Performance analysis for two-level parallel transient stability constrained optimal power flow

Graphics processing unit (GPU) has significantly increased the computing capacity of state-of-the-art high performance computing systems. This paper presents a novel GPU acceleration approach for transient stability-constrained optimal power flow (TSCOPF), which is one of the most computational challenging tasks in large-scale power system applications. Enabled by the revealed two-level decomposition parallelism in reduced-space interior point method, GPUs serve as plug-and-play coprocessors for time-consuming linear algebra operations in TSCOPF solving. Enhanced by multi-GPU processing and mixed-precision iterative refinement technique, the efficiency of solving TSCOPF is greatly improved without redesigning and reimplementing the existing algorithm framework. Numerical studies based on a series of test cases with up to 12 951 buses indicate the effectiveness of the proposed GPU-based approach for large-scale TSCOPF problems.

Transient stability constrained optimal power flow (TSC-OPF) is a well known problem in the literature. Several nu-merical based and heuristic based approaches have been applied to solve this problem so far. In the literature, it is shown that meta heuristic-population based methods are fast and effective in handling this problem. This paper proposes a heuristic method based workflow known as, search space reduction algorithm (SSRA) based workflow to solve the TSC-OPF problem. The SSRA based TSC-OPF solution make changes in active power generations of the generators in the power system such that both the goals, system economy of operation and stability requirements are reconciled, satisfying both the static and dynamic constraints. The proposed method is tested on 10 machine 39 bus system and results indicates that the proposed approach has capability to explore and exploit efficiently, the search range to reach a near global optimal point. The results are compared with different evolutionary algorithms like PSO and GWO, and the comparison results shows that the proposed method achieves more near global optimum compared with its counterparts. Further, the effect of the algorithm tuning parameters on its convergence characteristics has also been investigated in a systematic manner.

The transient stability constrained optimal power flow (TSC‐OPF) is a big challenge in the field of power systems because of its high complexity and extensive computation effort involved in its solution. This study presents a new approach to compute the transient stability constraint formulated by the critical clearing time (CCT) in TSC‐OPF. CCT has been determined by dual‐kriging, a space interpolation method which has primarily been used in natural resources evaluation. Given the huge dimensionality of the problem, Pareto analysis is firstly used to reduce the number of input variables in an initial database to those which are significant to compute CCT. With the reduced variables, a new database has been constructed using a design of experiment to obtain a reduced number of observed points. As a result of this approach, the sets of dynamic and transient stability constraints to be considered in the optimisation process are reduced to one single stability constraint with only a few variables. Ultimately, the size of the resulting optimisation problem is almost similar to that of a conventional optimal power flow. The effectiveness of the proposed method is tested on the New England 10‐machine 39‐bus system and the larger 50‐machine 145‐bus power system.

A novel approach based on both single machine equivalent (SIME) and trajectory sensitivity methods is proposed to formulate a transient stability-constrained optimal power flow (TSC-OPF) in the Euclidian space, where only one single stability constraint is necessary in the optimization problem to represent all dynamic and transient stability constraints of the multi-machine system, resulting in a tractable approach to the preventive control of transient stability in realistic power systems. A unified framework of time domain analysis is proposed, where the transient stability, trajectory sensitivity and SIME analyses are all combined to assess the system's stability and to compute the sensitivity coefficients of the proposed transient stability constraint. Based on these sensitivity coefficients, a non-heuristic selection criterion is proposed to perform the preventive control by rescheduling only a selected number of generators, which is the commonly accepted practice followed by the system's operators. The validity and the effectiveness of the proposed method is numerically demonstrated in the Mexican 46-machine, 190-bus system.

