TY - JOUR
T1 - Maximizing Renewable Energy Integration in the Egyptian Power Grid Using the Coronavirus Optimization Algorithm
AU - Paolo Ricci
AU - Marco De Luca
AU - Giulia Ferraro
AU - Antonio Russo
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2025
VL - 4
IS - 2
SP - 118
N2 - Global efforts are increasingly focused on achieving fully sustainable energy systems by mid-century to comply with the Paris Agreement targets. This study introduces a novel approach for the optimal operation of the Egyptian power grid, aiming to maximize renewable energy integration while minimizing voltage fluctuations and overall power losses. The optimization is implemented using the Corona Virus Optimization (CVO) algorithm. The proposed CVO method is benchmarked against the Teaching and Learning-Based Optimization (TLBO) algorithm for voltage deviation, power losses, and renewable energy share. The study uses real-world data on electricity demand, solar irradiation, and wind speed for representative winter and summer days. A 2020 Egyptian grid model is developed, simulated, and optimized using DIgSILENT software. Results demonstrate that CVO outperforms TLBO in enabling the grid to operate with the highest achievable share of renewable energy. This research contributes toward Egypt’s national objectives of reaching 20% and 42% renewable energy penetration by 2022 and 2035, respectively.
UR - https://isespub.com/s034068820
ER -