Soft Computing Techniques for Optimal Capacitor Placement - Complex System Modelling and Control Through Intelligent Soft Computations - page 608

Table 2 Load duration data

Low Medium Peak

Load level 0.5 1 1.4

Time interval (hrs) 1,000 6,760 1,000

Table 3 Parameters and their
values

Parameter Value

Cost of energy (US$), k e 0.06 US$/kWh

Cost of peak power loss
(US$), k p

168 US$/kW per
year

In fl ation rate (%), m 5 % per year

Creditor ' s interest rate (%), f

5 % per year

Maintenance cost in US$, M 2 % of the total cost

Capacitor installation cost
(US$), k inst

US$1,000

Capacitor cost (US$), k c (in
multiple of 150 kvar)

3 US$/kvar

1. Case I: Comparison of GA and PSO with P-1 (Static Cost Based Planning
Method), and
2. Case II: Comparison of PSO and NM-PSO with P-2 (Variable Cost Based
Planning Method).

In Case-I, the best method obtained is used for comparison with the P-2 and
NM-PSO combination (Kumar and Singh 2011 ). For these cases, the load duration
data is shown in Table 2 . For the simulation, performed on the MATLAB ® plat-
form, the different system parameters considered are shown in Table 3 .
The bus voltage constraints are taken as V min = 0.90 pu, and V max = 1.10 pu.

6.1 Candidate Bus Selection

For the selection of the candidate buses the bus sensitivity (BSi) i ) method has been
found superior than the loss sensitivity based method (Kumar et al. 2011 ). The
candidate buses are selected using the numerical value of BSi i in their descending
order, for IEEE 69-bus test system, as depicted in Fig. 13 . Amongst 69 buses, 9
buses, viz., 11, 12, 21, 48, 49, 50, 59, 61, and 64 are selected for the capacitor
placement.