未来智能配电网迫切需要能提供系统实时运行数据的状态估计技术，以保证配电网的安全、经济和智能化运行。现有配电网状态估计（DSSE）技术的主要问题是实时性不够和准确度不高。本项目围绕这两个问题，从电力市场对DSSE的影响这个角度出发，对贵州配电网实时状态估计技术开展研究。首先，协同融合相量量测单元、数据采集与监视控制系统和智能电表的数据，采用静态估计与动态估计相结合的方式构建DSSE框架以提高DSSE的实时性；其次，基于电力大数据采用机器学习算法建立智能电表和伪量测量估计模型来提高静态估计的精度，同时采用风险价值法建立量测系统优化配置模型对量测装置及量测量进行优化配置，从而提高DSSE的准确性；最后采用改进容积卡尔曼滤波法建立动态估计模型以提升DSSE的稳定性。预期提出的DSSE技术具有更好的实时性、准确性和稳定性，可促进智能电网相关研究方向的发展，为我国智能配电网建设提供借鉴。

The future smart distribution network urgently needs the state estimation technology that can provide the real-time operation data of the system so as to ensure the safe, economical and smart operation of the distribution network. The main problem of the existing technology of distribution system state estimation (DSSE) is that the real-time performance is not satisfied and the accuracy is not high. Focusing on these two issues, this project investigates the real-time state estimation technology of Guizhou distribution network from the perspective of the impact of electricity market on DSSE. First of all, aiming at improving the real-time performance of DSSE, we construct a DSSE framework by combining the technology of static estimation and dynamic estimation based on collaboratively fusing the data of phase measurement units, data acquisition and monitoring control system and smart meters; then in order to increase the accuracy of DSSE, machine learning algorithms are used to establish estimation models of smart meters and pseudo-measurements to improve the accuracy of static estimation model, and Value at risk method is adopted to build the optimization model of the measure system for optimizing measure equipments and measurements; finally, the stability of DSSE is improved by establishing dynamic estimation model with a modified method of Cubature Calman filter. It is expected that the proposed DSSE technology has higher real-time performance, accuracy and stability, which can promote the development of the relevant research direction of smart grid and provide reference for the construction of smart distribution network in our country.