Unbundling Smart Meter Services Through Spatiotemporal Decomposition Agents in DER-Rich Environment

Abstract

Smart meters and the advanced metering infrastructure facilitate distribution system operators (DSOs) to gather information on energy consumption at the customer level. With the increasing penetration of building-level intermittent distributed energy resources (DERs) behind the meter, DER information is not available to DSOs. At the same time, the smart meter enables users to participate in grid, with real-time information. Information for behind the meter is needed by the user to coordinate building-level assets for maximum benefits. The concept of unbundled smart meter (USM) needs agents to decompose smart meter measurements to provide service to DSOs as well as customers. In this article, we propose a spatiotemporal decomposition agent (STDA) for the USM based on artificial intelligence. The STDA can help users optimize their energy usage and help DSOs to utilize building assets for the grid operation. The energy usage strategy developed by the STDA is suitable for different users and can be customized by deep learning (DL) models according to the different energy consumption habits of each user. The power prediction performance results of various DL models and evaluation using a set of data from a Hawaii utility is presented. Also, STDA integration with home energy management systems to manage resources is presented and validated. STDA preprocesses the measurements before model training and provides the spatiotemporal decomposed forecasting.