Comparative study of bat & flower pollination optimization algorithms in highly stressed large power system

Optimal power flow is an important non-linear optimization task in power systems. In this process, the total power demand is distributed amongst the generating units such that each unit satisfies its generation limit constraints and the cost of power production is minimized. This paper presents a comparative study of new meta-heuristic optimization techniques namely bat and flower pollination algorithm for the optimal solution of optimal power flow problem such as minimizing the fuel cost of a thermal power plant. In this paper PSO is also taken just as a reference for measure the performance of the above two techniques. The numerical results clearly show that the bat algorithm gives better results than flower pollination algorithm in terms of fuel cost value and time required to reach global best solution. In order to illustrate the effectiveness of the proposed algorithm, it has been tested on highly stressed modified IEEE 300-bus test system.