Multiarea state estimation for distribution systems

Abstract

The large scale of real distribution systems makes difficult the development of computational tools for real-time monitoring of these systems. Thus, the decomposition of distribution networks into smaller sub-networks emerges as an alternative for this development, embracing the use of smaller models, instead of a single large-scale model, parallel computing and decentralized processing architectures.This chapter introduces decomposition methods to perform state estimation in large-scale distribution networks, employing the concepts of multiarea state estimation (MASE). A brief contextualization of scalability and decentralization is presented to emphasize the need of such architectures. Then, the main ideas of MASE are discussed. Two multiarea state estimation algorithms are presented, both make use of specialized methods for distribution system state estimation. However, one based on the traditional approach of the nodal voltage state estimation and the other on current-based model. Finally, numerical examples with both estimators illustrate the accuracy and computational aspects.

Authors 4

1. Júlio A. D. Massignan Aachen

   University of Cagliari · Universidade de São Paulo · RWTH Aachen University

   Affiliation as printed

   Department of Electrical and Computing Engineering, School of Engineering of São Carlos, University of São Paulo, São Carlos, São Paulo, Brazil

   Department of Electrical and Electronic Engineering

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems

   RWTH Aachen University

   University of Cagliari

   Department of Electrical and Computing Engineering, School of Engineering of São Carlos, University of São Paulo, , Brazil

2. João B. A. London Jr Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems

   University of Cagliari · Universidade de São Paulo · RWTH Aachen University

   Affiliation as printed

   Department of Electrical and Computing Engineering, School of Engineering of São Carlos, University of São Paulo, São Carlos, São Paulo, Brazil

   Department of Electrical and Electronic Engineering

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems

   RWTH Aachen University

   University of Cagliari

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, , Germany

3. Marco Pau Aachen E.ON Energy Research Center Institute for Automation of Complex Power Systems

   University of Cagliari · Universidade de São Paulo · RWTH Aachen University

   Affiliation as printed

   Department of Electrical and Computing Engineering, School of Engineering of São Carlos, University of São Paulo, São Carlos, São Paulo, Brazil

   Department of Electrical and Electronic Engineering

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems

   RWTH Aachen University

   University of Cagliari

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems, RWTH Aachen University, , Germany

4. Paolo Attilio Pegoraro Aachen

   University of Cagliari · Universidade de São Paulo · RWTH Aachen University

   Affiliation as printed

   Department of Electrical and Computing Engineering, School of Engineering of São Carlos, University of São Paulo, São Carlos, São Paulo, Brazil

   Department of Electrical and Electronic Engineering

   E.ON Energy Research Center, Institute for Automation of Complex Power Systems

   RWTH Aachen University

   University of Cagliari

   Department of Electrical and Electronic Engineering, University of Cagliari, , Italy