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

TL;DR

This paper presents a comprehensive big data analysis and visualization platform for renewable energy integration in distribution systems, utilizing advanced algorithms and GIS tools for optimized system operation and control.

Contribution

It introduces a novel integrated framework combining big data visualization, distributed optimization, and multi-timescale operation modeling for renewable energy in smart grids.

Findings

Effective visualization of heterogeneous data improves grid awareness.

Distributed algorithms achieve real-time optimal power flow solutions.

Multi-timescale scheduling reduces system costs with renewable integration.

Abstract

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. 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. Further more, a multi-timescale operation approach is modeled with a three-phase distributed system, which consists the hourly scheduling at substation level and the minutes power flow operation at feeder level. The system cost with renewable generation is minimized at the substation level. The given error distribution model of renewable generation is simulated with a chance constraint, and the derived deterministic form is modeled with Gaussian mixture 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 the nonconvexity of the three-phase unbalanced distribution system into a convex problem, which make sure to achieve the global optimal result. In the parallel manner, the ADMM is realizing getting the results in a short time.

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