Strengthening a Solar-Wind-Battery-Diesel Hybrid Energy System by Evaluating Effective Factors: An Application of Optimization Method

Abstract

This research addresses the challenge of meeting increasing global energy demands while transitioning from finite fossil fuel resources. The study focuses on optimizing a solar-wind-battery-diesel hybrid energy system using the Particle Swarm Optimization with Improved Inertia Weight (PSO-IIW) and Crow Search Optimization (CSO) algorithm. The research delves into detailed modeling of solar panels and wind turbines considering the vibrational aspects, incorporating factors such as solar radiation and wind speed for accurate system design. The study determines that diesel offers high energy density, surpassing gasoline by approximately 12.57 percentage points. The optimized hybrid system configuration includes 1,234 solar panels, 78 wind turbines, and 567 batteries. Gas engine systems, costing less with higher fuel ratios below 345.23, are compared with gas turbine drives, which exhibit higher annual costs due to increased battery and solar panel requirements. Post-calibration, each drive achieves a nominal capacity between 123 and 432 kW, emphasizing uniformity across all drives. The research underscores the economic benefits of larger capacities, particularly in gas turbine and gas engine drives, resulting in reduced annual costs. Tailored for hot climate conditions, this study provides a comprehensive understanding of hybrid system design parameters, constraints, and input variables.