Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

Montano-Martinez, Karen; Thakar, Sushrut; Ma, Shaoping; Soltani, Zahra; Member, Student; Vittal, Vijay; Fellow, Life; Khorsand, Mojdeh; Member, Senior; Rojas, Cynthia; Member,; IEEE,

doi:10.48550/arxiv.2105.14161

Cited by 2 publications

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“…This utility feeder, shown in Fig. 5, is an actual 12.47 kV, 9 km-long Arizona utility feeder with 3.8 MW of residential roof-top PV installed, leading to a penetration level of more than 200% (3.8 MW/1.6 MW) as compared to the feeder total gross load during peak PV production hours [46]. To simplify the calculation, we assume that all equipped PVs at residential customers are integrated into the secondary side of each distribution transformer.…”

Section: Numerical Resultsmentioning

confidence: 99%

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

Yuan

Weng

2023

IEEE Trans. Smart Grid

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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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Section: Numerical Resultsmentioning

confidence: 99%

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

Yuan

Weng

2023

IEEE Trans. Smart Grid

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“…Smart meter data can be automatically allocated to customer nodes using Euclidean distance as in [24]. A recent study proposes the use of advanced metering infrastructure (AMI) data from customer nodes, together with data acquisition system (DAS) measurements at MV-to-LV transformers to determine timeseries load profiles, comprising the unmetered loads [25].…”

Section: State Of the Artmentioning

confidence: 99%

Synergies Between Low Carbon Technologies in a Large-Scale MV/LV Distribution System

et al. 2022

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Power distribution systems are experiencing large deployment of behind-the-meter distributed generation and storage along with electrified transport and heat, requiring fundamental changes in planning and operation. There are increasing efforts to directly model real large-scale networks and conduct detailed analysis beyond conventional practices. Utilizing optimization methods to leverage distributed generation to the benefit of the distribution system and all customers is the ideal. However, progress towards adoption of such controls by system operators are impeded by concerns over privacy, regulatory and market issues in many jurisdictions. While these issues are being addressed, there may be opportunity to exploit the synergistic effect of mixing low carbon technologies (LCT). This paper presents a large-scale medium voltage (MV)-low voltage (LV) integrated system model building, scenario determination and analysis approach for distribution systems. Data with different formats from several databases are used in model building. In collaboration with the national distribution system operator (DSO) in Ireland (ESB Networks), a pilot study is conducted for a rural system in the Southwest of Ireland, highlighting the challenges of directly modelling real distribution systems and investigating the potential synergies between multiple low carbon technologies.

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Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data

Cited by 2 publications

References 0 publications

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

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

Synergies Between Low Carbon Technologies in a Large-Scale MV/LV Distribution System

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