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Dynamic state estimation for distribution networks with renewable energy integration

Author(s): Phuong H. Nguyen a,b*, Ganesh K. Venayagamoorthyb, Wil L. Klinga, Paulo F. Ribeiroa
a Eindhoven University of Technology, Den Dolech 2, Eindhoven, 5600MB, the Netherlands
b Real-Time Power and Intelligent Systems Laboratory, Holcombe Department of Electrical and Computer Engineering, Clemson University, 303D Riggs Hall, Clemson, SC 29634-0915, USA
International Journal of Smart Grid and Clean Energy, vol. 2, no. 3, October 2013: pp. 307–315
ISSN: 2315-4462
Digital Object Identifier: 10.12720/sgce.2.3.307-315

Abstract: The massive integration of variable and unpredictable Renewable Energy Sources (RES) and new types of load consumptions increases the dynamic and uncertain nature of the electricity grid. Emerging interests have focused on improving the monitoring capabilities of network operators so that they can have accurate insight into a network’s status at the right moment and predict its future trends. Though state estimation is crucial for this purpose to trigger control functions, it has been used mainly for steady-state analysis. The need for dynamic state estimation (DSE), however, is increasing for real-time control and operation. This paper addresses the important role of DSE over conventional static-state estimation in this new distribution network context. Computational burden mitigates the state-of-the-art utilizations of DSE in real large-scale networks, although DSE was introduced several decades ago. This paper the unscented Kalman filter (UKF) to alleviate computational burden with DSE. The UKF-based approach does not use a linearization procedure and thus outperforms the conventional Extended Kalman Filter based approach to cope with non-linear models. The performance of the UKF method is investigated with a simulation of an 18-bus distribution network on the real-time digital simulator (RTDS) platform. A distribution network with considerable integration of renewable energy production is used to evaluate the UKF-based DSE approach under different types of events.
Keywords: Dynamic state estimation, extended Kalman filter, unscented Kalman filter, renewable energy sources, distribution network

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