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C.D. López Torres
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Electrical power systems are becoming more interconnected and technologically diverse to accommodate ever increasing shares of non-dispatchable generation. These changes are imposing new requirements on the simulation of electrical power systems. One of these requirements is that simulations integrate models of different subsystems, developed by different experts, from different organizations, which may not wish to disclose the information embedded in their models. This, to study the interactions between neighboring, interconnected grids, or between existing grids and new devices. Another requirement is that they reproduce phenomena in a wider range of timescales, to study the interactions between subsystems with slow and fast dynamic behavior. One way for electrical power system simulations to comply with these requirements is with remote, natural waveform co-simulation. Co-simulation is a model integration approach in which each subsystem is simulated in a different simulator. These simulators exchange interface variables, at runtime, to represent interactions between the subsystems. Since the simulators can interact remotely, over a communication network, co-simulation has the advantage that the organization that owns the model needs not disclose it. It also has the advantage that each model can be simulated with the simulator for which it was intended, so organizations that use different simulators can collaborate without having to translate their models. If such a co-simulation is performed using natural waveform models, at a high time resolution, then it is also possible to reproduce a wide range of timescales, from slow to very fast phenomena. But the fact that such a co-simulation is performed remotely, with the communication delays this entails, and that its high time resolution translates into a high communication rate, make it rather slow. Thus, it is desirable to reduce the need for inter-simulator communication. In this thesis I explore a solution to this communication challenge, based on the hypothesis that slower phenomena are easier to predict. If the co-simulated phenomena can be classified as predictable according to some criterion, it should be possible to find expressions that predict interface variables, and that each simulator can use to compute its own inputs instead of expecting inputs to be communicated. I propose a criterion for classifying phenomena as predictable or unpredictable, as well as methods for finding these expressions based on an interpolated Fourier transform and Taylor-Kalman filters. Additionally, I propose a co-simulation algorithm where the simulators compute their own inputs while the co-simulated phenomena are predictable. After applying these ideas to the co-simulation of two different test systems, I was able to reduce the need for communication up to 60%. A co-simulation framework with these characteristics is a step towards more descriptive models and better performing simulations, and a tool that increases our ability to take better advantage of existing energy infrastructure, as well as to develop it further.
...
Electrical power systems are becoming more interconnected and technologically diverse to accommodate ever increasing shares of non-dispatchable generation. These changes are imposing new requirements on the simulation of electrical power systems. One of these requirements is that simulations integrate models of different subsystems, developed by different experts, from different organizations, which may not wish to disclose the information embedded in their models. This, to study the interactions between neighboring, interconnected grids, or between existing grids and new devices. Another requirement is that they reproduce phenomena in a wider range of timescales, to study the interactions between subsystems with slow and fast dynamic behavior. One way for electrical power system simulations to comply with these requirements is with remote, natural waveform co-simulation. Co-simulation is a model integration approach in which each subsystem is simulated in a different simulator. These simulators exchange interface variables, at runtime, to represent interactions between the subsystems. Since the simulators can interact remotely, over a communication network, co-simulation has the advantage that the organization that owns the model needs not disclose it. It also has the advantage that each model can be simulated with the simulator for which it was intended, so organizations that use different simulators can collaborate without having to translate their models. If such a co-simulation is performed using natural waveform models, at a high time resolution, then it is also possible to reproduce a wide range of timescales, from slow to very fast phenomena. But the fact that such a co-simulation is performed remotely, with the communication delays this entails, and that its high time resolution translates into a high communication rate, make it rather slow. Thus, it is desirable to reduce the need for inter-simulator communication. In this thesis I explore a solution to this communication challenge, based on the hypothesis that slower phenomena are easier to predict. If the co-simulated phenomena can be classified as predictable according to some criterion, it should be possible to find expressions that predict interface variables, and that each simulator can use to compute its own inputs instead of expecting inputs to be communicated. I propose a criterion for classifying phenomena as predictable or unpredictable, as well as methods for finding these expressions based on an interpolated Fourier transform and Taylor-Kalman filters. Additionally, I propose a co-simulation algorithm where the simulators compute their own inputs while the co-simulated phenomena are predictable. After applying these ideas to the co-simulation of two different test systems, I was able to reduce the need for communication up to 60%. A co-simulation framework with these characteristics is a step towards more descriptive models and better performing simulations, and a tool that increases our ability to take better advantage of existing energy infrastructure, as well as to develop it further.
