Modern power systems are highly complex due to increasing shares of intermittent renewable energy and distributed generation. Research requires computer simulation and modeling, and knowledge of methods and algorithms.
This book presents key concepts of modeling and simulation of power systems. The book introduces the two main families of techniques for computer-based simulation of dynamic systems, and methods that allow parallel simulation execution. The coverage includes digital simulation, topological methods, state space methods, parallelization methods, simulation under uncertainty, phasor simulation, switching systems simulation as well as real-time simulation and hardware in the loop testing. Examples, exercises and a set of simulation solvers implemented in Matlab® and Python are also provided.
Modeling and Simulation of Complex Power Systems is an invaluable tool for researchers in industry and academia, and advanced students.
Author(s): Antonello Monti, Andrea Benigni
Series: IET Energy Engineering Series, 118
Publisher: The Institution of Engineering and Technology
Year: 2022
Language: English
Pages: 321
City: London
Cover
Contents
About the authors
Additional contributors
1 Introduction
1.1 The structure of the book
1.2 How to use the book
Supplementary material
2 Digital simulation
2.1 Euler forward method
2.2 Backward Euler method
2.3 Trapezoidal rule method
2.4 Predictor and corrector method
2.5 Runge–Kutta methods
2.6 Adams–Bashforth and Adams–Moulton methods
2.7 Accuracy comparison
Exercises
References
3 Nodal methods
3.1 Nodal analysis
3.2 Matrix stamp
3.2.1 Resistor
3.2.2 Ideal current source
3.2.3 Real current source
3.2.4 Real voltage source
3.3 Modified nodal analysis
3.4 Resistive companion
3.4.1 Resistive companion solution flow
3.4.2 Inductor and capacitor in resistive companion
3.5 Numerical methods for the solution of linear systems
3.5.1 Gaussian elimination
3.6 Controlled sources
3.6.1 VCCS
3.6.2 VCVS
3.6.3 CCCS
3.6.4 CCVS
Exercises
References
4 State-space methods
4.1 State-space modeling
4.2 Circuit modeling
4.3 Discretization
4.4 Automated state-space modeling
4.5 Simulation of state-space model
4.6 Signal flow solver
4.7 From state-space to transfer function representation
Exercises
References
5 Parallelization methods
5.1 Introduction
5.2 Case study 1: parallelize the simulation of a ship power system
5.3 Case study 2: parallelize the simulation of the IEEE 34 and IEEE 123 distribution network
5.4 Diakoptics
5.5 State-space nodal method (SSN)
5.6 Transmission line modeling and the waveform relaxation-based method
5.7 Latency insertion method
5.7.1 Latency insertion method for power electronics simulation
5.7.2 Latency insertion method combined with state space and nodal methods
5.8 LB-LMC method
5.9 Exercises
References
6 Simulation under uncertainty
6.1 Introduction
6.2 Case studies
6.2.1 Case study 1: ship system analysis under uncertainty
6.2.2 Uncertainty sources in the simulation of distribution networks
6.3 Uncertainty and statistics
6.4 Monte Carlo
6.4.1 Theory
6.4.2 Computation of Monte Carlo simulations
6.4.3 QMC
6.5 Polynomial chaos
6.5.1 Theory
6.5.2 Statistical moments
6.5.3 Inner product calculation
6.5.4 Basic algebra using polynomial chaos
6.6 Non-intrusive polynomial chaos
6.6.1 Definition of collocation points
6.6.2 Evaluation
6.6.3 Expansion coefficients of the target variable
6.7 Exercises
References
7 Simulation language specification—Modelica
7.1 Example 1: Simulation of electrical and thermal
components considering the impact of a building
heating system on the voltage level in a distribution grid
7.2 Example 2: Static voltage assessment of a distribution grid with high penetration of photovoltaics
7.3 Example 3: Transient characteristics of synchronous generator models
7.4 Example 4: Simulation of electrical and mechanical
components considering the start of an asynchronous
induction machine
7.5 Introduction to Modelica
7.6 Fundamentals of the Modelica language
7.7 Hello World using Modelica
7.8 Electrical component modeling by equations
7.9 Object-oriented modeling by inheritance
7.10 System modeling by composition
7.11 Hybrid modeling
7.12 Further modeling formalisms
7.13 Implementation and execution of Modelica
7.14 Exercises
7.14.1 Task 1
7.14.2 Task 2
7.15 Exercises—solutions
7.15.1 Task 1—solution
7.15.2 Task 2—solution
References
8 Dynamic phasors
8.1 Simulation examples
8.1.1 Synchronous generator three-phase fault
8.1.2 Grid simulation using diakoptics
8.2 Introduction
8.3 Comparison to electromechanical simulation
8.4 Bandpass signals and baseband representation
8.5 Extracting dynamic phasors from real signals
8.6 Modeling dynamic systems using dynamic phasors
8.7 Dynamic phasor power system component models
8.7.1 Inductance model
8.7.2 Capacitance model
8.8 Dynamic phasors and resistive companion models
8.8.1 Inductance model
8.8.2 Capacitance model
8.9 Resistive companion simulation example
8.10 Accuracy
8.11 DP and EMT accuracy simulation example
8.12 Summary
References
9 Modeling of converters as switching circuits
9.1 Simulation of power electronics systems
9.2 Role of power electronics in power systems
9.3 Modelling and simulation of power electronics in power systems
9.4 Converter models
9.5 Averaged models
9.6 Averaged circuits
9.7 Averaged switching elements
9.7.1 Linearization
9.7.2 Considerations on the averaged models
9.8 State-space models
9.8.1 Continuous time models
9.8.2 Discrete time models
9.8.3 Generalized state-space models
9.8.4 Linearization of state-space models
9.9 Implementing a switch
9.9.1 Ideal switch
9.9.2 Switching of parameter value
9.9.3 Switching of companion source
9.10 Resistive companion model of converters
Problems
References
10 Real-time and hardware-in-the-loop simulation
10.1 Introduction
10.2 Model-based design and real-time simulation
10.3 General considerations about real-time simulation
10.3.1 The constraint of real-time
10.3.2 Stiffness issues
10.3.3 Simulator bandwidth considerations
10.3.4 Achieving very low latency for HIL application
10.3.5 Effective parallel processing for fast EMT simulation
10.3.6 FPGA-based multi-rate simulators
10.3.7 Advanced parallel solvers without artificial delays or stublines: application to active distribution networks
10.3.8 The need for iterations in real-time
10.4 Phasor-mode real-time simulation
10.5 Modern RTS requirements
10.5.1 Simulator I/O requirements
10.6 Rapid control prototyping and HIL testing
10.7 Power grids real-time simulation applications
10.7.1 Statistical protection system study
10.7.2 Monte Carlo tests for power grid switching surge system studies
10.7.3 Multi-level modular converter in HVDC applications
10.7.4 High-end super-large power grid simulations
10.8 Motor drive and FPGA-based real-time simulation applications
10.8.1 Industrial motor drive design and testing using CPU models
10.8.2 FPGA modeling of SRM and PMSM motor drives
10.9 Conclusion
References
11 Octsim/a solver for dynamic system simulation
11.1 Introduction
11.2 Solver description
11.3 Solver structure
11.4 Solver functionalities
11.5 Solver implementation and validation
11.5.1 Implementation details
11.5.2 Comparison with Simulink
11.5.3 Octsim code examples
11.5.4 Control system simulation
11.5.5 Electric circuit simulation
11.6 Example for hybrid system (buck converter with voltage control)
11.7 Conclusion
11.8 User manual
References
Index
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