Advances in Applied Energy最新文献_第7页

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 : 2025-03-01 Epub 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

Boosting direct-ethane solid oxide fuel cell efficiency with anchored palladium nanoparticles on perovskite-based anode 在钙钛矿基阳极上锚定钯纳米颗粒提高直接乙烷固体氧化物燃料电池效率

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-01 Epub Date: 2025-01-05 DOI: 10.1016/j.adapen.2025.100206

Shuo Zhai , Junyu Cai , Idris Temitope Bello , Xi Chen , Na Yu , Rubao Zhao , Xingke Cai , Yunhong Jiang , Meng Ni , Heping Xie

An efficient anode catalyst for hydrocarbon fuel in Solid Oxide Fuel Cells (SOFC) should possess a stable phase structure, high catalytic efficiency, and excellent coke resistance. However, traditional nickel-based anodes necessitate high steam-to-carbon ratios to prevent coking, complicating system design and reducing the overall performance. In this work, we report a nickel-free PrBaFe_1.9Pd_0.1O_5+δ perovskite as anode material for direct ethane SOFC, which demonstrates superior electroactivity and chemical stability. Under a reducing atmosphere, Pd nano-catalysts exsolved in-situ are uniformly anchored to the perovskite surface. Density functional theory analyses reveal that the Pd exsolution significantly improve ethane adsorption capacity, thereby reducing activation resistance and boosting catalytic performance. When used as an anode for an SDC electrolyte-supported SOFC, superior performance is achieved with the peak power densities (PPDs) of 702 and 377 mW cm^-2 at 800 °C when using hydrogen and almost dry ethane (3% H₂O) as fuel, respectively. Moreover, the cell exhibits a stable continuous operation over 90 h under almost dry ethane atmosphere at 178 mA cm⁻², presenting a promising pathway for developing high-performance, nickel-free SOFC anodes that simplify system design and improves efficiency when operating with hydrocarbon fuels, thus holding significant potential for practical SOFC applications.

固体氧化物燃料电池（SOFC）中高效的烃类燃料阳极催化剂应具有稳定的相结构、高的催化效率和优异的抗焦炭性能。然而，传统的镍基阳极需要高蒸汽碳比来防止结焦，使系统设计复杂化并降低整体性能。在这项工作中，我们报道了一种无镍PrBaFe1.9Pd0.1O5+δ钙钛矿作为直接乙烷SOFC的阳极材料，该材料具有优异的电活性和化学稳定性。在还原气氛下，原位溶解的钯纳米催化剂被均匀地固定在钙钛矿表面。密度泛函理论分析表明，Pd解液显著提高了乙烷吸附能力，从而降低了活化阻力，提高了催化性能。当用作SDC电解质支持的SOFC阳极时，在800°C下，当使用氢气和几乎干燥的乙烷（3% H2O）作为燃料时，其峰值功率密度（PPDs）分别为702和377 mW cm-2，具有优异的性能。此外，该电池在几乎干燥的乙烷气氛下，在178毫安厘米−2下稳定连续运行超过90小时，为开发高性能、无镍SOFC阳极提供了一条有前途的途径，可以简化系统设计，提高烃类燃料运行时的效率，从而具有实际SOFC应用的巨大潜力。

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引用次数: 0

Impact of heat pumps and future energy prices on regional inequalities 热泵和未来能源价格对区域不平等的影响

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-03-01 Epub Date: 2024-12-12 DOI: 10.1016/j.adapen.2024.100201

Jieyang Xu , Sebastian Mosbach , Jethro Akroyd , Markus Kraft

The adoption of heat pumps to displace the use of gas for domestic heating is a major component of the strategy to reduce emissions in the UK. This study examines the impact of adopting heat pumps on regional inequalities in the UK. An index is used to assess how variations in household fuel costs could affect regional disparities across different future price scenarios. The findings reveal that, at 2019 prices, most households would face higher heating costs with heat pumps. However, following the 2022 energy price shock, heat pump adoption would lead to lower heating costs for most households compared to gas heating. The effect is sensitive to the electricity-to-gas price ratio, with regions experiencing high fuel poverty being most vulnerable to negative impacts. By mapping these geospatial effects, the study enables the forecasting of future inequality trends, providing insights for informed policy development. The results suggest that, under appropriate price structures, heat pump adoption could contribute to both decarbonisation and reduced social inequality. An example mechanism for financial support to mitigate the impact of adopting heat pumps on inequality is demonstrated. This study highlights the novel capability of The World Avatar (TWA) approach to integrate cross-domain data sets, combining energy policy with social equity goals. By forecasting future inequality trends based on energy price scenarios, the study provides a route to valuable insights to support informed policy development, highlighting how the adoption of heat pumps can influence regional inequalities and emphasising the need for targeted interventions to support vulnerable regions.

