Pub Date : 2026-04-15Epub Date: 2026-01-31DOI: 10.1016/j.renene.2026.125335
Kaijie Hong , Zheng Li , Kang Fu, Guo Zhang, Ya Zhou
The rapid integration of solar photovoltaics (PV) into distribution networks frequently leads to congestion and reverse power flows, which limits the system's capacity for additional renewable generation. To address these challenges, this paper proposes a risk-constrained framework based on the concept of Virtual Multi-Energy Assets (VMEA). This framework offers a coordinated approach that combines hydrogen-based systems for long-term balancing, multi-technology storage, incentive-driven demand response, and dynamic network reconfiguration. A central feature of the model is the use of a downside risk constraint (DRC) to explicitly manage the risk of high costs in adverse scenarios, thereby ensuring robust operation under uncertainties in renewable generation and load. Numerical simulations on a test system show that the proposed framework reduces operational costs by up to 14%, lowers congestion by nearly 60%, and increases solar hosting capacity by over 40% compared to a base case. While the Risk-averse strategy results in a modest 4% cost increase relative to a Risk-neutral approach, it ensures secure operation under extreme uncertainty. These results demonstrate that the VMEA framework is an effective approach for enhancing the resilience and sustainability of distribution networks with high solar penetration.
{"title":"Risk-aware modeling of active distribution grids for sustainable solar hosting and congestion relief with virtual multi energy assets and demand flexibility","authors":"Kaijie Hong , Zheng Li , Kang Fu, Guo Zhang, Ya Zhou","doi":"10.1016/j.renene.2026.125335","DOIUrl":"10.1016/j.renene.2026.125335","url":null,"abstract":"<div><div>The rapid integration of solar photovoltaics (PV) into distribution networks frequently leads to congestion and reverse power flows, which limits the system's capacity for additional renewable generation. To address these challenges, this paper proposes a risk-constrained framework based on the concept of Virtual Multi-Energy Assets (VMEA). This framework offers a coordinated approach that combines hydrogen-based systems for long-term balancing, multi-technology storage, incentive-driven demand response, and dynamic network reconfiguration. A central feature of the model is the use of a downside risk constraint (DRC) to explicitly manage the risk of high costs in adverse scenarios, thereby ensuring robust operation under uncertainties in renewable generation and load. Numerical simulations on a test system show that the proposed framework reduces operational costs by up to 14%, lowers congestion by nearly 60%, and increases solar hosting capacity by over 40% compared to a base case. While the Risk-averse strategy results in a modest 4% cost increase relative to a Risk-neutral approach, it ensures secure operation under extreme uncertainty. These results demonstrate that the VMEA framework is an effective approach for enhancing the resilience and sustainability of distribution networks with high solar penetration.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125335"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186419","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 : 2026-04-15Epub Date: 2026-01-23DOI: 10.1016/j.renene.2026.125201
Wen-Ting Lin , Kangming Liu , Guo Chen , Jueyou Li , Degang Yang , Tingzhen Ming
With the increasing integration of renewable energy into regional power grids, significant spatial differences in carbon intensity have emerged. These differences highlight the need for carbon-aware workload allocation in geographically distributed Internet Data Centers, where aligning computational loads with low-carbon regions can enhance both environmental and economic outcomes. In this paper, we propose a two-stage optimization framework that integrates renewable-aware workload allocation and strategic carbon allowance procurement. In the first stage, a robust optimization model based on column-and-constraint generation is developed to manage uncertainties in workload demand and carbon prices, enabling stable and cost-effective workload distribution across regions with varying renewable energy penetration. In the second stage, a multi-class mean field game model is constructed to capture strategic interactions and behavioral heterogeneity among Internet Data Centers in carbon markets. We apply a Deep Galerkin Method to solve the resulting high-dimensional partial differential equations, yielding a robust and convergent procurement strategy. Simulation results demonstrate that the proposed framework achieves over 28% cost savings while ensuring carbon compliance and workload satisfaction. This study offers theoretical and practical insights for carbon-regulated Internet Data Center operations, and supports the broader integration of renewable energy in large-scale digital infrastructure.
