Applied Energy最新文献_第3页

A flexible hybrid-energy heat pump using efficient ionic liquids for sustainable solar cooling 一种灵活的混合能源热泵，使用高效的离子液体进行可持续的太阳能冷却

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127372

Yunren Sui , Zhixiong Ding , Zengguang Sui , Fuxiang Li , Haosheng Lin , Wei Wu

Heat pumps have been widely adopted as an effective solution for sustainable building cooling. Thermally-driven absorption heat pumps can significantly reduce electricity consumption; however, they are hindered by issues such as low reliability, limited applicability, low efficiency, and crystallization limitations. To address these challenges, this study introduces a hybrid-energy heat pump (HEHP) that offers enhanced flexibility, enabling a gradual transition from an absorption cycle to a compression cycle, utilizing novel refrigerant/ionic liquids (ILs) as working fluids to eliminate crystallization constraints. The cycle performance of H₂O/ILs, NH₃/ILs, HFC/ILs, and HFO/ILs is compared, with NH₃/[DMIM][DMP] identified as the most suitable alternative due to its high electrical coefficient of performance (

{COP}_{ele}

) of 19.2 and its significantly high compactness. The HEHP cycle employing NH₃/[DMIM][DMP] is designed for Hong Kong, with the energy efficiency of the solar absorption sub-cycle ranging from 0.31 to 0.50. As the solar collector area rises, the

{COP}_{ele}

rises from 6.8 to 19.8, while the unit cooling potential decreases from 1.75 kWh/m²/day to 0.64 kWh/m²/day. The levelized cooling cost initially decreases before increasing, reflecting the interplay between higher initial costs and reduced operation costs, with the lowest value (0.075 USD/kWh) occurring at a solar collector area of 600 m². With a relative improvement in demand met ratio of 27.7% to 47.5% and a reduction in electricity consumption of 39.4–110.0 MWh/year, the HEHP cycle demonstrates both high efficiency and flexible applicability for sustainable building cooling.

热泵作为可持续建筑制冷的有效解决方案已被广泛采用。热驱动吸收式热泵可显著降低用电量；然而，它们受到可靠性低、适用性有限、效率低和结晶限制等问题的阻碍。为了应对这些挑战，本研究引入了一种混合能源热泵（HEHP），它提供了更高的灵活性，能够从吸收循环逐渐过渡到压缩循环，利用新型制冷剂/离子液体（ILs）作为工作流体来消除结晶限制。比较了H2O/ILs、NH3/ILs、HFC/ILs和HFO/ILs的循环性能，其中NH3/[DMIM][DMP]因其19.2的高电性能系数（COPele）和显著的高致密性而被确定为最合适的替代品。采用NH3/[DMIM][DMP]的HEHP循环是为香港设计的，太阳能吸收子循环的能源效率为0.31至0.50。随着太阳能集热器面积的增加，COPele由6.8上升到19.8，而机组制冷潜力由1.75 kWh/m2/day下降到0.64 kWh/m2/day。平冷成本先下降后上升，反映了初始成本较高与运行成本降低之间的相互作用，最低值（0.075美元/千瓦时）出现在太阳能集热器面积为600 m2时。需求满足率相对提高27.7% ~ 47.5%，年耗电量减少39.4 ~ 110.0 MWh/年，在可持续建筑制冷方面具有较高的效率和灵活的适用性。

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引用次数: 0

How energy management strategy shapes optimal microgrid design: A comparative analysis for EV charging stations 能源管理策略如何塑造最佳微电网设计：电动汽车充电站的比较分析

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127352

Nahar F. Alshammari , Shahr Alshahr , Shimaa Barakat

Background

The rapid expansion of electric vehicle (EV) adoption places unprecedented strain on power distribution networks, driving demand for grid-connected charging stations integrated with renewable energy and hybrid energy storage systems (HESS). Current microgrid design practices often treat system sizing and control strategy as separate problems, resulting in suboptimal configurations.

