Dynamic state estimation for distribution networks with renewable energy integration

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.

Distribution system state estimation-A step towards smart grid

“…In [84], a UKF-based estimator is proposed for DNs considering renewable energy integration. Since DNs lack real-time measurements, network observability is achieved using pseudo measurements, which are generated using historical load forecasting.…”

Emerging role of photovoltaics for sustainably powering underdeveloped, emerging, and developed economies

“…The fluctuations of power and frequency in grids with large PV generation plants raise concerns about dynamic and transient stability, because energy harvesting by solar panels shuts off almost instantaneously, in comparison to wind turbines which actually have some kinetic energy available. Real-time monitoring and forecasting of PV generation and dynamic state estimation will be needed [45][46][47] along with weather forecasting capabilities [48] to overcome the intermittency problems.…”

An improved unscented Kalman filter based dynamic state estimation algorithm for electric distribution Systems

“…Although dynamic estimators have been proposed for transmission systems, very little attention has been given to distribution systems. In Nguyen et al (2013) a UKF-based estimator is proposed for distribution networks considering renewable energy integration. Similarly, another study related to the dynamic estimation of distribution systems can be found in Deshmukh et al (2014), where an EKF-based dynamic estimator is proposed for state estimation of distribution network and a volt/Var control is achieved using the state estimates.…”