{"title":"Carbon-aware optimization for Internet Data Centers with renewable generation: Robust workload allocation and carbon procurement via multi-class mean field game","authors":"Wen-Ting Lin , Kangming Liu , Guo Chen , Jueyou Li , Degang Yang , Tingzhen Ming","doi":"10.1016/j.renene.2026.125201","DOIUrl":"10.1016/j.renene.2026.125201","url":null,"abstract":"<div><div>With the increasing integration of renewable energy into regional power grids, significant spatial differences in carbon intensity have emerged. These differences highlight the need for carbon-aware workload allocation in geographically distributed Internet Data Centers, where aligning computational loads with low-carbon regions can enhance both environmental and economic outcomes. In this paper, we propose a two-stage optimization framework that integrates renewable-aware workload allocation and strategic carbon allowance procurement. In the first stage, a robust optimization model based on column-and-constraint generation is developed to manage uncertainties in workload demand and carbon prices, enabling stable and cost-effective workload distribution across regions with varying renewable energy penetration. In the second stage, a multi-class mean field game model is constructed to capture strategic interactions and behavioral heterogeneity among Internet Data Centers in carbon markets. We apply a Deep Galerkin Method to solve the resulting high-dimensional partial differential equations, yielding a robust and convergent procurement strategy. Simulation results demonstrate that the proposed framework achieves over 28% cost savings while ensuring carbon compliance and workload satisfaction. This study offers theoretical and practical insights for carbon-regulated Internet Data Center operations, and supports the broader integration of renewable energy in large-scale digital infrastructure.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125201"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096106","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 : 2026-04-15Epub Date: 2026-01-28DOI: 10.1016/j.renene.2026.125344
Zunshi Han , Hao Lu , Wenjun Zhao , Chuanxiao Zheng
Dust deposition on photovoltaic panels severely degrades power output. The combination of droplets and hydrophobic surfaces can effectively solve this problem. However, the underlying physics of droplet-based cleaning and its quantitative impact on generation remain poorly understood. This study employs an innovative multiphysics framework, integrating computational fluid dynamics with the discrete element method, coupled with a photovoltaic power prediction model that links dust deposition directly to photo-generation physics, to simulate droplet-mediated dust cleaning and its impact on power output. The droplet dynamics are analyzed using a multiphase volume of fluid model, and the dust particle behavior are revealed by Edinburgh elastic-plastic adhesion model. Smaller particles (dp ≤ 100 μm) are readily removed, achieving a dust removal rate of 22.7 % at dp = 50 μm. However, particles that agglomerate due to cohesive forces after droplet cleaning become difficult to remove. Droplet cleaning efficiency correlates with the Weber number. At Weber number = 1.91, both coverage radius and contact frequency reach optimal values, yielding a peak dust removal rate of 14.1 %. Coupling dust deposition density with cleaning efficiency results and inputting them into a photovoltaic power generation model indicates that each 1 g/m2 increase in deposition density causes a maximum power degradation of 2.26 %. Under droplet cleaning conditions with Weber number = 1.91, photovoltaic power significantly increases by 2.9 % under small particle conditions. This study provides theoretical basis and parameter optimization paradigms for self-cleaning design of super-hydrophobic photovoltaic.
