Advances in Applied Energy最新文献_第5页

Decarbonizing energy: Plastic waste trade for zero waste 2040 脱碳能源：塑料废物贸易实现零废物2040

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-01 DOI: 10.1016/j.adapen.2025.100216

Xiang Zhao , Fengqi You

Plastics are essential to human activities but also drive climate burdens and global pollution from energy-intensive material production and waste treatment. This study proposes and evaluates sustainable technology roadmaps under energy transitions to non-fossils to decarbonize the plastic life cycle and mitigate pollution across 202 global countries from 2024 to 2060. The results show that substituting plastic use, combined with advanced chemical recycling and carbon capture utilization powered by renewables, minimizes waste generation and pollution. In North America and Europe, replacing 56.7 % of plastics with glass, metal, and biodegradable alternatives coupled with chemical recycling can achieve zero annual waste by 2040 or 2035 with biomass-powered carbon capture and utilization. In African and Southeast Asian countries, this net-zero waste goal will be delayed to 2055 due to excessive plastic waste from global trade imports. Strategies including a 50 % cross-border tariff increment on plastic waste export and promoting alternative material use can reduce 87.45 % of global waste trade volume and surpluses to developed countries. This study advances the existing local and global plastic pollution mitigation strategies integrating energy decarbonization and transition to strengthen the United Nations Global Plastic Treaty towards minimum plastic pollution.

塑料对人类活动至关重要，但也造成了气候负担和能源密集型材料生产和废物处理造成的全球污染。本研究提出并评估了2024年至2060年全球202个国家向非化石能源转型的可持续技术路线图，以使塑料生命周期脱碳并减轻污染。结果表明，替代塑料的使用，结合可再生能源驱动的先进化学回收和碳捕集利用，可以最大限度地减少废物产生和污染。在北美和欧洲，用玻璃、金属和可生物降解的替代品取代56.7%的塑料，再加上化学回收，到2040年或2035年，通过生物质碳捕获和利用，可以实现零年浪费。在非洲和东南亚国家，由于全球贸易进口产生的塑料垃圾过多，这一净零废物目标将推迟到2055年。包括对塑料废物出口增加50%的跨境关税和促进替代材料使用在内的战略可以减少全球废物贸易量和对发达国家的顺差87.45%。本研究推进了现有的地方和全球塑料污染缓解战略，整合了能源脱碳和转型，以加强《联合国全球塑料条约》，实现塑料污染最小化。

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引用次数: 0

Impact of foresight horizons on energy system decarbonization pathways 展望视野对能源系统脱碳路径的影响

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-02-26 DOI: 10.1016/j.adapen.2025.100217

Rachel Maier , Johannes Behrens , Maximilian Hoffmann , Felix Kullmann , Jann M. Weinand , Detlef Stolten

Energy system optimization models often assume a perfect foresight of all defining future influences, assuming full knowledge of technological and socio-political developments over a long time horizon. In addition, many models impose annual emission reductions to prevent short-sighted decisions from deviating from these reduction paths. In this paper, we analyze different approaches to model foresight horizons and compare the influence of a perfect foresight approach with a rolling horizon and a purely myopic approach. For the first time, we explore the combination of incrementally increasing limited foresight horizons combined with a cumulative emission budget. Results from a case study of the German energy system indicate that the foresight horizon significantly affects the decarbonization trajectory. Short-sighted decisions that only consider the next five years even lead to infeasible pathways, failing the transition of the energy system by missing complying with the emission constraint. In contrast, long-sighted decision-making shows earlier investments in renewable energies and decarbonization, which can save billions of euros. Short-term cost-efficiency conflicts with long-term decarbonization goals, underscoring the importance of incorporating long-term perspectives into policy and decision-making, as well-defined decarbonization plans to achieve sustainable climate targets. This study underscores the importance of well-defined annual emissions targets and their achievement, highlighting the potential consequences of missing emissions targets.

