Pub Date : 2025-11-29DOI: 10.1016/j.apenergy.2025.127146
Le Wang , Fei Chen , Quan Zhou , Congju Li
With the continuous growth of global energy demand, issues related to energy security and environmental sustainability have attracted increasing attention. In recent decades, innovations in alternative energy technologies have significantly advanced the development of fuel cells as a promising clean energy solution. Traditional fuel cells utilize oxygen as the cathode oxidant; however, their application is limited in oxygen-deficient or anoxic environments. In contrast, hydrogen peroxide (H2O2), as a liquid oxidant, offers several advantages in terms of storage and transport, while also facilitating more direct reactions at the solid-liquid interface. Moreover, the two-electron reduction mechanism of H2O2 considerably enhances the reaction rate. This review summarizes the applications of H2O2 in fuel cells and highlights the research progress related to cathode catalysts. First, the use of H2O2 as a cathode oxidant in various types of fuel cells was explored. Second, the performance and challenges of precious metal catalysts, transition metal catalysts, and other catalysts in hydrogen peroxide reduction reactions (HPRR) are analyzed. Finally, the future research directions in this field are discussed.
{"title":"The application and research progress of hydrogen peroxide as cathode oxidant in fuel cells","authors":"Le Wang , Fei Chen , Quan Zhou , Congju Li","doi":"10.1016/j.apenergy.2025.127146","DOIUrl":"10.1016/j.apenergy.2025.127146","url":null,"abstract":"<div><div>With the continuous growth of global energy demand, issues related to energy security and environmental sustainability have attracted increasing attention. In recent decades, innovations in alternative energy technologies have significantly advanced the development of fuel cells as a promising clean energy solution. Traditional fuel cells utilize oxygen as the cathode oxidant; however, their application is limited in oxygen-deficient or anoxic environments. In contrast, hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>), as a liquid oxidant, offers several advantages in terms of storage and transport, while also facilitating more direct reactions at the solid-liquid interface. Moreover, the two-electron reduction mechanism of H<sub>2</sub>O<sub>2</sub> considerably enhances the reaction rate. This review summarizes the applications of H<sub>2</sub>O<sub>2</sub> in fuel cells and highlights the research progress related to cathode catalysts. First, the use of H<sub>2</sub>O<sub>2</sub> as a cathode oxidant in various types of fuel cells was explored. Second, the performance and challenges of precious metal catalysts, transition metal catalysts, and other catalysts in hydrogen peroxide reduction reactions (HPRR) are analyzed. Finally, the future research directions in this field are discussed.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127146"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622740","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127130
Kyungjin Yu , Munnyeong Choi , Adekanmi Miracle Adeyinka , Xiaoniu Du , Song-Yul Choe , Wooju Lee
Multi-stage Constant Current (MCC) is a widely used Fast Charging (FC) protocol that decreases its current amplitude to minimize degradation. Lithium Plating is the most popular degradation effect considered when designing fast charging protocols because of its high degradation rate and growth of dendrites, potentially leading to thermal runaway. Anode potential is popularly considered as lithium plating onset indicator; however, it is insufficient because of continuously varying electrochemical mechanisms under different operating conditions. This study proposes a real-time optimized MCC protocol with negative pulses (O-MCC + NP) to minimize charging time, considering degradation and heat generation. The proposed O-MCC + NP provides a double protection mechanism by not only suppressing the reaction of lithium plating but also promoting reaction of lithium stripping, thereby significantly enhancing battery safety. Based on a validated reduced-order electrochemical-thermal life model, the charging and negative pulse current amplitudes are optimized using two separate Nonlinear Model Predictive Control algorithms under constrained lithium plating overpotential and anode concentration gradients to prevent lithium plating. Pulse frequency is determined experimentally, reducing heat generation associated with diffusion resistance by Distribution of Relaxation Time (DRT) analysis. The proposed charging protocol is experimentally tested in a battery-in-the-loop system, showing a 16 % reduction in charging time and a 37 % reduction in capacity fade compared with those by the conventional MCC protocol by preventing lithium plating and promoting lithium stripping simultaneously.
