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

Nicolas Lopez Ramos; Altin  Hoti; Takeaki  Toma

doi:10.36941/ajis-2024-0051

Authors

Nicolas Lopez Ramos College of Engineering and Technology, American University of the Middle East, Kuwait
Altin Hoti College of Engineering and Technology, American University of the Middle East, Kuwait
Takeaki Toma College of Engineering and Technology, American University of the Middle East, Kuwait

DOI:

https://doi.org/10.36941/ajis-2024-0051

Keywords:

Hybrid Genetic-Firework Algorithm, Genetic Algorithm, Fireworks Algorithm, Monte Carlo Simulation, Renewable Energy Integration, Hybrid Microgrid

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.

Received: 12 January 2024 / Accepted: 19 February 2024 / Published: 5 March 2024