能源系统优化模型通常假设对所有决定性的未来影响都有完美的预见，假设对长期的技术和社会政治发展有充分的了解。此外，许多模型强制规定年度减排，以防止短视的决策偏离这些减排路径。本文分析了不同的预测视界模型方法，比较了具有滚动视界的完全预测方法和纯短视预测方法的影响。我们首次探讨了增量增长的有限远见视野与累积排放预算的结合。以德国能源系统为例进行的研究结果表明，前瞻性水平对脱碳轨迹有显著影响。只考虑未来五年的短视决策甚至会导致不可行的路径，因为没有遵守排放约束而导致能源系统转型失败。相比之下，有远见的决策表明，在可再生能源和脱碳方面的早期投资可以节省数十亿欧元。短期成本效益与长期脱碳目标相冲突，强调了将长期观点纳入政策和决策的重要性，因为明确的脱碳计划可以实现可持续的气候目标。本研究强调了明确的年度排放目标及其实现的重要性，强调了未达到排放目标的潜在后果。

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引用次数: 0

Reinforcement learning for vehicle-to-grid: A review 车辆到网格的强化学习：综述

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-02-08 DOI: 10.1016/j.adapen.2025.100214

Hongbin Xie , Ge Song , Zhuoran Shi , Jingyuan Zhang , Zhenjia Lin , Qing Yu , Hongdi Fu , Xuan Song , Haoran Zhang

The rapid development of Vehicle-to-Grid technology has played a crucial role in peak shaving and power scheduling within the power grid. However, with the random integration of a large number of electric vehicles into the grid, the uncertainty and complexity of the system have significantly increased, posing substantial challenges to traditional algorithms. Reinforcement learning has shown great potential in addressing these high-dimensional dynamic scheduling optimization problems. However, there is currently a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in Vehicle-to-Grid, which limits the further development of this technology in the Vehicle-to-Grid domain. To this end, this review systematically analyzes the application of reinforcement learning in Vehicle-to-Grid from the perspective of different stakeholders, including the power grid, aggregators, and electric vehicle users, and clarifies the effectiveness and mechanisms of reinforcement learning in addressing the uncertainty in power scheduling. Based on a comprehensive review of the development trajectory of reinforcement learning in Vehicle-to-Grid applications, this paper proposes a structured framework for method classification and application analysis. It also highlights the major challenges currently faced by reinforcement learning in the Vehicle-to-Grid domain and provides targeted directions for future research. Through this systematic review of reinforcement learning applications in Vehicle-to-Grid, the paper aims to provide relevant references for subsequent studies.

车联网技术的迅速发展，对电网内的调峰和电力调度起到了至关重要的作用。然而，随着大量电动汽车随机入网，系统的不确定性和复杂性显著增加，对传统算法提出了重大挑战。强化学习在解决这些高维动态调度优化问题方面显示出巨大的潜力。然而，目前对强化学习在车到网格中的应用缺乏全面的分析和系统的认识，这限制了该技术在车到网格领域的进一步发展。为此，本文从电网、聚合器和电动汽车用户等不同利益相关者的角度系统分析了强化学习在车辆到电网中的应用，阐明了强化学习在解决电力调度不确定性方面的有效性和机制。在全面回顾车辆到网格应用中强化学习发展轨迹的基础上，提出了一种用于方法分类和应用分析的结构化框架。它还强调了目前强化学习在车辆到网格领域面临的主要挑战，并为未来的研究提供了有针对性的方向。本文通过对强化学习在Vehicle-to-Grid中的应用进行系统综述，旨在为后续研究提供相关参考。

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引用次数: 0

Advancements and future outlook of Artificial Intelligence in energy and climate change modeling 人工智能在能源和气候变化建模中的进展与展望

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-28 DOI: 10.1016/j.adapen.2025.100211

Mobolaji Shobanke, Mehul Bhatt, Ekundayo Shittu

This paper explores the employment of artificial intelligence and machine learning to decipher strategic responses to incidences of climate change and to inform the management of energy systems. Given the increasing global dependence on sustainable and efficient energy solutions and the rise of artificial intelligence and machine learning, it has become imperative to evaluate existing routines in energy and climate change modeling to identify areas for further application. The process of conducting a systematic review of the contemporary literature highlights significant advances in optimization and predictive analytics within energy and climate change modeling systems driven by artificial intelligence and machine learning. This paper contributes to cutting-edge research on energy innovation, i.e., through the examination of the applications of artificial intelligence and machine learning in energy modeling and climate change assessments. The article bridges the gaps between research, development, and implementation with significant insights into the broader applications of artificial intelligence and machine learning in the analysis of future energy transitions and climate change mitigation and adaptation.