Consideration of transient stability constraints in optimal power flow (OPF) problems is increasingly important because modern power systems tend to operate closer to stability boundaries due to the rapid increase of electricity demand and the deregulation of electricity markets. Transient stability constrained OPF (TSCOPF) is however a nonlinear optimization problem with both algebraic and differential equations, which is difficult to be solved even for small power systems. This paper develops a robust and efficient method for solving TSCOPF problems based on differential evolution (DE), which is a new branch of evolutionary algorithms with strong ability in searching global optimal solutions of highly nonlinear and nonconvex problems. Due to the flexible properties of DE mechanism, the hybrid method for transient stability assessment, which combines time-domain simulation and transient energy function method, can be employed in DE so that the detailed dynamic models of the system can be incorporated. To reduce the computational burden, several strategies are proposed for the initialization, assessment and selection of solution individuals in evolution process of DE. Numerical tests on the WSCC three-generator, nine-bus system and New England ten-generator, 39-bus system have demonstrated the robustness and effectiveness of the proposed approach. Finally, in order to deal with the large-scale system and speed up the computation, DE is parallelized and implemented on a Beowulf PC-cluster. The effectiveness of the parallel DE approach is demonstrated by simulations on the 17-generator, 162-bus system.

Online transient stability assessment and preventive control are important measures to prevent power system blackouts. Based on the deep cross network (DCN), an online assessment and preventive control method for power system transient stability is proposed. The deep cross network is developed to estimate the critical clearing time (CCT) of the credible fault contingencies. Based on the deep cross network model, the sensitivity of the active power of the generator set to the fault critical clearing time is calculated and then the DCN model-embedded transient stability constrained optimal power flow (TSCOPF) model is proposed to compute the preventive generation re-dispatch strategy. This TSCOPF model is computed by successive linear programming assisted by the DCN. The simulation results of the IEEE 39-bus test system show that this method can give early warning to the operation mode with instability risk and generate preventive control strategies, which can effectively improve the safety level of power grid operation.

There are many voltage‐dependent loads (VDLs) in power systems. When the unit reserve supply is low, the transmission network adjusts the voltage of grid‐connected nodes to provide additional resources. Many reactive power devices with different regulatory performance levels and timescales must be coordinated to employ flexible VDLs to the maximum extent, and consequently, a transient stability‐constrained optimal power flow (TSCOPF) problem with mixed integers and multiperiod coupling must be solved. A multitimescale scheduling ‘day‐ahead‐intraday‐real‐time’ framework is designed for the optimal economic scheduling of VDLs. In the day‐ahead stage, the optimal scheduling period of the discrete reactive power control devices is determined. In the intraday stage, a rolling optimization model based on model predictive control is established, and the specific action times of the discrete reactive power devices are determined. In the real‐time stage, the optimal regulation strategy of the continuous reactive power control device is optimized by rolling. Then, to consider the speed and accuracy of the solution at the same time, a continuous sensitivity correction method is adopted to simplify and solve the original model. Finally, with a modified IEEE‐39 system and a modified IEEE‐118 system as examples, it is verified that the multitimescale scheduling framework maximizes the exploitation of the flexible resources of VDLs to ensure voltage security and stability and improve the economy of system operation under different scenarios.

A new transient stability‐constrained optimal power flow (TSCOPF) model for power system optimal operations is presented in this study. The proposed model is a non‐linear, non‐convex, discrete, and non‐differentiable optimisation problem, and very difficult to be solved by classic programming algorithms. In this study, an effective optimisation framework is developed to address the TSCOPF problem. The self‐adaptive differential evolution (SaDE) algorithm is employed in the framework, which can effectively eliminate the needs for the strategy selection process and at the same time improve the robustness of the DE search process. The benchmark New England 10‐generator, 39‐bus system is used to verify the efficacy of this algorithm. The simulation result obtained using SaDE is compared with both conventional mathematical method and the recently published heuristic methods for OPF problems. The simulation results reveal that the SaDE optimisation technique outperforms other algorithms in both terms of robustness and solution quality.