{"title":"Optimization of multi-stage constant currents fast charging protocol with negative pulses considering Lithium plating, stripping, and heat generation rates for Lithium-ion batteries","authors":"Kyungjin Yu , Munnyeong Choi , Adekanmi Miracle Adeyinka , Xiaoniu Du , Song-Yul Choe , Wooju Lee","doi":"10.1016/j.apenergy.2025.127130","DOIUrl":"10.1016/j.apenergy.2025.127130","url":null,"abstract":"<div><div>Multi-stage Constant Current (MCC) is a widely used Fast Charging (FC) protocol that decreases its current amplitude to minimize degradation. Lithium Plating is the most popular degradation effect considered when designing fast charging protocols because of its high degradation rate and growth of dendrites, potentially leading to thermal runaway. Anode potential is popularly considered as lithium plating onset indicator; however, it is insufficient because of continuously varying electrochemical mechanisms under different operating conditions. This study proposes a real-time optimized MCC protocol with negative pulses (O-MCC + NP) to minimize charging time, considering degradation and heat generation. The proposed O-MCC + NP provides a double protection mechanism by not only suppressing the reaction of lithium plating but also promoting reaction of lithium stripping, thereby significantly enhancing battery safety. Based on a validated reduced-order electrochemical-thermal life model, the charging and negative pulse current amplitudes are optimized using two separate Nonlinear Model Predictive Control algorithms under constrained lithium plating overpotential and anode concentration gradients to prevent lithium plating. Pulse frequency is determined experimentally, reducing heat generation associated with diffusion resistance by Distribution of Relaxation Time (DRT) analysis. The proposed charging protocol is experimentally tested in a battery-in-the-loop system, showing a 16 % reduction in charging time and a 37 % reduction in capacity fade compared with those by the conventional MCC protocol by preventing lithium plating and promoting lithium stripping simultaneously.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127130"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682111","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127134
Longxi Li
Energy sharing helps to promote the development of distributed energy systems from an individual mode to a group mode to realize the spatiotemporal complementarity of heterogeneous energy supply and consumption. When not considering the information of neighboring energy systems, an investor may not receive reasonable benefits to justify infrastructure investment costs, leading to suboptimal system design. This paper proposes a game-based equilibrium model to characterize the interactions between distributed energy systems and customers with flexible energy demand. On this basis, to guide energy sharing among distributed energy systems with flexible resources, a coordination mechanism is developed featuring a contribution-based asymmetric bargaining scheme, which allocates shared benefits proportionally to each participant’s marginal contribution to system-wide optimization. Furthermore, the planning model is constructed considering the equilibrium distribution of individual interests for multiple stakeholder’s coordination. Numerical results indicate that the coordinated design reduces the investment cost of stakeholders by 14.20 % compared to the individual design scenario, and decreases total costs from 5.28 million to 4.96 million dollars. The influence of natural gas prices and load profiles on the coordinated design results has been analyzed. This research offers a fresh perspective for the coordinated planning of distributed energy systems catering to flexible demand, highlighting its practical relevance for early-stage design challenges.
