Pub Date : 2026-01-30DOI: 10.1016/j.apenergy.2026.127433
Tayenne Dias de Lima, Pedro Faria, Zita Vale
Battery energy storage systems (BESS) play a critical role in enhancing the flexibility, reliability, and efficiency of residential energy management. Optimized scheduling of BESS is essential to maximize operational benefits while mitigating carbon emissions. Consequently, investigations addressing the enhanced operation of BESS in energy management systems are highly relevant. From this perspective, it is important to consider operational patterns that preserve the long-term performance and lifespan of batteries. This paper presents a mixed integer linear programming model for the optimal scheduling of home energy systems supported by solar generation and battery systems. After optimization, the model calculates battery degradation, considering both cycle and calendar aging effects. Additionally, a carbon emissions penalty was incorporated into the objective function to address environmental impacts. The model was coded in Python and solved through the CBC solver. The model was tested under different battery SOC limits and seasonal conditions (winter and summer), highlighting the role of BESS in reducing energy costs, emissions, and grid dependency while evidencing the impact of operational strategies on battery aging.
{"title":"Optimal scheduling of home energy systems considering battery aging and CO2 emissions","authors":"Tayenne Dias de Lima, Pedro Faria, Zita Vale","doi":"10.1016/j.apenergy.2026.127433","DOIUrl":"10.1016/j.apenergy.2026.127433","url":null,"abstract":"<div><div>Battery energy storage systems (BESS) play a critical role in enhancing the flexibility, reliability, and efficiency of residential energy management. Optimized scheduling of BESS is essential to maximize operational benefits while mitigating carbon emissions. Consequently, investigations addressing the enhanced operation of BESS in energy management systems are highly relevant. From this perspective, it is important to consider operational patterns that preserve the long-term performance and lifespan of batteries. This paper presents a mixed integer linear programming model for the optimal scheduling of home energy systems supported by solar generation and battery systems. After optimization, the model calculates battery degradation, considering both cycle and calendar aging effects. Additionally, a carbon emissions penalty was incorporated into the objective function to address environmental impacts. The model was coded in Python and solved through the CBC solver. The model was tested under different battery SOC limits and seasonal conditions (winter and summer), highlighting the role of BESS in reducing energy costs, emissions, and grid dependency while evidencing the impact of operational strategies on battery aging.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127433"},"PeriodicalIF":11.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076598","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 : 2026-01-30DOI: 10.1016/j.apenergy.2026.127421
Qianqian Wang , Haobin Xie , Binlin Dou , Weibo Zheng , Bing Li , Jim P. Zheng , Xiang Li , Pugalenthiyar Thondaiman , Pingwen Ming
Proton exchange membrane fuel cells suffer from membrane degradation induced by pinhole defects under asymmetric pressure. However, the quantitative influence of sub-10 μm pinholes, critical to understanding early-stage failure, is poorly understood. We present a novel mechanistic model of two-phase flow through a 3 μm pinhole, validated experimentally with a custom in-situ test bench and infrared thermography. The results quantitatively decouple the degradation mechanisms: (1) H₂ crossover shifts from diffusion- to convection-dominated when the anode-cathode pressure gradient exceeds 0.8 kPa, with a tenfold flux increase at 10 kPa. Sensitivity analysis identifies temperature as the dominant factor governing convective crossover. (2) Voltage loss comprises 50–100 mV from the pinhole itself and an additional 13.9–41.5 mV from asymmetric pressure, mainly due to increased oxygen reduction reaction activation loss. (3) Under symmetric pressure, the pinhole generates a negative current at the carbon paper surface at open circuit and a catalyst-layer hotspot 9.5 °C above operating temperature (105 °C). Although the negative current vanishes at 1500 mA cm−2, local current drops by 70% and the hotspot temperature rises by 5 °C. (4) Asymmetric pressure does not significantly change local current but further raises the catalyst-layer hotspot by 28 °C. Yet these intense localized hotspots remain macroscopically undetectable, producing only a 1–2 °C rise on the carbon paper surface and evading conventional infrared detection. Thus, while a pinhole primarily degrades electrical performance, asymmetric pressure dominates thermal degradation by exacerbating H₂ convection crossover. By establishing quantitative performance relationships and a defect-sensitivity framework, this work provides predictive insights and practical guidelines for enhancing PEMFC durability.
