NSF Award
2301411
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.<br/><br/>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.<br/><br/>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.
