Table 4.
Summary of studies based on single objective optimization
| S. No. | Author/Year | System components | Site | Mode | Combined methods | Proposed methods | Compared methods | Objective functions | Design constraints |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Gandomkar et al. 2005 [72] | PV-wind-biomass | Iran | On-grid | Genetic Algortihm (GA) Tabu Search (TS) | Hybrid GA-based TS | Genetic Algortihm (GA) | (−) PL | Maximum number of DEG, current capacity limit, upper and lower voltage limit, total capacity of DEG at distribution network, and inequality constraint |
| 2 | Dufo-Lopez et al. 2005 [73] | PV-diesel | Spain | Off-grid | Genetic Algorithms (GAs) | HOGA | HOMER | (−) NPC | Not specified |
| 3 | Dehghan et al. 2009 [74] | PV-wind-FC | Iran | Off-grid | Particle Swarm Optimization (PSO) Harmony Search (HS) | Hybrid PSO-based HS | Particle swarm optimization (PSO) | (−) NPC | The maximum permissible level of the Equivalent Loss Factor (ELF) index |
| 4 | Khatib et al. 2012 [75] | PV-wind-battery | Malaysia | Off-grid | Iterative Method Genetic Algorithm (GA) | Iterative-based GA | Not Specified | (−) TC | Energy balance, loss of load probability (LLP) and capacity of PV array, wind and battery |
| 5 | Hong et al. 2012 [76] | PV-wind-diesel | Taiwan | Off-grid | Markov model Genetic Algorithm (GA) | Markov model-based GA | Chronology model-based GA | (−) TC | Number of PV panels, wind turbines, and diesel generators, LLP and CO2 emissions |
| 6 | Katsigiannis et al. 2012 [77] | PV-wind-diesel-biodiesel-FC-battery | Greece | Off-grid | Simulated Annealing (SA) Tabu Search (TS) | SA-based TS | Individual SA Individual TS | (−) LCOE | Initial cost, Unmet load, capacity storage, fuel consumption, renewable fraction and components size limitation |
| 7 | Lujano et al. 2013 [78] | PV-wind-diesel-battery | Spain | Off-grid | Artificial Neural Network (ANN) Genetic Algorithm (GA) | ANN-based GA | Not Specified | (−) NPC | Probability of energy not supply limit and NPC |
| 8 | Askarzadeh et al. 2013 [79] | PV-wind-battery | USA | Off-grid | Discrete Simulated Annealing (DSA) Chaotic Search (CS) Harmony Search (HS) | DHSSA and DCHSSA | Discrete Simulated Annealing (DSA) | (−) TAC | Number of PV panels, wind turbines and batteries and maximum depth of discharge (DOD) |
| 9 | Garcia et al. 2014 [80] | Multiple DEG systems (Not specified) | Spain | On-grid | Teaching Learning-Based Optimization (TLBO) Differential Evolution (DE) | Modified Teaching Learning-Based Optimization (MTLBO) | Improving Particle Swarm Optimization (IPSO) | (−) PL | Inquality constraints |
| 10 | Abdelhak et al. 2014 [81] | PV-wind-battery | France | Off-grid | Genetic Algorithms (GA) Fuzzy Logic | Fuzzy-Adaptive Genetic Algorithm | Genetic Algorithm (GA) | (−) TC | Energy balance and state of charge (SOC) |
| 11 | Fetanat et al. 2015 [82] | PV-wind | Iran | Off-grid | Ant Colony Optimization for Continuous Domains () Integer Linear Programming (ILP) | -based ILP | Artificial Bee Colony (ABC) Genetic Algorithm (GA) Conventional Optimization B&B method | (−) TC | Number of PV panels, wind turbines, batteries |
| 12 | Zhou et al. 2016 [83] | PV-wind-battery-super capacitor | China | Off-grid | Particle Swarm optimization (PSO) Simulated Annealing (SA) | SAPSO | Particle Swarm optimization (PSO) | (−) LCC | Super capacitor charging and discharging, power surplus, and PL |
| 13 | Ahmadi et al. 2016 [84] | PV-wind-battery | Iran | Off-grid | Particle Swarm optimization (PSO) Big Bang-Big Crunch (BB-BC) Differential Evolution mutation operators | HBB-BC algorithm | Particle Swarm Optimization (PSO) Discrete Harmony Search (DHS) | (−) TPC | Number of hybrid system components, ENS, and charge quality of the battery |
