[17] |
2018 |
Improved grey wolf optimizer (IGWO) |
Minimizing generation cost, power loss, and voltage deviation |
Equality, generator, transformer, bus voltage, line loading, and installed reactive power resource constraints |
|
√ |
Improved rate of convergence with quality solution |
Voltage stability and power factor constraints are neglected |
[10] |
2018 |
Modified power loss index + Crow search (MPLI + CS) |
Minimize active power loss and cost |
Bus voltage, reactive power injected, complex power, capacitor size and power factor |
|
√ |
Reduced search space, accurate and quick convergence |
Voltage stability is not considered |
[18] |
2019 |
Voltage stability index + Genetic algorithm (VSI + GA) |
Minimize feeder current, voltage deviation and power losses |
Voltage and branch current carrying capacity |
√ |
√ |
Hourly variation of load demand is modelled |
Relaxed network constraints and single test system |
[7] |
2020 |
Enhance grey wolf algorithm (EGWA) |
Minimize total investment costs, maximize voltage profile, loading capacity, and benefits from the reduction of losses and purchased power |
Equality constraints, DG penetration level, power factor limit, CB size, node voltage, and branch current limits |
√ |
√ |
Improved performance, highly stable and superior capabilities |
Voltage stability and emission perspectives are ignored |
[3] |
2016 |
Tabu search + Chu–Beasley genetic algorithm (TS + CBGA) |
Minimize investment and operation costs |
Technical and operational constraints |
√ |
√ |
Very efficient and used for planning the system |
Single test system and stability constraint is ignored |
[19] |
2017 |
Grasshopper optimization algorithm + Cuckoo search algorithm (GOA + CSA) |
Minimize voltage deviation, line losses, and cost |
Equality, load bus voltage and DG capacity |
√ |
|
Less complexity with reduced computational time |
Limited type of DGs, voltage stability, and emission analysis are ignored |
[11] |
2017 |
Hybrid grey wolf optimizer (HGWO) |
Minimizing total real power losses |
Equality, Bus voltage, DG unit size, and power factor |
√ |
|
Algorithm performance is enhanced without tuning |
Demand uncertainties and reliability are not considered |
[20] |
2017 |
Harmony search algorithm + Particle artificial bee colony (HSA + PABC) |
Minimize real power loss, line loading, and voltage deviation |
Bus voltage, thermal limit of the lines, maximum power injection from DGs and CBs |
√ |
√ |
Enhanced performance with fast convergence |
Economic and voltage stability constraints are ignored |
[21] |
2018 |
HGWO-PSO |
Minimizing power losses |
Equality, bus voltage, line current, total generated power, and DG size |
√ |
|
Optimal solution with less iteration. |
Power factor and voltage stability constraints are ignored |
[13] |
2019 |
Multi-objective hybrid teaching learning-based optimization-grey wolf optimizer (MOHTLBOGWO) |
Minimizing power losses and improving reliability |
Equilibrium, bus voltage, DG size, and line capacity |
√ |
|
Improved speed of convergence and no local trapping |
Solar PV and wind resources are only considered |
[12] |
2019 |
Hybrid teaching-learning based optimization |
Minimize power losses, voltage deviation and maximize voltage stability index |
Equality, active and reactive power balance, voltage and thermal limits, and DG penetration |
√ |
|
Avoidance of local minima/maxima trappings and improved convergence |
Tuning of algorithm parameters are required; limited type of DGs |
[22] |
2019 |
Hybrid Whale optimization algorithm—Salp swarm algorithm (WOA-SSA) |
Minimize power losses and voltage deviation |
Bus voltage magnitude, DG number, and capacity |
√ |
|
More effective and better execution time |
Convergence is ignored, and limited types of DGs |
[23] |
2019 |
Hybrid weight improved particle swarm optimization + gravitational search algorithm (WIPSO + GSA) |
Maximize total cost benefit |
DG and capacitor power limits, voltage limits of bus |
√ |
√ |
Feeder’s failure rate is evaluated through compensation coefficients, greater convergence speed |
DGs with reactive power capabilities and stability are ignored |
[1] |
2020 |
Hybrid GA + PSO |
Minimize active, reactive power losses and voltage deviation |
Active and reactive power balance, voltage, line, and DG power limits |
√ |
|
More realistic, accurate, improved performance, and easy to apply |
Cost analysis, stability, and environmental factors are ignored |
[14] |
2020 |
Analytical hybrid PSO (AHPSO) |
Minimize total cost |
Real power of DG, angle deviation limit, and line current flow |
√ |
|
Modified 2/3rd rule is used, faster convergence |
No power factor and voltage stability assessment |
[24] |
2020 |
Hybrid Parameter improved PSO—Sequential quadratic programming (PIPSO-SQP) |
Minimize real power loss |
Net power flow, DG limit and node voltage |
√ |
|
Highly stable, rapid convergence and less computation time |
No power factor, cost analysis, and voltage stability assessment |
[25] |
2020 |
Hybrid Phasor PSO and GSA (PPSOGSA) |
Minimize active power losses |
Equality, bus voltage, THD of voltage, branch flow, DG and capacitor capacity, and positions |
√ |
√ |
Different constraints are used, solutions are effective, robust with high-quality and less no. of iterations |
Limited type of DGs, power factor constraint, stability, and economic issues are ignored |
[26] |
2020 |
Hybrid CBGA—Vortex search algorithm (CBGA- VSA) |
Minimize power loss |
Complex power and network voltage |
√ |
|
Successive approximation power flow is used. More efficient and better solution with low computational times |
Limited type of DGs, emission, and stability investigations are ignored |
[27] |
2021 |
Hybrid empirical discrete metaheuristic—Steepest descent method (EDM-SDM) |
Minimize power losses |
Active and reactive power balance, DG status and limits, and voltage |
√ |
|
High-quality and straightforward solutions with low tuning parameters |
Stability and economic evaluations are ignored |