Research and education in power system dynamics

Research overview

Currently it is widely accepted that a major barrier toward massive integration of renewable energy sources is the complex dynamic behavior of large-scale power systems. In many cases, the need to preserve system reliability and stability is a bottleneck, which practically prevents the use of such sources, despite their positive environmental impact and low cost. In addition, power systems with high penetration level of renewable energy sources will probably require new control methods and management strategies.

In light of these challenges we explore the fundamental limits of large-scale power systems, from the point of view of the system dynamics. For instance: how much power can be generated by renewable energy sources without critical consequences? Is 100% renewables energy integration a feasible goal in principle? What is the fundamental lower limit on the amount of stored energy in a system? Is it possible to operate a power system without energy storage devices, and if not, what is a lower limit on the energy stored in the system?

Lectures on power system dynamics
  1. This lecture is a short introduction to power system dynamics. It discusses the approximation of time-varying phasors, and reviews key aspects of the primary and secondary control methods.
  2. This lecture introduces the Direct-Quadrature-Zero (DQ0) transformation, shows how to use it to analyze linear networks, and discusses the relations between dq0 quantities and phasors.
  3. This lecture presents a dynamic model of the synchronous machine. We demonstrate how to use this model in power system simulations, and explain the relations between the machine's dq0 model and time-varying phasor model.
  4. This lecture focuses on management and control of energy storage devices. We explain how these devices are used for energy balancing, load leveling, peak shaving, and energy trading.
Homework Assignments: Assignment 1, Assignment 2, Assignment 3, Assignment 4, Research Assignment.
Software

This is a free software tool for analyzing the dynamics of power systems based on dq0 signals. It is designed to simulate and analyze power systems that include several generators and loads, and possibly a large transmission network. The software provides tools for constructing dynamic models of the system components, and enables analysis in the frequency domain or the time domain. The manual (including tutorial) and software provide simple explanations and examples that can help one get started.

This software package supports:

  • Dynamic analysis of large-scale networks.
  • Simulation of complete networks.
  • Transient simulations.
  • Small-signal analysis.

To get started,

  1. Download the software files from MATLAB Central, and copy them to a directory of your choice, e.g., C:\DQ0 dynamics.
  2. Setup the directory in your MATLAB path. In the MATLAB, go to File > Set Path... and click on Add with Subfolders.... Now, select the directory that contains the DQ0 dynamics folder.
  3. Save the path for future MATLAB sessions (usually administrator privileges are necessary).
  4. For more advanced installation options please see the MANUAL.
How to cite this research

We kindly request that publications derived from the use of this approach acknowledge this fact by citing reference(s) from the list below. Note: full text of the papers related to this research can be alternatively accessed here.

