An efficient and accurate solution for distribution system state estimation with multiarea architecture

Distribution system state estimation (DSSE) is an essential tool for the management and control of future distribution networks. Distribution grids are usually characterized by a very large number of nodes and different voltage levels. Moreover, different portions of the system can be operated by different distribution system operators. In this context, multiarea approaches are key tools to efficiently perform DSSE. This paper presents a novel approach for multiarea state estimation in distribution systems. The proposed algorithm is based on a two-step procedure, where the first-step local estimations are refined through a newly designed second step that allows the integration of the measurement information available in the adjacent areas. The main novelty in this paper is the mathematical analysis of the impact brought by possible measurements shared among different areas, which drives the design of a new efficient weighted least squares formulation of the second step to maximize the achievable estimation accuracy. Tests performed on the unbalanced IEEE 123-bus network prove the goodness of the new multiarea estimator proposed and show the accuracy and efficiency enhancements obtainable with respect to previous literature

Improving Electricity Distribution System State Estimation with AMR-Based Load Profiles

The ongoing battle against global warming is rapidly increasing the amount of renewable power generation, and smart solutions are needed to integrate these new generation units into the existing distribution systems. Smart grids answer this call by introducing intelligent ways of controlling the network and active resources connected to it. However, before the network can be controlled, the automation system must know what the node voltages and line currents defining the network state are.Distribution system state estimation (DSSE) is needed to find the most likely state of the network when the number and accuracy of measurements are limited. Typically, two types of measurements are used in DSSE: real-time measurements and pseudomeasurements. In recent years, finding cost-efficient ways to improve the DSSE accuracy has been a popular subject in the literature. While others have focused on optimizing the type, amount and location of real-time measurements, the main hypothesis of this thesis is that it is possible to enhance the DSSE accuracy by using interval measurements collected with automatic meter reading (AMR) to improve the load profiles used as pseudo-measurements.The work done in this thesis can be divided into three stages. In the first stage, methods for creating new AMR-based load profiles are studied. AMR measurements from thousands of customers are used to test and compare the different options for improving the load profiling accuracy. Different clustering algorithms are tested and a novel twostage clustering method for load profiling is developed. In the second stage, a DSSE algorithm suited for smart grid environment is developed. Simulations and real-life demonstrations are conducted to verify the accuracy and applicability of the developed state estimator. In the third and final stage, the AMR-based load profiling and DSSE are combined. Matlab simulations with real AMR data and a real distribution network model are made and the developed load profiles are compared with other commonly used pseudo-measurements.The results indicate that clustering is an efficient way to improve the load profiling accuracy. With the help of clustering, both the customer classification and customer class load profiles can be updated simultaneously. Several of the tested clustering algorithms were suited for clustering electricity customers, but the best results were achieved with a modified k-means algorithm. Results from the third stage simulations supported the main hypothesis that the new AMR-based load profiles improve the DSSE accuracy.The results presented in this thesis should motivate distribution system operators and other actors in the field of electricity distribution to utilize AMR data and clustering algorithms in load profiling. It improves not only the DSSE accuracy but also many other functions that rely on load flow calculation and need accurate load estimates or forecasts

Fast multi-area approach for distribution system state estimation

This paper presents the application of a fast two-step multi-area approach for state estimation in wide-area distribution systems. The possibility to perform Distribution System State Estimation (DSSE) in a multi-area framework, designing the methodology according to the configuration of the measurement infrastructure is assessed. In particular, the proposed methodology of multi-area DSSE considers areas with overlapping nodes and is generalized, with respect to previous literature, to deal with every possible measurement system. A new second step procedure has been also designed to obtain a computationally efficient refinement of the voltage profile. The proposed multi-area method can be performed in a decentralized way and with parallel processing, relying on limited data communication. Test results, obtained on the 123-bus IEEE test network, are presented and discussed