质子交换膜燃料电池在不对称压力下存在针孔缺陷导致的膜降解。然而，对于了解早期失效至关重要的10 μm以下针孔的定量影响却知之甚少。我们提出了一种新的3 μm针孔两相流机理模型，并通过定制的原位测试平台和红外热像仪进行了实验验证。结果表明：(1)当负极压力梯度超过0.8 kPa时，H₂交叉由扩散主导转变为对流主导，在10 kPa时通量增加10倍；敏感性分析表明温度是影响对流交叉的主要因素。(2)电压损失包括针孔本身产生的50-100 mV和不对称压力产生的13.9-41.5 mV，主要是由于氧还原反应活化损失的增加。(3)在对称压力下，针孔在开路时碳纸表面产生负电流，在工作温度（105℃）以上9.5℃处产生催化层热点。虽然负电流在1500 mA cm−2时消失，但局部电流下降70%，热点温度上升5°C。(4)不对称压力没有显著改变局部电流，但使催化层热点进一步升高28℃。然而，这些强烈的局部热点在宏观上仍然无法检测到，仅在碳纸表面产生1-2°C的升高，并逃避传统的红外检测。因此，虽然针孔主要降低电性能，但不对称压力通过加剧H₂对流交叉而主导热退化。通过建立定量性能关系和缺陷敏感性框架，这项工作为提高PEMFC耐久性提供了预测性见解和实用指南。
{"title":"Quantitative decoupling of electro-thermal degradation in PEMFCs: H₂ crossover through a single Sub-10 μm pinhole under asymmetric pressure","authors":"Qianqian Wang , Haobin Xie , Binlin Dou , Weibo Zheng , Bing Li , Jim P. Zheng , Xiang Li , Pugalenthiyar Thondaiman , Pingwen Ming","doi":"10.1016/j.apenergy.2026.127421","DOIUrl":"10.1016/j.apenergy.2026.127421","url":null,"abstract":"<div><div>Proton exchange membrane fuel cells suffer from membrane degradation induced by pinhole defects under asymmetric pressure. However, the quantitative influence of sub-10 μm pinholes, critical to understanding early-stage failure, is poorly understood. We present a novel mechanistic model of two-phase flow through a 3 μm pinhole, validated experimentally with a custom in-situ test bench and infrared thermography. The results quantitatively decouple the degradation mechanisms: (1) H₂ crossover shifts from diffusion- to convection-dominated when the anode-cathode pressure gradient exceeds 0.8 kPa, with a tenfold flux increase at 10 kPa. Sensitivity analysis identifies temperature as the dominant factor governing convective crossover. (2) Voltage loss comprises 50–100 mV from the pinhole itself and an additional 13.9–41.5 mV from asymmetric pressure, mainly due to increased oxygen reduction reaction activation loss. (3) Under symmetric pressure, the pinhole generates a negative current at the carbon paper surface at open circuit and a catalyst-layer hotspot 9.5 °C above operating temperature (105 °C). Although the negative current vanishes at 1500 mA cm<sup>−2</sup>, local current drops by 70% and the hotspot temperature rises by 5 °C. (4) Asymmetric pressure does not significantly change local current but further raises the catalyst-layer hotspot by 28 °C. Yet these intense localized hotspots remain macroscopically undetectable, producing only a 1–2 °C rise on the carbon paper surface and evading conventional infrared detection. Thus, while a pinhole primarily degrades electrical performance, asymmetric pressure dominates thermal degradation by exacerbating H₂ convection crossover. By establishing quantitative performance relationships and a defect-sensitivity framework, this work provides predictive insights and practical guidelines for enhancing PEMFC durability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127421"},"PeriodicalIF":11.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076599","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 : 2026-01-29DOI: 10.1016/j.apenergy.2026.127447
Sruthy V. , Preetha P.K. , Javier Rodríguez-García
As the marine transportation sector gradually transitions towards sustainable electrified solutions, the significance of charging solutions has been receiving considerable attention. The study is a compendium covering the viability, analysis, and sizing of floating charging stations (FCS) for electric vessels, in offshore locations, including the North Sea, Europe, and the Arabian Sea, India. The comprehensive investigation used real-time renewable energy data to determine the optimal sizing of offshore charging stations. MATLAB simulations using Genetic Algorithm and the Interior-Point Fmincon algorithm optimized the FCS system for minimum life cycle cost with loss of power supply probability as a reliability criterion to evaluate the sizing analysis. The economic assessment produced a levelized cost of energy of 10.74 INR/kWh and a payback period of 5.1 years for the North Sea FCS and 12.92 INR/kWh and 6.2 years for the Arabian Sea FCS. Economic factors like net present value and profitability index as well as the sensitivity analysis with respect to discount rates confirmed the FCS project's feasibility at the selected locations. The study lays the groundwork for future research on offshore charging stations for the deployment of electric vessel transit services.