{"title":"Coordinated design of multi-stakeholder distributed energy systems with flexible demand resources from an energy-sharing perspective","authors":"Longxi Li","doi":"10.1016/j.apenergy.2025.127134","DOIUrl":"10.1016/j.apenergy.2025.127134","url":null,"abstract":"<div><div>Energy sharing helps to promote the development of distributed energy systems from an individual mode to a group mode to realize the spatiotemporal complementarity of heterogeneous energy supply and consumption. When not considering the information of neighboring energy systems, an investor may not receive reasonable benefits to justify infrastructure investment costs, leading to suboptimal system design. This paper proposes a game-based equilibrium model to characterize the interactions between distributed energy systems and customers with flexible energy demand. On this basis, to guide energy sharing among distributed energy systems with flexible resources, a coordination mechanism is developed featuring a contribution-based asymmetric bargaining scheme, which allocates shared benefits proportionally to each participant’s marginal contribution to system-wide optimization. Furthermore, the planning model is constructed considering the equilibrium distribution of individual interests for multiple stakeholder’s coordination. Numerical results indicate that the coordinated design reduces the investment cost of stakeholders by 14.20 % compared to the individual design scenario, and decreases total costs from 5.28 million to 4.96 million dollars. The influence of natural gas prices and load profiles on the coordinated design results has been analyzed. This research offers a fresh perspective for the coordinated planning of distributed energy systems catering to flexible demand, highlighting its practical relevance for early-stage design challenges.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127134"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622739","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127137
Xuedong Yao , Shihong Zhang , Zeyu Liang , Jianhua Li , Chang Liu
Accurate photovoltaic (PV) detection from high-resolution remote sensing imagery plays a crucial role in assessing electricity generation potential and facilitating renewable energy management. While deep learning-based approaches have achieved significant performance in PV segmentation tasks, existing methods predominantly rely on single-scenario datasets to represent the specific feature distributions, limiting their capability to simultaneously generalize size and edge features of PV systems across diverse scenarios. To address this limitation, we propose PVSAM, a novel segmentation model that integrates zero-shot generalization capability of the Segment Anything Model (SAM) with geometric prompts tailored for PV panels. In PVSAM, we incorporate two specialized prompt modules as the knowledge-specific adapter to guide SAM for multi-scenario PV feature learning. Specifically, to improve adaptability to PV panels of various sizes, we construct a multi-scale prompt module that employs a multi-branch convolutional structure to effectively aggregate feature information with different receptive fields. To leverage the structural regularity of PV panels for refined semantic segmentations, we introduce an edge pyramid prompt module that explicitly reinforces multilevel shape features while strengthening the model's sensitivity to high-frequency boundary information. Extensive experiments on the PV01–03-08 (PV01, PV03, PV08), HRPVS and PVP datasets demonstrate that PVSAM can obtain the superior detection performance and outperform existing state-of-the-art methods with impressive F1 and IoU accuracy exceeding 90 % overall. Furthermore, the PVSAM method exhibits remarkable generalization performance in cross-scenario PV detection tasks, providing an effective solution for large-scale energy infrastructure monitoring.
{"title":"PVSAM: Adapting geometric prompts to segment anything model for photovoltaic detection in remote sensing imagery","authors":"Xuedong Yao , Shihong Zhang , Zeyu Liang , Jianhua Li , Chang Liu","doi":"10.1016/j.apenergy.2025.127137","DOIUrl":"10.1016/j.apenergy.2025.127137","url":null,"abstract":"<div><div>Accurate photovoltaic (PV) detection from high-resolution remote sensing imagery plays a crucial role in assessing electricity generation potential and facilitating renewable energy management. While deep learning-based approaches have achieved significant performance in PV segmentation tasks, existing methods predominantly rely on single-scenario datasets to represent the specific feature distributions, limiting their capability to simultaneously generalize size and edge features of PV systems across diverse scenarios. To address this limitation, we propose PVSAM, a novel segmentation model that integrates zero-shot generalization capability of the Segment Anything Model (SAM) with geometric prompts tailored for PV panels. In PVSAM, we incorporate two specialized prompt modules as the knowledge-specific adapter to guide SAM for multi-scenario PV feature learning. Specifically, to improve adaptability to PV panels of various sizes, we construct a multi-scale prompt module that employs a multi-branch convolutional structure to effectively aggregate feature information with different receptive fields. To leverage the structural regularity of PV panels for refined semantic segmentations, we introduce an edge pyramid prompt module that explicitly reinforces multilevel shape features while strengthening the model's sensitivity to high-frequency boundary information. Extensive experiments on the PV01–03-08 (PV01, PV03, PV08), HRPVS and PVP datasets demonstrate that PVSAM can obtain the superior detection performance and outperform existing state-of-the-art methods with impressive F1 and IoU accuracy exceeding 90 % overall. Furthermore, the PVSAM method exhibits remarkable generalization performance in cross-scenario PV detection tasks, providing an effective solution for large-scale energy infrastructure monitoring.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127137"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622734","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127055
