Hybrid Optimization of AC Transmission Expansion Planning for Augmenting Renewable Energy Integration: A Case Study of Kerala State Electricity Board’s Subsystem

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Shereena Gaffoor, Mariamma Chacko

10.14445/23488379/IJEEE-V12I4P112

How to Cite?

Shereena Gaffoor, Mariamma Chacko, "Hybrid Optimization of AC Transmission Expansion Planning for Augmenting Renewable Energy Integration: A Case Study of Kerala State Electricity Board’s Subsystem," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 4, pp. 170-190, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I4P112

Abstract:

The incorporation of renewable energy sources into power systems is the key factor in achieving de-carbonization, promising a future of energy that is not only cleaner but also more sustainable. This paper presents a multi-objective hybrid optimization method for AC Transmission Expansion Planning (TEP) in electrical power systems incorporating renewable energy sources using the IEEE 24 Reliability Test System. This is an extended work of Multi-Objective Hybrid Optimization for Renewable Energy Integrated Electrical Power Transmission Expansion Planning in DC systems proposed by the authors. The method combines the Genetic Algorithm and the Grey Wolf Optimization known as Grey Wolf with the Genetic Algorithm (GWGA), taking advantage of both methods to optimize the cost and load-shedding factors of power transmission systems with the objective of minimizing transmission losses. The results show that GWGA consistently reduces losses from 2.65 MW to 1.91 MW between the 100th and 500th iterations, demonstrating remarkable stability and convergence compared to all other conventional algorithms. A real system using a modelled subsystem of the Kerala State Electricity Board (KSEB) central zone for optimized TEP using GWGA is also done, and the results are presented. The results achieve the lowest transmission loss with an optimum value of 1.45 MW from 300 iterations onwards. It also minimizes power transmission expansion costs and reduces the risk of load shedding, enhancing the cost-effectiveness of renewable energy integration. This research work also addresses the difficulties encountered in the TEP electrical power system through the optimization of reinforcement lines, generators, and renewable energy sources. The findings presented here can potentially inform future transmission expansion strategies within the KSEB and serve as a model for similar systems globally.

Keywords:

Genetic Algorithm, Grey Wolf Optimization, Multi-objective, Renewable energy integration, Transmission expansion planning.

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