Prediction Models for Dynamic Decision Making in Smart Grid

The widespread use of smart meters and other sensors in smart grid has resulted in unprecedented amounts of data being generated at high spatial and temporal resolutions. This high volume data is being generated at a high velocity and comes from a variety of sources, and is designated as “big data” by researchers and practitioners in various domains, including the smart grid domain. Predictive modeling can be used to learn from this data about how electricity consumption patterns change over time and when peak demand periods occur. Utilities routinely face the challenge of ensuring uninterrupted electric supply during peak demand periods. The widespread practice to address this challenge is by demand response (DR), whereby utilities ask customers to reduce their consumption during peak demand periods according to a-priori agreements. For DR, utilities need to make decisions about when, by how much, and how to reduce consumption. While day-ahead predictions have long been used to make these decisions, in this dissertation, we address the problem of making predictions and decisions at a few hours’ advance notice whenever necessitated by the changing conditions of the grid. In particular, we formulate and address the problem of dynamic demand response (D2R) in smart grids that involves balancing supply and demand in real-time and adapting to dynamically changing conditions by automating and transforming the DR planning process. We also focus on the requirements and challenges of prediction