基于改进型快速寻优算法的微网经济负荷优化

Economic Environmental Optimization of a Microgrid Using an Improved Fast Evolutionary Programming Technique

本文旨在寻找一种改进型的快速渐近算法(IFEP),使得在一个典型微型智能电网下的操作成本,维护成本以及环境排放量都降到最低。这种方法利用高斯变换和柯西变换,从父辈中得到用于选择的更佳子代,它具有快速收敛性并能寻找全局最优解。应用该方法测试一个包含风力涡轮机、柴油发电机、微型燃气轮机和燃料电池的微电网,仿真结果表明,这种技术容易实现,能够在可接受的执行时间内,兼顾生产成本与排放成本最低时收敛。

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.