{"title":"Sizing analysis and economic feasibility evaluations of offshore floating electric vessel charging stations for sustainable development","authors":"Sruthy V. , Preetha P.K. , Javier Rodríguez-García","doi":"10.1016/j.apenergy.2026.127447","DOIUrl":"10.1016/j.apenergy.2026.127447","url":null,"abstract":"<div><div>As the marine transportation sector gradually transitions towards sustainable electrified solutions, the significance of charging solutions has been receiving considerable attention. The study is a compendium covering the viability, analysis, and sizing of floating charging stations (FCS) for electric vessels, in offshore locations, including the North Sea, Europe, and the Arabian Sea, India. The comprehensive investigation used real-time renewable energy data to determine the optimal sizing of offshore charging stations. MATLAB simulations using Genetic Algorithm and the Interior-Point Fmincon algorithm optimized the FCS system for minimum life cycle cost with loss of power supply probability as a reliability criterion to evaluate the sizing analysis. The economic assessment produced a levelized cost of energy of 10.74 INR/kWh and a payback period of 5.1 years for the North Sea FCS and 12.92 INR/kWh and 6.2 years for the Arabian Sea FCS. Economic factors like net present value and profitability index as well as the sensitivity analysis with respect to discount rates confirmed the FCS project's feasibility at the selected locations. The study lays the groundwork for future research on offshore charging stations for the deployment of electric vessel transit services.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127447"},"PeriodicalIF":11.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076597","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 : 2026-01-29DOI: 10.1016/j.apenergy.2026.127454
Alexander Kilian, Michael Bettermann, Hermann de Meer
This work proposes a multi-agent system aimed at increasing the computing sustainability of high-performance computing data centers that are distributed among several wind farms. The novel approach of wind turbines housing high-performance computing data centers seeks to maximize renewable energy usage by supplying the data centers with otherwise curtailed wind energy, thus increasing wind farm efficiency as well. To optimize data center operation in this unique environment, job execution should be prioritized during periods of high availability of renewable energy. When wind power generation is low, resource utilization should be continuously adjusted to minimize gray electricity consumption with high carbon intensity or high grid consumption costs. Furthermore, green service-level agreements are introduced allowing for more flexibility in terms of deadline compliance, thereby fostering energy-aware data center operation. The proposed multi-agent system realizes a moving-horizon, multi-objective optimization problem to find the best operational strategy, taking into account both sustainability and performance concerns, and is compared against a selection of baseline job scheduling strategies.
{"title":"Energy-optimized operation of a distributed data center infrastructure located in wind farms: a multi-agent system approach","authors":"Alexander Kilian, Michael Bettermann, Hermann de Meer","doi":"10.1016/j.apenergy.2026.127454","DOIUrl":"10.1016/j.apenergy.2026.127454","url":null,"abstract":"<div><div>This work proposes a multi-agent system aimed at increasing the computing sustainability of high-performance computing data centers that are distributed among several wind farms. The novel approach of wind turbines housing high-performance computing data centers seeks to maximize renewable energy usage by supplying the data centers with otherwise curtailed wind energy, thus increasing wind farm efficiency as well. To optimize data center operation in this unique environment, job execution should be prioritized during periods of high availability of renewable energy. When wind power generation is low, resource utilization should be continuously adjusted to minimize gray electricity consumption with high carbon intensity or high grid consumption costs. Furthermore, green service-level agreements are introduced allowing for more flexibility in terms of deadline compliance, thereby fostering energy-aware data center operation. The proposed multi-agent system realizes a moving-horizon, multi-objective optimization problem to find the best operational strategy, taking into account both sustainability and performance concerns, and is compared against a selection of baseline job scheduling strategies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127454"},"PeriodicalIF":11.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076600","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 : 2026-01-29DOI: 10.1016/j.apenergy.2026.127439
Md Arifin Arif , Fengyu Wang , Di Shi , Liang Sun
As new policies are adopted to transition toward emission-free electricity, effective metrics are needed to accurately quantify emissions. Locational Marginal Emission Rates (LMERs) effectively capture the change in emissions associated with a change in demand. This paper introduces a computationally efficient LMER calculation algorithm that considers reserve requirements and network constraints in the economic dispatch model. Unlike locational marginal prices (LMPs), which can be directly derived from shadow prices expressed in $/MWh, calculating LMERs requires identifying marginal units that adjust their outputs in response to infinitesimal changes in system parameters. The LMER calculation algorithms also compute the incremental output changes of these marginal units and utilize these output changes, combined with the respective emission rates to compute LMERs accurately. However, the inclusion of reserve requirements, which are essential in modern electricity markets to address the uncertainties introduced by contingencies and significant intermittent generation, complicates identification of marginal units and calculation of their corresponding output changes in response to the locational demand changes. Thus calculating LMERs become increasingly complex. The proposed algorithm for the calculation of LMERs considering the reserve requirement systematically identifies marginal units and their corresponding output changes to calculate LMERs. This algorithm has been tested on both a four-bus system and a synthetic Texas Test System to show its efficiency and validate its accuracy. Furthermore, this algorithm is examined with different levels of reserve requirements and hence analyzes the impact of reserve requirements on marginal units and LMERs.
