Andrea Angioni

Impact of Different Uncertainty Sources on a Three-Phase State Estimator for Distribution Networks

Distribution networks present their own features, significantly different from transmission systems features. For instance, loads are often unbalanced on the phases of the network. Moreover, the increasing amount of distributed generation, installed in an unplanned manner, is creating significant challenges. Such changing scenario imposes new operational requirements, such as distributed voltage control and demand side management. New performances are required for distribution system state estimators. In this paper, a study on the impact of different uncertainty sources on a state estimator designed for monitoring unbalanced distribution networks is presented. The impact of different measurement devices and levels of knowledge of the network behavior is analyzed and discussed using simulations performed on the 123-bus IEEE distribution network, which is commonly used as test network for this kind of study.

Impact of Pseudo-Measurements From New Power Profiles on State Estimation in Low-Voltage Grids

Low-voltage state estimation (SE) in distribution power system, because of lack of real-time measurements, must heavily rely on pseudo-measurements, primarily originating from standard load profiles (SLPs). Actual load profiles have been recently suspected of significantly deviating from the SLPs, and research work is ongoing to quantify and characterize this deviation and to synthesize more realistic profiles. The analysis of the propagation of error and uncertainty of load profiles in SE must be investigated, as distribution system SE in distribution is being experimentally deployed, and the use of any available source of data is under consideration. The findings provide hints on the accuracy to be expected and on corrections to be applied to classical pseudo-measurements.

Real-Time Monitoring of Distribution System Based on State Estimation

The development of the smart grid requires new monitoring systems able to support automation functionalities to control distributed energy resources. A real-time distribution system state estimator (DSSE) integrated with bad data processor is presented in this paper as a key element of the monitoring system. The developed DSSE is optimized for real-time applications, particularly, for computational efficiency, numerical stability, and robustness against measurements with large error. The DSSE is localized within an automation platform, which performs monitoring and control at substation level, from which the requirements for monitoring are derived. DSSEs located in different automation platform may be coordinated through multiarea algorithms, improving solutions time efficiency and robustness, but maintaining acceptable accuracy levels. The performance of real-time DSSE, both for single and multiarea is analyzed and discussed by means of real-time simulations performed in distribution medium voltage and low voltage networks.

A platform for testing monitoring systems for the power distribution grid

In future distribution network the need for automatic control and supervision has to be assisted by a monitoring system with high quality performance in terms of accuracy, reporting rate and robustness. However the number of measurement devices vs number of nodes buses is still very low. The increment in the measurement capacity can be achieved through distributed measurements, deploying of more devices, using existing measurements currently employed for other purposes (e.g. only protection, or energy billing) and gathering other heterogeneous measurements. Therefore, a measurement aggregator with monitoring functions should be able to produce meaningful and accurate results with very sparse, heterogeneous measurements. This paper proposes an architecture for testing this type of monitoring systems for distribution grids. The primary requirements for the development of this platform are: flexibility of the inputs, in terms of type, quantity and quality of measurements, and typical requirements for monitoring in modern smart grids. The key feature of the proposed platform is the modular structure, which allows total flexibility in terms of type of measurements and monitoring functions.

Impact of Pseudo-Measurements from new Load Profiles on State Estimation in Distribution Grids

State estimation proposals in distribution power systems heavily rely on pseudo-measurements, primarily originating from standard load profiles. Actual load profiles are suspected to deviate significantly from the standard load profiles, and research work is ongoing to determine the amount of this deviation and to find ways to synthesize more realistic profiles. The analysis of the propagation of error and uncertainty of load profiles in state estimation must be investigated, as state estimation in distribution is being experimentally deployed, and the use of any available source of data is under consideration. The findings should provide hints on the accuracy to be expected and on corrections to be applied to update classical pseudo-measurements.

Design and test of a real time monitoring system based on a distribution system state estimation

The development of the smart grid requires new monitoring systems feasible to support automation functionalities as control of DER. As the key element in the monitoring system, the real time distribution system state estimation (DSSE) integrated with bad data processor is presented in this work. The developed DSSE is suitable for the real time application because it has the benefits of more computational efficiency, numerical stability and robustness against the measurements with large error. The DSSE is localized within a automation platform, from which the requirements from the output and the specifications of the input are derived. The performance of the real time DSSE is analyzed and discussed by means of simulations performed in a 16 bus distribution network.

Coordinated voltage control in distribution grids with LTE based communication infrastructure

In modern distribution grids voltage problems are likely to be more common and more significant, due to the high penetration of Distributed Generation (DG) that can vary their in-feed in short times. Coordinating these DGs, either in a centralized or distributed fashion, to maintain voltage at prescribed limits may be one effective solution. However, this coordination system requires a reliable communication infrastructure to operate. This paper presents the role of a Quality of Service (QoS) enabled Long Term Evolution (LTE) network in supporting the exchange of measurement and control signals between control center and local controllers in a coordinated voltage control scheme. A lab setup, with a simulated real distribution grid and an LTE communication infrastructure that prioritizes the energy traffic, tests the feasibility of this communication solution.

Systematic method for the development of future active distribution network automation architectures

In response to the EU Mandate M/490, two crucial tools were developed for supporting the standardization of the Smart Grid: the Smart Architecture Model (SGAM) framework and the Use Case Methodology. This paper shows and exemplifies the use of these tools to incrementally develop the automation architecture for future active distribution power systems, by leveraging on existing automation architectures from literature and existing standards. This method for architecture development has been formalized and used in EU Project IDE4L, but it is generally applicable.

Bayesian Approach for Distribution System State Estimation With Non-Gaussian Uncertainty Models

To deal with the increasing complexity of distribution networks that are experiencing important changes, due to the widespread installation of distributed generation and the expected penetration of new energy resources, modern control applications must rely on an accurate picture of the grid status, given by the distribution system state estimation (DSSE). The DSSE is required to integrate all the available information on loads and generators power exchanges (pseudomeasurements) with the real-time measurements available from the field. In most cases, the statistical behavior of the measured and pseudomeasured quantities cannot be approximated by a Gaussian distribution. For this reason, it is necessary to design estimators that are able to use measurements and forecast data on power flows that can show a non-Gaussian behavior. In this paper, a DSSE algorithm based on Bayes’s rule, conceived to perfectly match the uncertainty description of the available input information, is presented. The method is able to correctly handle the measurement uncertainty of conventional and synchronized measurements and to include possible correlation existing between the pseudomeasurements. Its applicability to medium voltage distribution networks and its advantages, in terms of accuracy of both estimated quantities and uncertainty intervals, are demonstrated.

Bayesian distribution system state estimation in presence of non-Gaussian pseudo-measurements

Distribution System State Estimation (DSSE) is nowadays essential to enable the smart management of medium and low voltage grids. Due to the lack of a suitable measurement infrastructure, DSSE usually relies on the use of power injection pseudo-measurements derived from the knowledge of the historical and statistical behaviour of loads and generators. The uncertainty of these pseudo-measurements could not fit with the normal distribution typically considered in DSSE. For this reason, suitable approaches have to be designed both to model the pseudo-measurements uncertainty and to consider it in the DSSE process. This paper proposes a DSSE algorithm based on the Bayesian theory able to handle appropriately pseudo-measurements with any uncertainty distribution. The procedure used to cluster different categories of prosumers and to generate the pseudo-measurement parameters provided as input to the DSSE is also presented. Tests on a low voltage network show the applicability of the proposed approach and the associated benefits.