Methods

This study introduces a novel comparative co-design framework that simultaneously optimizes microgrid physical sizing for three distinct Energy Management Strategy (EMS) archetypes: Rule-Based, Reinforcement Learning using Deep Deterministic Policy Gradient, and a Hybrid approach. A multi-objective optimization using the NSGA-II algorithm was performed independently for each strategy to generate distinct Pareto-optimal fronts, allowing for a direct assessment of how control logic shapes the optimal design space.

Results

Applied to a PV-wind-battery-supercapacitor microgrid for an EV charging station in Dumat Al-Jandal, Saudi Arabia, the results reveal that control strategy choice dominantly determines environmental performance and battery health. The Rule-Based strategy achieved the highest emissions reduction (70.8 %) but incurred the highest battery degradation. Conversely, the Hybrid strategy reduced battery capacity loss by 49 % compared to the Rule-Based approach, albeit with increased grid reliance. The Reinforcement Learning strategy delivered a balanced performance, achieving competitive costs with high reliability. Furthermore, Monte Carlo sensitivity analysis confirmed the system's economic robustness, showing a Total Net Present Cost variance of less than 2.3 % under stochastic weather variability.

Conclusion

Statistical analysis across 30 independent runs confirmed the robustness of the optimization framework. This work establishes control strategy as a design cornerstone, providing quantitative guidance for aligning microgrid design with specific sustainability, economic, or longevity priorities in EV charging infrastructure.

电动汽车（EV）的迅速普及给配电网带来了前所未有的压力，推动了对集成可再生能源和混合能源存储系统（HESS）的并网充电站的需求。当前的微电网设计实践通常将系统规模和控制策略视为独立的问题，从而导致次优配置。本研究引入了一种新的比较协同设计框架，该框架同时优化了三种不同的能源管理策略（EMS）原型的微电网物理规模：基于规则的、使用深度确定性策略梯度的强化学习和混合方法。使用NSGA-II算法对每个策略独立执行多目标优化，以生成不同的帕累托最优前沿，从而可以直接评估控制逻辑如何形成最优设计空间。结果应用于沙特阿拉伯Dumat Al-Jandal电动汽车充电站的光伏-风力电池-超级电容器微电网，结果表明控制策略选择对环境性能和电池健康起主导作用。基于规则的策略实现了最高的减排（70.8%），但导致了最高的电池退化。相反，与基于规则的方法相比，混合策略减少了49%的电池容量损失，尽管增加了对电网的依赖。强化学习策略提供了平衡的性能，实现了具有竞争力的成本和高可靠性。此外，蒙特卡罗敏感性分析证实了该系统的经济稳健性，显示在随机天气变化下，总净当前成本方差小于2.3%。结论30次独立运行的统计分析证实了优化框架的稳健性。这项工作将控制策略作为设计基石，为将微电网设计与电动汽车充电基础设施的具体可持续性、经济性或寿命优先事项相结合提供定量指导。

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引用次数: 0

A review of congestion management methods for power distribution networks: Current practices and future challenges 配电网拥塞管理方法综述：当前实践与未来挑战

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2025.127342

Khawaja Khalid Mehmood , Ranier Alexsander Arruda Moura , Anne van der Molen , Reinaldo Tonkoski , Peter Tzscheutschler , Peter van der Wielen , Phuong Hong Nguyen

The increasing volume of connection requests for load and generation is putting pressure on the limited grid capacity of distribution networks (DNs), resulting in growing waiting lists. Consequently, distribution system operators (DSOs) are seeking fast and effective congestion management (CM) strategies to reduce delays and enable timely customer connections. In this paper, we present a comprehensive review of CM methods for DNs to assist DSOs in addressing this challenge. By taking into account the severity of customer impact, we categorize existing CM methods into four groups: (1) DSO-owned technical solutions, (2) tariff- and flexible contract-based solutions, (3) DSO-procured market-based solutions, and (4) DSO-direct interventions. Within DSO-owned solutions, we provide an in-depth review of network reinforcement and equipment control-based solutions. Next, we examine tariff and flexible contract-based solutions, including time-of-use (TOU) tariffs, TOU tariffs with incentives, dynamic tariffs and non-firm capacity contracts. The DSO-procured market-based solutions cover research on various market designs aimed at addressing congestion issues. Finally, we review DSO-direct interventions as last-resort approaches for CM. Additionally, we analyze research studies that propose underlying mathematical methods for CM, categorizing them into three groups: (1) deterministic analysis, (2) stochastic analysis, and (3) machine learning-based methods. For each study, we highlight key contributions along with our reflections on its applicability. For the most relevant methods, we also present simulation results to validate their working principles. Finally, we highlight future challenges in CM, offering insights for DSOs and researchers in developing effective CM solutions.

