Applied Energy最新文献_第6页

Coordinated dispatch of hybrid mobile power sources for distribution network restoration: A dynamic gradient masking embedded multi-agent meta-deep reinforcement learning method 配电网恢复混合移动电源协调调度：一种动态梯度掩蔽嵌入多智能体元深度强化学习方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-15 DOI: 10.1016/j.apenergy.2026.127377

Haopeng An , Yihao Xu , Guangdou Zhang , Yankai Xing , Yalong Mai , Olusola Bamisile , Qi Huang , Jian Li

The co-dispatch of electric vehicles (EVs) and mobile energy storage systems (MESSs) as mobile power sources (MPSs) has emerged as a critical means for the rapid restoration of post-disaster distribution networks (DNs). However, MESSs have DN restoration as a single objective, while private EVs pursue dual objectives: supporting DN restoration and fulfilling individual charging demands. The heterogeneous objectives of EVs and MESSs, coupled with their limited adaptability for rapid deployment in unpredictable post-disaster scenarios, result in coordination failures during deployment. To this end, this paper proposes a dynamic gradient masking embedded multi-agent meta-deep reinforcement learning (DGME-MAMDRL) strategy. A tailored reward function is designed to guide EVs in making correct decisions between charging and DN restoration. Each MPS is modelled as an independent agent within a Markov Game for DN restoration. The proposed strategy is applied to batches of pre-training tasks for knowledge extraction. A dynamic gradient masking mechanism is proposed and embedded within the strategy to enhance the prior knowledge extraction from different tasks. In this way, the agents need only a quick fine-tuning stage for post-disaster deployment. Case studies validate the effectiveness of the proposed strategy in the co-dispatch of hybrid MPSs and its capability for rapid deployment.

电动汽车（ev）和移动储能系统（MESSs）作为移动电源（mps）的协同调度已经成为灾后配电网（DNs）快速恢复的关键手段。然而，大众汽车将DN恢复作为单一目标，而私人电动汽车则追求双重目标：支持DN恢复和满足个人充电需求。电动汽车和MESSs的异构目标，加上它们在不可预测的灾后场景下快速部署的有限适应性，导致部署期间的协调失败。为此，本文提出了一种动态梯度掩蔽嵌入多智能体元深度强化学习（DGME-MAMDRL）策略。设计量身定制的奖励功能，引导电动汽车在充电和恢复DN之间做出正确的选择。每个MPS被建模为一个独立的代理在一个马尔可夫博弈DN恢复。将该策略应用于批量的预训练任务中进行知识提取。提出了一种动态梯度掩蔽机制，并将其嵌入到该策略中，以增强对不同任务的先验知识提取。通过这种方式，代理只需要为灾后部署进行快速微调。案例研究验证了所提出的混合mps协同调度策略的有效性及其快速部署的能力。

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引用次数: 0

Optimization design of hydrogen energy supported microgrid network capacity based on hydrogen energy equipment behavior pattern inversion under uncertain conditions 不确定条件下基于氢能设备行为模式反演的氢能支撑微电网容量优化设计

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-15 DOI: 10.1016/j.apenergy.2026.127393

Linghong Zeng , Zishan Yin , Fengyuan Guo , Pengyu Yue , Yixiang Ai , Hongchuan Qin , Jiashu Jin , Zhonghua Deng , Zhuo Wang , Xi Li

With the increasing drawbacks of fossil energy characterized by high consumption and emissions, the goals of carbon peaking and carbon neutrality have become central to structural transformation in the energy sector. The study addresses the dual challenges faced by regional hydrogen-supported microgrid networking, namely, uncertain source-load conditions and equipment health states. A novel networking optimization method is proposed that integrates uncertainty modeling, health assessment, and multi-criteria decision-making, aiming to achieve economically optimal, safe, and flexible collaborative operation of microgrids. First, probabilistic statistics and scenario generation techniques are employed to accurately quantify stochastic fluctuations from renewable generation and load demand, thereby enhancing the adaptability of networking schemes through multi-scenario simulations. Second, a dynamic state of health (SOH) assessment is introduced, embedding degradation models of proton exchange membrane fuel cells (PEMFCs) and proton exchange membrane electrolyzers (PEMECs) into the planning process to enable resource allocation optimization over time. Third, a multidimensional evaluation framework is constructed, encompassing operational costs, long-term health state variations, and total investment costs. Finally, under different source-load quantile scenarios, the proposed method demonstrates superior economic efficiency and robustness in PEMFC and PEMEC capacity configuration compared with conventional mixed-integer programming approaches. Moreover, the incorporation of SOH constraints significantly improves both energy output and economic performance of the system.

