Economic Generation and Scheduling of Power by Genetic Algorithm

The paper represents a genetic algorithm (GA) solution to the unit commitment problem for power generation in thermal power plant. GAs are general optimization techniques based on principles inspired from the biological evolution, using metaphors of mechanism such as natural selection, genetic recombination and survival of the fittest. A simple GA implementation using the standard crossover and mutation operators could locate near optimal solution. To solve the problem a two layer approach is used, the first layer uses a genetic algorithm to decide the on/off status of the units, and the second layer uses a non linear programming formulation solved by Lagrangian relaxation to perform the economic dispatch while meeting all plant and system constraints. In order to save the execution time the economic dispatch is only performed if the given unit commitment schedule is able to meet the load balance, energy and begin/end level constraints. The simulation results reveal that the features of easy implementation, convergence within an acceptable execution time, and highly optimal solution in solving the unit commitment problem can be achieved.