Book chapter
(2021)
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Ajay Shetgaonkar, Arcadio Perilla, Ilya Tyuryukanov, Digvijay Gusain, Claudio López, José Luis Rueda Torres
This chapter presents a general overview of the experience learned with the use of DIgSILENT PowerFactory in the design of theoretical lectures and practical sessions of a power system dynamics course at postgraduate level. This chapter focuses on the experiences acquired in the course that is part of the MSc program in Electrical Engineering of TU Delft, Department of Electrical Sustainable Energy. The discussion provided in this chapter focuses on power systems application with special focus on (i) Steady-state, Dynamic, (ii) Voltage Stability and (iii) rotor angle stability. The main objective of using PowerFactory at MSc level is to expose the postgraduate students to real-life application, however, without lack of generalisation this chapter is dedicated to the is to expose to the application above by using a very well-known two area-four machine test power system (2A4G), it gives students insights and experience with cases closer to actual power systems. Results of this pedagogical experience demonstrate the importance of incorporating appropriate power system simulations tools in the postgraduate level.
...
This chapter presents a general overview of the experience learned with the use of DIgSILENT PowerFactory in the design of theoretical lectures and practical sessions of a power system dynamics course at postgraduate level. This chapter focuses on the experiences acquired in the course that is part of the MSc program in Electrical Engineering of TU Delft, Department of Electrical Sustainable Energy. The discussion provided in this chapter focuses on power systems application with special focus on (i) Steady-state, Dynamic, (ii) Voltage Stability and (iii) rotor angle stability. The main objective of using PowerFactory at MSc level is to expose the postgraduate students to real-life application, however, without lack of generalisation this chapter is dedicated to the is to expose to the application above by using a very well-known two area-four machine test power system (2A4G), it gives students insights and experience with cases closer to actual power systems. Results of this pedagogical experience demonstrate the importance of incorporating appropriate power system simulations tools in the postgraduate level.
The complexity of energy systems increases as more renewable generation and energy storage technologies are added to the grid. Diverse energy carriers are becoming interconnected and the grids are getting reliant on communication networks for timely operation. The arising complexity is difficult to model with the existing mathematical models and using existing simulation tools due to confinement of these models and tools to a subset of the interconnected system. To overcome this challenge, combined simulation (co-simulation) methodology is being deployed. In co-simulation, multiple models and tools are being interconnected to truthfully represent reality. In this work, we review several aspects of co-simulation. First, we look at interconnecting transmission and distribution grid simulations in order to enable collaboration between transmission system operators (TSOs) and distribution system operators (DSOs). Next, we investigate co-simulation as means to dynamic model exchange between TSOs. Finally, we analyze co-simulation capabilities for running experiments in remotely connected research labs.
...
The complexity of energy systems increases as more renewable generation and energy storage technologies are added to the grid. Diverse energy carriers are becoming interconnected and the grids are getting reliant on communication networks for timely operation. The arising complexity is difficult to model with the existing mathematical models and using existing simulation tools due to confinement of these models and tools to a subset of the interconnected system. To overcome this challenge, combined simulation (co-simulation) methodology is being deployed. In co-simulation, multiple models and tools are being interconnected to truthfully represent reality. In this work, we review several aspects of co-simulation. First, we look at interconnecting transmission and distribution grid simulations in order to enable collaboration between transmission system operators (TSOs) and distribution system operators (DSOs). Next, we investigate co-simulation as means to dynamic model exchange between TSOs. Finally, we analyze co-simulation capabilities for running experiments in remotely connected research labs.
