Real-Time Stochastic Optimization of Complex Energy Systems on High-Performance Computers

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

Successfully implementing polymer flooding and maximizing benefits requires selecting best options of variables such as polymer concentration and slug, and number and location of new wells. Option-decisions combined generate thousands of scenarios. Therefore, even using smart algorithm optimizers to efficiently find the maximum of a business objective mathematical function can be a very time-consuming process. The objective of this article is to demonstrate a methodology to improve the chances of finding the maximum net present value (NPV) solution for planning an offshore polymer-flooding; this includes finding, for example, the minimum injection volume to more easily offshore operations. The reservoir and economy models included here were automatically coupled with software that encloses a smart numerical algorithm for searching complex maximum/minimum functions. The options of the previously mentioned decision variables were selected to maximize the NPV of the inverted five-spot polymer-flooding project under constrained rig availability. The process was conducted in three stages: Stage 1: potential value estimation Stage 2: narrowing options through deterministic numerical simulation Stage 3: A) numerical optimization of all decision variable options except the drilling sequence; B) numerical optimization of the drilling sequence A total of 379 scenarios were numerically forecasted in just a few months. The best scenario showed three times the NPV of the nonflooding case. Compared to the reference water-flooding scenario (i.e., all the same options but with the fluid injected), the NPV was 1.3 times greater, the water-oil ratio (WOR) was 0.45 times lower, and Np was 1.25 times greater. Unobvious scenarios, such as reducing the yearly drilling rig availability but extending drilling by four years, were revealed. A comparison of the working time for Stage 2 with Stages 3A and 3B showed that the numerical optimization is six times faster per scenario generated. This study demonstrates that the use of numerical algorithms of polymer flooding yields a significant incremental value over traditional deterministic simulations in a much shorter time frame and with fewer costs compared to previous steps related to building a reservoir model. It is expected to be applicable to all types of enhanced oil recovery (EOR) processes.

Although optimization algorithms have made significant progress in a wide range of power system applications, many research areas and case studies remain unexplored and require additional investigation. Therefore, this paper tackles the coordination process of overcurrent relays (OCRs) in large-scale and complicated networks comprising hundreds of decision variables and constraints. In this context, savannah bengal tiger optimizer (SBTO) is employed to solve the OCR’s coordination of the 42-bus and 30-bus power grids by optimizing pickup current, time dial, and operating curve. It is worth noting that the 42-bus network comprises 291 decision variables and 713 constraints while the 30-bus incorporates 333 decision variables and 1119 constraints: indicating challenging problem formulation to be optimized. Thus, high-performance computing is necessary to provide the necessary computational efficiency and real-time results since the suggested method requires parallel processing capabilities for large-scale simulations and high-resolution models. The objective function (OF) is adapted to have a minimal total operating time (TOT) along with fulfilling problem constraints. Exceptional results of the utilized SBTO are validated against other benchmark algorithms such as genetic algorithm, particle swarm optimizer, water cycle algorithm, and gray wolf optimizer. Obviously, SBTO effectively meets the coordination criteria by achieving a minimal OF and incurring only a limited number of constraint violations. Furthermore, SBTO attains the minimum possible TOT of 19.66 s among other competitors for the 42-bus study case. This approach highlights SBTO’s capability in effectively addressing highly complex optimization problems with numerous constraints.

Enabling simulation at the fifth rung of DFT: Large scale RPA calculations with excellent time to solution

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

Contingency analysis is a key function in the Energy Management System (EMS) to assess the impact of various combinations of power system component failures based on state estimates. Contingency analysis is also extensively used in power market operation for feasibility test of market solutions. Faster analysis of more cases is required to safely and reliably operate today's power grids which have a less margin and more intermittent renewable energy sources. Enabled by the latest development in the computer industry, high performance computing holds the promise of meet the need in the power industry. This paper investigates the potential of high performance computing for massive contingency analysis. The framework of “N-x” contingency analysis is established, and computational load balancing schemes are studied and implemented with high performance computers. Case studies of massive 300,000-contingency-case analysis using the Western Electricity Coordinating Council power grid model are presented to illustrate the application of high performance computing and demonstrate the performance of the framework and computational load balancing schemes.

