基于社区的弹性电力共享:最优空间集群

Y. Yamagata, H. Seya
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引用次数: 6

摘要

本文扩展了我们提出的(Yamagata and Seya 2012)基于社区的灾害弹性电力共享系统(DRESS)的概念,作为上网电价(FiT)的补充或替代方案,以实现城市二氧化碳中和。在这个系统中,广泛引入的太阳能光伏板(pv)产生的电力被储存到城市中“不使用的汽车”中。在东京市区的中心地区,几乎一半的汽车只在周末使用,而在工作日则停在那里。因此,如果这些汽车在未来被电动汽车(ev)取代,它们可能会在社区层面上使用车辆到电网(V2G)作为新的电池储能系统,这将存在巨大的新潜力。本研究扩展了我们之前的论文。首先,通过使用建筑物的实际地面面积,我们可以更准确地估计pv的供应潜力。结果表明:在全市范围内,将27%的停车电动汽车作为电池蓄电,可将每小时剩余电量(光伏供电减去需求)完全存储而不浪费,但在地方区域层面存在显著的空间差异。其次,在地理供需估算的基础上,通过梳理高储潜力区和低储潜力区,形成电力自给自足的弹性社区,检验当地电力共享的可能性。最后,利用Moran’s I指数分析了最优群落聚类。我们表明,如果考虑到停电风险的弹性,40%而不是27%是最优的电动汽车电力共享率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Community-based resilient electricity sharing: Optimal spatial clustering
This paper extends our proposing (Yamagata and Seya 2012) concept of a community-based disaster resilient electricity sharing system (DRESS) as a complement or an alternative to the feed-in-tariff (FiT) to achieve CO2 neutral in cities. In this system, electricity generated from widely introduced solar photovoltaic panels (PVs) is stored to the “cars not in use” in a city. In the central part of the Tokyo metropolitan area, almost half of the cars is used only on weekends and are kept parking during the weekdays. Hence, there exists a huge new potential if those cars are replaced by electric vehicles (EVs) in the future, namely they may be used as new battery storages using vehicle to grid (V2G) at a community level. This study extends our previous paper. Firstly, by using actual ground areas of buildings, we estimate PVs supply potential more accurately. The result shows that the hourly electricity surplus (PV supply minus demand) can be fully stored without waste if 27% of the parking EVs are used as battery storage at the whole city level, although there exist significant spatial differences at local district level. Secondly, based on the geographical demand-supply estimates, we check the possibility of local electricity sharing by combing high and low storage potential districts to form electricity self-sufficient resilient communities. Finally, we analyze the optimal community clustering using Moran's I index. We show that the 40%, instead of 27%, is an optimal EV electricity sharing rate, if we consider the resilience against black-out risk.
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