Advances in Applied Energy最新文献_第3页

Flexibility potential of electric vehicle charging: A trip chain analysis under bi-criterion stochastic dynamic user equilibrium 电动汽车充电灵活性潜力：双准则随机动态用户均衡下的行程链分析

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-01 DOI: 10.1016/j.adapen.2025.100240

Shuyi Tang, Yunfei Mu, Hongjie Jia, Xiaolong Jin, Xiaodan Yu

The widespread adoption of electric vehicles (EVs) creates opportunities to use EV charging load as a flexible resource to improve grid operation. In urban areas, EV users typically follow trip chains in their daily travel, offering temporal and spatial flexibility in EV charging. Specifically, charging at slow-charging spots at trip destinations is temporally flexible when the parking duration exceeds the required charging time. In contrast, charging at fast charging stations (FCSs) during trips is spatially flexible, with route and FCS choice influenced by traffic congestion, FCS charging prices, and user perception. In this paper, we propose a bi-criterion stochastic dynamic user equilibrium (SDUE) model with trip chain demand, which captures route and FCS choice of EV users and derives fast and slow charging loads. The model accounts for user response to traffic congestion and FCS charging prices, along with the randomness in user perception of trip utility. A quantitative evaluation is also presented on the spatial flexibility of fast charging driven by price incentives, and the temporal flexibility of slow charging enabled by long parking durations. A case study in Sioux Falls is conducted to evaluate the flexibility potential of EV charging, revealing that reduced randomness in user perception enhances the spatial flexibility potential of fast charging. Additionally, the temporal flexibility potential of slow charging varies across location types, such as home, work, and other locations, depending on arrival times and parking durations. This research provides key insights for optimizing grid management and enhancing EV integration into power systems.

电动汽车的广泛采用为利用电动汽车充电负荷作为改善电网运行的灵活资源创造了机会。在城市地区，电动汽车用户在日常出行中通常遵循出行链，这为电动汽车充电提供了时间和空间上的灵活性。具体而言，当停车时间超过充电时间时，在旅行目的地的慢速充电点充电具有暂时的灵活性。在出行过程中，快速充电站的充电具有空间灵活性，其路径和充电站的选择受到交通拥堵、充电站充电价格和用户感知的影响。本文提出了考虑出行链需求的随机动态用户平衡（SDUE）双准则模型，该模型捕捉电动汽车用户的路径选择和FCS选择，并推导出快速和慢速充电负荷。该模型考虑了用户对交通拥堵和FCS收费价格的反应，以及用户对出行效用感知的随机性。定量评价了价格激励下的快速充电的空间灵活性和停车时间长的慢速充电的时间灵活性。以苏福尔斯市为例，评估了电动汽车充电的灵活性潜力，发现用户感知随机性的降低增强了快速充电的空间灵活性潜力。此外，慢速充电的时间灵活性潜力因地点类型而异，如家庭、工作和其他地点，这取决于到达时间和停车时间。该研究为优化电网管理和提高电动汽车与电力系统的整合提供了关键见解。

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引用次数: 0

Enhancing flexibility in wind-powered hydrogen production systems through coordinated electrolyzer operation 通过协调电解槽操作，提高风力制氢系统的灵活性

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-01 DOI: 10.1016/j.adapen.2025.100228

Zhang Bai , Wenjie Hao , Qi Li , Rujing Yan , Bin Ding , Weiming Shao , Long Gao , Tieliu Jiang , Yongsheng Wang , Caifeng Wen

Wind-powered water electrolysis for hydrogen production is a sustainable and environmentally friendly energy technology. However, the inherent intermittency and variability of wind power, significantly damage the stability and efficiency of the hydrogen production system. To enhance the operational flexibility and system efficiency, a novel wind-hydrogen production system is proposed, which integrates a new coordination of the conventional alkaline electrolyzers (AEL) and proton exchange membrane electrolyzers (PEMEL), for optimizing the dynamic operation of the system under fluctuating wind power. The developed approach employs variational mode decomposition to classify wind power fluctuations into different frequency components, which are then allocated to suitable type of electrolyzers. The configurations of the developed system are optimized using the non-dominated sorting genetic algorithm, and the operating scenarios are dynamically analyzed through clustering techniques. Compared to the AEL-only system, the proposed system demonstrates significant enhancements, with energy efficiency and internal rate of return increased by 5.78 % and 10.65 %, respectively. Meanwhile, the coordinated operation extends the continuous operating time of the AEL by 7.08 %. The proposed approach enhances the economic viability and operational stability of wind-powered hydrogen production, providing a valuable reference for industrial green hydrogen applications.

