Online economic environmental optimization of a Microgrid using an Improved Fast Evolutionary programming technique

This paper aims at exploring the application of an Improved Fast Evolutionary algorithm (IFEP) to determine the economic load sharing scenario in a typical Microgrid by minimizing the cost incurred for operation, maintenance and emissions. This approach utilizes both Gaussian and Cauchy mutations for creation of offspring's from the same parent and better ones are chosen for next generation. Hence IFEP has fastest convergence and highest potential of finding nearly global solution. The proposed method has been tested on a sample Microgrid consisting of a photovoltaic array, a wind turbine, a diesel generator, a micro turbine and a fuel cell, and the results are compared with that of other prevalent methods. The simulation results reveal that the developed technique is easy to implement, has converged within an acceptable execution time and yields highly optimal solution for Combined Economic and Emission Dispatch with minimum operating cost and minimum emission cost.