采用热泵取代燃气用于家庭供暖是英国减少排放战略的主要组成部分。本研究考察了采用热泵对英国地区不平等的影响。使用一个指数来评估家庭燃料成本的变化如何影响未来不同价格情景下的地区差异。调查结果显示，以2019年的价格计算，大多数家庭将面临更高的热泵供暖成本。然而，在2022年能源价格冲击之后，与燃气供暖相比，热泵的采用将使大多数家庭的供暖成本降低。这种影响对电力与天然气的价格比很敏感，燃料高度贫困的地区最容易受到负面影响。通过绘制这些地理空间效应，该研究能够预测未来的不平等趋势，为明智的政策制定提供见解。结果表明，在适当的价格结构下，采用热泵可以有助于脱碳和减少社会不平等。一个财政支持机制的例子，以减轻采用热泵对不平等的影响。本研究强调了世界化身（TWA）方法整合跨领域数据集的新能力，将能源政策与社会公平目标结合起来。通过基于能源价格情景预测未来不平等趋势，该研究为支持知情政策制定提供了有价值的见解，强调了热泵的采用如何影响区域不平等，并强调了有针对性的干预措施以支持脆弱地区的必要性。

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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 : 2025-03-01 Epub 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

Reviewing the complexity of endogenous technological learning for energy system modeling 回顾能源系统建模中内生技术学习的复杂性

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-01 Epub Date: 2024-10-19 DOI: 10.1016/j.adapen.2024.100192

Johannes Behrens , Elisabeth Zeyen , Maximilian Hoffmann , Detlef Stolten , Jann M. Weinand

Energy system components like renewable energy technologies or electrolyzers are subject to decreasing investment costs driven by technological progress. Various methods have been developed in the literature to capture model-endogenous technological learning. This review demonstrates the non-linear relationship between investment costs and production volume, resulting in non-convex optimization problems and discuss concepts to account for technological progress. While iterative solution methods tend to find future energy system designs that rely on suboptimal technology mixes, exact solutions leading to global optimality are computationally demanding. Most studies omit important system aspects such as sector integration, or a detailed spatial, temporal, and technological resolution to maintain model solvability, which likewise distorts the impact of technological learning. This can be improved by the application of methods such as temporal or spatial aggregation, decomposition methods, or the clustering of technologies. This review reveals the potential of those methods and points out important considerations for integrating endogenous technological learning. We propose a more integrated approach to handle computational complexity when integrating technological learning, that aims to preserve the model's feasibility. Furthermore, we identify significant gaps in current modeling practices and suggest future research directions to enhance the accuracy and utility of energy system models.

可再生能源技术或电解槽等能源系统组件的投资成本受技术进步的驱动而不断降低。文献中提出了各种方法来捕捉模型内生的技术学习。本综述论证了投资成本与产量之间的非线性关系，这导致了非凸优化问题，并讨论了考虑技术进步的概念。虽然迭代求解方法往往能找到依赖次优技术组合的未来能源系统设计，但实现全局最优的精确求解方法对计算要求很高。大多数研究忽略了重要的系统方面，如部门整合或详细的空间、时间和技术分辨率，以保持模型的可解决性，这同样扭曲了技术学习的影响。应用时空聚合、分解方法或技术聚类等方法可以改善这种情况。本综述揭示了这些方法的潜力，并指出了整合内生技术学习的重要考虑因素。我们提出了一种更综合的方法，用于处理整合技术学习时的计算复杂性，旨在保持模型的可行性。此外，我们还指出了当前建模实践中存在的重大差距，并提出了未来的研究方向，以提高能源系统模型的准确性和实用性。

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引用次数: 0

Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases 城市多能源系统的数字化--数字孪生在生命周期各阶段的应用进展

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-01 Epub Date: 2024-10-28 DOI: 10.1016/j.adapen.2024.100196