负载和发电的连接请求量不断增加，给有限的配电网容量带来了压力，导致等待名单不断增加。因此，配电系统运营商（dso）正在寻求快速有效的拥塞管理（CM）策略，以减少延迟并实现及时的客户连接。在本文中，我们提出了一个全面的审查CM方法的域名，以协助dso应对这一挑战。考虑到客户影响的严重程度，我们将现有的CM方法分为四组：(1)dso拥有的技术解决方案，(2)基于关税和灵活合同的解决方案，(3)dso采购的市场解决方案，以及(4)dso直接干预。在dso拥有的解决方案中，我们提供了对网络加固和设备控制解决方案的深入审查。接下来，我们研究了基于关税和灵活合同的解决方案，包括分时电价（TOU）、带激励的分时电价、动态电价和非企业产能合同。dso采购的基于市场的解决方案涵盖了旨在解决拥堵问题的各种市场设计的研究。最后，我们回顾了dso直接干预作为CM的最后手段。此外，我们分析了提出CM基础数学方法的研究，将它们分为三组：(1)确定性分析，(2)随机分析和(3)基于机器学习的方法。对于每项研究，我们都强调了关键贡献以及我们对其适用性的思考。对于大多数相关的方法，我们也给出了仿真结果来验证它们的工作原理。最后，我们强调了CM的未来挑战，为dso和研究人员提供了开发有效CM解决方案的见解。

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引用次数: 0

WeTRaC: Scalable EV charging demand forecasting for heavy-duty fleets WeTRaC：重型电动汽车充电需求预测

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127365

Alexander Aushev, Joel Anttila, Yancho Todorov, Ari Hentunen, Mikko Pihlatie

The rapid expansion of electric vehicles (EVs) in response to stricter emissions targets presents formidable challenges for power systems, particularly in scaling EV charging infrastructure to meet growing demands from heavy-duty fleets. Such demands are shaped by complex spatio-temporal interdependencies, such as weather conditions, traffic density, routes, and charging infrastructure, leading to imprecise charging demand predictions by the existing models that do not fully address all factors. This study introduces the Weather Traffic Routes and Chargers (WeTRaC), a predictive framework that unifies graph neural networks (GNNs) with physics-based vehicle simulations and open global data to produce high-precision forecasts of heavy-duty (i.e., buses and trucks) EV charging needs. Forecasts are generated at the vehicle level along routes and then aggregated to fleet- or corridor-level demand using probabilistic priors over vehicle attributes. We validate its performance through large-scale simulations (including ten international virtual corridor case studies) and real-world truck data from Finland, revealing a 500-fold computational speedup over conventional physics-based approaches at only a marginal (

\approx

4%) accuracy trade-off. By identifying peak periods and locations of corridor demand for specified fleets, WeTRaC can effectively mitigate grid overload and accelerate the transition toward zero-emission transport.

为了应对更严格的排放目标，电动汽车（EV）的迅速扩张给电力系统带来了巨大的挑战，特别是在扩大电动汽车充电基础设施以满足重型车队不断增长的需求方面。这些需求受到复杂的时空相互依赖关系的影响，如天气条件、交通密度、路线和充电基础设施，导致现有模型的充电需求预测不精确，不能完全解决所有因素。本研究介绍了天气交通路线和充电器（WeTRaC），这是一个预测框架，将图形神经网络（gnn）与基于物理的车辆模拟和开放的全球数据相结合，以产生重型（即公共汽车和卡车）电动汽车充电需求的高精度预测。预测是沿着路线在车辆级别生成的，然后使用车辆属性的概率先验聚合到车队或走廊级别的需求。我们通过大规模模拟（包括10个国际虚拟走廊案例研究）和来自芬兰的真实卡车数据验证了其性能，结果显示，与传统的基于物理的方法相比，其计算速度提高了500倍，而精度仅为边际（≈4%）。通过确定特定车队的高峰时段和通道需求位置，WeTRaC可以有效缓解电网过载，加速向零排放运输的过渡。

