Pub Date : 2025-12-17DOI: 10.1016/j.apenergy.2025.127212
Ganglei Zhao, Xiang Li, Changxing Liu, Huiliu Zhang, Po Li
The wavelet transform (Wav), due to its capability of effectively allocating high- and low-frequency energy, has been widely applied in real-time energy management strategy (EMS) for hybrid energy storage systems (HESS). However, the time delay generated by Wav during signal processing can reduce the overall effectiveness of HESS. In this paper, a real-time hardware platform for HESS is built to study the effect of Wav on the performance of batteries (Bat) and supercapacitors (SC) with and without neural networks for delay compensation. Furthermore, a current-prediction-based predictive adaptive wavelet transform method is proposed to enhance system real-time performance and examine the potential risks of speed prediction. Rapid control prototype experimental results show that, compared with conventional Wav, the predictive wavelet transform can reduce SC peak current by approximately 7.6 % and 15.6 % in urban and highway conditions, respectively, and decrease energy cycling losses by about 5 %. The proposed predictive adaptive wavelet transform further reduces peak current by approximately 12.6 % and 20 %, decreases energy cycling losses by 14 % and 20 %, and extends battery lifetime by 1.5 %.
{"title":"Experimental validation of real-time energy management for hybrid energy storage systems based on predictive wavelet transforms","authors":"Ganglei Zhao, Xiang Li, Changxing Liu, Huiliu Zhang, Po Li","doi":"10.1016/j.apenergy.2025.127212","DOIUrl":"10.1016/j.apenergy.2025.127212","url":null,"abstract":"<div><div>The wavelet transform (Wav), due to its capability of effectively allocating high- and low-frequency energy, has been widely applied in real-time energy management strategy (EMS) for hybrid energy storage systems (HESS). However, the time delay generated by Wav during signal processing can reduce the overall effectiveness of HESS. In this paper, a real-time hardware platform for HESS is built to study the effect of Wav on the performance of batteries (Bat) and supercapacitors (SC) with and without neural networks for delay compensation. Furthermore, a current-prediction-based predictive adaptive wavelet transform method is proposed to enhance system real-time performance and examine the potential risks of speed prediction. Rapid control prototype experimental results show that, compared with conventional Wav, the predictive wavelet transform can reduce SC peak current by approximately 7.6 % and 15.6 % in urban and highway conditions, respectively, and decrease energy cycling losses by about 5 %. The proposed predictive adaptive wavelet transform further reduces peak current by approximately 12.6 % and 20 %, decreases energy cycling losses by 14 % and 20 %, and extends battery lifetime by 1.5 %.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127212"},"PeriodicalIF":11.0,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788238","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 : 2025-12-16DOI: 10.1016/j.apenergy.2025.127229
K. Malleswararao , Inga Bürger , Aldo Cosquillo Mejia , Seon Tae Kim , Marc Linder
Efficient utilization of waste heat is a crucial method to meet global energy demands and carbon neutrality. High Temperature mechanical Heat Pumps (HTHPs) are vital in this context but are limited by evaporator temperature constraints. This study proposes an innovative approach to upgrading and reintegrating waste heat from HTHPs (105–140 °C) by coupling them with a dual reactor salt hydrate based Thermo-Chemical Energy Storage (TCES) system. Operating in a quasi-continuous mode, the system utilizes the waste heat from the HTHP to drive hydration (discharge) in one reactor and dehydration (charge) in another. The key to heat upgrading lies in the evaporator of the TCES system, which governs system performance. Therefore, an empirical relation has been developed to determine its optimum temperature as a function of waste heat temperature, heat upgrade temperature, and the thermal properties of the salt hydrate. Subsequently, the system performance with K2CO3-H2O was assessed by applying the first law of thermodynamics, with the evaporator temperature of the TCES varied from 100 °C to 90 °C. Lowering the evaporator temperature of the TCES enhances thermal output but is constrained by the HTHP's temperature requirements. The system delivers 55.4 kW per kg/s of air with a heat upgrade efficiency of 45.7 %, using waste heat at 140 °C and the evaporator of the TCES at 100 °C. This study attempts to establish a framework for designing efficient thermally driven cascaded heat pumps.
