传统配电网无功优化和重构大多是单独进行研究的，缺乏不同优化技术的协调与配合。建立了一种有源配电网无功和重构协同优化数学模型，结合配电网无功优化和重构两种优化方式，根据配电网的实际情况，实现二者的协调运行。以年综合成本最小作为目标函数，在满足网络功率平衡、节点电压幅值、网络辐射状运行等约束条件下，采用改进的灰狼算法进行求解。针对传统灰狼算法种群多样性低、容易陷入局部最优解以及运行速度慢的问题，提出在灰狼更新策略的基础上增加烟花算法爆炸机制，同时为了提高计算效率和求解精度，将烟花算法用于整数解寻优，并引入非线性规划算法对连续解进行寻优。以IEEE33节点配电网为例进行4种不同场景的验证，结果表明，所提出的协同优化模型能够有效降低网损和年综合成本，抑制节点电压波动水平，同时显示出改进算法收敛速度和计算精度的优越性。

The reactive power optimization and reconfiguration of traditional distribution network are mostly studied separately, lacking the coordination and cooperation of different optimization techniques. A mathematical model of reactive power and reconfiguration collaborative optimization of active distribution network was established. Combined with the two optimization methods of reactive power optimization and reconfiguration of distribution network, the coordinated operation of the two was realized according to the actual situation of distribution network. Taking the minimum annual comprehensive cost as the objective function, the improved grey wolf algorithm was used to solve the problem under the constraints of network power balance, node voltage amplitude and network radial operation. Aiming at the problems of low population diversity, easy to fall into local optimal solution and slow running speed of traditional grey wolf algorithm, it is proposed to increase the explosion mechanism of fireworks algorithm on the basis of grey wolf update strategy. At the same time, in order to improve the computational efficiency and solution accuracy, the fireworks algorithm was used for integer solution optimization, and the nonlinear programming algorithm was introduced to optimize the continuous solution. The IEEE33 node distribution network was taken as an example to verify four different scenarios. The results show that the proposed collaborative optimization model can effectively reduce the network loss and annual comprehensive cost, suppress the node voltage fluctuation level, and show the superiority of the improved algorithm in convergence speed and calculation accuracy.