Fig 1.
Flow chart of applying the GTO in solving stochastic UC problem.
Table 1.
Forecasted load data.
Table 2.
The data for the10-thermal units.
Fig 2.
The convergence curve for the GTO handling the DUC (case 1).
Fig 3.
The commitment scheduleing of thermal units of the DUC (case 1).
Fig 4.
The output powers of the thermal units at the DUC (case 1).
Fig 5.
A statistical comparison for cost reduction by different optimizers at DUC.
Table 3.
The cost comparison between the GTO and other well-known algorithms without considering VPE (case 1).
Fig 6.
The convergence curves comparison of GTO, GWO, WOA, and AHA with VPE (case 1).
Table 4.
The cost comparison between the GTO and other well-known algorithms with VPE (case 1).
Fig 7.
The produced scenarios of the load demand during 12.00–13.00 h by applying 1000 MCSs.
Fig 8.
The convergence curve for the GTO handling the SUC at uncertain load demand (case2).
Fig 9.
The commitment scheduleing of thermal units of the SUC at uncertainty of the load demand (case2).
Fig 10.
The output powers of the thermal units of the SUC at uncertainty of the load demand (case 2).
Table 5.
Costs’ results for case 2.
Fig 11.
Mean value and SD of wind speed over 24-hr.
Fig 12.
Mean value and SD of solar irradiance over 24-hr.
Fig 13.
The produced scenarios of the wind speed during 12.00–13.00 h by applying 1000 MCSs.
Fig 14.
The produced scenarios of the solar irradiance during 12.00–13.00 h by applying 1000 MCSs.
Fig 15.
The commitment scheduleing of thermal units of the SUC at uncertainty of the load demand and RE resources (case3).
Fig 16.
The output powers obtained of the thermal units at the SUC with uncertainty of the load demand and RE resources (case 3).
Fig 17.
The convergence curve for the GTO handling the SUC at uncertainty of the load demand and RE resources (case3).
Fig 18.
The convergence curves comparison between case 2 and case 3.
Table 6.
Specification of wind turbine.
Table 7.
Specification of solar unit.
Table 8.
Costs comparison between case 2 and case 3.