Maximum Loadability Enhancement with a Hybrid Optimization Method

Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions

Multi objective Adaptive Tumbling Bacterial Foraging in VAR Solutions for Sustainable Power System Operation

The application of the developed optimization technique Multi Objective Adaptive Tumbling Bacterial Foraging (MOATBFO) was introduced to solve the multi objective Reactive Power Planning (RPP) problems. The objective of conventional RPP problems is to minimize the total power losses in a system. However, in this study, the aspect of security was also taken into consideration in terms of voltage stability condition in solving RPP problems. Hence, the RPP problem is now termed as security constrained RPP (SCRPP) and generalized into a multi objective function via weighted sum method that labeled as MOSCRPP. The best minimum voltage solution for the network is aimed in ensuring the sustainable power system operation.  In order to verify the performance of the proposed technique were used for MOSCRPP in the IEEE 57 bus system thus the comprehensive analyses were also conducted with other multi objective Meta heuristic Evolutionary Programming (Meta-EP). From the results it shows that the multi objective ATBFO optimization is able to give better overall improvement in the objective functions for SCRPP problems