随着化石能源高消耗、高排放的弊端日益凸显，碳峰值和碳中和目标已成为能源行业结构转型的核心。该研究解决了区域氢支持微电网面临的双重挑战，即不确定的源负荷条件和设备健康状态。为实现微电网经济最优、安全灵活的协同运行，提出了一种集不确定性建模、健康评估和多准则决策于一体的网络优化方法。首先，采用概率统计和场景生成技术，准确量化可再生能源发电和负荷需求的随机波动，通过多场景模拟，增强组网方案的适应性。其次，引入动态健康状态（SOH）评估，将质子交换膜燃料电池（pemfc）和质子交换膜电解槽（PEMECs）的降解模型嵌入到规划过程中，以实现资源的长期优化配置。第三，构建了包含运营成本、长期健康状态变化和总投资成本的多维评估框架。最后，在不同的源负荷分位数情景下，与传统的混合整数规划方法相比，所提出的方法在PEMFC和PEMEC容量配置方面具有更好的经济性和鲁棒性。此外，SOH约束的结合显著提高了系统的能量输出和经济性能。

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引用次数: 0

Fault diagnosis of photovoltaic arrays at ports under small-sample and imbalanced data conditions 小样本不平衡数据条件下港口光伏阵列故障诊断

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-14 DOI: 10.1016/j.apenergy.2026.127401

Zhiya Xiao , Daogui Tang , Qianneng Zhang , Hamidreza Arasteh , Josep M. Guerrero , Enrico Zio

Photovoltaic (PV) power generation is increasingly deployed in ports to support green and low-carbon development. However, the harsh operating environment and the scarcity of fault data in newly installed PV arrays hinder accurate and reliable fault diagnosis. To address the issues of data imbalance and fault sample scarcity typically encountered during the initial deployment of PV arrays in port areas, this study proposes an enhanced oversampling algorithm, Adaptive K-nearest neighbor and Dynamic Random-disturbance-based Synthetic Minority Over-sampling Technique (AKDRSMOTE), for fault data augmentation. Furthermore, a hybrid strategy based on improved Harris Hawks Optimization (IHHO) optimized support vector machine (SVM) is proposed to enhance the diagnostic performance.Experimental results demonstrate that under small-sample and imbalanced data conditions, the proposed approach effectively identifies various complex PV faults. The model achieves an accuracy of 93.42%, an F1-score of 88.09%, and a Kappa coefficient of 92.89%, all of which outperform traditional fault detection techniques. These findings substantiate the accuracy, robustness, and stability of the proposed method in complex port environments and highlight its strong potential for real-world engineering applications in intelligent PV system operation and maintenance.

为支持绿色低碳发展，港口越来越多地部署光伏发电。然而，新安装的光伏阵列运行环境恶劣，故障数据稀缺，阻碍了其准确可靠的故障诊断。为了解决港口地区光伏阵列初始部署过程中通常遇到的数据不平衡和故障样本稀缺性问题，本研究提出了一种增强的过采样算法——自适应k近邻和基于动态随机扰动的合成少数派过采样技术（AKDRSMOTE），用于故障数据的增强。在此基础上，提出了一种基于改进Harris Hawks Optimization （IHHO）优化支持向量机（SVM）的混合策略来提高诊断性能。实验结果表明，在小样本和不平衡数据条件下，该方法能有效识别各种复杂PV故障。该模型的准确率为93.42%，f1分数为88.09%，Kappa系数为92.89%，均优于传统的故障检测技术。这些研究结果证实了该方法在复杂港口环境中的准确性、鲁棒性和稳定性，并突出了其在智能光伏系统运行和维护中的实际工程应用潜力。