{"title":"Innovative approaches to waste heat recovery: Coupling high temperature vapour compression heat pumps with salt hydrate thermochemical systems","authors":"K. Malleswararao , Inga Bürger , Aldo Cosquillo Mejia , Seon Tae Kim , Marc Linder","doi":"10.1016/j.apenergy.2025.127229","DOIUrl":"10.1016/j.apenergy.2025.127229","url":null,"abstract":"<div><div>Efficient utilization of waste heat is a crucial method to meet global energy demands and carbon neutrality. High Temperature mechanical Heat Pumps (HTHPs) are vital in this context but are limited by evaporator temperature constraints. This study proposes an innovative approach to upgrading and reintegrating waste heat from HTHPs (105–140 °C) by coupling them with a dual reactor salt hydrate based Thermo-Chemical Energy Storage (TCES) system. Operating in a quasi-continuous mode, the system utilizes the waste heat from the HTHP to drive hydration (discharge) in one reactor and dehydration (charge) in another. The key to heat upgrading lies in the evaporator of the TCES system, which governs system performance. Therefore, an empirical relation has been developed to determine its optimum temperature as a function of waste heat temperature, heat upgrade temperature, and the thermal properties of the salt hydrate. Subsequently, the system performance with K<sub>2</sub>CO<sub>3</sub>-H<sub>2</sub>O was assessed by applying the first law of thermodynamics, with the evaporator temperature of the TCES varied from 100 °C to 90 °C. Lowering the evaporator temperature of the TCES enhances thermal output but is constrained by the HTHP's temperature requirements. The system delivers 55.4 kW per kg/s of air with a heat upgrade efficiency of 45.7 %, using waste heat at 140 °C and the evaporator of the TCES at 100 °C. This study attempts to establish a framework for designing efficient thermally driven cascaded heat pumps.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127229"},"PeriodicalIF":11.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788268","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 : 2025-12-16DOI: 10.1016/j.apenergy.2025.127238
Ke Jiang , Haolin Jiang , Liang Zhang , Yang Luan , Tongxi Zheng , Mingxin Liu , Xunkang Su , Yihui Feng , Guolong Lu , Zhenning Liu
Proton exchange membrane fuel cells (PEMFCs) are promising candidates for zero‑carbon power generation; however, the complexity of flow field design remains a key barrier to large-scale application. In this work, a novel asymmetric microchannel flow field (AMFF) with under-rib channels is proposed to enhance oxygen distribution and improve water removal, particularly at high current densities. To optimize the proposed structure, an AI-aided auto fast design system (AAFDS) was applied. This framework integrates a multi-objective AI optimization algorithm directly with a multi-physics simulation model, enabling fully automated and simultaneous optimization of multiple geometric parameters without the need for pre-generated datasets or surrogate model training. Nineteen parameters of the AMFF were optimized to maximize current density, improve gas distribution uniformity, and reduce pressure drop. The optimized AMFF achieved a 7.22 % increase in peak power density, and the uniformity of current density and gas distribution at the peak-power operating point improved by 20.9 % and 24.7 %, respectively. Moreover, the system completed 400 design iterations within 140 h, representing an 8.6-fold increase in efficiency compared with manual optimization. Validation through simulations and full-scale experiments confirmed the robustness of the design, especially under high current density, high temperature, and high humidity conditions. This study introduces a novel structural approach for PEMFC flow field design and demonstrates the potential of AI-assisted optimization to accelerate the development of high-performance fuel cell systems.