{"title":"Synergistic optimization analysis of dust cleaning efficiency and power generation enhancement on super-hydrophobic photovoltaic panel for droplets with different Weber numbers","authors":"Zunshi Han , Hao Lu , Wenjun Zhao , Chuanxiao Zheng","doi":"10.1016/j.renene.2026.125344","DOIUrl":"10.1016/j.renene.2026.125344","url":null,"abstract":"<div><div>Dust deposition on photovoltaic panels severely degrades power output. The combination of droplets and hydrophobic surfaces can effectively solve this problem. However, the underlying physics of droplet-based cleaning and its quantitative impact on generation remain poorly understood. This study employs an innovative multiphysics framework, integrating computational fluid dynamics with the discrete element method, coupled with a photovoltaic power prediction model that links dust deposition directly to photo-generation physics, to simulate droplet-mediated dust cleaning and its impact on power output. The droplet dynamics are analyzed using a multiphase volume of fluid model, and the dust particle behavior are revealed by Edinburgh elastic-plastic adhesion model. Smaller particles (<em>d</em><sub><em>p</em></sub> ≤ 100 μm) are readily removed, achieving a dust removal rate of 22.7 % at <em>d</em><sub><em>p</em></sub> = 50 μm. However, particles that agglomerate due to cohesive forces after droplet cleaning become difficult to remove. Droplet cleaning efficiency correlates with the Weber number. At Weber number = 1.91, both coverage radius and contact frequency reach optimal values, yielding a peak dust removal rate of 14.1 %. Coupling dust deposition density with cleaning efficiency results and inputting them into a photovoltaic power generation model indicates that each 1 g/m<sup>2</sup> increase in deposition density causes a maximum power degradation of 2.26 %. Under droplet cleaning conditions with Weber number = 1.91, photovoltaic power significantly increases by 2.9 % under small particle conditions. This study provides theoretical basis and parameter optimization paradigms for self-cleaning design of super-hydrophobic photovoltaic.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125344"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096108","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 : 2026-04-15Epub Date: 2026-01-30DOI: 10.1016/j.renene.2026.125339
Yao Gao , Jian Zhang , Ke Song , Bolun Hao , Zhongdong Zhao , Jie Li
To achieve high-value utilization of biomass resources, this study elucidates the selective extraction characteristics of oxygenated compounds by investigating the influence of reaction temperature (240–360 °C) and time (0–120 min). Thermal dissolution (TD) experiments were conducted on three representative agricultural/forestry and marine biomass materials—Pine sawdust (Ps), corncob (Cb), and Enteromorpha prolifera (Ep), using solvents with distinct properties: ethanol (ET) and tetrahydrofuran (THF). The results showed that ET consistently outperformed THF in soluble yield across all biomass types. The highest yield (82.35%) was achieved from Ps at 300 °C for 90 min using ET. GC/MS analysis revealed that the soluble products were primarily composed of oxygenated compounds, including phenols, alcohols, esters, etc. Saccharides were detected in Ep extracts (10.39%) under the conditions of 300 °C and 90 min. ET exhibited selectivity towards ethers and esters, while THF demonstrated selectivity towards phenols, alcohols, esters, and furans. Temperature and reaction time significantly influenced the component distribution of soluble products, with similar trends observed for three biomasses. Analysis (ultimate analysis, XPS, and FTIR) showed TD decreased biomass oxygen content, transferring oxygenated structures to the soluble products. The remaining residues, rich in carbon (30.04–67.24%), are suitable for further application. This research clarifies that TD is a dual-purpose sustainable energy technology: it valorizes biomass into high-value biofuel precursors and carbon-dense solid fuels, providing a viable pathway to integrate biomass into the global sustainable energy chain and reduce fossil reliance.
{"title":"Study of thermal dissolution characteristics of oxygenated structures in biomass","authors":"Yao Gao , Jian Zhang , Ke Song , Bolun Hao , Zhongdong Zhao , Jie Li","doi":"10.1016/j.renene.2026.125339","DOIUrl":"10.1016/j.renene.2026.125339","url":null,"abstract":"<div><div>To achieve high-value utilization of biomass resources, this study elucidates the selective extraction characteristics of oxygenated compounds by investigating the influence of reaction temperature (240–360 °C) and time (0–120 min). Thermal dissolution (TD) experiments were conducted on three representative agricultural/forestry and marine biomass materials—Pine sawdust (Ps), corncob (Cb), and <em>Enteromorpha prolifera</em> (Ep), using solvents with distinct properties: ethanol (ET) and tetrahydrofuran (THF). The results showed that ET consistently outperformed THF in soluble yield across all biomass types. The highest yield (82.35%) was achieved from Ps at 300 °C for 90 min using ET. GC/MS analysis revealed that the soluble products were primarily composed of oxygenated compounds, including phenols, alcohols, esters, etc. Saccharides were detected in Ep extracts (10.39%) under the conditions of 300 °C and 90 min. ET exhibited selectivity towards ethers and esters, while THF demonstrated selectivity towards phenols, alcohols, esters, and furans. Temperature and reaction time significantly influenced the component distribution of soluble products, with similar trends observed for three biomasses. Analysis (ultimate analysis, XPS, and FTIR) showed TD decreased biomass oxygen content, transferring oxygenated structures to the soluble products. The remaining residues, rich in carbon (30.04–67.24%), are suitable for further application. This research clarifies that TD is a dual-purpose sustainable energy technology: it valorizes biomass into high-value biofuel precursors and carbon-dense solid fuels, providing a viable pathway to integrate biomass into the global sustainable energy chain and reduce fossil reliance.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125339"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186258","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 : 2026-04-15Epub Date: 2026-02-02DOI: 10.1016/j.renene.2026.125377
