Techno-Economic Feasibility Analysis of Grid-Connected Microgrid Design by Using a Modified Multi-Strategy Fusion Artificial Bee Colony Algorithm


The present work investigates the techno-economic solution that can address the problem of rural electrification. To maintain a continuous power supply to this village area, a grid-connected microgrid system was designed that consists of solar photovoltaic (SPV) and battery energy storage systems (BESS). The recently introduced multi-strategy fusion artificial bee colony (MFABC) algorithm was hybridized with the simulated annealing approach and is referred to as the MFABC+ algorithm. This was employed to determine the optimal sizing of different components comprising the integrated system as well as to maximize the techno-economic objectives. For validation, the simulation results obtained by the MFABC+ algorithm are compared with the results obtained using HOMER software, the particle swarm optimization algorithms and the original MFABC algorithm. It was revealed that the MFABC+ algorithm has a better convergence rate and the potential ability to provide compromising results in comparison to these existing optimization tools. It was also discovered through the comprehensive evaluation that the proposed system has the potential capability to meet the electricity demand of the village for 24 × 7 at the lowest levelized cost of electricity.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
Additional Information: ©2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/).
Uncontrolled Keywords: energy management,levelized cost of electricity,microgrid,nature-inspired optimization algorithm,renewable energy,rural electrification
Publication ISSN: 1996-1073
Last Modified: 14 May 2024 07:19
Date Deposited: 04 Jan 2021 11:20
Full Text Link:
Related URLs: https://www.mdp ... 6-1073/14/1/190 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-01-01
Accepted Date: 2020-12-25
Authors: Singh, Sweta
Slowik, Adam
Kanwar, Neeraj
Meena, Nand K. (ORCID Profile 0000-0002-4092-3921)



Version: Published Version

License: Creative Commons Attribution

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