Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers

Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2021) Optimal power flow incorporating stochastic wind and solar generation by metaheuristic optimizers. Microsystem Technologies, 27 (9). 3263 -3277. ISSN 0946-7076. (Published)

Preview	Pdf Optimal power flow incorporating stochastic wind and solar .pdf Download (139kB) | Preview
	Pdf Optimal power flow incorporating stochastic wind and solar_FULL.pdf Restricted to Repository staff only Download (1MB)

Abstract

Optimal power flow (OPF) is one of the complex problems in power system operation that includes multi-modal, large-scale, non-convex and non-linear constrained optimization problems. Due to these features, solving the OPF problem is becoming an active topic to be solved by power engineers and researchers. In this paper, recent metaheuristic algorithms namely Grasshopper Optimization Algorithm (GOA), Black Widow Optimization Algorithm, Grey Wolves Optimizer, Ant Lion Optimizer, Particles Swarm Optimization, Gravitational Search Algorithm, Moth-Flame Optimization and Barnacles Mating Optimizer (BMO) will be used to solve three objective functions of OPF problem viz. (1) cost minimization of the power generation that consists of thermal, stochastic wind and solar power generations, (2) power loss minimization, and (3) combined cost and emission minimization of power generations. To assess the performance of these selected metaheuristic algorithms on OPF, a modified IEEE 30-bus system that incorporate the stochastic wind and solar power generators will be used. Statistical studies are performed to identify the effectiveness of algorithms under consideration. Test results suggest that BMO performs better compared to the rest of algorithms and demonstrate that it can be effective alternative for the OPF problem solution.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Acoustic generators; Constrained optimization; Electric load flow; Solar power plants; Stochastic systems
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical & Electronic Engineering Faculty of Computing
Depositing User:	Dr. Mohd Herwan Sulaiman
Date Deposited:	03 Aug 2022 03:08
Last Modified:	03 Aug 2022 03:09
URI:	http://umpir.ump.edu.my/id/eprint/33825
Download Statistic:	View Download Statistics

Actions (login required)

View Item