本文探讨了人工智能和机器学习的应用，以破译气候变化事件的战略反应，并为能源系统的管理提供信息。鉴于全球对可持续和高效能源解决方案的日益依赖以及人工智能和机器学习的兴起，评估能源和气候变化建模的现有常规以确定进一步应用的领域已成为当务之急。对当代文献进行系统回顾的过程强调了人工智能和机器学习驱动的能源和气候变化建模系统中优化和预测分析的重大进展。本文通过研究人工智能和机器学习在能源建模和气候变化评估中的应用，为能源创新的前沿研究做出了贡献。本文弥合了研究、开发和实施之间的差距，对人工智能和机器学习在分析未来能源转型和减缓和适应气候变化方面的更广泛应用提供了重要见解。

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引用次数: 0

Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy 通过AIoT、GIS和气象学协同推进建筑立面太阳能潜力评估

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-27 DOI: 10.1016/j.adapen.2025.100212

Kechuan Dong , Qing Yu , Zhiling Guo , Jian Xu , Hongjun Tan , Haoran Zhang , Jinyue Yan

The assessment of building solar potential plays a pivotal role in Building Integrated Photovoltaics (BIPV) and urban energy systems. While current evaluations predominantly focus on rooftop solar resources, a comprehensive analysis of building facade BIPV potential is often lacking. This study presents an innovative methodology that harnesses state-of-the-art Artificial Intelligence of Things (AIoT) techniques, Geographic Information Systems (GIS), and Meteorology to develop a model for accurately estimating spatial–temporal building facade BIPV potential considering 3 Dimension (3D) shading effect. Here, we introduce a zero-shot Deep Learning framework for detailed parsing of facade elements, utilizing cutting-edge techniques in Large-scale Segment Anything Model (SAM), Grounding DINO (Detection Transformer with improved denoising anchor boxes), and Stable Diffusion. Considering urban morphology, 3D shading impacts, and multi-source weather data enables a meticulous estimation of solar potential for each facade element. The experimental findings, gathered from a range of buildings across four countries and an entire street in Japan, highlight the effectiveness and applicability of our approach in conducting comprehensive analyses of facade solar potential. These results underscore the critical importance of integrating shadow effects and detailed facade elements to ensure accurate estimations of PV potential.

建筑太阳能潜力评估在建筑综合光伏系统和城市能源系统中起着至关重要的作用。虽然目前的评估主要集中在屋顶太阳能资源，但对建筑立面BIPV潜力的综合分析往往缺乏。本研究提出了一种创新的方法，利用最先进的物联网人工智能（AIoT）技术、地理信息系统（GIS）和气象学来开发一个模型，用于准确估计考虑三维（3D）阴影效应的建筑立面BIPV时空潜力。在这里，我们介绍了一个零采样深度学习框架，用于详细解析立面元素，利用大规模分段任意模型（SAM）、接地DINO（改进去噪锚盒检测变压器）和稳定扩散中的尖端技术。考虑到城市形态、3D阴影影响和多源天气数据，可以对每个立面元素的太阳能潜力进行细致的估计。实验结果来自四个国家的一系列建筑和日本的一整条街道，强调了我们在立面太阳能潜力进行综合分析时的有效性和适用性。这些结果强调了整合阴影效果和详细立面元素的重要性，以确保准确估计PV潜力。

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引用次数: 0

Integrating water availability for electrolysis into energy system modeling 将电解用水纳入能源系统建模

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-27 DOI: 10.1016/j.adapen.2025.100208

Julian Walter , Lina Fischer , Sandra Venghaus , Albert Moser

In recent years, temperature records have been broken all over the world and the global temperature keeps rising. As a result, fresh water availability will diminish ever more and more due to droughts and extreme weather events. Water is a key part of many central aspects of life but will also become important in the future for electrolysis to synthesize hydrogen, a promising energy carrier in energy systems for the transition from fossil to renewable energy. Current energy system optimization models neglect water as an input for electrolysis when focusing on electricity. In this study, we present a method for implementing water as an input in energy system optimization models, with constraints for freshwater availability and seawater processing. We apply our method to one scenario and investigate the impact on the European energy system with highly-detailed spatial and temporal resolutions. The results indicate a relocation of electrolysis capacities of 10% and an increase of methane imports and methanation capacities. The effects suggest that water should be considered in energy system optimization in the future.

