Genetic-based algorithm for power economic load dispatch

Abstract

An improved genetic algorithm with multiplier updating (IGAMU) to solve practical power economic load dispatch (PELD) problems of different sizes and complexities with non-convex cost curves, where conventional mathematical methods are inapplicable, is developed. The improved genetic algorithm (IGA) provides an improved evolutionary direction operator and a migrating operator, enabling it to efficiently search and actively explore solutions. Multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function, which is adopted to manage the system constraints of PELD problems. The proposed IGAMU integrates the IGA with the MU. Two practical examples are employed to demonstrate that the proposed algorithm has the benefits of straightforwardness, ease of implementation, better effectiveness than previous methods, better effectiveness and efficiency than the genetic algorithm (GA) with MU (GA–MU), automatic adjustment of the randomly assigned penalty to an appropriate value and the requirement for only a small population when applied to real-life PELD operations.