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

合作研究：电力系统灵活性：度量、评估和算法

• 批准号: 2045978
• 负责人: Lei Fan
• 金额: $ 15.46万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045978&HistoricalAwards=false
• 关键词: Collaborative; Research; Power; System; Flexibility

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.

该项目旨在建立关键指标，并开发一个全面的评估框架，以评估电力系统的灵活性。该项目将通过支持从业人员和研究人员更好地了解和提高电力系统灵活性，为电力行业带来变革性变化，这一点尤其重要，因为可再生能源的渗透率增加加剧了净负荷的波动性和不可预测性。建议的工作包括i）定义度量来描述复杂的柔性信息，ii）开发一个统一的数学建模框架与各种底层建模结构，和iii）设计广泛适用的，高效的，可扩展的解决方案方法，为不同的度量或应用组合。该项目的智力优势包括从不同角度丰富对系统灵活性的理解，使跨电力系统的灵活性进行比较，并为电力系统规划和运营提供安全和弹性增强方向。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)采用多个柔性指标可以满足研究单个节点和整个系统柔性的需要，而单一的柔性指标不能充分揭示净负荷的高维可行域信息。(2)柔性度量评估模型涉及非线性、离散性和非凸性，与线性模型相比，其求解难度要大得多。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，两阶段和多阶段配方，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紧迫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价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Distributionally Robust Unit Commitment With Flexible Generation Resources Considering Renewable Energy Uncertainty

• DOI: 10.1109/tpwrs.2022.3149506
• 发表时间: 2022-11
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Siyuan Wang;Chaoyue Zhao;Lei Fan;R. Bo

Active Gamma-Ray Log Pattern Localization With Distributionally Robust Reinforcement Learning

• DOI: 10.1109/tgrs.2023.3278491
• 发表时间: 2023
• 期刊: IEEE Transactions on Geoscience and Remote Sensing
• 影响因子: 8.2
• 作者: Yuan Zi;Lei Fan;Xuqing Wu;Jiefu Chen;Shirui Wang;Zhu Han

Passive-seismic sensor placement optimization for geologic carbon storage

地质碳储存的被动地震传感器放置优化

• DOI: 10.1016/j.geoen.2023.212473
• 发表时间: 2024
• 期刊: Geoenergy Science and Engineering
• 作者: Zi, Yuan;Fan, Lei;Wu, Xuqing;Chen, Jiefu;Han, Zhu
• 通讯作者: Han, Zhu

Optimal Data Center Energy Management with Hybrid Quantum-Classical Multi-Cuts Benders' Decomposition Method

采用混合量子经典多切割 Bender 分解法的最优数据中心能源管理

• DOI: 10.1109/tste.2023.3309244
• 发表时间: 2024
• 期刊: IEEE Transactions on Sustainable Energy
• 影响因子: 8.8
• 作者: Zhao, Zhongqi;Fan, Lei;Han, Zhu
• 通讯作者: Han, Zhu

Distributionally Robust Optimal Sensor Placement Method for Site-Scale Methane-Emission Monitoring

• DOI: 10.1109/jsen.2022.3214176
• 发表时间: 2022-12
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Yuan Zi;Lei Fan;Xuqing Wu;Jiefu Chen;Zhu Han