Modified Flower Pollination Algorithm for Optimal Power Flow in Transmission Congestion

Artificial intelligence (AI) is an attractive and popular paradigm for providing any machine/system, the ability to carry out tasks in a ‘smart’ way. Population-based metaheuristics utilize the intelligence available in nature to search for optimal solutions of complex problems. Randomization prevents the search from being trapped at local optima. This paper presents a model based on recently developed modified flower pollination algorithm (MFPA) to solve the problem of transmission congestion management (CM) in competitive electricity market by real power rescheduling of generating units. The performance of the proposed algorithm is tested for single line outage, increased demand and variation in line power limits using modified IEEE 30 bus and IEEE 57 bus test systems. The results are compared with basic FPA, particle swarm optimization (PSO), random search method (RSM) and simulated annealing (SA).