Table 1 Synopsis of indicated hybrid ser optimization methods.
Ref. | Optimized parameters | Optimization method | Objective function | Benefits | Drawbacks |
|---|---|---|---|---|---|
Fathy et al.1 | PV/WT/BS/DG | SSO | Minimize COE and LPSP | Basic and clear strategy | The SSO algorithm suffers from poor exploitation, slow convergence, and imbalanced exploration and exploitation processes. |
Belboul et al.2 | PV/WT/BS/DG | MOSSO | Minimize COE and LPSP | More economic and reliable | |
Zhu et al.3 | PV/WT/BS/DG/TCT | IMOGWO | Minimize CACS and DPSP | Fewer parameters, simple principles, and implementing easily | Poor local search capability and slow convergence rate |
Jasim et al.4 | PV/WT/biomass | GWCSO | Levelized Cost of Energy | Efficient random walks and balanced mixing | Slow convergence, low precision and poor local search capability |
Samy et al.5 | PV/ wind/ FC | Firefly algorithm (FA). | performance and the total cost | efficient and an easy-to-implement algorithm | Big tasks are difficult to put in Algorithms |
Ghenai et al.6 | PV/PEM fuel cell/DG | Modeling and simulation (M&S) | Providing energy for cruises in Sweden | Able to test a product or system works before building it | One significant downside of simulation is its lack of precision. |
Fard et al.7 | PV/ Battery System | HOMER software | Minimize COE and CO2 emission | Determine the best mix of resources for the least-cost solution | Homer need excess detailed data |
Borhanazad et al.9 | PV/ WT/ BS/DG | MOPSO | Find the best system configuration and sizing of components | The best algorithm | He did not focus on reducing the price of energy and CO2 emission |
Ghorbani et al.10 | WT/PV/BS | GA-PSO and MOPSO | Minimize costs and improve reliability | Falls easily into a local optimum in a multi-dimensional space and has a lower iterative convergence rate | The algorithm is capable of doing more tasks that they did not mention |
Ramli et al.11 | PV/WT/BS | MATLAB and HOMER | Electricity production and energy cost | System efficiency and capacity factors are included | Unclear optimization method |
Bukar et al.12 | PV/WT/BS/DG | GOA | Cost of energy (COE) and deficiency of power supply probability (DPSP) | Clear EMS is followed | Several calculation steps are involved |
Sarkar et al.13 | PV/ WT/ BS/ biomass | HOMER | Minimize LPSP | A simple PSCAD model is proposed. | Excess detailed are required |
Ren et al.14 | PV/ Fuel Cell/ BS | NA | Reducing CO2 emission | An important aspect studied is CO2 emissions | The method of action is not clear, as are the results |
Nicu Bizon15. | WT / Fuel Cell | ESC | Minimize MPP | The Extremum Seeking Control (ESC) algorithm is efficient optimization tool | This work requires a lot of information, which takes a lot of time |
Moghaddam et al.16 | PV/WT / Fuel Cell | Flower pollination algorithm | Reduce the total net present cost (TNPC) | Excellent methodology | The results are difficult to understand and long |
Ramli et al.17 (2018) | PV/Wind/diesel | MOSaDE | Analyze LPSP, COE, RF | It takes into account real weather data for the area | Fuel consumption is great |
Gharibi et al.18 | PV/FC/DG | Multi-objective crow search | Minimize COE and LPSP | Different RESs scenarios are presnted | The calculation method is very large, which wastes a lot of time in extracting the results |
Amshidi et al.19 | PV/FC/DG | Minimize COE and LPSP | Simple and easy to understand | ||
Maleki et al.25 | PV/WT/BS/DG | PSO | Reducing CO | A new topology is taken up in this article | Probability indicators are neglected |
Meisheng et al.28 | PV/Bio generator /DG/BS | HOMER | Minimize COE end CO2 emission | Taking into account the gases emitted | Complex simulation |
Mahmoud et al.29 | PV/WT / fuel cell | Marine predators algorithm | Minimize COE | Good strategy for the algorithm | Early convergence of the chosen algorithm, poor search ability, and stagnation in the local optimum. |
