AMPS: Online and Model-Free Optimization of Power and Energy Systems

由于可再生能源发电和分布式能源（DER）的激增，电力系统正在经历快速变化。当前最先进的网络监测和控制方法需要适应这种新兴的设置，特别是它们必须能够科普可控点数量的增加和可以在不到一秒的时间内发生变化的更快的网络级动态。该项目推进了一个程序，以设计新的算法，该算法将运行得更快，因此能够在快速的时间尺度上优化电网，并且还能够处理模型中的不确定性和误差。这些算法将适用于能源以外的其他关键基础设施，包括交通和工业自动化。对电力和能源部门的直接影响，如交流电网和风电场的运营，将是更有效地利用电力和更可靠地执行电压和电流限制。该项目的一个间接影响是通过本科生参与、研究生辅导和课程编制、针对初中和高中科学、技术、工程和数学（STEM）营地的外联活动、广泛的传播活动，现有办法不足以处理数据和作出决定，在高度动态的环境中操作的复杂电力系统中，由于以下缺点：i）用于与监视和控制任务相关联的优化问题的批量解决方案方法可能无法在与分布的可变性匹配的时间尺度上提供解决方案，水平的能量资源和不可控的资产; ii）基于模型的优化方法需要准确的网络知识;以及iii）针对批量/静态解决方案设计的算法的朴素在线实现可能不提供最优性和收敛性保证。在这个项目中提出的工作将显着扩展理论和加速方法的应用-成功地应用于批量优化-随时间变化的优化网络和网络系统的成本，约束条件和问题的输入过程中的算法步骤的执行演变。该项目进一步研究了在线无导数策略的开发，以处理成本函数的未知梯度，该策略适用于网络模型未知或难以准确估计的情况。主要的努力致力于具有挑战性的优化设置，涉及约束和不可微的条款，在时变目标。所提出的努力集中在严格的收敛分析，其目的是在执行的算法（而不是渐近界）的最佳解决方案的跟踪误差方面得出的结果，并使线性收敛率随时间变化的设置。在电力系统领域，拟议的研究重温经典的监测和优化任务-状态估计，交流最优潮流，偏航控制在风电场只是举几个-在动态运行条件下;所提出的理论方法能够实现真实的-该奖项反映了NSF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。

Power systems are undergoing a rapid change due to the proliferation of renewable-based generation and distributed energy resources (DERs). Current state-of-the-art methods for network monitoring and control need to adapt this emerging setting, and in particular they must be able to cope with an increased number of controllable points and faster network-level dynamics that can change in less than a second. This project advances a program to design new algorithms that will run faster, and therefore be able to optimize the power grid at fast time scales, and that will also be able to handle uncertainty and errors in the model. The algorithms will apply to other critical infrastructures beyond energy, including transportation and industrial automation. The direct impact to the power and energy sector, such as the AC power grid and the operation of wind farms, will be a more efficient use of power and more reliable enforcement of voltage and current constraints. An indirect impact of the project is the training of a new generation of students through undergraduate student involvement, graduate student mentoring and curriculum development, outreach activities targeted at middle and high school Science, Technology, Engineering and Mathematics (STEM) camps, broad dissemination activities, and industrial outreach.Existing approaches are not adequate for data-processing and decision-making in complex power systems operating in highly dynamic environments because of the following drawbacks: i) batch solution approaches for optimization problems associated with monitoring and control tasks may fail to provide solutions at a time scale that match variability of distribution-level energy resources and non-controllable assets; ii) model-based optimization approaches require accurate knowledge of the network; and, iii) a naive online implementation of algorithms that are designed for a batch/static solution may not provide optimality and convergence guarantees. The proposed work in this project will significantly extend theory and application of accelerated methods -- successfully applied in batch optimization -- to time-varying optimization of networks and networked systems where costs, constraints and problem inputs evolve during the execution of the algorithmic steps. The project further investigates the development of online derivative-free strategies to deal with unknown gradients of the cost function, which apply when the network model is unknown or difficult to estimate accurately. Major efforts are devoted to the challenging optimization settings that involve constraints and non-differentiable terms in the time-varying objective. The proposed efforts focus on rigorous convergence analysis; the aim is to derive results in terms of error in the tracking of an optimal solution during the execution of the algorithms (rather than asymptotic bounds), and enable linear convergence rates in time-varying settings. Within the power systems area, the proposed research revisits classical monitoring and optimization tasks -- state estimation, AC optimal power flow, and yaw control in wind farms just to mention few -- under dynamic operating conditions; the proposed theoretical approach enables a real-time execution of these tasks.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.