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
Houssem R. E. H. Bouchekara, Mohammad Shoaib Shahriar, Usama Bin Irshad, Yusuf Abubakar Sha aban, M. A. Parvez Mahmud, Muhammad Sharjeel Javaid, Makbul A.M. Ramli, Shahjadi Hisan Farjana
Abstract
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.