A robust approach to chance constrained optimal power flow with renewable generation

Lubin, Miles; Dvorkin, Yury; Backhaus, Scott N.

doi:10.1109/TPWRS.2015.2499753

Title: A robust approach to chance constrained optimal power flow with renewable generation

Abstract

Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a robust chance constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. In conclusion, deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.

Authors:

Lubin, Miles ^[1]; Dvorkin, Yury ^[2]; Backhaus, Scott N. ^[3]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Univ. of Washington, Seattle, WA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Publication Date:: Thu Sep 01 00:00:00 EDT 2016

Research Org.:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1329591

Report Number(s):: LA-UR-15-22280
Journal ID: ISSN 0885-8950

Grant/Contract Number:: AC52-06NA25396

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Power Systems

Additional Journal Information:: Journal Volume: 31; Journal Issue: 5; Journal ID: ISSN 0885-8950

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 29 ENERGY PLANNING, POLICY, AND ECONOMY; 24 POWER TRANSMISSION AND DISTRIBUTION; Energy Sciences

Citation Formats


                    Lubin, Miles, Dvorkin, Yury, and Backhaus, Scott N. A robust approach to chance constrained optimal power flow with renewable generation.  United States: N. p., 2016. 
Web.  doi:10.1109/TPWRS.2015.2499753.

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                    Lubin, Miles, Dvorkin, Yury, & Backhaus, Scott N. A robust approach to chance constrained optimal power flow with renewable generation.  United States.  https://doi.org/10.1109/TPWRS.2015.2499753

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                    Lubin, Miles, Dvorkin, Yury, and Backhaus, Scott N. Thu .  
"A robust approach to chance constrained optimal power flow with renewable generation".  United States.  https://doi.org/10.1109/TPWRS.2015.2499753.  https://www.osti.gov/servlets/purl/1329591.

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@article{osti_1329591,

  title        = {A robust approach to chance constrained optimal power flow with renewable generation},

  author       = {Lubin, Miles and Dvorkin, Yury and Backhaus, Scott N.},

  abstractNote = {Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a robust chance constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. In conclusion, deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.},

  doi          = {10.1109/TPWRS.2015.2499753},

  journal      = {IEEE Transactions on Power Systems},

  number       = 5,

  volume       = 31,

  place        = {United States},

  year         = {Thu Sep 01 00:00:00 EDT 2016},

  month        = {Thu Sep 01 00:00:00 EDT 2016}

}

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Works referencing / citing this record:

Chance-Constrained OPF Based on Polynomials Approximation and Cornish–Fisher Expansion
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Cai, Yunfeng; Wang, Liang; Zhou, Jianhua
Iranian Journal of Science and Technology, Transactions of Electrical Engineering, Vol. 44, Issue 4
DOI: 10.1007/s40998-020-00316-6

SR-based chance-constrained economic dispatch for power systems with wind power
journal, July 2019

Qin, Chao; Zeng, Yuan
IET Generation, Transmission & Distribution, Vol. 13, Issue 13
DOI: 10.1049/iet-gtd.2018.6549

A Survey of Real-Time Optimal Power Flow
journal, November 2018

Mohagheghi, Erfan; Alramlawi, Mansour; Gabash, Aouss
Energies, Vol. 11, Issue 11
DOI: 10.3390/en11113142

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Title: A robust approach to chance constrained optimal power flow with renewable generation

Abstract

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