## 1 Introduction  
Reactive power optimization is an effective means to ensure the safe and economic operation of power system. The reasonable reactive power distribution can reduce the network loss [1; 2], improve the voltage quality [3; 4], and maintain the normal operation of the power grid. Reactive power optimization is a nonlinear mixed integer programming problem with multiple objectives, and generally, the solution of existing methods is to make hypothesis and simplifications of the optimal model, and then solve it with iterative optimization. The optimization algorithm can be roughly divided into two types: the traditional optimization method such as interior point method [5], sequential quadratic programming [6], and artificial intelligence algorithms such as genetic algorithm [7], particle swarm algorithm [8], and multi-agent technology [9]. With a large number of distributed generators [10; 11; 12], energy storages and demand side response control equipment [13; 14; 15; 16] access to distribution network, the number of control variables in reactive power optimization increased significantly, while the convergence cannot be guaranteed and the computation time of traditional mathematical methods increased. The results of artificial intelligence algorithms are instable and easy to fall into local optimum. In order to improve the convergence, accuracy and computing speed of reactive power optimization, the existing literature mainly studied on the simplification of the model and the improvement of the algorithm. Literature [17] proposed a method to optimally set the reactive power contributions of distributed energy resources present in distribution systems with the goal of regulating bus voltages, then the reactive power optimization was modeled as a convex quadratic program and solved based on the alternating direction method of multipliers efficiently. Literature [18] proposed an advanced loss reduction approach to achieve the optimal control coordination among multiple capacitors and DERs. The proposed approach and solution were developed on the basis of the detailed multi-phase distribution network modeling and the state-of-the-art optimization technology. The effectiveness of the proposed approach was demonstrated on practical utility distribution circuits with varying degree of unbalance and model complexity. Literature [19] presented a mixed-integer linear programming model to solve the simultaneous transmission network expansion planning and reactive power planning problem. The proposed model considered reactive power, off-nominal bus voltage magnitudes, power losses, multistage expansion, and security constraints. The use of a mixed-integer linear programming (MILP) model guaranteed convergence to optimality by using existing classical optimization methods. Although the above research has improved the efficiency and convergence of reactive power optimization, it has not been separated from the limitations of the traditional algorithm model and iterative optimization. In recent years, big data technology has been paid more and more attention by experts and scholars in various fields. The basic idea of big data is to guide the system operation or production practice in the future by means of analyzing a large amount of data generated from practice or production system, finding a certain law, and establishing the corresponding data model [20; 21]. The distribution network contains many nodes, the database has accumulated a lot of historical data, and the power load showed a certain cyclical characteristics, which make it possible to apply the theory and method of big data to reactive power optimization in distribution network. At present, the research and application of big data in distribution network is still in the initial stages. The relevant research focuses on multi-source data fusion and data storage technology [22; 23], application requirements and typical scene analysis [24; 25; 26], running state analysis [27; 28; 29] and other fields, and it has made some achievements.