Applied Energy最新文献_第10页

Photovoltaic material selection and multi-objective building design optimization for enhancing energy performance of building integrated photovoltaics (BIPV) systems 提高建筑集成光伏系统能效的光伏材料选择和多目标建筑设计优化

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-26 DOI: 10.1016/j.apenergy.2025.127270

Yifan Wu , Haozhou Huang , Yan Cao , Bo Wang , Xin Li , Xiaozhen Song , Zhenqian Pang , Tengyao Jiang , Yanghua Lu , Gang Tan

Building-integrated photovoltaic (BIPV) systems hold significant potential for on-site renewable energy generation, reducing reliance on grid-supplied energy and lowering carbon emissions from building operations. Despite these advantages, BIPV systems have rarely been leveraged to inform architectural design or comprehensively evaluated when incorporating diverse solar cell technologies. This study systematically analyzes five photovoltaic materials for BIPV applications, including crystalline silicon (Si), cadmium telluride (CdTe), copper indium gallium selenide (CIGS), perovskite, and organic solar cells. A multi-objective optimization framework is employed to determine optimal building design parameters, including window-to-wall ratio (WWR), orientation, aspect ratio, and roof tilt angle. Results reveal that the most energy-efficient BIPV system combines Si solar cells for roofs, CdTe for walls, and perovskite for windows. The minimization of optimal WWR is preferred under the state-of-the-art PV technologies. Contrary to the conventional design of individual PV panels (south-facing with a tilt angle matching local latitude), the highest power generation for BIPV systems is achieved when the building's long axis and roof are oriented southwest, with the roof tilt angle adjusted within ±10° of local latitude. Optimization enhances the system's power generation by an average of 16.6 % and reduces net energy consumption by 17.4 %. If implemented in the design of newly constructed buildings across China, this strategy could reduce CO₂ emissions by over 805 million tons annually.

建筑一体化光伏（BIPV）系统在现场可再生能源发电方面具有巨大的潜力，减少了对电网供应能源的依赖，降低了建筑运营中的碳排放。尽管有这些优势，BIPV系统很少被用于建筑设计或综合评估各种太阳能电池技术。本研究系统分析了五种用于BIPV应用的光伏材料，包括晶体硅（Si）、碲化镉（CdTe）、硒化铜铟镓（CIGS）、钙钛矿和有机太阳能电池。采用多目标优化框架确定最优建筑设计参数，包括窗墙比、朝向、纵横比和屋顶倾斜角。结果表明，最节能的BIPV系统将硅太阳能电池用于屋顶，CdTe用于墙壁，钙钛矿用于窗户。在最先进的光伏技术下，最优水比的最小化是首选。与单个光伏板的传统设计（朝南，倾斜角度与当地纬度相匹配）相反，当建筑的长轴和屋顶朝向西南时，BIPV系统的最高发电量可以实现，屋顶倾斜角度在当地纬度的±10°范围内调整。优化后的系统发电量平均提高16.6%，净能耗降低17.4%。如果在中国新建建筑的设计中实施这一策略，每年可减少超过8.05亿吨的二氧化碳排放。

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引用次数: 0

Interpretable distributionally robust optimization for cyber-physical power systems under attack and renewable uncertainty 网络物理电力系统在攻击和可再生不确定性下的可解释分布鲁棒优化

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-25 DOI: 10.1016/j.apenergy.2025.127277

Gaosen Dong , Zhengfeng Ming , Jizhong Zhu

This paper develops a unified tri-level distributionally robust optimization (DRO) framework for resilient defense planning in cyber-physical power systems with adversarial attacks and renewable uncertainties. By integrating labeled discrete-event system (DES) modeling with convex DRO, the proposed method systematically captures symbolic state degradation and uncertainty propagation via a product automaton with reliability labels, supporting explainable resilience evaluation.

A KL-divergence-based ambiguity set is adopted to jointly model attack and renewable uncertainty, and the robust defense problem is solved via a stabilized dual reformulation and log-sum-exp optimization. Extensive simulations on representative scenarios demonstrate that the method significantly improves supply reliability, shortens recovery time, and reduces operational costs compared to deterministic and single-layer DRO baselines.