近年来，世界各地的气温记录不断被打破，全球气温持续上升。因此，由于干旱和极端天气事件，淡水供应将越来越少。水是生命许多核心方面的关键部分，但在未来也将成为电解合成氢的重要组成部分，氢是能源系统中从化石能源向可再生能源过渡的一种有前途的能源载体。当前的能源系统优化模型在关注电力时忽略了水作为电解的输入。在这项研究中，我们提出了一种将水作为能源系统优化模型的输入的方法，并对淡水可用性和海水处理进行了约束。我们将我们的方法应用于一个场景，并以非常详细的空间和时间分辨率调查对欧洲能源系统的影响。结果表明，电解能力将迁移10%，甲烷进口量和甲烷化能力将增加。这些结果表明，在未来的能源系统优化中应考虑水。

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引用次数: 0

A review of participatory modelling techniques for energy transition scenarios 能源转换情景参与式建模技术综述

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-26 DOI: 10.1016/j.adapen.2025.100215

Jair K.E.K. Campfens , Mert Duygan , Claudia R. Binder

Energy transitions are pivotal for sustainability, yet their complexity and uncertainty pose significant challenges for effective planning and implementation. Participatory modelling has emerged as a promising approach to support these transitions, as it involves incorporating stakeholders' perspectives into models and policy designs, which helps integrate their mental models and preferences into simulations. This paper reviews the current state of participatory modelling in transition research for energy scenarios. Drawing on a comprehensive literature review and semi-structured interviews, we extract findings by evaluating participatory modelling techniques against criteria such as normative dimensions, non-linearity, actors and agency, uncertainty and emergence. Findings reveal that techniques like Cross-Impact Balance analysis and Fuzzy Cognitive Mapping excel in incorporating normative aspects and capturing diverse actor perspectives, yet they face challenges in addressing non-linearity and uncertainty. Bayesian Networks and Agent-Based Models are strong in managing uncertainty and modelling emergent behaviours but show limitations in normative aspects. Our findings provide a foundation for scholars and practitioners in the field of socio-technical energy transitions to select participatory modelling techniques best suited to their specific research contexts. This review also highlights gaps between theoretical potential and practical application of participatory modelling techniques. Bridging these gaps requires methodological advancement and a more rigorous application in empirical studies. To this end, future directions for blending techniques are discussed to better address the complexities of energy transitions.

能源转型对可持续发展至关重要，但其复杂性和不确定性对有效规划和实施构成了重大挑战。参与式建模已成为支持这些转变的一种有希望的方法，因为它涉及将利益相关者的观点纳入模型和政策设计，这有助于将他们的心理模型和偏好整合到模拟中。本文综述了参与式模型在能源情景转换研究中的现状。通过全面的文献综述和半结构化访谈，我们通过评估参与式建模技术对标准（如规范维度，非线性，行动者和代理，不确定性和出现）的发现。研究结果表明，交叉影响平衡分析和模糊认知映射等技术在整合规范方面和捕捉不同参与者视角方面表现出色，但它们在处理非线性和不确定性方面面临挑战。贝叶斯网络和基于代理的模型在管理不确定性和模拟紧急行为方面很强大，但在规范方面表现出局限性。我们的研究结果为社会技术能源转型领域的学者和从业者选择最适合其特定研究背景的参与式建模技术提供了基础。本综述还强调了参与式建模技术的理论潜力和实际应用之间的差距。弥合这些差距需要方法上的进步和在实证研究中更严格的应用。为此，讨论了混合技术的未来发展方向，以更好地解决能源转换的复杂性。