B. Koirala , H. Cai , F. Khayatian , E. Munoz , J.G. An , R. Mutschler , M. Sulzer , C. De Wolf , K. Orehounig

Urban multi-energy systems (UMES) incorporating distributed energy resources are vital to future low-carbon energy systems. These systems demand complex solutions, including increased integration of renewables, improved efficiency through electrification, and exploitation of synergies via sector coupling across multiple sectors and infrastructures. Digitalization and the Internet of Things bring new opportunities for the design-build-operate workflow of the cyber-physical urban multi-energy systems. In this context, digital twins are expected to play a crucial role in managing the intricate integration of assets, systems, and actors within urban multi-energy systems. This review explores digital twin opportunities for urban multi-energy systems by first considering the challenges of urban multi energy systems. It then reviews recent advancements in digital twin architectures, energy system data categories, semantic ontologies, and data management solutions, addressing the growing data demands and modelling complexities. Digital twins provide an objective and comprehensive information base covering the entire design, operation, decommissioning, and reuse lifecycle phases, enhancing collaborative decision-making among stakeholders. This review also highlights that future research should focus on scaling digital twins to manage the complexities of urban environments. A key challenge remains in identifying standardized ontologies for seamless data exchange and interoperability between energy systems and sectors. As the technology matures, future research is required to explore the socio-economic and regulatory implications of digital twins, ensuring that the transition to smart energy systems is both technologically sound and socially equitable. The paper concludes by making a series of recommendations on how digital twins could be implemented for urban multi energy systems.

包含分布式能源资源的城市多能源系统（UMES）对未来的低碳能源系统至关重要。这些系统需要复杂的解决方案，包括增加可再生能源的集成度、通过电气化提高效率，以及通过多个部门和基础设施之间的部门耦合利用协同效应。数字化和物联网为网络-物理城市多能源系统的设计-建造-运行工作流程带来了新的机遇。在此背景下，数字孪生有望在管理城市多能源系统中资产、系统和参与者的复杂集成方面发挥关键作用。本综述首先探讨了城市多能源系统所面临的挑战，从而探讨了城市多能源系统的数字孪生机遇。然后回顾数字孪生架构、能源系统数据类别、语义本体和数据管理解决方案的最新进展，以应对日益增长的数据需求和建模复杂性。数字孪生提供了一个客观、全面的信息库，涵盖了整个设计、运行、退役和再利用生命周期的各个阶段，加强了利益相关者之间的协同决策。本综述还强调，未来的研究应侧重于扩大数字孪生的规模，以管理城市环境的复杂性。一个关键的挑战仍然是确定标准化的本体，以实现能源系统和部门之间的无缝数据交换和互操作性。随着技术的成熟，未来的研究需要探索数字孪生的社会经济和监管影响，确保向智能能源系统的过渡在技术上是合理的，在社会上是公平的。本文最后就如何在城市多能源系统中实施数字孪生提出了一系列建议。

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引用次数: 0

A review of mixed-integer linear formulations for framework-based energy system models 基于框架的能源系统模型的混合整数线性公式综述

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-01 Epub Date: 2024-09-20 DOI: 10.1016/j.adapen.2024.100190

Maximilian Hoffmann , Bruno U. Schyska , Julian Bartels , Tristan Pelser , Johannes Behrens , Manuel Wetzel , Hans Christian Gils , Chuen-Fung Tang , Marius Tillmanns , Jan Stock , André Xhonneux , Leander Kotzur , Aaron Praktiknjo , Thomas Vogt , Patrick Jochem , Jochen Linßen , Jann M. Weinand , Detlef Stolten

Optimization-based frameworks for energy system modeling such as TIMES, ETHOS.FINE, or PyPSA have emerged as important tools to outline a cost-efficient energy transition. Consequently, numerous reviews have compared the capabilities and application cases of established energy system optimization frameworks with respect to their model features or adaptability but widely neglect the frameworks’ underlying mathematical structure. This limits their added value for users who not only want to use models but also program them themselves.

To address this issue, we follow a hybrid approach by not only reviewing 63 optimization-based frameworks for energy system modeling with a focus on their mathematical implementation but also conducting a meta-review of 68 existing literature reviews.

Our work reveals that the basic concept of network-based energy flow optimization has remained the same since the earliest publications in the 1970s. Thereby, the number of open-source available optimization frameworks for energy system modeling has more than doubled in the last ten years, mainly driven by the uptake of energy transition and progress in computer-aided optimization.

To go beyond a qualitative discussion, we also define the mathematical formulation for a mixed-integer optimization model comprising all the model features discussed in this work. We thereby aim to facilitate the implementation of future object-oriented frameworks and to increase the comprehensibility of existing ones for energy system modelers.