  1. J. Belikov and Y. Levron, "Uses and misuses of quasi-static models in modern power systems," IEEE Transactions on Power Delivery, 33, pp. 3263-3266, 2018.
    Quasi-static models, also known as time-varying phasor models, have been used for many years for dynamic analysis and stability studies in power systems. However, the long track of success of using these models in a broad spectrum of applications resulted in blurring the boundaries between uses and misuses of time-varying phasors. Specifically, one possible misconception is that quasi-static models are always accurate enough when the system dynamics are slow in comparison to the nominal system frequency. This letter shows that in some cases, this assumption is inaccurate and may lead to misleading conclusions regarding the system dynamics and stability.
  2. J. Belikov and Y. Levron, "A sparse minimal-order dynamic model of power networks based on dq0 signals," IEEE Transactions on Power Systems, 33, pp. 1059-1067, 2018.
    Today the dq0 reference frame is mainly used for modeling and control of traditional electric machines and small power sources. A current challenge is to merge various dq0-based models appearing in recent literature to obtain a complete model of a large power system. To this end, in this paper we propose a model describing the dynamics of large transmission networks based on dq0 quantities. The proposed model is based on a standard network topology, uses sparse system matrices, and is of minimal order. We also demonstrate how this model may be used to construct a small-signal description of a complete system that includes the transmission network, generators, and loads. Results are illustrated on the basis of a long transmission line, and using the 118-bus test case network. This paper is accompanied by a free software package.
  3. J. Belikov and Y. Levron, "Integration of long transmission lines in large-scale dq0 dynamic models," Electrical Engineering, 100, pp. 1219-1228, 2018.
    The dq0 transformation is increasingly used today to model distributed sources, complex loads, renewable generators, and power electronics-based devices. This paper presents a dynamic model of long transmission lines that is based entirely on dq0 quantities, and demonstrates how such a model may be integrated with emerging dq0 models of large-scale networks. The model is first developed in the frequency domain and then converted to the time domain, using a state-space representation which inputs and outputs are dq0 signals. The proposed approach may be used to evaluate the stability and dynamic behavior of power systems that include long transmission lines, taking advantage of the dq0 reference frame inherent benefits. This is demonstrated on the basis of a 7-bus network, which shows how long transmission lines influence the network dynamics and stability. The proposed models and examples are provided as a part of an open-source software.
  4. D. Baimel, J. Belikov, J. M. Guerrero, and Y. Levron, "Dynamic modeling of networks, microgrids, and renewable sources in the dq0 reference frame: A survey," IEEE Access, 5, pp. 21323-21335, 2017.
    With increasing the penetration of distributed and renewable sources into power grids, and with increasing the use of power electronics-based devices, the dynamic behavior of large-scale power systems is becoming increasingly complex. These recent developments have led to several models attempting to simplify the analysis of dynamic phenomena, among them are models based on the dq0 transformation. Many recent works present dq0-based models of various power system components, ranging from small renewable sources to complete networks. The purpose of this paper is to review and categorize these works, with an objective to promote a straightforward modeling and the analysis of complex systems, based on dq0 quantities. This paper opens by recalling basic concepts of the dq0 transformation and dq0-based models. We then review several recent works related to dq0 modeling and analysis, considering the models of passive components, complete passive networks, synchronous machines, wind turbine systems, photovoltaic inverters, and others.
  5. Y. Levron and J. Belikov, "Open-source software for modeling and analysis of power networks in the dq0 reference frame," The 12th IEEE PES PowerTech Conference, Manchester, UK, pp. 1-6, 2017.
    This paper joins the developing trend of modeling distributed generators based on dq0 quantities, and proposes an open-source software for modeling and analysis of large-scale power networks based on dq0 signals. The dynamic models describing the network are provided as state-space objects that are sparse and of minimal order. The software supports integration of various models of synchronous machines, loads and renewable sources, and enables analysis of the complete system, including the feedback between the active units and the transmission network. In addition, functions are provided for computing time-domain transients, and for evaluating the system stability. The package contains several examples demonstrating modeling and stability analysis of small microgrids containing renewable sources, as well as of large networks with a variety of generators and loads.
  6. J. Belikov and Y. Levron, "Comparison of time-varying phasor and dq0 dynamic models for large transmission networks," International Journal of Electrical Power & Energy Systems, 93, pp. 65-74, 2017.