{"title":"Locational marginal emission rates calculation considering reserve requirements","authors":"Md Arifin Arif , Fengyu Wang , Di Shi , Liang Sun","doi":"10.1016/j.apenergy.2026.127439","DOIUrl":"10.1016/j.apenergy.2026.127439","url":null,"abstract":"<div><div>As new policies are adopted to transition toward emission-free electricity, effective metrics are needed to accurately quantify emissions. Locational Marginal Emission Rates (LMERs) effectively capture the change in emissions associated with a change in demand. This paper introduces a computationally efficient LMER calculation algorithm that considers reserve requirements and network constraints in the economic dispatch model. Unlike locational marginal prices (LMPs), which can be directly derived from shadow prices expressed in $/MWh, calculating LMERs requires identifying marginal units that adjust their outputs in response to infinitesimal changes in system parameters. The LMER calculation algorithms also compute the incremental output changes of these marginal units and utilize these output changes, combined with the respective emission rates to compute LMERs accurately. However, the inclusion of reserve requirements, which are essential in modern electricity markets to address the uncertainties introduced by contingencies and significant intermittent generation, complicates identification of marginal units and calculation of their corresponding output changes in response to the locational demand changes. Thus calculating LMERs become increasingly complex. The proposed algorithm for the calculation of LMERs considering the reserve requirement systematically identifies marginal units and their corresponding output changes to calculate LMERs. This algorithm has been tested on both a four-bus system and a synthetic Texas Test System to show its efficiency and validate its accuracy. Furthermore, this algorithm is examined with different levels of reserve requirements and hence analyzes the impact of reserve requirements on marginal units and LMERs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127439"},"PeriodicalIF":11.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076596","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 : 2026-01-29DOI: 10.1016/j.apenergy.2026.127420
S. Mazzoni, M. Vellini, M. Gambini
Following up on European Regulatory plans towards carbon neutrality targets by exploiting cost-effective, reliable and easy-to-implement solutions based on hydrogen penetration in the Hard-to-Abate sectors are a challenge. Under this umbrella, the authors proposed a methodological approach to model the demand and supply of HTA sector needs (e.g. electricity, heat), integrated with proprietary databases of H2-specific production costs and related CO2 emission factors, and of HTA sectors (e.g. refinery, paper production, glass & steel manufacturing) specific consumptions (electricity, heat) and emissions per production unit. The authors presented an H2-CH4 blending model capable of assessing blended fuel CO2 emission factors and OPEX through maps. The first map shows that achieving a specific decarbonization target, as an example, 20% in respect of the current configuration, requires up to 70% blending of blue H2 (80 kg CO2/MWh emission factor) or only 50% blending of green H2 (near-zero CO2 emissions). The second map incorporates LCOH and Carbon Tax to evaluate economic feasibility. In a case study with CH4 priced at 70 EUR/MWh and CO2 Tax of 100 EUR/ton, green H2 remains costlier, while blue H2 blending leads to a slight OPEX reduction of 2 EUR/MWh, since Carbon Tax is applied. Thanks to these maps, a sensitivity analysis varying H2 blending fraction with CH4 has been performed for five HTA sectors, highlighting CO2 emissions reduction potential, up to 70% in the sectors with larger heat demands, such as Oil&Gas, and evaluating OPEX in respect to the reference scenario, showing that at the current CO2 Tax of almost 100 EUR/ton and for the actual LCOH the decarbonisation economic viability would require the support of regulation and environmental policies implementation.