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引用次数: 0

Mapping price dynamics across electricity market designs: A functional data approach with STL decomposition 跨电力市场设计的价格动态映射：一种具有STL分解的功能数据方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127390

Miao Yu, Philipp Sibbertsen

This study develops a functional data analysis (FDA) framework to investigate how electricity price dynamics respond to changes in energy generation across countries with different market structures. Using Seasonal-Trend decomposition using Loess (STL) decomposition, electricity prices are separated into trend, seasonal, and irregular components to distinguish market-driven cycles from policy-driven signals. Functional principal component analysis (FPCA) is applied to extract shared and country-specific seasonal features, while multivariate functional response analysis (FRA) quantifies the time-varying influence of energy generation on price. The analysis covers six countries, Germany, the United Kingdom, the United States, France, China, and Brazil, which represent a spectrum of liberalized, semi-regulated, and regionally fragmented electricity markets. The results reveal that renewables in liberalized systems drive immediate price volatility, while policy coordination in semi-regulated systems leads to delayed and smoothed price adjustments. By linking institutional design with functional elasticity patterns, the study offers a unified approach to evaluating the interplay between price signals and energy generation in diverse regulatory contexts.

本研究开发了一个功能数据分析（FDA）框架，以调查电价动态如何响应不同市场结构国家的能源生产变化。采用黄土（STL）分解的季节趋势分解方法，将电价分为趋势、季节和不规则成分，以区分市场驱动周期和政策驱动信号。功能主成分分析（FPCA）用于提取共享和特定国家的季节性特征，而多元功能响应分析（FRA）量化能源发电对价格的时变影响。该分析涵盖了六个国家：德国、英国、美国、法国、中国和巴西，这些国家代表了一系列自由化、半监管和区域分散的电力市场。结果表明，自由化系统中的可再生能源驱动直接的价格波动，而半管制系统中的政策协调导致延迟和平滑的价格调整。通过将制度设计与功能弹性模式联系起来，该研究提供了一种统一的方法来评估不同监管背景下价格信号与能源生产之间的相互作用。

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引用次数: 0

Synergies between power-to-vehicle, smart grids and renewable energy communities: Dynamic and thermoeconomic analysis 电力到汽车、智能电网和可再生能源社区之间的协同效应：动态和热经济分析

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127359

Francesco Calise, Francesco L. Cappiello, Luca Cimmino, Massimo Dentice d’Accadia, Maria Vicidomini

This study investigates the synergies among renewable energy communities (RECs) powered by photovoltaic (PV) systems, electric storage units, and power-to-vehicle (PtV) strategies within a smart district located in Naples, Southern Italy. The district comprises 60 residential buildings, each equipped with a 36.4 kW rooftop PV system, and a diversified fleet of electric vehicles (EVs), including light EVs – such as e-scooters, e-bikes, electric motorcycles, and ultra-compact city cars – and conventional electric cars. The smart grid also integrates a 5 MWh lithium-ion battery system to enhance renewable electricity self-consumption. The methodology combines real traffic data from the Google Maps Distance Matrix API, representing the travel behavior of commuters, students, and local residents, with dynamic energy simulation. An in-house Python tool was developed to process these data and generate representative traffic curves for different vehicle types. The resulting profiles were incorporated into TRNSYS 18 for dynamic simulation of the entire district. All system components were modeled through detailed, time-dependent representations, while building performance was simulated based on geometric and thermo-physical characteristics of the envelopes and calibrated against measured data. The energy performance of the proposed smart district was compared with that of a conventional one powered by natural gas and grid electricity. Results indicate a 75% reduction in primary energy consumption for transportation when adopting the EV fleet, alongside a moderate increase in overall electricity demand mitigated by the use of light EVs. The economic analysis yields a Simple Payback period (SPB) of seven years for the entire system. Light EVs achieve SPB below three years – except for electric motorcycles, which reach approximately 8 years – while conventional electric cars remain less economically attractive due to higher capital costs. Overall, the findings demonstrate that integrating diverse EV fleets within RECs can significantly enhance energy independence, lower carbon emissions, and improve the economic feasibility of sustainable urban mobility.

