Open AccessA Multi-objective Hybrid Optimization for renewable energy integrated Electrical Power Transmission Expansion Planning
Author(s) -
Shereena Gaffoor,
Mariamma Chacko
Publication year - 2022
Publication title -
international journal of electrical and computer engineering systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.141
H-Index - 4
eISSN - 1847-7003
pISSN - 1847-6996
DOI - 10.32985/ijeces.13.2.1
Subject(s) - genetic algorithm , mathematical optimization , renewable energy , electric power system , minification , computer science , transmission (telecommunications) , heuristic , population , engineering , power (physics) , reliability engineering , electrical engineering , mathematics , telecommunications , physics , quantum mechanics , demography , sociology
Due to the large size of conventional electrical power transmission systems and the large number of uncertainties involved, achieving the most favourable Transmission Expansion Planning solution turns out to bealmost impossible. The proposed method intends to develop a novel method to solve Transmission Expansion Planning problems in electric power systems incorporating renewable energy sources like wind turbines and Photo Voltaic array using IEEE 24 Reliability Test System. For enhancing the efficiency of search processes and to make its use easier on diverse networks and operations, the hybridization of two renowned meta-heuristic algorithms known as Grey Wolf Optimization (GWO) and Genetic Algorithm (GA) termed as Grey Wolf with Genetic Algorithm (GWGA)is adopted. A novel distance factor based on the best position and current position of the solution in Grey wolf optimization is introducedand proposed for the hybridization technique and gives a quick and promising solution with reduced computational time. The GWO and GA algorithms are combined suitably to achieve the advantages of both algorithms. With this proposed model, the investment cost of the transmission line and the maximum amount of power that can be distributed to the consumer is optimized with an objective of minimum load shedding. Among the state-of-the-art optimization techniques considered, a remarkable performance percentage improvement in the expansion plan in terms of cost reduction and load shedding minimizationhas been obtained in GWO, but when hybridized with GA, an improvement of 13.42% in cost function and 18.65% in load shedding is achieved for apopulation size of 60. Hence, the proposed method guarantees to generate the best solution with a faster convergence resulting in reduced computational time.