Quantum-Inspired Evolutionary Programming for Economic FACTS Allocation in Power Systems: Advancing Quantum Computing Applications

Quantum Mechanics and Quantum Computing stand at the forefront of a computational revolution, promising the ability to solve problems previously deemed unsolvable. This has sparked interest in leveraging these disciplines to address complex computational challenges across various fields. Among the innovative approaches emerging from this intersection is Quantum Inspired Evolutionary Programming (QIEP), a technique that skillfully combines the principles of quantum mechanics and quantum computing with the robustness of evolutionary algorithms. By incorporating core concepts of quantum mechanics, such as qubit representation, superposition, and entanglement, QIEP significantly enhances the efficacy of evolutionary programming, offering more efficient solutions for complex tasks. Despite the promising capabilities of quantum computing and its algorithms, their practical application, particularly in areas such as power system optimization, has remained limited. Our research explores the application of the QIEP methodology, with a specific focus on optimizing the allocation of Flexible AC Transmission System (FACTS) devices while considering economic criteria. Although these FACTS devices play a crucial role in improving controllability, increasing power transfer capacity, enhancing stability, and maintaining voltage levels within desired thresholds in power systems, their installation is limited to a few locations from a vast pool of potential sites due to economic consideration. Consequently, the allocation process presents a complex optimization problem, which can be effectively addressed through algorithms like QIEP. Through a comparative study with traditional genetic algorithms (GAs), our analysis showcases the superior efficiency and accuracy of QIEP, even on classical computing platforms. Notably, QIEP achieves a 35% reduction in FACTS allocation costs while maintaining the total system cost at its minimum level. Moreover, despite operating in a similar computing environment, QIEP completes the optimization process 10% faster than its GA counterparts.