    In recent years, with increasing penetration of small distributed generators and fast power electronics based devices, the assumption of quasi-static phasors is becoming increasingly inaccurate. In order to describe fast dynamic behavior and rapid amplitude and phase variations, more accurate dynamic models based on the dq0 transformation are used. To better understand the differences between these two models, in this work we compare their relative accuracy when applied to large-scale transmission networks. In this light, the present work describes the two types of models using similar terminology, which is based on dq0 quantities. Based on this result, we show that quasi-static models may be obtained from dq0 models at low frequencies, and that there exists a frequency range in which quasi-static model approximates the dq0 model well. The obtained results allow to estimate the frequency after which the quasi-static model cannot accurately describe the system dynamics, and dq0 models should be used instead.
  7. Y. Levron and J. Belikov, "Modeling power networks using dynamic phasors in the dq0 reference frame," Electric Power Systems Research, 144, pp. 233–242, 2017.
    The dynamic behavior of large power systems has been traditionally studied by means of time-varying phasors, under the assumption that the system is quasi-static. However, with increasing integration of fast renewable and distributed sources into power grids, this assumption is becoming increasingly inaccurate. In this paper, we present a dynamic model of general transmission and distribution networks that uses dynamic phasors in the dq0 reference frame. The model is formulated in the frequency domain, and is based on the network frequency dependent admittance matrix. We also present a simplified version of this model that is obtained by a first-order Taylor approximation of the dynamic equations. The proposed models extend the quasi-static model to higher frequencies, while employing dq0 signals that are static at steady-state, and therefore combine the advantages of high bandwidth and a well-defined operating point. The models are verified numerically using the 9-, 30-, and 118-bus test-case networks. Simulations show that frequency responses of all models coincide at low frequencies and diverge at high frequencies. In addition, responses of the dq0 model in the time domain and in the abc reference frame are very close to those of the transient model.
  8. Y. Levron and J. Belikov, "Reduction of power system dynamic models using sparse representations," IEEE Transactions on Power Systems, 32, pp. 3893-3900, 2017.
    This paper proposes a model reduction technique that simplifies the dynamic equations of complex power networks, using sparse representations of the system matrices. Instead of removing components from the state vector, elements from the system matrices are eliminated such that these matrices become sparse. This is achieved by three different numeric algorithms that approximate the original system model using fewer nonzero elements. These algorithms lead to simpler models, since the complexity of operations involving sparse matrices is primarily affected by the matrices density. Furthermore, this approach enables to identify significant dynamic relations between units in the network. The proposed methods are demonstrated on several test-case systems with 9 and 2383-buses. In these examples, more than 90% of the elements in the system matrices are eliminated.
  1. A. Fahima, R. Ofir, J. Belikov, and Y. Levron, "Minimal energy storage required for stability of low inertia distributed sources," International Energy Conference, Limassol, Cyprus, pp. 1-5, 2018.
    Recently there have been extensive research efforts to identify possible adverse effects of distributed sources and power electronics based devices when integrated into existing power grids, where two main challenges are low rotational inertia and stability. This paper studies the dynamics and stability of two simple systems: an ideal power source and a simple synchronous machine, both connected to an infinite bus. The objective in both cases is to determine analytically the minimal storage device that is necessary for stability. One objective of this analysis is educational - to demonstrate the crucial function of local energy storage as part of any power source, and specifically to show that ideal power sources are unstable when no local energy storage is present. Another objective is to approximate the size of local storage devices in practical designs, using simple analytic expressions and limited data.
  2. D. Akinyele, J. Belikov and Y. Levron, "Battery storage technologies for electrical applications: Impact in stand-alone photovoltaic systems," Energies, 10, pp. 1-39, 2017.
    Batteries are promising storage technologies for stationary applications because of their maturity, and the ease with which they are designed and installed compared to other technologies. However, they pose threats to the environment and human health. Several studies have discussed the various battery technologies and applications, but evaluating the environmental impact of batteries in electrical systems remains a gap that requires concerted research efforts. This study first presents an overview of batteries and compares their technical properties such as the cycle life, power and energy densities, efficiencies and the costs. It proposes an optimal battery technology sizing and selection strategy, and then assesses the environmental impact of batteries in a typical renewable energy application by using a stand-alone photovoltaic (PV) system as a case study. The greenhouse gas (GHG) impact of the batteries is evaluated based on the life cycle emission rate parameter. Results reveal that the battery has a significant impact in the energy system, with a GHG impact of about 36–68% in a 1.5 kW PV system for different locations. The paper discusses new batteries, strategies to minimize battery impact and provides insights into the selection of batteries with improved cycling capacity, higher lifespan and lower cost that can achieve lower environmental impacts for future applications.
