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A GIS-MOPSO integrated method for optimal design of grid-connected HRES for educational buildings

Mokhtara, Charafeddine; Negrou, Belkhir; Settou, Noureeddine ; Bouferrouk, Abdessalem; Yao, Yufeng ; Messaoudi, Djilali

Authors

Charafeddine Mokhtara mokhtara.chocho@gmail.com

Belkhir Negrou

Noureeddine Settou

Abdessalem Bouferrouk

Yufeng Yao Yufeng.Yao@uwe.ac.uk
Professor in Aerospace Engineering

Djilali Messaoudi



Contributors

Abstract

 Abstract. In this paper, an optimal design of a grid-connected PV-battery-hydrogen hybrid renewable energy system (HRES) at a University campus in Ouargla, Algeria is carried out. To achieve this goal, geographical information system (GIS), CAD software and multi-objective particle swarm optimization (MOPSO) are used. First, the rooftop's solar energy potential, optimal zones to install PV panels and selection of the PV system's best installation are determined , considering many design criteria. Thus, based on these outcomes, optimal sizing of the proposed hybrid system is then performed using MATLAB. Cost of energy, loss of power supply probability, and renewable usage are the objectives to be optimized. Here, an energy management strategy is adopted to select the most adequate storage option at each simulation time step. In this study, selling of the excess hydrogen gas has suggested instead of selling electricity to the grid. Results show that standard multi crystalline PV panels with an inclination angle of 17° is the best installation. In addition, the obtained optimal HRES, which includes PV/battery/hydrogen has a renewable usage of 90%, and cost of energy of only 0.22 $/kWh with high reliability.

Presentation Conference Type Conference Paper (unpublished)
Start Date Nov 27, 2019
APA6 Citation Mokhtara, C., Negrou, B., Settou, N., Bouferrouk, A., Yao, Y., & Messaoudi, D. (2019, November). A GIS-MOPSO integrated method for optimal design of grid-connected HRES for educational buildings. Paper presented at 3rd International Symposium on Sustainable Hydrogen, Algiers
Keywords Solar PV; Hybrid Energy Storage; Multi Objective Particle Swarm Optimization; GIS; Hydrogen
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