考虑城市形态的复杂城市地区气候弹性能源系统设计:技术综述

IF 13 Q1 ENERGY & FUELS
Kavan Javanroodi , A.T.D. Perera , Tianzhen Hong , Vahid M Nik
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引用次数: 1

摘要

由于气候变化和快速城市化,城市能源基础设施正面临越来越多的挑战。特别是,随着城市的不断扩张和人口密度的增加,城市形态和能源系统之间的联系变得越来越重要。实现气候中和增加了另一层复杂性,凸显了解决这种关系的必要性,以制定可持续城市能源基础设施的有效战略。极端气候事件的发生也可能引发系统组件的级联故障,导致长期停电。本文通过全面的文献综述,深入探讨了将城市形态纳入能源系统模型的挑战,并提出了一个新的框架来增强互联系统的弹性。该审查强调,需要综合模型来深入了解城市能源系统的设计和运行,并解决气候变化和城市化对能源系统的连锁故障、互联性和复合影响。它还探讨了新出现的挑战和机遇,包括对高质量数据的需求、大数据的利用以及人工智能和机器学习等先进技术在城市能源系统中的集成。所提出的框架整合了城市形态分类、中尺度和微尺度气候数据,以及考虑城市形态、气候变化和极端事件影响的设计和操作过程。鉴于极端气候事件的普遍性和应对气候变化战略的必要性,该研究强调了改进能源系统模型以适应未来气候变化的重要性,同时认识到城市基础设施内部的相互联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing climate resilient energy systems in complex urban areas considering urban morphology: A technical review

The urban energy infrastructure is facing a rising number of challenges due to climate change and rapid urbanization. In particular, the link between urban morphology and energy systems has become increasingly crucial as cities continue to expand and become more densely populated. Achieving climate neutrality adds another layer of complexity, highlighting the need to address this relationship to develop effective strategies for sustainable urban energy infrastructure. The occurrence of extreme climate events can also trigger cascading failures in the system components, leading to long-lasting blackouts. This review paper thoroughly explores the challenges of incorporating urban morphology into energy system models through a comprehensive literature review and proposes a new framework to enhance the resilience of interconnected systems. The review emphasizes the need for integrated models to provide deeper insights into urban energy systems design and operation and addresses the cascading failures, interconnectivity, and compound impacts of climate change and urbanization on energy systems. It also explores emerging challenges and opportunities, including the requirement for high-quality data, utilization of big data, and integration of advanced technologies like artificial intelligence and machine learning in urban energy systems. The proposed framework integrates urban morphology classification, mesoscale and microscale climate data, and a design and operation process to consider the influence of urban morphology, climate variability, and extreme events. Given the prevalence of extreme climate events and the need for climate-resilient strategies, the study underscores the significance of improving energy system models to accommodate future climate variations while recognizing the interconnectivity within urban infrastructure.

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来源期刊
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
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