基于改进量子差分进化的含分布式电源的配电网无功优化

差分进化算法（DE）已被证明为解决无功优化问题的有效方法.随着越来越多的分布式电源并网，对配电网潮流、电压均有一定改变，同时也影响了DE的鲁棒性和性能.本文在研究DE基础上，针对其收敛过早、局部搜索能力较差的缺陷，分析了量子计算思想和人工蜂群算法的优势，提出改进量子差分进化混合算法（IQDE）.通过量子编码思想提高了种群个体的多样性，人工蜂群算法的观察蜂加速进化操作和侦查蜂随机搜索操作分别提高了算法的局部搜索和全局搜索性能.建立以有功网损最小为目标的数学模型，将IQDE算法和DE算法分别用于14节点和30节点标准数据集进行大量仿真实验.实验结果表明，IQDE算法用更少的收敛时间、更小的种群规模便可以获得与DE算法相同甚至更佳的优化效果，并且可以很好的应用于解决难分布式电源的配电网无功优化问题.

An Improved Quantum Differential Evolution Algorithm for Optimization and Control in Power Systems Including DGs

Differential evolution algorithm (DE) has been proved to be an effective way for solving the optimal reactive power flow (ORPF) problem. As distributed generations (DGs) are introduced into the system, there is a certain impact on power flow and voltage of the power system, which affects the robustness and effectiveness of DE. On the basis of DE, aiming at its limitation of premature convergence and poor search ability, this paper discusses about how to improve it with quantum encoding and artificial bee colony (ABC) algorithm and proposes a hybrid algorithm, which is called improved quantum differential evolution algorithm (IQDE). The idea of quantum encoding increases the individual diversity while the accelerating evolution operation of the onlooker bees improves local search ability of DE. At the same time, the random search operation of the scout bees improves global search ability of DE. In the last, the effectiveness of IQDE is verified by simulations on the IEEE 14-bus system and 30-bus system including DGs. The experimental results show that with less convergence time and smaller population size, IQDE can obtain an even or better optimization effect compared with DE and can be applied to ORPF problem of power system including DGs.