Automated Generation Algorithm for Synthetic Medium Voltage Radial Distribution Systems

To introduce more automation in the distribution level of the power system, increasingly more data are needed to serve as input to control algorithms. To examine the complex interaction between the various layers of the system and verify the effectiveness of automation, difficult to obtain, realistic test systems are necessary. An algorithm for automatically synthesizing realistic medium voltage distribution feeders, and thus circumvent the data access problem, is presented. The algorithm treats distribution system feeders as graphs with nodes and edges, each with various properties, and leverages this structure to search for emerging statistical patterns. Using a large data set from a DSO in The Netherlands, clear statistical distributions are identified linking properties, such as load, node degree, or cable length, to the feeder structure. Specifically, many properties are linked to a node's or edge's distance, in hops, from the primary substation. With consideration for standard engineering practices, the statistical trends are exploited in the synthesis process, to generate feeders, which display similar characteristics to the real samples. The KL-divergence is used in the evaluation of analysis and synthesis results. Beyond solving the data access problem, the use of automatically generated, synthetic, distribution systems will enable testing and validation techniques, such as Monte Carlo simulations, which are currently not possible in this field, where single test cases are the norm.