Droop Control Optimization for Improved Power Sharing in AC Islanded Microgrids Based on Centripetal Force Gravity Search Algorithm

Abstract

The urgent demand for clean and renewable energy sources has led to the emergence of the microgrid (MG) concept. MGs are small grids connecting various micro-sources, such as diesel, photovoltaic wind, and fuel cells. They operate flexibly, connected to the grid, standalone, and in clusters. In AC MG control, a hierarchical system consists of three levels: primary, secondary, and tertiary. It monitors and ensures MG stability, power quality, and power sharing based on the specifications of governing protocols. Various challenging transient disturbances exist, such as generator tripping, secondary control failure due to communication delay, and drastic load changes. Although several optimal power sharing methods have been invented, they pose complex control requirements and provide limited improvement. Therefore, in this paper, a novel optimized droop control is proposed using a metaheuristic multi-objective evolutionary algorithm called the Centripetal Force-Gravity Search Algorithm (CF-GSA) to improve the droop performance of power sharing, voltage and frequency stability, and power quality. CF-GSA is an improved algorithm designed to address the issue of local solutions commonly encountered in optimization algorithms. The effectiveness and superiority of the proposed method are validated through a series of simulations. The results of these simulations show that the proposed multi-objective optimization droop control method works well to fix problems caused by power sharing errors in isolated AC microgrids that have to deal with high inductive loads and changes in line impedance.

Metaheuristic Optimization of Maximum Power Point Tracking in PV Array under Partial Shading

“…These optimization methodologies draw inspiration from natural and social behaviors and present a strategy to deliver resilient and effective solutions across a wide range of application domains. Metaheuristic optimization algorithms, such as Particle Swarm Optimization (PSO) [11], Genetic Algorithm (GA) [12], and Ant Colony Optimization (ACO) [13] have been investigated as potential substitutes for conventional approaches in MPPT for PV arrays under partial shading. Optimization techniques have been the subject of many studies in an attempt to improve energy harvesting efficiency.…”