Overall, this work bridges symbolic modeling and convex risk-aware optimization, enabling practical, interpretable, and robust grid operation under multi-layer uncertainty.

针对具有对抗性攻击和可再生不确定性的网络物理电力系统的弹性防御规划问题，提出了统一的三层分布式鲁棒优化框架。该方法将标记离散事件系统（DES）建模与凸DRO相结合，通过带有可靠性标签的产品自动机系统地捕获符号状态退化和不确定性传播，支持可解释弹性评估。采用基于kl -发散度的模糊集对攻击和可再生不确定性进行联合建模，通过稳定对偶重构和对数和经验优化解决鲁棒防御问题。对代表性场景的大量模拟表明，与确定性和单层DRO基线相比，该方法显著提高了供电可靠性，缩短了恢复时间，并降低了运营成本。总的来说，这项工作将符号建模和凸风险感知优化连接起来，实现了多层不确定性下实用、可解释和稳健的网格运行。

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引用次数: 0

Short-term regional EV charging load forecasting based on GAT and GRU with trip distribution estimation 基于行程分布估计的GAT和GRU的电动汽车充电负荷短期区域预测

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-25 DOI: 10.1016/j.apenergy.2025.127262

Jia He , Qing-Yu Liu , Yan-Lei Hu , Wen-Long Shang , Haibo Chen , Washington Ochieng

Accurate forecasting of electric vehicle (EV) charging demand is crucial for developing coordinated charging strategies and reducing the negative impacts of large-scale, uncoordinated EV integration on power grid operations. Although previous studies have proposed predictive models using traffic simulations and machine learning with both dynamic and static features, most existing methods still fail to capture inter-nodal demand correlations, especially those involving long-distance nodes and heterogeneous data sources. To overcome these limitations, this study introduces STGR-Net, a novel short-term regional forecasting framework that integrates Graph Attention Networks (GAT), Gated Recurrent Units (GRU), and traffic distribution principles. The framework combines weekly aggregated charging data with fine-grained time series to extract spatio-temporal correlations, with particular focus on interactions among distant nodes. Based on these correlations, three matrices are constructed: (1) a dynamic gravity matrix from charging volume patterns, (2) a dynamic correlation matrix using inter-nodal Pearson coefficients, and (3) a static physical adjacency matrix. These components collectively form a novel triple-stream graph attention architecture. Temporal features are captured by parallel GRU encoders operating on three temporal sequences: recent, daily-periodic, and weekly-periodic. Each encoder is augmented with Reversible Instance Normalization (RevIN) to mitigate distributional shifts across different time periods. An adaptive gating mechanism further fuses temporal features with multi-dimensional spatial representations before prediction. Experiments on large-scale datasets from Beijing and Shenzhen show that STGR-Net achieves significant improvements in prediction accuracy over benchmark models. Ablation studies further confirm the contribution of the triple-stream graph architecture. The framework also shows strong practical utility for grid load management, charging service optimization, and infrastructure planning, supported by its efficient and accurate regional forecasting capability.

准确预测电动汽车充电需求对于制定协调充电策略，减少大规模、不协调的电动汽车并网对电网运行的负面影响至关重要。尽管之前的研究已经提出了基于流量模拟和机器学习的动态和静态特征的预测模型，但大多数现有方法仍然无法捕获节点间的需求相关性，特别是那些涉及远距离节点和异构数据源的需求相关性。为了克服这些限制，本研究引入了STGR-Net，这是一个新的短期区域预测框架，它集成了图注意网络（GAT）、门控循环单元（GRU）和流量分布原则。该框架将每周汇总的收费数据与细粒度时间序列相结合，以提取时空相关性，特别关注远距离节点之间的相互作用。基于这些相关性，构建了三个矩阵：(1)基于充电体积模式的动态重力矩阵，(2)基于节点间Pearson系数的动态关联矩阵，以及(3)静态物理邻接矩阵。这些组件共同形成了一种新颖的三流图注意力架构。时间特征由并行GRU编码器捕获，操作在三个时间序列上：最近的、每日周期的和每周周期的。每个编码器都增加了可逆实例规范化（RevIN），以减轻不同时间段的分布变化。自适应门控机制在预测前进一步将时间特征与多维空间表征融合。在北京和深圳的大规模数据集上进行的实验表明，与基准模型相比，STGR-Net的预测精度有了显著提高。消融研究进一步证实了三流图架构的贡献。该框架具有高效、准确的区域预测能力，在电网负荷管理、充电服务优化和基础设施规划等方面具有较强的实用价值。

