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Due to the large volume of heterogeneous data provided by both the customerand the grid side, a big data visualization platform is built to discover thehidden useful knowledge for smart grid (SG) operation, control and situationawareness. An open source cluster computing framework based on Apache Spark isconsidering to discover the hidden knowledge of the bag-data. And the data istransmitted with a Open System Interconnection (OSI) model to the datavisualization platform in a high-speed communication architecture. Google Earthand global geographic information system (GIS) are used to design thevisualization platform and realize the result with the test bench.  The alternating direction method of multipliers (ADMM) is used to compute theoptimal power flow in distributed manner. The proposed network reconfigurationis solved in parallel manner with the limited switches and the strong computingcapability.  Further more, a multi-timescale operation approach is modeled with athree-phase distributed system, which consists the hourly scheduling atsubstation level and the minutes power flow operation at feeder level. Thesystem cost with renewable generation is minimized at the substation level. Thegiven error distribution model of renewable generation is simulated with achance constraint, and the derived deterministic form is modeled with Gaussianmixture model (GMM) with genetic algorithm-based expectationmaximization(GAEM). The system cost is further reduced with the OPF in real-time (RT)scheduling. The semidefinite programming (SDP) is used to relax thenonconvexity of the three-phase unbalanced distribution system into a convexproblem, which make sure to achieve the global optimal result. In the parallelmanner, the ADMM is realizing getting the results in a short time.

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