Hybrid Dragonfly and Pattern Search Algorithm Applied to Dynamic Economic Dispatch Problem

Abstract

Background: In this paper, a novel hybridisation of the dragonfly algorithm (DFA) with the pattern search (PS) algorithm is applied to the dynamic economic dispatch (DED) problem. The DED problem is non-convex, non-linear, and non-smooth and considering practical constraints such as the loading effect of the valve point and ramp rate limits. The conventional DFA is stuck in the local optima and converges prematurely. Introduction: The characteristics of the optimality of the electric power system are dependent on reliability and high economy. Economic dispatch (ED) significantly contributes in deriving the optimal solutions for the operation of a power system. ED aims to generate electric power at the optimum cost among all generating units in order to satisfy the load demand considering all practical and operational constraints. Practical constraints, such as the effect of steam valves, the dynamic behaviour of ramp rate limits, losses due to transmission of power, and prohibited operating zones, convert the linear and convex problem to a non-convex and non-linear problem. Method: Planning and scheduling of output electric power from committed generating units to fulfill the load demand for a scheduled period are termed as DED. The practical generators of thermal power experience the effect of steam valve, real-time ramp rate limit, and technical constraints. DED satisfies all practical, technical, and operational constraints. The DFA was a newly proposed method taking the inspiration of the behaviour of dragonflies for hunting and migrating towards food. The random movement of dragonfly clusters depicts their static characteristics for exploring in the local search space for exploitation competencies, whereas the dynamic behaviour of swarms is used for exploring the global search space by moving in a single direction for a long distance. Result: The efficiency of the proposed method is validated for six well-known benchmark functions. The hybrid technique is compared with the conventional DFA. The proposed hybrid technique combining of the DFA and PS algorithm is also applied for four different test systems with diverse generating units. The proposed technique shows better efficiency for the optimum cost as compared to other recently applied techniques. Conclusion: To overcome difficulties, a PS method is hybridised with the original DFA. The application of the proposed technique improves the capability of search and convergence property The validation of the proposed method by applying to the DED problem shows its effectiveness for generation scheduling and estimating.