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Renewable Energy Integration on a High Inflation Economic Scenario by Means of Firework Algorithm, Genetic Algorithm and Monte Carlo Simulation

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  • Nicolas Lopez Ramos
  • Altin Hoti
  • Takeaki Toma

Abstract

This paper presents a solution to the Renewable Energy Integration Problem (REIP) by finding the Optimal Configuration of components required in a hybrid microgrid located in Kuwait, such that the cost of energy (COE) is minimized when considering several components such as: solar panels, wind turbines, electric batteries, converters, inverters, diesel generators and connection to the power grid. The optimal configuration is found by evaluating the interaction and effects of several combinations of components via Monte-Carlo simulation, and such configurations are in turn optimized by means of 2 alternative stochastic algorithms: The Genetic Algorithm and the Fireworks Algorithm. The two approaches are compared, concluding that the Fireworks Algorithm provides more variety of configurations along the iterations before reaching convergence. The evaluation by Monte-Carlo simulation is calculated, by means of Present Worth (PW) with a minimum attractive rate of return (MARR) set to 7 percent to represent a high inflation rate-scenario, concluding that both methods can be safely used to optimize the design of hybrid micro-grids under high economical stress.

Suggested Citation

  • Nicolas Lopez Ramos & Altin Hoti & Takeaki Toma, 2023. "Renewable Energy Integration on a High Inflation Economic Scenario by Means of Firework Algorithm, Genetic Algorithm and Monte Carlo Simulation," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 12, November.
    • Handle: RePEc:bjz:ajisjr:2505
    DOI: https://doi.org/10.36941/ajis-2023-0172
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    References listed on IDEAS

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    1. Xingcai, Li & Kun, Niu, 2018. "Effectively predict the solar radiation transmittance of dusty photovoltaic panels through Lambert-Beer law," Renewable Energy, Elsevier, vol. 123(C), pages 634-638.
    2. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
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