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引用次数: 0

Bi-level planning of data centers with coupled electricity-heat-computation system using data-driven scenario generation for representing uncertainties 采用数据驱动情景生成表示不确定性的电-热耦合数据中心双层规划

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-14 DOI: 10.1016/j.apenergy.2026.127369

Lingfang Yang , Yujie Lin , Mohammad Shahidehpour , Yuanyi Chen , Qiang Yang

Along with the rapidly increasing data traffic, data centers (DTCs) have considered the expansion of their server capacity, which has led to higher energy consumption. To deal with this issue, an effective solution is presented for the coupled electricity-heat-computation system (CEHCS) in DTCs. However, CEHCS uncertainties of loads and energy resources can pose additional challenges for the optimal planning of DTC. Therefore, this work proposes a bi-level expansion planning of DTCs, which considers data-driven scenario generation for representing CEHCS uncertainties. First, a diffusion model is designed using the operational scenario generation (DMOSG) method to characterize the multi-dimensional randomness of multivariate time series, where a one-dimensional U-net works as the denoising network with an interpretable latent output space. Then, a DTC expansion planning model with CEHCS is formulated considering the safe operation temperature of CPUs, where the operational status of each server is individually characterized to increase the proposed model's fidelity. Although the DMOSG helps address the randomness of input scenarios for planning, it is also necessary to consider the uncertainty hedging for energy scheduling. Thus, chance constraints are included for BESSs to cope with risks during CEHCS dispatch. A heuristic solution is proposed for the bi-level scheme to solve the DTC planning model. At the upper level, the number of servers to be added is determined by the particle swarm optimization (PSO) algorithm. The upper-level solution is submitted to the lower level, where the performance cost of DTC planning and scheduling decisions is obtained by the Gurobi solver. Then, the DTC performance cost is returned to the upper level iteratively for calculating the optimal DTC planning results. The proposed DTC planning solution with CEHCS is validated through case studies, where the numerical results confirm the accuracy of the proposed characterization of operational uncertainty, as well as the cost-effectiveness and improved energy efficiency of the DTC expansion planning scheme.

随着数据流量的快速增长，数据中心（dtc）已经开始考虑服务器容量的扩展，这导致了更高的能耗。针对这一问题，提出了一种有效的解决方案，用于直流输电系统的电-热耦合计算系统。然而，CEHCS负荷和能源的不确定性给直接控制系统的优化规划带来了额外的挑战。因此，本研究提出了一种双层dtc扩展规划，该规划考虑了数据驱动的场景生成，以表示CEHCS的不确定性。首先，利用操作场景生成（DMOSG）方法设计了一个扩散模型来表征多元时间序列的多维随机性，其中一维U-net作为具有可解释潜在输出空间的去噪网络。然后，考虑cpu的安全运行温度，建立了具有CEHCS的DTC扩展规划模型，其中每个服务器的运行状态都被单独表征，以提高模型的保真度。虽然DMOSG有助于解决规划输入场景的随机性，但在能源调度中也需要考虑不确定性对冲。因此，在CEHCS调度过程中，bess需要考虑机会约束来应对风险。提出了一种求解DTC规划模型的启发式双级方案。在上层，要增加的服务器数量由粒子群优化（PSO）算法确定。上层解决方案提交给下层，下层通过Gurobi求解器获得DTC规划和调度决策的性能代价。然后，将DTC性能成本迭代返回到上一级，计算最优DTC规划结果。通过实例研究验证了基于CEHCS的DTC规划方案，其中数值结果证实了所提出的运行不确定性表征的准确性，以及DTC扩展规划方案的成本效益和提高的能源效率。

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引用次数: 0

Sequential operation of residential energy hubs using physics-based economic nonlinear MPC 基于物理的经济非线性MPC的住宅能源枢纽顺序运行

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-14 DOI: 10.1016/j.apenergy.2026.127402

Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, Pavol Bauer

The operation of residential energy hubs with multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to different carrier dynamics, hybrid storage coordination and high-dimensional action-spaces. Energy management systems oversee their operation, deciding the set points of the primary control layer. This paper presents a novel 2-stage economic model predictive controller for electrified buildings including physics-based models of the battery degradation and thermal systems. The hierarchical control operates in the Dutch sequential energy markets. In particular common assumptions regarding intra-day markets (auction and continuous-time) are discussed as well as the coupling of the different storage systems. The best control policy it is best to follow continuous time intra-day in the summer and the intra-day auction in the winter. This sequential operation comes at the expense of increased battery degradation. Lastly, under our controller, the realized short-term flexibility of the thermal energy storage is marginal compared to the flexibility delivered by stationary battery pack and electric vehicles with bidirectional charging.