{"title":"Decarbonization pathways of Hard-to-Abate sectors through hydrogen blending solutions","authors":"S. Mazzoni, M. Vellini, M. Gambini","doi":"10.1016/j.apenergy.2026.127420","DOIUrl":"10.1016/j.apenergy.2026.127420","url":null,"abstract":"<div><div>Following up on European Regulatory plans towards carbon neutrality targets by exploiting cost-effective, reliable and easy-to-implement solutions based on hydrogen penetration in the Hard-to-Abate sectors are a challenge. Under this umbrella, the authors proposed a methodological approach to model the demand and supply of HTA sector needs (e.g. electricity, heat), integrated with proprietary databases of H2-specific production costs and related CO<sub>2</sub> emission factors, and of HTA sectors (e.g. refinery, paper production, glass & steel manufacturing) specific consumptions (electricity, heat) and emissions per production unit. The authors presented an H2-CH4 blending model capable of assessing blended fuel CO<sub>2</sub> emission factors and OPEX through maps. The first map shows that achieving a specific decarbonization target, as an example, 20% in respect of the current configuration, requires up to 70% blending of blue H2 (80 kg CO<sub>2</sub>/MWh emission factor) or only 50% blending of green H2 (near-zero CO<sub>2</sub> emissions). The second map incorporates LCOH and Carbon Tax to evaluate economic feasibility. In a case study with CH4 priced at 70 EUR/MWh and CO<sub>2</sub> Tax of 100 EUR/ton, green H2 remains costlier, while blue H2 blending leads to a slight OPEX reduction of 2 EUR/MWh, since Carbon Tax is applied. Thanks to these maps, a sensitivity analysis varying H2 blending fraction with CH4 has been performed for five HTA sectors, highlighting CO<sub>2</sub> emissions reduction potential, up to 70% in the sectors with larger heat demands, such as Oil&Gas, and evaluating OPEX in respect to the reference scenario, showing that at the current CO<sub>2</sub> Tax of almost 100 EUR/ton and for the actual LCOH the decarbonisation economic viability would require the support of regulation and environmental policies implementation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127420"},"PeriodicalIF":11.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076636","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 : 2026-01-29DOI: 10.1016/j.apenergy.2026.127438
Ling Chang , Haibo Yu , Minghan Yang , Ziheng Zhang , Shuai Chen , Jianye Wang
Accurate long-term forecasting of operating parameters in nuclear power plants (NPPs) is crucial for safety and cost-effective maintenance. However, the complexity and uncertainty of reactors, along with the high-dimensional and large-scale operating data, present challenges in capturing intricate dynamic behaviors and long-term dependencies. This paper presents NPP-GPT, which for the first time investigates the potential of using pre-trained Large Language Model (LLM) to forecast long-term parameters from historical NPP data without explicit prompt engineering. Considering the modal disparity between textual pre-training data and numerical energy data, NPP-GPT employs a two-stage cross-modal transfer learning strategy that preserves the native next-token forecasting capability of LLMs while unlocking their potential for precise energy forecasting. First, the modal gap is bridged through input embedding reconstruction and Self-Supervised Learning (SSL). Second, domain-specific energy knowledge is integrated via LoRA fine-tuning. The framework was rigorously validated using data from an established advanced nuclear energy research platform, focusing on a Chinese Pressurized Water Reactor (CPR-1000). Comprehensive experiments covering diverse operational scenarios, including normal and multiple fault conditions, demonstrated that NPP-GPT outperforms both classical and advanced time-series forecasting models in accuracy and generalization, especially in long-term forecasting and under conditions with noise and missing data. This study offers a novel and generalizable solution for forecasting tasks in energy sectors.