本研究调查了位于意大利南部那不勒斯的一个智能区内，由光伏（PV）系统、电力存储单元和电力到汽车（PtV）策略供电的可再生能源社区（RECs）之间的协同效应。该地区包括60栋住宅楼，每栋都配备了36.4千瓦的屋顶光伏系统，以及多样化的电动汽车（ev）车队，包括轻型电动汽车（如电动滑板车、电动自行车、电动摩托车和超紧凑型城市汽车）和传统电动汽车。智能电网还集成了一个5兆瓦时的锂离子电池系统，以提高可再生电力的自我消耗。该方法结合了来自谷歌地图距离矩阵API的真实交通数据，代表了通勤者、学生和当地居民的出行行为，以及动态能源模拟。我们开发了一个内部Python工具来处理这些数据，并为不同类型的车辆生成具有代表性的交通曲线。所得的剖面图被纳入TRNSYS 18，用于整个地区的动态模拟。所有系统组件都通过详细的、与时间相关的表示进行建模，而建筑性能则基于围护结构的几何和热物理特性进行模拟，并根据测量数据进行校准。将拟建的智能小区的能源性能与天然气和电网供电的传统小区进行了比较。结果表明，当采用电动汽车车队时，交通运输的一次能源消耗减少了75%，同时轻型电动汽车的使用减轻了总体电力需求的适度增加。经济分析得出整个系统的简单回收期（SPB）为7年。轻型电动汽车实现SPB的时间不超过3年（电动摩托车除外，约为8年），而传统电动汽车由于资本成本较高，在经济上仍然不那么有吸引力。总体而言，研究结果表明，在区域经济区内整合不同类型的电动汽车车队可以显著提高能源独立性，降低碳排放，并提高可持续城市交通的经济可行性。

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引用次数: 0

Challenges and applications of hosting capacity analysis in DER-rich power systems 富der电力系统承载能力分析的挑战与应用

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2025.127323

Ashish Kumar Karmaker , Yang Du , Jiajia Yang , Mohan Jacob

Increasing trends of distributed energy resources (DERs) in power system networks have led to discussions on hosting capabilities. This paper presents a structured review of academic and industrial studies from the past decade, identifying key methodological challenges and applications of hosting capacity analysis covering both transmission and distribution networks. Unlike earlier reviews that focus mainly on distribution networks, this paper addresses hosting capacity analysis for both transmission- and distribution-levels, emphasizing forecasting, steady-state and transient constraints, and application-oriented perspectives for diverse DERs. This review identifies three principal research directions: (a) role of forecasting, scenario selection, performance factors, and control strategies in hosting capacity limits, (b) consideration of steady-state and transient constraints in violation checking, and (c) translation of hosting capacity insights into practical applications for grid planning, operational scheduling, resilience enhancement, operational coordination, and market participation. By synthesizing recent academic and industrial trends, this paper offers actionable insights and strategic recommendations to empower transmission and distribution system operators and drive future applications of hosting capacity analysis in DER-centric grid infrastructures.

电力系统网络中分布式能源（DERs）的增长趋势引发了对托管能力的讨论。本文对过去十年的学术和工业研究进行了结构化的回顾，确定了涵盖输电和配电网络的托管能力分析的关键方法挑战和应用。与之前主要关注配电网络的综述不同，本文讨论了输电和配电级别的托管容量分析，强调了预测、稳态和瞬态约束以及面向应用的视角。本文确定了三个主要研究方向：(a)预测、场景选择、性能因素和控制策略在托管容量限制中的作用；(b)在违规检查中考虑稳态和暂态约束；(c)将托管容量见解转化为电网规划、运行调度、弹性增强、运行协调和市场参与的实际应用。通过综合最近的学术和工业趋势，本文提供了可操作的见解和战略建议，以增强输配电系统运营商的能力，并推动以der为中心的电网基础设施中托管容量分析的未来应用。