Fasheng Liu , Chen Yin , Shuguang Li
—This paper presents a novel framework for virtual energy hub (VEH) optimization that integrates multiple energy carriers, including electrical, thermal, renewable energy units, and water. To meet diverse demands while considering energy markets signal. The proposed model contains conventional energy sources like combined heat and power units, gas boiler, and wind generation with more complex units like electrical vehicle parking lot (EVPL) and water desalination systems. In order to take into account uncertainties related to renewable generation and load variations, the proposed approach utilizes a demand response program for both electrical and thermal networks. In particular, deep asynchronous gradient policy (DAGP) has been adopted for solving the decision-making optimization problem by interacting with its agent with the environment. There are two deep neural networks (DNNs) in the architecture of DAGP to exploit the optimal policy for the VEH operation equipped with EVPL: i) an actor neural network generates optimal actions for allocation of energy units, and ii) a critic neural network is implemented to evaluate the quality of applied actions through estimating a pre-defined reinforcement signal. By training the capability of DNNs, the DAGP aims to facilitate energy trading in the electrical market while the agent responds to fluctuations in market price. Simulation tests on VEH under various scenarios reveal the feasibility of the suggested VEH optimization methodology (realized by the DAGP agent) to improve efficiency and reduce operational costs under uncertain situations.
{"title":"Deep asynchronous gradient policy for cost-effective optimization of virtual energy hubs under uncertainty","authors":"Fasheng Liu , Chen Yin , Shuguang Li","doi":"10.1016/j.renene.2026.125377","DOIUrl":"10.1016/j.renene.2026.125377","url":null,"abstract":"<div><div>—This paper presents a novel framework for virtual energy hub (VEH) optimization that integrates multiple energy carriers, including electrical, thermal, renewable energy units, and water. To meet diverse demands while considering energy markets signal. The proposed model contains conventional energy sources like combined heat and power units, gas boiler, and wind generation with more complex units like electrical vehicle parking lot (EVPL) and water desalination systems. In order to take into account uncertainties related to renewable generation and load variations, the proposed approach utilizes a demand response program for both electrical and thermal networks. In particular, deep asynchronous gradient policy (DAGP) has been adopted for solving the decision-making optimization problem by interacting with its agent with the environment. There are two deep neural networks (DNNs) in the architecture of DAGP to exploit the optimal policy for the VEH operation equipped with EVPL: <em>i</em>) an actor neural network generates optimal actions for allocation of energy units, and <em>ii</em>) a critic neural network is implemented to evaluate the quality of applied actions through estimating a pre-defined reinforcement signal. By training the capability of DNNs, the DAGP aims to facilitate energy trading in the electrical market while the agent responds to fluctuations in market price. Simulation tests on VEH under various scenarios reveal the feasibility of the suggested VEH optimization methodology (realized by the DAGP agent) to improve efficiency and reduce operational costs under uncertain situations.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125377"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186239","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 : 2026-04-15Epub Date: 2026-01-25DOI: 10.1016/j.renene.2026.125334
Yuting Yang , Donglan Zha , Yihui Wu
In the context of expanding photovoltaic applications, optimizing the spatial distribution of photovoltaic systems at the city level presents a major challenge. However, existing studies have limitations in accounting for the constraints affecting photovoltaic deployment. This study developed a sophisticated framework combining geographic information system technology and multicriteria decision-making method for photovoltaic deployment assessment, incorporating affordability, techno-economic feasibility, development intensity, and infrastructure factors. It assessed photovoltaic deployment capacity in 297 Chinese cities, including both utility-scale photovoltaic and distributed-scale photovoltaic and revealed the spatial heterogeneity in photovoltaic generation supply-demand situation. Our findings indicate a clear spatial mismatch between the utility-scale photovoltaic generation potential and power demand, particularly in Hotan, and Jiuquan with intensive photovoltaic industries. Furthermore, 55 cities in China are unsuitable for further utility-scale photovoltaic development due to land limitations. Distributed-scale photovoltaic deployment capacity in the northwest cities like Hami and Ordos can be reduced to lower power absorption costs. By contrast, it is suggested to conduct large-scale distributed photovoltaic deployment in the megacities, super-large cities, and large cities with a deployment intensity larger than 10 GW. Our study clarifies an implementation roadmap for city-level capacity deployment of utility-scale and distributed-scale photovoltaic, focusing on photovoltaic resources effective allocation.
