Optimal size allocation of superconducting magnetic energy storage system based unit commitment

SMES has gained developments in recent years because of its high efficiency with no site limitation, and faster response to meet the peak load demands. By incorporating high efficient Superconducting magnetic energy storage systems (SMES) has a greater impact on daily load scheduling of thermal units and pave the way for optimal unit commitment to meet the load demands with reduced load shedding. In this paper, IEEE 10 unit thermal unit system is incorporated with and without SMES and the results are analysed for studying the impact of SMES in unit commitment scheduling. For this purpose effective Modified Lagrangian relaxation based Particle swarm optimization (LR-PSO) algorithm is used for calculating the operating cost and the results are compared with existing algorithm using MATLAB.