Optimal partitioning in distributed state estimation considering a modified convergence criterion

TL;DR

This paper proposes a modified convergence criterion and an optimal partitioning technique for distributed state estimation in power systems, aiming to improve robustness, reduce communication, and enhance reliability in smart grids with renewable energy sources.

Contribution

It introduces a novel convergence criterion and an optimal partitioning method tailored for distributed state estimation in complex power grids.

Findings

The modified convergence criterion improves estimation efficiency.

Optimal partitioning enhances communication and reliability.

The proposed methods outperform recent DSE approaches.

Abstract

Distributed state estimation (DSE) is considered as a more robust and reliable alternative for centralized state estimation (CSE) in power system. Especially, taking into account the future power grid, so called smart grid in which bi-directional transfer of energy and information happens, and renewable energy sources with huge indeterminacy are applied more than before. Combining the mentioned features and complexity of the power network, there is a high probability that CSE face problems such as communication bottleneck or security/reliability issues. So, DSE has the potential to be considered as a solution to solve the mentioned issues. In this paper, first, a modified convergence criterion is proposed and has been tested for different approaches of DSE problem, considering the most important factors such as iteration number, convergence rate, and data needed to be transferred to/from each area. Then, an optimal partitioning technique has been implemented for clustering the system into different areas. Besides the detailed analysis and comparison of recent DSE methods, the proposed partitioning method's effectiveness has been shown in this paper.