由于不同的载体动力学、混合存储协调和高维行动空间，具有多种能源载体（电力、热能、流动性）的住宅能源中心的运行面临着重大挑战。能源管理系统监督它们的运行，决定主要控制层的设定值。本文提出了一种新的两阶段经济模型预测控制器，包括基于物理模型的电池退化和热系统。分级控制在荷兰顺序能源市场运行。特别是关于日内市场（拍卖和连续时间）的常见假设以及不同存储系统的耦合进行了讨论。最好的控制策略是夏季最好遵循连续时间日内，冬季最好遵循日内拍卖。这种顺序操作的代价是增加电池退化。最后，在我们的控制器下，与固定电池组和双向充电的电动汽车所提供的灵活性相比，所实现的热能储存的短期灵活性是微不足道的。

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引用次数: 0

Coordinated frequency regulation of fixed-variable speed pumped storage hybrid systems: an adaptive control framework integrating dynamic prediction and rolling optimization 定变速抽水蓄能混合系统的协调频率调节：一种结合动态预测和滚动优化的自适应控制框架

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-13 DOI: 10.1016/j.apenergy.2026.127375

He Wang , Xiaoqiang Tan , Lihao Li , Chaoshun Li , Alexandre Presas

Pumped-storage hydropower (PSH) remains one of the most reliable and mature large-scale energy-storage technologies and plays a critical role in balancing power systems with high shares of renewable generation. This study addresses the coordination challenge in hybrid systems arising from the differing fast-slow regulation characteristics of fixed and variable-speed units, and proposes a multi-operating condition, cost-aware active predictive control framework that jointly optimizes regulation performance and cost. First, frequency-regulation dynamics and unit response characteristics are analyzed using a mechanistic model, and a linear parameter-varying fast-prediction model that captures multi-operating behavior is derived. An active, condition-prediction-adaptive optimization strategy is formulated, which combines model linearization decomposition, predictive-performance reconstruction and online allocation of control actions. Simulation results demonstrate that the proposed strategy achieves coordinated optimization of integrated regulation performance under multiple constraints, improving regulation reliability and response consistency. Specifically, compared with conventional optimization methods, it reduces the transient frequency deviation by >20 % and maximum unit speed deviation by >30 %, while substantially lowering regulation costs and exploiting the complementary potential across multi-operating conditions. The work provides a systematic technical pathway for the operation and offers valuable insights for improving flexibility of PSH.

抽水蓄能水力发电（PSH）是目前最可靠、最成熟的大型储能技术之一，在平衡高可再生能源发电比例的电力系统中发挥着关键作用。本研究解决了固定和变速机组不同的快慢调节特性在混合动力系统中引起的协调挑战，并提出了一个多工况、成本意识的主动预测控制框架，共同优化调节性能和成本。首先，利用机理模型分析了调频动力学和机组响应特性，并推导了一个能够捕捉多工况行为的线性变参数快速预测模型。提出了一种结合模型线性化分解、预测性能重构和控制动作在线分配的主动、状态预测自适应优化策略。仿真结果表明，该策略实现了多约束条件下综合调控性能的协调优化，提高了调控可靠性和响应一致性。具体而言，与传统优化方法相比，该方法将暂态频率偏差降低了20%，最大单位转速偏差降低了30%，同时大大降低了调节成本，并在多工况下发挥了互补潜力。该工作为PSH的操作提供了系统的技术途径，为提高PSH的灵活性提供了有价值的见解。

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引用次数: 0

Lean multi-agent deep reinforcement learning for uncertainty handling in the energy management of networked microgrids 网络化微电网能源管理中不确定性处理的精益多智能体深度强化学习

