Refined Genetic Algorithm for Short Term Generation Scheduling Problem in Regulated and Deregulated Power System

In the new restructured electricity power industry, a Generation Company (GENCO) with thermo-electric amenities faces the optimal problem of how to attain a maximum profit by considering the price uncertainties. In deregulated power markets, the maximization of profit is different from the minimization of cost because generation companies no longer have the obligation to serve the whole demand. They may choose to generate less than the demand, which allows more flexibility in unit commitment schedules. In the proposed method, the Lagrange multipliers are updated using GA technique to overcome the convergence problems with LR. Here the system constraints have been relaxed in the objective function by using Lagrange multipliers. The relaxed problem is then solving through the dual optimization procedure. A case study based on the standard IEEE 39-bus system is presented to illustrate the proficiency of the proposed refined Genetic Algorithm technique and the simulation results are compared with those obtained from traditional unit commitment.