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

Nicolas Lopez  Ramos; Altin  Hoti; Takeaki  Toma

doi:10.36941/ajis-2023-0172

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-2023-0172

Keywords:

Genetic Algorithm, Fireworks Algorithm, Monte Carlo Simulation, Renewable Energy, Optimization, Hybrid Microgrid, Inflation

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.

Received: 18 August 2023 / Accepted: 25 October 2023 / Published: 5 November 2023