Investigation of technoeconomic optimization for sizing renewable energy systems using metaheuristic and hybrid algorithms

Abstract

The escalating global adoption of Renewable Energy (RE) comes from declining costs and expanding electricity demand, driven by environmental imperatives to mitigate carbon emissions and reduce reliance on fossil fuels. Morocco, leveraging its abundant solar and wind resources, represents this transition, with a significant portion of its electricity demand met by RE sources. This study focuses on optimizing grid-connected Hybrid Renewable Energy Systems (HRES) in Morocco using technoeconomic approaches, including metaheuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO) and Artificial Bee Colony (ABC) and hybrid methods combining these latter with Modified Marquardt Gradient Descent (MGD). The primary objective is to optimize the Levelized Cost of Electricity (LCOE) for a HRES. The proposed system achieves an annual energy production exceeding 5000 kWh for capacities up to 20 kWp, which is sufficient to meet a daily load demand of 17.12 kWh (equivalent to 6248.8 kWh per year) while ensuring a Renewable Energy Fraction (REF) of at least 80%. According to evaluations conducted in eleven Moroccan cities, hybrid approaches notably, MGD-GWO emerges as a consistently effective solution, demonstrating superior performance in all cities. The lowest overall average LCOE for all cities is 0.114464 $/kW. Among them, the lowest value was recorded in Dakhla at 0.0662 $/kW using a wind capacity of 4781.892 kWp with an initial cost of 5308$. The study's findings offer valuable insights for researchers, policymakers, and investors seeking optimal strategies for sizing RE systems and integrating renewables into the energy landscape.