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引用次数: 0

Day-ahead electricity price forecasting method integrating multi-scale hypergraph features and dual-layer transformer 结合多尺度超图特征和双层变压器的日前电价预测方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127396

Liuyu Yang, Yuan An, Gang Zhang, Tuo Xie, Mengxin Liu

Accurate forecasting of spot electricity prices is critical yet challenging due to the multi-scale temporal coupling, nonlinear volatility, and complex spatial dependencies influenced by supply-demand fluctuations, extreme weather, and transmission topology. This study proposes a novel day-ahead price forecasting model integrating multi-scale hypergraph features with a dual-layer Transformer. A hypergraph is constructed based on price trend similarity to capture spatial dependencies at local, global, and full-fusion levels. High-relevance exogenous variables are selected using the maximum information coefficient (MIC), and a two-tier Transformer separately models temporal and spatial dynamics. Spectral hypergraph convolution is introduced to generate dynamic spatial representations. The model is evaluated on real-world data from the Guangdong electricity market using both single-day and rolling forecast tasks. Compared with the second-best model, RMSE, MAE, and MAPE are reduced by 9.23%, 12.00%, and 21.74%, respectively, with R² improved by 2.25%. Additionally, SHAP analysis quantifies feature contributions, forming a closed-loop feature selection and validation process with MIC. The results demonstrate that incorporating multi-scale dynamic modeling and spatiotemporal feature fusion can significantly enhance forecasting accuracy.

由于受供需波动、极端天气和输电拓扑影响的多尺度时间耦合、非线性波动和复杂的空间依赖关系，准确预测现货电价至关重要，但也具有挑战性。本文提出了一种结合双层变压器的多尺度超图特征的日前电价预测模型。基于价格趋势相似度构建超图，以捕获局部、全局和全融合水平的空间依赖关系。使用最大信息系数（MIC）选择高相关性外生变量，并使用双层Transformer分别对时间和空间动态进行建模。引入光谱超图卷积生成动态空间表示。该模型在广东电力市场的实际数据上进行了评估，使用了单日和滚动预测任务。与次优模型相比，RMSE、MAE和MAPE分别降低了9.23%、12.00%和21.74%，R2提高了2.25%。此外，SHAP分析量化了特征贡献，与MIC形成了一个闭环特征选择和验证过程。结果表明，结合多尺度动态建模和时空特征融合可以显著提高预测精度。

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引用次数: 0

Low-carbon economic dispatch for microgrid-integrated charging stations: A cost-oriented bi-layer optimization framework 微网集成充电站低碳经济调度：以成本为导向的双层优化框架

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127358

Yihao Meng , Yuan Zou , Guodong Du , Xudong Zhang , Zhaolong Zhang

Driven by the low-carbon economy imperative, charging stations (CSs) integrated with renewable energy microgrids (MGs) have gained significant attention as critical infrastructure for advancing transportation electrification. However, the integration combines their inherent uncertainties, leading to suboptimal operational performance. To address this challenge, a cost-oriented bi-layer dispatch framework is developed by incorporating proximal policy optimization (PPO) into a model predictive control (MPC) foundation. This framework simultaneously optimizes the microgrid-integrated charging stations' (MGCSs) low-carbon economic operating costs and the charging fulfillment of electric vehicles (EVs). The proposed framework bypasses the explicit prediction of uncertainties inherent in the traditional “predict-then-optimize” framework and reduces MPC's reliance on precise parameter settings. Additionally, a power allocation strategy based on a cooperative game model (CGM) is established, which ensures fair charging among EVs through dynamic urgency indicators and enables a closed-loop optimization for maximizing charging fulfillment through the aggregated urgency feedback. Simulations using real-world EV data demonstrate the effectiveness of the proposed framework, outperforming various MPC-based benchmarks.

