosic biomass as an alternative to fossil fuels. However, the recalcitrant structure of lignocellulose and the reliance on harsh chemical pretreatments remain major barriers to efficient bioethanol production. The present study addresses the challenge of efficiently converting lignocellulosic biomass into bioethanol without relying on harsh chemicals, focusing on <em>Cynodon dactylon</em> (Bermuda grass), an underutilized and abundant feedstock. A novel microwave-assisted deep eutectic solvent pretreatment system, composed of potassium carbonate and glycerol, was developed and applied to the biomass. Subsequently, simultaneous saccharification and co-fermentation (SSCF) using <em>Saccharomyces cerevisiae</em> NCIM 3219 and <em>Kluyveromyces marxianus</em> MTCC 1389 facilitated effective conversion of both C5 and C6 sugars into ethanol. Process optimization was conducted using response surface methodology (RSM) and a hybrid artificial neural network-genetic algorithm (ANN-GA) modeling approach. The ANN-GA model outperformed RSM in predictive capability, achieving a maximum ethanol of 23.84 ± 0.45 g/L compared to 14.85 ± 0.32 g/L with RSM. Overall, this work presents a novel, chemical-free valorization route for Bermuda grass and offers a promising framework for optimizing lignocellulosic bioethanol production through advanced modeling, contributing to Sustainable Development Goals on clean energy and responsible resource utilization.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124959"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683497","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124920
Han Xu , Chang Xi , Rui Min , Weijun Li , Shi-Jie Cao
Urban photovoltaic (PV) deployment is expanding rapidly as a cornerstone of global decarbonization, yet its unintended side effect, the photovoltaic heat island (PVHI), poses emerging risks to urban microclimates, public health, and community acceptance. Green roof has the potential to mitigate urban heat islands, however, current research rarely accounts for the dynamic interactions between PV arrays and green roofs. This study developed a high-resolution bidirectional feedback framework to evaluate photovoltaic green roofs (PVGR) and introduced a new power generation cooling index () to unify energy yield and thermal regulation, thereby enabling further optimization of PVGR parameters including photovoltaic layout and plant configuration. Results revealed that conventional PV arrays elevated air temperature by 0.2–0.9 °C, whereas PVGR reduced it by 1.8–2.9 °C relative to PV alone. Waste heat from PV modules enhanced plant transpiration by 23.3–28.7 W/m3, producing a synergistic cooling benefit and modestly boosting PV efficiency. Optimal configuration (PV vertical distance from plant 1.3 m, row spacing 1.2 m, tilt angle 25°, coverage 20 %; plant height 1 m, leaf area density 1.5 m2/m3) maximized combined performance. These findings provide scalable design principles to balance renewable energy expansion with urban heat adaptation, bridging the gap between clean energy deployment, urban climate adaptation, and public acceptance.
{"title":"Synergistic cooling and energy gains of photovoltaic green roofs for sustainable cities","authors":"Han Xu , Chang Xi , Rui Min , Weijun Li , Shi-Jie Cao","doi":"10.1016/j.renene.2025.124920","DOIUrl":"10.1016/j.renene.2025.124920","url":null,"abstract":"<div><div>Urban photovoltaic (PV) deployment is expanding rapidly as a cornerstone of global decarbonization, yet its unintended side effect, the photovoltaic heat island (PVHI), poses emerging risks to urban microclimates, public health, and community acceptance. Green roof has the potential to mitigate urban heat islands, however, current research rarely accounts for the dynamic interactions between PV arrays and green roofs. This study developed a high-resolution bidirectional feedback framework to evaluate photovoltaic green roofs (PVGR) and introduced a new power generation cooling index (<span><math><mrow><msub><mi>I</mi><mrow><mi>δ</mi><mo>‐</mo><mi>E</mi></mrow></msub></mrow></math></span>) to unify energy yield and thermal regulation, thereby enabling further optimization of PVGR parameters including photovoltaic layout and plant configuration. Results revealed that conventional PV arrays elevated air temperature by 0.2–0.9 °C, whereas PVGR reduced it by 1.8–2.9 °C relative to PV alone. Waste heat from PV modules enhanced plant transpiration by 23.3–28.7 W/m<sup>3</sup>, producing a synergistic cooling benefit and modestly boosting PV efficiency. Optimal configuration (PV vertical distance from plant 1.3 m, row spacing 1.2 m, tilt angle 25°, coverage 20 %; plant height 1 m, leaf area density 1.5 m<sup>2</sup>/m<sup>3</sup>) maximized combined performance. These findings provide scalable design principles to balance renewable energy expansion with urban heat adaptation, bridging the gap between clean energy deployment, urban climate adaptation, and public acceptance.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124920"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734903","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124960
