Chance-constrained optimal power flow with non-parametric probability distributions of dynamic line ratings

Nicola Viafora*, Stefanos Delikaraoglou, Pierre Pinson, Joachim Holbøll

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Compared to Seasonal Line Rating (SLR), Dynamic Line Rating (DLR) allows for higher power flows on overhead transmission lines, depending on the actual weather conditions. Nevertheless, the potential of DLR has to be traded off against the additional uncertainty associated with varying ratings. This paper proposes a DC-Optimal Power Flow (DCOPF) algorithm that accounts for DLR uncertainty by means of Chance-Constraints (CC). The goal is to determine the optimal day-ahead dispatch taking the cost of reserve procurement into account. The key contribution of this paper consists in considering both non-parametric predictive distributions of DLR and the combined wind power uncertainty in the optimization problem. Our results highlight the benefits of DLR in wind-dominated power systems, assuming typical risk aversion levels in the line rating estimation.

Original languageEnglish
Article number105389
JournalInternational Journal of Electrical Power and Energy Systems
Volume114
Number of pages10
ISSN0142-0615
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Chance constraints
  • Dynamic line rating
  • Non-parametric distribution
  • Uncertainty
  • Wind power

Cite this

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title = "Chance-constrained optimal power flow with non-parametric probability distributions of dynamic line ratings",
abstract = "Compared to Seasonal Line Rating (SLR), Dynamic Line Rating (DLR) allows for higher power flows on overhead transmission lines, depending on the actual weather conditions. Nevertheless, the potential of DLR has to be traded off against the additional uncertainty associated with varying ratings. This paper proposes a DC-Optimal Power Flow (DCOPF) algorithm that accounts for DLR uncertainty by means of Chance-Constraints (CC). The goal is to determine the optimal day-ahead dispatch taking the cost of reserve procurement into account. The key contribution of this paper consists in considering both non-parametric predictive distributions of DLR and the combined wind power uncertainty in the optimization problem. Our results highlight the benefits of DLR in wind-dominated power systems, assuming typical risk aversion levels in the line rating estimation.",
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author = "Nicola Viafora and Stefanos Delikaraoglou and Pierre Pinson and Joachim Holb{\o}ll",
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Chance-constrained optimal power flow with non-parametric probability distributions of dynamic line ratings. / Viafora, Nicola; Delikaraoglou, Stefanos; Pinson, Pierre; Holbøll, Joachim.

In: International Journal of Electrical Power and Energy Systems, Vol. 114, 105389, 01.01.2020.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Chance-constrained optimal power flow with non-parametric probability distributions of dynamic line ratings

AU - Viafora, Nicola

AU - Delikaraoglou, Stefanos

AU - Pinson, Pierre

AU - Holbøll, Joachim

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Compared to Seasonal Line Rating (SLR), Dynamic Line Rating (DLR) allows for higher power flows on overhead transmission lines, depending on the actual weather conditions. Nevertheless, the potential of DLR has to be traded off against the additional uncertainty associated with varying ratings. This paper proposes a DC-Optimal Power Flow (DCOPF) algorithm that accounts for DLR uncertainty by means of Chance-Constraints (CC). The goal is to determine the optimal day-ahead dispatch taking the cost of reserve procurement into account. The key contribution of this paper consists in considering both non-parametric predictive distributions of DLR and the combined wind power uncertainty in the optimization problem. Our results highlight the benefits of DLR in wind-dominated power systems, assuming typical risk aversion levels in the line rating estimation.

AB - Compared to Seasonal Line Rating (SLR), Dynamic Line Rating (DLR) allows for higher power flows on overhead transmission lines, depending on the actual weather conditions. Nevertheless, the potential of DLR has to be traded off against the additional uncertainty associated with varying ratings. This paper proposes a DC-Optimal Power Flow (DCOPF) algorithm that accounts for DLR uncertainty by means of Chance-Constraints (CC). The goal is to determine the optimal day-ahead dispatch taking the cost of reserve procurement into account. The key contribution of this paper consists in considering both non-parametric predictive distributions of DLR and the combined wind power uncertainty in the optimization problem. Our results highlight the benefits of DLR in wind-dominated power systems, assuming typical risk aversion levels in the line rating estimation.

KW - Chance constraints

KW - Dynamic line rating

KW - Non-parametric distribution

KW - Uncertainty

KW - Wind power

U2 - 10.1016/j.ijepes.2019.105389

DO - 10.1016/j.ijepes.2019.105389

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JO - International Journal of Electrical Power & Energy Systems

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