中文摘要：

以有功网损期望值最小为优化目标，以节点电压的合格概率大于一定的阈值为约束条件，建立了同时考虑风能、太阳能分布式发电出力和负荷随机波动的配电网无功优化模型。目标函数和约束项中所涉及的概率潮流由一种结合传统解析法的基于全概率公式的计算方法求得。使用化学反应算法对所建优化模型进行求解。在同时接入风能、太阳能分布式电源的33节点和69节点系统上对所提方法进行了验证，得到了具有概率统计意义的最优方案。通过与包括遗传算法（genetic algorithm，GA）、Stud GA（stud geneticalgorithm）、生物地理学算法（biogeography based optimization，BB01和粒子群算法（particle swar moptimization，PSO）在内的多个智能算法对比，验证了所构建的化学反应算法在求解上述无功优化模型时性能更加稳定。

英文摘要：

Taking the minimum expectation of active network loss as the optimization objective and the qualified probability of nodal voltage, which larger than a certain threshold, as the constraint, a reactive power optimization model of distribution network, in which the output fluctuation of distributed wind power generation and PV generation as well as the random fluctuation of load are considered simultaneously, is established. The probabilistic power flows involved in objective function and constraints are solved by a complete probability formula based computing method that combines with traditional analytical method. The established reactive power optimization model is solved by chemical reaction optimization （CRO）. The proposed model is verified by IEEE 33-bus system and PG＆E 69-bus system respectively, to which the distributed PV generation and wind power generation are simultaneously added, and an optimal scheme possessing the meaning of probability statistics is achieved. Comparing the constructed CRO algorithm with other intelligent algorithms, such as genetic algorithm （GA）, stud genetic algorithm （Stud GA）, biogeography based optimization （BBO） and particle swarm optimization （PSO）, it is validated that the constructed CRO algorithm possesses more stable performance when it is used to solve above-mentioned reactive power optimization model.