Multi-objective Chaotic-Enhanced Competitive Swarm Optimizer (CECSO) algorithm based optimal scheduling of microgrid with renewable energy sources

The microgrid (MG) is a compact power distribution system that serves consumers within a limited area by functioning autonomously using distributed generators (DG) or in conjunction with other small grids or the central grid. The power generation by fossil-fueled generators results in the injection of undesirable pollutants into the atmosphere. Integrating renewable energy sources (RES) in the MG minimizes the pollutants emitted into the atmosphere. It is always a challenge for power engineers to attain a compromise between economic power generation and reduced emissions of pollutants into the environment. Economic Load Dispatch (ELD) and Emission Dispatch (EMD) are specific challenges within an MG that focus on efficiently scheduling DG units to reduce fuel expenses and environmental emissions. This paper proposes a multi-objective-based optimal DG scheduling in a renewable-integrated islanded MG, resulting in a compromised solution between fuel costs and environmental emissions. A Pareto-based Chaotic-Enhanced Competitive Swarm Optimizer (CECSO) Algorithm is proposed for multi-objective optimization. In addition to multi-objective optimization, ELD and EMD problems are solved separately to analyze the effectiveness of the proposed CECSO algorithm and are compared with other variants of CSO. Ten chaotic maps are utilized to improve the efficiency of the Enhanced Competitive Swarm Optimizer (ECSO) algorithm to avoid trapping in local optima and to enhance convergence speed. The result outcomes are contrasted with evolutionary algorithms previously utilized in research to demonstrate the superiority of the proposed method.