风力水电解制氢是一种可持续、环保的能源技术。然而，风力发电固有的间歇性和可变性，严重损害了制氢系统的稳定性和效率。为了提高系统运行的灵活性和效率，提出了一种新型的风力制氢系统，该系统将传统的碱性电解槽（AEL）和质子交换膜电解槽（PEMEL）集成在一起，以优化系统在波动风力下的动态运行。所开发的方法采用变分模态分解将风电波动划分为不同的频率分量，然后将其分配给合适类型的电解槽。采用非支配排序遗传算法对所开发的系统进行配置优化，并通过聚类技术对运行场景进行动态分析。与纯ael系统相比，该系统的能源效率和内部收益率分别提高了5.78%和10.65%。同时，协同运行使AEL的连续运行时间延长了7.08%。该方法提高了风力制氢的经济可行性和运行稳定性，为工业绿色氢应用提供了有价值的参考。

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引用次数: 0

Physics-informed modularized neural network for advanced building control by deep reinforcement learning 基于深度强化学习的先进建筑控制的物理信息模块化神经网络

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-08-11 DOI: 10.1016/j.adapen.2025.100237

Zixin Jiang, Xuezheng Wang, Bing Dong

Physics-informed machine learning (PIML) provides a promising solution for building energy modeling and can be used as a virtual environment to enable reinforcement learning (RL) agents to interact and learn. However, how to integrate physics priors efficiently, evaluate the effectiveness of physics constraints, balance model accuracy and physics consistency, and enable real-world implementation remain open challenges. To address these gaps, this study introduces a Physics-Informed Modularized Neural Network (PI-ModNN), which integrates physics priors through a physics-informed model structure, loss functions, and hard constraints. A new evaluation matrix called “temperature response violation” is developed to quantify the physical consistency of data-driven building dynamic models under varying control inputs and training data sizes. Additionally, a physics prior evaluation framework based on “rule importance” is proposed to quantify the contribution of each individual physical priors, offering guidance on selecting appropriate PIML techniques. The results indicate that incorporating physical priors does not always improve model performance; inappropriate physical priors could decrease model accuracy and consistency. However, hard constraints effectively enforce model consistency. Furthermore, we present a general workflow for developing control-oriented PIML models and integrating them with deep reinforcement learning (DRL). Following this framework, a case study of implementation DRL in an office space for three months demonstrates potential energy savings of 31.4%. Finally, we provide a general guideline for integrating data-driven models with advanced building control through a four-step evaluation framework, paving the way for reliable and scalable implementation of advanced building controls.

物理信息机器学习（PIML）为建筑能源建模提供了一个有前途的解决方案，可以用作虚拟环境，使强化学习（RL）代理能够交互和学习。然而，如何有效地整合物理先验，评估物理约束的有效性，平衡模型准确性和物理一致性，并使现实世界的实施仍然是一个开放的挑战。为了解决这些问题，本研究引入了一种物理信息模块化神经网络（PI-ModNN），该网络通过物理信息模型结构、损失函数和硬约束集成了物理先验。为了量化不同控制输入和训练数据大小下数据驱动的建筑动态模型的物理一致性，建立了一个新的评价矩阵“温度响应违逆”。此外，提出了一个基于“规则重要性”的物理先验评价框架，以量化每个物理先验的贡献，为选择合适的PIML技术提供指导。结果表明，加入物理先验并不一定能提高模型的性能；不适当的物理先验会降低模型的准确性和一致性。然而，硬约束有效地加强了模型的一致性。此外，我们提出了开发面向控制的PIML模型并将其与深度强化学习（DRL）集成的一般工作流程。在此框架下，一个在办公空间实施DRL三个月的案例研究表明，潜在的节能效果为31.4%。最后，我们通过四步评估框架提供了将数据驱动模型与先进建筑控制集成的一般指南，为可靠和可扩展的先进建筑控制实施铺平了道路。

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引用次数: 0

A critical review of use cases and insights from a large dataset of smart thermostats 对智能恒温器大型数据集的用例和见解进行了批判性回顾

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-08-08 DOI: 10.1016/j.adapen.2025.100236

Han Li , William O’Brien , Vivian Loftness , Erica Cochran Hameen , Tianzhen Hong