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引用次数: 0

Power-to-hydrogen-ammonia coordination and planning for integrated energy systems with ammonia co-firing and waste heat recovery 氨共烧和余热回收综合能源系统的电力-氢-氨协调与规划

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127258

Jialong Zhou , Fulin Fan , Xuerong Ye , Zhengyao Wang , Jingkai Xu , Hang Zhang , Jinhai Jiang , Rui Xue , Chuanyu Sun , Kai Song , Siew Hwa Chan

With the continuously increasing penetration of renewable generation into integrated energy systems (IES), the limited capacity for renewable energy integration has emerged as a critical bottleneck, resulting in significant renewable curtailments and suboptimal operation. An alternative to putting renewable generation onto electrical grids is to produce ammonia from surplus renewables, which can be transported and co-fired with coal at thermal power plants, reducing carbon emissions and enhancing operational flexibility of IES. To promote the co-development of renewables and green ammonia within IES, the paper proposes a bi-level optimisation model for IES coordination and planning with power-to-hydrogen (P2H) and power-to-ammonia (P2A) systems. The lower-level model schedules daily multi-energy flows and their mutual conversion within an electricity-gas-heat IES to minimise overall operating costs on each typical day. Capital and operating expenses of P2H and P2A systems together with the carbon trading revenue and IES operating cost savings achieved by coal-ammonia co-firing and waste heat recovery are translated into a net present value, which is then maximised to suggest the best production and storage capacities of P2H and P2A components in the upper level. The proposed bi-level model is tested in the context of a modified IEEE 39-node electrical grid combined with 20-node gas and 6-node heat networks based on 2019, 2030 and 2050 techno-economic parameters, respectively. In addition to the power-to-hydrogen-ammonia technologies, the joint use of P2H and hydrogen electrification for spatio-temporal shift of renewables is evaluated in terms of their capacities and resulting economics as comparison. Furthermore, the sensitivity of P2H-P2A sizing results and system economics to coal unit costs, carbon trading prices and electrolyser unit costs is explored, respectively, offering insights into feasible pathways for deployment of P2H-P2A technologies alongside renewables in future IES.

随着可再生能源发电在综合能源系统（IES）中的渗透率不断提高，可再生能源集成容量有限已成为一个关键瓶颈，导致大量可再生能源弃电和次优运行。将可再生能源发电纳入电网的另一种选择是，从剩余的可再生能源中生产氨，这种氨可以在热电厂运输并与煤共烧，从而减少碳排放并提高IES的运营灵活性。为了促进可再生能源和绿色氨在IES内的共同发展，本文提出了一个双级优化模型，用于电力制氢（P2H）和电力制氨（P2A）系统的IES协调和规划。低级模型安排每日多能流及其在电-气-热IES内的相互转换，以最小化每个典型日的总体运行成本。P2H和P2A系统的资本和运营费用以及碳交易收入和煤氨共烧和余热回收所实现的IES运营成本节约转化为净现值，然后将其最大化，以表明上层P2H和P2A组件的最佳生产和储存能力。本文在基于2019年、2030年和2050年技术经济参数的改进的IEEE 39节点电网与20节点燃气网络和6节点热力网络相结合的背景下对所提出的双层次模型进行了测试。除了电力制氢氨技术外，还对P2H和氢电气化在可再生能源时空转移中的联合使用进行了评估，并对其容量和由此产生的经济效益进行了比较。此外，还分别探讨了P2H-P2A分级结果和系统经济对煤炭单位成本、碳交易价格和电解器单位成本的敏感性，为未来IES中部署P2H-P2A技术和可再生能源的可行途径提供了见解。

