Optimal sizing of hybrid photovoltaic/diesel/battery nanogrid using a parallel multiobjective PSO-based approach: Application to desert camping in Hafr Al-Batin city in Saudi Arabia

Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research...

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Bibliographic Details
Published in:Energy reports Vol. 7; pp. 4360 - 4375
Main Authors: Bouchekara, Houssem R.E.H., Shahriar, M.S., Irshad, U.B., Sha’ aban, Y.A., Mahmud, M.A. Parvez, Javaid, M.S., Ramli, Makbul A.M., Farjana, Shahjadi Hisan
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2021
Elsevier
Subjects:
Batteries
Diesel generator
Multiobjective optimization
Nanogrid
Particle swarm optimization
Solar energy
Diesel generator
Nanogrid
Batteries
Particle swarm optimization
Multiobjective optimization
Solar energy
ISSN:2352-4847, 2352-4847
Online Access:Get full text
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Summary:Designing a nanogrid involves intricate considerations. Its primary system components, including PV systems, inverter type and control, batteries, and diesel generator, always offer a trade-off among conflicting design objectives – the cost of electricity and reliability, for example. This research proposes a synergistic Parallel Multiobjective PSO-based approach (PMOPSO), a merger of four optimization methods to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The merged approaches are the Speed-Constrained Multiobjective Particle Swarm Optimization (SMPSO), MultiObjective Particle Swarm Optimization Algorithm Based on Decomposition (MPSO-D), Novel multiobjective particle swarm optimization (NMPSO), and Competitive Mechanism-Based Multiobjective Particle Swarm Optimizer (CMPSO). The developed approach allows the designer/operator to test multiple component models based on cost and reliability and choose the design that gives the best-suited solution. The four combined algorithms are run in parallel, and the obtained solutions are aggregated together in an archive pool where only non-dominated solutions are kept. A desert camp in the sub-urban area of Hafr Al-Batin city, situated in the Western region of Saudi Arabia, is used as a test case. The approach obtains a well-spread and large Pareto Front (PF), offering many options (solutions) to the designer/operator in a single run. The results achieved a superior set of solutions than those obtained by using each of the four combined PSO-based algorithms individually. Therefore, the developed technique provides improved and viable design solutions for a hybrid nanogrid.
ISSN:2352-4847
2352-4847
DOI:10.1016/j.egyr.2021.07.015