The next several years will see the maturing of a collection of technologies that will enable fully and transparently distributed computing environments. Networks will be used to configure independent computing, storage, and I/O elements into "virtual systems" that are optimal for solving a particular problem. This environment will make the most powerful computing systems those that are logically assembled from network-based components and will also make those systems available to a widespread audience. Anticipating that the necessary technology and communications infrastructure will be available in the next 3 to 5 years, we are developing and demonstrating prototype applications that test and exercise the currently available elements of this configurable environment. The Lawrence Berkeley Laboratory (LBL) Information and Computing Sciences and Research Medicine Divisions have collaborated with the Pittsburgh Supercomputer Center to demonstrate one distributed application that illuminates the issues and potential of using networks to configure virtual systems. This application allows the interactive visualization of large three-dimensional (3D) scalar fields (voxel data sets) by using a network-based configuration of heterogeneous supercomputers and workstations. The specific test case is visualization of 3D magnetic resonance imaging (MRI) data. The virtual system architecture consists of a Connection Machine-2 (CM-2) that performs surface reconstruction from the voxel data, a Cray Y-MP that renders the resulting geometric data into an image, and a workstation that provides the display of the image and the user interface for specifying the parameters for the geometry generation and 3D viewing. These three elements are configured into a virtual system by using several different network technologies. This paper reviews the current status of the software, hardware, and communications technologies that are needed to enable this configurable environment. These interdependent technologies include: (1) user interface and application program construction methodologies, (2) the interprocess communication (IPC) mechanisms used to connect the software modules of the application, (3) the network protocols and interface hardware used by the IPC for communicating between modules running on separate and independent computing system elements, (4) the telecommunications infrastructure that provides the low-level data transfer functions for the networks that connect the geographically distributed elements used by the application, and (5) the nature of the functional elements that will be connected to form virtual systems.

In the 50+ years since the first humans landed on the moon, computing has grown at breakneck speed. We are faced with another challenge that is just as daunting, and just as important to overcome—modernizing the North American electric power grid—and high-performance computing (HPC) systems with specialized software will be an important element in rising to this challenge. We describe at a high level how software developed in the ExaSGD project addresses this “moonshot” goal by utilizing exascale computing and a novel high performance solver software stack to support the mission of decarbonizing power grid operations in an environment of uncertain weather and climate. To reach the exascale benchmark the team has made a number of first-of-their-kind innovations, including novel method for stochastic optimization, fine grained parallel methods for modeling power systems, and GPU resident sparse numerical linear solvers.

The increased communication bandwidth demands of high performance computing (HPC) systems calling at the same time for reduced latency and increased power efficiency have designated optical interconnects as the key technology in order to achieve the target of exascale performance. In this realm, technology advances have to be accompanied by the development of corresponding design and simulation tools that support end-to-end system modeling in order to evaluate the performance benefits offered by optical components at system scale. In this paper, we present recent advances on electro-optical printed circuit boards (EOPCB) technology development pursued within the European FP7 PhoxTroT research program and directed toward system-scale performance benefits in real HPC workload applications. We report on high-density and multilayered EOPCBs together with all necessary building blocks for enabling true optical blade technology, including multimode polymer-based single- and dual-layer EOPCBs, a board-compatible optically interfaced router chip, and passive board-level connectors. We also demonstrate a complete optical blade design and evaluation software simulation framework called OptoHPC that tailors optical blade technology development toward optimized performance at HPC system scale, allowing for its validation with synthetic workload benchmark traffic profiles and for reliable comparison with existing HPC platforms. The OptoHPC simulator is finally utilized for evaluating and comparing a 384-node HPC system relying on optically enabled blades with the state-of-the-art Cray XK7 HPC network when performing with a range of synthetic workload traffic profiles, revealing the significant throughput and delay improvements that can be released through application-oriented optical blade technology.

