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Configuration optimization of a renewable hybrid system including biogas generator, photovoltaic panel and wind turbine: Particle swarm optimization and genetic algorithms

Author

Listed:
  • Ali Heydari

    (Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran)

  • Zahra Sayyah Alborzi

    (��Department of Chemistry, Valiasr Technical College, Tehran, Iran)

  • Younes Amini

    (��Nuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute, Tehran, Iran)

  • Amin Hassanvand

    (�Department of Polymer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran)

Abstract

The main contribution of this paper is to formulate the problem of optimal design of renewable wind/solar/biomass hybrid system for grid-independent applications in a region of Iran and to compare the genetic algorithm (GA) and performance of particle swarm optimization (PSO) on this optimization problem. There are many types of research on solar and wind hybrid energy systems, but research on solar/wind/biomass hybrid energy systems is rare. The biomass energy system can be used as a support and complementary system along with wind and solar energy systems. This paper studies the optimum design of a biomass/PV/wind energy system for independent applications. The objective of the optimum design problem is to minimize the total net present cost (TNPC) of the PV/wind/biomass system during its lifetime subject to some constraints by adjusting three decision variables, namely the swept area of wind turbines, the area of PV panels and the capacity of biogas generators. For this aim, two efficient metaheuristic techniques of GA and PSO are used to solve the optimization problem. Simulation results show that PV/biomass system is the most cost-effective one for supplying the demanded load. Moreover, PSO leads to better results than GA.

Suggested Citation

  • Ali Heydari & Zahra Sayyah Alborzi & Younes Amini & Amin Hassanvand, 2023. "Configuration optimization of a renewable hybrid system including biogas generator, photovoltaic panel and wind turbine: Particle swarm optimization and genetic algorithms," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-22, May.
    • Handle: RePEc:wsi:ijmpcx:v:34:y:2023:i:05:n:s0129183123500699
    DOI: 10.1142/S0129183123500699
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    Cited by:

    1. Zhang, Liwu & Zhu, Guanghui & Chao, Yanpu & Chen, Liangbin & Ghanbari, Afshin, 2023. "Simultaneous prediction of CO2, CO, and NOx emissions of biodiesel-hydrogen blend combustion in compression ignition engines by supervised machine learning tools," Energy, Elsevier, vol. 282(C).

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