Table 5.
Summary of studies based on multi-objective design
| S. No. | Author/Year | System components | Site | Mode | Hybrid Algorithm | Novel Algorithm | Compared Methods | Objective function | Constraints |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Tahani et al. 2015 [96] | PV-wind-battery | Iran | Off-grid |
Flower Pollination Algorithm (FPA) Simulated Annealing (SA) |
FPA/SA |
Genetic Algorithm (GA) Particle swarm optimization (PSO) |
(−) LPSP (+) Cumulative savings |
PV panel tilt angle, number of PV panels and number of batteries |
| 2 | Lan et al. 2015 [97] | PV-diesel-battery | A Ship | On-grid |
Multi-Objective Particle Swarm Optimization (MOPSO) Non-dominated Sorting Genetic Algorithm (NSGA-II) |
MOPSO/NSGA-II | Not Specified |
(−) TC (−) CO2 emission |
Number of hybrid system components, ENS and charge quality of the battery |
| 3 | Kefyat et al. 2015 [98] | Multiple renewable distributed energy generation units (not specified) | Iran | Off-grid |
Ant Colony Optimization (ACO) Artificial Bee Colony (ABC) |
ACO-ABC |
Artificial Bee Colony (ABC) Particle swarm optimization with constriction factor approach (PSO-CFA) |
(−) PL (+) VSI (−) GHG Emission (−) COE |
Equality constraints |
| 4 | Khare et al. 2016 [99] | PV-wind-diesel-battery | Iran | Off-grid |
Many optimizing liaisons (MOL) Teaching{learning based optimization (TLBO) |
Hybrid MOL-TLBO |
Simple genetic algorithm (SGA) Particle swarm optimization (PSO) Many optimizing liaison (MOL) Teaching{learning based optimization (TLBO) Improved teaching{learning based optimization (ITLBO) |
(−) TAC (−) LOLP (−) CO2 emission |
Energy balance, and number of wind turbines, PV panels, batteries and Diesel generators |
| 5 | Ma et al. 2016 [100] | PV-wind-battery | China | Off-grid |
Particle swarm optimization (PSO) Genetic algorithm (GA) |
Natural selection particle swarm optimization (NSPSO) | Genetic algorithm (GA) |
(−) LPSP (−) LEP (−) LCC (−) K1 |
Number of type 1 and 2 of PV panels, type 1 and type 2 of wind turbines and batteries |
| 6 | Dufo-Lopez et al. 2016 [65] | PV-wind-diesel-battery | Tindouf | On-grid |
Multi-objective evolution- ary algorithm (MOEA) Genetic algorithm (GA) |
MOEA-based GA | Not Specified |
(−) NPC (+) HDI (+) JC |
Power balance, excess energy and state of charge (SOC) |
| 7 | Cho et al. 2016 [101] | PV-wind-diesel-battery | South Korea | Off-grid |
Teaching Learning-Based Optimization (TLBO) Clonal Selection Algorithm (CSA) |
TLBO-CS |
Particle Swarm Optimization (PSO) Genetic algorithm (GA) |
(−) TAC (+) LPSP (+) Fuel cost |
Number of PV panels, wind turbines, batteries, and Diesel generators and charge value of the battery |
| 8 | Yammani et al. 2016 [102] | PV-wind-micro turbine- FC | India | On-grid |
Shuffled frog-leap algorithm (SFLA) Bat algorithm (BAT) |
Shuffled bat algorithm (ShBAT) |
Genetic Algorithm (GA) Shuffled frog-leap algorithm (SFLA) Bat algorithm (BAT) |
(−) PL (+) VSI (+) Line flow capacity index |
Equality and Inequality con- straints |
| 9 | Bakshi et al. 2017 [103] | PV-wind-battery | India | On-grid |
Modified Human Opinion Dynamics (MHOD) Gravitational Search Algo- rithmm (GSA) |
MHODGSA | Gravitational Search Algorithmm (GSA) |
(−) PL (+) VSI |
Power balance equality con- straints of the power ow equations |
| 10 | Abdelshafy et al. 2018 [104] | PV-wind-hydrogen- diesel-battery | Egypt | On-grid |
Particle Swarm Optimiza- tion (PSO) Grey Wolf Optimizer (GWO) |
PSO{GWO |
Classical Particle Swarm Optimization (PSO) Classical Grey Wolf Optimizer (GWO) |
(−) TAC (−) CO2 emission |
Number of hybrid system components, energy storage capacity of hydrogen tank, renewable fraction and DOD |
| 11 | Bhullar et al. 2018 [105] | Multiple renewable DEG units (not specified) | India | On-grid |
Artificial Bee Colony (ABC) Cuckoo Search (CS) |
ABC-CS |
Genetic Algorithm (GA) Particle Swarm Optimization (PSO) GA-PSO |
(−) Power loss (+) Voltage pro-file |
Bus voltages constraints and real-reactive power limits of generators |
