Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

Authors

  • SwornaKokila M L Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India
  • Venkatarathinam R Department of Information Technology, Sri BalajiChockalingam Engineering College, A.C.S. Nagar, Irumbedu, Arni, Thiruvannamalai District, Tamil Nadu, India
  • Rose Bindu Joseph P Department of Mathematics, Dayananda Sagar College of Engineering, Bangalore, India
  • M. A. Manivasagam Computer Science and Engineering, Siddartha Institute of Science and Technology, Andhra Pradesh, India
  • Kakarla Hari Kishore Department of Electronics and Communication Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

DOI:

https://doi.org/10.13052/dgaej2156-3306.3915

Keywords:

Demand shift, peak demand, response time, autonomous computation, energy optimization, policy optimization

Abstract

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time (<0.4 seconds), are used to evaluate the model’s performance. The output of the method excelled all of the other current methods that were taken into account. The system’s rapid response time and its positive environmental impact further highlight its potential in managing smart grid resources effectively.

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Author Biographies

SwornaKokila M L, Department of Computing Technologies, School of Computing, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, TN, India

SwornaKokila M L was born in Kanyakumari, India, on April 29,1982. She received her B.E. degree Computer Science and Engineering from and M.E. degree in Computer Science and Engineering from ManonmaniumSundaranar University, Tirunelveli, India, in 2004 and 2008. Currently she is working as an Assistant Professor in Department of Computing Technology, SRM Institute of Science and Technology, Tamilnadu, India. Her current research interests include Image Processing, video surveillance, Machine Learning, Deep learning and Computer vision. She is a Life Member of the Indian Society for Technical Education (ISTE).

Venkatarathinam R, Department of Information Technology, Sri BalajiChockalingam Engineering College, A.C.S. Nagar, Irumbedu, Arni, Thiruvannamalai District, Tamil Nadu, India

Venkatarathinam R is currently working as Professor in the Department of Information Technology and Vice principal at SBC Engineering College, India. He received his PhD Degree from Dr. M.G.R Educational and Research Institute. His research areas of interest are Mobile Computing, Computer Networks and Internet of Things. He has published more than 10+ Journals in Scopus and SCI indexed journals. He also attended and conducted several faculty development programs in recent technologies.

Rose Bindu Joseph P, Department of Mathematics, Dayananda Sagar College of Engineering, Bangalore, India

Rose Bindu Joseph P is currently working as an Associate Professor in the Department of Mathematics at Christ Academy Institute for Advanced Studies, Bangalore. She received her Ph.D. in Mathematics from VIT University, Vellore in the field of Interval Type-2 Fuzzy Theory. She has qualified NET for lectureship by CSIR-UGC. She holds a Master’s degree and bachelor’s degree in Mathematics from Mahatma Gandhi University, Kerala. She has more than 15 years of experience in academia and research. She has published more than 15 research papers in Scopus indexed journals and presented papers in many international conferences. Her research interests include fuzzy theory, machine learning, soft computing and artificial intelligence.

M. A. Manivasagam, Computer Science and Engineering, Siddartha Institute of Science and Technology, Andhra Pradesh, India

M. A. Manivasagam received the B.E. Degree from Computer Science and Engineering Discipline, University of Madras, the M.E. Degree in Computer Science and engineering from Anna University and Ph.D. Degree from the Computer Science and engineering discipline, St.Peter’s Institute of Higher education and Research, India, 2019. He is currently a Professor in the Department of Computer Science and Engineering, Siddartha Institute of Science and Technology, Andhra Pradesh, India. His research interests include real time applications in Wireless Sensor Networks, the Internet of Things, Artificial Intelligence and Machine Learning.

Kakarla Hari Kishore, Department of Electronics and Communication Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

Kakarla Hari Kishore was born in Vijayawada, Andhra Pradesh, India. He received B.Tech (ECE) from JNTUH, M.Tech from SK University, A.P, India. He received his PhD in the field of VLSI from K L University, India. He received his Postdoctoral Fellowship (PDF) in Malaysia. He is working as Professor in ECE. He has published 4 IEEE Transactions and more than 130 research articles published in International Journals/Conferences. He has filled 07 patents and published 3 Textbooks. He had received several awards like the International Book of Records and the Indian Book of Records for the highest number of citations and H-index in Scopus/WoS. He is Editorial Board Member/Reviewer for several International Journals/Conferences. His research interests include VLSI Design, Fault Tolerance and Digital Testing, and SoC. He is a life member of ISTE, IE, IACSIT and IAENG.

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Published

2023-10-30

How to Cite

M L, S. ., R, V. ., Joseph P, R. B. ., Manivasagam, M. A. ., & Kishore, K. H. . (2023). Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques. Distributed Generation &Amp; Alternative Energy Journal, 39(01), 111–136. https://doi.org/10.13052/dgaej2156-3306.3915

Issue

2024: Vol 39 Iss 1

Section

Digital twin for Accelerating Sustainability in Energy Automation and Smart Grid