Optimizing Rural MG’s Performance: A Scenario-Based Approach Using an Improved Multi-Objective Crow Search Algorithm Considering Uncertainty

In recent years, the growth of utilizing rural microgrids (RMGs) has been accompanied by various challenges. These necessitate the development of appropriate models for optimal generation in RMGs and RMGs’ coordination. In this paper, two distinct models for RMGs are presented. The first model includes an islanded rural microgrid (IRMG) and the second model consists of three RMGs that are interconnected with one another and linked to the distribution network. The proposed models take into account the uncertainty in load, photovoltaics (PVs), and wind turbines (WTs) with consideration of their correlation by using a scenario-based technique. Three objective functions are defined for optimization: minimizing operational costs including maintenance and fuel expenses, reducing voltage deviation to maintain power quality, and decreasing pollution emissions from fuel cells and microturbines. A new optimization method, namely the Improved Multi-Objective Crow Search Algorithm (IMOCSA), is proposed to solve the problem models. IMOCSA enhances the standard Crow Search Algorithm through three key improvements: an adaptive chaotic awareness probability to better balance exploration and exploitation, a mutation mechanism applied to the solution repository to prevent premature convergence, and a K-means clustering method to control repository size and increase algorithmic efficiency. Since the proposed problem is a multi-objective non-linear optimization problem with conflicting objectives, the idea of the Pareto front is used to find a group of optimal solutions. To assess the effectiveness and efficiency of the proposed models, they are implemented in two different case studies and the analysis and results are illustrated.