A New Quantum-Inspired Binary PSO for Thermal Unit Commitment Problems

This paper proposes a new binary particle swarm optimization (BPSO) approach inspired from quantum computing, so-called quantum-inspired BPSO (QBPSO), for solving the unit commitment (UC) problems. Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems of power systems, the BPSO algorithm has some drawbacks such as premature convergence when handling heavily constrained problems. The proposed QBPSO combines the conventional BPSO with the concept and principles of quantum computing such as a quantum bit and superposition of states. The QBPSO adopts a Q-bit individual for the probabilistic representation, which replaces the velocity update procedure in the particle swarm optimization. This paper also proposes an efficient rotation gate for updating Q-bit individuals to improve the searching capability of the quantum computing. To verify the performance of the proposed QBPSO, it is applied to the test systems of up to 100-units with 24-hour demand horizon.