{"title":"City-level photovoltaic deployment roadmap in China","authors":"Yuting Yang , Donglan Zha , Yihui Wu","doi":"10.1016/j.renene.2026.125334","DOIUrl":"10.1016/j.renene.2026.125334","url":null,"abstract":"<div><div>In the context of expanding photovoltaic applications, optimizing the spatial distribution of photovoltaic systems at the city level presents a major challenge. However, existing studies have limitations in accounting for the constraints affecting photovoltaic deployment. This study developed a sophisticated framework combining geographic information system technology and multicriteria decision-making method for photovoltaic deployment assessment, incorporating affordability, techno-economic feasibility, development intensity, and infrastructure factors. It assessed photovoltaic deployment capacity in 297 Chinese cities, including both utility-scale photovoltaic and distributed-scale photovoltaic and revealed the spatial heterogeneity in photovoltaic generation supply-demand situation. Our findings indicate a clear spatial mismatch between the utility-scale photovoltaic generation potential and power demand, particularly in Hotan, and Jiuquan with intensive photovoltaic industries. Furthermore, 55 cities in China are unsuitable for further utility-scale photovoltaic development due to land limitations. Distributed-scale photovoltaic deployment capacity in the northwest cities like Hami and Ordos can be reduced to lower power absorption costs. By contrast, it is suggested to conduct large-scale distributed photovoltaic deployment in the megacities, super-large cities, and large cities with a deployment intensity larger than 10 GW. Our study clarifies an implementation roadmap for city-level capacity deployment of utility-scale and distributed-scale photovoltaic, focusing on photovoltaic resources effective allocation.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125334"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186240","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 : 2026-04-15Epub Date: 2026-01-31DOI: 10.1016/j.renene.2026.125354
Camilla Lops , Ida Bruno , Maira Aracne , Mariano Pierantozzi
The accelerating transition toward electrification and decarbonization increases the need for accurate, scalable forecasting tools to guide renewable energy deployment. Most existing approaches treat climate prediction and spatial suitability analysis as independent tasks, constraining the integration of short-term forecasts into site selection frameworks. This study addresses this gap by investigating how Artificial Intelligence, specifically Gated Recurrent Units, can enhance renewable energy site planning through improved short-term climatic forecasting embedded into spatial decision-making frameworks. The primary contribution lies in directly integrating Machine Learning-based climate predictions with physics-based energy production models within a unified multi-criteria framework applicable to both photovoltaic and wind technologies. The proposed models predict key climatic variables (solar irradiance, temperature, wind speed, and direction) over a 3-day horizon. Performance is evaluated by comparing Machine Learning forecasts and a regional climate model against weather-station observations at two Italian sites. Results demonstrate substantial improvements: for photovoltaic systems, prediction errors decrease by 11%–60% with consistently higher correlations across seasons; for wind energy, errors decrease by 13%–27%. By coupling data-driven climate forecasting with physics-based energy models, the framework enhances the accuracy, robustness, and spatial relevance of renewable energy assessments, providing more reliable support for site planning and grid integration decisions.
