Adaptation and Hybridization in Computational Intelligence

Abstract

The increasing costs of fuels and operations of power generating units necessitate the development of optimization methods for economic dispatch (ED) problems. Classical optimization techniques such as direct search and gradient methods often fail to find global optimum solutions. Modern optimization techniques are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This chapter presents a novel method to determine the feasible optimal solutions of the ED problems utilizing the newly developed Bat Algorithm (BA). The proposed BA is based on the echolocation behavior of bats. This technique is adapted to solve non-convex ED problems under different nonlinear constraints such as transmission losses, ramp rate limits, multi-fuel options and prohibited operating zones. Parameters are tuned to give the best results for these problems. To describe the efficiency and applicability of the proposed algorithm, we will use four ED test systems with non-convexity. We will compare our results with some of the most recently published ED solution methods. Comparing with the other existing techniques, the proposed approach can find better solutions than other methods. This method can be deemed to be a promising alternative for solving the ED problems in real systems. Keywords: Economic dispatch,