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

Author:

Vishnoi Shreya1,Nikolovski Srete2ORCID,Raju More1ORCID,Kirar Mukesh Kumar1,Rana Ankur Singh3ORCID,Kumar Pawan4ORCID

Affiliation:

1. Maulana Azad National Institute of Technology Bhopal, Bhopal 462003, India

2. 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

3. Department of Electrical and Electronics Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli 620015, India

4. Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India

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.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

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