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-13 DOI: 10.1016/j.apenergy.2026.127354

Ayodele Benjamin Esan , Hussain Shareef , Ahmad K. ALAhmad

This study introduces a Lean Multi-Agent Deep Reinforcement Learning (L-MADRL) framework for energy management of networked microgrids (NMGs) with multiple electricity retailers (ERs) and microgrids (MGs) under renewable and load uncertainties. Coordinating energy exchanges in such systems is challenging due to the need for market efficiency, technical feasibility, and scalability. The proposed framework combines a multi-agent Deep Q-Network (DQN) with a single-level reformulation of a bi-level optimization model. In this formulation, the upper level maximizes ER profits and the network’s available transfer capability (ATC), while the lower-level MG cost minimization is replaced by Karush–Kuhn–Tucker (KKT) conditions, yielding a mathematical program with equilibrium constraints (MPEC). This hybrid design offers two benefits: (i) technical constraints such as power flow limits, generator capacities, and market rules are embedded in the MPEC, freeing DRL agents from constraint enforcement and improving learning stability and policy reliability, and (ii) explicit ATC consideration enhances power transfer efficiency and enables network-aware coordination. Performance was evaluated on PJM 5-bus and IEEE 14-bus test systems against deterministic, risk-neutral (RNSO), and risk-averse (RASO) stochastic optimization. Results show that in the 5-bus case, L-MADRL reduced MG costs by 10.3% and increased ER profits by 3.7%, while in the 14-bus case costs decreased by 2.6% and profits rose by 11.4%. L-MADRL also improved ATC, exceeding the best benchmark by 32% in the 5-bus system and by 30% initially and 10% at peak in the 14-bus system. Across all cases, runtimes remained below 3 s, highlighting the framework’s scalability and computational efficiency.

本研究引入了一个精益多智能体深度强化学习（L-MADRL）框架，用于可再生能源和负荷不确定性下具有多个电力零售商（er）和微电网（mg）的网络微电网（nmg）的能量管理。由于需要市场效率、技术可行性和可扩展性，在这样的系统中协调能源交换是具有挑战性的。提出的框架将多智能体深度q网络（DQN）与双级优化模型的单级重构相结合。在该公式中，上层最大化了ER利润和网络的可用传输能力（ATC），而下层的MG成本最小化被Karush-Kuhn-Tucker （KKT）条件所取代，产生了一个具有平衡约束（MPEC）的数学程序。这种混合设计提供了两个好处：(i)技术约束，如功率流限制，发电机容量和市场规则嵌入在MPEC中，使DRL代理免于约束执行，提高学习稳定性和策略可靠性；（ii）明确的ATC考虑提高了电力传输效率，实现了网络感知协调。针对确定性、风险中性（RNSO）和风险厌恶（RASO）随机优化，在PJM 5总线和IEEE 14总线测试系统上对性能进行了评估。结果表明，在5辆公交车的情况下，L-MADRL降低了MG成本10.3%，增加了ER利润3.7%，而在14辆公交车的情况下，成本降低了2.6%，利润增加了11.4%。L-MADRL也提高了ATC，在5总线系统中超过最佳基准32%，在14总线系统中最初和峰值分别超过30%和10%。在所有情况下，运行时间都保持在3秒以下，突出了框架的可伸缩性和计算效率。

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引用次数: 0

State-of-the-art review of deep-sea wind turbine: installation, cost, instability, flow-induced oscillation, failure, maintenance, and power cost 深海风力涡轮机的最新研究综述：安装、成本、不稳定性、流致振荡、故障、维护和电力成本

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-13 DOI: 10.1016/j.apenergy.2026.127361