{"title":"NPP-GPT: Forecasting nuclear power plants operating parameters using pre-trained large language model","authors":"Ling Chang , Haibo Yu , Minghan Yang , Ziheng Zhang , Shuai Chen , Jianye Wang","doi":"10.1016/j.apenergy.2026.127438","DOIUrl":"10.1016/j.apenergy.2026.127438","url":null,"abstract":"<div><div>Accurate long-term forecasting of operating parameters in nuclear power plants (NPPs) is crucial for safety and cost-effective maintenance. However, the complexity and uncertainty of reactors, along with the high-dimensional and large-scale operating data, present challenges in capturing intricate dynamic behaviors and long-term dependencies. This paper presents NPP-GPT, which for the first time investigates the potential of using pre-trained Large Language Model (LLM) to forecast long-term parameters from historical NPP data without explicit prompt engineering. Considering the modal disparity between textual pre-training data and numerical energy data, NPP-GPT employs a two-stage cross-modal transfer learning strategy that preserves the native next-token forecasting capability of LLMs while unlocking their potential for precise energy forecasting. First, the modal gap is bridged through input embedding reconstruction and Self-Supervised Learning (SSL). Second, domain-specific energy knowledge is integrated via LoRA fine-tuning. The framework was rigorously validated using data from an established advanced nuclear energy research platform, focusing on a Chinese Pressurized Water Reactor (CPR-1000). Comprehensive experiments covering diverse operational scenarios, including normal and multiple fault conditions, demonstrated that NPP-GPT outperforms both classical and advanced time-series forecasting models in accuracy and generalization, especially in long-term forecasting and under conditions with noise and missing data. This study offers a novel and generalizable solution for forecasting tasks in energy sectors.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127438"},"PeriodicalIF":11.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076651","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 : 2026-01-29DOI: 10.1016/j.apenergy.2026.127370
Cheng Dai , Junqi Pan , Xianggen Liu , Sahil Garg , Sherif Moussa , Chahinaz Kandouci
The objective of this study is to address the challenges posed by high energy overhead and the complexity of ensuring quality of service (QoS) for vehicular edge computing in dynamic environments. To this end, this paper investigates the task offloading problem for vehicular edge computing networks in urban areas where there are task offloading hotspots. The objective is twofold: first, to minimize the system energy expenditure, and second, to ensure the service quality. To this end, Unmanned Aerial Vehicles (UAVs) are introduced as mobile offloading nodes to physically reduce the signal transmission distance and lower the system energy consumption. To this end, we propose a resource allocation optimization framework centered on a novel Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Our algorithm’s novelty lies in its integration of a dual-critic mechanism for robust and stable Q-value estimation and a maximum entropy framework to enhance exploration efficiency in complex environments. This intelligent algorithm is synergistically coupled with a clustering-based UAV deployment strategy to handle this dynamic problem. This strategy dynamically and autonomously achieves the optimal resource allocation and UAV deployment, with the objective of reducing the system energy overhead and guaranteeing the QoS. Simulation results demonstrate that this framework significantly enhances resource allocation efficiency. Compared to the original MADDPG algorithm, it reduces task costs by , and compared to the fixed offloading position scheme, it reduces task costs by . This study offers a valuable reference point and practical insights for reducing energy overhead and optimizing resource allocation in edge computing for vehicular networking.
{"title":"An enhanced MADDPG framework for joint energy and QoS optimization in UAV-assisted vehicular edge computing system","authors":"Cheng Dai , Junqi Pan , Xianggen Liu , Sahil Garg , Sherif Moussa , Chahinaz Kandouci","doi":"10.1016/j.apenergy.2026.127370","DOIUrl":"10.1016/j.apenergy.2026.127370","url":null,"abstract":"<div><div>The objective of this study is to address the challenges posed by high energy overhead and the complexity of ensuring quality of service (QoS) for vehicular edge computing in dynamic environments. To this end, this paper investigates the task offloading problem for vehicular edge computing networks in urban areas where there are task offloading hotspots. The objective is twofold: first, to minimize the system energy expenditure, and second, to ensure the service quality. To this end, Unmanned Aerial Vehicles (UAVs) are introduced as mobile offloading nodes to physically reduce the signal transmission distance and lower the