Multi-Objective Optimization for Smart Energy Grids Using Synergistic Fibroblast Optimization Algorithm

A promising energy scheduling algorithm is the most important component for determining energy allocation and efficient energy management in the smart grid. In this paper, Synergistic Fibroblast Optimization (SFO) based energy scheduling scheme for the smart grid is proposed to solve multi-objectives, namely, reduce electricity consumption cost, maximize the usage of renewable resources and effective utilization of resources connected in a smart grid system. A well detailed case study is conducted to monitor the electricity consumption in the real time environment, and the proposed scheduling strategy is simulated to validate the performance of algorithm. Evaluation of experimental results demonstrated that multi-objective SFO algorithm obtains significant electricity cost reduction and maximizes resource utilization when compared to other most popular algorithms, such as, First Fit, Best Fit, Firefly algorithm (FA), Particle Swarm Optimization (PSO) and Invasive Weed Optimization (IWO).