Real-Time Simulation and Validation of Interconnected Microgrid Load Frequency Control With Uncertainties Using PDN-I$^{\lambda }$ D$^{\mu }$ Controller Based on Improved Smell Agent Optimization

Interconnected microgrids are vulnerable to load fluctuations and uncertainties in renewable energy generation due to a lack of profound grid support and deficient inertia. Disruption of microgrid frequency stability leads to blemish various system apparatus and loads that are sensitive to frequency. This article depicts an intelligent optimization algorithm-based
$\text{PDN}-I^\lambda D^{\mu }(\text{PDNFID})$
control strategy for load frequency control of interconnected microgrids by incorporating the effects of plug-in hybrid electric vehicles (PHEVs) with coordinated vehicle-to-grid (V2G) control. V2G control has designed based on the battery state-of-charge (SOC) and controlled by the optimized SOC deviation method. An intelligent algorithm, chaotic smell agent optimization with adaptive control strategy (ChSAOACS), has been developed that optimizes the fractional order proportional–integral–derivative and the proposed proportional–derivative filter (PDN) with fractional order integral and derivative (
$\text{PDN}-I^\lambda D^{\mu }$
) controllers concurrently to attain sturdy efficacy. Two scenarios have been investigated to assess the proposed control strategy's capability in an interconnected microgrid with rapid load changes, disturbance at the input of renewable energy units, and PHEV impacts. The outcomes of the proposed ChSAOACS-(
$\text{PDN}-I^\lambda D^{\mu }$
) controller has been contrasted with conventional metaheuristic algorithms, such as PSO and DE. This article validates the performance of the proposed controller on a closed-loop system to confirm the potentiality of its practicality with OPAL-RT-based real-time experimental and simulation results. Outcomes show that the proposed controller is robust and responds quickly.