State Estimation For Active Distribution Networks

As the electrical industry continues to develop, one sector that still faces a range of concerns is the electrical distribution system. Excessive industrialization and inadequate billing are just a few issues that have plagued this electrical sector as it advances into the smart grid environment. Research is necessary to explore the possible solutions in fixing these problems and developing the distribution sector into an active and smart system. The Handbook of Research on New Solutions and Technologies in Electrical Distribution Networks is a collection of innovative research on the methods and applications of solving major issues within the electrical distribution system. Some issues covered within the publication include distribution losses, improper monitoring of system, renewable energy integration with micro-grid and distributed energy sources, and smart home energy management system modelling. This book is ideally designed for power engineers, electrical engineers, energy professionals, developers, technologists, policymakers, researchers, academicians, industry professionals, and students seeking current research on improving this key sector of the electrical industry.

This book offers a broad and detailed view about how traditional distribution systems are evolving smart/active systems. The reader will be able to share the view of a number of researchers directly involved in this field. For this sake, philosophical discussions are enriched by the presentation of theoretical and computational tools. A senior reader may incorporate some concepts not available during his/her graduation process, whereas new Engineers may have contact with some material that may be essential to his/her practice as professionals.

A guide to the role of static state estimation in the mitigation of potential system failures With contributions from a noted panel of experts on the topic, Advances in Electric Power and Energy: Static State Estimation addresses the wide-range of issues concerning static state estimation as a main energy control function and major tool for evaluating prevailing operating conditions in electric power systems worldwide. This book is an essential guide for system operators who must be fully aware of potential threats to the integrity of their own and neighboring systems. The contributors provide an overview of the topic and review common threats such as cascading black-outs to model-based anomaly detection to the operation of micro-grids and much more. The book also includes a discussion of an effective mathematical programming approach to state estimation in power systems. Advances in Electric Power and Energy reviews the most recent developments in the field and: Offers an introduction to the topic to help non-experts (and professionals) get up-to-date on static state estimation Covers the essential information needed to understand power system state estimation written by experts on the subject Discusses a mathematical programming approach Written for electric power system planners, operators, consultants, power system software developers, and academics, Advances in Electric Power and Energy is the authoritative guide to the topic with contributions from experts who review the most recent developments.

Offering an up-to-date account of the strategies utilized in state estimation of electric power systems, this text provides a broad overview of power system operation and the role of state estimation in overall energy management. It uses an abundance of examples, models, tables, and guidelines to clearly examine new aspects of state estimation, the testing of network observability, and methods to assure computational efficiency. Includes numerous tutorial examples that fully analyze problems posed by the inclusion of current measurements in existing state estimators and illustrate practical solutions to these challenges. Written by two expert researchers in the field, Power System State Estimation extensively details topics never before covered in depth in any other text, including novel robust state estimation methods, estimation of parameter and topology errors, and the use of ampere measurements for state estimation. It introduces various methods and computational issues involved in the formulation and implementation of the weighted least squares (WLS) approach, presents statistical tests for the detection and identification of bad data in system measurements, and reveals alternative topological and numerical formulations for the network observability problem.

Cyber-Physical Power System State Estimation updates classic state estimation tools to enable real-time operations and optimize reliability in modern electric power systems. The work introduces and contextualizes the core concepts and classic approaches to state estimation modeling. It builds on these classic approaches with a suite of data-driven models and non-synchronized measurement tools to reflect current measurement trends required by increasingly more sophisticated grids. Chapters outline core definitions, concepts and the network analysis procedures involved in the real-time operation of EPS. Specific sections introduce power flow problem in EPS, highlighting network component modeling and power flow equations for state estimation before addressing quasi static state estimation in electrical power systems using Weighted Least Squares (WLS) classical and alternatives formulations. Particularities of the state estimation process in distribution systems are also considered. Finally, the work goes on to address observability analysis, measurement redundancy and the processing of gross errors through the analysis of WLS static state estimator residuals. Develops advanced approaches to smart grid real-time monitoring through quasi-static model state estimation and non-synchronized measurements system models Presents a novel, extended optimization, physics-based model which identifies and corrects for measurement error presently egregiously discounted in classic models Demonstrates how to embed cyber-physical security into smart grids for real-time monitoring Introduces new approaches to calculate power flow in distribution systems and for estimating distribution system states Incorporates machine-learning based approaches to complement the state estimation process, including pattern recognition-based solutions, principal component analysis and support vector machines

State Estimation in Electric Power Systems: A Generalized Approach provides for the first time a comprehensive introduction to the topic of state estimation at an advanced textbook level. The theory as well as practice of weighted least squares (WLS) is covered with significant rigor. Included are an in depth analysis of power flow basics, proper justification of Stott's decoupled method, observability theory and matrix solution methods. In terms of practical application, topics such as bad data analysis, combinatorial bad data analysis and multiple snap shot estimation are covered. The book caters both to the specialist as well as the newcomer to the field. State estimation will play a crucial role in the emerging scenario of a deregulated power industry. Many market decisions will be based on knowing the present state of the system accurately. State Estimation in Electric Power Systems: A Generalized Approach crystallizes thirty years of WLS state estimation theory and practice in power systems and focuses on techniques adopted by state estimation developers worldwide. The book also reflects the experience of developing industrial-grade state estimation software that is used in the USA, South America, and many other places in world.

The feasible operation region (FOR) allows capturing the aggregated flexibility potential of DER within radial distribution networks, while respecting the technical restrictions of both plants and grid. This thesis proposes a novel approach to compute the FOR, the Linear Flexibility Aggregation (LFA) method, based on the solution of a sequence of linear OPF. With the objective of reducing the computation time, without compromising the accuracy of the assessed FOR. It is shown that the proposed method provides a considerable reduction in processing time compared to similar methods, e.g. Monte-Carlo simulations or non-linear OPF-based methods.