Optimal power flow solutions to power systems with wind energy using a highly effective meta-heuristic algorithm

Thi Minh Chau Le (Department of Electric Power Systems, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi)
Xuan Chau Le (Faculty of Mechanical and Electrical, Naval Academy, Nhatrang)
Ngoc Nguyen Phuong Huynh (Faculty of Electrical Engineering, The University of Danang - University of Science and Technology, Danang)
Anh Tuan Doan (Faculty of Electrical Engineering, The University of Danang - University of Science and Technology, Danang)
Thanh Viet Dinh (Faculty of Electrical Engineering, The University of Danang - University of Science and Technology, Danang)
Minh Quan Duong (Faculty of Electrical Engineering, The University of Danang - University of Science and Technology, Danang)

International Journal of Renewable Energy Development
Vol 12, No 3 (2023): May 2023

This paper implements two novel meta-heuristic algorithms, including the Coati optimization algorithm (COA) and War strategy optimization (WSO) for determining the optimal solutions to the optimal power flow problem incorporating the use of wind turbines (WTs). Two objective functions are considered in this study, including minimizing the entire electricity generation expenditure (EEGE) with the value point effect and minimizing the voltage fluctuation index (VFI). IEEE 30-bus system is chosen to conduct the whole study and validate the efficiency of the two applied methods. Furthermore, DFIG WTs are used in grids with varying power output and power factor ranges. The comparison of the results obtained from the two methods in all case studies reveals that WSO is vastly superior to COA in almost all aspects. In addition, the positive contributions of WTs to the EEGE and VFI while they are properly placed in the grid are also clarified by using WSO. As a result, WSO is acknowledged as a highly effective search method for dealing with such optimal power flow (OPF) problems considering the presence of renewable energy sources.