{"title":"AI-assisted design and optimization of novel asymmetric microchannel flow fields for proton exchange membrane fuel cells","authors":"Ke Jiang , Haolin Jiang , Liang Zhang , Yang Luan , Tongxi Zheng , Mingxin Liu , Xunkang Su , Yihui Feng , Guolong Lu , Zhenning Liu","doi":"10.1016/j.apenergy.2025.127238","DOIUrl":"10.1016/j.apenergy.2025.127238","url":null,"abstract":"<div><div>Proton exchange membrane fuel cells (PEMFCs) are promising candidates for zero‑carbon power generation; however, the complexity of flow field design remains a key barrier to large-scale application. In this work, a novel asymmetric microchannel flow field (AMFF) with under-rib channels is proposed to enhance oxygen distribution and improve water removal, particularly at high current densities. To optimize the proposed structure, an AI-aided auto fast design system (AAFDS) was applied. This framework integrates a multi-objective AI optimization algorithm directly with a multi-physics simulation model, enabling fully automated and simultaneous optimization of multiple geometric parameters without the need for pre-generated datasets or surrogate model training. Nineteen parameters of the AMFF were optimized to maximize current density, improve gas distribution uniformity, and reduce pressure drop. The optimized AMFF achieved a 7.22 % increase in peak power density, and the uniformity of current density and gas distribution at the peak-power operating point improved by 20.9 % and 24.7 %, respectively. Moreover, the system completed 400 design iterations within 140 h, representing an 8.6-fold increase in efficiency compared with manual optimization. Validation through simulations and full-scale experiments confirmed the robustness of the design, especially under high current density, high temperature, and high humidity conditions. This study introduces a novel structural approach for PEMFC flow field design and demonstrates the potential of AI-assisted optimization to accelerate the development of high-performance fuel cell systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127238"},"PeriodicalIF":11.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788270","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 : 2025-12-16DOI: 10.1016/j.apenergy.2025.127232
Urbana Kawsar Mitali , Arshad Hussain , Md. Billal Hossain , Syed Shaheen Shah , Karnan Manickavasakam , Bong-Joong Kim , Md. Abdul Aziz , A.J. Saleh Ahammad
Research into novel materials for rechargeable batteries has increased due to the growing need for long-lasting, high-performance energy storage solutions. One of the most important components is the separator, which ensures the safe and quick passage of ions while maintaining system stability. Covalent organic frameworks (COFs) are porous crystalline polymers that exhibit remarkable chemical stability and a variety of topologies. Their applications as components for next-generation battery separators have grown. This study examines current advances in the design, fabrication, and application of COF-based separators in a variety of rechargeable battery systems, including lithium-ion, lithium‑sulfur, sodium‑sulfur, zinc‑sulfur, and zinc‑iodine batteries. We explore into the way COFs' distinct structural properties, such as structured nanochannels, variable functional groups, and high porosity, can improve ion selectivity, electrolyte wettability, and prevent dendritic and polysulphide movement. We also discuss the relationship between structure and property, synthesis methods, and critical performance criteria, with an emphasis on recent developments in COF membrane design. The study concluded by outlining the present problems and offering suggestions for the future development of scalable, highly effective COF-based separators for the upcoming generation of energy storage devices.
{"title":"Recent advances in COF-based separators for rechargeable batteries: Design, properties, and applications","authors":"Urbana Kawsar Mitali , Arshad Hussain , Md. Billal Hossain , Syed Shaheen Shah , Karnan Manickavasakam , Bong-Joong Kim , Md. Abdul Aziz , A.J. Saleh Ahammad","doi":"10.1016/j.apenergy.2025.127232","DOIUrl":"10.1016/j.apenergy.2025.127232","url":null,"abstract":"<div><div>Research into novel materials for rechargeable batteries has increased due to the growing need for long-lasting, high-performance energy storage solutions. One of the most important components is the separator, which ensures the safe and quick passage of ions while maintaining system stability. Covalent organic frameworks (COFs) are porous crystalline polymers that exhibit remarkable chemical stability and a variety of topologies. Their applications as components for next-generation battery separators have grown. This study examines current advances in the design, fabrication, and application of COF-based separators in a variety of rechargeable battery systems, including lithium-ion, lithium‑sulfur, sodium‑sulfur, zinc‑sulfur, and zinc‑iodine batteries. We explore into the way COFs' distinct structural properties, such as structured nanochannels, variable functional groups, and high porosity, can improve ion selectivity, electrolyte wettability, and prevent dendritic and polysulphide movement. We also discuss the relationship between structure and property, synthesis methods, and critical performance criteria, with an emphasis on recent developments in COF membrane design. The study concluded by outlining the present problems and offering suggestions for the future development of scalable, highly effective COF-based separators for the upcoming generation of energy storage devices.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127232"},"PeriodicalIF":11.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788272","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 : 2025-12-16DOI: 10.1016/j.apenergy.2025.127206
Arshad Mahmood , Charles O.P. Marpaung
In Pakistan, implementing the long overdue fiscal reforms such as indirect tax rationalization and subsidies removal can not only generate substantial government revenue but also reduce energy consumption and pollutant emissions. The intended outcomes of environmental reforms–reducing local pollutants or GHG emissions–can therefore be achieved at relatively low costs if such policies are complemented with comprehensive fiscal overhauling of the economy. This study aimed to assess the economic and environmental implications of these policies under different scenarios using a recursive dynamic computable general equilibrium modeling approach. The main findings of the study reveal that both tax rationalization and subsidies removal policies will lead to reduced energy consumption and pollutant emissions while positively impacting GDP. In the combined scenarios where a carbon tax is imposed in conjunction with fiscal reforms, the energy sector will bear the greatest impact due to the upward adjustment of various energy product prices under all the policies considered. Moreover, the relative impact of reducing energy use and GHG emissions per unit of GDP loss will be greater under the combined carbon tax and fiscal policies scenarios than under the independent carbon tax scenario.
