An Evolutionary Programming based Neuro-Fuzzy Technique for Multiobjective Generation Dispatch with Nonsmooth Fuel Cost and Emission Level Functions

A combined approach involving an EP based fuzzy coordination and an ANN(artificial neural network) methods along with a heuristic rule based search algorithm has been propounded in this paper in order to obtain the best fit optimal generation schedules for multiobjective generation dispatch problem with non-smooth characteristic functions satisfying various practical constraints. Initially, the economy objective function is minimized, followed by minimization of emission level objective function. Then, both the objectives are mixed through a fuzzy coordination method to form a fuzzy decision making (FDM) function. Maximizing the FDM function then solves the original two-objective problem. The minimization and maximization tasks of this optimization problem are solved by the evolutionary programming technique and the results are trained through a radial basis function ANN to reach a preliminary generation schedule. Since, some practical constraints may be violated in the preliminary stage, a heuristic rule based search algorithm is developed to reach a feasible best compromising generation schedule which satisfies all practical constraints in the final stage. The proposed EP based neuro-fuzzy technique has been applied to standard IEEE-30 bus test system and the results are presented. Simulation results indicate that the accuracy and the capability of very fast computation of generation schedule by this technique seem to be very promising for its suitability for on-line multiobjective generation dispatching with any kind of characteristic functions. This technique can be extended to other higher test case systems as well with suitable assumptions.