Methodology for multiarea state estimation solved by a decomposition method

• The state estimation is addressed by a decentralized scheme with minimum information exchange.
• The proposed methodology is the decomposition method called optimality condition decomposition.
• The proposed strategy converges faster due to it does not require the multipliers’ updating.

As power systems are large interconnected systems with a high degree of complexity, the control and operation of such systems become a challenging task. Thus, large-scale power systems are mostly operated as interconnected subsystems. In this paper, the state estimation problem is addressed through a decentralized optimization scheme with minimum information exchange among subsystems. This paper focuses on a methodology for solving the multiarea state estimation problem by a decomposition method. This method is derived from the Lagrangian relaxation method and is named optimality condition decomposition (OCD). Results are presented for the IEEE 118-buses test power system, which has been split into two and three subsystems.