Abstract
This paper presents the application of modified cuckoo search algorithm (MCSA) for solving economic load dispatch (ELD) problems. The MCSA method is developed to improve the search ability and solution quality of the conventional CSA method. In the MCSA, the evaluation of eggs has divided the initial eggs into two groups, the top egg group with good quality and the abandoned group with worse quality. Moreover, the value of the updated step size in MCSA is adapted as generating a new solution for the abandoned group and the top group via the Levy flights so that a large zone is searched at the beginning and a local zone is foraged as the maximum number of iterations is nearly reached. The MCSA method has been tested on different systems with different characteristics of thermal units and constraints. The result comparison with other methods in the literature has indicated that the MCSA method can be a powerful method for solving the ELD.
Highlights
Economic Load Dispatch (ELD) problem is one of the major optimization issues in power system operation
The Modified Cuckoo Search Algorithm (MCSA) has been tested on different systems corresponding to the formulated problems including 13unit system considering valve point loading effects with two load demands of 1800 MW and 2520 MW, 20-unit system with quadratic cost function and transmission losses, systems up to 160 units considering valve point loading effects and multiple fuel options, and systems up to 90 units considering prohibited operating zones and spinning reserve
The MCSA method has been successfully applied for solving ELD problems with different objective functions such as quadratic fuel cost function, nonconvex fuel cost function, and multiple fuel cost function of thermal units considering different constraints such as transmission losses, prohibited operating zones and generation limits
Summary
Economic Load Dispatch (ELD) problem is one of the major optimization issues in power system operation. The applicability of the conventional methods to the problem is limited to systems with a convex objective function These methods can only be applied to the systems where the cost function of each generator is approximately represented by a simple quadratic function and the effects of valve-points are ignored [12]. The potential of DE is the fast convergence characteristic [28] and the advantage can yield higher probability of searching toward a local optimum or getting premature convergence This drawback could be overcome by employing a larger population. Several hybrid methods have been proposed for solving the ELD problems such as hybrid GA–PSO method [31], Hybrid Stochastic Search (HSS) [32], hybrid PSO-SQP [33], and hybrid genetic algorithm [34] These hybrid methods can obtain better solution quality than each member method. These methods suffer a difficulty of the proper selection of many controllable parameters for dealing with different problems
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