Fuzzy unit commitment for cost minimization in power system planning

Unit commitment is among of the key elements in power system planning. Unit commitment is extensively applied by the power utilities to plan the optimal dispatch of generating units in the system. In the deregulated power system industry, it is important to consider several uncertainty constraints during the planning process. This research proposes the application of fuzzy set modeling to determine the uncertainty constraints. Several intelligence techniques including Particle Swarm Optimization, Ant Colony Optimization, and Differential Evolution Immunized Ant Colony Optimization approaches have been used to optimize the fuzzy unit commitment problem. The verification process was performed on IEEE 30-Bus Reliable Test System (RTS). Comparative studies among PSO, ACO and DEIANT indicate the superiority of DEIANT in solving the fuzzy unit commitment problem.