Hong Tan , Shun Chen , Zhenjia Lin , Qiujie Wang , Mohamed A. Mohamed
Electrolytic water hydrogen production is an effective method for achieving the absorption of excess renewable energy and peak shaving and valley filling in the power system. However, when facing large-scale and long-distance hydrogen transportation needs, existing hydrogen transportation strategies struggle to transport hydrogen economically and flexibly from hydrogen production plants (HPPs) to various hydrogen users. To this end, this paper proposes a distributionally robust optimization (DRO) scheduling model for the electric‑hydrogen integrated energy system (EHIES) based pipeline-road collaborative hydrogen transportation (PRCHT). Firstly, by analyzing the transportation mechanism of hydrogen-blended pipelines and combining the relationship between the pipeline's storage and the gas pressure at both ends, this work constructs a quasi-dynamic transportation model for natural gas hydrogen blending with a variable hydrogen blending ratio. Next, by employing the improved McCormick technique and piecewise linearization method, the quasi-dynamic model is transformed into a mixed-integer linear programming (MILP) model. Furthermore, by integrating the trailer-based hydrogen transportation model, a PRCHT model is developed. Finally, considering the high uncertainty in wind power output, a DRO scheduling model for the integrated electricity‑hydrogen energy system based on Wasserstein distance is proposed. The DRO model is then transformed into a MILP problem using the conditional value-at-risk (CVaR) approximation method. The simulation results demonstrate that the proposed scheduling model reduces the total system cost by 19.43 % compared to the constant hydrogen blending ratio benchmark, while preventing 22.68 % of potential hydrogen load shedding relative to the natural-gas-pipeline-exclusive transport model. Meanwhile, the employed algorithm improves computational efficiency and achieves a robust optimization of the scheduling decisions by balancing system robustness and economic performance.
{"title":"Distributionally robust scheduling of electric‑hydrogen integrated energy systems based on pipeline-road coordinated hydrogen transportation","authors":"Hong Tan , Shun Chen , Zhenjia Lin , Qiujie Wang , Mohamed A. Mohamed","doi":"10.1016/j.apenergy.2025.127055","DOIUrl":"10.1016/j.apenergy.2025.127055","url":null,"abstract":"<div><div>Electrolytic water hydrogen production is an effective method for achieving the absorption of excess renewable energy and peak shaving and valley filling in the power system. However, when facing large-scale and long-distance hydrogen transportation needs, existing hydrogen transportation strategies struggle to transport hydrogen economically and flexibly from hydrogen production plants (HPPs) to various hydrogen users. To this end, this paper proposes a distributionally robust optimization (DRO) scheduling model for the electric‑hydrogen integrated energy system (EHIES) based pipeline-road collaborative hydrogen transportation (PRCHT). Firstly, by analyzing the transportation mechanism of hydrogen-blended pipelines and combining the relationship between the pipeline's storage and the gas pressure at both ends, this work constructs a quasi-dynamic transportation model for natural gas hydrogen blending with a variable hydrogen blending ratio. Next, by employing the improved McCormick technique and piecewise linearization method, the quasi-dynamic model is transformed into a mixed-integer linear programming (MILP) model. Furthermore, by integrating the trailer-based hydrogen transportation model, a PRCHT model is developed. Finally, considering the high uncertainty in wind power output, a DRO scheduling model for the integrated electricity‑hydrogen energy system based on Wasserstein distance is proposed. The DRO model is then transformed into a MILP problem using the conditional value-at-risk (CVaR) approximation method. The simulation results demonstrate that the proposed scheduling model reduces the total system cost by 19.43 % compared to the constant hydrogen blending ratio benchmark, while preventing 22.68 % of potential hydrogen load shedding relative to the natural-gas-pipeline-exclusive transport model. Meanwhile, the employed algorithm improves computational efficiency and achieves a robust optimization of the scheduling decisions by balancing system robustness and economic performance.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127055"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622658","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127136
Atefeh Alirezazadeh , Vahid Disfani , Jan Kleissl
In smart energy communities (ECs) with distributed energy resources (DERs) such as solar photovoltaic (PV) and electric vehicle (EV) battery storage, their prosumer members can achieve economic benefits through power scheduling. Peer-to-peer (P2P) energy exchange provides a localized and decentralized market for prosumers within an EC to participate in demand response (DR) programs and to trade energy directly among themselves. Supplying electricity to these EVs should be scheduled to minimize greenhouse gas emissions. This paper presents a comprehensive framework for a P2P energy market for power exchange and communication between ECs in the presence of solar PV, flexible loads, and EVs to maximize the economic benefits and social welfare of participants and minimize emissions from charging EVs. The proposed model incorporates a Community Energy Management Center (CEMC) that coordinates internal and external energy flows by optimizing day-ahead scheduling based on local generation forecasts, DR incentives, and upstream market prices. Building units in ECs are used to host parking for EVs from outside the community to reduce costs for users and alleviate urban traffic congestion. This strategy not only increases infrastructure utilization but also generates additional revenue for ECs. Case studies demonstrate the effectiveness of the model in reducing operational costs, improving load flexibility, and mitigating emissions while ensuring high levels of energy self-supply.
