Power Distribution System State Estimation

Description

State estimation is a key function for real-time operation and control of electrical power systems since its role is to provide a complete, coherent, and reliable network real-time model used to set up other real-time operation and control functions. In recent years it has extended its applications to monitoring active distribution networks with distributed energy resources. The inputs of a conventional state estimator are a redundant collection of real-time measurement, load and production forecasts and a mathematical model that relates these measurements to the complex nodal voltages, which are taken as the state variables of the system. The goal of state estimation is to adjust models so that they are closer to observed values and deliver better forecasts. In power systems, this is key to maintaining power quality and operating generation and storage units well. This book, written by international authors from industry and universities, systematically addresses state estimation in power distribution systems. Chapters convey techniques for distribution system state estimation, such as classical methods, three-phase network modelling, power flow calculation, fast decoupled approaches and their new application via complex per unit normalization, the Bayesian method, and multiarea state estimation. Also, synchronized and non-synchronized measurements with different sample rates, real-time monitoring, and practical experiences of distribution state estimation are covered. Researchers involved with electrical power and electrical distribution systems, professionals working in utilities, advanced students and PhD students will find this work essential reading. Contents About the Editors Preface 1 Introduction 2 Real-time monitoring of distribution systems brazilian experience 2.1 Introduction 2.2 Brazilian distribution system monitoring 2.3 Description of the RTMT implemented in COPEL 2.3.1 Step 1: Data preprocessing 2.3.2 Step 2: Real-time load modeling 2.4 Application results 2.4.1 In-field verification 2.4.2 Application in service restoration 2.4.3 Application in distribution system state estimation 2.4.4 Application for real-time load forecasting 2.5 Concluding remarks References 3 Practical experiences of distribution state estimation in real life 3.1 Introduction 3.2 Problem settings 3.2.1 Background 3.2.2 Sources of the problems 3.3 Industrial-grade product 3.3.1 Requests for practical implementation in real life 3.3.2 Problems of practical application and how to solve them 3.4 Where are we today? 3.5 Where are we going, and what awaits us? 3.6 Conclusion 3.6.1 Achieved to date 3.6.2 In front of us Appendix – Schneider Electric DMS Novi Sad LLC References 4 Three-phase network model for steady-state analysis of distribution systems 4.1 Introduction 4.1.1 Distribution system state estimation 4.1.2 The unbalanced and asymmetrical nature of distribution systems 4.2 Three-phase two-port models 4.2.1 Three-phase two-port admittance model 4.2.2 Polar coordinates 4.2.3 Rectangular coordinates 4.3 Models of the physical components of a distribution system 4.3.1 Distribution lines 4.3.2 Power transformers 4.3.3 Voltage regulators 4.3.4 Loads 4.3.5 Shunt capacitors and reactors 4.3.6 Distributed generation 4.3.7 Energy storage 4.3.8 Electric vehicles 4.3.9 Static compensators (D-FACTS) 4.4 Concluding remarks References 5 Current-based power flow calculation methods for distribution systems 5.1 Introduction 5.2 Basics of the current-based three-phase power flow 5.2.1 State variables 5.2.2 Specified quantities and bus types 5.2.3 Load modelling 5.3 Branch current–based and admittance matrix–based load flows 5.3.1 State variables 5.3.2 Bus types and equivalent specified quantities 5.3.3 General modelling 5.3.4 Computational aspects and discussion 5.4 Backward/forward sweep load flow 5.4.1 Basic aspects 5.4.2 General modelling 5.4.3 Computational aspects and discussion 5.5 Case studies 5.5.1 Data preparation 5.5.2 Execution of the AMBLF 5.5.3 Execution of the BCBLF 5.5.4 Execution of the BFSLF 5.6 Conclusions and special remarks References 6 Classical methods applied to distribution system state estimation 6.1 Introduction 6.2 Historical notes 6.3 Basics of BCB and AMB state estimators 6.3.1 State variables 6.3.2 Equivalent measurements 6.3.3 Variances of the equivalent measurements 6.3.4 Reference bus 6.3.5 WLS solution via normal equation 6.4 Jacobian matrix of the AMBSE 6.4.1 Derivatives of the complex current injections 6.4.2 Derivatives of the complex current flows 6.4.3 Derivatives of the equivalent voltages 6.4.4 Derivatives of the complex voltages of the reference bus 6.5 Jacobian matrix of the BCBSE 6.5.1 Derivative