A novel hybrid jellyfish search optimizer and sine cosine algorithm for optimal scheduling of real power for controlling congestion in deregulated power system environment
Year: 2026
Type: article
Abstract: This manuscript presents a novel Hybrid Jellyfish Search Optimizer-Sine Cosine Algorithm (hJSO-SCA) for controlling congestion cost in the power system framework. The proposed research work consists of 2 objectives: Firstly, to select the most sensitive generators for power rescheduling based on the Generator Sensitivity Factor, which is calculated by utilizing the power flow status of congested transmission lines. Secondly, to apply the proposed hJSO-SCA to achieve the minimum possible congestion cost. The formulation of hJSO-SCA has been done to enhance the exploration and exploitation phases of JSO with the integration of the SCA feature in the JSO optimization framework. Test results of hJSO-SCA on 23 conventional benchmark functions showed that the performance of hJSO-SCA has been enhanced during the exploration and exploitation phases. The 39-bus New England and IEEE-118 bus test systems have been used to validate the proposed approach with hJSO-SCA. Research outcomes highlighted that hJSO-SCA has reduced congestion cost by 17.19%, 13.48%, 10.18%, 7.26%, 4.20%, and 3.48% for the 39-bus system and 12.48%, 11.87%, 9.54%,7.12%, 6.05%, and 3,16% for the 118 bus system when compared to Salp swarm algorithm, Butterfly Optimization Algorithm, Harris Hawks Optimization, Gradient Based Optimization, Sine Cosine Algorithm, Gray Langurs Optimizer, Artificial Protozoa Optimizer, and Jellyfish Search Optimizer. The proposed hJSO-SCA has the least computational time as compared to the other referred optimization approaches and has successfully alleviated congestion with a significant reduction in the congestion cost.
Source: Next Energy
Author: Kaushik Paul
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,280
OpenAlex ID: W7168877375