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High Stress Economic Scenario on Renewable Energy Integration with Genetic-Firework Hybrid Algorithm

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  • Nicolas Lopez Ramos
  • Altin Hoti
  • Takeaki Toma

Abstract

This work models a hard economic scenario in which inflation rate is set to 7%, the price of diesel is increasing, the price of electricity purchased from the power grid is inflated and there is a top limit for daily purchasable electricity on a region, in which there is an attempt to introduce renewable energy on a private property of the size of a residential house of 5 people. The optimal microgrid configuration is approximated by the new Hybrid Genetic-Fireworks Algorithm working in conjunction with a Monte Carlo simulation to find the annual worth, and comparing results with a Genetic Algorithm and a Fireworks Algorithm. The components considered are: solar panels, wind turbines, diesel generators, electric batteries, converters, and a connection to the power grid. The objective is to maximize annual worth. The results show that a cost of energy (COE) of 2.0603 USD per kWh is achievable in such scenario, and recommends the further use of the Hybrid Genetic-Fireworks Algorithm for this type or Renewable Energy Integration studies, as it outperformed their 2 counterparts in this work.

Suggested Citation

  • Nicolas Lopez Ramos & Altin Hoti & Takeaki Toma, 2024. "High Stress Economic Scenario on Renewable Energy Integration with Genetic-Firework Hybrid Algorithm," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 13, March.
  • Handle: RePEc:bjz:ajisjr:2562
    DOI: https://doi.org/10.36941/ajis-2024-0051
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    References listed on IDEAS

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    1. 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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