{"title":"Impact of carbon taxation under fiscal reforms: A computable general equilibrium analysis for the Pakistan economy","authors":"Arshad Mahmood , Charles O.P. Marpaung","doi":"10.1016/j.apenergy.2025.127206","DOIUrl":"10.1016/j.apenergy.2025.127206","url":null,"abstract":"<div><div>In Pakistan, implementing the long overdue fiscal reforms such as indirect tax rationalization and subsidies removal can not only generate substantial government revenue but also reduce energy consumption and pollutant emissions. The intended outcomes of environmental reforms–reducing local pollutants or GHG emissions–can therefore be achieved at relatively low costs if such policies are complemented with comprehensive fiscal overhauling of the economy. This study aimed to assess the economic and environmental implications of these policies under different scenarios using a recursive dynamic computable general equilibrium modeling approach. The main findings of the study reveal that both tax rationalization and subsidies removal policies will lead to reduced energy consumption and pollutant emissions while positively impacting GDP. In the combined scenarios where a carbon tax is imposed in conjunction with fiscal reforms, the energy sector will bear the greatest impact due to the upward adjustment of various energy product prices under all the policies considered. Moreover, the relative impact of reducing energy use and GHG emissions per unit of GDP loss will be greater under the combined carbon tax and fiscal policies scenarios than under the independent carbon tax scenario.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127206"},"PeriodicalIF":11.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788269","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 : 2025-12-16DOI: 10.1016/j.apenergy.2025.127234
Zhiguo Mu , You Lv , Fang Fang , Jizhen Liu
Increasing integration of volatile renewable energy threatens grid security and raises demand for flexibility services such as deep peak shaving and rapid load adjustment from conventional coal-fired power plants (CFPPs). Energy storage systems (ESS) significantly enhance the load-adjustment capability of CFPPs, but the economic operation of thermal plants coupled with ESS is increasingly critical. In this study, a molten salt energy storage system (MSESS) is integrated with a CFPP to facilitate rapid and economical load adjustment. Two charging schemes are designed and analyzed for rapid and economical operation of the coupled system. To address limitations of traditional control strategies, a hierarchical control strategy based on multivariable fuzzy reinforcement learning is proposed. The strategy comprises an upper-level multivariable fuzzy control (MFC) to eliminate tracking errors in load adjustment and a lower-level reinforcement learning agent using a twin-delayed deep deterministic policy gradient (TD3) algorithm. The coupled system's flexibility demand is decomposed into a load-adjustment subproblem and an economic-operation subproblem, solved by the upper-level MFC and lower-level RL agent, respectively. A multi-criteria objective function is formulated, considering load-tracking cost, energy storage status, and coal consumption. A 48-h simulation under automatic power control (APC) regulation demonstrates that the proposed hierarchical RL-MFC strategy improves the load regulation performance of the coupled system by 1.05 % and enhances operational economy by 1.40 % compared to conventional strategies. Additionally, the proportion of the energy storage system operating within its optimal state-of-charge (SOC) range increased by 16.57 %
