Optimal Structuring of Microgrid Distributed State Estimation

• Published in: Electric power components and systems Vol. 49; no. 15; pp. 1278 - 1288
• Main Authors: Moselimizadeh, Erfan, Rastegar, Mohammad, Shabani, Faridoon, Hassan Asemani, Mohammad
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 08.06.2022; Taylor & Francis Ltd
• Subjects: Distributed generation; distributed state estimation; Electrical loads; Estimators; Flow equations; genetic algorithm; Kalman filters; microgrid; Minimum cost; Observability (systems); Optimization; optimization problem; Power flow; State estimation
• ISSN: 1532-5008; 1532-5016
• DOI: 10.1080/15325008.2022.2055676

Although distributed state estimation in the microgrids facilitates the application of monitoring and supervision, optimal methods for installation of meters and estimators, and determining optimal links between them have remained scarce. Thus, this study solves an optimization problem to determine the optimal number and location of the meters, the optimal number of estimators, optimal categorizing of meters for transferring data to each estimator, and the optimal determination of links between the estimators. To this end, the number of meters and the estimation errors in a Kalman filter-based distributed state estimation are minimized in this paper, subject to power flow equations of the microgrid, convergence condition of the state estimation, and observability of the microgrid. The proposed method is examined on a microgrid consisting of three distributed resources, one storage system, and three electrical loads. The results show that 10 meters and 4 estimators with proper communication can meet the constraints and perform state estimation with minimum cost and error. The optimality and effectiveness of the results are investigated by designing various studies.