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引用次数: 0

Sky cooling-driven radiant-capacitive hydronic system for all-day building cooling 天冷驱动辐射-电容式水循环系统的全天候建筑冷却

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127260

Davide Forte , Eduardo González-Cruz , Lorenzo Pattelli , Claudio Belotti , Gloria Pérez , Pietro Asinari , Matteo Fasano

Daytime Radiative Cooling (DRC) surfaces enable heat rejection by emitting infrared radiation to the sky while reflecting solar radiation, allowing for sub-ambient cooling even under direct sunlight. This study develops and validates a transient numerical model of a DRC-based hydronic cooling system designed for building applications. The system integrates ceiling-mounted radiant capacitive modules (RCMs) with outdoor sky radiators (SRs) that dissipate indoor heat to outer space, cooling down a heat transfer fluid. The model is validated using experimental data from a full-scale demonstrator using a commercially available DRC emitter and is employed to assess system performance for a single-family building during a typical cooling season in the cities of Madrid and Rome. Compared to a system limited to nighttime radiative cooling, the DRC-enhanced setup delivers seasonal energy performance improvements of +6.2 % with a commercial DRC material and +10.3 % with an ideal broadband emitter. The study further investigates the effects of varying the surface area ratio between SRs and RCMs and alternative SR placements (rooftop vs. external surface). A fully passive building model with a DRC roof is also considered for comparison. Results show that the DRC-hydronic system can consistently maintain indoor thermal comfort throughout the cooling season, achieving seasonal energy efficiency ratios (SEER) up to 35 times higher than those of conventional air conditioning systems for the case studies analyzed, although the two systems differ in controllability and application scenarios. These findings highlight the strong potential of DRC-integrated hydronic cooling as a highly energy-efficient and sustainable alternative for the climate control of residential buildings.

白天辐射冷却（DRC）表面通过向天空发射红外辐射同时反射太阳辐射来散热，即使在阳光直射下也可以实现亚环境冷却。本研究开发并验证了用于建筑应用的基于drc的水力冷却系统的瞬态数值模型。该系统集成了天花板安装的辐射电容模块（rcm）和室外天空散热器（SRs），将室内热量散发到外太空，冷却传热流体。该模型使用商用DRC发射器的全尺寸演示器的实验数据进行验证，并用于评估马德里和罗马城市典型冷却季节的单户建筑的系统性能。与仅限于夜间辐射冷却的系统相比，采用商用DRC材料的DRC增强型装置可提供+ 6.2%的季节性能源性能改进，采用理想的宽带发射器可提供+ 10.3%的季节性能源性能改进。该研究进一步调查了不同的SR和rcm之间的表面积比以及不同的SR放置位置（屋顶与外表面）的影响。一个完全被动的建筑模型与DRC屋顶也被考虑进行比较。结果表明，尽管两种系统在可控性和应用场景上存在差异，但在整个制冷季节，DRC-hydronic系统都能持续保持室内热舒适，其季节性能效比（SEER）比传统空调系统高出35倍。这些发现突出了drc集成水循环冷却作为住宅建筑气候控制的高能效和可持续替代方案的巨大潜力。

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引用次数: 0

Two-layer coordinated operation of multi-energy system considering carbon-oriented collaborative pricing mechanism via two-stage stochastic programming approach 考虑碳导向协同定价机制的两阶段随机规划多能系统两层协同运行