Munisamy Vijayalaxmi , Prathapan Thevamudhan
The growing use of renewable energy and Internet of Things (IoT) devices in smart grids introduces challenges in managing variable generation and changing energy demands. This study proposes a novel hybrid energy management approach that combines the Lotus Effect Optimization (LEO) algorithm with a Quantum Complete Graph Neural Network (QCGNN) to enhance battery utilization and minimize energy costs while maintaining system stability. The QCGNN model is used to forecast system performance, while LEO optimizes energy consumption and reduces electricity costs. The proposed model is excluded in MATLAB and benchmarked against optimization techniques including Earthworm Optimization Algorithm (EWOA), Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). Results display the proposed method achieves lower costs over one-day, one-week, and one-year scenarios, with a computation time of 95.31 s, which is less than the existing techniques. It demonstrates better transient response and stability during rapid load and renewable generation changes, highlighting its robustness and practical applicability. This method provides an efficient way to manage energy in IoT-enabled smart grids.
{"title":"Enhancing energy management for internet of things enabled smart grids with the LEO-QCGNN approach","authors":"Munisamy Vijayalaxmi , Prathapan Thevamudhan","doi":"10.1016/j.renene.2025.124960","DOIUrl":"10.1016/j.renene.2025.124960","url":null,"abstract":"<div><div>The growing use of renewable energy and Internet of Things (IoT) devices in smart grids introduces challenges in managing variable generation and changing energy demands. This study proposes a novel hybrid energy management approach that combines the Lotus Effect Optimization (LEO) algorithm with a Quantum Complete Graph Neural Network (QCGNN) to enhance battery utilization and minimize energy costs while maintaining system stability. The QCGNN model is used to forecast system performance, while LEO optimizes energy consumption and reduces electricity costs. The proposed model is excluded in MATLAB and benchmarked against optimization techniques including Earthworm Optimization Algorithm (EWOA), Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). Results display the proposed method achieves lower costs over one-day, one-week, and one-year scenarios, with a computation time of 95.31 s, which is less than the existing techniques. It demonstrates better transient response and stability during rapid load and renewable generation changes, highlighting its robustness and practical applicability. This method provides an efficient way to manage energy in IoT-enabled smart grids.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124960"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734892","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124953
Minghao Liu , Bingyan Li , Yanhu Mu , Fujun Niu , Jing Luo , Fei Yin , Xiangbing Kong
Solar radiation shielding (SRS) is a mitigation technique designed to directly reduce surface temperature and specifically engineered to protect permafrost beneath roadway embankments. However, its inherent structural instability substantially hinders its implementation and assessment of long-term cooling efficacy. Decades-long monitoring data of air and ground temperatures from 2006 to 2021 were collected at an SRS embankment section along the Qinghai-Xizang Railway to examine its long-term cooling performance for permafrost foundations. Field results indicate that the SRS structure effectively mitigates solar radiation impacts, thereby reducing near-surface air temperatures throughout the year. It provides sustained cooling for underlying permafrost foundations to depths exceeding 15.0 m, with rapid cooling observed during the initial 7–8 years post-installation. The SRS structure also elevated permafrost table and induced permafrost aggradation. To improve operational reliability, the SRS structure was optimized into a concrete shading board (CSB), and simulation-driven analysis proved that integrating the new CSB with crushed-rock sloped embankments synergistically enhances overall cooling capacity by leveraging winter convective cooling and fully utilizing shading effect. This study validates the long-term effectiveness of the SRS technique for climate-resilient infrastructures and highlights its potential for integrating passive cooling technologies into renewable energy systems for transportation in permafrost regions.