Residential buildings consume a significant portion (17 % in 2023) of the global primary energy. Smart thermostat has become a proven technology in the residential building sector that offers insights into energy efficiency, HVAC system operation, and indoor thermal comfort of occupants. Although there are an increasing number of studies using the available large scale smart thermostat dataset, there lacks a holistic review of the existing literature to understand what applications have been conducted and what outcomes have been offered. This paper reviews 57 articles published between January 2015 and March 2025 using the open access ecobee Donate Your Data (DYD) dataset, where >200,000 customers participated in the voluntary data donation program. Articles are analyzed by major application areas including occupant behavior and IEQ assessment, energy performance evaluation, HVAC operations and controls, and building thermal dynamics. Two major limitations of the DYD dataset are the lack of measured energy use of HVAC systems and the coarse city-level building location information and limits applications requiring energy use data and introduces errors in ignoring the urban microclimate effects influencing a home’s operation and performance. Gaps and challenges of using the ecobee thermostat dataset for research were analyzed. Future efforts should focus on improving data collection and fusing other datasets with the ecobee DYD dataset to unlock new applications and improve analytics accuracy. Furthermore, AI emerges as a powerful tool to help clean up, integrate, and analyze the thermostat dataset, create and calibrate energy models, as well as inferring residential building operation and performance at scale.

住宅建筑消耗了全球一次能源的很大一部分（2023年为17%）。智能恒温器已经成为住宅建筑领域的一项成熟技术，它提供了对能源效率、暖通空调系统运行和居住者室内热舒适的见解。尽管越来越多的研究使用了现有的大规模智能恒温器数据集，但缺乏对现有文献的全面审查，以了解已经进行了哪些应用以及已经提供了哪些结果。本文回顾了2015年1月至2025年3月期间发表的57篇文章，使用开放获取的ecobee捐赠数据（DYD）数据集，其中有20万客户参与了自愿数据捐赠计划。文章分析了主要应用领域，包括居住者行为和IEQ评价，能源性能评价，暖通空调运行和控制，以及建筑热动力学。DYD数据集的两个主要限制是缺乏HVAC系统的实测能源使用和粗略的城市级建筑位置信息，限制了需要能源使用数据的应用，并引入了忽略影响家庭运行和性能的城市微气候效应的错误。分析了使用ecobee恒温器数据集进行研究的差距和挑战。未来的工作应该集中在改进数据收集和融合其他数据集与ecobee DYD数据集，以解锁新的应用程序和提高分析的准确性。此外，人工智能成为一种强大的工具，可以帮助清理、整合和分析恒温器数据集，创建和校准能源模型，以及大规模推断住宅建筑的运营和性能。

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引用次数: 0

Climate-resilient energy systems planning via system-informed identification of stressful events 通过系统信息识别压力事件进行气候适应型能源系统规划

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-08-06 DOI: 10.1016/j.adapen.2025.100235

Francesco De Marco, Jacob Mannhardt, Alfredo Oneto, Giovanni Sansavini

As the energy mix increasingly relies on weather-dependent renewable sources, energy systems become more vulnerable to climate variability and extremes. However, current planning approaches struggle to incorporate climate uncertainty in the design phase while maintaining computational tractability. We address this challenge by developing a framework that combines system-informed scenario reduction and stochastic optimization to design climate-resilient energy systems. Our method reduces data complexity by identifying representative climate scenarios that capture stress events through system response. Remarkably, five distinct patterns of multi-day energy shortages emerge across Europe, each characterized by different combinations of renewable resource availability and demand profiles. Stochastic optimization then incorporates these representative climate scenarios with their associated probabilities to design energy systems that are resilient across the full spectrum of climate variability. Results show that climate-resilient designs consistently outperform conventional single-climate designs, achieving lower costs (on average 14.8 bn EUR) for equivalent resilience levels. We identify two trade-off regions with different marginal costs of resilience: a low-resilience and a high-resilience region where marginal costs increase fivefold. Despite higher costs, trade-offs between the cost of resilience investments against energy not supplied justify pursuing the high levels of resilience. Combinations of onshore wind and hydrogen storage emerge as effective mitigation against multi-day events of energy shortage. This framework provides energy planners and policymakers with quantifiable insights into resilience investment strategies and technology selection for future climate-aware energy planning.

