Combined Transmission and Distribution State-Estimation for Future Electric Grids

TL;DR

This paper introduces a fast, convex, and robust distributed method for combined transmission and distribution AC state-estimation, enabling more coordinated and hierarchical grid control for future electric power systems.

Contribution

It presents the first practical, circuit-theoretic, distributed AC state-estimation methodology for joint T&D networks that is scalable, robust, and suitable for large, privacy-sensitive systems.

Findings

Accurate state estimates with low root mean-square error.

Scalable algorithm performance on large test networks.

Efficient parallelism using node-tearing techniques.

Abstract

Proliferation of grid resources on the distribution network along with the inability to forecast them accurately will render the existing methodology of grid operation and control untenable in the future. Instead, a more distributed yet coordinated approach for grid operation and control will emerge that models and analyzes the grid with a larger footprint and deeper hierarchy to unify control of disparate T&D grid resources under a common framework. Such approach will require AC state-estimation (ACSE) of joint T&D networks. Today, no practical method for realizing combined T&D ACSE exists. This paper addresses that gap from circuit-theoretic perspective through realizing a combined T&D ACSE solution methodology that is fast, convex and robust against bad-data. To address daunting challenges of problem size (million+ variables) and data-privacy, the approach is distributed both in memory and computing resources. To ensure timely convergence, the approach constructs a distributed circuit model for combined T&D networks and utilizes node-tearing techniques for efficient parallelism. To demonstrate the efficacy of the approach, combined T&D ACSE algorithm is run on large test networks that comprise of multiple T&D feeders. The results reflect the accuracy of the estimates in terms of root mean-square error and algorithm scalability in terms of wall-clock time.