Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Abstract

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency.

Optimizing DC Microgrid: Fuzzy PI Controller with Metaheuristic Energy Storage Management

“…Fig. 7a and 7b clearly demonstrate that the optimized FGS-PI by improved GWO technique produces the lowest settling time value (0.02 s) compared to hybrid PSO-GWO optimized PI controller (0.17 s) [40], which indicates that the suggested approach results in an improvement of 11.76% in settling time. Further observations from Fig.…”

“…The simulation results as shown in Fig. 8a and 8b demonstrate that the battery tracking current and battery dynamic stress reduction are improved in the proposed system compared to a conventional PI, GWO PI, and hybrid PSO-GWO PI controller [40], which leads to accurate tracking of power sharing between DGs and EESs. As shown in Fig.…”

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

“…Among the categories of optimization algorithms, metaheuristics are often used to optimize dynamic control problems [ 43 ]. This is due to their natural inspiration characteristics, soft computing, and simplicity of implementation [ 16 ]. Metaheuristics are subdivided into either swarm intelligence and evolutionary or physical creature algorithms.…”

“…The latter is frequently employed to optimize the performance of complex dynamic systems. This is due to the intensified search for diversified solutions to obtain the best optimal solution in a reasonable time [ 16 ]. However, there is often a trade-off between exploitation and exploration when a single optimization algorithm is applied to optimize the performance of a complex system [ 17 ].…”

“…The PSO mimics natural interactions in fish schools or flocks of birds. It applies the random search technique in population movement using swarm intelligence to solve optimization problems [ 16 ]. In ABC optimization, the foraging behavior is applied by a bunch of honey bees searching for a food source.…”