A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model

Qureshi, Muhammad Umer; Grijalva, Santiago; Reno, Matthew J.; Deboever, Jeremiah; Zhang, Xiaochen; Broderick, Robert Joseph

doi:10.1109/TSTE.2018.2833748

Title: A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model

Abstract

Understanding the impact of distributed photovoltaic (PV) resources on various elements of the distribution feeder is imperative for their cost effective integration. A year-long quasi-static time series (QSTS) simulation at 1-second granularity is often necessary to fully study these impacts. However, the significant computational burden associated with running QSTS simulations is a major challenge to their adoption. In this paper, we propose a fast scalable QSTS simulation algorithm that is based on a linear sensitivity model for estimating voltage-related PV impact metrics of a three-phase unbalanced, nonradial distribution system with various discrete step control elements including tap changing transformers and capacitor banks. Here, the algorithm relies on computing voltage sensitivities while taking into account all the effects of discrete controllable elements in the circuit. Consequently, the proposed sensitivity model can accurately estimate the state of controllers at each time step and the number of control actions throughout the year. For the test case of a real distribution feeder with 2969 buses (5469 nodes), 6 load/PV time series power profiles, and 9 voltage regulating elements including controller delays, the proposed algorithm demonstrates a dramatic time reduction, more than 180 times faster than traditional QSTS techniques.

Authors:

^[1]; Grijalva, Santiago ^[1]; Reno, Matthew J. ^[2]; Deboever, Jeremiah ^[1];

^[1]; Broderick, Robert Joseph ^[2]

Georgia Inst. of Technology, Atlanta, GA (United States)
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Mon May 07 00:00:00 EDT 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1524210

Report Number(s):: SAND-2019-5854J
Journal ID: ISSN 1949-3029; 675797

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Sustainable Energy

Additional Journal Information:: Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 1949-3029

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY

Citation Formats


                    Qureshi, Muhammad Umer, Grijalva, Santiago, Reno, Matthew J., Deboever, Jeremiah, Zhang, Xiaochen, and Broderick, Robert Joseph. A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model.  United States: N. p., 2018. 
Web.  doi:10.1109/TSTE.2018.2833748.

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                    Qureshi, Muhammad Umer, Grijalva, Santiago, Reno, Matthew J., Deboever, Jeremiah, Zhang, Xiaochen, & Broderick, Robert Joseph. A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model.  United States.  https://doi.org/10.1109/TSTE.2018.2833748

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                    Qureshi, Muhammad Umer, Grijalva, Santiago, Reno, Matthew J., Deboever, Jeremiah, Zhang, Xiaochen, and Broderick, Robert Joseph. Mon .  
"A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model".  United States.  https://doi.org/10.1109/TSTE.2018.2833748.  https://www.osti.gov/servlets/purl/1524210.

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@article{osti_1524210,

  title        = {A Fast Scalable Quasi-Static Time Series Analysis Method for PV Impact Studies Using Linear Sensitivity Model},

  author       = {Qureshi, Muhammad Umer and Grijalva, Santiago and Reno, Matthew J. and Deboever, Jeremiah and Zhang, Xiaochen and Broderick, Robert Joseph},

  abstractNote = {Understanding the impact of distributed photovoltaic (PV) resources on various elements of the distribution feeder is imperative for their cost effective integration. A year-long quasi-static time series (QSTS) simulation at 1-second granularity is often necessary to fully study these impacts. However, the significant computational burden associated with running QSTS simulations is a major challenge to their adoption. In this paper, we propose a fast scalable QSTS simulation algorithm that is based on a linear sensitivity model for estimating voltage-related PV impact metrics of a three-phase unbalanced, nonradial distribution system with various discrete step control elements including tap changing transformers and capacitor banks. Here, the algorithm relies on computing voltage sensitivities while taking into account all the effects of discrete controllable elements in the circuit. Consequently, the proposed sensitivity model can accurately estimate the state of controllers at each time step and the number of control actions throughout the year. For the test case of a real distribution feeder with 2969 buses (5469 nodes), 6 load/PV time series power profiles, and 9 voltage regulating elements including controller delays, the proposed algorithm demonstrates a dramatic time reduction, more than 180 times faster than traditional QSTS techniques.},

  doi          = {10.1109/TSTE.2018.2833748},

  journal      = {IEEE Transactions on Sustainable Energy},

  number       = 1,

  volume       = 10,

  place        = {United States},

  year         = {Mon May 07 00:00:00 EDT 2018},

  month        = {Mon May 07 00:00:00 EDT 2018}

}

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https://doi.org/10.1109/TSTE.2018.2833748

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Cited by: 18 works

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Fig. 1: Geometric interpretation of equation (1) for a single load and a single PV power injection profile.

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