Solution methodology of unit commitment (UC) using genetic algorithms (GA) is presented. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as minimization of the total objective function while satisfying the associated constraints. Problem specific operators are proposed for the satisfaction of time dependent constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented.

A differential evolution approach to solve optimal power flow problem with multiple and competing objectives is presented. Two sub-problems of optimal power flow namely active power dispatch and reactive power dispatch are considered. The problem is formulated as a nonlinear constrained true multi-objective optimisation problem with competing objectives. Constrain-domination approach have been used to handle inequality constraints, which eliminates the use of penalty factors. The performance of the proposed approach was tested on standard IEEE 30-bus system and is compared with a conventional method. The result demonstrates the capability of the proposed approach to generate diverse and well-distributed Pareto-optimal solutions.