Table 1.

A compilation of review studies on sizing methodologies of hybrid renewable energy systems

S. No.	Ref.	Year	Systems covered	Topics covered	Highlights
1	Banos et al. [25]	2011	Includes all possible combination of hybrid renewable energy systems	Reviewed single and multi-objective optimizations for renewable energy based hybrid systems Optimization techniques reviewed:  Traditional approaches  Heuristic optimization methods  Pareto-optimization techniques	Concludes that heuristic approaches, parallel processing, and Pareto front-based multi-objective optimization are most promising methods for solving hybrid system design problems
2	Fadae and Radzi [26]	2012	Review focused on different combinations of PV/wind/diesel/battery based hybrid systems	Studied multi-objective optimizations for hybrid renewable energy system Optimization techniques discussed:  Multi-objective evolutionary algorithms (MOEA)	Evolutionary algorithms such as GA and PSO are identified to be promising to find solution for many objective optimizations.
3	Khatib et al. [27]	2013	Study concentrates on various combinations of on/off-grid PV/wind/diesel based hybrid systems	Reviewed size optimization methodologies for standalone PV systems:  Artificial intelligence methods  Intuitive approaches  Numerical approaches  Analytical approaches Grid-connected sizing optimization techniques discussed are:  Artificial intelligence methods  Intuitive approaches  Numerical approaches Software based approach: HOMER	Artificial intelligence methods are recommended to solve size optimization problem of renewable energy based hybrid system components
4	Sinha and Chandel [28]	2014	Review covers on various combinations PV/wind/battery based hybrid systems	Software tools reviewed are: HOMER, RETScreen, HYBRID2, iHOGA, NSEL, TRNSYS, IGRHYSO, HYBRIDS, RAPSIM, SOMES, SOLSTOR, HySim, HybSim, IPSYS, HySys, Dymola/Modelica, ARES, SOLSIM, and Hybrid Designer	Concludes HOMER as a faster, easier, and most widely used tool to evaluate the many possible system combinations.
5	Fathima and Palanisamy [29]	2015	Study concentrates mostly on PV, wind, diesel, and energy storage hybrid systems	Reviewed sizing objectives, control and energy management of renewable energy based hybrid systems Mathematical model for PV, wind, diesel generators, and energy storage systems are studied	Promising methods mentioned are: Tabu Search (TS), Honey Bee Mating Algorithm (HBMA), Multicriteria Decision Analysis Optimization (MDAO), Bacterial Foraging Algorithm (BFA), Biogeography based optimization (BBO) Artificial Immune System Algorithm (AISA), Firefly Algorithms (FA) PSO are suitable for high dimensional problems than GA
				Reviewed various sizing methodologies used:  Differential evolution (DE)  Genetic algorithm (GA)  Particle swarm optimization (PSO)  Simulated annealing (SA)  Ant colony algorithm (ACS) Software based approach:  HOMER  GAMS  HYBRID2  RETSCREEN
6	Khare et al. [30]	2016	Concentrated on PV/wind based hybrid systems	Discussed feasibility analysis, modeling, reliability issues, and control aspects of PV/wind based hybrid systems Optimization techniques reviewed are:  Genetic algorithm (GA)  Particle swarm Optimization (PSO)  Fuzzy  Neural  Game theory	Iterative and artificial intelligence methods are useful for size optimization of PV-wind hybrid systems, but it requires to be improved
7	Al-falahi et al. [31]	2017	Focused on standalone PV/wind based hybrid systems	Reviewed various PV/wind combinations and configurations for standalone application, design parameters, and evaluation criteria for power system reliability Sizing methodologies reviewed are:  Classical techniques  Artificial intelligence methods  Hybrid algorithms Software tools for hybrid PV/wind systems mentioned are:  HOMER Pro  HOMER  iHOGA	Study suggests that artificial intelligence methods and hybrid algorithms provide more accurate optimization solutions than classical techniques
8	Anoune et al. [32]	2018	Focused only on standalone PV/wind based hybrid systems	Discussed different solar/wind combinations and configurations for standalone system, design parameters, and evaluation criteria Sizing methodologies reviewed are:  Classical techniques  Artificial intelligence methods  Multi-objective approach Software based approach: HOMER, iHOGA, HYBRID2, TRNSYS, HYDROGEMS, INSEL, ARES, SOLSIM, SOMES, H2RES	Artificial intelligence and heuristic approaches are most suitable and efficiency for solving hybrid renewable energy optimization problems
9	Lian et al. [24]	2019	Review coveres all forms of renewable energy sources based hybrid systems	Reviewed various operating mode (Standalone, grid-connected), various structure of system (presence/absence of conventional source, presence/absence of storage, renewable energy used) and system performance indicators Sizing methodologies reviewed are: Analytical method, Probabilistic method, Iterative method, Numerical method, Graphic construction method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Artificial bee colony (ABC), Cuckoo search (CS), Hybrid methods Software tools reviewed are:  HOMER, iHOGA, HYBRIDS, HYBRIDS2  HOMER  iHOGA  HYBRIDS  HYBRIDS2	Hybrid optimization methods are recommended for hybrid renewable energy systems research to avoid the limitations of one methodology due to their greater adaptability and optimization performance
10	Emad et al. [33]	2020	Study covers PV/wind/battery system	Mathematical model for wind, solar, and energy storage systems are studied Discussed various architecture of power source configuration: DC, AC, and hybrid DC/AC coupled configurations Reviewed hybrid system Economical parameters: Net Present Cost (NPC), Life Cycle Cost (LCC), Total Annual Cost (TAC), and Cost of Energy (COE) Sizing methods reviewed are:  Classical Techniques  Meta-heuristic Techniques Classical and Meta-heuristic Techniques Software tool reviewed is: Homer	Article conclude that meta-heuristic optimization techniques are more accurate and required less computational time than classical techniques