Collaborative Research: Power System Flexibility: Metric, Assessment, and Algorithm

该NSF项目旨在建立关键指标并开发一个全面的评估框架来评估电力系统的灵活性。该项目将为电力行业带来革命性的变化，支持从业者和研究人员更好地了解和提高电力系统的灵活性，这一点尤其关键，因为可再生能源的日益普及加剧了净负荷的波动性和不可预测性。所提出的工作包括i)定义度量来描述复杂的柔性信息，ii)开发具有各种底层建模结构的统一数学建模框架，以及iii)为不同的度量或应用组合设计广泛适用、高效和可扩展的解决方案。该项目的学术价值包括从不同的角度丰富了对系统灵活性的理解，使电力系统的灵活性能够进行比较，并为电力系统的规划和运行提供了一个安全和增强韧性的方向。该项目的更广泛影响包括推进优化和人工智能跨学科领域的理论基础，以及将尖端学习技术应用于传统工程领域。该研究将通过提出三个方面的创新科学方法来应对现有文献中的挑战。(1)多个柔度指标的使用满足了研究单个节点和整个系统柔度的需要，而单一的指标不能充分揭示净负荷的高维可行域的信息。(2)涉及非线性、离散性和非凸性的柔性度量评估模型比线性度量评估模型更难求解。为了处理复杂的情况，该项目将使用广泛的建模技术，包括混合整数、两阶段和多阶段公式，以降低测量灵活性的复杂性。(3)提出的混合整数规划和深度学习算法将显著提高在时间苛刻的环境中获取关键信息以进行灵活性评估的效率，并满足运营实践的要求。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This NSF project aims to establish critical metrics and develop a comprehensive evaluation framework to assess the flexibility of power systems. The project will bring transformative change to the power industry by supporting practitioners and researchers to better understand and improve power system flexibility, which is especially crucial due to the volatility and unpredictability of net loads intensified by the increasing penetration of renewable energy. The proposed work includes i) defining metrics to describe complicated flexibility information, ii) developing a unified mathematical modeling framework with various underlying modeling structures, and iii) designing broadly applicable, efficient, and scalable solution approaches for different metric or application combinations. The intellectual merits of the project include enriching the understanding of system flexibility from different perspectives, enabling the comparison of flexibility across power systems, and providing a safety and resilience enhancement direction for power system planning and operations. The broader impacts of the project include advancing the theoretical foundations in the interdisciplinary field of optimization and artificial intelligence as well as applying cutting-edge learning techniques into traditional engineering fields.The study will address challenges in the existing literature by proposing innovative scientific methods in three aspects. (1) The employment of multiple flexibility metrics satisfies the needs of investigating flexibility on individual buses and the whole system, while a single metric is not able to reveal enough information on the high dimensional feasible regions of net loads. (2) The flexibility metric assessment models that involve nonlinearity, discreteness, and nonconvexity, are significantly harder to solve compared to their linear counterparts. To handle the complex cases, the project will use a wide range of modeling techniques, including mixed-integer, two-stage and multistage formulations to reduce the complexity of measuring flexibility. (3) The proposed hybrid mixed-integer programming and deep learning algorithm will significantly improve the efficiency to obtain critical information for flexibility assessment in a time-critical environment and meet the requirement of operations practice.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.