{"title":"Hierarchical fuzzy RL control strategy and economic evaluation for molten salt energy storage systems","authors":"Zhiguo Mu , You Lv , Fang Fang , Jizhen Liu","doi":"10.1016/j.apenergy.2025.127234","DOIUrl":"10.1016/j.apenergy.2025.127234","url":null,"abstract":"<div><div>Increasing integration of volatile renewable energy threatens grid security and raises demand for flexibility services such as deep peak shaving and rapid load adjustment from conventional coal-fired power plants (CFPPs). Energy storage systems (ESS) significantly enhance the load-adjustment capability of CFPPs, but the economic operation of thermal plants coupled with ESS is increasingly critical. In this study, a molten salt energy storage system (MSESS) is integrated with a CFPP to facilitate rapid and economical load adjustment. Two charging schemes are designed and analyzed for rapid and economical operation of the coupled system. To address limitations of traditional control strategies, a hierarchical control strategy based on multivariable fuzzy reinforcement learning is proposed. The strategy comprises an upper-level multivariable fuzzy control (MFC) to eliminate tracking errors in load adjustment and a lower-level reinforcement learning agent using a twin-delayed deep deterministic policy gradient (TD3) algorithm. The coupled system's flexibility demand is decomposed into a load-adjustment subproblem and an economic-operation subproblem, solved by the upper-level MFC and lower-level RL agent, respectively. A multi-criteria objective function is formulated, considering load-tracking cost, energy storage status, and coal consumption. A 48-h simulation under automatic power control (APC) regulation demonstrates that the proposed hierarchical RL-MFC strategy improves the load regulation performance of the coupled system by 1.05 % and enhances operational economy by 1.40 % compared to conventional strategies. Additionally, the proportion of the energy storage system operating within its optimal state-of-charge (SOC) range increased by 16.57 %</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127234"},"PeriodicalIF":11.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788271","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 : 2025-12-15DOI: 10.1016/j.apenergy.2025.127225
A. Ademollo , N. Ulivi , L. Ferretti , F. Serafini , C. Carcasci , C. Pacini
Solar energy is set to play a pivotal role in the energy transition. However, the large-scale deployment of Photovoltaic (PV) systems competes with agricultural land use, creating a need for integrated approaches that balance energy generation and food production. Agrivoltaic (AV) systems offer a promising solution by combining farming with PV electricity generation, addressing both energy and resource efficiency within the food-energy-water nexus. Here, we present a modelling framework that couples PV power production, high-resolution shading mapping, process-based biomass growth and economic analysis, and apply it to a one-hectare AV field in Sesto Fiorentino, Tuscany, Italy, driven by 5-min meteorological data at 13 cm × 6.5 cm spatial resolution. Results show ground-level irradiance reductions up to 55 % beneath the panels during the potato-growing season. Despite this reduction in irradiance, the spatial distribution of simulated tuber growth diverges from shading patterns: moderately shaded zones can exhibit up to 6 % higher production owing to delayed senescence and improved water-use efficiency. Overall, annual potato yield under AV averaged 26.8 t/ha—15 % below the full-light reference—yet achieved a land equivalent ratio of 1.58, indicating synergistic land-use benefits. Economic analysis distinguishes between abandoned and conventionally cultivated farmland: on abandoned land with 70 % electricity self-consumption, AV achieves an internal rate of return of 13 % (payback in 10 years) versus 21 % (payback in 6 years) for ground-mounted PV, while on conventional farmland the mitigation of crop revenue losses under AV narrows the return gap.
太阳能将在能源转型中发挥关键作用。然而，大规模部署光伏（PV）系统与农业用地竞争，因此需要采用平衡能源生产和粮食生产的综合方法。农业光伏（AV）系统通过将农业与光伏发电相结合，解决了食物-能源-水关系中的能源和资源效率问题，提供了一个很有前途的解决方案。在这里，我们提出了一个建模框架，将光伏发电、高分辨率阴影映射、基于过程的生物质增长和经济分析结合起来，并将其应用于意大利托斯卡纳Sesto Fiorentino一公顷的AV田，由13 cm × 6.5 cm空间分辨率的5分钟气象数据驱动。结果表明，在马铃薯生长季节，面板下方的地面辐照度减少了55%。尽管辐照度降低，但模拟块茎生长的空间分布与遮荫模式不同：适度遮荫区由于延迟衰老和提高水分利用效率，产量可提高6%。总体而言，在全光照条件下，马铃薯年平均产量为26.8吨/公顷，比全光照条件下的参考产量低15%，但土地当量比为1.58，表明土地利用效益协同。经济分析区分了废弃农田和传统耕地：在自用电量为70%的废弃土地上，自动发电的内部回报率为13%（10年回收期），而地面安装光伏的内部回报率为21%（6年回收期），而在传统农田上，自动发电减少了作物收入损失，缩小了回报差距。