{"title":"A new model of energy management for maximum social welfare and minimum carbon dioxide emissions considering parking sharing","authors":"Atefeh Alirezazadeh , Vahid Disfani , Jan Kleissl","doi":"10.1016/j.apenergy.2025.127136","DOIUrl":"10.1016/j.apenergy.2025.127136","url":null,"abstract":"<div><div>In smart energy communities (ECs) with distributed energy resources (DERs) such as solar photovoltaic (PV) and electric vehicle (EV) battery storage, their prosumer members can achieve economic benefits through power scheduling. Peer-to-peer (P2P) energy exchange provides a localized and decentralized market for prosumers within an EC to participate in demand response (DR) programs and to trade energy directly among themselves. Supplying electricity to these EVs should be scheduled to minimize greenhouse gas emissions. This paper presents a comprehensive framework for a P2P energy market for power exchange and communication between ECs in the presence of solar PV, flexible loads, and EVs to maximize the economic benefits and social welfare of participants and minimize <span><math><msub><mrow><mtext>CO</mtext></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions from charging EVs. The proposed model incorporates a Community Energy Management Center (CEMC) that coordinates internal and external energy flows by optimizing day-ahead scheduling based on local generation forecasts, DR incentives, and upstream market prices. Building units in ECs are used to host parking for EVs from outside the community to reduce costs for users and alleviate urban traffic congestion. This strategy not only increases infrastructure utilization but also generates additional revenue for ECs. Case studies demonstrate the effectiveness of the model in reducing operational costs, improving load flexibility, and mitigating <span><math><mtext>{CO}{2}</mtext></math></span> emissions while ensuring high levels of energy self-supply.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127136"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622735","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127097
Hongbin Xie , Haoran Zhang , Ge Song , Jingyuan Zhang , Hongdi Fu , Liyu Zhang , Nianru Chen , Xuan Song
With the rapid growth of electric vehicle (EV) ownership, the deep integration of power grids, renewable energy, and transportation systems has led to the emergence of highly coupled hydrogen–electric distributed networks. In this complex environment of multi-energy coordinated operation, EV charging management systems face not only supply–demand imbalances caused by renewable fluctuations but also multiple external risks such as extreme weather, natural disasters, and cyberattacks, which impose higher demands on system resilience. To address the limitations of existing studies—most of which focus on steady-state or small-disturbance scenarios and lack coordinated optimization strategies for extreme events and uncertainties—this paper proposes a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning framework integrated with a risk characterization mechanism. The framework builds a dynamic simulation environment integrating EV charging facilities and hydrogen–electric hybrid energy storage systems, and introduces diffusion models to enrich the distribution of risk features in training data, thereby improving the perception and identification of rare and extreme risk events. An attention-based information filtering module and a low-frequency, high-efficiency communication strategy are designed to reduce communication costs and latency while enhancing coordination efficiency among agents in high-dimensional, long-horizon scenarios. Experimental results, evaluated on multi-dimensional resilience indicators including risk loss, response capability, and overall system resilience, demonstrate that the proposed method outperforms other reinforcement learning algorithms in enhancing resilience, reducing operational costs, and improving cross-scenario generalization. The diffusion model also shows strong adaptability to extreme risk disturbances. The proposed algorithm achieves an average reduction of approximately 27.8 % in operational cost compared to the best-performing baseline across all test scenarios and disturbance levels. This result is obtained from repeated trials and averaged outcomes, covering a wide range of risk types and intensities, and demonstrates high statistical reliability.
