Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

Wu, Jiaqi; Yuan, Jingyi; Weng, Yang; Ayyanar, Raja

doi:10.1109/TSG.2022.3196943

Title: Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

Journal Article · Sun Jan 01 00:00:00 EST 2023 · IEEE Transactions on Smart Grid

DOI:https://doi.org/10.1109/TSG.2022.3196943· OSTI ID:1906412

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School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

The widespread use of distributed energy sources (DERs) raises significant challenges for power system design, planning, and operation, leading to wide adaptation of tools on hosting capacity analysis (HCA). Traditional HCA methods conduct extensive power flow analysis. Due to the computation burden, these time-consuming methods fail to provide online hosting capacity (HC) in large distribution systems. To solve the problem, we first propose a deep learning-based problem formulation for HCA, which conducts offline training and determines HC in real time. The used learning model, long short-term memory (LSTM), implements historical time-series data to capture periodical patterns in distribution systems. However, directly applying LSTMs suffers from low accuracy due to the lack of consideration on spatial information, where location information like feeder topology is critical in nodal HCA. Therefore, we modify the forget gate function to dual forget gates, to capture the spatial correlation within the grid. Such a design turns the LSTM into the Spatial-Temporal LSTM (ST-LSTM). Moreover, as voltage violations are the most vital constraints in HCA, we design a voltage sensitivity gate to increase accuracy further. The results of LSTMs and ST-LSTMs on feeders, such as IEEE 34-, 123-bus feeders, and utility feeders, validate our designs.

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Research Organization:: Arizona State Univ., Tempe, AZ (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

Grant/Contract Number:: EE0008773

OSTI ID:: 1906412

Alternate ID(s):: OSTI ID: 1906414; OSTI ID: 1963767

Journal Information:: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Vol. 14 Journal Issue: 1; ISSN 1949-3053

Publisher:: Institute of Electrical and Electronics EngineersCopyright Statement

Country of Publication:: United States

Language:: English

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