of the magnitude of the current flows 6.5.2 Derivative of the complex current injections 6.5.3 Derivative of the complex current flows 6.5.4 Derivative of the equivalent voltages 6.5.5 Derivatives of the complex voltages of the reference bus 6.6 Computational aspects 6.6.1 AMBSE algorithm 6.6.2 BCBSE algorithm 6.7 Case studies 6.7.1 Solving the AMBSE 6.7.2 Solving the BCBSE 6.8 Conclusion References 7 Fast-decoupled power flow method for active distribution systems 7.1 Introduction 7.1.1 Power flow analysis for active DS 7.2 Basics of Newton–Raphson-based power flow 7.2.1 Fundamentals of the power flow analysis 7.2.2 NRPF formulation 7.2.3 Fast-decoupled approach 7.3 cpu-based fast-decoupled power flow for distribution systems 7.3.1 Fundamentals of the complex per unit normalization 7.3.2 cpu-based power flow algorithm 7.4 Multilevel voltage analyses 7.5 Case studies and performance evaluation 7.5.1 Two-bus test system—convergence example 7.5.2 141-bus distribution feeder 7.6 Final remarks References 8 Fast-decoupled distribution system state estimation 8.1 Introduction 8.2 Fast-decoupled weighted least-squares state estimation 8.2.1 Conventional weighted least-squares state estimation 8.2.2 Decoupled formulation 8.2.3 Model-decoupled state estimator 8.2.4 Algorithm-decoupled state estimator 8.3 Bus-section level modeling in state estimation 8.3.1 Generalized decoupled formulation 8.4 cpu-based fast-decoupled DSSE 8.4.1 Data and measurement under cpu normalization 8.4.2 Conventional measurement data in cpu system 8.5 Measurement simulator 8.6 cpu-based real-time distribution system network modeling algorithm 8.7 Case studies and performance evaluation 8.8 Final remarks References 9 Bayesian approach for distribution system state estimation 9.1 Introduction 9.2 Power statistics and pseudo-measurements 9.3 Bayesian approach for state estimation in distribution systems 9.3.1 Measurement model 9.3.2 A Bayes framework for DSSE 9.3.3 Measurement handling 9.3.4 Prior description 9.3.5 Numerical computation of the Bayesian DSSE 9.4 Examples of Bayesian distribution system state estimation 9.5 Concluding remarks References 10 Multiarea state estimation for distribution systems 10.1 Introduction 10.1.1 Large-scale distribution systems and motivations for MASE 10.2 Multiarea State Estimation 10.2.1 Terminology, definitions, and classifications of multiarea state estimators 10.2.2 Hierarchical architecture 10.2.3 Distributed architecture 10.3 MASE for distribution systems 10.3.1 Two-step method with branch current estimator 10.3.2 Bayesian inference method with nodal voltage estimator 10.4 Application examples of MASE for distribution systems 10.4.1 Example I: Two-step multiarea DSSE 10.4.2 Example II: Bayesian inference multiarea DSSE 10.5 Concluding remarks References 11 Including synchronized and non-synchronized measurements with different sample rates in distribution system state estimation 11.1 Introduction 11.2 Data sources for DSSE 11.2.1 Measurement data used in DSSE 11.3 Temporal aspects of DSSE 11.3.1 Dynamic state estimation concepts and introduction to Kalman filters 11.3.2 Dynamic, forecasting-aided and tracking state estimation 11.4 Multistages state estimators based on quasi-dynamic techniques 11.4.1 Limitations of Kalman filter-based methods 11.4.2 Consensus DSSE method 11.4.3 Matrix completion-based system state update with granular measurements 11.4.4 Implementations of matrix completion-based DSSE 11.4.5 Numerical results 11.5 Bayesian information fusion approach for multistage DSSE 11.5.1 Bayesian inference concepts and application in DSSE 11.5.2 Posterior inference via orthogonal methods 11.5.3 Bayesian credibility test 11.5.4 Numerical results 11.6 Applications of DSSE and challenges 11.7 Concluding remarks References 12 State estimation for low voltage distribution grids 12.1 The need for DSSE in LVDGs 12.2 Classical DSSE approach 12.2.1 WLS DSSE 12.2.2 State variables and measurement functions 12.2.3 Kron’s reduction: four wires to three wires 12.3 Challenges for DSSE in LVDGs 12.3.1 Limited sensing 12.3.2 Limited system knowledge 12.3.3 Slow reporting rate – asynchronous measurements 12.3.4 Nonzero neutral voltage 12.4 Enhancing DSSE 12.4.1 Addressing asynchronous measurements using historical data 12.4.2 Addressing the nonzero neutral voltage 12.5 Future directions 12.5.1 State estimation for the whole distribution grid 12.5.2 Leveraging information from PV and EV inverters 12.5.3 Validation in a real-time hardware-in-the-loop framework 12.6 Concluding remarks References 13 Conclusions 13.1 Historical context 13.2 Alternative modeling and approaches for DSSE 13.3 Future perspectives for the evolution of the DSSE process 13.4 Final remarks Index Back Cover