Quantum-Inspired Evolutionary Programming: A Case Study on Optimum Power Generation Scheduling

Quantum mechanics (QM) and quantum computation (QC) are driving a computational revolution, with transformative potential in areas like drug discovery, battery design, and fusion energy. However, despite these promising developments, the application of quantum algorithms has largely been restricted to solving abstract mathematical problems, with significant areas such as power system optimization still awaiting the transformative impact of QC. In this context, this paper implements Quantum-Inspired Evolutionary Programming (QIEP), a novel method that integrates quantum principles-such as qubit representation and superposition-into evolutionary programming. By applying QIEP to power generation scheduling, a complex power system management task, we demonstrate its superiority over traditional genetic algorithms (GA), achieving a 24% reduction in power loss and substantial cost savings. This advancement highlights QIEP's effectiveness in power system optimization and its practical application to real-world problems.