{"title":"Enhancing resilience of electric vehicle charging management in hydrogen–electric coupled distribution networks: A risk-characterization multi-agent reinforcement learning approach","authors":"Hongbin Xie , Haoran Zhang , Ge Song , Jingyuan Zhang , Hongdi Fu , Liyu Zhang , Nianru Chen , Xuan Song","doi":"10.1016/j.apenergy.2025.127097","DOIUrl":"10.1016/j.apenergy.2025.127097","url":null,"abstract":"<div><div>With the rapid growth of electric vehicle (EV) ownership, the deep integration of power grids, renewable energy, and transportation systems has led to the emergence of highly coupled hydrogen–electric distributed networks. In this complex environment of multi-energy coordinated operation, EV charging management systems face not only supply–demand imbalances caused by renewable fluctuations but also multiple external risks such as extreme weather, natural disasters, and cyberattacks, which impose higher demands on system resilience. To address the limitations of existing studies—most of which focus on steady-state or small-disturbance scenarios and lack coordinated optimization strategies for extreme events and uncertainties—this paper proposes a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning framework integrated with a risk characterization mechanism. The framework builds a dynamic simulation environment integrating EV charging facilities and hydrogen–electric hybrid energy storage systems, and introduces diffusion models to enrich the distribution of risk features in training data, thereby improving the perception and identification of rare and extreme risk events. An attention-based information filtering module and a low-frequency, high-efficiency communication strategy are designed to reduce communication costs and latency while enhancing coordination efficiency among agents in high-dimensional, long-horizon scenarios. Experimental results, evaluated on multi-dimensional resilience indicators including risk loss, response capability, and overall system resilience, demonstrate that the proposed method outperforms other reinforcement learning algorithms in enhancing resilience, reducing operational costs, and improving cross-scenario generalization. The diffusion model also shows strong adaptability to extreme risk disturbances. The proposed algorithm achieves an average reduction of approximately 27.8 % in operational cost compared to the best-performing baseline across all test scenarios and disturbance levels. This result is obtained from repeated trials and averaged outcomes, covering a wide range of risk types and intensities, and demonstrates high statistical reliability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127097"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622297","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127145
Yufan Zhang , Fengqi You
Controlled environment agriculture (CEA) provides a sustainable solution for food production, but high energy costs hinder its widespread adoption. Using commercial Plant Factories with Artificial Lighting (PFALs) as an example, we demonstrate that selling reserve capacity to renewable-rich power systems, i.e., by committing to decrease (up-reserve) or increase (down-reserve) electricity consumption from flexible electrical devices, can significantly reduce energy costs while maintaining optimal food production climates. Results indicate a cost reduction of up to 87 % per kilogram of food produced, with an average reduction of 82 % across cities. Up-reserve is primarily achieved by reducing consumption for heating or cooling, while down-reserve is supplied by increasing lighting consumption. PFALs with smaller electrical device capacities benefit more from the proposed model. The relative cost reduction per m2 doubles when the capacities are quartered. In addition to analyzing cost reduction, we also examine electricity consumption and the associated carbon emissions. Compared to the benchmark, the new economic model can lead to lower emissions when providing up-reserve or when no reserve is activated. However, in the case of down-reserve provision, achieving lower emissions depends on the relative carbon emission intensity. Our findings improve the cost competitiveness of PFALs and suggest a promising economic transition for CEA with flexible electricity use.
