Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles

Amil Daraz; Irfan Ahmed Khan; Abdul Basit; Suheel Abdullah Malik; Salman A AlQahtani; Guoqiang Zhang

doi:10.1016/j.heliyon.2024.e28073

Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles

Heliyon. 2024 Mar 12;10(6):e28073. doi: 10.1016/j.heliyon.2024.e28073. eCollection 2024 Mar 30.

Authors

Amil Daraz¹, Irfan Ahmed Khan², Abdul Basit¹, Suheel Abdullah Malik³, Salman A AlQahtani⁴, Guoqiang Zhang¹

Affiliations

¹ School of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
² Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
³ Department of Computer and Electrical Engineering, FET, International Islamic University, Islamabad 44000, Pakistan.
⁴ Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.

Abstract

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

Keywords: Electrical vehicles; Hybrid power system and fractional order controller; Load frequency control; Renewable energy sources; White shark optimization.