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引用次数: 0

Adaptive reinforcement learning for energy management – A progressive approach to boost climate resilience and energy flexibility 用于能源管理的自适应强化学习——一种提高气候适应能力和能源灵活性的渐进方法

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-22 DOI: 10.1016/j.adapen.2025.100213

Vahid M. Nik , Kavan Javanroodi

Energy management in urban areas is challenging due to diverse energy users, dynamics environmental conditions, and the added complexity and instability of extreme weather events. We incorporate adaptive reinforcement learning (ARL) into energy management (EM) and introduce a novel approach, called ARLEM. An online, value-based, model-free ARL engine is designed that updates its policy periodically and partially by replacing less favorable actions with those better adapted to evolving environmental conditions. Multiple policy update mechanisms are assessed, varying based on the frequency and length of updates and the action selection criteria. ARLEM is tested to control the energy performance of typical urban blocks in Madrid and Stockholm considering 17 future climate scenarios for 2040–2069. Each block contains 24 buildings of different types and ages. In Madrid, ARLEM is tested for a summer with two heatwaves and in Stockholm for a winter with two cold waves. Three performance indicators are defined to evaluate the effectiveness and resilience of different control approaches during extreme weather events. ARLEM demonstrates an ability to increase climate resilience in the studied blocks by increasing energy flexibility in the network and reducing both average and peak energy demands while affecting indoor thermal comfort marginally. Since the approach does not require any information about the system dynamics, it is easy to cope with the complexities of building systems and technologies, making it an affordable technology to control large urban areas with diverse types of buildings.

由于能源用户的多样性、动态环境条件以及极端天气事件的复杂性和不稳定性，城市地区的能源管理具有挑战性。我们将自适应强化学习（ARL）整合到能量管理（EM）中，并引入了一种称为ARLEM的新方法。设计了一个在线的、基于价值的、无模型的ARL引擎，该引擎定期更新其策略，部分地通过将不太有利的操作替换为更适合不断变化的环境条件的操作。评估多个策略更新机制，根据更新的频率和长度以及操作选择标准而变化。ARLEM经过测试，以控制马德里和斯德哥尔摩典型城市街区的能源性能，考虑到2040-2069年的17种未来气候情景。每个街区包含24座不同类型和年龄的建筑。在马德里，ARLEM测试了一个有两次热浪的夏天，在斯德哥尔摩测试了一个有两次寒潮的冬天。定义了三个性能指标来评估极端天气事件中不同控制方法的有效性和弹性。ARLEM通过增加网络中的能源灵活性，降低平均和峰值能源需求，同时略微影响室内热舒适，证明了在所研究的街区中增加气候适应能力的能力。由于该方法不需要任何关于系统动力学的信息，因此很容易处理建筑系统和技术的复杂性，使其成为一种经济实惠的技术，可以控制具有不同类型建筑物的大型城市地区。

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引用次数: 0

Understanding bidding strategies of intermittent renewables in negative price environments: A theoretical and empirical analysis 负电价环境下间歇性可再生能源竞价策略的理论与实证分析

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-15 DOI: 10.1016/j.adapen.2025.100209

Qinghu Tang , Hongye Guo , Daniel S. Kirschen , Chongqing Kang

Negative electricity prices have become increasingly prevalent with the growing penetration of intermittent renewable energy sources worldwide. Although it is widely thought that the negative prices are primarily driven by intermittent renewable energies, the bidding decision theory behind this phenomenon remains underexplored. This paper seeks to illuminate the bidding theory of intermittent renewables under negative electricity prices through not only a theoretical model but also an empirical analysis of its real-world counterpart. First, we propose a comprehensive intermittent renewable bidding decision model considering both forward contract and spot market, as well as income from both the energy market and green energy incentive, which significantly influence bidding behavior under negative price conditions. Next, we develop a data-driven approach to estimate the model’s embedded parameters using publicly available market data, enabling direct comparison with real-world counterparts. Finally, on the basis of the proposed model, we analyze the actual bid records in comparison to the optimal bidding decisions from three perspectives: strategy, behavior, and profit. Empirical results show that the proposed model can explain 80% of the bidding strategies employed by intermittent renewable power plants in a real-world market, including suboptimal strategies. Furthermore, some empirical evidence can help understand the intrinsic relationship between bidding rationality and negative price severity.