由于能源结构越来越依赖于依赖天气的可再生能源，能源系统变得更容易受到气候变化和极端事件的影响。然而，目前的规划方法难以在保持计算可追溯性的同时，将气候不确定性纳入设计阶段。我们通过开发一个框架来解决这一挑战，该框架结合了系统知情情景减少和随机优化来设计气候适应性能源系统。我们的方法通过识别通过系统响应捕获压力事件的代表性气候情景来降低数据复杂性。值得注意的是，整个欧洲出现了五种不同的多日能源短缺模式，每种模式都以可再生资源的可用性和需求概况的不同组合为特征。然后，随机优化将这些有代表性的气候情景与其相关的概率结合起来，设计出在整个气候变率范围内具有弹性的能源系统。结果表明，气候弹性设计始终优于传统的单一气候设计，在同等弹性水平下实现更低的成本（平均148亿欧元）。我们确定了两个具有不同弹性边际成本的权衡区域：低弹性区域和边际成本增加五倍的高弹性区域。尽管成本较高，但在弹性投资成本与未供应能源之间进行权衡，证明了追求高水平弹性的合理性。陆上风能和氢储存的结合成为缓解多日能源短缺事件的有效手段。该框架为能源规划者和政策制定者提供了可量化的关于弹性投资战略和未来气候意识能源规划技术选择的见解。

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引用次数: 0

District heating network topology optimization and optimal co-planning using dynamic simulations 基于动态仿真的区域供热网络拓扑优化与最优协同规划

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-07-24 DOI: 10.1016/j.adapen.2025.100233

Jonathan Vieth, Jan Westphal, Arne Speerforck

District heating networks play a critical role in the transition of the heating supply of buildings to renewable sources. The transition from coal-fired or gas-fired generation units to heat pumps requires new planning methods for district heating networks, since the efficiency of a heat pump is affected strongly by the supply temperature of the district heating network. Therefore, a co-planning approach including the operation of the district heating network in the planning process is required. This paper presents a novel co-planning approach consisting of two steps. First, an optimal district heating network topology is generated from real geo-referenced data. To determine the optimal topology, a new algorithm designed specifically for district heating networks is presented. Next, a simulation model is automatically generated from the respective topology. An optimization is used for the co-planning approach to select an optimal generation unit, find the optimal supply temperature, and dimension the pipes of the district heating network. In contrast to conventional district heating network planning procedures, the optimization includes a full-year dynamic simulation of the district heating network. The result of the planning process is a full y parameterized district heating network with a matching supply temperature. Furthermore, the use of simulation models allows the results to be reused for sensitivity analyses. This is illustrated by examining the selection of generation units under different

{CO}_{2}

price scenarios.

区域供热网络在建筑向可再生能源供热的过渡中发挥着关键作用。从燃煤或燃气发电机组过渡到热泵需要新的区域供热网络规划方法，因为热泵的效率受到区域供热网络供应温度的强烈影响。因此，在规划过程中需要采用包括区域供热网络运行在内的共同规划方法。本文提出了一种由两个步骤组成的新型协同规划方法。首先，根据实际地理参考数据生成最优区域供热网络拓扑结构。为了确定最优拓扑，提出了一种专门针对区域供热网络的新算法。接下来，从各自的拓扑中自动生成仿真模型。采用优化的协同规划方法，选择最优发电机组，确定最优供热温度，确定区域供热管网的管道尺寸。与传统的区域供热网络规划程序相比，优化包括区域供热网络的全年动态模拟。规划过程的结果是一个具有匹配供应温度的全参数化区域供热网络。此外，模拟模型的使用允许结果被重新用于敏感性分析。这可以通过检查不同二氧化碳价格情景下发电机组的选择来说明。

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引用次数: 0

Providing load flexibility by reshaping power profiles of large language model workloads 通过重塑大型语言模型工作负载的功率配置文件来提供负载灵活性

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-07-15 DOI: 10.1016/j.adapen.2025.100232