| 12 | Senthil et al. 2018 [106] | PV-Wind-FC | India |
Particle Swarm Optimiza- tion algorithm (PSO) Nelder-Mead Algorithm (NM) |
Hybrid Nelder Mead-Particle Swarm Optimiza- tion (HNMPSO) |
Genetic Algorithm (GA) Particle Swarm Optimization algo- rithm (PSO) |
(−) Power Loss | Power balance constraints, Bus voltage constraint, Bus voltage stability margin, and DG penetration constraint in network | |
| 13 | Nowdeh et al. 2019 [107] | PV-wind | India | On-grid |
Teaching-Learning Based Optimization (TLBO) Grey Wolf Optimizer (GWO) |
MOHTLBOGWO | In comparison with each of the meth- ods being used individually |
(−) Power losses (−) ENS |
Equilibrium power, bus volt- ages, DG generation limits, and line capacity |
| 14 | Sambaiah et al. 2019 [108] | PV-wind | India | On-grid |
Grey Wolf Optimizer (GWO) Differential Evolution (DE) |
Hybrid GWO | Particle Swarm Optimization (PSO) |
(−) PL (+) Voltage sta- bility (+) Network security index |
Equality and inequality con- straints |
| 15 | Battapothula et al. 2019 [109] | Multiple distributed energy generation units (not specified) | India | On-grid |
Shuffled Frog Leap Algorithm (SFLA) Teaching and Learn- ing Based Optimization (TLBO) |
SFL-TLBO | In comparison with each of the meth- ods being used individually |
(−) Voltage devia- tion (−) PL (−) DGs cost (−) The energy consumption of electric vehicle users |
Charging stations constraints and DG constraints |
| 16 | Radosavljević, et al. 2020 [110] | PV-wind | Serbia | On-grid |
Particle Swarm Optimiza- tion algorithm (PSO) Gravitation search algo- rithm (GSA) |
PPSOGSA |
ACO-ABC Modifed teaching-learning based opti- mization (MTLBO) Symbiotic Organism search (SOS) |
(−) Total energy loss (+) Voltage profit |
Power Flow Constraints, bus Voltage and Branch Load Constraints, and renewable distributed generation Capac- ity Constraints |
| 17 | Rezaeimozafar et al. 2020 [111] | Renewable resources, EV charging stations, and energy storage systems | Iran | On-grid |
Genetic Algorithm (GA) Particle Swarm Optimiza- tion (PSO) |
GA-PSO |
NSGA-II DE GA E-PSO |
(−) PL (−) Voltage fluc tuations (−) Demand sup- plying costs |
Demand-Supply Balance, bus Voltage Limitations, line Cur- rent Constraint, and pricing Constraints |
| 18 | Abuelrub et al. 2020 [112] | PV-wind-battery | India | On/Off-grid |
Biogeography-based optimi- sation (BBO) Particle Swarm Optimization algorithm (PSO) |
GPSBBO |
Non-dominated sorting genetic algorithm (NSGA-II) Multi-objective particle swarm optimization algorithm(MOPSO) |
(−) TC (−) System index of reliability |
Power balance between the generated and consumed power |
| 19 | Sultan et al. 2021 [113] | PV-wind-FC | Egypt | On/Off-grid |
Artificial Ecosystem Optimization (AEO) Sine Cosine Algorithm (SCA) |
Improved Artificial Ecosystem Optimization (IAEO) |
Artificial Ecosystem Optimization (AEO) Particle Swarm Optimization (PSO) Salp Swarm Algorithm (SSA) Grey Wolf Optimizer (GWO) |
(−) COE (−) LPSP (−) Excess energy |
Equality and inequality constraints |
| 20 | Suman et al. 2021 [114] | PV-wind-bio-generator- diesel-battery | India | Off-grid |
Grey Wolf Optimizer (GWO) Particle Swarm Optimization (PSO) |
Hybrid PSO-GWO |
Ant Lion Optimisation (ALO) Teaching Learning Based Optimisa- tion (TLBO) Whale optimisation algorithm (WOA) Cuckoo search algorithm (CSA) Artificial Bee Colony (ABC) |
(−) COE (−) LPSP (−) Excess energy |
Number of hybrid system components |
| 21 | Aliabadi et al. 2021 [115] | PV-wind-battery | Iran | On-grid |
Crow search algorithm (CSO) Particle Swarm Optimiza- tion algorithm (PSO) |
Improved Crow search algorithm (ICSO) |
Crow search algorithm (CSO) Particle Swarm Optimization algo- rithm (PSO) Manta ray foraging optimization (MRFO) |
(−) Cost (−) Loss (−) Voltage profile |
Number of hybrid system components, size of batteries, Network bus voltage con- straint, Allowable current constraint, Peak capacity of each renewable DG constraint, and Power balance constraint |
(−)-Minimize; (+)-Maximize