Building Large-Scale U.S. Synthetic Electric Distribution System Models

Mateo, Carlos; Postigo, Fernando; Cuadra, F. de; Román, Tomás Gómez San; Elgindy, Tarek; Dueñas, Pablo; Krishnan, Venkat; Palmintier, Bryan

doi:10.1109/tsg.2020.3001495

Cited by 49 publications

(29 citation statements)

References 36 publications

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“…Without attempting to be exhaustive, we list a number of sources that offer open access to unbalanced power flow data sets: BetterGrids (various formats) 3 DR POWER (various formats) 4 EPRI test circuits (OpenDSS) 5 PNNL “taxonomy of prototypical feeders” (Gridlab‐D) 6 NREL “synthetic models for advanced, realistic testing: distribution systems and scenarios” (OpenDSS and others; Mateo et al, 2020; Palmintier et al, 2021) CSIRO “MV taxonomy” (PowerFactory, PSS/SINCAL; Berry et al, 2013) 7 …”

Section: Modeling Distribution Networkmentioning

confidence: 99%

“…• PNNL "taxonomy of prototypical feeders" (Gridlab-D) 6 • NREL "synthetic models for advanced, realistic testing: distribution systems and scenarios" (OpenDSS and others; Mateo et al, 2020;Palmintier et al, 2021) • CSIRO "MV taxonomy" (PowerFactory, PSS/SINCAL; Berry et al, 2013) 7 The share of LV networks in those data sources is limited, therefore we call out some of the key data sets:…”

Section: Representative Networkmentioning

confidence: 99%

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Applications of optimization models for electricity distribution networks

Claeys

Vanin

Geth

2021

WIREs Energy & Environment

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Increased penetration of low‐carbon technologies, such as residential photovoltaic systems, electric vehicles, and batteries, can potentially cause voltage quality issues in distribution networks. Active distribution networks adopt control schemes where these assets are actively managed to prevent potential issues, increasing the network utilization. Mathematical optimization is a key technology in enabling such applications, either directly as the underlying solution, or for benchmarking effectiveness. As networks are operated closer to their engineering limits, models representing distribution network physics become increasingly important. This article reviews how distribution networks are modeled with varying degrees of detail in the context of optimization problems. It goes on to catalog the applications that use such models, and ends with an overview of toolchains to implement them, to enable the transition from the passive to active management of the distribution system. This article is categorized under: Energy Systems Analysis > Systems and Infrastructure

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Section: Modeling Distribution Networkmentioning

confidence: 99%

Section: Representative Networkmentioning

confidence: 99%

Applications of optimization models for electricity distribution networks

Claeys

Vanin

Geth

2021

WIREs Energy & Environment

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“…The synthetic distribution system is constructed using the U.S. Reference Network Model tool (RNM-US) [22], which adapted the European Reference Network Model tool (RNM) [27] for U.S.-style networks. Fig.…”

Section: Distribution Synthesis For Highly Detailed Synthetic Gridsmentioning

confidence: 99%

“…At the distribution level, synthetic grids have been constructed by placing secondaries and distribution transformers, and then designing medium-voltage components to support the low-voltage infrastructure [22]. For U.S.-style systems, phase-balancing algorithms are applied on the medium-voltage network to provide a realistically balanced network across three-phase trunks and single-phase laterals [23].…”

Section: Introductionmentioning

confidence: 99%

“…Building on the author's previous work of SMART-DS that creates synthetic distribution systems [22], this paper presents a bottom-up methodology to generate data sets that contain a combined synthetic T&D system that is based on actual customer and street locations. Commercially obtained parcel information is utilized to model the load in a highly-detailed manner, where each end-use customer is geographically located, and the load value is estimated according to the size and usage type of each parcel.…”

Section: Introductionmentioning

confidence: 99%

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Building Highly Detailed Synthetic Electric Grid Data Sets for Combined Transmission and Distribution Systems

Wert

Birchfield

et al. 2020

IEEE Open J. Power Energy

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This paper introduces a methodology for building synthetic electric grid data sets that represent fictitious, yet realistic, combined transmission and distribution (T&D) systems. Such data sets have important applications, such as in the study of the wide-area interactions of distributed energy resources, in the validation of advanced control schemes, and in network resilience to severe events. The data sets created here are geographically located on an actual North American footprint, with the enduser load information estimated from land parcel data. The grid created to serve these fictional but realistic loads is built starting with low-voltage and medium-voltage distribution systems in full detail, connected to distribution and transmission substations. Bulk generation is added, and a high-voltage transmission grid is created. This paper explains the overall process and challenges addressed in making the combined case. An example test case, syn-austin-TDgrid-v03, is shown for a 307,236-customer case located in central Texas, with 140 substations, 448 feeders, and electric line data at voltages ranging from 120 V to 230 kV. Such new combined test cases help to promote high quality in the research on large-scale systems, particularly since much actual power system data are subject to data confidentiality. The highly detailed, combined T&D data set can also facilitate the modeling and analysis of coupled infrastructures.

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