{"title":"Sustained performance and structural optimization of a solar radiation shielding technique for cooling permafrost foundations under climate warming","authors":"Minghao Liu , Bingyan Li , Yanhu Mu , Fujun Niu , Jing Luo , Fei Yin , Xiangbing Kong","doi":"10.1016/j.renene.2025.124953","DOIUrl":"10.1016/j.renene.2025.124953","url":null,"abstract":"<div><div>Solar radiation shielding (SRS) is a mitigation technique designed to directly reduce surface temperature and specifically engineered to protect permafrost beneath roadway embankments. However, its inherent structural instability substantially hinders its implementation and assessment of long-term cooling efficacy. Decades-long monitoring data of air and ground temperatures from 2006 to 2021 were collected at an SRS embankment section along the Qinghai-Xizang Railway to examine its long-term cooling performance for permafrost foundations. Field results indicate that the SRS structure effectively mitigates solar radiation impacts, thereby reducing near-surface air temperatures throughout the year. It provides sustained cooling for underlying permafrost foundations to depths exceeding 15.0 m, with rapid cooling observed during the initial 7–8 years post-installation. The SRS structure also elevated permafrost table and induced permafrost aggradation. To improve operational reliability, the SRS structure was optimized into a concrete shading board (CSB), and simulation-driven analysis proved that integrating the new CSB with crushed-rock sloped embankments synergistically enhances overall cooling capacity by leveraging winter convective cooling and fully utilizing shading effect. This study validates the long-term effectiveness of the SRS technique for climate-resilient infrastructures and highlights its potential for integrating passive cooling technologies into renewable energy systems for transportation in permafrost regions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124953"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683489","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}
The growing integration of renewable energy into modern power systems requires advanced planning methods to maintain reliability and resilience, especially during extreme weather events. This paper presents a new planning framework that optimizes the integration of renewables by strategically dividing the power grid into self-sufficient zones. The framework introduces two key innovations. First, it uses a power flow control device (Thyristor-Controlled Series Capacitor or TCSC) to dynamically adjust the electrical connections within the grid, creating fewer but stronger and more manageable zones that can better accommodate renewable sources like wind farms (WF) and photovoltaic (PV). Second, it employs a robust decision-making approach, entitled p-robust optimization, to effectively manage the uncertainties from variable renewable generation and dynamic electricity demand, ensuring that the planning decisions remain reliable even under worst-case scenarios. Tested on a standard IEEE 30-bus system, the results indicate that the method reduces the number of necessary grid partitions by 7.3 % while boosting the system's capacity to host renewable energy by 18.4 %. The optimization method also lowers a key measure of operational risk by 41.2 %, which makes the grid design more stable and reliable.