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127298

Jie Chen , Panfeng Wu , Wu Chen , Josep M. Guerrero , Zhewen Niu , Zhengmao Li

To achieve low-carbon goals in a multi-energy system, coupled with an Electricity-Hydrogen-Transportation (E-H-T) system with a high penetration level of renewables, this paper proposes a dual-layer two-stage Stochastic Programming (SP) method based on the Carbon-Oriented Collaborative Pricing Mechanism (COCPM). Firstly, a dual-layer scheduling model is formulated, integrating dynamic conversions and interactive coupling of multi-energy flows. This model leverages a carbon flow tracking feedback mechanism to accurately characterize the dynamic carbon flow feedback within both layers. Using the pricing criteria derived from the dynamic carbon flow, the COCPM is employed to promote low-carbon energy consumption decisions, thereby achieving coordinated source-load interaction. Secondly, a two-stage SP approach, enhanced with a decision-making method based on confidence intervals, is employed to robustly handle uncertainties from renewable generation and demand fluctuations. Then, KKT optimality conditions, synergistically integrated with adaptive piecewise linearization techniques and a rigorous Big-M complementarity enforcement scheme, are applied to reformulate the original bi-level nonlinear optimization problem into an equivalent single-layer Mixed-Integer Linear Programming (MILP) model, ensuring computationally tractable and provably near-optimal solutions. Finally, a test system based on the E33-H6-T14 system is applied, and simulation results validate that our method can achieve low-carbon, economical, and robust operation under uncertainties.

为实现多能源系统的低碳目标，结合可再生能源渗透率高的电氢运输（E-H-T）系统，提出一种基于碳导向协同定价机制（COCPM）的双层两阶段随机规划（SP）方法。首先，建立了集成多能流动态转换和交互耦合的双层调度模型；该模型利用碳流跟踪反馈机制来准确表征两层内的动态碳流反馈。利用动态碳流的定价准则，利用COCPM促进低碳能源消费决策，从而实现源负荷协调交互。其次，采用基于置信区间的决策方法增强两阶段SP方法，鲁棒处理来自可再生能源发电和需求波动的不确定性。然后，将KKT最优性条件与自适应分段线性化技术和严格的大- m互补执行方案协同集成，将原始的双层非线性优化问题重新表述为等效的单层混合整数线性规划（MILP）模型，确保计算可处理且可证明的近最优解。最后，建立了基于E33-H6-T14系统的测试系统，仿真结果验证了该方法在不确定条件下能够实现低碳、经济、鲁棒运行。

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引用次数: 0

Hysteresis and integrity in multiphase hydrogen storage: a review of flow, rock, and monitoring challenges 多相储氢的迟滞和完整性：对流动、岩石和监测挑战的回顾

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127207

Dennis Sabato Chinamo , Xiao-Qiang Bian , Renshi Nie , Natacha Diane Ngasse Moudio , Daniel Chege Reuben

Underground hydrogen storage (UHS) is increasingly recognized as a cornerstone technology for large-scale energy transition, yet its implementation remains hindered by limited understanding of hysteresis and coupled multiphysics interactions that control storage efficiency and containment security. Experimental, modeling, and field evidence are synthesized to elucidate the mechanisms governing hydrogen (H₂) trapping, wettability alteration, chemo-biomechanical feedbacks, and integrity evolution across scales, integrated within a framework of three interconnected pillars: hysteresis, integrity, and monitoring. Key research gaps are identified, including the scarcity of H₂-specific experiments under reservoir-relevant conditions, limited understanding of upscaling and heterogeneity representation, inadequacy of current simulation tools for dynamic and coupled processes, and insufficient integration of monitoring technologies. Based on these findings, a structured research roadmap is proposed, encompassing multiscale hysteresis characterization, chemo-bio-mechanical coupling, machine-learning-enhanced, THMC modeling, and multi-sensor data fusion. This assessment provides a unified framework for bridging laboratory observations and field-scale uncertainties, thereby supporting the development of predictive, secure, and economically viable UHS systems.

