Economic Dispatch Problems Using Backtracking Search Optimization

Economic Dispatch Problems Using Backtracking Search Optimization

Kuntal Bhattacharjee (Institute of Technology, Nirma University, Ahmedabad, Gujarat, India)
Copyright: © 2018 |Pages: 22
DOI: 10.4018/IJEOE.2018040102


The purpose of this article is to present a backtracking search optimization technique (BSA) to determine the feasible optimum solution of the economic load dispatch (ELD) problems involving different realistic equality and inequality constraints, such as power balance, ramp rate limits, and prohibited operating zone constraints. Effects of valve-point loading, multi-fuel option of large-scale thermal plants, system transmission loss are also taken into consideration for more realistic application. Two effective operations, mutation and crossover, help BSA algorithms to find the global solution for different optimization problems. BSA has the capability to deal with multimodal problems due to its powerful exploration and exploitation capability. BSA is free from sensitive parameter control operations. Simulation results set up the proposed approach in a better stage compared to several other existing optimization techniques in terms quality of solution and computational efficiency. Results also reveal the robustness of the proposed methodology.
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1. Introduction

Economic Load Dispatch (ELD) optimization problem is the fundamental issue to achieve an economic power system operation by dispatching the generated power to fulfill the load demand. The main aim of ELD is to minimize the total generation cost, while satisfying the operational constraints. The classical calculus-based methods (El-Keib et al.,1994), Linear programming method (Fanshel et al., 1964) have been successfully implemented to solve ELD problems by minimizing the fuel cost function of each generator. However, due to consideration of valve point loading and other complicated constraints like ramp rate limit, prohibited operating zone prevents the classical methods from obtaining global optima. Dynamic Programming (DP) methodology was recommended by Wood and Wollenberg, (Wood et al., 1984) to solve ELD problems. This technique has no such burden on the nature of the cost curves, but suffers from the curse of dimensionality and thus it is not suitable for applications.

Previously, several attempts such as genetic algorithm (GA) (Walters et al., 1993), evolutionary programming (EP) (Jayabharathi et al., 2005), simulated annealing (SA) (Panigrahi et al.,2006), particle swarm optimization (PSO) (Gaing et al., 2003), Ant Colony Optimization (ACO) (Hou et al., 2002), Differential Evolution (DE) (Nomana & Iba, 2008), Artificial Immune System (AIS) (Panigrahi et al., 2007), Bacterial Foraging Algorithm (BFA) (Panigrahi et al., 2008a), Biogeography-based Optimization (BBO) (Bhattacharya et al., 2010a), etc., have shown great potential in solving the nonlinear ELD problems. The above-mentioned based techniques have ability to handle any types of constraints in solving linear or nonlinear ELD problems. However, these methods have some common disadvantages like (i) their execution time is more than double to present efficient techniques like BSA; (ii) dependent on a lot of input control parameters; (iii) do not guarantee global best solutions; rather they often achieve a near global optimal solution.

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