Optimal Operation of Droop Control in Microgrids Using Different Techniques Optimization: Review

Abstract

Microgrids are small power systems and can operate in two modes: island mode and gridconnected. Switching between these two modes may cause a change in the load, which causes
disturbances that affect the operation of the microgrid (MG), as the load change leads to a change in
the voltage and frequency of the system so the operating control problem main issue for the microgrids
that is need addressed during operation. A control system is required for accurate synchronization,
system protection, and load reduction in an imbalance scenario, as well as to achieve system stability
while supplying robust and efficient electricity to the microgrids. Droop control is one of the common
methods used in the microgrid (MG) to adjust the real power and reactive power and control the
system voltage and frequency. However, the traditional droop control suffers from problems in the
accuracy of load distribution, line impedance mismatch, and slow dynamic response, as a result,
parameter values must be carefully chosen. To address these issues, many techniques have been used,
one of which is the optimization techniques. This paper reviews five different optimization techniques
based on metaheuristic optimization algorithms applied to microgrids that address some of the
drawbacks of droop control by optimizing droop control parameters for optimal flexible microgrid
(MG) operation. These techniques include Particle Swarm Optimization (PSO), Genetic Algorithm
(GA), Grey Wolf Optimization (GWO), Grasshopper Optimization Algorithm (GOA), and Salp Swarm
Algorithm (SSA)