Unbundling Smart Meter Services Through Spatio-Temporal Decomposition Agents in DER-rich Environment

Journal Article · Mon Feb 22 00:00:00 EST 2021 · IEEE Transactions on Industrial Informatics

DOI:https://doi.org/10.1109/tii.2021.3060870· OSTI ID:1810251

Qin, Chuan ^[1]; Srivastava, Anurag ^[2]; Davies, Kevin ^[3]

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1. Washington State Univ., Pullman, WA (United States); Washington State University
2. Washington State Univ., Pullman, WA (United States)
3. Univ. of Hawaii at Manoa, Honolulu, HI (United States)

Smart meters and the advanced metering infrastructure (AMI) 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, smart meter enables users to participate in grid, with real-time information. Information for behind the meter is needed by 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 DSO as well as customers. In this paper, we propose a Spatio-Temporal Decomposition Agent (STDA) for USM based on Artificial Intelligence (AI). STDA can help users optimize their energy usage, help DSO to utilize building assets for the grid operation. The energy usage strategy developed by 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. Furthermore, STDA integration with Home Energy management Systems (HEMS) to manage resources is presented and validated. STDA pre-processes the measurements before model training, and provides the spatio-temporal decomposed forecasting.

• Research Organization: Washington State Univ., Pullman, WA (United States)
• Sponsoring Organization: USDOE Office of Electricity (OE)
• Grant/Contract Number: IA0000025; OE0000878
• OSTI ID: 1810251
• Report Number(s): OE0000878--WSUpaper-3
• Journal Information: IEEE Transactions on Industrial Informatics, Journal Name: IEEE Transactions on Industrial Informatics Journal Issue: 1 Vol. 18; ISSN 1551-3203
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
Decomposition analytic
deep learning
intelligent agent
photovoltaic
short-term load forecasting
smart grid
smart meter extension
unbundled smart meter