在低碳经济的推动下，与可再生能源微电网（mg）相结合的充电站（CSs）作为推进交通电气化的关键基础设施受到了广泛关注。然而，这种集成结合了它们固有的不确定性，导致了次优的操作性能。为了解决这一挑战，将近端策略优化（PPO）纳入模型预测控制（MPC）基础，开发了一个以成本为导向的双层调度框架。该框架同时优化了微电网集成充电站（MGCSs）的低碳经济运行成本和电动汽车的充电实现。提出的框架绕过了传统“预测-然后优化”框架中固有的不确定性的明确预测，并减少了MPC对精确参数设置的依赖。建立了基于合作博弈模型（CGM）的电力分配策略，通过动态紧急度指标确保电动汽车之间的公平充电，并通过聚合紧急度反馈进行闭环优化，实现充电实现最大化。使用真实世界EV数据的模拟证明了所提出框架的有效性，优于各种基于mpc的基准测试。

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引用次数: 0

Lightweight and efficient tubular SOFC design for UAV applications: multi-physics modeling and performance optimization 用于无人机应用的轻质高效管状SOFC设计：多物理场建模和性能优化

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-01-12 DOI: 10.1016/j.apenergy.2026.127353

Feifan Huang, Yeqing Ling, Long Wang, Peng Yuan, Li Sun, Tao Li

Solid oxide fuel cells (SOFCs) are promising power sources for unmanned aerial vehicles (UAVs), yet their widespread application is hindered by the inherent contradiction between the demand for rapid dynamic response and the constraints of heat and mass transfer. Traditional decoupled control strategies struggle to resolve the complex, coupled conflicts between heat and mass transfer under high dynamic loads. To address this challenge, this study utilizes a validated multi-physics model to develop and validate an advanced integrated synergistic control strategy. The investigation first reveals the highly asymmetric dynamic response of the single cell to flow velocity adjustments and innovatively proposes that this response signature can be used for online efficiency optimization. Furthermore, the study demonstrates that single feedforward strategies are inherently flawed: aggressive pre-heating induces power overshoot and fuel starvation, while a simple flow velocity increase prolongs thermal stabilization due to its convective cooling effect. To resolve this dilemma, an integrated synergistic control strategy that intelligently couples active pre-heating and dynamic flow velocity is proposed. Validation under a typical UAV mission profile shows that, compared to baseline control, the synergistic strategy shortens the stabilization time by 90 %, completely eliminates dynamic undershoot, delivers a steady-state power output up to 70 % higher during maneuvering, and reduces the peak thermal stress by over 35 %. Additionally, the superiority of a multi-channel anode design in mitigating coking risk is confirmed. Overall, the proposed synergistic strategy effectively resolves the conflict between rapid response and stability offering critical insights and a practical framework for managing transient thermos-electrochemical couplings which constitutes a necessary step toward realizing high performance SOFC propulsion in actual UAV flight missions.

固体氧化物燃料电池（sofc）是一种很有前景的无人机动力源，但其快速动态响应需求与传热传质约束之间的内在矛盾阻碍了其广泛应用。传统的解耦控制策略难以解决高动态载荷下复杂的传质传热耦合冲突。为了应对这一挑战，本研究利用一个经过验证的多物理场模型来开发和验证一种先进的集成协同控制策略。该研究首次揭示了单个电池对流速调节的高度不对称动态响应，并创新性地提出了该响应特征可用于在线效率优化。此外，研究表明，单一的前馈策略本身就存在缺陷：剧烈的预热会导致功率超调和燃料短缺，而简单的流速增加则会由于对流冷却效应而延长热稳定时间。为了解决这一难题，提出了一种智能耦合主动预热和动态流速的综合协同控制策略。在典型无人机任务轮廓下的验证表明，与基线控制相比，协同策略将稳定时间缩短了90%，完全消除了动态下冲，在机动过程中提供高达70%的稳态功率输出，并将峰值热应力降低了35%以上。此外，还证实了多通道阳极设计在降低焦化风险方面的优越性。总体而言，所提出的协同策略有效地解决了快速响应和稳定性之间的冲突，为管理瞬态热电化学耦合提供了关键见解和实用框架，这是在实际无人机飞行任务中实现高性能SOFC推进的必要步骤。

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