Application of SMES and Fuel Cell System Combined With Liquid Hydrogen Vehicle Station to Renewable Energy Control

Application of SMES and Fuel Cell System Combined With Liquid Hydrogen Vehicle Station to Renewable Energy Control
复制标题

DOI:
10.1109/tasc.2011.2175687
复制
发表时间:
2012-06
影响因子:
1.8
通讯作者:
T. Hamajima;H. Amata;T. Iwasaki;N. Atomura;M. Tsuda;D. Miyagi;T. Shintomi;Y. Makida;T. Takao;K. Munakata;Masataka Kajiwara
T. Hamajima;H. Amata;T. Iwasaki;N. Atomura;M. Tsuda;D. Miyagi;T. Shintomi;Y. Makida;T. Takao;K. Munakata;Masataka Kajiwara
中科院分区:
物理与天体物理3区
文献类型:
--
作者:
T. Hamajima;H. Amata;T. Iwasaki;N. Atomura;M. Tsuda;D. Miyagi;T. Shintomi;Y. Makida;T. Takao;K. Munakata;Masataka Kajiwara

文献摘要

被引文献

相似文献

减少全球二氧化碳排放量是世界各国的当务之急,因此应大量供应对环境友好的可再生能源作为电力。针对风电涡轮机和光伏发电等可再生能源的大量投入会导致电网不稳定的问题,提出了一种由电解氢燃料电池和车载液氢冷却的超导储能系统组成的先进超导电力调节系统(ASPCS)。ASPCS具有利用SMES补偿可再生能源波动的功能,SMES具有快速响应和大I/O功率,并且具有适度的响应和大容量。从经济的角度来看,SMES采用临界温度为39 K的超导体缠绕,因为它通过热虹吸系统冷却,以保持对可燃气体的安全性。ASPCS通过应用卡尔曼滤波算法的统计预测方法有效地实现了功率平衡。通过对大量风电数据的趋势预测,对SMES的容量进行了优化。针对典型的风力发电机,评估了ASPCS的总体电效率。
It is an urgent issue to reduce global carbon-dioxide in the world, and hence the renewable energy, that is environmentally friendly, should be supplied as a large amount of the electric power. Since installation of a large amount of the fluctuating renewable energy, such as wind turbine and photovoltaic, will cause the power utility network unstable, we propose an advanced superconducting power conditioning system (ASPCS) that is composed of Electrolyzer-Hydrogen-FC and SMES cooled with liquid hydrogen from a station for vehicles. The ASPCS has a function of compensating the fluctuating renewable energy with SMES that has quick response and large I/O power, and with that has moderate response and large capacity. The SMES is wound with superconductor with a critical temperature of 39 K from an economical point of view, because it is cooled with through a thermo-siphon system to keep safety against a flammable gas. The ASPCS effectively fulfills a power balance by applying a statistical prediction method of Kalman filter algorithm. The capacity of SMES is optimized by using the trend prediction for a number of wind power data. The overall electric efficiency of the ASPCS is evaluated for a typical wind generator.