{"title":"Bridging climate modeling and Artificial Intelligence for enhanced renewable energy forecasting and siting","authors":"Camilla Lops , Ida Bruno , Maira Aracne , Mariano Pierantozzi","doi":"10.1016/j.renene.2026.125354","DOIUrl":"10.1016/j.renene.2026.125354","url":null,"abstract":"<div><div>The accelerating transition toward electrification and decarbonization increases the need for accurate, scalable forecasting tools to guide renewable energy deployment. Most existing approaches treat climate prediction and spatial suitability analysis as independent tasks, constraining the integration of short-term forecasts into site selection frameworks. This study addresses this gap by investigating how Artificial Intelligence, specifically Gated Recurrent Units, can enhance renewable energy site planning through improved short-term climatic forecasting embedded into spatial decision-making frameworks. The primary contribution lies in directly integrating Machine Learning-based climate predictions with physics-based energy production models within a unified multi-criteria framework applicable to both photovoltaic and wind technologies. The proposed models predict key climatic variables (solar irradiance, temperature, wind speed, and direction) over a 3-day horizon. Performance is evaluated by comparing Machine Learning forecasts and a regional climate model against weather-station observations at two Italian sites. Results demonstrate substantial improvements: for photovoltaic systems, prediction errors decrease by 11%–60% with consistently higher correlations across seasons; for wind energy, errors decrease by 13%–27%. By coupling data-driven climate forecasting with physics-based energy models, the framework enhances the accuracy, robustness, and spatial relevance of renewable energy assessments, providing more reliable support for site planning and grid integration decisions.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125354"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186245","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 : 2026-04-15Epub Date: 2026-02-10DOI: 10.1016/j.renene.2026.125425
Ning Yang , Yifan Hu , Yuanjing Zhao , Ziqi Wei , Jingyi Zhang , Bowen Wang , Yuhang Zhang , Yaoxuan Shang , Gang Wang , Lei Xing
Achieving carbon neutrality by 2050 is a central global objective that is accelerating the transition to sustainable hydrogen. Water electrolysis, particularly the hydrogen evolution reaction (HER), offers a promising pathway for clean hydrogen production. Nonetheless, high catalyst cost, limited durability, and sluggish alkaline kinetics remain significant obstacles. This review systematically examines the design and application of carbon-based composite catalysts for HER, spanning carbon-based materials and electrodes to hydrogen production devices and, ultimately, the broader hydrogen energy system. We also outline upgrade pathways for hydrogen production devices and optimization strategies for the hydrogen energy system, with particular attention to China's growing energy demand and continued dependence on fossil fuels. We analyze advanced structural strategies, including transition-metal doping (e.g., iron, cobalt, and nickel), heteroatom modification (e.g., nitrogen-doped graphene), and hierarchical architectures (e.g., three-dimensional porous frameworks and single-atom catalysts). The latest progress in the development and optimization of catalyst materials was summarized, with an emphasis on methods to improve catalytic activity and stability. We compare the advantages and disadvantages of four electrolyzer types. In addition, we provide an overview of the global hydrogen landscape, with emphasis on China's progress in hydrogen production, storage, and utilization. This review highlights the roles of heteroatom doping, active-site engineering, and synergy between the electrolyte and the catalyst in improving HER performance. We examine the effects of temperature management and flow-channel design on hydrogen production devices within integrated systems that leverage artificial intelligence and multi-energy coupling. Finally, we discuss current limitations of water electrolysis for hydrogen production and outline future directions, including improving catalyst robustness under complex conditions, reducing production costs, simplifying synthesis methods, upgrading the design of existing hydrogen production devices, adopting interdisciplinary approaches for mechanism analysis, integrating artificial intelligence with regional, large-scale hydrogen energy systems, and reducing avoidable hydrogen energy losses to improve efficiency. These insights aim to advance water electrolysis technology, support the global carbon-neutrality goal, and guide future research on sustainable hydrogen production. Compared with previous reviews that mainly focus on catalyst synthesis and intrinsic activity, this work uniquely bridges carbon-based catalyst design with electrode engineering, electrolyzer technologies, and hydrogen energy systems, providing a unified framework to accelerate the development of efficient, durable, and scalable hydrogen production for global carbon-neutrality goals.