{"title":"Policy-constrained agrivoltaics in Italy: a potato case study linking shading, crop and economics","authors":"A. Ademollo , N. Ulivi , L. Ferretti , F. Serafini , C. Carcasci , C. Pacini","doi":"10.1016/j.apenergy.2025.127225","DOIUrl":"10.1016/j.apenergy.2025.127225","url":null,"abstract":"<div><div>Solar energy is set to play a pivotal role in the energy transition. However, the large-scale deployment of Photovoltaic (PV) systems competes with agricultural land use, creating a need for integrated approaches that balance energy generation and food production. Agrivoltaic (AV) systems offer a promising solution by combining farming with PV electricity generation, addressing both energy and resource efficiency within the food-energy-water nexus. Here, we present a modelling framework that couples PV power production, high-resolution shading mapping, process-based biomass growth and economic analysis, and apply it to a one-hectare AV field in Sesto Fiorentino, Tuscany, Italy, driven by 5-min meteorological data at 13 cm × 6.5 cm spatial resolution. Results show ground-level irradiance reductions up to 55 % beneath the panels during the potato-growing season. Despite this reduction in irradiance, the spatial distribution of simulated tuber growth diverges from shading patterns: moderately shaded zones can exhibit up to 6 % higher production owing to delayed senescence and improved water-use efficiency. Overall, annual potato yield under AV averaged 26.8 t/ha—15 % below the full-light reference—yet achieved a land equivalent ratio of 1.58, indicating synergistic land-use benefits. Economic analysis distinguishes between abandoned and conventionally cultivated farmland: on abandoned land with 70 % electricity self-consumption, AV achieves an internal rate of return of 13 % (payback in 10 years) versus 21 % (payback in 6 years) for ground-mounted PV, while on conventional farmland the mitigation of crop revenue losses under AV narrows the return gap.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127225"},"PeriodicalIF":11.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788280","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 : 2025-12-15DOI: 10.1016/j.apenergy.2025.127228
Dalia Rabie , Hooman Farzaneh
Modern power grids require adaptive demand-side management, yet existing mechanisms often rely on static strategies that fail to reflect consumer heterogeneity. This study develops a demand response program (DRP)-based Energy Management System (ESM) as a novel framework designed to overcome these limitations. The innovation lies in a hybrid methodology that integrates a tri-actor welfare optimization model, capturing generators, service providers, and consumers, alongside a dynamic Multi-Criteria Decision Making (MCDM) approach. The proposed DRP-based ESM autonomously allocates the most suitable program between a Price-Based Program (PBP) and an Incentive-Based Program (IBP) on an hourly basis. Empirical validation uses the Japanese electricity market data, disaggregated across four consumer segments: residential, industrial, commercial offices, and wholesale/retail facilities. Results confirm that no single DRP is universally optimal; rather, effectiveness depends on temporal and sectoral contexts. Sensitivity analysis indicates that, PBPs dominate during valley hours, accounting for up to 75 % of allocations due to lower tariffs that enhance affordability and improve load factor recovery. During off-peak periods, the DRP-based ESM alternates between PBPs and IBPs depending on hourly demand conditions. On low-demand days, IBPs represent 66.67 % of selections, while during the Obon holiday, PBPs are chosen for roughly 75 % of operating hours, effectively managing heightened demand variability. Findings demonstrate that the proposed DRP-based ESM adapts effectively to changes in demand magnitude, elasticity, and operator priorities. The results underscore the importance of behavioral context in shaping DRP performance and highlight the potential of dynamic, data-driven program selection to enhance grid flexibility and consumer welfare.
