Table 1.

Summary of recent work on the application of metaheuristic for the optimum planning of grid-independent microgrids.

Refs.	Microgrid element	Main objective	Decision variables	Methodology	Validator Optimization algorithm	Location	Strength	Weakness
[27]	PV-WT-FC-${\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$-BT	Minimize TSC	The capacity of WT, PV, BT bank, H2 tank, and${\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$	HS	SA, HOMER software	Rafsanjan, Iran	-The proposed algorithm is easy to implement and has a fair balance between exploitation and exploration phase	-The need to fine-tune the algorithm parameters of for different applications -Effect of $T^{\mathit{amb}}$ has been ignored in the considered PV model
[26]	PV-biomass-WT-BT	Minimize ASC	The capacity of WT, PV, biomass gastifier, and BT bank	ABC	PSO, HOMER	Patiala, Punjab India	-ABC has a less number of control parameters compared to PSO -The methodology is highly efficient in solving multi-modal optimization problems	-ABC algorithm is computationally demanding. -Solutions offered doesn’t represent a trade-off between multiple objectives
[6]	PV-BT-WT	Minimize NPC	The capacity of WT, PV, and BT bank	MFOA	PSO, GA-PSO, PSO	Hengam Island, Iran	-Employed data compression-based model reduction technique to alleviate computational intensiveness of the simulation	-MFOA is highly susceptible to changes in the values of the algorithm parameters
[25]	${\mathrm{B}\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$-PV-BT-H2 tank	Minimize LCOE	The capacity of PV, ${\mathrm{B}\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$, WT, FC, H2 tank, and BT bank	GA	ESA	Cities in the US	-In GA information is shared between the population to generate new individuals	-The technique is very vulnerable to the choice of the values of the operators
[21]	PV-${\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$-WT-BT	Minimize ASC & LPSP	The capacity of BT bank, PV array, and WT	IACO	ACO	Zhejiang, China	-The proposed method has high exploration power because of parallel searches -Made attempt to improve IACO -EMS is not considered.	-ACO poorly perform on a continuous optimization problem
[23]	PV-BT-WT-${\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$	Minimize LCOE	The capacity of BT bank, PV array,${\mathrm{D}}_{\mathrm{g}\mathrm{e}\mathrm{n}}$	IPSO	PSO	Unspecified tropical island, Singapore	-Made attempt to improve the innate weakness of premature convergences in PSO.	-PSO algorithm is prone to premature convergence.
[22]	PV-BT-WT	Minimize LCC & LDR	The Capacity BT bank, WT, PV array	FA	APSO, BA, GEWA	Bouzareah, Algeria	-The methodology is efficient for computing multi-modal optimization problem.	-FA is memory-less in nature.
[30]	WT-PV-BT	Minimize TSC & LPSP	The capacity of PV array, WT, inverter and BT bank	GA-PSO	PSO, GA	Suburbs of Tehran.	-Exploration of the search landscape is improved.	-Slow convergence process
[20]	BT-PV-H2 Tank	Minimize LCC & LPSP	The capacity of FC, EL, H2 tank, WT, and PV array	Hybrid HS-SA-CS	SA, HS	Davarzan, Iran	-Proposed a hybrid algorithm to enhance the efficiency of solving continuous optimization problem.	-An excessive number of call function required
[19]	PVT-WT-BT-MT-TESS-boiler	Minimize TAC & LPSP	Size of BT bank, WT, PVT collector, boiler and TESS	E-PSO	DE, GA, PSO, HS	Rafsanjan, Iran	-Devised an evolutionary PSO to reduce the inherent weakness of PSO due to premature convergences.	-High computational flexibility and poor local search ability. -Computational intractable in some applications.