DIgSILENT PowerFactory is among the most widely adopted power system analysis tools in research and industry. It provides a comprehensive library of device models and it allows users to define their own. Models for dynamic simulation can be defined in the DIgSILENT Simulation Language (DSL). When the functionality of DSL is insufficient, new DSL functions can be defined in C or C++. However, C and C++ can be challenging for inexperienced programmers. Furthermore, every time the C or C++ code is modified, it needs to be recompiled and PowerFactory needs to be restarted for the changes to take effect, which slows down the workflow, model development, and inhibits rapid prototyping. In this paper we present an open source library that allows users to call Python functions and methods from DSL with minimal effort. Python is a powerful and much easier to use language than C or C++. Additionally, Python programs do not need to be compiled. Furthermore, with this library PowerFactory does not need to be restarted every time the Python code is changed. To illustrate what can be accomplished with our library we present three example use cases related to load modeling, co-simulation, and fault detection based on machine learning. The examples show that it becomes straightforward to enhance DSL with Python and that sophisticated models can be produced with reduced effort using popular open source Python libraries. As a consequence, PowerFactory users gain access to enhanced modeling capabilities and user-friendliness, and a more speedy workflow, which is beneficial for rapid prototyping.
...
DIgSILENT PowerFactory is among the most widely adopted power system analysis tools in research and industry. It provides a comprehensive library of device models and it allows users to define their own. Models for dynamic simulation can be defined in the DIgSILENT Simulation Language (DSL). When the functionality of DSL is insufficient, new DSL functions can be defined in C or C++. However, C and C++ can be challenging for inexperienced programmers. Furthermore, every time the C or C++ code is modified, it needs to be recompiled and PowerFactory needs to be restarted for the changes to take effect, which slows down the workflow, model development, and inhibits rapid prototyping. In this paper we present an open source library that allows users to call Python functions and methods from DSL with minimal effort. Python is a powerful and much easier to use language than C or C++. Additionally, Python programs do not need to be compiled. Furthermore, with this library PowerFactory does not need to be restarted every time the Python code is changed. To illustrate what can be accomplished with our library we present three example use cases related to load modeling, co-simulation, and fault detection based on machine learning. The examples show that it becomes straightforward to enhance DSL with Python and that sophisticated models can be produced with reduced effort using popular open source Python libraries. As a consequence, PowerFactory users gain access to enhanced modeling capabilities and user-friendliness, and a more speedy workflow, which is beneficial for rapid prototyping.
Co-simulation has become increasingly popular as a tool for dealing with the unprecedented complexity of modern engineering systems, such as electrical power systems and the AC circuits that compose them. Co-simulation is useful when migrating the models of each subsystem to a single monolithic simulator is either impractical or impossible, and the need for understanding the interactions between the subsystems does not leave room for model simplifications. However, co-simulation can suffer from long execution times, caused by the overhead introduced by exchanging variables between simulators. In this paper, we propose a method that mitigates this overhead by decoupling the simulators whenever their inputs become predictable. We applied this method to the co-simulation of an AC circuit composed of two subsystems and obtained speedups of up to 39% with errors that remain around 1% most of the time. Although questions regarding the scalability of the method persist, these results indicate that the method has the potential to make co-simulation an even more valuable tool for the user.
...
Co-simulation has become increasingly popular as a tool for dealing with the unprecedented complexity of modern engineering systems, such as electrical power systems and the AC circuits that compose them. Co-simulation is useful when migrating the models of each subsystem to a single monolithic simulator is either impractical or impossible, and the need for understanding the interactions between the subsystems does not leave room for model simplifications. However, co-simulation can suffer from long execution times, caused by the overhead introduced by exchanging variables between simulators. In this paper, we propose a method that mitigates this overhead by decoupling the simulators whenever their inputs become predictable. We applied this method to the co-simulation of an AC circuit composed of two subsystems and obtained speedups of up to 39% with errors that remain around 1% most of the time. Although questions regarding the scalability of the method persist, these results indicate that the method has the potential to make co-simulation an even more valuable tool for the user.