{"title":"A novel modeling framework for demand response-based energy management systems in smart electricity markets, using optimization and multi-criteria decision making techniques","authors":"Dalia Rabie , Hooman Farzaneh","doi":"10.1016/j.apenergy.2025.127228","DOIUrl":"10.1016/j.apenergy.2025.127228","url":null,"abstract":"<div><div>Modern power grids require adaptive demand-side management, yet existing mechanisms often rely on static strategies that fail to reflect consumer heterogeneity. This study develops a demand response program (DRP)-based Energy Management System (ESM) as a novel framework designed to overcome these limitations. The innovation lies in a hybrid methodology that integrates a tri-actor welfare optimization model, capturing generators, service providers, and consumers, alongside a dynamic Multi-Criteria Decision Making (MCDM) approach. The proposed DRP-based ESM autonomously allocates the most suitable program between a Price-Based Program (PBP) and an Incentive-Based Program (IBP) on an hourly basis. Empirical validation uses the Japanese electricity market data, disaggregated across four consumer segments: residential, industrial, commercial offices, and wholesale/retail facilities. Results confirm that no single DRP is universally optimal; rather, effectiveness depends on temporal and sectoral contexts. Sensitivity analysis indicates that, PBPs dominate during valley hours, accounting for up to 75 % of allocations due to lower tariffs that enhance affordability and improve load factor recovery. During off-peak periods, the DRP-based ESM alternates between PBPs and IBPs depending on hourly demand conditions. On low-demand days, IBPs represent 66.67 % of selections, while during the Obon holiday, PBPs are chosen for roughly 75 % of operating hours, effectively managing heightened demand variability. Findings demonstrate that the proposed DRP-based ESM adapts effectively to changes in demand magnitude, elasticity, and operator priorities. The results underscore the importance of behavioral context in shaping DRP performance and highlight the potential of dynamic, data-driven program selection to enhance grid flexibility and consumer welfare.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127228"},"PeriodicalIF":11.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788281","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 : 2025-12-15DOI: 10.1016/j.apenergy.2025.127272
Ryan Fasti, Dawen Li
Perovskite solar cells (PSCs) combine exceptional power conversion efficiencies with low-cost solution-based processing but remain hindered by environmental instability, mechanical fragility on flexible substrates, and lead leakage. This review evaluates polymer-enabled self-healing strategies for both rigid and flexible PSCs. Rigid PSCs benefit from polymeric scaffolds that sequester volatile degradation products to enable in situ perovskite recrystallization, environmental stimulus-responsive additives that continuously neutralize emerging defects, and self-healing outer encapsulants that autonomously mend impact-induced damage while also reducing lead leakage. In flexible devices, dynamic supramolecular networks based on hydrogen bonding, ionic interactions, and reversible covalent linkages facilitate autonomous repair of bending-induced microcracks, leading to nearly complete restoration of initial performance after extensive deformation cycles. Multifunctional polymer design holds promise for enhancing intrinsic stability by passivating defects at grain boundaries, enhancing extrinsic resistance to moisture and thermal stress, and increasing mechanical robustness under cyclic bending. Future research directions include expanding self-healing capabilities across all PSC functional layers, optimizing healing kinetics, and addressing scalability for industrial manufacturing. Overall, polymer-enabled self-healing approaches are poised to play a critical role in the commercialization of highly robust perovskite-based photovoltaics with enhanced operational stability.
{"title":"Polymer-enabled self-healing and performance recovery: a route to significantly enhance durability and extend lifespan of perovskite-based photovoltaics","authors":"Ryan Fasti, Dawen Li","doi":"10.1016/j.apenergy.2025.127272","DOIUrl":"10.1016/j.apenergy.2025.127272","url":null,"abstract":"<div><div>Perovskite solar cells (PSCs) combine exceptional power conversion efficiencies with low-cost solution-based processing but remain hindered by environmental instability, mechanical fragility on flexible substrates, and lead leakage. This review evaluates polymer-enabled self-healing strategies for both rigid and flexible PSCs. Rigid PSCs benefit from polymeric scaffolds that sequester volatile degradation products to enable in situ perovskite recrystallization, environmental stimulus-responsive additives that continuously neutralize emerging defects, and self-healing outer encapsulants that autonomously mend impact-induced damage while also reducing lead leakage. In flexible devices, dynamic supramolecular networks based on hydrogen bonding, ionic interactions, and reversible covalent linkages facilitate autonomous repair of bending-induced microcracks, leading to nearly complete restoration of initial performance after extensive deformation cycles. Multifunctional polymer design holds promise for enhancing intrinsic stability by passivating defects at grain boundaries, enhancing extrinsic resistance to moisture and thermal stress, and increasing mechanical robustness under cyclic bending. Future research directions include expanding self-healing capabilities across all PSC functional layers, optimizing healing kinetics, and addressing scalability for industrial manufacturing. Overall, polymer-enabled self-healing approaches are poised to play a critical role in the commercialization of highly robust perovskite-based photovoltaics with enhanced operational stability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127272"},"PeriodicalIF":11.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788291","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 : 2025-12-15DOI: 10.1016/j.apenergy.2025.127245
