Optimal Design and Mathematical Modeling of Hybrid Solar PV–Biogas Generator with Energy Storage Power Generation System in Multi-Objective Function Cases

Takele Ferede Agajie, Armand Fopah-Lele, Isaac Amoussou, Ahmed Ali, Baseem Khan () and Emmanuel Tanyi
Additional contact information
Takele Ferede Agajie: Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, Buea P.O. Box 63, Cameroon
Armand Fopah-Lele: Department of Mechanical Engineering, Faculty of Engineering and Technology, University of Buea, Buea P.O. Box 63, Cameroon
Isaac Amoussou: Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, Buea P.O. Box 63, Cameroon
Ahmed Ali: Department of Electrical and Electronic Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2006, South Africa
Baseem Khan: Department of Electrical and Electronic Engineering Technology, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2006, South Africa
Emmanuel Tanyi: Department of Electrical and Electronic Engineering, Faculty of Engineering and Technology, University of Buea, Buea P.O. Box 63, Cameroon

Sustainability, 2023, vol. 15, issue 10, 1-26

Abstract: This study demonstrates how to use grid-connected hybrid PV and biogas energy with a SMES-PHES storage system in a nation with frequent grid outages. The primary goal of this work is to enhance the HRES’s capacity to favorably influence the HRES’s economic viability, reliability, and environmental impact. The net present cost (NPC), greenhouse gas (GHG) emissions, and the likelihood of a power outage are among the variables that are examined. A mixed solution involves using a variety of methodologies to compromise aspects of the economy, reliability, and the environment. Metaheuristic optimization techniques such as non-dominated sorting whale optimization algorithm (NSWOA), multi-objective grey wolf optimization (MOGWO), and multi-objective particle swarm optimization (MOPSO) are used to find the best size for hybrid systems based on evaluation parameters for financial stability, reliability, and GHG emissions and have been evaluated using MATLAB. A thorough comparison between NSWOA, MOGWO, and MOPSO and the system parameters at 150 iterations has been presented. The outcomes demonstrated NSWOA’s superiority in achieving the best optimum value of the predefined multi-objective function, with MOGWO and MOPSO coming in second and third, respectively. The comparison study has focused on NSWOA’s ability to produce the best NPC, LPSP, and GHG emissions values, which are EUR 6.997 × 106, 0.0085, and 7.3679 × 106 Kg reduced, respectively. Additionally, the simulation results demonstrated that the NSWOA technique outperforms other optimization techniques in its ability to solve the optimization problem. Furthermore, the outcomes show that the designed system has acceptable NPC, LPSP, and GHG emissions values under various operating conditions.

Keywords: photovoltaic; hybrid renewable energy source; NPC; CO 2 emissions; LPSP; energy storage; PHES; SMES; biogas; metaheuristic optimization; NSWOA; MOGWO; MOPSO (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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