Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information

Adriano Arrigo; Jalal Kazempour; Zacharie De Greve; Jean-François Toubeau; Francois Vallée

doi:10.1109/PowerTech46648.2021.9494771

Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information

Adriano Arrigo
, Jalal Kazempour
, Zacharie De Greve
, Jean-François Toubeau
, Francois Vallée

University of Mons

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational
results achieved and the computational time.

Original language	English
Title of host publication	Proceedings of 2021 IEEE PowerTech
Number of pages	6
Publisher	IEEE
Publication date	2021
ISBN (Print)	978-1-6654-1173-8
DOIs	https://doi.org/10.1109/PowerTech46648.2021.9494771
Publication status	Published - 2021
Event	2021 IEEE Madrid PowerTech - Virtual Event - from the Alberto Aguilera Campus of Comillas University, Madrid, Spain Duration: 28 Jun 2021 → 2 Jul 2021 Conference number: 14 https://www.powertech2021.com/

Conference

Conference	2021 IEEE Madrid PowerTech
Number	14
Location	Virtual Event - from the Alberto Aguilera Campus of Comillas University
Country/Territory	Spain
City	Madrid
Period	28/06/2021 → 02/07/2021
Internet address	https://www.powertech2021.com/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Distributionally robust optimization
Space-time dependencies
Optimal power flow
Out-of-sample analysis

Access to Document

10.1109/PowerTech46648.2021.9494771

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title = "Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information",

abstract = "This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational results achieved and the computational time.",

keywords = "Distributionally robust optimization, Space-time dependencies, Optimal power flow, Out-of-sample analysis",

author = "Adriano Arrigo and Jalal Kazempour and \{De Greve\}, Zacharie and Jean-Fran{\c c}ois Toubeau and Francois Vall{\'e}e",

year = "2021",

doi = "10.1109/PowerTech46648.2021.9494771",

language = "English",

isbn = "978-1-6654-1173-8",

booktitle = "Proceedings of 2021 IEEE PowerTech",

publisher = "IEEE",

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note = "2021 IEEE Madrid PowerTech ; Conference date: 28-06-2021 Through 02-07-2021",

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T1 - Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information

AU - Arrigo, Adriano

AU - Kazempour, Jalal

AU - De Greve, Zacharie

AU - Toubeau, Jean-François

AU - Vallée, Francois

N1 - Conference code: 14

PY - 2021

Y1 - 2021

N2 - This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational results achieved and the computational time.

AB - This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational results achieved and the computational time.

KW - Distributionally robust optimization

KW - Space-time dependencies

KW - Optimal power flow

KW - Out-of-sample analysis

U2 - 10.1109/PowerTech46648.2021.9494771

DO - 10.1109/PowerTech46648.2021.9494771

M3 - Article in proceedings

SN - 978-1-6654-1173-8

BT - Proceedings of 2021 IEEE PowerTech

PB - IEEE

T2 - 2021 IEEE Madrid PowerTech

Y2 - 28 June 2021 through 2 July 2021

ER -

Enhanced Wasserstein Distributionally Robust OPF With Dependence Structure and Support Information

Abstract

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UN SDGs

Keywords

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