ERI: Variational Quantum Algorithm for Power System Simulation

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。量子计算已被证明在解决许多问题方面比经典计算机具有指数级优势。仅使用量子计算机实现指数优势可能需要数十年的研究，因为它需要具有数千量子位和长相干时间的通用容错量子计算机。变分量子算法（VQAs）作为量子算法与经典算法的混合，在许多问题上都表现出比经典算法指数级的优势。提高大容量电力系统的安全性和可靠性可以节省数十亿美元的经济损失。为了确保电力系统的可靠性和安全性，暂态稳定性模拟和应急分析是公用事业和独立系统运营商非常频繁执行的基本任务（即使是实时的，即需要在不到一分钟的时间内产生解决方案）。大型电力系统中的发电机、变压器和输电线路的暂态稳定仿真和事故分析是一个非常具有挑战性的问题，因为它具有很高的维数。现有的基于经典计算机的工具很难对大型电力系统进行这种偶然性分析，特别是在考虑多个部件同时故障的情况下。由于电力、天然气和通信系统之间的相互依赖，以及大量的可再生发电机正在被纳入电力系统，造成了规模的显著增加，这使困难进一步复杂化。暂态稳定仿真和偶然性分析本质上是用微分代数方程（DAEs）来建模的，微分代数方程也可以表示许多其他工程问题。一般来说，经典算法在求解高维DAEs时具有难以处理的计算负担。本项目提出了新的vqa来解决DAEs作为一个新的范例。更广泛的影响活动包括(a)传播研究成果，以激励电力和能源界加速量子计算的研究和开发，以解决具有挑战性的工程问题，(b)阿尔弗雷德大学量子计算和电力系统的课程改进，(c)让代表性不足的群体和本科生参与研究，以及(d)通过外展活动教育公众和K-12。提出的工作目标是开发新的vqa，可以有效地解决具有可再生能源的大型电力系统的高维模拟问题（例如，暂态稳定性和N-k应急分析）的时间相关非线性微分方程，这将是同类中的第一个。该方法是：1)为一般时变非线性微分方程开发VQA，其中将全面研究VQA的两个核心组件（变分分析和优化算法）；2)开发用于电力系统暂态稳定性模拟的哈密顿和量子非线性处理单元（QNPU），其中QNPU是VQA的第三个核心组件；3)开发用于高渗透可再生能源电力系统N-k偶然性分析的VQA。当具有几百个量子比特的噪声中等规模量子计算机在几年内可用时，本项目开发的VQAs有望实现比纯经典算法的指数优势，以解决具有显着意义的瞬态稳定性模拟和N-k偶然性分析问题。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2)Quantum computing has been proven to have an exponential advantage over classical computers in solving many problems. Realizing the exponential advantage using only a quantum computer will potentially take decades of research as it needs a universal fault-tolerant quantum computer with thousands of qubits and long coherence time. Variational quantum algorithms (VQAs), as hybrid quantum and classical algorithms, have shown an exponential advantage over classical algorithms for various problems. Enhancing the security and reliability of bulk power systems can save billions of economic losses. To ensure power system reliability and security, transient stability simulation and contingency analysis are essential tasks that are executed very frequently (even in real time, i.e., a solution needs to be produced in less than one minute) at utilities and independent system operators. Transient stability simulation and contingency analysis for a large power system with many generators, transformers, and transmission lines are extremely challenging problems due to the very-high dimensionality. Existing tools based on classical computers have great difficulty in performing such contingency analysis for a large power system, especially when considering the simultaneous failure of multiple components. The difficulty is further complicated by the significant increase in dimensionality caused by the interdependency between power, gas, and communication systems and by the fact that a significant number of renewable generators are being integrated into power systems. Transient stability simulation and contingency analysis are essentially modeled as differential algebraic equations (DAEs) which also can represent many other engineering problems. In general, classical algorithms have an intractable computational burden for solving very-high-dimensional DAEs. This project proposes new VQAs to solve DAEs as a new paradigm. Broader impact activities include (a) disseminating research results to inspire the power and energy community to accelerate research and development in quantum computing for challenging engineering problems, (b) curriculum enhancement on quantum computing and power systems at Alfred University, (c) involving students from underrepresented groups and undergraduates in research, and (d) educating the public and K-12 through outreach activities.The goal of the proposed work is to develop new VQAs that can efficiently solve time-dependent nonlinear differential equations for very-high-dimensional simulation problems (e.g., transient stability and N-k contingency analysis) of large-scale power systems with renewables, which will be the first of its kind. The approach is to: 1) develop VQAs for general time-dependent nonlinear differential equations, where two core components (variational ansatz and optimization algorithm) of a VQA will be comprehensively investigated, 2) develop Hamiltonian and quantum nonlinear processing unit (QNPU) for power system transient stability simulation, where QNPU is the third core component of the VQA, 3) develop VQAs for N-k contingency analysis of power systems with high-penetration renewables. When noisy intermediate-scale quantum computers with a few hundred qubits are available in several years, the VQAs developed from this project are expected to realize exponential advantage over pure classical algorithms to solve transient stability simulation and N-k contingency analysis problems of remarkable significance.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.