Modeling and Simulation of Complex Power Systems
2: RWTH-Aachen, Germany
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.
Inspec keywords: graphical user interfaces; integration; educational courses; power engineering computing; Petri nets; data visualisation; design engineering; HVDC power convertors; object-oriented methods; power grids; HVDC power transmission
Other keywords: educational courses; power grids; design engineering; integration; Petri nets; HVDC power transmission; data visualisation; HVDC power convertors; graphical user interfaces; object-oriented methods
Subjects: Power system protection; Education and training; General electrical engineering topics; Control system analysis and synthesis methods; d.c. transmission; AC-DC power convertors (rectifiers); DC-AC power convertors (invertors); General and management topics; Power engineering computing
- Book DOI: 10.1049/PBPO118E
- Chapter DOI: 10.1049/PBPO118E
- ISBN: 9781785614040
- e-ISBN: 9781785614057
- Page count: 322
- Format: PDF
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Front Matter
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1 Introduction
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Simulation is a key activity in every engineering domain. It is basically impossible today to imagine a design activity that does not include a simulation verification.
The role of simulation has been actually growing more and more in practice. While the main role of simulation in the past has been to replicate reality, it has been more and more moving to anticipate reality. While in the past, it was mostly used to better understand the existing systems, it is now driving the design process so that we may say that the target is now on reality to replicate simulation. Today every complex modern system is first built-in simulation and then realized in reality. Simulation becomes then a formal method of specification that can better summarize the requirements, thanks to the fact that it is strictly a mathematical process.
Nevertheless, simulation is also still used to better understand the systems that we already built and operate. A simulation model can be for example used to define and analyze "what if" scenarios for an infrastructure.
Simulation in the power system has always been a very important activity, mostly because an experimental activity, strictly speaking, is basically impossible. While testing can be performed on a real grid, it is anyway impossible to perform comprehensive testing or testing that can impact wide areas such as a full transmission system. Also, simulation has been always offering a safer way to understand critical situations such as faults.
Real-time simulation has in the recent years added a new dimension to the power system research offering close to reality experiments in a different scale from small-field tests. The further development in the direction of hardware in the loop and power hardware in the loop has extended the concept of testing and validation in the power engineering domain.
All these considerations should make clear how the simulation science is a key asset for a modern power engineer. On the other hand, when we deal with simulators, we typically deal with a sort of black box. As good engineering practice it is then critical to have an understanding of what is inside the box.
In the trend to simulation-driven engineering, it is in fact very important to deeply understand how simulators work to be sure that we always take educated decisions in the process. Building trust in the simulation results is a key activity for every engineer. A modern engineer is not likely to develop a new simulation platform but it is definitely likely to use one or more of them for the everyday job.
The purpose of this book is to look "under the hood" of modern simulators to help engineers developing their understanding of when and why the simulations results from a given tool can be trusted.
In effect, there is not a single simulator able to tackle all the questions that a power engineer may face, but there are several tools that are better suited for a given question.
Different commercial platforms use different modeling approaches and each approach may face limitations in a given condition. In this respect, this book will not define the perfect solution for every question, but it will provide an unbiased guide to different simulation approaches presenting pros and cons of the different solutions.
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2 Digital simulation
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The main purpose of this chapter is to review some basic aspect of numerical integration of differential equations. Implicit and explicit as well as single and multistep algorithms have been considered. References [1-3] are used for this paragraph. References [1,2] are very well-known texts about numerical integration of differential equations and can be used for a deep understanding of the math behind what is presented in this chapter, Ref. [3] is a simulation textbook that - in the first chapter - offers a very well done and comprehensive review of integration methods applied to simulation of continuous system.
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3 Nodal methods
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A method that historically played a leading role for the modeling of electric circuit for transient simulation is the resistive companion [1] also often known as Dommel algorithm [2].
We now proceed presenting the principles of nodal analysis [3] and modified nodal analysis [4] that are on the basis of the resistive companion algorithm. Nodal analysis and modified nodal analysis are first presented with reference to DC circuit, immediately after is shown how, with resistive companion, nodal analysis and modified nodal analysis are used to solve circuit containing dynamic linear elements. To conclude how resistive companion can be extended to solve the more general family of non-linear dynamic network is illustrated.
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4 State-space methods
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Besides nodal methods another very common and widely used family of modeling approaches is the one based on state-space representation. Simulations solvers and tools based on this approach typically implement what is normally referred as signal flow solution process, the most well known commercial tool of this type is Simulink® from MathWorks®. This type of solver has been widely used for the modeling of mechanical, thermal, and hydraulic systems but it was less successful in the modeling of electrical systems. First of all, explicit integration better fits signal flow solvers but at the same time explicit integration methods often are not suitable for the integration of electrical systems, especially if the interest is in using relatively large time steps. Moreover, those types of solvers - as state-space modeling approach in general - make the automatic creation of system level models by coupling of components models difficult and not always possible; this is extremely critical for large power systems composed of hundreds of components. At the same time, state-space modeling is also extremely important as modeling method for control engineering independently of its use for simulation purposes. In this chapter, we provide a concise review of state-space modeling and its use for the simulation of electrical systems, for the reader interested in diving more in deep on the topic [1] represent a good starting point for the use of state-space modeling in simulation and [2] for the use of state-space modeling for control purposes.
