A novel state estimation method for distribution networks with low observability based on linear AC optimal power flow model

• Published in: Electric power systems research 2024-03, Vol.228, p.110085, Article 110085
• Main Authors: Zeraati, Mehdi, Sheibani, Mohammad Reza, Jabari, Farkhondeh, Heydarian-Forushani, Ehsan
• Format: Article
• Language: English
• Subjects: AC optimal power flow; Linear programming; Phasor measurement unit; Power distribution network; State estimation
• ISSN: 0378-7796
• DOI: 10.1016/j.epsr.2023.110085

•A linear optimization procedure based on the ACOPF for state estimation in distribution networks.•Suitable for distribution networks with low observability (limited number of real measurements).•A developed MILP problem with high speed convergence and ensuring the global optimal solution.•Comparing the algorithm performance to counterpart nonlinear model as well as conventional WLS method.•Minimizing the state estimation error as objective function to provide accurate DSSE results for DSOs. The state estimation (SE) problem is one of the key functions in real-time control and management of modern electrical distribution networks. However, due to the complex network configuration and the limited number of measuring devices in these networks, the SE problem faces many challenges. In this manuscript, a new state estimation methodology is proposed based on the AC optimal power flow (ACOPF) model for distribution networks with a small number of real-time measurements. The proposed method is formulated in the form of a mixed integer linear programming problem (MILP), where the estimated states are regarded as the decision variables in the optimization problem. The load consumption values are also assumed as the decision variables where the estimation limits of these variables are determined by pseudo-measurements taken from the historical data. The main objective function is to maximize the state estimation accuracy, while the power flow equations are defined as the problem constraints. The efficiency of the proposed methodology is evaluated by implementing on the test networks and comparing the estimation results accuracy with the conventional weighted least squares (WLS) method. The obtained numerical results show the superior performance of OPF-based SE model to WLS method. Moreover, linearizing the formulation demonstrates an acceptable precision while ensuring convergence and optimum solution in low observability conditions. [Display omitted]