Chance Constraints for Improving the Security of AC Optimal Power Flow

Lubin, M.; Dvorkin, Y.; Roald, L.

doi:10.1109/TPWRS.2018.2890732

Title: Chance Constraints for Improving the Security of AC Optimal Power Flow

Abstract

This paper introduces a scalable method for improving the solutions of ac optimal power flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The aim of this paper is on providing solutions that are more robust to short-term deviations, and that optimize both the initial operating point and a parametrized response policy for control during fluctuations. We formulate this as a chance-constrained optimization problem. To obtain a tractable representation of the chance constraints, we introduce a number of modeling assumptions and leverage recent theoretical results to reformulate the problem as a convex, second-order cone program, which is efficiently solvable even for large instances. Our experiments demonstrate that the proposed procedure improves the feasibility and cost performance of the OPF solution, while the additional computation time is on the same magnitude as a single deterministic AC OPF calculation.

Authors:

Lubin, M. ^[1];

^[2];

^[3]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
New York Univ. (NYU), NY (United States)
Univ. of Wisconsin, Madison, WI (United States)

Publication Date:: 2019-01-03

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

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

OSTI Identifier:: 1525830

Report Number(s):: LA-UR-18-22568
Journal ID: ISSN 0885-8950

Grant/Contract Number:: 89233218CNA000001

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Power Systems

Additional Journal Information:: Journal Volume: 34; Journal Issue: 3; Journal ID: ISSN 0885-8950

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; Mathematical model; Uncertainty; Generators; Reactive power; Production; Load flow; Voltage control

Citation Formats


                    Lubin, M., Dvorkin, Y., and Roald, L. Chance Constraints for Improving the Security of AC Optimal Power Flow.  United States: N. p., 2019. 
Web.  doi:10.1109/TPWRS.2018.2890732.

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                    Lubin, M., Dvorkin, Y., & Roald, L. Chance Constraints for Improving the Security of AC Optimal Power Flow.  United States.  https://doi.org/10.1109/TPWRS.2018.2890732

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                    Lubin, M., Dvorkin, Y., and Roald, L. Thu .  
"Chance Constraints for Improving the Security of AC Optimal Power Flow".  United States.  https://doi.org/10.1109/TPWRS.2018.2890732.  https://www.osti.gov/servlets/purl/1525830.

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@article{osti_1525830,

  title        = {Chance Constraints for Improving the Security of AC Optimal Power Flow},

  author       = {Lubin, M. and Dvorkin, Y. and Roald, L.},

  abstractNote = {This paper introduces a scalable method for improving the solutions of ac optimal power flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The aim of this paper is on providing solutions that are more robust to short-term deviations, and that optimize both the initial operating point and a parametrized response policy for control during fluctuations. We formulate this as a chance-constrained optimization problem. To obtain a tractable representation of the chance constraints, we introduce a number of modeling assumptions and leverage recent theoretical results to reformulate the problem as a convex, second-order cone program, which is efficiently solvable even for large instances. Our experiments demonstrate that the proposed procedure improves the feasibility and cost performance of the OPF solution, while the additional computation time is on the same magnitude as a single deterministic AC OPF calculation.},

  doi          = {10.1109/TPWRS.2018.2890732},

  journal      = {IEEE Transactions on Power Systems},

  number       = 3,

  volume       = 34,

  place        = {United States},

  year         = {2019},

  month        = {1}

}

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Cited by: 23 works

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Figures / Tables:

Table I: EXPLICIT AND IMPLICIT (IMPL.) RESPONSE POLICIES

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