ERI: Data-Driven Analysis and Dynamic Modeling of Residential Power Demand Behavior: Using Long-Term Real-World Data from Rural Electric Systems

ERI：住宅电力需求行为的数据驱动分析和动态建模：使用农村电力系统的长期真实数据

• 批准号: 2301411
• 负责人: Long Zhao
• 金额: $ 19.87万
• 依托单位: South Dakota School of Mines and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2301411&HistoricalAwards=false
• 关键词: ERI; Data; Driven; Analysis; Dynamic

As distributed energy resources (e.g., rooftop solar, energy storage) continue to grow among households, more houses will act as virtual power plants, sending electricity back to the power grid and significantly changing power system operations. Existing load analysis and forecasting models fail to adequately reflect consumer characteristics, which are crucial for understanding residential power demand patterns. To improve power system reliability and stability, this project introduces a novel data-driven approach to analyze and explicitly model power demand behavior at the household level using real-world long-term data. This research has several significant impacts on both theory and practice. First, the proposed model can be readily expanded to other domains, such as water usage. Second, it significantly contributes to the marketing literature by reconciling mixed findings on incentive effectiveness in changing consumer behavior, particularly in green consumption. Third, a new "Smart Grid and Data Science" curriculum will be developed, fostering student interest in combining power engineering and social science. Annual data-driven modeling competitions for K-12 students will be organized to encourage more students, especially females, to study power engineering and data science. A joint power engineering program will be developed to promote STEM research and education, increasing Native American representation in STEM careers. This project can help electric co-ops develop behavior-based demand response programs, reducing peak demand charges and contributing to global decarbonization goals. Finally, the project supports U.S. electric utilities in creating effective incentive programs that ensure both firm profit and consumers’ sustainable use of electricity.The research goal is to develop a new data-driven approach to reveal macro- and micro-residential power demand patterns, through matching 15-minute resolution smart meter data with consumer surveys, along with local parcel data and meteorological data, to achieve a comprehensive understanding of residential power demand behavior at a disaggregated level. This will lay the foundation for research focused on data-driven analysis and modeling for next-generation power grids. The key tool employed in the project is utilizing the Singular-Value-Decomposition (SVD) to identify and characterize power demand behavior. An Eigen Demand Behavior Extraction method, based on SVD, is proposed for characterizing demand behavior using 15-minute smart meter data. The primary contribution of this project will be the development of a data-driven Temperature-Time-Day-based (TTD) model for residential power demand behavior, relying solely on real-world data. The power demand behavior will be represented as a discrete-time dynamical system with outdoor temperature and time of a day coordinates evolving over days.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.

随着分布式能源（例如屋顶太阳能、储能）在家庭中的不断增长，更多的房屋将充当虚拟发电厂，将电力输送回电网，并显着改变现有的负荷分析和预测模型。充分反映消费者特征，这对于理解住宅电力需求模式至关重要，为了提高电力系统的可靠性和稳定性，该项目引入了一种新颖的数据驱动方法，利用现实世界的长期电力分析和明确建模家庭层面的电力需求行为。这项研究对术语数据有几个重大影响。首先，所提出的模型可以很容易地扩展到其他领域，例如用水量；其次，它通过协调有关改变消费者行为的激励有效性的不同研究结果，对营销文献做出了重大贡献，尤其是在绿色消费方面。将开发新的“智能电网和数据科学”课程，培养学生对电力工程和社会科学相结合的兴趣，并将为 K-12 学生组织年度数据驱动建模竞赛，以鼓励更多学生，特别是女性学习。电力工程和数据科学的联合力量。将开发工程计划来促进 STEM 研究和教育，增加美国原住民在 STEM 职业中的代表性。该项目可以帮助电力合作社制定基于行为的需求响应计划，减少高峰需求费用并为全球脱碳目标做出贡献。项目支持美国电力公司制定有效的激励计划，确保公司利润和消费者可持续用电。该研究目标是开发一种新的数据驱动方法，通过匹配 15 个项目来揭示宏观和微观住宅电力需求模式- 分钟分辨率的智能电表数据消费者调查以及当地包裹数据和气象数据，以在分类层面上全面了解居民电力需求行为，这将为重点关注下一代电网的数据驱动分析和建模奠定基础。该项目中使用的工具是利用奇异值分解（SVD）来识别和表征电力需求行为，提出了一种基于 SVD 的特征需求行为提取方法，用于使用 15 分钟智能电表来表征需求行为。该项目的主要贡献将是开发基于数据驱动的住宅电力需求行为的温度-时间-日（TTD）模型，电力需求行为将表示为：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。