A Novel Improved Crow Search Algorithm to alleviate congestion in power system transmission lines

Congestion management is one of the most critical issues in the operation of deregulated power systems. This research work proposes a Congestion Management (CM) approach considering the optimal real power rescheduling of power system generators. The Generator Sensitivity Factors (GSF) have been considered to select the most sensitive generators that would participate in CM. An Improved Crow Search Algorithm (ICSA) has been formulated to minimize the congestion cost. The stages of exploration and exploitation of the Crow Search Algorithm (CSA) have been modified with the incorporation of the dynamic awareness probability and Levy flight approach for the formulation of ICSA in CM. The CM cost optimization problem was validated using the standard 39-bus new England system and the IEEE-118 bus system. A comparison analysis with other optimization techniques has also been carried over to further assess the performance of the proposed ICSA approach. The obtained results showed that the congestion cost achieved with ICSA has been reduced by 1.84%, 4.01%, 6.25%, when compared to the Differential Evolution (DE) Algorithm, Grey Wolf Optimization (GWO), and Crow Search Algorithm (CSA), respectively, for the 39-bus system. For the IEEE 118-bus system, the application of ICSA has curtailed the congestion cost by 2.50%, 8.55%, and 12.50%, when compared to the congestion cost achieved with DE, GWO, and CSA, correspondingly. It is observed that the performance of ICSA for CM cost has superseded the other approaches adopted for CM.(C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

Congestion management is a critical issue in the operation of deregulated power systems. This research proposes an approach that considers optimal real power rescheduling of generators to manage congestion. An improved crow search algorithm is used to minimize congestion costs, and the results show that this approach outperforms other optimization techniques.