Fig 1.
SORPD probabilistic uncertainty handling techniques.
Table 1.
Comparison of the proposed method with existing hybrid optimization approaches based on MCS and SBA methods for solving the SORPD problem.
Table 2.
Reduced Scenarios for Load, Irradiance, Wind Speed, and Associated Probabilities.
Fig 2.
The obtained scenarios of the load by the MCS.
Fig 3.
The obtained scenarios of the wind speed by MCS.
Fig 4.
The obtained scenarios of the solar irradiance by MCS.
Fig 5.
Optimal system parameters for SEPL: (a) voltage, (b) tap ratio, (c) reactive power with RERs integration, and (d) voltage, (e) tap ratio, (f) reactive power without RERs integration.
Table 3.
Description of algorithm parameters.
Table 4.
Simulation results for with and without RERs uncertainties.
Fig 6.
The power generated for the SEPL.
Table 5.
Simulation Results for SEVS with and without RERs uncertainties.
Fig 7.
Optimal system parameters for SEVS: (a) voltage, (b) tap Ratio, (c) reactive power with RERs integration, and (d) voltage, (e) tap ratio, (f) reactive power without RERs integration.
Fig 8.
The power generated for the SEVS.
Fig 9.
Convergence curves of MDO and other methods for SEPL, (a) with integration of RERs and (b) without integration of RERs.
Fig 10.
Convergence curves of MDO and other optimization methods for SEVS, (a) with integration of RERs and (b) without integration of RERs.
Fig 11.
The system voltage profile: (a) SEPL with RERs, (b) SEPL without RERs, (c) SEVS with RERs, and (d) SEVS without RERs.
Fig 12.
Classification of objective functions using box plots: (a) SEPL with considering RERs, (b) SEPL without considering RERs, (c) SEVS with considering RERs, and (d) SEVS without considering RERs.
Table 6.
Sensitivity analysis of SEPL under different number of scenarios.
Table 7.
Sensitivity analysis of SEVS under different number of scenarios.
Table 8.
Statistical comparison for , and SEVS for different algorithms in the two case studies for 25 runs.