地下储氢（UHS）越来越被认为是大规模能源转型的基石技术，但其实施仍然受到控制储氢效率和安全壳安全性的滞后和耦合多物理场相互作用的有限理解的阻碍。综合实验、建模和现场证据来阐明控制氢（H2）捕获、润湿性改变、化学-生物力学反馈和跨尺度完整性进化的机制，并将其整合在三个相互关联的支柱框架内：滞后、完整性和监测。关键的研究空白被确定，包括缺乏在水库相关条件下的h2特异性实验，对升级和异质性表征的理解有限，当前动态和耦合过程模拟工具的不足，以及监测技术的集成不足。基于这些发现，提出了一个结构化的研究路线图，包括多尺度滞后表征、化学-生物-机械耦合、机器学习增强、THMC建模和多传感器数据融合。该评估为连接实验室观测和现场规模的不确定性提供了一个统一的框架，从而支持开发预测性、安全性和经济上可行的UHS系统。

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引用次数: 0

Explainable machine learning unveils a critical trade-off in SOFCs: The role of cathode-to-anode reaction site ratio 可解释的机器学习揭示了sofc中一个关键的权衡：阴极-阳极反应位点比的作用

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127287

Li Duan, Yinghao Zhou, Zilin Yan, Zehua Pan, Zheng Zhong

Solid oxide fuel cell (SOFC) research recognizes the critical importance of electrode reaction sites for cell performance. This study, for the first time, systematically investigates the influence of the cathode to anode reaction site ratio, denoted as

λ

, on SOFC performance and reliability. The ratio

λ

is defined as the thickness scaled ratio of cathode double phase boundary (DPB) area to anode triple phase boundary (TPB) length. Using integrated experiments, multiphysical modeling, and explainable AI (XAI), we characterize the effects of

λ

on maximum power density and failure probability under various operating conditions. Results demonstrate that

λ

is the most significant factor affecting power density and substantially influences failure probability. A strong nonlinear relationship exists between

λ

and both metrics, with optimal

λ

shifting dynamically with temperature and gas flow. At higher temperatures (

T \geq 1023.15

K) and sufficient flow (

Q \geq 50

SCCM),

λ

of approximately 3000 nm enables high power density (

>

1.1 W/cm²) and low failure probability (

<

0.01). At

T = 973.15

K, maintaining

λ

near 1750 nm improves power output by over 15 %. This work reveals a fundamental performance reliability trade off governed by

λ

and identifies key microstructural parameters for both electrode optimization.

固体氧化物燃料电池（SOFC）的研究认识到电极反应位点对电池性能的重要性。本研究首次系统地研究了负极反应位比（λ）对SOFC性能和可靠性的影响。λ定义为阴极双相边界（DPB）面积与阳极三相边界（TPB）长度的厚度缩放比。利用综合实验、多物理模型和可解释人工智能（XAI），我们表征了λ对各种运行条件下最大功率密度和故障概率的影响。结果表明，λ是影响功率密度最显著的因素，对失效概率有很大影响。λ与两个指标之间存在强烈的非线性关系，且最优λ随温度和气流动态变化。在较高的温度（T≥1023.15 K）和足够的流量（Q≥50 SCCM）下，λ约为3000 nm可实现高功率密度（>1.1 W/cm2）和低故障概率（<0.01）。在T=973.15 K时，λ保持在1750 nm附近可以提高15%以上的功率输出。这项工作揭示了由λ控制的基本性能可靠性权衡，并确定了两个电极优化的关键微结构参数。

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引用次数: 0

A digital twin framework for intelligent electric vehicle charging optimization in smart manufacturing systems 智能制造系统中智能电动汽车充电优化的数字孪生框架

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127281

Chunting Liu , Ruyu Liu , Xiufeng Liu

The electrification of industrial vehicle fleets introduces complex coordination challenges in dynamic manufacturing environments, where vehicle availability directly influences operational continuity. This paper proposes a novel Digital Twin (DT) framework that integrates discrete-event simulation with a multi-objective optimization engine for intelligent electric vehicle (EV) charging. The system employs a hierarchical rolling-horizon strategy that accounts for battery states, production demands, and dynamic electricity pricing. Simulation studies across four representative manufacturing scenarios, evaluating five charging strategies including uncontrolled, first-come-first-served (FCFS), and our intelligent optimization, demonstrate the effectiveness of the proposed approach. Results reveal that the intelligent strategy delivers substantial energy cost reductions (up to 54.4 %), improved carbon efficiency, and increased infrastructure utilization. Compared to FCFS, which incurs 36.4–37.2 % higher energy and emission burdens, the intelligent framework consistently supports more sustainable and efficient charging. Scenario-specific variations in operational throughput offer opportunities for adaptive algorithmic refinement. These findings provide a scalable, modular, and data-driven solution for integrating EV charging infrastructure as a co-optimized component of smart manufacturing systems.