system energy consumption. To this end, we propose a resource allocation optimization framework centered on a novel Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Our algorithm’s novelty lies in its integration of a dual-critic mechanism for robust and stable Q-value estimation and a maximum entropy framework to enhance exploration efficiency in complex environments. This intelligent algorithm is synergistically coupled with a clustering-based UAV deployment strategy to handle this dynamic problem. This strategy dynamically and autonomously achieves the optimal resource allocation and UAV deployment, with the objective of reducing the system energy overhead and guaranteeing the QoS. Simulation results demonstrate that this framework significantly enhances resource allocation efficiency. Compared to the original MADDPG algorithm, it reduces task costs by <span><math><mn>24</mn><mi>%</mi></math></span>, and compared to the fixed offloading position scheme, it reduces task costs by <span><math><mn>31.3</mn><mi>%</mi></math></span>. This study offers a valuable reference point and practical insights for reducing energy overhead and optimizing resource allocation in edge computing for vehicular networking.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127370"},"PeriodicalIF":11.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076601","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 : 2026-01-28DOI: 10.1016/j.apenergy.2026.127432
Kunxiang Liu , Bo Liu , Yu Wang , Haijiang Wang , Jun Yang , Chen Zhao
In the global energy transition, hydrogen fuel cells have drawn a lot of attention as a clean energy source. Developing new energy vehicles that rely on hydrogen fuel cells as their primary power source is crucial to reaching net-zero carbon emissions. As the central component and key to the overall operation of new energy vehicles, fuel cell energy management (FCEM) is crucial, particularly for enhancing durability and fuel economy. However, the literature screening process in existing bibliometric studies is often opaque and lacks publicly available criteria, leading to irreproducible findings. To address this, we propose a transparent and reproducible bibliometric framework that integrates an enhanced Word2Vec model for systematic literature screening. Our AI-driven screening method, based on calculating the similarity of titles, abstracts, and keywords, is validated by achieving 91.4751% alignment with the Web of Science (WOS) relevance ranking, offering a quantifiable and automated alternative to opaque screening processes. Using this framework, we systematically analyze the characteristics of FCEMS-related scholarship in terms of publication journals, country geographic distribution, institutional collaborations, author collaborations, and keyword co-occurrence frequencies. The analysis reveals a pattern of policy-associated growth: post-2015, China contributes to 45% of the global FCEM literature, likely benefiting from the national hydrogen energy strategy. Furthermore, we detail FCEMS strategies including rule-based, optimization-based, and learning-based approaches, summarize their research progress in applications such as vehicles, aircraft, and ships, and analyze future research trends from multiple perspectives. This work represents the first integration of bibliometrics with natural language processing (NLP) for algorithmic literature screening, and its inaugural application in the FCEMS domain.
在全球能源转型中，氢燃料电池作为一种清洁能源备受关注。开发以氢燃料电池为主要动力源的新能源汽车对于实现净零碳排放至关重要。燃料电池能量管理（FCEM）作为新能源汽车整体运行的核心部件和关键，对于提高耐久性和燃油经济性至关重要。然而，现有文献计量学研究中的文献筛选过程往往是不透明的，缺乏可公开获得的标准，导致不可重复的发现。为了解决这个问题，我们提出了一个透明和可重复的文献计量框架，该框架集成了一个增强的Word2Vec模型，用于系统的文献筛选。我们的人工智能驱动的筛选方法基于计算标题、摘要和关键词的相似度，与Web of Science （WOS）相关排名的一致性达到91.4751%，为不透明的筛选过程提供了可量化和自动化的替代方案。在此框架下，我们从发表期刊、国家地理分布、机构合作、作者合作和关键词共现频率等方面系统分析了fcems相关学术研究的特征。分析揭示了一种与政策相关的增长模式：2015年后，中国贡献了全球45%的氢能源文献，可能受益于国家氢能战略。在此基础上，详细介绍了基于规则的、基于优化的和基于学习的FCEMS策略，总结了它们在车辆、飞机和船舶等领域的研究进展，并从多个角度分析了未来的研究趋势。这项工作代表了文献计量学与自然语言处理（NLP）在算法文献筛选中的首次整合，以及它在FCEMS领域的首次应用。
{"title":"Fuel cell energy management strategies (FCEMS): a Word2Vec-driven bibliometric framework for trend mapping and algorithmic advancements","authors":"Kunxiang Liu , Bo Liu , Yu Wang , Haijiang Wang , Jun Yang , Chen Zhao","doi":"10.1016/j.apenergy.2026.127432","DOIUrl":"10.1016/j.apenergy.2026.127432","url":null,"abstract":"<div><div>In the global energy transition, hydrogen fuel cells have drawn a lot of attention as a clean energy source. Developing new energy vehicles that rely on hydrogen fuel cells as their primary power source is crucial to reaching net-zero carbon emissions. As the central component and key to the overall operation of new energy vehicles, fuel cell energy management (FCEM) is crucial, particularly for enhancing durability and fuel economy. However, the literature screening process in existing bibliometric studies is often opaque and lacks publicly available criteria, leading to irreproducible findings. To address this, we propose