Treffer: An improved swarm intelligence for power system economic operations based on optimal power generation to control congestion in transmission channels.

Power system congestion is a key challenge in the deregulated era. Finding ways to transmit power without congestion is crucial. Achieving optimal generator power output with the application of evolutionary algorithms is a primary method to manage this issue. This paper proposes a novel modified whale optimization algorithm (MWOA) for the optimal power generation to address the power system economic operations for the nonlinear transmission congestion management cost problem. The MWOA has been developed by introducing two correction factors in the exploration and exploitation phases that enhances its performance and coordination in these two phases. The MWOA strikes a good balance between exploring and exploiting WOA phases. Its capabilities are validated using benchmark functions and compared to other heuristic algorithms. The MWOA has been applied to accomplish minimum congestion cost by optimal adjustment in the generator power output while alleviating congestion in the lines. Generator sensitivity factors are computed to identify the most sensitive generators that are participating in the power rescheduling process. The results show that MWOA lowered congestion costs by 6.00%, 4.45%, 3.14%, and 2.30% for the 39-bus system, 7.91%, 4.29%, 2.57%,1.69% for the 30-bus system, and 10.44%, 8.48%, 7.36%, 5.17%, 2.00% for 118-bus system when compared to FEP, GA, DE, and original WOA. Statistical and comparative analysis has shown that with MWOA there has been a considerable reduction in the congestion cost, system losses, amount of power rescheduled, and improved in the system bus voltages when contrasted with other referred optimization algorithms. [ABSTRACT FROM AUTHOR]