Yi Wang, Qinglai Guo, Min Chen

The emergence of large language models (LLM) has driven a significant increase of AI workload in data center power demand. Renewable-powered solutions to decarbonizing LLM workload and reducing electricity costs are faced with the combined volatility of stochastic user requests and renewable energy. The key to removing the barriers in sustainable AI development lies in the adjustable capability of LLM power profiles. Therefore, this paper focuses on exploring the potential load flexibility of LLM workload and proposes a coordinated scheduling framework, notably, without computing performance degradation. Driven by the existence of the energy-optimal core frequency for graphics processing units (GPU), the energy-performance decoupling phenomenon is discovered and proved, where collaborative scaling in GPU quantity and frequency can change power but not computing performance. Motivated by this, the framework slows down the fine-tuning cluster and utilizes idle GPU resources from the inference cluster to maintain the computing performance of fine-tuning tasks. Consequently, the power consumption of the total cluster is reduced, which provides a fresh source of load flexibility. Furthermore, the framework employs dynamic frequency scaling to more flexibly modify the power profile of the expanded fine-tuning cluster. The computing performance is particularly guaranteed through temporal coupling constraints. In a simulated study supported by real-world data, the results prove a 6.8% power-saving ability and 11.3% cost-saving gains on average.

大型语言模型（LLM）的出现推动了数据中心电力需求中人工智能工作负载的显著增加。可再生能源解决方案既可以降低LLM工作量，又可以降低电力成本，同时还面临着随机用户需求和可再生能源的综合波动性。消除人工智能可持续发展障碍的关键在于LLM功率配置的可调能力。因此，本文重点探讨LLM工作负载的潜在负载灵活性，并提出一个协调的调度框架，特别是在不降低计算性能的情况下。在图形处理单元（GPU）能量最优核心频率存在的驱动下，发现并证明了能量性能解耦现象，即GPU数量和频率的协同缩放可以改变功耗，但不会改变计算性能。在此驱动下，框架降低了微调集群的速度，并利用推理集群的空闲GPU资源来维持微调任务的计算性能。因此，整个集群的功耗降低了，这为负载灵活性提供了新的来源。此外，该框架还采用了动态频率缩放，可以更灵活地修改扩展后的微调簇的功率分布。通过时间耦合约束特别保证了计算性能。在一个由真实数据支持的模拟研究中，结果证明了平均节省6.8%的功率和11.3%的成本收益。

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引用次数: 0

Transforming building retrofits: Linking energy, equity, and health insights from The World Avatar 改造建筑：从世界化身链接能源，公平和健康见解

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-07-11 DOI: 10.1016/j.adapen.2025.100230

Jiying Chen , Jiaru Bai , Jieyang Xu , Feroz Farazi , Sebastian Mosbach , Jethro Akroyd , Markus Kraft

The upgrading of energy-inefficient buildings is a critical part of the energy transition. Holistic analyses that foster informed and equitable policy interventions require interoperable data. We apply a principled approach that leverages The World Avatar to create a virtual knowledge graph underpinned by machine-understandable data representations. This approach provides a common terminology to integrate heterogeneous data sources to support multi-scale analysis of building energy retrofit options. We consider a case study in the UK based on the holistic analysis of household-level energy performance data, public health statistics and socio-economic metrics across geographic hierarchies. The analysis identifies regions with critical retrofit necessities, revealing disparities between these imperatives and extant policy levers. Granular retrofit targets are proposed to optimise resource allocation to the most vulnerable areas. Bespoke retrofit strategies are developed for 14.4 million households in the UK, providing actionable insights to support the targeted application of ‘fabric-first’ or ‘system-led’ retrofit pathways.

节能建筑的改造是能源转型的重要组成部分。促进知情和公平的政策干预的整体分析需要可互操作的数据。我们采用了一种原则性的方法，利用The World Avatar来创建一个虚拟的知识图谱，该图谱以机器可理解的数据表示为基础。这种方法提供了一个通用的术语来集成异构数据源，以支持建筑能源改造方案的多尺度分析。我们考虑了一个案例研究在英国基于家庭层面的能源绩效数据，公共卫生统计和跨地理层次的社会经济指标的整体分析。该分析确定了具有关键改造需求的地区，揭示了这些需求与现有政策杠杆之间的差异。提出了细化改造目标，以优化资源分配到最脆弱的地区。为英国1440万户家庭开发了定制改造策略，提供了可操作的见解，以支持“织物优先”或“系统主导”改造途径的目标应用。

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引用次数: 0

Net Zero without the gridlock through peer-to-peer coordinated flexibility 通过点对点协调灵活性实现零网络

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-29 DOI: 10.1016/j.adapen.2025.100231