{"title":"Offering reserve capacity to renewable-rich power systems can cut plant factory energy costs by up to 87 %","authors":"Yufan Zhang , Fengqi You","doi":"10.1016/j.apenergy.2025.127145","DOIUrl":"10.1016/j.apenergy.2025.127145","url":null,"abstract":"<div><div>Controlled environment agriculture (CEA) provides a sustainable solution for food production, but high energy costs hinder its widespread adoption. Using commercial Plant Factories with Artificial Lighting (PFALs) as an example, we demonstrate that selling reserve capacity to renewable-rich power systems, i.e., by committing to decrease (up-reserve) or increase (down-reserve) electricity consumption from flexible electrical devices, can significantly reduce energy costs while maintaining optimal food production climates. Results indicate a cost reduction of up to 87 % per kilogram of food produced, with an average reduction of 82 % across cities. Up-reserve is primarily achieved by reducing consumption for heating or cooling, while down-reserve is supplied by increasing lighting consumption. PFALs with smaller electrical device capacities benefit more from the proposed model. The relative cost reduction per m<sup>2</sup> doubles when the capacities are quartered. In addition to analyzing cost reduction, we also examine electricity consumption and the associated carbon emissions. Compared to the benchmark, the new economic model can lead to lower emissions when providing up-reserve or when no reserve is activated. However, in the case of down-reserve provision, achieving lower emissions depends on the relative carbon emission intensity. Our findings improve the cost competitiveness of PFALs and suggest a promising economic transition for CEA with flexible electricity use.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127145"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682110","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.127127
Aitor Cendoya, Frederic Ransy, Bentao Guo, Andres Hernandez, Olivier Dumont, Vincent Lemort
Carnot Batteries (CBs) are a promising option for energy storage, acting as a buffer for the variability from renewables and enabling multi-energy integration and dispatch, converting electricity to heat and back to electricity. Although techno-economic studies report promising costs and high feasibility, especially when components from both cycles are shared in long-term storage, there are few prototypes, and the technology readiness level remains near 4. This paper presents a reversible Rankine-based CB designed for integration with an abandoned flooded mine. The system is under construction, being the largest machine of its type. A physics-based model was developed and validated against manufacturer data to assess performance under realistic constraints. The key focus is the role of auxiliaries and temperature-glide control. By actively modulating secondary-loop pump rotational speed, the Organic Rankine Cycle (ORC) achieves up to a 36 % increase in efficiency and the Heat Pump (HP) mode up to 20 % increase in relative efficiency to a constant-glide strategy. Highlighting that no single pair of glide settings is optimal across the full operating envelope, underscoring the need for adaptive control. Neglecting auxiliaries leads to substantial errors: a relative difference of 24 % in round-trip efficiency (RTE) can be achieved when auxiliaries are omitted, resulting in unrealistic performance values and, consequently, an unrealistic feasibility. With auxiliaries and constraints included, the modelled charge–discharge RTE ranges from 22.8 % to 34.7 %, lower than conventional storage but consistent with reported limits for CB technology. However, CBs can also supply industrial heat, reject heat to district heating networks, and/or deliver cooling, making RTE efficiency an incomplete metric for this technology. The analysis indicates that efficiency depends more on operating conditions than on component selection. This highlights that, for CBs connected to low-temperature storage, auxiliary components are decisive for performance. Achieving high efficiency requires water pumps with high part-load efficiency (including both pump and motor), refrigerant pumps capable of high efficiency at low net positive suction head, and the deployment of active control laws governing charge management and pump operation.