随着间歇性可再生能源在世界范围内的日益普及，负电价变得越来越普遍。尽管人们普遍认为负电价主要是由间歇性可再生能源驱动的，但这一现象背后的投标决策理论仍未得到充分探讨。本文试图通过理论模型和实证分析来阐明负电价条件下间歇性可再生能源的竞价理论。首先，考虑远期合约和现货市场，以及能源市场收益和绿色能源激励对负价格条件下竞价行为的影响，提出了一个综合的间歇性可再生能源竞价决策模型。接下来，我们开发了一种数据驱动的方法，使用公开可用的市场数据来估计模型的嵌入参数，从而能够与现实世界的对应对象进行直接比较。最后，在此模型的基础上，我们从策略、行为和利润三个角度分析了实际投标记录与最优投标决策的比较。实证结果表明，该模型可以解释现实市场中间歇性可再生能源电厂80%的竞价策略，包括次优策略。此外，一些经验证据有助于理解投标理性与负价格严重程度之间的内在关系。

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引用次数: 0

A newly developed spatially resolved modelling framework for hydrogen valleys: Methodology and functionality 新开发的氢谷空间解析建模框架：方法和功能

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-01-05 DOI: 10.1016/j.adapen.2025.100207

Friedrich Mendler , Christopher Voglstätter , Nikolas Müller , Tom Smolinka , Marius Holst , Christopher Hebling , Barbara Koch

Regional initiatives, like the European hydrogen valleys, aim to solve the simultaneous absence of green hydrogen production, infrastructure, and application with coordinated development of the whole supply chain. A new model framework was developed to bridge the gap between linearised energy system models and detailed plant simulations that allows for dynamic, nonlinear simulation and optimisation of regional hydrogen systems from electricity generation to hydrogen application. The model incorporates different supply algorithms for electricity and hydrogen, representing both bilateral contracts and flexible markets. A case study demonstrates the application of the framework within a representative hydrogen valley in Germany, showing how the model can identify optimal configurations of hydrogen production, storage, and distribution infrastructure to minimise the levelized cost of hydrogen. The influence of different spatial resolutions, exchange control algorithms, and boundary conditions chain are evaluated. A too coarse spatial resolution can underestimate system cost by up to 10 % while the allowance of both bilateral hydrogen contracts and a flexible market algorithm can increase hydrogen utilisation and reduce cost by up to 15 %. An autarkic supply of hydrogen demands was possible for 7.60 €/kg, while the option to use grid electricity reduces costs to 6.37 €/kg and the option to import hydrogen to 6.60 €/kg, based on the assumptions for electricity and hydrogen prices. This work contributes to the evolving field of hydrogen economy by providing a sophisticated tool for policymakers and industry stakeholders worldwide to plan and optimise regional hydrogen valleys effectively.

区域倡议，如欧洲氢谷，旨在通过整个供应链的协调发展，解决绿色氢生产、基础设施和应用同时缺乏的问题。开发了一个新的模型框架，以弥合线性能源系统模型和详细的工厂模拟之间的差距，允许从发电到氢应用的区域氢系统的动态、非线性模拟和优化。该模型结合了电力和氢气的不同供应算法，代表了双边合同和灵活的市场。一个案例研究展示了该框架在德国一个具有代表性的氢谷中的应用，展示了该模型如何确定氢生产、储存和分配基础设施的最佳配置，以最大限度地降低氢的平均成本。评估了不同空间分辨率、交换控制算法和边界条件链的影响。过于粗糙的空间分辨率可能会低估高达10%的系统成本，而双边氢合同和灵活的市场算法可以提高氢的利用率并降低高达15%的成本。根据电力和氢气价格的假设，自给自足的氢气需求供应可能为7.60欧元/公斤，而使用电网电力的选择将成本降低至6.37欧元/公斤，进口氢气的选择将成本降低至6.60欧元/公斤。这项工作通过为全球政策制定者和行业利益相关者提供有效规划和优化区域氢谷的复杂工具，为不断发展的氢经济领域做出了贡献。

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引用次数: 0