Multi-objective optimal control of renewable energy based autonomous AC microgrid using dandelion optimisation

Abstract

This paper presents multi-objective dandelion optimization (DEO) control of an autonomous microgrid consisting of a PV array, along with a battery energy storage system (BES) and diesel generator (DG) system for rural areas that are not connected to the main grid. This configuration aims at the maximum extraction of solar energy by using maximum power point tracking (MPPT) using a boost converter with dandelion optimization (DEO)-based incremental conductance (INC) algorithm. The INC algorithm uses the dandelion algorithm to calculate the ideal step size (δ), that significantly enhances the maximum power point tracking and increases the efficiency of algorithm. The DEO optimization also gives optimized value of gains of PI controller of bidirectional converter of battery for better DC bus voltage regulation. DC link voltage has less variations during steady state and dynamic conditions. Generic sigmoid function-based- modified variable step size least mean square (GS-MVSS-LMS) adaptive VSC control provides solutions for power quality issues such as harmonic elimination, and load leveling and regulates the voltage at PCC. The suggested control method performs better in terms of steady-state error reduction, convergence rate, transition tracking effectiveness, and self-coherence. The main features of this AC microgrid are the operation of a solar array at its MPPT, less utilization of diesel generators, and reactive power compensation. The performance of the proposed microgrid topology is studied under various dynamic conditions and performs satisfactorily as per IEEE 519 standards. MATLAB/ simpower tools are used for the simulation of system.