{"title":"Design and modelling of a reversible HP/ORC Carnot battery tailored for waste heat integration in flooded mines","authors":"Aitor Cendoya, Frederic Ransy, Bentao Guo, Andres Hernandez, Olivier Dumont, Vincent Lemort","doi":"10.1016/j.apenergy.2025.127127","DOIUrl":"10.1016/j.apenergy.2025.127127","url":null,"abstract":"<div><div>Carnot Batteries (CBs) are a promising option for energy storage, acting as a buffer for the variability from renewables and enabling multi-energy integration and dispatch, converting electricity to heat and back to electricity. Although techno-economic studies report promising costs and high feasibility, especially when components from both cycles are shared in long-term storage, there are few prototypes, and the technology readiness level remains near 4. This paper presents a reversible Rankine-based CB designed for integration with an abandoned flooded mine. The system is under construction, being the largest machine of its type. A physics-based model was developed and validated against manufacturer data to assess performance under realistic constraints. The key focus is the role of auxiliaries and temperature-glide control. By actively modulating secondary-loop pump rotational speed, the Organic Rankine Cycle (ORC) achieves up to a 36 % increase in efficiency and the Heat Pump (HP) mode up to 20 % increase in relative efficiency to a constant-glide strategy. Highlighting that no single pair of glide settings is optimal across the full operating envelope, underscoring the need for adaptive control. Neglecting auxiliaries leads to substantial errors: a relative difference of 24 % in round-trip efficiency (RTE) can be achieved when auxiliaries are omitted, resulting in unrealistic performance values and, consequently, an unrealistic feasibility. With auxiliaries and constraints included, the modelled charge–discharge RTE ranges from 22.8 % to 34.7 %, lower than conventional storage but consistent with reported limits for CB technology. However, CBs can also supply industrial heat, reject heat to district heating networks, and/or deliver cooling, making RTE efficiency an incomplete metric for this technology. The analysis indicates that efficiency depends more on operating conditions than on component selection. This highlights that, for CBs connected to low-temperature storage, auxiliary components are decisive for performance. Achieving high efficiency requires water pumps with high part-load efficiency (including both pump and motor), refrigerant pumps capable of high efficiency at low net positive suction head, and the deployment of active control laws governing charge management and pump operation.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 127127"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145682125","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 : 2025-11-29DOI: 10.1016/j.apenergy.2025.126996
Wei Wang , Hengrui Ma , Bo Wang , Jianfeng Zheng , Zhilu Liu , Tiange Li , David Wenzhong Gao
The electricity‑hydrogen integrated system (E-HIS) represents a promising paradigm for future energy systems. Accordingly, developing rational dispatch strategies is essential to ensure its secure, stable, and economical operation. Given the inherent uncertainty of renewable energy sources (RES), enhancing the robustness of dispatch decisions is critical. Moreover, due to the temporal coupling characteristics of E-HIS, dispatch decisions must be non-anticipative; that is, dispatch decisions at any given time must be based solely on the decisions made in the previous period and the current uncertainties, without relying on future uncertainties. To address these challenges, a representative E-HIS is formulated based on the structural features of the electricity and hydrogen subsystems. A multi-stage robust dispatch model is then proposed, considering the operating states of the electrolyzer (EL) and generator, RES uncertainty, and the non-anticipative characteristics of the generator, battery energy storage system (BESS), and hydrogen tank (HT). To solve the model efficiently, a decoupled fast robust dual dynamic programming (D-FRDDP) algorithm is developed. Finally, case studies based on modified 6-bus and 69-bus E-HIS systems are conducted to validate the effectiveness of the proposed model and algorithm.
{"title":"Scheduling of electricity‑hydrogen integrated system under renewable energy sources uncertainty: Non-anticipativity and robust feasibility","authors":"Wei Wang , Hengrui Ma , Bo Wang , Jianfeng Zheng , Zhilu Liu , Tiange Li , David Wenzhong Gao","doi":"10.1016/j.apenergy.2025.126996","DOIUrl":"10.1016/j.apenergy.2025.126996","url":null,"abstract":"<div><div>The electricity‑hydrogen integrated system (<em>E</em>-HIS) represents a promising paradigm for future energy systems. Accordingly, developing rational dispatch strategies is essential to ensure its secure, stable, and economical operation. Given the inherent uncertainty of renewable energy sources (RES), enhancing the robustness of dispatch decisions is critical. Moreover, due to the temporal coupling characteristics of <em>E</em>-HIS, dispatch decisions must be non-anticipative; that is, dispatch decisions at any given time must be based solely on the decisions made in the previous period and the current uncertainties, without relying on future uncertainties. To address these challenges, a representative <em>E</em>-HIS is formulated based on the structural features of the electricity and hydrogen subsystems. A multi-stage robust dispatch model is then proposed, considering the operating states of the electrolyzer (EL) and generator, RES uncertainty, and the non-anticipative characteristics of the generator, battery energy storage system (BESS), and hydrogen tank (HT). To solve the model efficiently, a decoupled fast robust dual dynamic programming (D-FRDDP) algorithm is developed. Finally, case studies based on modified 6-bus and 69-bus <em>E</em>-HIS systems are conducted to validate the effectiveness of the proposed model and algorithm.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"404 ","pages":"Article 126996"},"PeriodicalIF":11.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145622741","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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