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A Thermal Unit Commitment Approach Using an Improved Quantum Evolutionary Algorithm

机译：基于改进量子进化算法的热电机组组合方法

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摘要

This article presents a new approachfor 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.
机译：本文介绍了一种使用量子启发式进化算法解决单元承诺问题的新方法。单元承诺问题是一个复杂的，具有严格约束的非线性混合整数组合优化问题。本文提出了一种改进的量子进化算法，可以有效地解决单位承诺问题。量子启发式进化算法被认为是一种受量子计算启发的新型进化算法，它基于量子计算的概念和原理，例如量子位和状态叠加。提出的改进的量子进化算法同时采用了简化的旋转门和减小旋转角的方法，以提高传统量子启发式进化算法的收敛性能。建议的简化旋转门可以在没有查找表的情况下确定旋转角，而常规旋转门需要预定义的查找表来确定旋转角。另外，所提出的减小旋转角度的方法提供了沿着迭代的线性减小的旋转角度的大小。此外，本文还包括基于启发式的约束处理技术，以处理单位承诺问题中的最小上/下时间和旋转备用约束。由于过多的纺丝储备可能会导致高昂的运营成本，因此还引入了单位停用策略以提高解决方案质量。为了证明所提出的改进的量子进化算法的性能，将其应用于具有24小时需求范围的高达100个单元的大型电力系统。

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