Study On The Optimal Location Of Superconducting Magnetic Energy Storage(SMES) In Power System

With the successful development of High Temperature Superconductor (HTS) and continuous progress of Low Temperature Engineering,application of superconductivity in power systems is becoming a social focus;FACTS(Flexible AC Transmission System),based on the power electronic and control technology, is one of the frontiers of power systems. As the perfect combination of these two revolutionary technologies, SMES(Superconducting Magnetic Energy Storage) is the studied object of the present paper,Distributed Superconducting Magnetic Energy Storage is an innovative application of SMES that combines several SMESs to work in coordinate.Firstly,this paper expands the significance of applying SMES,especially for power systems in our country,sets up whole understanding of SMES application study,analyzes the basic structure and principle of SMES,expands the significance of Power Conditioning System (PCS) applied in SMES,and compares Current Source Converter and Voltage Source Converter in structure and application range, and overviews study achievements of SMES in various countries based on the recent publications,points out research direction in future and wide application vision.Afterward,the effectiveness and ability of SMES to enhance stability for power system is tested in far-going range by digital simulation based on a One Machine Infinite Bus system with SMES in the environment of MATLAB/Simulink. Experiment results verified the conclusion obtained.Finally,introduces the solution approaches to the optimal locations problem in power system for the usual FACTS devices like TCSC,UPFC,SVC, respectively, and evaluates the used approaches for determining the optimal locations of FACTS, then introduces Extended Equal Area Criterion (EEAC) to be applied to the SMES optimal location problem and proposes the concept of stability margin. Next, detailedly analyses the optimal location problem in the aspects of allocation principal, analysis approach, selecting indicator and optimization indicator, discusses some random factors like load and generators output met in the optimal problem, and proposes to use Monte-Carlo algorithm to solve the random problems.