Optimal power flow for large-scale power system with shunt FACTS using efficient parallel GA
The main disadvantage of GAs is the high CPU time execution and the qualities of the solution deteriorate with practical large-scale optimal power flow (OPF) problems. This paper presents an efficient parallel GA (EPGA) for the solution of large-scale OPF with consideration of practical generators constraints. The length of the original chromosome is reduced successively based on the decomposition level and adapted with the topology of the new partition. Partial decomposed active power demand added as a new variable and searched within the active power generation variables of the new decomposed chromosome. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). Numerical results on three test systems IEEE 30-Bus, IEEE 118-Bus and 15 generation units with prohibited zones are presented and compared with results of others competitive global approach. The results show that the proposed approach can converge to the optimum solution, and obtains the solution with high accuracy.
optimal power flow large-scale facts genetic algorithm fuzzy logic shunt compensators