Squirrel Search Optimizer: Nature Inspired Metaheuristic Strategy for Solving Disparate Economic Dispatch Problems

Abstract

In this paper, a new meta-heuristic algorithm, called Squirrel Search Optimizer (SSO) is applied to solve various types of economic load dispatch (ELD) problems. The SSO mimics the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. In SSO algorithm, predator presence behavior and a seasonal monitoring condition are employed to increase the search ability of the algorithm, and to balance the exploitation and exploration. The key idea of the suggested approach is to determine the optimal generation scheduling by minimizing total generation cost of units while satisfying various constraints such as power balance constraint, prohibited operating zones (POZ), ramp rate constraints and operating limits of generators. The different generating unit's characteristics, quadratic fuel cost function, non-convex fuel cost function and multiple fuel options (MFO) are also considered. The feasibility of the proposed algorithm is tested on four different power test systems having different sizes and intricacies. The results are examined in terms of both solution quality and the computational efficiency, and compared with the other approaches in the literature. The comparisons prove the robustness and effectiveness of the proposed algorithm and show that it could be used as a consistent optimizer for solving various ELD problems.