At first thought, one may not realize the need for a special issue of Parallel and Distributed Computing Practices focused on Cluster Computing when our community has already invested years of research and spent a wealth of resources on traditional Big Iron supercomputers, for instance, the SGI/Cray Origin and IBM SP. Academia, industry, and national laboratories are still using, and for the foreseeable future, will continue to use, supercomputers to solve both grand-challenge scale and high-throughput applications. Then why the interest and new popularity of research into clusters for high-performance parallel and distributed applications? Clusters incorporate the tremendous successes that are attributable to our parallel and distributed processing community. Over the next decade, clusters will span the entire range of high-performance computing platforms. Instead of being restricted solely to supercomputers for achieving high-performance computing, the spectrum of available platforms will spread out to Beowulf-class clusters, to superclusters, and on to supercomputers. Moving along this continuum represents increasing computational resources, which in turn allows the capability for solving larger classes of applications or instances of problems. Compute-bound applications on workstations often may take advantage of the extra horsepower provided by symmetric multiprocessors. Applications with even larger computational requirements and a high-degree of concurrency may use Beowulf clusters, that is, clusters that are home-built from mass-market, commercial off-the-shelf workstations and networks, for instance, PC's interconnected by Fast Ethernet. A supercluster may contain commodity SMP nodes interconnected with scalable, low-latency, high-bandwidth networks, and is integrated by a system vendor rather than the computational scientist. Applications with greater complexity, for example, grand challenges from the sciences, demand scalable systems with high-performance, interconnects, perhaps coupled with terascale I/O subsystems, and thus, are appropriate for these superclusters. Finally, supercomputers, the largest of the clusters, may provide the ultimate platform for the highest-fidelity models and simulations and data-intensive applications. At each step up this ladder of cluster architectures, the system cost increases approximately by an order of magnitude, but having the high-performance capability to solve the problem of interest may require an entry point at any of these levels, the Beowulf, or supercluster, or even supercomputer platform. Cluster computing with Beowulf- or superclusters, thus, may be one or two orders of magnitude less expensive than a supercomputer, and provide a cost-effective solution and capability that was not available on a workstation. But this cost comparison measures just the basic system price. What about the applications? As recently as a few years ago, the lack of a canonical high-performance architectural paradigm meant that migrating to a new computer system, even from the same vendor, typically required redesigning applications to efficiently use the high-performance system. Thus, the application must be redesigned with parallel algorithms and libraries that are optimized for each high-performance system. This process of porting, redesigning, optimizing, debugging, and analyzing, the application may in fact be prohibitively expensive, and certainly frustrating when the process is completed just in time to greet the next generation system! That was then, this is now. With the successful standardization of system-level interfaces, many of these issues no longer hinder our expedited use of these new, high-performance clusters. The message-passing interface (MPI) is a clear example of a standard that has contributed significantly to the success of cluster computing. Supercomputer vendors working together with the high-performance computing community embarked on a process to standardize the user-level interface and behavior for passing messages between concurrent tasks. Now, using ordinary sequential compilers and vendor-supplied or freely-available implementations of this message-passing interface, applications can be implemented efficiently on one supercomputer and ported with little or no effort to other parallel machines. The message-passing paradigm, thus, provides a reasonably good canonical high-performance model and methodology for implementing high-performance applications. An algorithm that exhibits efficient execution and scaling on one machine then is likely to perform well on another with little or no additional programming effort. Using this cluster computing paradigm, algorithms typically are optimized by minimizing the number of remote memory accesses (reducing the amount of messages passed), and carefully organizing the data placement to increase memory locality. Thus, high-performance algorithms and applications that have been optimized for supercomputers, using the same cluster paradigm, may be appropriate for Beowulf and superclusters. Cluster computing, realizing the importance of a standard high-performance programming environment that applies equally well to both ends of the architectural spectrum, highly leverages these and other successes from parallel and distributed processing research and development. This is in an exciting, new era for high-performance computing where the canonical architectural paradigm stretches from home-built Beowulf clusters, to superclusters, and on to supercomputers. But cluster computing faces some challenges ahead of it. Continued research is required for designing 1) scalable, high-performance algorithms, applications, and methodologies, that can tolerate the communication overheads, and adapt to hybrid, SMP, and heterogeneous, nodes; 2) high-performance computer architectures, scalable interconnection networks, and I/O subsystems; and 3) scalable systems software, compilers, and monitoring tools. Cluster computing has come of age, and it is high time for the parallel and distributed processing community to lay the groundwork for our own legacy of success stories. David A. Bader University of New Mexico

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.

