Optimizing Distributed Generation Resources for Power Grid Quality Improvement Using Hybrid Optimization Technique

Abstract

Distributed generation (DG) integration is a critical requirement in modern distribution networks to enhance power grid quality. An essential aspect of this endeavor involves strategically placing and sizing various DG resources optimally. The primary objective of this study is to determine an efficient approach for allocating Photovoltaic (PV) and Wind Turbine resources to minimize overall power losses within the grid. To achieve this goal, we introduce a novel hybrid optimization technique that combines Particle Swarm Optimization (PSO) with Genetic Algorithms (GAs). Furthermore, we explore the influence of factors such as variations in PV daily consumption, load profile fluctuations, penetration levels, and changing climatic conditions on the optimization process across two comprehensive case studies presented in this research. Our modeling results reveal that the hybrid PSO-GA approach outperforms traditional PSO in terms of integration speed, power loss reduction, and enhancements to grid quality, including voltage and frequency profiles. Additionally, this study highlights the dynamic impact of load curve fluctuations and climate changes on the optimal location and capacity of DG resources, leading to a substantial reduction in grid power losses.

Keywords:

• wind turbines
• solar PV
• optimization algorithm
• genetic algorithm and optimum placement of RES

ACKNOWLEDGMENT

There is no acknowledgment involved in this work.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

No participation of humans takes place in this implementation process.

AUTHORS’ CONTRIBUTION

All authors are contributed equally to this work.

DATA AVAILABILITY STATEMENT

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

DISCLOSURE STATEMENT

Conflict of Interest is not applicable in this work.

HUMAN AND ANIMAL RIGHTS

No violation of Human and Animal Rights is involved.

Additional information

Funding

No funding is involved in this work.

Notes on contributors

Sengolrajan Thanasingh

Sengolrajan Thanasingh was born in 1982 in Manadikuppam, Panruti taluk, Cuddalore District, Tamilnadu, India. He has obtained B.E (Electrical and Electronics) and M.E (Power Electronics & Drives) from Annamalai University and Arunai Engineering College, Tiruvannamalai in 2003 and 2010 respectively. He has obtained Ph.D in Power Electronics from Annamalai University in 2018. He is presently an Associate Professor in the department of Electrical and Electronics Engineering of Kongunadu College of Engineering and Technology (Autonomous), Trichy (D.T) where he has servicing for 4.10 years from 2018 and as Assistant Professor & Head in the department of Electronics and Instrumentation Engineering of Arunai Engineering College, Tiruvannamalai for 9.2 years since 2006, where he has put in a total service of 14.4 years. His research areas of interest are: modeling, simulation and intelligent control for Z-source inverters, derivatives of Z-source inverters, Trinary source inverters, Renewable Energy Systems, E-Vehicles, IoT and Sensor technology. He has published 27 international journals, presented 40 technical papers in various national/international conferences, authored 8 books and filed 5 patents. He has received 4 national recognitions for providing mentor support to projects exhibited in national level.

A. N. Sasikumar

A. N. Sasikumar is currently working as an Associate Professor in Computer Science & Engineering department at Panimalar Engineering College, Chennai. He received M.E from Sathyabama University, Chennai in 2017 and MCA from Bharathidasan University in 2001. His area of research includes Artificial Intelligence, Machine Learning, Deep Learning, Computer Networks and Data Analytics. He has published 14 articles in journals and presented 5 conferences in international conferences. He can be contacted at [email protected].

K. Mahendran

K. Mahendran received his B.E (Electronics and Communication Engineering degree from Anna University, India in 2009. He received his M.E (Communication Systems) from Anna University, India in 2013. He received his Ph.D. in the specialization of Electronics and communication Engineering from Annamalai University, India. He currently working as Assistant Professor in Saveetha Engineering College, Chennai, India. His research interest includes Antennas, Metamaterials, MIMO systems and 5G wireless Communication. He is reviewer for many reputed international journals. Email: [email protected]

R. Saranya

R. Saranya received the Engineer degree in Information Technology from Mailam Engineering College in 2010. she received the Master degree in Computer Science and Engineering from Prist University, Puducherry, India in 2013. She is currently working as an Assistant Professor in the Department of Artificial Intelligence and Data Science at Rajalakshmi Institute of Technology, Chennai, India. Her area of interests includes Image processing, BlockChain Technology, Data Science, Artificial Intelligence and Internet of Things. She has published 2 articles in peer reviewed International journals and presented 6 papers in International conferences. She can be contacted at email: [email protected]