Modified NSGA-II for day-ahead multi-objective thermal generation scheduling

In this paper, a novel approach is proposed to solve the day-ahead multi-objective thermal generation scheduling problem. The proposed method combines the principles of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) with problem specific crossover and mutation operators. Heuristics are used in the initial population by seeding the random population with a Priority list based solution for better convergence. The penalty-parameter-less constrained binary tournament method is used as the selection operator to efficiently handle the constraints. Constrain-domination relation is used as the non-dominated classification procedure to classify the population into non-dominated fronts in presence of constraints. Lambda-iteration method is probabilistically used for assigning the economic/environmental real power dispatch to solve the problem. The proposed method is effectively applied to a large scale 60 generating unit power system for short-term generation scheduling problem. It is found that the presented approach gives good convergence to obtain the Pareto-optimal solutions.