  3. Y. Levron, J. M. Guerrero, and Y. Beck, "Optimal power flow in microgrids with energy storage," IEEE Transactions on Power Systems, 28, pp. 3226-3234, 2013.
    Energy storage may improve power management in microgrids that include renewable energy sources. The storage devices match energy generation to consumption, facilitating a smooth and robust energy balance within the microgrid. This paper addresses the optimal control of the microgrid's energy storage devices. Stored energy is controlled to balance power generation of renewable sources to optimize overall power consumption at the microgrid point of common coupling. Recent works emphasize constraints imposed by the storage device itself, such as limited capacity and internal losses. However, these works assume flat, highly simplified network models, which overlook the physical connectivity. This work proposes an optimal power flow solution that considers the entire system: the storage device limits, voltages limits, currents limits, and power limits. The power network may be arbitrarily complex, and the proposed solver obtains a globally optimal solution.
  4. Y. Levron and D. Shmilovitz, "Power systems' optimal peak-shaving applying secondary storage," Electric Power Systems Research, 89, pp. 80-84, 2012.
    Energy storage devices can facilitate more efficient energy management by regulating the peak of generated power. Managing the stored energy usually presents a complicated optimization problem. In this paper, we show an optimal “peak shaving” strategy, that enables minimization of the power peak and derive an analytic design method for attaining optimal peak shaving. The analysis reveals the lowest possible peak, given only the load's demand profile and the storage capacity. The effects of losses in the storage device are analyzed numerically, showing the increase of power peak associated with the increase of loss.
  5. Y. Levron and D. Shmilovitz, "Optimal power management in fueled systems with finite storage capacity," IEEE Transactions on Circuits and Systems I: Regular Papers, 57, pp. 2221-2231, 2009.
    Fueled power systems using secondary energy storage are analyzed. A generic model of such systems is suggested, and an optimal power management strategy that maximizes efficiency is derived analytically. The model and optimal management solution emphasizes the constraint imposed by finite storage capacity. The optimal generated energy is established independently of the system's capacity, and load, and general characteristics of it are derived and proved. The analytic solution provides an intuitive comprehension into the optimal power management, without needing numeric simulations.
  1. Y. Levron, J. Belikov, and D. Baimel, "A tutorial on dynamics and control of power systems with distributed and renewable energy sources based on the DQ0 transformation," Applied Sciences, 8, pp. 1-48, 2018.
    In light of increasing integration of renewable and distributed energy sources, power~systems are undergoing significant changes. Due to the fast dynamics of such sources, the system is in many cases not quasi-static, and cannot be accurately described by time-varying phasors. In such systems the classic power flow equations do not apply, and alternative models should be used instead. In this light, this paper offers a tutorial on the dynamics and control of power systems with distributed and renewable energy sources, based mainly on the dq0 transformation. The paper opens by recalling basic concepts of dq0 quantities and dq0-based models. We then explain how to model and analyze passive networks, synchronous machines, three-phase inverters, and how to systematically construct dq0-based models of complex systems. We also highlight the idea that dq0 models may be viewed as a natural extension of time-varying phasor models, and discuss the correct use and validity of each approach.
  2. D. Baimel, J. Belikov, J. M. Guerrero, and Y. Levron, "Dynamic modeling of networks, microgrids, and renewable sources in the dq0 reference frame: A survey," IEEE Access, 5, pp. 21323-21335, 2017.
    With increasing the penetration of distributed and renewable sources into power grids, and with increasing the use of power electronics-based devices, the dynamic behavior of large-scale power systems is becoming increasingly complex. These recent developments have led to several models attempting to simplify the analysis of dynamic phenomena, among them are models based on the dq0 transformation. Many recent works present dq0-based models of various power system components, ranging from small renewable sources to complete networks. The purpose of this paper is to review and categorize these works, with an objective to promote a straightforward modeling and the analysis of complex systems, based on dq0 quantities. This paper opens by recalling basic concepts of the dq0 transformation and dq0-based models. We then review several recent works related to dq0 modeling and analysis, considering the models of passive components, complete passive networks, synchronous machines, wind turbine systems, photovoltaic inverters, and others.
Contact (✉)
Yoash Levron
The Andrew and Erna Viterbi Faculty of Electrical Engineering,
Technion—Israel Institute of Technology,
Haifa 3200003, Israel
E-mail: Send Mail
  Juri Belikov
Department of Software Science,
Tallinn University of Technology,
Akadeemia tee 15a, 12618 Tallinn, Estonia
E-mail: Send Mail