High performance computing promotes the solution of large mathematical models of complex mechanical systems and lays a foundation for the theoretical analysis of complicated and difficult problems in the mechanical field. At the same time, the high reliability transmission of wireless data makes it possible for a variety of motion state mechanical systems to measure and control. In this paper, we present the energy loss model of the wet axle transmission system based on bond graph theory, and obtain the heat source boundary condition of the thermal analysis of the wet axle by calculating the mathematical model of the energy loss. Moreover, thermal network method is proposed to analysis heat accumulation and disequilibrium of multistage closed wet axle, by analyzing heat source distribution and heat transfer characteristics among machine elements, oil and air, finally the mathematic model of thermal analysis is established. The thermal numerical calculation of the thermal network for the complex mechanical system and the verification of the whole vehicle test for the results of the thermal calculation have been realized, then thermal optimization is done for wet axle heat equilibrium. Theory and experiment results indicate that heat accumulation in wet hub and heat disequilibrium of different parts are severe especially under efficient operating condition; thermal network calculation and optimization results verified by real vehicle experiment are true and valid. The research proves that high performance computer and high reliable wireless data transmission technology have promoted the optimization design of complex mechanical system.

Research into risky decision making (RDM) has become a multidisciplinary effort. Conversations cut across fields such as psychology, economics, insurance, and marketing. This broad interest highlights the necessity for collaborative investigation of RDM to understand and manipulate the situations within which it manifests. A holistic understanding of RDM has been impeded by the independent development of diverse RDM research methodologies across different fields. There is no software specific to RDM that combines paradigms and analytical tools based on recent developments in high-performance computing technologies. This paper presents a toolkit called RDMTk, developed specifically for the study of risky decision making. RDMTk provides a free environment that can be used to manage globally-based experiments while fostering collaborative research. The incorporation of machine learning and high-performance computing (HPC) technologies in the toolkit further open additional possibilities such as scalable algorithms and big data problems arising from global scale experiments.

Synchronized Phasor Measurement Units (PMUs) are becoming a key element of monitoring, protection, control and state estimation applications in power systems and are the basis for the implementation of Wide Area Monitoring Systems (WAMSs). This paper investigates the possibility of using a software-only PTP (Precision Time Protocol) synchronization method to synchronize different Phasor Measurement Units (PMUs) and how this synchronization impacts the State Estimation. In this context, as a case study, the output data of the PMUs have been used to estimate the state of a power system of reduced size and complexity. A laboratory experimental setup has been built to evaluate the performance achievable with the proposed synchronization solution. The results thus obtained and the performance analysis are discussed in this paper.

High performance computing (HPC) is, today, a key technology to solve large problems in science, engineering, and business by utilizing tremendous computing power which has been evolving to the future. HPC Asia, which is an international conference series on HPC technologies in Asia Pacific region, and was held in the past several times in several countries in Asia regional sites to discuss the issues on high performance computing and to exchange information of research and development results in HPC.

In April, 1999, the National Science Foundation (NSF)-sponsored National Computational Science Alliance (the Alliance) opened its new 7000 square foot advanced technology training and demonstration facility in the Washington, DC metropolitan area. Called the Alliance Center for Collaboration, Education, Science, and Software (ACCESS), it is a new technology transfer and learning mechanism for emerging and advanced technologies, high performance computing, communications, and information technologies. The paper discusses the development of this highly flexible facility for collaborative research and education for high performance computing (HPC) science and engineering users. The leading edge site for ACCESS and the alliance is the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. ACCESS is staffed by NCSA. The paper discusses planning, designing and developing this innovative site, and meeting technological, aesthetic, spatial, and other challenges in setting up this multiple-use, multiple-partner center. It will make recommendations to those wishing to set up similar facilities.