An evolutionary programming based neuro-fuzzy technique for multi-objective generation dispatch with non-smooth characteristic functions

With the advent of stochastic search algorithms, the simulated annealing [2] and the genetic algorithms [5] were devoted to solving the highly non-linear economic dispatch problems without restrictions to the shape of fuel cost functions. Yang et al [6], have developed an efficient general economic dispatch algorithm for units with non-smooth fuel cost functions based on EP technique. In this work the authors have compared the results of ED problems when solved by genetic algorithm, simulated annealing and EP. They have shown that the EP method is able to give a cheaper schedule at a less computation time. Hanzhenget al [7], described a solution method for unit commitment using Lagrangian relaxation combined with evolutionary programming. Hotaet al [8], have developed an evolutionary programming based algorithm for solution of short-term hydrothermal scheduling problem. They have also shown that when compared to simulated annealing based algorithm for short-term hydrothermal scheduling, EP based algorithm is able to obtain a cheaper hydrothermal schedule at reduced execution time. In this paper, a novel evolutionary programming (EP) based neuro-fuzzy technique is proposed to solve the multi-objective generation dispatch problem with non-smooth characteristic functions i.e., fuel cost and emission level functions. The stochastic mechanics, which combine offspring creation based on the performance of current trial solutions and competition & selection based on successive generations, form a considerably robust scheme for large-scale real-valued combinatorial optimization. The weaknesses of the algorithms mentioned above are circumvented. The proposed EP approach is capable of not only solving the multi-objective generation dispatch problem with any type of fuel cost and emission level functions, analytical or empirical curves, but also obtaining the global or near global minimum solution considering transmission losses within the reasonable execution time. Encoding and decoding schemes essential in the genetic algorithm approach are not needed; considerable computation time can thus be saved.