An intelligent hybrid technique for optimal generator rescheduling for congestion management in a deregulated power market

Congestion not only affects the power flow, but also leads certain issues, like market power, market inefficiency and security. When the transmission line exceeds their limits congestion is occurred (voltage, thermal, stability). Congestion management is a technique that helps to deal the issue corresponding to congestion. Many methods have been developed to manage congestion, and also several countries execute various strategies for the smooth functioning of their network. In this manuscript, the rescheduling of congestion management in a deregulated environment using DA-MRFO is proposed. The proposed hybrid technique is the combined execution of both the dragonfly algorithm (DA) and manta ray foraging optimization (MRFO). Dragonfly algorithm is enhanced using Manta ray Foraging optimization (MRFO), hence it is named DA-MRFO technique. The proposed method is used to alleviate transmission grid congestion on group-based electricity market via reprogramming active power of generators and also to reprogram the generator power. Congestion is the major Independent System Operator (ISO) concern on deregulated electricity market that is traditionally controlled by reprogramming generator output power. However, the effects of changes in the generator output power on the overloaded line flow are not identical. All the generators do not represent a desirable approach for congestion management. Here, a generator sensitivity factor is adapted for supporting the optimal generator selection in a congestion management (CM). In a congestion relief process, it is provided at the lowest possible cost. The reduction of power flow with collection of congested lines is probable through coordinated response of reactive energy dispatch as wind farms. The proposed approach is executed in modified IEEE 30 bus system and IEEE 57 bus system, then the efficiency is compared with the various existing optimization approaches.