A Thermal Unit Commitment Approach Using an Improved Quantum Evolutionary Algorithm

Abstract This article presents a new approach for solving unit commitment problems using a quantum-inspired evolutionary algorithm. The unit commitment problem is a complicated non-linear and mixed-integer combinatorial optimization problem with heavy constraints. This article proposes an improved quantum evolutionary algorithm to effectively solve unit commitment problems. The quantum-inspired evolutionary algorithm is considered a novel evolutionary algorithm inspired by quantum computing, which is based on the concept and principles of quantum computing such as the quantum bit and the superposition of states. The proposed improved quantum evolutionary algorithm adopts both the simplified rotation gate and the decreasing rotation angle approach in order to improve the convergence performance of the conventional quantum-inspired evolutionary algorithm. The suggested simplified rotation gate can determine the rotation angle without a lookup table, while the conventional rotation gate requires a predefined lookup table to determine the rotation angle. In addition, the proposed decreasing rotation angle approach provides the linearly decreasing magnitude of rotation angle along the iteration. Furthermore, this article includes heuristic-based constraint treatment techniques to deal with the minimum up/down time and spinning reserve constraints in unit commitment problems. Since the excessive spinning reserve can incur high operation costs, the unit de-commitment strategy is also introduced to improve the solution quality. To demonstrate the performance of the proposed improved quantum evolutionary algorithm, it is applied to the large-scale power systems of up to 100-unit with 24-hr demand horizon.