Wei Gan , Yue Zhou , Jianzhong Wu , Philip C. Taylor

In the pursuit of Net Zero, the rapid adoption of electric vehicles, heat pumps, and distributed generation is placing unprecedented pressure on low-voltage electrical distribution networks. Can these networks adapt and evolve without facing gridlock? Our study proposes an innovative peer-to-peer coordinated flexibility strategy that has the potential to significantly transform the landscape. By aggregating individual flexibility through peer-to-peer coordination, this approach enhances local power balance, mitigates gridlock, and safeguards individual benefits. Through a novel large-scale network analysis method based on statistically similar networks, we have quantified the maximal potential of peer-to-peer coordinated flexibility in alleviating gridlock and deferring network expansion. Using real-world UK low-voltage electrical distribution network data and authoritative distributed energy resources roadmaps, our findings reveal that peer-to-peer coordinated flexibility can reduce peak power flows by up to 20 % and enable as much as 91 % of UK residential low-voltage electrical distribution networks to meet peak demand without gridlock by 2050, significantly reducing the need for network expansion. Furthermore, with the adoption of peer-to-peer coordinated flexibility, the network's peak is projected to occur between 2045–2050, postponing it by 8–10 years compared to scenarios without it. These results underscore the critical role of peer-to-peer coordinated flexibility and serve as a benchmark for the co-development of future grids and flexible resources when addressing associated implementation challenges such as technological infrastructure and consumer engagement.

在追求净零排放的过程中，电动汽车、热泵和分布式发电的迅速普及给低压配电网络带来了前所未有的压力。这些网络能够适应和发展而不面临僵局吗？我们的研究提出了一种创新的点对点协调灵活性策略，它有可能显著改变景观。这种方法通过点对点协调聚合个人灵活性，增强了地方权力平衡，缓解了僵局，保障了个人利益。通过一种基于统计相似网络的新型大规模网络分析方法，我们量化了对等协调灵活性在缓解交通拥堵和延缓网络扩张方面的最大潜力。使用真实的英国低压配电网络数据和权威的分布式能源路线图，我们的研究结果显示，点对点协调的灵活性可以减少峰值功率流高达20%，并使多达91%的英国住宅低压配电网络在2050年之前满足峰值需求而不会出现僵局，大大减少了对网络扩展的需求。此外，随着点对点协调灵活性的采用，预计网络峰值将在2045-2050年之间出现，与没有它的情况相比，它将推迟8-10年。这些结果强调了点对点协调灵活性的关键作用，并在解决技术基础设施和消费者参与等相关实施挑战时，作为未来电网和灵活资源共同开发的基准。

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引用次数: 0

Weather conditions severely impact optimal direct air capture siting 天气条件严重影响最佳的直接空气捕获选址

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-06-13 DOI: 10.1016/j.adapen.2025.100229

Henrik Wenzel , Freia Harzendorf , Kenneth Okosun , Thomas Schöb , Jann Michael Weinand , Detlef Stolten

Direct air capture (DAC) is rapidly gaining attention as a key technological approach to mitigating climate change. While techno-economic assessments increasingly incorporate DAC, they often overlook the influence of weather variability on both energy demand and plant productivity. In this study, we analyze how local weather patterns affect the two most promising DAC approaches: the solid sorbent and the liquid solvent processes. We reveal for a German case study, that the integration of DAC with renewable energy sources necessitates temporal and spatial considerations, as fluctuations in energy supply and demand can significantly impact operational feasibility. We demonstrate energy demand fluctuations of DAC exceeding 100 % over the course of a year and estimate future DAC costs in Germany in a range from 197 €/t_CO2 to 1035 €/t_CO2, depending on the region and technology. These results emphasize the need for detailed, site-specific assessments to ensure future cost-optimal DAC deployment.

直接空气捕获（DAC）作为缓解气候变化的关键技术途径正迅速受到关注。虽然技术经济评估越来越多地纳入DAC，但它们往往忽略了天气变化对能源需求和植物生产力的影响。在这项研究中，我们分析了当地天气模式如何影响两种最有前途的DAC方法：固体吸附剂和液体溶剂工艺。我们对德国的一个案例研究表明，DAC与可再生能源的整合需要考虑时间和空间因素，因为能源供需的波动会对运营可行性产生重大影响。我们证明了在一年的时间里，DAC的能源需求波动超过100%，并估计德国未来的DAC成本在197欧元/吨二氧化碳到1035欧元/吨二氧化碳之间，具体取决于地区和技术。这些结果强调需要进行详细的、具体地点的评估，以确保未来成本最优的DAC部署。

引用次数: 0