Given the complexity, scale and heterogeneity of modern power systems, many comprehensive simulation tasks are almost impossible to carry out without collaboration from multiple institutions. One way to approach collaborative simulation is through co-simulation. In this approach each institution contributes a model and the simulation is run distributedly. Co-simulation environments can be centrally orchestrated or decentrally orchestrated, each having its own set of advantages and challenges. In this paper we analyse the merits and challenges of co-simulation for collaborative simulation between institutions through two simple co-simulation environments, one centralized and the other decentralized. We argue that a co-simulation environment for collaboration between institutions should provide functionality like standard co-simulation interfaces for a variety of simulation tools, remote management and con?guration, model compatibility checks, and failure detection and recovery. An environment with these characteristics can be easily adopted by a wide range of institutions, which would greatly aid in tackling the complexity of modern electrical power systems.
...
Given the complexity, scale and heterogeneity of modern power systems, many comprehensive simulation tasks are almost impossible to carry out without collaboration from multiple institutions. One way to approach collaborative simulation is through co-simulation. In this approach each institution contributes a model and the simulation is run distributedly. Co-simulation environments can be centrally orchestrated or decentrally orchestrated, each having its own set of advantages and challenges. In this paper we analyse the merits and challenges of co-simulation for collaborative simulation between institutions through two simple co-simulation environments, one centralized and the other decentralized. We argue that a co-simulation environment for collaboration between institutions should provide functionality like standard co-simulation interfaces for a variety of simulation tools, remote management and con?guration, model compatibility checks, and failure detection and recovery. An environment with these characteristics can be easily adopted by a wide range of institutions, which would greatly aid in tackling the complexity of modern electrical power systems.
Co-simulation for Cyber Security Analysis
Data Attacks against Energy Management System
It is challenging to assess the vulnerability of a cyber-physical power system to data attacks from an integral perspective. In order to support vulnerability assessment except analytic analysis, suitable platform for security tests needs to be developed. In this paper we analyze the cyber security of energy management system (EMS) against data attacks. First we extend our analytic framework that characterizes data attacks as optimization problems with the objectives specified as security metrics and constraints corresponding to the communication network properties. Second, we build a platform in the form of co-simulation - coupling the power system simulator DIgSILENT PowerFactory with communication network simulator OMNeT++, and Matlab for EMS applications (state estimation, optimal power flow). Then the framework is used to conduct attack simulations on the co-simulation based platform for a power grid test case. The results indicate how vulnerable of EMS to data attacks and how co-simulation can help assess vulnerability.
...
It is challenging to assess the vulnerability of a cyber-physical power system to data attacks from an integral perspective. In order to support vulnerability assessment except analytic analysis, suitable platform for security tests needs to be developed. In this paper we analyze the cyber security of energy management system (EMS) against data attacks. First we extend our analytic framework that characterizes data attacks as optimization problems with the objectives specified as security metrics and constraints corresponding to the communication network properties. Second, we build a platform in the form of co-simulation - coupling the power system simulator DIgSILENT PowerFactory with communication network simulator OMNeT++, and Matlab for EMS applications (state estimation, optimal power flow). Then the framework is used to conduct attack simulations on the co-simulation based platform for a power grid test case. The results indicate how vulnerable of EMS to data attacks and how co-simulation can help assess vulnerability.
Python Scripting for DIgSILENT powerfactory
Leveraging the python API for scenario manipulation and analysis of large datasets
The need to set up and simulate different scenarios, and later analyse the results, is widespread in the power systems community. However, scenario management and result analysis can quickly increase in complexity as the number of scenarios grows. This complexity is particularly high when dealing with modern smart grids. The Python API provided with DIgSILENT PowerFactory is a great asset when it comes to automating simulation-related tasks. Additionally, in combination with the well-established Python libraries for data analysis, analysis of results can be greatly simplified. This chapter illustrates the synergic relationship that can be established between DIgSILENT PowerFactory and a set of Python libraries for data analysis by means of the Python API, and the simplicity with which this relationship can be established. The examples presented here show that it can be beneficial to exploit the Python API to combine DIgSILENT PowerFactory with other Python libraries and serve as evidence that the possible applications are mainly limited by the creativity of the user.