{"title":"Boosting renewable hosting capacity via TCSC-enhanced transmission system planning: P-robust stochastic approach","authors":"Esmaeil Valipour, Ramin Nourollahi, Mehrdad Tarafdar Hagh, Kazem Zare, Saeid Ghassem Zadeh","doi":"10.1016/j.renene.2025.124922","DOIUrl":"10.1016/j.renene.2025.124922","url":null,"abstract":"<div><div>The growing integration of renewable energy into modern power systems requires advanced planning methods to maintain reliability and resilience, especially during extreme weather events. This paper presents a new planning framework that optimizes the integration of renewables by strategically dividing the power grid into self-sufficient zones. The framework introduces two key innovations. First, it uses a power flow control device (Thyristor-Controlled Series Capacitor or TCSC) to dynamically adjust the electrical connections within the grid, creating fewer but stronger and more manageable zones that can better accommodate renewable sources like wind farms (WF) and photovoltaic (PV). Second, it employs a robust decision-making approach, entitled p-robust optimization, to effectively manage the uncertainties from variable renewable generation and dynamic electricity demand, ensuring that the planning decisions remain reliable even under worst-case scenarios. Tested on a standard IEEE 30-bus system, the results indicate that the method reduces the number of necessary grid partitions by 7.3 % while boosting the system's capacity to host renewable energy by 18.4 %. The optimization method also lowers a key measure of operational risk by 41.2 %, which makes the grid design more stable and reliable.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124922"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683494","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124927
Sayed Zafar Abbas , Dhanasekaran Vikraman , Zulfqar Ali Sheikh , Syed Muhammad Zain Mehdi , Iftikhar Hussain , Jeung Choon Goak , Hyun-Seok Kim , Jongwan Jung , Sajjad Hussain , Naesung Lee
Synthesis of cost-effective, high-performance electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) remains challenging. Herein, we report a novel approach to the synthesis of efficient electrocatalyst using silicon (Si), nickel (Ni), and carbon nanotube (CNT) on a Kovar (nickel-cobalt ferrous alloy) substrate via screen-printing and vacuum annealing. Initially, Si nanoparticles were screen-printed on a Kovar substrate. Subsequent vacuum annealing induces a solid-state diffusion reaction between Si and substrate, resulting in the formation of an Fe2NiSi Heusler phase that enriched catalytically active Fe and Ni metals. To further enhance catalytic activity, the unreacted Si was eliminated by introducing Ni nanoparticles, converting Si to an electrically conductive NiSi phase. Furthermore, these phases agglomerate at high temperatures, resulting in poor catalytic activities. The incorporation of CNT suppressed agglomeration, improved conductivity, and enhanced surface area. Additionally, the mechanical exfoliation strategy which is the key innovation of this study offers high exposure to active sites by protruding buried catalytic sites. The resulting CNT-Integrated Fe2NiSi-NiSi electrocatalyst demonstrated low overpotentials of 57 mV for HER and 200 mV for OER at 10 mA cm−2. As a bifunctional catalyst, it delivered an electrolyzer cell voltage of 1.491 V, comparable to conventional electrode systems.
为析氢反应（HER）和析氧反应（OER）合成经济高效的电催化剂仍然是一个挑战。在此，我们报告了一种利用硅（Si）、镍（Ni）和碳纳米管（CNT）在Kovar（镍钴铁合金）衬底上通过丝网印刷和真空退火合成高效电催化剂的新方法。最初，硅纳米颗粒被丝网印刷在Kovar衬底上。随后的真空退火诱导了Si和衬底之间的固态扩散反应，导致Fe2NiSi Heusler相的形成，该相富集了催化活性Fe和Ni金属。为了进一步提高催化活性，通过引入Ni纳米颗粒将未反应的Si消除，将Si转化为导电的NiSi相。此外，这些相在高温下结块，导致催化活性差。碳纳米管的掺入抑制了团聚，改善了电导率，并增加了表面积。此外，机械剥离策略是本研究的关键创新，通过突出埋藏的催化位点，提供了高暴露于活性位点的机会。得到的碳纳米管集成Fe2NiSi-NiSi电催化剂在10 mA cm−2下，HER过电位为57 mV， OER过电位为200 mV。作为一种双功能催化剂，它提供的电解槽电压为1.491 V，与传统电极系统相当。
{"title":"Innovative mechanical exfoliation process of screen-printed CNT-integrated Fe2NiSi-NiSi electrocatalyst for efficient water-splitting","authors":"Sayed Zafar Abbas , Dhanasekaran Vikraman , Zulfqar Ali Sheikh , Syed Muhammad Zain Mehdi , Iftikhar Hussain , Jeung Choon Goak , Hyun-Seok Kim , Jongwan Jung , Sajjad Hussain , Naesung Lee","doi":"10.1016/j.renene.2025.124927","DOIUrl":"10.1016/j.renene.2025.124927","url":null,"abstract":"<div><div>Synthesis of cost-effective, high-performance electrocatalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) remains challenging. Herein, we report a novel approach to the synthesis of efficient electrocatalyst using silicon (Si), nickel (Ni), and carbon nanotube (CNT) on a Kovar (nickel-cobalt ferrous alloy) substrate via screen-printing and vacuum annealing. Initially, Si nanoparticles were screen-printed on a Kovar