🔍 Research: Multi-Objective Optimization of Hybrid Renewable Energy System (HRES) Sizing Using NSGA-II
This repository presents the simulation and optimization of a standalone Hybrid Renewable Energy System (HRES) designed to supply power to a rural community in Udhruh, southern Jordan.
Technologies: Python • NumPy • Pandas • PVLib • AI Optimization (NSGA-II) • HRES
Sizing optimization of HRES components: PV, Wind, Battery, and Diesel Generator (DG)
Multi-objective algorithm: NSGA-II
EMS: Greedy logic (PV → Wind → Battery → DG → Load Shedding)
| Objective | PV (N) | Wind (N) | Battery (N) | DG (N) | Investment Cost ($) | Operational Cost ($/yr) | CO₂ Emissions (kg/yr) | DG Contribution (%) |
|---|---|---|---|---|---|---|---|---|
| Minimum Investment | 3 | 1 | 2 | 15 | 4,883 | 18,565 | 28,569 | 98.23 |
| Minimum Operational | 39 | 1 | 2 | 15 | 15,983 | 18,145 | 26,681 | 78.13 |
| Minimum CO₂ Emissions | 40 | 4 | 50 | 15 | 89,715 | 58,588 | 15,025 | 4.23 |
| Best Trade-off | 3 | 1 | 6 | 16 | 13,980 | 24,909 | 23,607 | 32.97 |