...
The need to set up and simulate different scenarios, and later analyse the results, is widespread in the power systems community. However, scenario management and result analysis can quickly increase in complexity as the number of scenarios grows. This complexity is particularly high when dealing with modern smart grids. The Python API provided with DIgSILENT PowerFactory is a great asset when it comes to automating simulation-related tasks. Additionally, in combination with the well-established Python libraries for data analysis, analysis of results can be greatly simplified. This chapter illustrates the synergic relationship that can be established between DIgSILENT PowerFactory and a set of Python libraries for data analysis by means of the Python API, and the simplicity with which this relationship can be established. The examples presented here show that it can be beneficial to exploit the Python API to combine DIgSILENT PowerFactory with other Python libraries and serve as evidence that the possible applications are mainly limited by the creativity of the user.
Conference paper
(2017)
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Miloš Cvetković, Kaikai Pan, Claudio David López, Rishabh Bhandia, Peter Palensky
Distributed energy resources (DERs) have seen significant expansion in utilization over the past decade. This expansion is best observed with the rooftop solar panels whose penetration has substantially grown in terms of deployed MWs. With the transformation of the grid towards more distributed supply of electricity, a new set of challenges arise. Although the challenges for adoption of DERs are plenty which span across technical, economical and policy domain, in this paper we discuss simulation challenges within two particular domains, cyber-security and voltage stability. For addressing each of these challenges, co-simulation has shown to be a promising path to take. Co-simulation (or combined simulation) represents the connection of two or more simulation tools with the goal of addressing a particular problem that neither one of these tools could address individually. Within each of these domains, we discuss the aspects for the design of co-simulation that one must consider when addressing the problem. The discussion is followed by short simulation examples.
...
Distributed energy resources (DERs) have seen significant expansion in utilization over the past decade. This expansion is best observed with the rooftop solar panels whose penetration has substantially grown in terms of deployed MWs. With the transformation of the grid towards more distributed supply of electricity, a new set of challenges arise. Although the challenges for adoption of DERs are plenty which span across technical, economical and policy domain, in this paper we discuss simulation challenges within two particular domains, cyber-security and voltage stability. For addressing each of these challenges, co-simulation has shown to be a promising path to take. Co-simulation (or combined simulation) represents the connection of two or more simulation tools with the goal of addressing a particular problem that neither one of these tools could address individually. Within each of these domains, we discuss the aspects for the design of co-simulation that one must consider when addressing the problem. The discussion is followed by short simulation examples.
Conference paper
(2017)
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Claudio David López, Arjen A. van der Meer, Miloš Cvetković, Peter Palensky
The unprecedented complexity of modern power systems has created a need for analyzing the interactions between different power system areas, which requires detailed physical models of all involved grids. However, a single institution seldom has access to enough information to build a complete model of a multi-area system. Additionally, such a model would be too labor-intensive to build and too computationally expensive to simulate. Co-simulation is an alternative that allows different institutions (TSOs, DSOs, research institutes, etc.) to simulate cooperatively by interconnecting their simulation tools, without having to disclose their grid models, and while sharing both the burden of model development and the computational load of the co-simulation. We present a co-simulation environment designed for researching the variable-rate (variable time step size) synchronization methods needed in a multi-institution setting. The environment can couple an arbitrary number of instances of DIgSILENT PowerFactory running on different virtual servers, at different rates, each representing a different area. An example use case with a three area system illustrates some of the main features of this environment. Errors bellow 5 % are evidence that this type of co-simulation is feasible, but long execution times point to additional challenges.
...