{"title":"Sustainable carbon-based composites for hydrogen evolution: catalyst engineering and performance enhancement pathways","authors":"Ning Yang , Yifan Hu , Yuanjing Zhao , Ziqi Wei , Jingyi Zhang , Bowen Wang , Yuhang Zhang , Yaoxuan Shang , Gang Wang , Lei Xing","doi":"10.1016/j.renene.2026.125425","DOIUrl":"10.1016/j.renene.2026.125425","url":null,"abstract":"<div><div>Achieving carbon neutrality by 2050 is a central global objective that is accelerating the transition to sustainable hydrogen. Water electrolysis, particularly the hydrogen evolution reaction (HER), offers a promising pathway for clean hydrogen production. Nonetheless, high catalyst cost, limited durability, and sluggish alkaline kinetics remain significant obstacles. This review systematically examines the design and application of carbon-based composite catalysts for HER, spanning carbon-based materials and electrodes to hydrogen production devices and, ultimately, the broader hydrogen energy system. We also outline upgrade pathways for hydrogen production devices and optimization strategies for the hydrogen energy system, with particular attention to China's growing energy demand and continued dependence on fossil fuels. We analyze advanced structural strategies, including transition-metal doping (e.g., iron, cobalt, and nickel), heteroatom modification (e.g., nitrogen-doped graphene), and hierarchical architectures (e.g., three-dimensional porous frameworks and single-atom catalysts). The latest progress in the development and optimization of catalyst materials was summarized, with an emphasis on methods to improve catalytic activity and stability. We compare the advantages and disadvantages of four electrolyzer types. In addition, we provide an overview of the global hydrogen landscape, with emphasis on China's progress in hydrogen production, storage, and utilization. This review highlights the roles of heteroatom doping, active-site engineering, and synergy between the electrolyte and the catalyst in improving HER performance. We examine the effects of temperature management and flow-channel design on hydrogen production devices within integrated systems that leverage artificial intelligence and multi-energy coupling. Finally, we discuss current limitations of water electrolysis for hydrogen production and outline future directions, including improving catalyst robustness under complex conditions, reducing production costs, simplifying synthesis methods, upgrading the design of existing hydrogen production devices, adopting interdisciplinary approaches for mechanism analysis, integrating artificial intelligence with regional, large-scale hydrogen energy systems, and reducing avoidable hydrogen energy losses to improve efficiency. These insights aim to advance water electrolysis technology, support the global carbon-neutrality goal, and guide future research on sustainable hydrogen production. Compared with previous reviews that mainly focus on catalyst synthesis and intrinsic activity, this work uniquely bridges carbon-based catalyst design with electrode engineering, electrolyzer technologies, and hydrogen energy systems, providing a unified framework to accelerate the development of efficient, durable, and scalable hydrogen production for global carbon-neutrality goals.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125425"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186248","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 : 2026-04-15Epub Date: 2026-02-02DOI: 10.1016/j.renene.2026.125368
Hamza Fiaz , Fahim Abdul Gafoor , Ali AlMasabai , Panagiotis Liatsis , TieJun Zhang , Maryam R. AlShehhi
Conventional mirror cleaning strategies for solar power plants are water-intensive and costly, so targeted cleaning is desired by identifying the dust-concentrated solar collectors. This paper utilizes a two-phase computational fluid dynamics (CFD) approach integrated with unsupervised clustering to predict the dust distribution on the solar field at a 100 MW-scale concentrated solar power (CSP) plant. The meteorological data (wind speed, direction, and relative humidity) recorded over a full year and geometrical parameters (size, layout, and number of solar collectors) are sourced directly from an operational CSP plant to develop a high-fidelity simulation model. Two distinct cleaning cycles (4 days each) for solar collectors with and without sandstorm are evaluated for model validation. Our results indicate that the root mean square error (RMSE) of model for the regular cleaning cycle varies from 7.1% to 8.1%, while for the irregular cleaning cycle (with sandstorm), it varies from 7.2% to 12.4% between the first and the last day of the cleaning cycle respectively. The proposed CFD approach is employed to generate spatially varying reflectivity data across weather conditions (varying wind speeds, humidity levels, and wind directions), effectively eliminating the need of expensive experimental setups. Unsupervised clustering is then utilized to classify the inherent trends between the meteorological conditions and reflectance loss. Based on this analysis, a dust distribution model featuring five distinct classes is developed to predict dust accumulation on solar mirrors throughout the year, enabling sustainable targeted cleaning.
