<p>Conventional energy sources, such as coal and oil, contribute significantly to pollution and climate change, which explains our urgent need for cleaner, more efficient alternatives. The concept of the Energy Internet has emerged as a promising solution to these challenges. At the heart of the conceived network are energy routers that allow optimal energy distribution across the network through advanced routing protocols. In this paper, we propose a novel real-time, semi-decentralized energy routing protocol that combines Quantum Genetic Algorithms with a modified Greedy Search Algorithm to address the Efficient Path Problem and determine the optimal subset of producers for each consumer. The Greedy Search Algorithm is adapted to solve the path optimization problem, while the quantum algorithm is used for producer subset selection. Additionally, we implement a dynamic scheduling algorithm to handle congestion and collisions effectively. Simulations were conducted in Python on networks of varying sizes, and the results were compared with existing approaches. The findings demonstrate that the proposed protocol outperforms similar methods in both speed and the quality of optimal solutions.</p>