substrate. Subsequent vacuum annealing induces a solid-state diffusion reaction between Si and substrate, resulting in the formation of an Fe<sub>2</sub>NiSi Heusler phase that enriched catalytically active Fe and Ni metals. To further enhance catalytic activity, the unreacted Si was eliminated by introducing Ni nanoparticles, converting Si to an electrically conductive NiSi phase. Furthermore, these phases agglomerate at high temperatures, resulting in poor catalytic activities. The incorporation of CNT suppressed agglomeration, improved conductivity, and enhanced surface area. Additionally, the mechanical exfoliation strategy which is the key innovation of this study offers high exposure to active sites by protruding buried catalytic sites. The resulting CNT-Integrated Fe<sub>2</sub>NiSi-NiSi electrocatalyst demonstrated low overpotentials of 57 mV for HER and 200 mV for OER at 10 mA cm<sup>−2</sup>. As a bifunctional catalyst, it delivered an electrolyzer cell voltage of 1.491 V, comparable to conventional electrode systems.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124927"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734899","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124958
Hao Wu , Li Zhao , Hengyu Guo , Dan Zhang , Xiaowei Li , Zhongjie Li , Yan Peng
Solid-liquid triboelectric nanogenerators currently suffer from an incomplete theoretical framework, which limits the accurate prediction of their energy conversion mechanisms and electrical performance. To address this challenge, this work proposes a novel solid-liquid electromechanical coupling model, inspired by the solid-solid sliding mode and boundary layer theory. A key innovation lies in replacing solid materials in the traditional solid-solid sliding model with liquid boundary layers, enabling the first theoretical investigation of electromechanical characteristics of the solid-liquid triboelectric nanogenerators at the boundary layer scale. The model is verified via a U-anti-rolling tank based simulation platform, and the solid-liquid electrical performance formula is derived through curve fitting of experimental data from a custom-built tribo-experimental setup. Theoretical analysis reveals that the conversion rate from water's dissipated kinetic energy to electrical energy reaches 11.41 %, while experimental validation under low-frequency excitations confirms the model's generalization and effectiveness. Further exploration of the mapping relationship between electrical performance and anti-rolling behavior during the roll motion of the tank shows that electrical output first increases and then decreases with rising rolling angles, and gradually declines with higher initial water levels in the tank. This model fills the gap in the theoretical system of solid-liquid triboelectric nanogenerators, offers a reliable tool for performance prediction, and provides valuable insights for optimizing the coupling between dissipated kinetic energy utilization and electrical energy replenishment in anti-rolling sensing applications.
{"title":"Tribo-mechano transduction of solid-liquid triboelectric nanogenerators via boundary layer theory","authors":"Hao Wu , Li Zhao , Hengyu Guo , Dan Zhang , Xiaowei Li , Zhongjie Li , Yan Peng","doi":"10.1016/j.renene.2025.124958","DOIUrl":"10.1016/j.renene.2025.124958","url":null,"abstract":"<div><div>Solid-liquid triboelectric nanogenerators currently suffer from an incomplete theoretical framework, which limits the accurate prediction of their energy conversion mechanisms and electrical performance. To address this challenge, this work proposes a novel solid-liquid electromechanical coupling model, inspired by the solid-solid sliding mode and boundary layer theory. A key innovation lies in replacing solid materials in the traditional solid-solid sliding model with liquid boundary layers, enabling the first theoretical investigation of electromechanical characteristics of the solid-liquid triboelectric nanogenerators at the boundary layer scale. The model is verified via a U-anti-rolling tank based simulation platform, and the solid-liquid electrical performance formula is derived through curve fitting of experimental data from a custom-built tribo-experimental setup. Theoretical analysis reveals that the conversion rate from water's dissipated kinetic energy to electrical energy reaches 11.41 %, while experimental validation under low-frequency excitations confirms the model's generalization and effectiveness. Further exploration of the mapping relationship between electrical performance and anti-rolling behavior during the roll motion of the tank shows that electrical output first increases and then decreases with rising rolling angles, and gradually declines with higher initial water levels in the tank. This model fills the gap in the theoretical system of solid-liquid triboelectric nanogenerators, offers a reliable tool for performance prediction, and provides valuable insights for optimizing the coupling between dissipated kinetic energy utilization and electrical energy replenishment in anti-rolling sensing applications.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124958"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734827","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}
