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Optimal cost and feasible design for grid-connected microgrid on campus area using the robust-intelligence method

Optimal cost and feasible design for grid-connected microgrid on campus area using the... In this paper, a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost. The campus microgrid needs to be optimized with further investigation, especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply. A modified combination of genetic algorithm and particle swarm optimization (MGAPSO) is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system. Then, a sustainable energy-management system is also defined to analyse the characteristics of the microgrid. The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods. The MGAPSO optimization reduces the system cost by up to 11.99% compared with the conventional methods. In the rest of the paper, the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics. Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clean Energy Oxford University Press

Optimal cost and feasible design for grid-connected microgrid on campus area using the robust-intelligence method

Almas Prakasa, Mohamad; Subiyanto, Subiyanto
Clean Energy , Volume 6 (1): 18 – Dec 21, 2021
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References (40)

Publisher
Oxford University Press
Copyright
© The Author(s) 2021. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy
ISSN
2515-4230
eISSN
2515-396X
DOI
10.1093/ce/zkab046
Publisher site
See Article on Publisher Site

Abstract

In this paper, a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost. The campus microgrid needs to be optimized with further investigation, especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply. A modified combination of genetic algorithm and particle swarm optimization (MGAPSO) is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system. Then, a sustainable energy-management system is also defined to analyse the characteristics of the microgrid. The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods. The MGAPSO optimization reduces the system cost by up to 11.99% compared with the conventional methods. In the rest of the paper, the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics. Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

Journal

Clean Energy – Oxford University Press

Published: Dec 21, 2021

Keywords: distributed energy and smart grid; renewable-energy system optimization; MGAPSO algorithm

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