Slow Coherency Based Cutset Determination Algorithm for Large Power Systems

Modern electric power systems are large and complicated, and, in many regions, the generation and transmission systems are operating near their limits. Eigenanalysis is one of the tools used to analyze the behavior of these systems. Standard eigenvalue methods require that simplified models be used for these analyses; however, these simplified models do not adequately model all of the characteristics of large power systems. Thus, new eigenanalysis methods that can analyze detailed power system models are required. The primary objectives of the work described in this report were I) to determine the availability of eigenanalysis algorithms that are better than methods currently being applied and that could be used an large power systems and 2) to determine if vector supercomputers could be used to significantly increase the size of power systems that can be analyzed by a standard power system eigenanalysis code. At the request of the Bonneville Power Administration, the Pacific Northwest Laboratory (PNL) conducted a literature review of methods currently used for the eigenanalysis of large electric power systems, as well as of general eigenanalysis algorithms that are applicable to large power systems. PNL found that a number of methods are currently being used for the this purpose, and all seem to work fairly well. Furthermore, most of the general eigenanalysis techniques that are applicable to power systems have been tried on these systems, and most seem to work fairly well. One of these techniques, a variation of the Arnoldi method, has been incorporated into a standard power system eigenanalysis package. Overall, it appears that the general purpose eigenanalysis methods are more versatile than most of the other methods that have been used for power systems eigenanalysis. In addition, they are generally easier to use. For some problems, however, it appears that some of the other eigenanalysis methods may be better. Power systems eigenanalysis requires the computation of eigenvalues of nonsymmetric matrices. Such computations are fairly difficult, however, and they constitute an area of active research. Thus, research in this area should be closely monitored, and, as new methods become available, they should be tested on large power systems. PNL also investigated the use of vector supercomputers to enlarge the size of power systems that can be analyzed by MASS, a standard power system eigenanalysis code. MASS was converted to run on a Cray supercomputer. On a conventional computer, MASS is limited to power system problems with about 500 states because of computer time constraints. Running MASS on a Cray X-MP EA/232, however, PNL found that a problem with about 2200 states could be solved in about 26 minutes. Furthermore, by moving to a larger Cray, it should be possible to use MASS to analyze power systems with 5,000 to 10,000 states. Problems with 5,000 states would probably take about 5 Cray hours, while a problem with 10,000 states would probably take about 43 Cray hours, though the actual execution times will depend on the type of Cray used. Problems requiring 5 Cray hours are not uncommon. Problems requiring 43 Cray hours, however, would be fairly expensive. Thus, power systems with about 5000 states probably represent an upper limit on the size of problems that one would want to routinely solve using a Cray version of MASS.

A power system is composed of various components such as generators, transformers, transmission lines, switching devices and loads. They have their mathematical model and graphical representation. Sometimes, a power system’s change of topology occurs due to events like short circuits, lightning striking a transformer, or a reconfiguration of the transmission system. In this thesis, a new way of simulating large scale power systems is presented from the modeling point of view. In the literature, a lot of modeling methods and mathematical tools are available to tackle this subject. However, this thesis mainly focuses on the time domain simulation of large scale power systems - and in particular, transients which appear after a change of topology. A change of topology in electrical networks impact time domain simulations on two levels. The first impact is that it is necessary to update or re-compute the set of equations. The computation time of this action on the topology can be significant - especially for large scale power systems. The second impact of this change of topology is the transient that will occur. Usually, this change will impose to numerically compute fast oscillations in currents and voltages until they reach a new steady state...

The present work is based on developing a control strategy to mitigate the impact of reduced inertia due to significant DFIG penetration in a large power system. The paper aims to design a supplementary control for the DFIG power converters such that the effective inertia contributed by these wind generators to the system is increased. The paper also proposes the idea of adjusting pitch compensation and maximum active power order to the converter in order to improve inertial response during the transient with response to drop in grid frequency. Results obtained on a large realistic power system indicate that the frequency nadir following a large power impact in the form of generators dropping out is effectively improved with the proposed control strategy. The proposed control is also validated against the sudden wind speed change in the form of wind gust downs and wind ramp downs occurring in conjunction with the generators dropping out. A beneficial impact in terms of damping power system oscillations is also observed, which is validated by eigenvalue analysis. The affected mode is then excited with a large disturbance in time domain. The damping improvement observed in time domain and subsequent Prony analysis support the result obtained from eigenvalue analysis.

The quality of power system operating need to assess maturity of small oscillation stability issues of regional power grid thoroughly, analysis level of operating, controller installation allocating and parameter optimization scheme from different develop stage of regional power grid accurately. The assessment scheme is based on damping torque analysis of real large scale power system. Small oscillation stability is to be evaluated through the mechanism of damping torque analysis with automatic voltage regulator and other power system stabilizers. By analyzing the characteristic of the link between damping torque provided or transferred through each generator and the oscillation mode detected in the power system, the maturity indice has been established. This indice may cover the information of the contribution of controllers towards the power system and the dispatching within each generator in power system. Practical application effects in a real large scale power grid indicates this assessment can thoroughly describe the action of control elements in the power grid, help to depict the whole picture of impact applying power system controllers. A novel maturity evaluation of small oscillation in power grid is presented.

This paper presents transient stability assessment of a large actual power system using the probabilistic neural network (PNN) with enhanced feature selection and extraction method. The investigated large power system is divided into five smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. An enhanced feature selection and extraction methods are then incorporated to reduce the input features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with enhanced feature selection and extraction methods reduces the time taken to train the PNN without affecting the accuracy of the classification results.

The economic power dispatch (EPD) is an integral part of the power system, and the major roles and purpose of its use are for achieving a reliable and efficient operation out of power system generation networks, and this operation should be obtained by minimizing the generator fuel cost. Getting optimal solutions to EPD problem requires efficient optimization algorithms. Novel Bat Algorithm (NBA) is one of the most recent methods and it has already proven its efficiency and reliability for solving the EPD problem. This paper proposes Novel Bat Algorithm (NBA) in order to solve the EPD problem based on the large scale power system. The NBA has proved its efficiency and it gave a perfect performance for small-scale systems compared with the original Bat algorithm (BA), because of considering the Doppler Effect and assumed that bat can move between various habitats. The study of the EPD for large-scale power systems is more important than study it for small ones, because all power systems in countries are considered large networks. This is why all modern studies focus on the study of EPD for large systems. To test the performance of NBA during small and large scale power system, we have applied it to many systems, including 3-thermal units, 6-thermal units, 31-Iraqi thermal units and IEEE 40-thermal units respectively, with transmission losses and generator limits, and we have made a comparison of the obtained results of NBA with other optimization methods. The Iraqi thermal units have been utilized as a new data.

In this paper electromagnetic transient (EMT) simulation of large scale power systems using graphics processing unit (GPU) based computing is demonstrated. As the size of power system networks increases, the simulation time using conventional central processing units (CPUs) based simulation increases drastically. This paper proposes a hybrid CPU-GPU environment for fast large scale power systems simulation. In this scheme the GPU is mainly deployed to perform the computationally intensive part of the simulation in parallel on its built-in multiple processing cores, and the CPU is assigned for other sequential jobs like flow control of the simulation and storing output data, etc. The GPU-based approach is used to simulate a network with 900 Buses, and it is shown that the CPU-GPU based implementation is 70 times faster than the conventional CPU-based implementations.

This paper presents a Lagrangian relaxation based technique for solving the power system thermal unit commitment problem. As a general approach, Lagrangian relaxation has proven to be an efficient method of solving the thermal unit commitment problem. The specific technique described in this paper focuses on finding a feasible commitment, then attempting to improve on the optimality of the solution. A new method for processing ramp rate constraints is also introduced. The algorithm has been implemented on a large scale power system and results are shown using actual power system data.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A solution to the economic dispatch using EP based SA algorithm on large scale power system

This paper presents a new efficient solution approach for solving the unit commitment schedule of thermal generating units of a realistic large scale power system. The approach is based on the equivalencing like concept of network reduction which reduces the number of units in the large power system to the lowest possible number according to their fuel/generation cost characteristics. The reduced system is then solved and from that overall solution to the original unit commitment problem of the entire system is obtained. Test results for real-life systems of up to 79 units and comparisons with results obtained using Lagrangian relaxation and truncated dynamic programming (DP-TC) are also reported.

The general focus of this study is to design a multilevel deep learning model that provides big data analytics and emergency management knowledge. A big data covariance analysis approach has been used to find multilevel representations of data based on prior knowledge from large scale power systems. For purpose of meeting requirements of incremental knowledge discovery, an adaptive regression algorithm is presented. Given the multilevel operating status and development trend of power system, the emergency management techniques are then proposed to produce intelligent decision making support. In this paper, a multilevel clustered hidden Markov model based global optimization approach is considered for power system emergency management problem, which is an extension of the conventional optimal power flow problem. The objective is defined to generate operation mode that minimizes multilevel cost while satisfying different constraints. To demonstrate the effectiveness of the presented approach, this paper carefully compared the discriminatory power of knowledge discovery models that utilize deep learning with dimensionality reduction based method and machine learning without dimensionality reduction based method. The experimental results showed that the proposed multilevel deep learning approach consistently outperformed the traditional machine learning method. The emergency management of large scale power system may also benefit from the modified hidden Markov model and global optimization.

A robust coordinated AVR+PSS, called the desensitized Four Loop regulator, has been designed for the French power system and is currently being implemented. It is shown here that it is possible to transform the structure of this controller into any standard IEEE AVR+PSS structure. The AVR+PSSs obtained in this manner are efficient for damping all kinds of electromechanical oscillations of a large scale power system (including inter-area oscillations). This approach is combined with modal analysis techniques. Its efficiency has been verified on several large scale systems and is illustrated here on a modified version of the New England system. The obtained AVR+PSSs have a good robustness against significant changes in the characteristics of the network.

This paper proposes a nonlinear optimization interior point (IP) method for the determination of maximum loadability in a power system. Details of the implementation of pure primal-dual and predictor-corrector primal-dual IP algorithms are presented. It is shown that most of the computational effort of the algorithm is taken by the formation and factorization of the augmented Hessian matrix of the IP algorithm. The size of this matrix can be as large as ten times the number of buses in the system. Comparisons of the two IP implementations with large scale power systems with as many as 4000 buses are presented. It is shown that the IP algorithm constitutes an effective method for the determination of the maximum loadability in a power system.

To estimate the potential to reinforce resilience in a power system under severe power deficit conditions, this study investigates the flexibility of space and water heating systems equipped with heat pumps in approximately one million single-family houses. The main contribution lies in the quantification of available power and corresponding duration of reduced power linked to the thermal comfort consequence of these resilience efforts. In this context, an integrated physics-based model is developed that involves a heat pump with space and water heating systems. For the power system with a dimensioning fault of 1.45 GW, the flexibility levels found range between 2.1 GW and 0.5 GW, at outdoor temperatures ranging between −10 °C and 10 °C respectively. An example result is that the power system can be relieved with 2.1 GW for 5 h and 0.8 GW for the next 12 h, with a consequence of the indoor and water temperatures dropping from 20 °C and 55 °C, to 15 °C and 44 °C, respectively at −10 °C outdoor temperature. Furthermore, the average electric power consumption increases to 2.9 GW during the recovery of indoor temperatures over 24 h. Finally, a modified Nordic-32 bus system with a large share of renewable power installations is proposed. In this system, the role of flexibility is demonstrated in limiting the instantaneous frequency deviation during the loss of a major generation. Thus, this study clearly shows the potential of single-family houses to reinforce resilience for a duration ranging from seconds to several hours. • Power support for disruptive grid events using flexibility from heat pumps. • Utilisation of physics based models for estimation of flexibility potential. • Flexibility quantification from space and water heating in about a million houses. • Flexibility potential for various time periods. • Flexibility potential range is 2.1-0.5 GW, at temperatures ranging from −10 °C to 10 °C.

Large scale power systems can be decomposed into areas each consisting of tightly coupled machines connected to other areas through weak connections. This kind of decomposition preserves the network structure and offers possibilities in developing reduced-order models and simplified direct stability analysis. Time scale separation is inherent in this decomposition. This paper investigates the use of slow and fast energy functions for the stability analysis of two-time-scale systems. We show rigorously the existence of a slow manifold and use this manifold for calculating slow energy in the system to any desired degree of accuracy. When the mode of instability is between areas (i.e., slow) it is shown that the slow energy accurately predicts the critical clearing time.