The unprecedented complexity of modern power systems has created a need for analyzing the interactions between different power system areas, which requires detailed physical models of all involved grids. However, a single institution seldom has access to enough information to build a complete model of a multi-area system. Additionally, such a model would be too labor-intensive to build and too computationally expensive to simulate. Co-simulation is an alternative that allows different institutions (TSOs, DSOs, research institutes, etc.) to simulate cooperatively by interconnecting their simulation tools, without having to disclose their grid models, and while sharing both the burden of model development and the computational load of the co-simulation. We present a co-simulation environment designed for researching the variable-rate (variable time step size) synchronization methods needed in a multi-institution setting. The environment can couple an arbitrary number of instances of DIgSILENT PowerFactory running on different virtual servers, at different rates, each representing a different area. An example use case with a three area system illustrates some of the main features of this environment. Errors bellow 5 % are evidence that this type of co-simulation is feasible, but long execution times point to additional challenges.
Applied Cosimulation of Intelligent Power Systems
Implementing Hybrid Simulators for Complex Power Systems
Journal article
(2017)
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Peter Palensky, Arjen A. Van Der Meer, Claudio David López, Arun Joseph, Kaikai Pan
Smart grids link various types of energy technologies, such as power electronics, machines, grids, and markets, via communication technology, which leads to transdisciplinary, multidomain systems. Simulation packages for assessing the system integration of components typically cover only one subdomain, while greatly simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. The article "Cosimulation of Intelligent Power Systems: Fundamentals, Software Architecture, Numerics, and Coupling," published in the March 2017 issue of this magazine [88], covered the fundamental concepts of this topic, and this follow-up article covers the applied aspects of the subject.
...
Smart grids link various types of energy technologies, such as power electronics, machines, grids, and markets, via communication technology, which leads to transdisciplinary, multidomain systems. Simulation packages for assessing the system integration of components typically cover only one subdomain, while greatly simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. The article "Cosimulation of Intelligent Power Systems: Fundamentals, Software Architecture, Numerics, and Coupling," published in the March 2017 issue of this magazine [88], covered the fundamental concepts of this topic, and this follow-up article covers the applied aspects of the subject.
Cosimulation of Intelligent Power Systems
Fundamentals, Software Architecture, Numerics, and Coupling
Smart grids link various types of energy technologies-such as power electronics, machines, grids, and markets-via communication technology, which leads to a transdisciplinary, multidomain system. Simulation packages for assessing system integration of components typically cover only one subdomain, while simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. This article, part 1 of 2 on this subject, covers fundamental concepts. Part 2 will appear in a future issue of IEEE Electrification Magazine and cover applications.
...
Smart grids link various types of energy technologies-such as power electronics, machines, grids, and markets-via communication technology, which leads to a transdisciplinary, multidomain system. Simulation packages for assessing system integration of components typically cover only one subdomain, while simplifying the others. Cosimulation overcomes this by coupling subdomain models that are described and solved within their native environments, using specialized solvers and validated libraries. This article discusses the state of the art and conceptually describes the main challenges for simulating intelligent power systems. This article, part 1 of 2 on this subject, covers fundamental concepts. Part 2 will appear in a future issue of IEEE Electrification Magazine and cover applications.
Conference paper
(2017)
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Miloš Cvetković, Harish Krishnappa, Claudio David López, Rishabh Bhandia, José Rueda Torres, Peter Palensky
This paper discusses and compares two approaches to address technical challenges in performing collaborative studies of power system dynamics. On one side, we consider the model migration approach which is an essential piece of dynamic model exchange. On the other side, we look at the co-simulation approach which is used to couple simulation tools together. The approaches are compared in terms of accuracy and the most probable reasons for discrepancy between dynamic responses are discussed. We complement the discussion and conclusions with the simulation results on a simple test system.
...
This paper discusses and compares two approaches to address technical challenges in performing collaborative studies of power system dynamics. On one side, we consider the model migration approach which is an essential piece of dynamic model exchange. On the other side, we look at the co-simulation approach which is used to couple simulation tools together. The approaches are compared in terms of accuracy and the most probable reasons for discrepancy between dynamic responses are discussed. We complement the discussion and conclusions with the simulation results on a simple test system.