基于优化的能源系统建模框架（如 TIMES、ETHOS.FINE 或 PyPSA）已成为勾勒具有成本效益的能源转型的重要工具。因此，许多评论都比较了现有能源系统优化框架在模型功能或适应性方面的能力和应用案例，但普遍忽视了框架的底层数学结构。为了解决这个问题，我们采用了一种混合方法，不仅对 63 个基于优化的能源系统建模框架进行了综述，重点关注其数学实现，而且还对 68 篇现有文献综述进行了元综述。我们的工作表明，自 20 世纪 70 年代最早发表以来，基于网络的能源流优化的基本概念一直未变。因此，在过去十年中，能源系统建模的开源优化框架数量翻了一番还多，这主要是受能源转型和计算机辅助优化技术进步的推动。为了超越定性讨论，我们还定义了一个混合整数优化模型的数学公式，该模型包含了本文讨论的所有模型特征。因此，我们的目标是促进未来面向对象框架的实施，并提高现有框架对能源系统建模人员的可理解性。

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引用次数: 0

Integrating material recycling and remanufacturing in energy system optimization modeling: A review and showcase 将材料回收和再制造纳入能源系统优化建模：回顾与展示

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-01 Epub Date: 2024-11-22 DOI: 10.1016/j.adapen.2024.100198

Sebastian Zwickl-Bernhard

This paper addresses the currently overlooked yet urgent topic of material recycling and remanufacturing in energy system optimization modeling, making three substantial contributions. First, it presents a comprehensive review of relevant studies on material demand, flows, and recycling from a techno-economic perspective and highlights the critical gap in existing energy system optimization models, in which material recycling and remanufacturing is not yet adequately integrated. Second, the paper introduces a general mathematical framework for incorporating material recycling and remanufacturing as a technology and investment option into typical energy system optimization models. Third, the paper demonstrates the practical application of this framework by examining the material recycling potential within the solar module expansion plan of the European Union. It explores the main drivers under which material recycling becomes economically competitive, considering various global and regional solar market conditions. Specifically, it investigates how different energy policies — such as incentivizing European Union manufacturing, limiting import shares, and implementing a circular economy constraint — affect the optimal remanufacturing capacities and achievable shares of recycling-based additions to meet the expansion targets until 2050.

本文探讨了能源系统优化建模中目前被忽视但又亟待解决的材料回收和再制造问题，并做出了三项重大贡献。首先，本文从技术经济学的角度全面回顾了材料需求、流动和回收利用方面的相关研究，并强调了现有能源系统优化模型中存在的关键差距，即材料回收和再制造尚未被充分纳入其中。其次，本文介绍了一个通用数学框架，用于将材料回收和再制造作为一种技术和投资选择纳入典型的能源系统优化模型。第三，本文通过研究欧盟太阳能模块扩张计划中的材料回收潜力，展示了这一框架的实际应用。考虑到全球和地区太阳能市场的各种情况，本文探讨了材料回收利用在经济上具有竞争力的主要驱动因素。具体而言，它研究了不同的能源政策--如激励欧盟制造业、限制进口份额和实施循环经济约束--如何影响最佳再制造能力和可实现的基于回收的新增份额，以满足 2050 年前的扩张目标。

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引用次数: 0

Advancing state of health estimation for electric vehicles: Transformer-based approach leveraging real-world data 推进电动汽车的健康状况评估：基于变压器的利用真实世界数据的方法

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-01 Epub Date: 2024-09-04 DOI: 10.1016/j.adapen.2024.100188

Kosaku Nakano , Sophia Vögler , Kenji Tanaka

The widespread adoption of electric vehicles (EVs) underscores the urgent need for innovative approaches to estimate their lithium-ion batteries’ state of health (SOH), which is crucial for ensuring safety and efficiency. This study introduces SOH-TEC, a transformer encoder-based model that processes raw time-series battery and vehicle-related data from a single EV trip to estimate the SOH. Unlike conventional methods that rely on lab-experimented battery cycle data, SOH-TEC utilizes real-world EV operation data, enhancing practical application. The model is trained and evaluated on a real-world dataset collected over nearly three years from three EVs. This dataset includes reliable SOH labels obtained through periodic constant-current full-discharge tests using a chassis dynamometer. Despite the challenges posed by noisy EV real-world data, the model shows high accuracy, with a mean absolute error of 0.72% and a root mean square error of 1.17%. Moreover, our proposed pre-training strategies with unlabeled data, particularly SOH ordinal comparison, significantly enhance the model’s performance; using only 50% of the labeled data achieves results nearly identical to those obtained with the full dataset. Self-attention map analysis reveals that the model primarily focuses on stationary or consistent driving periods to estimate SOH. While the study is constrained by a dataset featuring repetitive driving patterns, it highlights the significant potential of transformer for SOH estimation in EVs and offers valuable insights for future data collection and model development.

