Abstract
In this study, we propose an algorithm based on a recently proposed metaheuristic called the Harris Hawk Optimization (HHO). We utilized a dynamic control strategy to enhance the exploration capability. To further avoid trapping in local optima, we incorporate opposition-based learning (OLB) and a multi-restart strategy. The proposed algorithm is used to solve economic load dispatch (ELD) problems with non-smooth cost functions. The ELD problems have a very large search space and therefore it is difficult to find global optimum using analytical methods. The results of the proposed approach are very competitive compared with notable results from previous research.
Highlights
The economic load dispatch (ELD) problem is one of the most vital optimization problems in a power system
Inspired by the success of the Dynamic Harris Hawk Optimization (DHHO) algorithm[26], this study developed a multi-restart strategy with opposition-based learning to further improve the performance of the DHHO
To further address the premature convergence problem, we introduced a multi- restart (MR) scheme as follows: Let K be constant, ΔT1,T2 be the difference between the fitness value found at iteration T2, where T2 = T1 + K, with respect to the fitness value found at iteration T1
Summary
The economic load dispatch (ELD) problem is one of the most vital optimization problems in a power system. In the ELD problem, the cost function for each generator has been approximately represented by a single quadratic function, and the valve-point effects were ignored [1] This would often introduce inaccuracy into the resulting dispatch. The ELD problem with valve-point effects is represented as a non-smooth optimization problem having complex and nonconvex characteristics with heavy equality and inequality constraints, which makes the challenge of finding the global optimum hard Methods that avoid this approximation of the actual unit curve model without sacrificing computational time would prove very valuable. In 2007, Chiang [19] developed an improved genetic algorithm 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.
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