Study on Unit Commitment Problem Considering Large-Scale Superconducting Magnetic Energy Storage Systems

被引：16

作者：

Chen, Zhen ^{[1
]}

Xiao, Xian Yong ^{[1
]}

Li, Chang Song ^{[1
]}

Zhang, Yin ^{[1
]}

Zheng, Zi Xuan ^{[1
]}

机构：

[1] Sichuan Univ, Coll Elect Engn & Informat Technol, Chengdu 610065, Peoples R China

来源：

IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY | 2016年 / 26卷 / 07期

关键词：

Daily load leveling; large-scale SMES; operation cost reduction; particle swarm optimization; unit commitment;

D O I：

10.1109/TASC.2016.2598353

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Large-scale superconducting magnetic energy storage (SMES) systems with the advantages of high storage efficiency and no site limitation have significant impacts on the optimal operation and daily load leveling in modern power systems. In this paper, a combinatorial unit commitment (UC) optimization model incorporating large-scale SMES systems is investigated to minimize operation cost of the currently existing power system. A hybrid particle swarm optimization algorithm is employed to intelligently schedule the thermal units and the SMES system simultaneously. The results obtained from an IEEE ten-unit test system case show that the UC-SMES model is favored for UC optimization with significant operation cost reduction achieved in the daily load leveling applications.

引用

页数：6

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