{"title":"A CFD-based spatiotemporal mapping of dust deposition on solar fields using unsupervised clustering for targeted cleaning","authors":"Hamza Fiaz , Fahim Abdul Gafoor , Ali AlMasabai , Panagiotis Liatsis , TieJun Zhang , Maryam R. AlShehhi","doi":"10.1016/j.renene.2026.125368","DOIUrl":"10.1016/j.renene.2026.125368","url":null,"abstract":"<div><div>Conventional mirror cleaning strategies for solar power plants are water-intensive and costly, so targeted cleaning is desired by identifying the dust-concentrated solar collectors. This paper utilizes a two-phase computational fluid dynamics (CFD) approach integrated with unsupervised clustering to predict the dust distribution on the solar field at a 100 MW-scale concentrated solar power (CSP) plant. The meteorological data (wind speed, direction, and relative humidity) recorded over a full year and geometrical parameters (size, layout, and number of solar collectors) are sourced directly from an operational CSP plant to develop a high-fidelity simulation model. Two distinct cleaning cycles (4 days each) for solar collectors with and without sandstorm are evaluated for model validation. Our results indicate that the root mean square error (RMSE) of model for the regular cleaning cycle varies from 7.1% to 8.1%, while for the irregular cleaning cycle (with sandstorm), it varies from 7.2% to 12.4% between the first and the last day of the cleaning cycle respectively. The proposed CFD approach is employed to generate spatially varying reflectivity data across weather conditions (varying wind speeds, humidity levels, and wind directions), effectively eliminating the need of expensive experimental setups. Unsupervised clustering is then utilized to classify the inherent trends between the meteorological conditions and reflectance loss. Based on this analysis, a dust distribution model featuring five distinct classes is developed to predict dust accumulation on solar mirrors throughout the year, enabling sustainable targeted cleaning.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125368"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186326","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 : 2026-04-15Epub Date: 2026-02-05DOI: 10.1016/j.renene.2026.125400
Abraham Ayobamiji Awosusi , Dilber Uzun Ozsahin
Understanding how renewable-based-energy innovation influences ecological sustainability remains a critical yet underexplored issue. This study fills this gap by examining the effects of renewable-based-energy innovation, political risk, and economic growth on load capacity factor (LF) in Japan. It further advances existing literature through a disaggregated analysis of renewable-based-energy innovation specifically solar, wind, and biomass innovation, to assess their individual environmental impacts. Given the non-normal distribution of the dataset and the need to capture heterogeneous relationships, this study used recently introduced wavelet based-quantile methodologies: wavelet-quantile Phillips-Perron, wavelet-quantile regression and wavelet-quantile correlation. The empirical results indicate that biomass and solar innovations enhance ecological quality by increasing LF across all quantiles and horizons, while economic growth reduces ecological quality, reflecting ongoing environmental pressures from expansion. Wind innovation shows a negative short-term impact but becomes beneficial in the medium and long terms, reflecting technological gains. Conversely, political risk exerts mixed short-term effects but negatively affects ecological quality in the long term, indicating that persistent instability undermines environmental governance. These findings emphasize the need to expand investment in biomass and solar technologies, introduce short-term support for wind energy to manage initial ecological costs, and strengthen political and institutional stability for sustained ecological gains.
{"title":"Assessing the role of renewable-based energy innovation, economic growth, and political risk in shaping Japan's load capacity factor: A wavelet-based quantile analysis","authors":"Abraham Ayobamiji Awosusi , Dilber Uzun Ozsahin","doi":"10.1016/j.renene.2026.125400","DOIUrl":"10.1016/j.renene.2026.125400","url":null,"abstract":"<div><div>Understanding how renewable-based-energy innovation influences ecological sustainability remains a critical yet underexplored issue. This study fills this gap by examining the effects of renewable-based-energy innovation, political risk, and economic growth on load capacity factor (LF) in Japan. It further advances existing literature through a disaggregated analysis of renewable-based-energy innovation specifically solar, wind, and biomass innovation, to assess their individual environmental impacts. Given the non-normal distribution of the dataset and the need to capture heterogeneous relationships, this study used recently introduced wavelet based-quantile methodologies: wavelet-quantile Phillips-Perron, wavelet-quantile regression and wavelet-quantile correlation. The empirical results indicate that biomass and solar innovations enhance ecological quality by increasing LF across all quantiles and horizons, while economic growth reduces ecological quality, reflecting ongoing environmental pressures from expansion. Wind innovation shows a negative short-term impact but becomes beneficial in the medium and long terms, reflecting technological gains. Conversely, political risk exerts mixed short-term effects but negatively affects ecological quality in the long term, indicating that persistent instability undermines environmental governance. These findings emphasize the need to expand investment in biomass and solar technologies, introduce short-term support for wind energy to manage initial ecological costs, and strengthen political and institutional stability for sustained ecological gains.</div></div>","PeriodicalId":419,"journal":{"name":"Renewable Energy","volume":"262 ","pages":"Article 125400"},"PeriodicalIF":9.1,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186370","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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