Pub Date : 2025-12-04DOI: 10.1016/j.renene.2025.124939
Wen Zhang , Guohua Xing , Junjie Tao , Mingtong Wei , Yanru Wu , Yin Liu , José F. Gallego
Heliostats, typically deployed in open and wind-prone regions, possess a large reflective surface area supported by lightweight, flexible structures, making them highly susceptible to wind loads. Wind-induced deformations can significantly compromise the precision required for accurate solar tracking and concentrating efficiency of heliostats. This study introduces a numerical multi-physics methodology that couples computational fluid dynamics (CFD), finite element modeling (FEM), and ray-tracing optical simulations to investigate the effects of heliostat postures and wind direction angles on concentrating efficiency across four solar terms: spring equinox, summer solstice, autumn equinox, and winter solstice. Based on solar tracking principles and site-specific meteorological data for wind direction and speed, variations in heliostat concentrating efficiency are evaluated under both calm and windy conditions. The direct normal irradiance (DNI) is integrated over time, and the cumulative solar energy per unit mirror area is compared for a representative day at each solar term. The results reveal that wind-induced deformations reduce concentrating efficiency by 10.35 %–24.44 %, particularly in the peripheral regions of the mirror panel, corresponding to an average reduction of approximately 17.23 % in thermal energy capture. Moreover, as the wind direction angle increases, the efficiency loss tends to decrease. Among the four solar terms, the winter solstice exhibits the highest efficiency loss and the greatest fluctuation in concentrating efficiency. The findings of this study provide a numerical basis for structural optimization and the enhancement of optical performance in heliostats under complex wind conditions.
{"title":"Multi-physics investigation of concentrating efficiency degradation in heliostats caused by wind-induced deformations","authors":"Wen Zhang , Guohua Xing , Junjie Tao , Mingtong Wei , Yanru Wu , Yin Liu , José F. Gallego","doi":"10.1016/j.renene.2025.124939","DOIUrl":"10.1016/j.renene.2025.124939","url":null,"abstract":"<div><div>Heliostats, typically deployed in open and wind-prone regions, possess a large reflective surface area supported by lightweight, flexible structures, making them highly susceptible to wind loads. Wind-induced deformations can significantly compromise the precision required for accurate solar tracking and concentrating efficiency of heliostats. This study introduces a numerical multi-physics methodology that couples computational fluid dynamics (CFD), finite element modeling (FEM), and ray-tracing optical simulations to investigate the effects of heliostat postures and wind direction angles on concentrating efficiency across four solar terms: spring equinox, summer solstice, autumn equinox, and winter solstice. Based on solar tracking principles and site-specific meteorological data for wind direction and speed, variations in heliostat concentrating efficiency are evaluated under both calm and windy conditions. The direct normal irradiance (DNI) is integrated over time, and the cumulative solar energy per unit mirror area is compared for a representative day at each solar term. The results reveal that wind-induced deformations reduce concentrating efficiency by 10.35 %–24.44 %, particularly in the peripheral regions of the mirror panel, corresponding to an average reduction of approximately 17.23 % in thermal energy capture. Moreover, as the wind direction angle increases, the efficiency loss tends to decrease. Among the four solar terms, the winter solstice exhibits the highest efficiency loss and the greatest fluctuation in concentrating efficiency. The findings of this study provide a numerical basis for structural optimization and the enhancement of optical performance in heliostats under complex wind conditions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"258 ","pages":"Article 124939"},"PeriodicalIF":9.1,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734828","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}
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