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

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.