Journal
ENERGY REPORTS
Volume 7, Issue -, Pages 4360-4375Publisher
ELSEVIER
DOI: 10.1016/j.egyr.2021.07.015
Keywords
Batteries; Diesel generator; Multiobjective optimization; Nanogrid; Particle swarm optimization; Solar energy
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Funding
- University of Hafr Al-Batin, Saudi Arabia [G-109-2020]
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Designing a nanogrid involves intricate considerations and trade-offs among conflicting design objectives. This research proposes a synergistic Parallel Multiobjective PSO-based approach to optimally design a hybrid photovoltaic/diesel/battery nanogrid. The approach combines four optimization methods and achieves superior solutions compared to using each method individually.
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. (C) 2021 Published by Elsevier Ltd.
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