HyperTran: High-Performance Transient Stability Simulation of Power Systems on Modern Parallel Computing Hardware

HyperTran：在现代并行计算硬件上对电力系统进行高性能暂态稳定性仿真

• 批准号: 2226826
• 负责人: Hantao Cui
• 金额: $ 32万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226826&HistoricalAwards=false
• 关键词: HyperTran; Performance; Transient; Stability; Simulation

Computer simulation is a widely used but computationally intensive method for predicting the transient stability of power systems following large disturbances. Currently, simulation performance lags behind industry demand for online, real-time applications and research need for data-driven applications, particularly for practically sized systems with high penetration of renewable energy. This NSF project aims to develop a high-performance framework that enables data, task, and job parallelisms for transient stability simulations to scale to the full capacity of contemporary and future parallel computing hardware. The proposed framework will bring transformative changes to the understanding of how power system models should be represented and how computational workflows should be structured to take advantage of modern parallel computers. The intellectual merits of the project include a) accelerating the building and solving phases of differential-algebraic equations (DAE) through the design of parallel-enabled software representations of power system models, and b) the identification and utilization of computational methods and hardware devices based on the characteristics of simulation test cases. The broader impacts of the project include the dissemination of research findings via open-source software and publications, integrated research and education activities, and the potential to enhance the stability of the power grid infrastructure.Three tasks have been identified to accomplish the goal. Task 1 will create software representations of power models and computational workflows to enable staged data and task parallelisms for the DAE building process on CPUs and Graphics Processing Units (GPUs). It will ensure correct results from concurrent executions by coordinating the updating of equations shared across models while optimizing caches. Task 2 will develop adaptive dispatchers to identify and apply the most efficient hardware and solution algorithms for given power system cases, considering system size, acceleration techniques, and practical constraints. Task 3 will investigate pipelining algorithms for parallelizing multi-scenario jobs on heterogeneous hardware to build and solve DAEs for maximized hardware utilization. Upon successful completion, the project is expected to have established a novel, high-performance framework for modern computing hardware that will markedly accelerate the simulation of power system dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

计算机仿真是一种广泛使用但计算量大的方法，用于预测电力系统在大扰动后的暂态稳定性。目前，仿真性能落后于行业对在线实时应用的需求以及数据驱动应用的研究需求，特别是对于可再生能源渗透率高的实际规模的系统。该 NSF 项目旨在开发一个高性能框架，使瞬态稳定性仿真的数据、任务和作业并行能够扩展到当代和未来并行计算硬件的全部能力。所提出的框架将为人们理解如何表示电力系统模型以及如何构建计算工作流程以利用现代并行计算机带来变革。该项目的智力优点包括a）通过设计电力系统模型的并行软件表示来加速微分代数方程（DAE）的构建和求解阶段，以及b）计算方法和硬件设备的识别和利用根据模拟测试用例的特点。该项目更广泛的影响包括通过开源软件和出版物传播研究成果、综合研究和教育活动以及增强电网基础设施稳定性的潜力。已确定了实现该目标的三项任务。任务 1 将创建电源模型和计算工作流程的软件表示，以便为 CPU 和图形处理单元 (GPU) 上的 DAE 构建过程实现分阶段数据和任务并行性。它将通过协调跨模型共享的方程的更新同时优化缓存来确保并发执行的正确结果。任务 2 将开发自适应调度程序，以识别和应用针对给定电力系统情况的最有效硬件和解决算法，同时考虑系统规模、加速技术和实际约束。任务 3 将研究用于在异构硬件上并行化多场景作业的流水线算法，以构建和解决 DAE，从而最大限度地提高硬件利用率。成功完成后，该项目预计将为现代计算硬件建立一个新颖的高性能框架，将显着加速电力系统动力学的模拟。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

Bus Admittance Matrix Revisited: Performance Challenges on Modern Computers

重新审视总线导纳矩阵：现代计算机的性能挑战

• DOI: 10.1109/oajpe.2024.3366117
• 发表时间: 2024-01
• 期刊: IEEE Open Access Journal of Power and Energy
• 影响因子: 3.8
• 作者: Cui; Hantao
• 通讯作者: Hantao