Wei-Bo Chen , Hongey Chen , Shih-Chun Hsiao
This study examines the long-term climate-driven trends in global wind-solar power complementarity from 1940 to 2024, employing the ERA5 reanalysis dataset to evaluate wind power density (WPD) and solar power density (SPD) dynamics. Advanced statistical methods, including the Mann-Kendall test, Theil-Sen estimator, Spearman's rank correlation coefficient (SRCC), composite variability index (CVI), and K-means clustering, were utilized to analyze spatiotemporal patterns and their implications for hybrid renewable energy systems. The results indicate a significant global WPD increase of 0.66 ± 0.06 W/m2 per year, with notable enhancements in the Southern Ocean (+18 W/m2 per year, 1998–2024) and North Atlantic (+10 W/m2 per year), contrasted by a decline in the equatorial Pacific (−20 W/m2 per year, 1998–2024). SPD exhibits a historical decline of −0.038 W/m2 per year (1940–2009) due to global dimming, followed by an increase of 0.054 W/m2 per year (2010–2024) driven by global brightening. Seasonal complementarity analyses reveal strong anticorrelation in the North Atlantic and Southern Ocean during DJF (CVI: 0.52–0.76, SRCC: −0.4 to −0.7), while equatorial regions display synchronized variability (CVI: 0.88–1.0). The Northern Hemisphere (NH) exhibits the strongest complementarity in JJA (CVI: 0.8221, SR: 8.90 %), and the Southern Hemisphere (SH) in DJF (CVI: 0.8922, SR: 5.39 %). K-means clustering categorizes regions into four classes, identifying Class 1 regions (e.g., Northern Europe, India, Brazil, North Atlantic, Southern Ocean) as optimal for hybrid systems, reducing relative storage needs by up to 8.90 % (when compared to a baseline scenario with no complementarity) and unmet demand hours by 20 %. Conversely, Class 4 regions (e.g., Southeast Asia, Central Africa, Central Pacific) exhibit minimal complementarity, necessitating substantial storage solutions. These findings highlight the critical role of wind-solar complementarity in enhancing energy resilience, providing a robust foundation for strategic planning of sustainable energy systems under evolving climate conditions.
{"title":"Climate-driven evolution of global wind-solar complementarity for hybrid energy systems (1940–2024)","authors":"Wei-Bo Chen , Hongey Chen , Shih-Chun Hsiao","doi":"10.1016/j.apenergy.2025.127245","DOIUrl":"10.1016/j.apenergy.2025.127245","url":null,"abstract":"<div><div>This study examines the long-term climate-driven trends in global wind-solar power complementarity from 1940 to 2024, employing the ERA5 reanalysis dataset to evaluate wind power density (WPD) and solar power density (SPD) dynamics. Advanced statistical methods, including the Mann-Kendall test, Theil-Sen estimator, Spearman's rank correlation coefficient (<em>SRCC</em>), composite variability index (<em>CVI</em>), and K-means clustering, were utilized to analyze spatiotemporal patterns and their implications for hybrid renewable energy systems. The results indicate a significant global WPD increase of 0.66 ± 0.06 W/m<sup>2</sup> per year, with notable enhancements in the Southern Ocean (+18 W/m<sup>2</sup> per year, 1998–2024) and North Atlantic (+10 W/m<sup>2</sup> per year), contrasted by a decline in the equatorial Pacific (−20 W/m<sup>2</sup> per year, 1998–2024). SPD exhibits a historical decline of −0.038 W/m<sup>2</sup> per year (1940–2009) due to global dimming, followed by an increase of 0.054 W/m<sup>2</sup> per year (2010–2024) driven by global brightening. Seasonal complementarity analyses reveal strong anticorrelation in the North Atlantic and Southern Ocean during DJF (<em>CVI</em>: 0.52–0.76, <em>SRCC</em>: −0.4 to −0.7), while equatorial regions display synchronized variability (<em>CVI</em>: 0.88–1.0). The Northern Hemisphere (NH) exhibits the strongest complementarity in JJA (<em>CVI</em>: 0.8221, SR: 8.90 %), and the Southern Hemisphere (SH) in DJF (<em>CVI</em>: 0.8922, SR: 5.39 %). K-means clustering categorizes regions into four classes, identifying Class 1 regions (e.g., Northern Europe, India, Brazil, North Atlantic, Southern Ocean) as optimal for hybrid systems, reducing relative storage needs by up to 8.90 % (when compared to a baseline scenario with no complementarity) and unmet demand hours by 20 %. Conversely, Class 4 regions (e.g., Southeast Asia, Central Africa, Central Pacific) exhibit minimal complementarity, necessitating substantial storage solutions. These findings highlight the critical role of wind-solar complementarity in enhancing energy resilience, providing a robust foundation for strategic planning of sustainable energy systems under evolving climate conditions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"405 ","pages":"Article 127245"},"PeriodicalIF":11.0,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145788292","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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