| 14 | Maleki et al. 2016 [43] | PV-wind-battery | Iran | Off-grid | Particle Swarm Optimization (PSO) Monte Carlo Simulation (MCS) | PSOMCS | Not Specified | (−) TAC | Number of PV panels, wind turbines, batteries and SOC |
| 15 | Tito et al. 2016 [85] | PV-wind-battery | New Zealand | Off-grid | Exhaustive Search Method Genetic Algorithm (GA) | GA-based Exhaustive Search | Not Specified | (−) TC | Number of PV panels, wind turbines, and batteries |
| 16 | Maleki et al. 2016 [86] | PV-wind-battery | Iran | Off-grid | Harmony Search (HS) Chaotic Search (CS) | HSBCS | NHarmony search (HS) | (−) LCC | LPSP, swept area of wind turbine’s blades, total area occupied by PV panels and number of batteries |
| 17 | Guangqian et al. 2018 [87] | PV-wind-biodiesel-battery | Iran | On-grid | Harmony Search (HS) Simulated Annealing (SA) | HHSSAA | Harmony Search (HS) Simulated Annealing (SA) | (−) LCC | Number of PV panels, wind turbines and batteries and DOD |
| 18 | Peng et al. 2018 [88] | PV-wind-RO-battery | Iran | Off-grid | Simulated annealing (SA) chaotic search (CS) Harmony search (HS) Improving Harmony search (IHS) | HSCS, IHSCS, HSSA, IHSSA, SACS, HSCSSA, and IHSCSSA | Particle Swarm Optimization (PSO) Artificial Bee Swarm optimization (ABSO) Tabu Search (TS) Individual hybridization algorithm | (−) TLCC | The maximum total area occupied by the PV arrays, the maximum total area swept by the wind turbine blades, the maximum number of batteries, LPSP and SOC. |
| 19 | Kumar et al. 2018 [89] | PV-FC | India | Off-grid | Cuckoo Search (CS) Nelder-Mead (NM) Algorithm | Hybrid Nelder-Mead-based Cuckoo Search (HNMCS) Algorithm | Particle Swarm Optimization algorithm (PSO) Cuckoo Search (CS) Nelder-Mead (NM) Algorithm | (−) PL | Power balance of the power flow equations |
| 20 | Zhang et al. 2019 [90] | PV-wind-hydrogen | Iran | Off-grid | Chaotic Search (CS) Harmony Search (HS) Simulated Annealing (SA) | CS-HS-SA | Chaotic Search (CS) Harmony Search (HS) Simulated Annealing (SA) | (−) TLCC | Number of hydrogen tank, PV surface area, and the area swept by the wind turbine blades, LPSP. |
| 21 | Mellouk et al. 2019 [91] | PV-wind-CSP-CPV-battery | Morocco | On-grid | Genetic Algorithm (GA) Particle Swarm Optimization algorithm (PSO) | P-GA-PSO | Genetic Algorithm (GA) Particle Swarm Optimization algorithm (PSO) | (−) LCOE | Energy balance , unmet load, and inequality constraints |
| 22 | Khan et al. 2019 [92] | PV-wind-battery | Iran | Off-grid | Teaching Learning Based Optimization (TLBO) Enhanced Differential Avolution (EDE) Salp Swarm Algorithm (SSA) | Enhanced Evolutionary Sizing Algorithms (EESAs) | Teaching Learning-based optimization (TLBO) Enhanced Differential Evolution (EDE) Salp Swarm Algorithm (SSA) | (−) TAC | Number of hybrid system components, LPSP and DOD |
| 23 | Cai et al. 2020 [93] | PV-diesel-battery | Iran | Off-grid | Simulated Annealing (SA) Improved Harmony sSarch (IHS) | IHS-based SA | Simulated Annealing (SA) Improved Harmony Search (IHS) | (−) TLCC | Number of batteries, diesel generator fuel usage, and surface area of PVs |
| 24 | Khan et al. 2020 [94] | PV-wind-battery | Iran | Off-grid | Jaya Optimization Algorithm Teaching Learning-Based Optimization (TLBO) | Hybrid Jaya-Teaching-Learning-Based Optimization (JLBO) | Jaya Optimization Algorithm Teaching Learning-Based Optimization (TLBO) Genetic Algorithm (GA) | (−) TAC | Number of hybrid system components, LPSP and charge quality of the battery |
| 25 | Jahannoosh et al. 2021 [95] | PV-wind-hydrogen | Iran | Off-grid | Grey Wolf Optimiser (GWO) Sine Cosine Algorithm (SCA) | Hybrid Grey Wolf Optimiser-based Sine Cosine Algorithm (HGWOSCA) | Particle Swarm Optimization (PSO) Sine Cosine Algorithm (SCA) Grey Wolf Optimiser (GWO) | (−) LSCS | Number of hybrid system components and Load interruption probability (LIP) |
(−)-Minimize; (+)-Maximize