## 6 Simulation Results  
The decision-making variables in the above equations include the generator bus voltage, tap changers, reactive power generated by the capacitors, optimal location and capacity of the SVC and TCSC. The reactive power flow also changes; thus, ABC can be used to achieve the most optimal case. To evaluate the performance of this method, it is applied to the standard 30-bus network represented in the attachment. Information on the network is found in [17]. There are two capacitor banks that are also installed on buses 5 and 24 with a maximum value of 4.3 and 19 MVAr, respectively. The voltage magnitude in generator buses and load buses varies between 0.96 to 1.05 and 0.94 to 1.06, respectively. To determine the optimal location and capacity, one SVC and TCSC are installed, which are considered as a sequence with three cells for the optimization problem, where the first two cells represent the location (SVC bus number and line number for the TCSC) and the third cell represents capacity. According to the generator bus model for the SVC, its optimal reactive capacity is calculated using the power flow [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. The capacitance range of the SVC is between \(-50\) to \(50\) MVAr. The simulation results of the 30-bus network that include the system's decision-making variables and performance parameters for the proposed algorithm (MABC), the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and also the genetic algorithm (GA) are given in Table 1.  
The optimal solutions of the six objective functions mentioned for the proposed modified artificial bee colony (MABC) algorithm are compared with the optimal solutions of the GA, PSO and ABC algorithms. In all cases, the MABC algorithm has a lower loss and costs and also a better voltage profile than three other algorithms. The power system total active power loss is 12.36 MW for the MABC algorithm when the f1 function is considered as an objective function. The total losses are 12.83 MW, 12.94 MW and 12.85 MW for the PSO, GA and ABC algorithms respectively. Also, the reactive power supply costs are 394.5 \(\upmu\)s/h for PSO, 397.2 \(\upmu\)s/h for GA, 387.2 \(\upmu\)s/h for ABC and 381.6 \(\upmu\)s/h for MABC. The average voltage of the buses for the second objective function, \(f_{2}\), is maximum. However, the value of this parameter for the other functions is acceptable. The third objective function (\(f_{3}\)), which is the cost of supplying reactive power, is the minimum of 350.1 \(\upmu\)s/h, while it is 481 and 553 \(\upmu\)s/h for \(f_{5}\) and \(f_{6}\) for the proposed MABC algorithm. Also, in this case, the reactive power supply cost by using MABC is less than the PSO, GA and ABC algorithms. By using loss as the objective function (\(f_{4}\)), the loss is reduced to 11.52 MW for the MABC. The losses are 12.77 MW for PSO, 12.32 MW for GA and 11.89 MW for the ABC algorithm. In the objective function (\(f_{4}\)), the reactive output power of the generators is reduced, and the reactive output power of the capacitor banks and the SVC is increased to reduce the costs. As the power flowing through the lines is reduced, the loss is also reduced. Using two 19.8 and 4.3 MVAr capacitor banks, one 40.3 MVAr SVC on bus 28 and a TCSC with 0.32% compensation on line 8, the cost of supplying the reactive power of the network is reduced and voltage deviation is at a minimum. The voltage profile is shown in Figure 5 to evaluate the voltage stability and the voltage profile changes in the different objective functions.