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
- Amanda Q. Okronipa, Jones Y. Nyame, Adoption of health information systems in emerging economies: Evidence from Ghana , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Deepa S, Sripriya T, Radhika M, Jeneetha J. J, Experimental evaluation of artificial intelligence assisted heart disease prediction using deep learning principle , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Lakshminarayani A, A Shaik Abdul Khadir, A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Amresh Kumar Singh, Manjit Singh Chhetri, Pushyamitra Mishra, Toughness and Ductile Brittle Transition Temperature of Different Mineral Filler Reinforced TPOs Composites , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Mohiyuddeen Hafzal, Gayathri B.J., M. Meghana Shet, Shaping the future: Education and skill development for Viksit Bharat@2047 , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Vandana, PANKAJ KUMAR, Vikas Jangra, Ambrish Pandey, An empirical study on the print suitability of hybrid modulated screen and digitally modulated screen in offset printing process , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Rajeshwar Mukherjee, Uday S. Dixit, Understanding cosmopsychism based on stochastic electrodynamics from the perspective of the Indian knowledge system , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Swetha Rajkumar, Jayaprasanth Devakumar, LSTM based data driven fault detection and isolation in small modular reactors , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Shashank Suman, Prashant Kumar, Seasonal Estimation in Primary Productivity of Akilpur Lake in Dighwara, Saran (Bihar) , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
<< < 5 6 7 8 9 10 11 12 13 14 > >>
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