###### Abstract  
In this paper, a reactive power optimization method based on historical data is investigated to solve the dynamic reactive power optimization problem in distribution network. In order to reflect the variation of loads, network loads are represented in a form of random matrix. Load similarity (LS) is  
Based on the one-day-ahead load forecasting, dynamic RPO determines the reactive power control devices action sequence in next day, with the purpose to reduce daily network losses, improve voltage quality and avoid excessive operation [5]. Distributed generation (DG) in DN makes RPO a more complex problem. A trust-region sequential quadratic programming (TRSQP) method is proposed in [6] to solve the RPO problem for distribution networks with DG. With wind power and photovoltaic power introduced into distribution network, the effects of wind generation and photovoltaic generation have been taken into consideration in RPO problems for DN. Uncertain wind power is considered in [7] and photovoltaic power is considered in [8] when optimizing reactive power in DN. The traditional RPO methods are mathematical model-based methods, and there are two levels. One level is the derivative-based methods using sensibility matrix, Jacobi matrix, Hessian matrix, _etc._ The second level is the stochastic searching algorithms based methods, such as GA, PSO, _etc._ Although the traditional methods can formulate accurate mathematical model, many iterations and a lot of time are required in the solution process. Most previous studies on RPO mainly focused on improving the performance of mathematical programming based and stochastic search algorithm. In addition, the load model is often treated as several simple and fixed typical types. Little effort was focused on utilizing data analysis method on historical data of the RPO. With a big data method, regularity of RPO can be found to avoid time-consuming iterative calculation and reduce computing time, improving the real-time capability. Some achievement has been made in studies on big data applications in power system currently. In [9], a big data architecture designed for smart grids was proposed based on random matrix theory (RMT). However, the investigation on RPO in DN with big data technology has not yet been carried out. Exploring the regularity in RPO from the historical data of power system, combining with the characteristics of loads can introduce new approach in DN. Large random matrix theory, with its advantages to deal with mass data, has already been applied to many fields, including signal detection [10], _etc._ In this paper, the focus of the study is mainly devoted to the sampled covariance matrix's largest eigen value. Random matrix theory is a big subject with application in many disciplines of science [11], engineering [12], communication [13] and finance [14]. The data of power system shows considerable randomness with the influence of weather, finance, sociocultural, _etc._ Thus, it is necessary to introduce random matrix theory into power system analysis. The amount and kind of data in our living world have been exploding. Big data analysis will become a key basis of competition, underpinning new waves of productivity growth and innovation [15]. As the power grid moves to smart grid, the power system has to deal with a large amount of data collected from millions of sensors and integrate series sets of data analytics and applications [16]. Therefore, it is necessary to introduce big data analysis technology into power grid management. With the help of big data technology, we can make corrective, predictive, distributed and adaptive decisions [17]. A big data RPO method based on historical data and random matrix (RM) is presented in this paper, whose target is to solve the day-ahead RPO (DPRO) problem by combining with historical load and dispatching scheme of reactive power control devices. Network loads are expressed in a form of RM in this paper. Load similarity (LS) is defined to measure the degree of similarity between the loads in different days.