A TLBO algorithm-based optimal sizing in a standalone hybrid renewable energy system
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.10Keywords:
Renewable Energy System, Hybrid System, TLBO algorithm, Standalone RES, PV SystemDimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Depletion of fossil fuels, increase in fuel prices, and global warming have motivated the utilization of renewable energy resources like solar and wind, as they are eco-friendly. Due to the stochastic nature of PV and wind, using a single energy source is not reliable and uneconomical as it results in system over-sizing. Integration of renewable sources such as PV and wind can significantly increase energy reliability compared to single-source systems. PV and wind hybrid systems are economically advantageous in isolated areas for providing continuous and quality power due to their inherent complementary characteristics and availability in most areas. Utilizing grid-tied renewable energy resources is also economical and reliable to overcome power outages in remote areas. This study proposes a TLBO algorithm for optimal design and sizing of HRES in both standalone and grid-connected modes due to its simplicity and fewer parameters to adjust. The objective of the optimization problem in standalone, as well as the grid-connected mode, is to minimize the LCE and maximize the system reliability and renewable energy integration while satisfying the system constraints and load demand. The number of PV panels, wind turbines, and batteries is taken as decision variables optimally determined by the proposed optimization algorithm. The simulations are carried out in MATLAB software. The effectiveness of TLBO in designing and sizing the hybrid system is investigated, and its performance is compared with other well-known optimization algorithms PSO; the TLBO provides the best optimal solution, better performance, and faster convergence speed compared to different algorithmsAbstract
How to Cite
Downloads
Similar Articles
- M. Menaha, J. Lavanya, Crop yield prediction in diverse environmental conditions using ensemble learning , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Iniyan, A. Banumathi, Brower blowfish nash secured stochastic neural network based disease diagnosis for medical WBAN in cloud environment , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- A. R. Jasmine Begum, M. Parveen, S. Latha, IoT based home automation with energy management , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Olivia C. Gold, Jayasimman Lawrence, Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Rajesh Kumar Singh, Abhishek Kumar Mishra, Ramapati Mishra, Hand Gesture Identification for Improving Accuracy Using Convolutional Neural Network(CNN) , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- R. Kalaiselvi, P. Meenakshi Sundaram, Unified framework for sybil attack detection in mobile ad hoc networks using machine learning approach , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- P. Vinnarasi, K. Menaka, Advanced hybrid feature selection techniques for analyzing the relationship between 25-OHD and TSH , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Vijai K. Visvanathan, Karthikeyan Palaniswamy, Thanarajan Kumaresan, Green ammonia: catalysis, combustion and utilization strategies , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Gulshan Makkad, Lalsingh Khalsa, Vinod Varghese, Fractional thermoviscoelastic damping response in a non-simple micro-beam via DPL and KG nonlocality effect , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Rajeev P. R., K. Aravinthan, A novel approach for metrics-based software defect prediction using genetic algorithm , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 2 3 4 5 6 7 8 9 10 11 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Muthuvel Balasubramanian, Jonnakuti V. G. Rama Rao, Surya C. P. R. Sanaboina, Vavilala Venkatesh, Amalodbhavi Sanaboina, Tracking and control of power oscillation dampings in transmission lines using PV STATCOM , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper

