SCALE: A hybrid MPI and multithreading based work stealing approach for massive contingency analysis in power systems

An expert system is developed to determine the steady state security of a power system after a contingency. Line, transformer and generator outages are considered. For any load in the system, overloaded branches are determined by comparing the system load to three load levels closest to the actual system load and using generator shift or branch outage distribution factors. The expert system, in conjunction with the DC load flow, is designed to identify the harmless and harmful contingencies as well as the overload branches. If a contingency cannot be determined as harmless or harmful then an AC load flow is suggested. Turbo-Prolog is used for symbolic manipulations and reasoning. The developed system is applied to a 12 bus power system. The results indicate that the expert system can be used in contingency screening as well as online operation to assist the operator. >

Security is an important aspect of modern large-scale infrastructure networks. In one hand, there has been a significant improvement in the efficiency and reliability of systems due to enhanced interconnection of intelligent devices. On the other hand, this provides an opportunity for strategic adversaries to exploit the vulnerabilities of the network and cause damage. An example of an infrastructure system that is a part of our daily lives, and is vulnerable to adversarial attacks is the power network. Enhancing power grids' security, performance and resilience has been an important research topic in engineering. A significant penetration of information communication technologies (ICT) in modern power systems, renders this network vulnerable to cyber attacks from strategic malicious entities. According to The North American Electric Reliability Corporation (NERC), every power system should be operated such that failure of a single component should not leave the rest of components heavily loaded. This is called N-1 rule in power networks. However higher order contingency conditions are inevitable due to cooperated cyber attacks or natural calamities. As a result N-k contingency analysis is an important notion of security and resilience of power grids. The central challenge of higher order cyber-physical contingency analysis is the exponential blow-up of the attack surface due to a large number of attack vectors. This gives rise to computational challenges in devising efficient attack mitigation strategies. Due to these challenges, most of the tools are effective in analysing contingencies caused by failure of one or two components in the power network. Moreover, current tools for contingency analysis do not consider the strategic and smart attackers, and consider different attack profiles as faults in the system. However, smart attackers can exploit such naive defense strategies and cause a significant damage to the system. In this work, we focus on challenges due to higher order contingency conditions resulting from cyber-attacks. Our approach provides efficient tools to consider higher order cyber-physical contingency analysis in a game-theoretic frame work, which enables the operator to exploit the strategic actions of the attacker. Moreover, we provide computationally efficient algorithms to deal with higher order contingency conditions, and find the optimal defense strategies which cannot be exploited by the smart attacker. Our contributions in this thesis are as follows: \textbf{Cyber-Physical contingency analysis modeling using Game-Theoretic framework:} We develop a game-theoretic model in which different actions of the attacker and the defender lead to different contingency conditions. We formulate the

Direct stability analysis using an energy function has recently been widely used for angular and voltage stability analysis in electric power systems. Considerable efforts have concentrated on seeking energy functions for lossy power systems, accomplished only for the simple two-bus system. A new approach is presented to derive an energy function by using the complex integral of bus current equations with respect to bus voltages. Under an assumption that all transmission lines have a uniform R/X ratio, it is shown that the energy function can be developed for multimachine power systems with losses. The proposed energy function is tested on sample systems by comparing the time simulation method, which shows the validity of the proposed method.

Improvements on the line outage distribution factor for power system security analysis

Nowadays, in power systems, we still lack the existence of standardized test systems that can be used to benchmark the performance and solution quality of proposed optimization techniques. Several authors report that the electric load pattern is very complex. It is therefore necessary to develop new methods for design of test cases for economic analysis in power systems. Therefore, we compared two methods to generate test systems: time series model and a method simulating stable random variables based on the use of Chambers-Mallows-Stuck. This paper describes a method for simulating stable random variables in the generation of test systems for economic analysis in power systems. A study focused on generating test electrical systems through stable distribution to model for unit commitment problem in electrical power systems. Usually, the instances of test systems in unit commitment are generated using normal distribution, but the behavior of electrical demand does not follow a normal distribution; in this work, simulation data are based on a new method. For empirical analysis, we used three original systems to obtain the demand behavior and thermal production costs. Numerical results illustrate the applicability of the proposed method by solving several unit commitment problems directly and through the Lagrangian relaxation of the original problem. All Rights Reserved © 2015 Universidad Nacional Autonoma de Mexico, Centro de Ciencias Aplicadas y Desarrollo Tecnologico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.

In this paper, analysis and ranking of single contingency due to the outage of transmission lines for a large scale power system of the Kurdistan Region (KR) are presented. Power System Simulator software (PSS®E33) is used to simulate the Kurdistan Region power system network and perform the contingency analysis for single line outage. This analysis is essential in order to predict and evaluate the voltage stability in case of contingency occurrence to know the most severe case and plan for managing it. All possible transmission line outages of the network are tested individually. After each branch disconnects, load flow analysis are applied by using Newton Raphson method then all bus voltages are recorded, and compared with them before the contingency. Voltage performance index is calculated for all possible contingencies to rank them according to their severity and determine the most severe contingency which is corresponding to the highest value of performance index. Also, the contingencies which cause load loss and amount of this load are observed.

The modern world is increasingly reliant on technology and consequently the systems which power it. Portable power systems are used in a wide variety of applications to varying degrees of success. Within the power range of 10 – 5000W the current power systems are not adequately ‘portable’. Thus the aim of this thesis is to recommend chosen portable power systems which are best suited to certain portable power applications.A comprehensive, critical analysis of relevant portable power systems; micro combustion engines, fuel cells and batteries has led to accurate recommendations being made on the benefits of each in various portable power applications of unmanned aerial vehicles, military services, personal electronics and portable generators.An extensive literature review has been undertaken and research compiled on the previously stated portable power applications and portable power systems. From this research, a relevant assessment framework was created to perform a comparative analysis of each power system. Using a multi-criteria decision making analytical hierarchy process, a matrix of criteria necessary for portable power applications was made presenting weightings for each criteria, quantifying their importance. A ranking system was defined to enable each power systems features in relation to each stated criteria to be ranked. Multiplying the weightings and rankings together resulted in weighted rankings for each power system in relation to each portable power application.Analysing the results obtained from the assessment framework indicated that micro combustion engines were best suited to all portable power applications. Batteries were a close second in all applications and fuel cells ranked significantly lower than both of these systems in all applications. Whilst micro combustion engines are not currently commercially available, they have incredible potential in the portable power market, and if research and improvements continue they will be a preferred choice to a battery in future. Currently batteries are still the best product on the market for portable power applications due to their maturity and commercial success.It is recommended that in future the further criteria be established and a wider range of rankings used to improve the accuracy of results. It is also recommended that in future the data the rankings are based off be updated, as fuel cells and micro combustion engines are a quickly improving technology whose features will change in future.

In contingency analysis of large-scale power systems, the high-order contingencies involving a relatively large number of component failures are traditionally assumed to have a very low occurrence probability. However, this may underestimate the probability of some N-k contingencies and thus pose risk to the reliable system operations considering that those contingencies would cause severe consequences. In this paper, we investigate the problem of hidden N-k contingencies and cast the problem of evaluating the hidden probability of an N-k contingency as a mixed integer linear programming problem. The simulation results on the IEEE 14-bus and 118-bus systems verify the effectiveness of the proposed model.

Clusters, composed of symmetric multiprocessor (SMP) machines and heterogeneous machines, have become increasingly popular for high‐performance computing. Message‐passing libraries, such as message‐passing interface (MPI) and parallel virtual machine (PVM), are de facto parallel programming libraries for clusters that usually consist of homogeneous and uni‐processor machines. For SMP machines, MPI is combined with multithreading libraries like POSIX Thread and OpenMP to take advantage of the architecture. In addition to existing parallel programming libraries that are in C/C++ and FORTRAN programming languages, the Java programming language presents itself as another alternative with its object‐oriented framework, platform neutral byte code, and ever‐increasing performance. This paper presents a new parallel programming model and a library, VCluster, which implements this model. VCluster is based on migrating virtual threads instead of processes to support clusters of SMP machines more efficiently. The implementation uses thread migration, which can be used in dynamic load balancing. VCluster was developed in pure Java, utilizing the portability of Java to support clusters of heterogeneous machines. Several applications are developed to illustrate the use of this library and compare the usability and performance of VCluster with other approaches. Copyright © 2007 John Wiley & Sons, Ltd.

This work deals with assessing the reliability of wind energy sources incorporated into a microgrid system to increase grid stability. As the share of power generated from wind energy continues to increase due to environmental concerns and to reduce carbon footprints, the case study proposed is designing an effective wind energy system for a smart building environment to reduce the burden on the grid. Incorporating wind energy systems into grid-connected buildings increases the reliability of the microgrid system for smart building environments. Applying smart techniques with integrated automation in the built environment can play a key role in decreasing peak demand for load development in smart cities. This can also enhance the overall efficiency of power grids, reduce waste, and enable the effective use of energy resources. Furthermore, researchers are finding new ways to collect energy from renewable sources and power-built environments due to dwindling fossil fuel resources and increased environmental awareness. The analysis of wind-connected power systems has been initiated to satisfy the increasing demand for reliable supply systems by consumers. This has prompted the use of wind energy systems with controlled techniques to enhance the reliability of power systems and reduce carbon footprints generated by fossil fuel-based power systems. Therefore, the main purpose of this work is to understand the economic prospects of wind energy sources for smart building environments, reliability analysis of wind energy sources-based power systems, reduce carbon footprints, and balance power demand through wind energy systems. The Progressive Wind Model Taylor-Sequence Proliferation (PWMTSP) and Innovative Six Parameters Based Progressive Beta Creative Model (ISPBPBCM) approaches are proposed to understand the reliability of wind energy-based power systems in smart building environments. The work also provides an economic analysis of the proposed low-cost wind power system with different capacities for residential consumers in selected geographic location. Eight cases are considered for the proposed effective wind energy system for smart building environments ranging from 2 MW to 32 MW. Hence, the economic analysis of the proposed systems is discussed for residential users. All eight cases are eco-friendly and reduce carbon emissions as these generated units from wind energy sources need not be purchased from fossil fuel-based plants, which increases the reliability of the proposed system. The expected energy not supplied (EENS) in the planned work increases as wind power capacity increases. Loss of load expectation (LOLE), loss of load probability (LOLP), and failure rate all decrease with the installation of more wind power systems. Moreover, it can be seen that mean time to repair (MTTR) improves when wind energy capacity rises incrementally from 2 MW to 32 MW. The proposed method is anticipated to be adaptable to various types of wind energy systems where dependability is a crucial component of system design and scalable to large wind integrated power systems.

A novel tool for transient stability analysis of large-scale power systems: Its application to the KEPCO system

Nowadays, the increasing energy demand, development of smart grids, and the combination of different energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate in more complex and uncertain conditions. Therefore, analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze these systems’ behavior, advanced metering and condition monitoring devices and systems are utilized, which generate huge amounts of data. Assessment of these data is approximately impossible by conventional methods and requires powerful data mining procedures. Machine learning, deep learning, and a variety of regression, classification, and clustering algorithms are powerful tools to use in these issues. These procedures can be utilized for load/demand forecasting, demand response evaluation, defect/fault detection in electrical equipment, power system analysis and control, cybersecurity, and renewable energy generation prediction. Understanding the structure and functioning of each learning method is therefore one of the most important issues in the application of them to solve power system problems. In this chapter, we will introduce and discuss selected methods of data mining based on their learning, structure, formulation, mode of operation, and application in power systems. Literature on machine learning and deep learning procedures, train and test process of networked methods, and, finally, applications of each procedure are presented in this chapter.

Nowadays, the increment of energy demand in the world as well as the development of smart grids and the combination of different types of energy systems have led to the complexity of power systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of renewable energies have made electricity networks operating in more complex and uncertain conditions. Also, consumers and especially users of sensitive load tend to have access to a reliable and sustainable power supply. Therefore, power producers need a variety of long- and short-term planning methodologies for attaining to sustainable investment, production, and operation. Analysis of traditional power and energy systems requires physical modeling and extensive numerical computation. To analyze the behavior of these systems, advanced metering, and condition monitoring devices and systems are utilized, which generate a huge amount of data. Evaluation of these data is approximately impossible by conventional or numerical methods, and it requires powerful data mining procedures. Regression, classification, and clustering applications of machine learning and deep learning methods are powerful tools to use for dealing with such issues. These procedures can be utilized for load/demand forecasting, renewable energy generation forecasting, demand response evaluation, and power system analysis. Understanding the problem and functioning of each learning methods is, therefore, one of the most important issues in the application of such approaches to solve power system problems. Accordingly, in this chapter, the authors will introduce and discuss selected applications of machine learning and deep learning based on their learning, structure, mode of operation, and application in the load forecasting of power systems. Literature review on machine learning and deep learning applications in load forecasting will be presented in this chapter.

Frequent subgraph mining (FSM) has important applications in areas such as bioinformatics, social networks and others. In this paper, we present a highly scalable approach called ParGraph that can efficiently mine from a single graph in both distributed as well as shared-memory based systems. In a distributed environment, we can leverage the local memory of multiple compute nodes for storing a large number of intermediate states for enumerating patterns. To address the skewness in the pattern generation tree, our approach uses a novel hybrid load balancing scheme to efficiently distribute workload across both processes and threads. Our experiments demonstrate good speedups using message passing interface (MPI) and OpenMP threads.

In this paper we propose an improved quasi-Monte Carlo method for solving Linear Algebra problems. We show that by using low-discrepancy sequences both the convergence and the CPU time of the algorithm are improved. Two parallelization schemes using the Message Passing Interface with static and dynamic load balancing are proposed. The dynamic scheme is useful for computing in the GRID environment.