电动汽车（EV）的广泛应用凸显了对创新方法的迫切需求，以估算其锂离子电池的健康状况（SOH），这对确保安全和效率至关重要。本研究介绍了 SOH-TEC，这是一种基于变压器编码器的模型，可处理来自单次电动汽车行程的原始时间序列电池和车辆相关数据，以估算 SOH。与依赖实验室实验电池循环数据的传统方法不同，SOH-TEC 利用真实世界的电动汽车运行数据，提高了实际应用能力。该模型在近三年来从三辆电动汽车收集的真实世界数据集上进行了训练和评估。该数据集包括通过使用底盘测功机进行定期恒流全放电测试获得的可靠 SOH 标签。尽管嘈杂的电动汽车真实世界数据带来了挑战，但该模型显示出很高的准确性，平均绝对误差为 0.72%，均方根误差为 1.17%。此外，我们提出的使用未标注数据进行预训练的策略，尤其是 SOH 排序比较，显著提高了模型的性能；仅使用 50%的标注数据就能获得与使用完整数据集几乎相同的结果。自我注意力图分析表明，该模型主要侧重于静止或持续驾驶时段来估计 SOH。虽然这项研究受到以重复驾驶模式为特征的数据集的限制，但它强调了变压器在电动汽车 SOH 估算方面的巨大潜力，并为未来的数据收集和模型开发提供了宝贵的见解。

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引用次数: 0

Multi-scale electricity consumption prediction model based on land use and interpretable machine learning: A case study of China 基于土地利用和可解释机器学习的多尺度用电预测模型：中国案例研究

IF 13 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2024-12-01 Epub Date: 2024-10-28 DOI: 10.1016/j.adapen.2024.100197

Haizhi Luo , Yiwen Zhang , Xinyu Gao , Zhengguang Liu , Xiangzhao Meng , Xiaohu Yang

The prediction of electricity consumption plays a vital role in promoting sustainable development, ensuring energy security and resilience, facilitating regional planning, and integrating renewable energy sources. A novel electricity consumption characterization and prediction model based on land use was proposed. This model achieves land-use subdivision to provide highly correlated variables; exhibits strong interpretability, thereby revealing even marginal effects of land use on electricity consumption; and demonstrates high performance, thereby enabling large-scale simulations and predictions. Using 297 cities and 2,505 counties as case studies, the key findings are as follows: (1) The model demonstrates strong generalization ability (R² = 0.91), high precision (Kappa = 0.77), and robustness, with an overall prediction accuracy exceeding 80 %; (2) The marginal impact of industrial land on electricity consumption is more complex, with more efficiency achieved by limiting its area to either 104.3 km² or between 288.2 and 657.3 km²; (3) The marginal impact of commercial and residential land on electricity consumption exhibits a strong linear relationship (R² > 0.80). Restricting the scale to 11.3 km² could effectively mitigate this impact. Mixed commercial and residential land is advantageous for overall electricity consumption control, but after exceeding 43.5 km², separate layout considerations for urban residential land are necessary; (4) In 2030, Shanghai's electricity consumption is projected to reach 155,143 million kW·h, making it the highest among the 297 cities. Meanwhile, Suzhou Industrial Park leads among the 2,505 districts with a consumption of 30,996 million kW·h; (5) Identify future electricity consumption hotspots and clustering characteristics, evaluate the renewable energy potential in these hotspot areas, and propose targeted strategies accordingly.

用电量预测在促进可持续发展、确保能源安全和弹性、促进区域规划以及整合可再生能源方面发挥着至关重要的作用。本文提出了一种基于土地利用的新型用电特征描述和预测模型。该模型实现了土地利用的细分，提供了高度相关的变量；表现出很强的可解释性，从而揭示了土地利用对用电量的边际效应；并表现出很高的性能，从而实现了大规模的模拟和预测。以 297 个城市和 2,505 个县作为案例研究，主要发现如下：(1) 模型具有较强的泛化能力（R2 = 0.91）、较高的精度（Kappa = 0.77）和稳健性，总体预测精度超过 80%；(2) 工业用地对用电量的边际影响较为复杂，将其面积限制在 104.3 平方公里或 288.2 至 657.3 平方公里之间可提高效率；(3) 商业用地和住宅用地对用电量的边际影响呈现出较强的线性关系（R2 >0.80）。将规模限制在 11.3 平方公里可有效缓解这一影响。商住混合用地对整体用电控制有利，但超过 43.5 km2 后，城市居住用地的布局需要单独考虑；（4）预计 2030 年，上海用电量将达到 1551.43 亿 kW-h，在 297 个城市中居首位。同时，苏州工业园区的用电量为 309.96 亿 kW-h，在 2,505 个区中居首位；（5）确定未来的用电热点和集群特征，评估这些热点地区的可再生能源潜力，并提出相应的针对性策略。

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