工业车队的电气化在动态制造环境中引入了复杂的协调挑战，其中车辆的可用性直接影响运营的连续性。针对智能电动汽车充电问题，提出了一种将离散事件仿真与多目标优化引擎相结合的数字孪生（DT）框架。该系统采用分层滚动地平线策略，考虑电池状态、生产需求和动态电价。四种典型制造场景的仿真研究，评估了五种充电策略，包括不受控制的，先到先得（FCFS），以及我们的智能优化，证明了所提出方法的有效性。结果表明，智能战略带来了大量的能源成本降低（高达54.4%），提高了碳效率，提高了基础设施利用率。与高36.4 - 37.2%的能源和排放负担的FCFS相比，智能框架始终支持更可持续和高效的充电。操作吞吐量中特定于场景的变化为自适应算法改进提供了机会。这些发现为将电动汽车充电基础设施集成为智能制造系统的协同优化组件提供了可扩展、模块化和数据驱动的解决方案。

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引用次数: 0

Profitability and scalability for waste-to-energy supply chains 废物转化能源供应链的盈利能力和可扩展性

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-12-24 DOI: 10.1016/j.apenergy.2025.127050

Craig Bakker , Timothy E. Seiple

Waste-to-Energy (WtE) conversion technologies have the potential to simultaneously reduce waste disposal costs, treatment-related emissions, and carbon intensity while producing valuable energy services. However, questions remain regarding the specific conditions under which emerging WtE technologies are feasible to deploy. In this paper, we develop an optimization-based siting method to assess cost-effective WtE processor locations, throughput scales, and profitability based on a geolocated waste inventory. Using Hydrothermal Liquefaction (HTL) as an example WtE technology, we develop calibrated scaled capital and operating expense cost curves based on literature data to study the techno-economic characteristics of WtE supply chains. In preparation for later solving the siting optimization, we use this paper to present and analyze the model that describes the behavior of the organic waste management system under four different HTL deployment configurations: Co-Processing, where HTL plants send biocrude to existing conventional refineries via assumed pipelines; Co-Location, where biorefineries must be integrated with HTL plants; and two alternative Standalone Biorefining cases, where distributed HTL plants transport biocrude intermediate via either assumed pipelines or trucking to centralized biorefineries. We also define several gate fee calculations representing different profit distribution options to investigate how bioproduct value could impact WtE supply chain economics in terms of waste producer and processor profit or cost reduction. The model and parameter analysis presented here serve as the basis for optimal WtE siting analyses to be presented in a subsequent paper.

废物发电技术有可能同时降低废物处置成本、与处理有关的排放和碳强度，同时提供有价值的能源服务。然而，关于新兴WtE技术在哪些具体条件下可行部署的问题仍然存在。在本文中，我们开发了一种基于优化的选址方法，以评估具有成本效益的WtE处理器位置，吞吐量规模和基于地理位置的废物清单的盈利能力。以水热液化（HTL）为例，在文献数据的基础上，建立了经校准的规模资本和运营费用成本曲线，研究了水热液化供应链的技术经济特征。为了为以后解决选址优化问题做准备，我们使用本文提出并分析了描述四种不同HTL部署配置下有机废物管理系统行为的模型：协同处理，HTL工厂通过假设的管道将生物原油输送到现有的传统炼油厂；协同选址，生物精炼厂必须与HTL工厂整合；以及两种可选的独立生物精炼案例，其中分布式html工厂通过假定的管道或卡车将生物原油中间体运输到集中的生物炼油厂。我们还定义了几种代表不同利润分配选项的门票费计算，以研究生物产品价值如何在废物生产者和处理者利润或成本降低方面影响垃圾处理供应链经济学。本文提出的模型和参数分析将作为后续文章中提出的最优WtE选址分析的基础。

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