Frequency Stabilization in an Interconnected Micro-Grid Using Smell Agent Optimization Algorithm-Tuned Classical Controllers Considering Electric Vehicles and Wind Turbines

Shreya Vishnoi, Srete Nikolovski (), More Raju (), Mukesh Kumar Kirar, Ankur Singh Rana and Pawan Kumar
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Shreya Vishnoi: Maulana Azad National Institute of Technology Bhopal, Bhopal 462003, India
Srete Nikolovski: Power Engineering Department, Faculty of Electrical Engineering, Computer Science and Information Technology, J. J. Strossmayer University of Osijek, K. Trpimira 2B, HR-31000 Osijek, Croatia
More Raju: Maulana Azad National Institute of Technology Bhopal, Bhopal 462003, India
Mukesh Kumar Kirar: Maulana Azad National Institute of Technology Bhopal, Bhopal 462003, India
Ankur Singh Rana: Department of Electrical and Electronics Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli 620015, India
Pawan Kumar: Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India

Energies, 2023, vol. 16, issue 6, 1-25

Abstract: In micro-grids (MGs), renewable energy resources (RESs) supply a major portion of the consumer demand. The intermittent nature of these RESs and the stochastic characteristics of the loads cause a frequency stabilization issue in MGs. Owing to this, in the present manuscript, the authors try to uncover the frequency stabilization/regulation issue (FRI) in a two-area MG system comprising wind turbines (WTs), an aqua-electrolyzer, a fuel cell, a bio-gas plant, a bio-diesel plant, diesel generation (DG), ship DG, electric vehicles and their energy storage devices, flywheels, and batteries in each control area. With these sources, the assessment of the FRI is carried out using different classical controllers, namely, the integral (I), proportional plus I (PI), and PI plus derivative (PID) controllers. The gain values of these I, PI, and PID controllers are tuned using the recently proposed smell agent optimization (SAO) algorithm. The simulation studies reveal the outstanding performance of the later controller compared with the former ones in view of the minimum settling period and peak amplitude deviations (overshoots and undershoots). The SAO algorithm shows superior convergence behavior when tested against particle swarm optimization and the firefly algorithm. The SAO-PID controller effectively performs in continuously changing and increased demand situations. The SAO-PID controller designed in nominal conditions was found to be insensitive to wide deviations in load demands and WT time constants.

Keywords: electric vehicles; frequency regulation; micro-grid; PID controller; smell agent optimization; wind turbine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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