Bayesian approach for distribution system state estimation

IEEE Transactions on Instrumentation and Measurement, 2017

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 Estimator (DSSE). DSSE is required to integrate all the available information on loads and generators power exchanges (pseudomeasurements) with the realtime 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, is demonstrated.

2016 IEEE International Workshop on Applied Measurements for Power Systems (AMPS), 2016

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 duly consider it in the DSSE process. This paper proposes a DSSE algorithm based on the Bayesian theory able to properly handle pseudomeasurements 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.

International Transactions on Electrical Energy Systems, 2016

The article presents a robust real-life distribution state estimation (DSE) that is integrated in the distribution management system. The DSE optimization procedure is developed in full accordance with the nature of distribution networks and their back/forward sweep-based load flow calculation. Thus, DSE is conceptually different from the traditional (transmission) state estimation. The article proves that the proposed DSE, even with relatively low level of telemetry and large dimensions of distribution networks, not only is possible as a function with value of its own but also provides a foundation for other important distribution management system functions. The procedure is the result of both research studies carried out in the last several years and real-life applications worldwide. Thus, this article offers DSE as an industrygrade product.

—This paper provides a survey of techniques for state estimation in electric power distribution systems. While state estimation has been applied in the monitoring and control of electricity transmission systems for several decades, it has not been widely implemented in distribution grids to date. However, with the recent drive towards more actively-managed, intelligent power distribution networks (" smart grids ") and the improvements in monitoring and communications infrastructure , Distribution System State Estimation (DSSE) has been receiving significant research interest. DSSE presents a number of unique challenges due to the characteristics of distribution grids, and many of the well-established methods used in transmission systems cannot be applied directly. This paper provides a detailed survey of the available methods for DSSE, reviewing around 70 papers from the major journals. In addition, it discusses the potential for applying Advanced Metering Infrastructure (AMI) data and computational intelligence methods in DSSE.

2018

Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for on-line monitoring and analyses. The requirements of Distribution System State Estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state of the art technology, major hurdles and challenges in DSSE development. The opportunities, paradigm shift and future research directions that could facilitate the need of DSSE are discussed.

IEEE Transactions on Power Systems, 2017

Transition to a sustainable energy environment results in aggregated generator and load dynamics in the distribution network. State estimation is a key function in building adequate network models for online monitoring and analyzes. The requirements of distribution system state estimation (DSSE) is becoming stringent because of the needs of new system modeling and operation practices associated with integration of distributed energy resources and the adoption of advanced technologies in distribution network. This paper summarizes the state-of-the-art technology, major hurdles, and challenges in DSSE development. The opportunities, paradigm shift, and future research directions that could facilitate the need of DSSE are discussed.

2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014

In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on a linear approximation of the power flow equations for distribution networks, which is computationally more efficient than standard nonlinear weighted least squares (WLS) estimators. We show via numerical simulation that the proposed strategy performs similarly to standard WLS on a small test feeder. A key advantage of the proposed approach is that it provides explicit off-line computation of the estimation error confidence intervals, which we use to explore the trade-offs between number of PMUs, PMU placement and measurement uncertainty. Since the estimation error in distribution systems tends to be dominated by uncertainty in loads and scarcity of instrumented nodes, the linearized method along with the use of high-precision PMUs may be a suitable way to facilitate on-line state estimation where it was previously impractical.

IEEE Transactions on Instrumentation and Measurement, 2018

The effective management of future smart grids is strictly related to the accurate knowledge of the network operating conditions via Distribution System State Estimation (DSSE). To achieve this target, the measurement infrastructure of the distribution systems needs a substantial upgrade. However, obvious economic limitations prevent a massive deployment of measurement instruments on the field in short times. As a consequence, ad hoc meter placement techniques have to be applied to define location and type of a minimum set of measurements required to obtain the desired accuracy performance. This paper presents a mathematical analysis showing the impact of current and power measurements on the accuracy of DSSE results. The goal is to provide the analytical tools to identify the best placement for additional current or power measurements when applying an incremental deployment of meters in the distribution system. The analysis has a general validity but has also a clear impact on practical situations when the monitoring systems is upgraded starting from an existing infrastructure. Tests on a 95bus sample grid are presented in order to validate the found mathematical results and to highlight the associated benefits in a meter placement perspective.

Renewable and Sustainable Energy Reviews

State estimation (SE) is well-established at the transmission system level of the electricity grid, where it has been in use for the last few decades and is a most vital component of energy management systems employed in the monitoring and control centers of electric transmission systems. However, its use for the monitoring and control of power distribution systems (DSs) has not yet been widely implemented because DSs have been majorly passive with uni-directional power flows. This scenario is now changing with the advent of smart grid, which is changing the nature of electric distribution networks by embracing more dispersed generation, demand responsive loads, and measurements devices with different data rates. Thus, the development of distribution system state estimation (DSSE) tool is inevitable for the implementation of protection, optimization, and control techniques, and various other features envisioned by the smart grid concept. Due to the inherent characteristics of DS different from those of transmission systems, transmission system state estimation (TSSE) is not applicable directly to distribution systems. This paper is an attempt to present the state-of-the-art on distribution system state estimation as an enabler function for smart grid features. It broadly reviews the development of DSSE, and challenges faced by its development, and 2 33 various DSSE algorithms, as well as identifies some future research lines for DSSE.

IEEE Transactions on Instrumentation and Measurement, 2014

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.