Renewable Energy Integration in Distribution System -- Synchrophasor Sensor based Big Data Analysis, Visualization, and System Operation

With the increasing attention of the renewable energy implementation in distributed power system, the hybrid smart grid (SG) operation is mainly featured by distributed renewable generation, data visualization, data prediction in high accuracy and operation cost minimization. Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to discover the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster computing framework based on Apache Spark is considering to discover the hidden knowledge of the bag-data. And the data is transmitted with a Open System Interconnection (OSI) model to the data visualization platform in a high-speed communication architecture. Google Earth and global geographic information system (GIS) are used to design the visualization platform and realize the result with the test bench. A short-term load forecasting approach is designed based on support vector regression (SVR) to provide a high accuracy load forecasting for the network reconfiguration. The nonconvexity of the three-phase balanced optimal power flow is relaxed to a optimal power flow (OPF) problem with the second-order cone program (SOCP). The alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. The proposed network reconfiguration is solved in parallel manner with the limited switches and the strong computing capability.