Research on Microgrid Optimal Scheduling Based on the Salp Swarm Algorithm

Abstract

To address uneven population distribution and premature convergence of the standard Salp Swarm Algorithm (SSA) in grid-connected microgrid scheduling, this study develops an Improved Salp Swarm Algorithm (ISSA). Sine chaotic mapping is used for population initialization. A distance-adaptive follower update is then employed, in which the weight is determined by the normalized distance between an individual and the current best solution, and an acceptance-based Lévy perturbation is applied to the best solution to enhance escape from local optima. The resulting ISSA is applied to a microgrid scheduling model that minimizes economic operation and environmental costs. Benchmark function and microgrid case studies are used to evaluate optimization accuracy, convergence behavior and scheduling feasibility.