Collaborative Research: Data-driven Power Systems Control with Stability Guarantees

合作研究：数据驱动的电力系统控制与稳定性保证

基本信息

批准号：
2153937
负责人：
Baosen Zhang
金额：
$ 10万
依托单位：
University of Washington
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：
2022-03-15 至 2025-02-28
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2153937&HistoricalAwards=false
关键词：
Collaborative Research Data driven Power

项目摘要

This NSF project aims to design a new data-driven power system control framework with stability guarantee. Power systems are experiencing a period of rapid changes due to the proliferation of renewable generation and distributed energy resources including solar, electric vehicles, and batteries. Many of these new technologies are interfaced with the grid through power electronic interfaces (i.e., inverters) that can be controlled at a much faster timescale compared to conventional machines. However, how to leverage such flexibility is nontrivial due to the nonlinearity, complexity, and uncertainty in the underlying power network. This project will bring transformative changes by developing new reinforcement learning (RL) algorithms for inverter-based frequency and voltage control with formal stability guarantees. The intellectual merits of the project include (i) a novel framework that bridges Lyapunov control theory and RL, therefore providing stability guarantee for learning-based controllers; (ii) neural network structure design that ensures stability constraint is met by design. The broader impacts of the project include various of new courses development and research opportunities for students interested in both energy systems and machine learning/AI.The proposed research consists of three thrusts. Thrust 1 focuses on developing the algorithmic framework that integrates RL with Lyapunov stability constraints, which serves as a foundation to later thrusts. Specifically, we will leverage analytical models to construct Lyapunov functions and engineer the structure of neural network-based controllers to meet the stability constraints. Thrust 2 uses machine learning to discover new Lyapunov functions for realistic power system models and design stable control policies. Thrust 3 integrates the theory and algorithms developed in Thrusts 1 and 2, and robustifies the controllers against modeling error, and network topology re-configurations in both transmission and distribution grids. The contributions of the project are two-folded. On the theoretical side, the proposed research bridges classic control and learning, where control theory provides the structural constraints that guarantee a controller is stable, and RL with neural networks searches over the large parametric spaces to find the best performing controllers that have this structure. On the practical side, our approach clears a critical hurdle in applying RL to power systems by guaranteeing the stability of the learned policy. We envision our framework will serve as the basis for future learning-based smart power system control architectures.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.

该NSF项目旨在设计具有稳定性保证的新的数据驱动电源系统控制框架。由于可再生能源生成和分布式能源（包括太阳能，电动汽车和电池）的扩散，电力系统正在经历一段时间的快速变化。与传统机器相比，这些新技术中的许多通过电力电子界面（即逆变器）连接到网格（即逆变器）。但是，如何利用这种灵活性是由于基础功率网络中的非线性，复杂性和不确定性而不是平凡的。该项目将通过为基于逆变器的频率和正式稳定性保证的基于逆变器的频率和电压控制的新增强学习（RL）算法带来变革性变化。该项目的智力优点包括（i）一个桥梁Lyapunov控制理论和RL的新颖框架，因此为基于学习的控制者提供了稳定保证；（ii）确保通过设计满足稳定性约束的神经网络结构设计。该项目的更广泛影响包括对能源系统和机器学习感兴趣的学生的各种新课程开发和研究机会。拟议的研究包括三个推力。推力1专注于开发将RL与Lyapunov稳定性约束集成的算法框架，这是后来推力的基础。具体而言，我们将利用分析模型来构建Lyapunov功能，并设计基于神经网络控制器的结构以满足稳定性约束。推力2使用机器学习来发现新的Lyapunov功能，以实现现实的电源系统模型和设计稳定的控制策略。推力3整合了推力1和2中开发的理论和算法，并鲁棒化控制器免于建模误差，以及在传输和分布网格中重新配置网络拓扑。该项目的贡献是两折。从理论方面来说，拟议的研究桥梁经典的控制和学习，其中控制理论提供了确保控制器稳定的结构约束，并且在较大的参数空间上搜索神经网络，以找到具有这种结构的最佳性能控制器。从实际方面来说，我们的方法通过保证学习政策的稳定性将RL应用于电力系统的关键障碍。我们设想我们的框架将作为基于未来的基于学习的智能电力系统控制体系结构的基础。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响标准，被认为值得通过评估来获得支持。