Marine predator algorithm based PD‐(1+PI) controller for frequency regulation in multi‐microgrid system

Abstract

Renewable generation uncertainty, dynamic load change, and system parameter variation play a significant role in the performance degradation of non-linear multi-microgrid (MMG) systems. As a result, intelligent control becomes the need of the hour for assisting superlative attribute-based consistent electric power. The application of marine predator algorithm (MPA)-based cascaded PD-(1+PI) controller for Automatic Generation Control (AGC) of MMG system is a novel work. A maiden attempt of the MPA is proposed to optimize the parameters of the cascaded PD-(1+PI) controller using the integral time absolute error criterion. To demonstrate its superiority, the proposed algorithm is compared to the genetic algorithm, differential evolution, and grey wolf optimization. MPA then applied to conventional controller PID, cascaded PD-PI controller and proposed PD-(1+PI) controller for frequency control in multi-microgrid system. The robustness of the suggested controller is verified over PID and PD-PI controller by taking step and random load perturbation and integrating the renewable sources like solar and wind with their uncertain nature. The simulation of the investigated interconnected microgrid is carried out in MATLAB/SIMULINK environment. Finally, detailed simulation and hardware in the loop experimental results are presented to confirm the practicality of the proposed approach.

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

“…In this work, the problem of RDG's allocation was solved when utilizing the recent metaheuristic technique of the marine predator algorithm (MPA), which is one of the newest optimization techniques that simulates the foraging behavior and movement of marine predators [32]. Recently, various researchers applied the MPA algorithm to different power system problems: solving the combined heat and power economic dispatch problem in [33], solving single-and multi-objective optimal power flow problems in [34], the optimal reactive power dispatch problem with high penetration of RDGs in [35], the process of optimizing the simultaneous network reconfiguration and DG allocation in EDS in [36,37], frequency regulation for automatic generation control in the multi-microgrid system in [38], stability of frequency in power systems integrated with wind energy in [39], and extracting the optimized parameters of three-photovoltaic solar cell models based on the three-diode model in [40].…”

Survey of load frequency control strategies in a Microgrid

“…Since the number of control nodes is higher with these controllers, an improvement in the system performance is expected [93]. For this reason, a PD-(1 + PI)-controller is used in [94] to ensure the frequency deviation stability in a microgrid. The parameters of this controller are optimized by a metaheuristic algorithm.…”

Resilient and Sustainable PD-(1+PI) Controller for a Smart grid in Uncertain Environments

“…Grasshopper-tuned cascaded fuzzy PD-PI controller has been reported for an interconnected three-area system with wind and solar thermal system and a conventional one [16]. A multi-microgrid system's MPA-tuned PD-(1+PI) controller has been proposed in [17], similarly, a fractional order PID controller has been presented in [18], and a cascade controller PD-P-PID has been proposed in [19]. In this paper, a sustainable PD-(1+PI) controller has been suggested and tuned using the Parasitism Predator Algorithm (PPA) for frequency stability and power balance.…”