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5 Parallelization methods
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The complexity of modern energy systems poses significant challenges on how these systems are planned, designed and operated. The design of each part (subsystem, component, algorithm) is a challenge due to the interactions and dimensions of the problem. This challenge cannot be simplified via de-coupling without risking the loss of essential dynamic behaviors. The impact of individual elements on the system, and vice versa, the impact of the system on individual elements, may not be inferred analytically, and the traditional design spiral maybe inadequate. In this context the use of numerical simulation tools becomes a critical need. Simulation is already a fundamental tool that supports design and analysis in many engineering fields. At the same time, despite a long history of use, traditional approaches and commercial tools are severely limited when treating complex, temporally and spatially distributed systems such as modern energy systems, partially because still largely based on serial execution algorithms. With time scales that span over 10 orders of magnitude and with systems of very large size that cannot be easily partitioned, high parallelizable simulation methods that ensure a computationally effective and scalable solution are needed.
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6 Simulation under uncertainty
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Effects of uncertain random elements within systems are expected to be very relevant in future energy systems due to the volatility of pervasive new energy sources (particularly large and small renewable sources, and consumer-owned small sources) and the presence of communication networks (e.g. random communication delays). These uncertain phenomena add to uncertainty sources typical of classical energy networks: load behavior, prices, and components reliability. Uncertainty can be introduced into systems by processes that are too complicated to model deterministically (i.e. exactly), and thus the processes are assumed to be stochastic (i.e. random). Moreover, the uncertainty of a system can come from having limited exact knowledge of elements of said system leading to these elements to be considered random. As uncertain/random quantities can impact the behavior of a system, modeling methods for uncertainty quantification are of great importance and should be considered at all stages of design and operation, including supporting real-time optimization and control.
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7 Simulation language specification - Modelica
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The Modelica language is designed for modeling complex systems with components from multiple domains. We leverage the holistic modeling capability considering electrical and thermal domain. Such considerations are becoming increasingly relevant due to the stronger coupling of both domains in modern distribution grids. In this example, we consider the impact of a building heating system on the voltage level in a low-voltage network.
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8 Dynamic phasors
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The following simulation represents a three-phase fault for the synchronous generator as described in Kundur [1] (Example 3.1). The simulation is conducted with dynamic phasor (DP) variables shifted by the nominal frequency of 60 Hz. Figure 8.1 shows the currents at the synchronous generator terminal. The fault occurs at 0.1 s and is cleared at 0.2 s. In this simulation, the clearing is instantly without waiting for a zero crossing of the current.
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9 Modeling of converters as switching circuits
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In this chapter, various models of switching circuits are presented, and thus of power converters, suitable for use in device level control design and system analysis. Each model derivation and formulation demonstrate the challenges, sources of limitation of accuracy, and dynamics, in particular related to linearization and numerical issues. The user of the model should combine the purposes of the model with the characteristics discussed here to make a knowledgeable choice or model type and formulation.
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10 Real-time and hardware-in-the-loop simulation
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This chapter has briefly presented various industrial applications of real-time simulation in the fields of power systems, motor drives, avionics, and robotics. A brief description of the model based design methodology was also presented together with a discussion of the main challenges encountered in the design of RTSs. It was demonstrated that the most complex applications found during the integration of very complex MMC HVDC grids can now be simulated in real-time to test actual control equipment using simulators based on standard computer systems.
As modern engineering projects become more complex, often with tight budgets and shortened development times, simulation technologies are becoming increasingly crucial to their success. It is believed that modern engineering curricula would benefit from the inclusion of real-time simulation technology courses because of widespread usage of simulation technology, both by industry and by researchers.
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11 Octsim/a solver for dynamic system simulation
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The major motivation for the design of Octsim library has been to provide a practical, reliable, and free tool for Octave with Simulink-like capabilities that can be applied to academic purposes.
Octsim is primarily a code-based solver, but visual and graphical enhancements will be introduced in the future. We envision the Octsim library adding a graphical user interface to go along with its textual functionality. Additionally, we aim to develop a parser for XML files related to the Simulink model. This design step can significantly improve the interoperability of Octsim since it provides the user a possibility to build the graphical diagram in Simulink and run the solver in Octsim. Another limitation is the current vision of Octsim supplies only fixed-step solver. But the variable-step will be developed in the further vision. The last point is that Octsim executes the code from top to bottom, in order to execute the blocks in a correct order, the sequence of the blocks should be well defined. One possible solution to this is to employ the Petri nets that could determine the next state of the system, validate if the system can be executed or identify if there is an algebraic loop.
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Back Matter
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