This research article presents an innovative approach to enhance sustainable power generation and grid support by integrating real-time modeling and optimization with Molten Salt Energy Storage (MSES) and a Supercritical Steam Cycle (s-SC). As renewable energy usage grows, intermittent resource availability challenges grid stability and reliable power supply. To address this, we develop a system that merges real-time modeling and optimization for precise control of MSES and s-SC components. This integration ensures uninterrupted energy generation, storage, and distribution, optimizing renewable energy use during high-demand periods. Mathematical models and simulations assess the system's dynamic behavior, performance, and economic viability. Rigorous techno-economic analysis highlights cost-effectiveness and environmental benefits. Findings reveal exceptional energy efficiency and grid support, making it a promising solution for sustainable power generation and grid stability amid renewable energy growth. Real-time modeling and optimization emerge as crucial components in modern energy systems. The Combined Heat and Power (CHP) system achieves 56% energy efficiency with off-design impacts considered and 63.61% without. Also, the overall system exergy efficiency decreased from 73.36% at design to approximately 63.55% under off-design scenarios. Regarding the economic aspect, the levelized cost of storage (LCOS) for the CHP system is estimated at 114.4 €/MWh with off-design conditions and 106.8 €/MWh without.

Uncertainty parameters of battery energy storage integrated grid and their modeling approaches: A review and future research directions

With the rapid development of UHV AC / DC hybrid power grid, it is required that the Network Analysis Application have the ability of unified analysis and high-performance computing. In this paper, the time-consuming analysis of each sub-function of Network Analysis Application is carried out to realize the performance bottleneck analysis of the whole process of network analysis. It is proposed that the performance of network analysis should be improved in three aspects: data input and output, topology analysis and core computing of Network Analysis Application. In the aspect of data input, the grid model data service is constructed to realize the combination of measurement reading and model reading, and the parallel model verification and boundary equivalence are completed; In the aspect of data output, it can be parallelized by table, block and equipment; In the aspect of topology analysis, the shared memory programming model OpenMP is adopted, and based on the fork/join parallel mode, its parallelization is realized; In the aspect of core computing, the existing parallel computing methods are summarized, and through the actual power grid simulation analysis, it puts forward the parallel computing mode applicable to different scale power grids and different applications. Finally, the effectiveness of this method is verified by comparing the optimized performance of state estimation.

Recent research has emphasised that an increasing number of enterprises need computation environments for executing HPC (High Performance Computing) applications. Rather than paying the cost of ownership and possess physical, fixed capacity clusters, enterprises can reserve or rent resources for undertaking the required tasks. With the emergence of new computation paradigms such as cloud computing it has become possible to solve a wider range of problems due to their capability to handle and process massive amounts of data. On the other hand, given the pressing regulatory requirement to reduce the carbon footprint of our built environment, significant researching efforts have been recently directed towards simulation-based building energy optimisation with the overall objective of reducing energy consumption. Energy optimisation in buildings represents a class of problems that requires significant computation resources and generally is a time consuming process especially when undertaken with building simulation software, such as EnergyPlus. In this paper we present how a HPC based cloud model can be efficiently used for running and deploying EnergyPlus simulation-based optimisation in order to fulfil a number of objectives related to energy consumption. We describe and evaluate the establishment of such an application-based environment, and consider a cost perspective to determine the efficiency over several cases we explore. This study identifies the following contributions: (i) a comprehensive examination of issues relevant to the HPC community, including performance, cost, user perspectives and range of user activities, (ii) a comparison of two different execution environments such as HTCondor and CometCloud and determine their effectiveness in supporting simulation-based optimisation and (iii) a detailed performance analysis to locate the limiting factors of these execution environments.

The power grid is becoming far more complex as a result of the grid evolution meeting an information revolution. Due to the penetration of smart grid technologies, the grid is evolving as an unprecedented speed and the information infrastructure is fundamentally improved with a large number of smart meters and sensors that produce several orders of magnitude larger amounts of data. How to pull data in, perform analysis, and put information out in a real-time manner is a fundamental challenge in smart grid operation and planning. The future power grid requires high performance computing to be one of the foundational technologies in developing the algorithms and tools for the significantly increased complexity. New techniques and computational capabilities are required to meet the demands for higher reliability and better asset utilization, including advanced algorithms and computing hardware for large-scale modeling, simulation, and analysis. This chapter summarizes the computational challenges in smart grid and the need for high performance computing, and present examples of how high performance computing might be used for future smart grid operation and planning.

Space station static and dynamic analyses using parallel methods