基于储氢和氢动力汽车的配电网弹性增强策略

IF 1.6 Q4 ENERGY & FUELS
Pingping Xie, Ruoxuan Zhao, Yue Chen, Yinguo Yang, Qiming Yang, Yingming Lin, Gengfeng Li
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引用次数: 0

摘要

从提高配电网氢气渗透率作为应对极端事件和提高电网弹性的手段出发,提出了一种提高配电网弹性的新策略。它概述了一种全面的方法,重点是在氢渗透配电系统(HPDS)中调度氢储存(HS)和氢汽车(HV),将战略分为灾前和灾后阶段。首先,在灾前阶段,建立高压电网和高压电网模型,收集运行数据,促进灾后快速响应,同时建立电网和道路网络耦合模型,优化高压电网的路由和调度。随后,灾后阶段将重点放在调度模型上,该模型旨在通过详细分析高频和高压调度策略,将负载损失和经济成本降至最低,平衡即时电力支持与成本效益。最后,本文通过一个案例研究证明了这一战略的有效性,强调了网络弹性和恢复的显著改善,并强调了氢技术在增强基础设施弹性方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Resilience enhancement strategies for power distribution network based on hydrogen storage and hydrogen vehicle

Resilience enhancement strategies for power distribution network based on hydrogen storage and hydrogen vehicle

In light of the increasing hydrogen permeability in distribution networks as a means to cope with extreme events and improve network resilience, this paper introduces a novel strategy for enhancing power distribution network resilience. It outlines a comprehensive approach that focuses on dispatching hydrogen storage (HS) and hydrogen vehicle (HV) within hydrogen penetrated distribution systems (HPDS), segmenting the strategy into pre-disaster and post-disaster stages. Firstly, in the pre-disaster stage, models for HS and HVs are established to gather operational data and facilitate rapid post-disaster response, alongside a coupled electric grid and road network model for optimising HV routing and dispatch. Subsequently, the post-disaster stage focuses on a scheduling model that aims to minimise load power losses and economic costs, balancing immediate power support with cost-effectiveness through detailed analysis of HS and HV dispatch strategies. Finally, this paper demonstrates the effectiveness of this strategy via a case study, highlighting significant improvements in network resilience and recovery and underscoring the potential of hydrogen technologies in enhancing infrastructure resilience.

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来源期刊
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
审稿时长
11 weeks
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