Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements

Author:

Kumar Pramod1,Swarnkar Nagendra Kumar1,Mahela Om Prakash23ORCID,Khan Baseem34ORCID,Anand Divya56,Singh Aman67,Mazon Juan Luis Vidal68ORCID,Alharithi Fahd S.9ORCID

Affiliation:

1. Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur, India

2. Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, Rajasthan, India

3. Engineering Research and Innovation Group (ERIG), Universidad Internacional Iberoamericana, Campeche, C.P. 24560, Mexico

4. Department of Electrical Engineering, Hawassa University, Hawassa, Ethiopia

5. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India

6. Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, Santander 39011, Spain

7. Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India

8. Department of Engineering, Universidad Internacional Iberoamericana, Arecibo 00613, Puerto Rico, USA

9. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Abstract

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031.

Funder

Taif University

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Modeling and Simulation

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