a transparent and reproducible bibliometric framework that integrates an enhanced Word2Vec model for systematic literature screening. Our AI-driven screening method, based on calculating the similarity of titles, abstracts, and keywords, is validated by achieving 91.4751% alignment with the Web of Science (WOS) relevance ranking, offering a quantifiable and automated alternative to opaque screening processes. Using this framework, we systematically analyze the characteristics of FCEMS-related scholarship in terms of publication journals, country geographic distribution, institutional collaborations, author collaborations, and keyword co-occurrence frequencies. The analysis reveals a pattern of policy-associated growth: post-2015, China contributes to 45% of the global FCEM literature, likely benefiting from the national hydrogen energy strategy. Furthermore, we detail FCEMS strategies including rule-based, optimization-based, and learning-based approaches, summarize their research progress in applications such as vehicles, aircraft, and ships, and analyze future research trends from multiple perspectives. This work represents the first integration of bibliometrics with natural language processing (NLP) for algorithmic literature screening, and its inaugural application in the FCEMS domain.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127432"},"PeriodicalIF":11.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146049141","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 : 2026-01-28DOI: 10.1016/j.apenergy.2026.127451
Haeseong Shin , Dohyung Jang , Hee-Sun Shin , Sungtae Park , Sanggyu Kang
Green hydrogen production through water electrolysis (WE) powered by renewable energy offers a promising pathway for decarbonization but faces challenges related to cost, variability, and stable off-grid operation. This study proposes an optimal design and operational strategy for an off-grid green hydrogen production system integrating photovoltaic (PV) generation, alkaline water electrolysis, proton exchange membrane water electrolysis (PEMWE), and battery energy storage systems (BESS). A dynamic simulation framework using one-minute PV irradiance data was developed to capture short-term renewable fluctuations and evaluate the interactions among the electrolyzers and BESS under real-time operation. The optimal system configuration was determined as 120 MW PV, 100 MW PEMWE, and 34.8 MWh BESS, achieving a Levelized Cost of Hydrogen (LCOH) of $10.77/kg under base conditions. Sensitivity analyses indicated that a 20% reduction in PV CAPEX reduced the LCOH to $9.81/kg, while doubling the BESS C-rate or halving the AWE minimum load range further decreased LCOH by 5–10%. These results demonstrate that integrating dynamic modeling with techno-economic evaluation enables a realistic and comprehensive assessment of off-grid hydrogen systems, providing practical guidance for the cost-effective and stable production of green hydrogen under renewable energy variability.
{"title":"High-resolution dynamic modeling and techno-economic optimization of off-grid PV–electrolysis–BESS systems for green hydrogen production","authors":"Haeseong Shin , Dohyung Jang , Hee-Sun Shin , Sungtae Park , Sanggyu Kang","doi":"10.1016/j.apenergy.2026.127451","DOIUrl":"10.1016/j.apenergy.2026.127451","url":null,"abstract":"<div><div>Green hydrogen production through water electrolysis (WE) powered by renewable energy offers a promising pathway for decarbonization but faces challenges related to cost, variability, and stable off-grid operation. This study proposes an optimal design and operational strategy for an off-grid green hydrogen production system integrating photovoltaic (PV) generation, alkaline water electrolysis, proton exchange membrane water electrolysis (PEMWE), and battery energy storage systems (BESS). A dynamic simulation framework using one-minute PV irradiance data was developed to capture short-term renewable fluctuations and evaluate the interactions among the electrolyzers and BESS under real-time operation. The optimal system configuration was determined as 120 MW PV, 100 MW PEMWE, and 34.8 MWh BESS, achieving a Levelized Cost of Hydrogen (LCOH) of $10.77/kg under base conditions. Sensitivity analyses indicated that a 20% reduction in PV CAPEX reduced the LCOH to $9.81/kg, while doubling the BESS C-rate or halving the AWE minimum load range further decreased LCOH by 5–10%. These results demonstrate that integrating dynamic modeling with techno-economic evaluation enables a realistic and comprehensive assessment of off-grid hydrogen systems, providing practical guidance for the cost-effective and stable production of green hydrogen under renewable energy variability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"409 ","pages":"Article 127451"},"PeriodicalIF":11.0,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146076650","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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