Faheem Ullah, Md. Mahbub Alam

Offshore wind power, especially from deep-sea floating turbines, is a renewable energy source that has proven its potential thanks to the strength and reliability of available wind resources. This review paper highlights the challenges of deep-sea floating wind turbines, focusing on installation, cost, instability, flow-induced oscillation, failure, maintenance, and optimization to enhance economic feasibility and accelerate wind energy adoption. Deploying floating structures in deep water is theoretically feasible but requires expensive and logistically difficult specialized installation vessels and equipment. Major obstacles include expensive startup and running costs, structural fatigue and wear from vibrations and instability, and material deterioration in harsh environments. While structural stability is a key design achievement, effective maintenance is essential for ensuring turbine reliability, availability, and long-term operation. Enhancing operational cost-effectiveness is closely linked to the adoption of autonomous maintenance technologies, predictive analytics, and advanced control systems. The key to achieving a competitive levelized cost of electricity compared to other energy resources is an optimized floating wind turbine design that minimizes both capital costs and operational expenditure while attaining higher efficiency. To increase the economic feasibility of floating wind turbines and promote the worldwide shift to renewable energy, this study emphasizes the necessity of ongoing research and development throughout all life cycle phases.

海上风力发电，特别是深海浮动涡轮机，是一种可再生能源，由于现有风力资源的强度和可靠性，已经证明了它的潜力。本文重点介绍了深海浮式风力涡轮机的安装、成本、不稳定性、流致振荡、故障、维护和优化等方面的挑战，以提高经济可行性并加速风能的采用。在深水中部署浮动结构在理论上是可行的，但需要昂贵且后勤困难的专业安装船只和设备。主要障碍包括昂贵的启动和运行成本，结构疲劳和振动和不稳定造成的磨损，以及恶劣环境下材料的劣化。虽然结构稳定性是一个关键的设计成果，有效的维护是必不可少的，以确保涡轮机的可靠性，可用性和长期运行。提高运营成本效益与采用自主维护技术、预测分析和先进控制系统密切相关。与其他能源相比，实现具有竞争力的电力成本的关键是优化的浮动风力涡轮机设计，使资本成本和运营支出最小化，同时实现更高的效率。为了提高浮动风力涡轮机的经济可行性，并促进全球向可再生能源的转变，本研究强调了在整个生命周期阶段进行持续研究和开发的必要性。

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引用次数: 0

Recent advances in supercapacitor electrode materials based on MOF-derived transition metal sulfides 基于mof衍生过渡金属硫化物的超级电容器电极材料研究进展

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-13 DOI: 10.1016/j.apenergy.2026.127349

Sweta R. Dash , Mangal S. Yadav , Rakesh K. Sahoo , A.L. Sharma

The development of next-generation energy storage technologies demands innovative material design strategies that can meet performance, scalability, and sustainability requirements. Metal-organic frameworks (MOFs) and their derived transition metal sulfides (TMSs) have attracted significant attention in the field of electrochemical supercapacitors due to their highly tunable architectures, extensive surface areas, and excellent electrical conductivity. This review aims to bridge the knowledge gap between electrochemists and researchers working on MOFs and MOF-derived TMSs. It begins by outlining the fundamental concepts of energy storage systems, including various charge storage mechanisms, while highlighting the critical interdependence among material properties, electrode architecture, and device-level parameters that collectively impact performance. The review then delves into the design principles of MOFs and their derived TMSs, focusing on key parameters that determine their effectiveness in high-rate electrochemical energy storage (EES) applications. Furthermore, it provides a comprehensive discussion on the strategies employed to improve the EES performance of MOF-derived TMSs in comparison to conventional TMS materials, along with their practical implementation in supercapacitor technologies. Finally, special attention is given to detailed investigations of charge storage mechanisms, incorporating both in-situ and ex-situ experimental techniques, and their correlation with theoretical insights derived from density functional theory (DFT) calculations.

下一代储能技术的发展需要创新的材料设计策略，以满足性能、可扩展性和可持续性要求。金属有机骨架（mof）及其衍生的过渡金属硫化物（tms）由于具有高度可调的结构、广泛的表面积和优异的导电性，在电化学超级电容器领域引起了广泛的关注。本文旨在弥补电化学学者和研究人员在mof和mof衍生的tms方面的知识差距。首先概述了能量存储系统的基本概念，包括各种电荷存储机制，同时强调了材料特性、电极结构和设备级参数之间的关键相互依存关系，这些参数共同影响性能。然后深入研究了mof及其衍生的tms的设计原则，重点讨论了决定其在高速电化学储能（EES）应用中有效性的关键参数。此外，本文还全面讨论了与传统TMS材料相比，用于提高mof衍生TMS的EES性能的策略，以及它们在超级电容器技术中的实际应用。最后，特别关注电荷存储机制的详细研究，结合原位和非原位实验技术，以及它们与密度泛函理论（DFT）计算得出的理论见解的相关性。

