Nature-inspired metaheuristics search algorithms for solving the economic load dispatch problem of power system: A comparison study

This work proposes a new approach in addressing Economic Load Dispatch (ELD) optimization problem in power unit systems using nature-inspired metaheuristics search algorithms. Solving such a problem requires a degree of maximization of the economic pact of a power network system, where this is possible with some existing population-based metaheuristic search algorithms. The key issue to be handled here is how to maximize the economic benevolence of a power network under a variety of operational constraints, taking into account the reduction in the generated fuel cost as well as the aggregate power loss in the transmission power network system. Some nature-inspired metaheuristics will be explored. Meanwhile, we shall focus our attention on a newly developed nature-inspired search algorithm, referred to as the Crow Search Algorithm or CSA for short, as well as the Differential Evolution (DE) that is commonly known as a metaheuristic. The CSA emerged to light by simulating the intelligent flocking behavior of crows. The practicability of the proposed approach-based CSA was conducted to common types of power generators, including three and six buses (nodes) in addition to the IEEE 30-bus standard system. The results of the presented approaches were compared to other results developed using existing nature-inspired metaheuristic algorithms like particle swarm optimization and genetic algorithms and also compared to traditional approaches such as quadratic programming method. The results reported here support that CSA has achieved an outstanding performance in solving the problem of ELD in power systems, demonstrating their good optimization capabilities through arriving at a combination of power loads that consummate the constraints of the ELD problem while simultaneously lessening the entire fuel cost. The experimental results also showed that the CSA solutions were capable of maximizing the reliability of the power supplied to the customers, and also reducing both the generated power cost and the loss of power in the transmission power systems.

Citation Key Alias: lens.org/053-057-671-070-617