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引用次数: 0

Sustainable production of alternative aviation fuel via thermolytic conversion of plastic waste: techno-economic analysis and life cycle assessment 通过塑料废物热分解转化可持续生产替代航空燃料：技术经济分析和生命周期评估

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127399

Junyoung Park , Dongho Choi , Hyukwon Kwon , Taewoo Lee , Eilhann E. Kwon , Jaewon Lee , Hyungtae Cho

This study proposes scalable process for producing alternative aviation fuel from plastic waste, particularly high-density polyethylene (HDPE), through pyrolysis. Prior to process design, HDPE pyrolysis experiments were conducted at 500, 600, and 700 °C to examine temperature effect on aviation fuel production. The aviation fuel yields were 24.0, 20.8, and 3.0 wt% at 500, 600, and 700 °C, respectively, indicating that 500 and 600 °C were most effective. Based on these findings, two aviation fuel production processes (AFP-500 and AFP-600) were developed, integrating pyrolysis at 500 and 600 °C with catalytic cracking. Notably, catalytic cracking was employed to convert wax produced during pyrolysis process. Simulation results showed that HDPE feed rate of 5000 kg h⁻¹ yielded 1523 and 1159 kg h⁻¹ of aviation fuel in AFP-500 and AFP-600, respectively. Techno-economic analysis (TEA) revealed that the levelized cost of production (LCOP) for AFP-500 and AFP-600 were 0.017 and 0.035 USD MJ⁻¹, respectively, indicating that 500 °C is the optimal pyrolysis temperature. Additionally, the LCOP of AFP-500 is 40–77% lower than that of sustainable aviation fuels (SAFs). Life cycle assessment (LCA) results demonstrated net GHG emissions of 0.050 and 0.073 kgCO₂e MJ⁻¹ for AFP-500 and AFP-600, 43% and 18% lower than fossil-based fuel. Eco-efficiency analysis (EEA) was performed to evaluate sustainability of aviation fuel production from HDPE via proposed processes. Aviation fuel produced from HDPE via AFP-500 exhibited the highest eco-efficiency compared with SAFs and that derived from AFP-600. These findings suggest that AFP-500 offers a viable pathway for producing alternative aviation fuel from HDPE.

本研究提出了一种可扩展的工艺，通过热解从塑料废物，特别是高密度聚乙烯（HDPE）中生产替代航空燃料。在工艺设计之前，进行了HDPE在500、600和700℃下的热解实验，以考察温度对航空燃料生产的影响。在500、600和700°C时，航空燃油产率分别为24.0%、20.8%和3.0 wt%，表明500和600°C时最有效。基于这些发现，开发了两种航空燃料生产工艺（AFP-500和AFP-600），将500°C和600°C热解与催化裂化相结合。值得注意的是，催化裂化对热解过程中产生的蜡进行了转化。仿真结果表明，在5000 kg h - 1的HDPE进给量下，AFP-500和AFP-600分别产生1523和1159 kg h - 1的航空燃料。技术经济分析（TEA）表明，AFP-500和AFP-600的平准化生产成本（LCOP）分别为0.017和0.035 USD MJ−1，表明500℃为最佳热解温度。此外，AFP-500的LCOP比可持续航空燃料（SAFs）低40-77%。生命周期评估（LCA）结果表明，AFP-500和AFP-600的温室气体净排放量分别为0.050和0.073 kgCO₂e MJ - 1，比化石燃料低43%和18%。通过生态效率分析（EEA）来评估HDPE航空燃料生产过程的可持续性。通过AFP-500生产的HDPE航空燃料与从AFP-600衍生的SAFs相比，表现出最高的生态效率。这些发现表明，AFP-500为从HDPE中生产替代航空燃料提供了一条可行的途径。

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