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Distributed stochastic reserve scheduling in AC power systems with uncertain generation. / Rostampour, Vahab; Ter Haar, Ole; Keviczky, Tamas.

In: IEEE Transactions on Power Systems, Vol. 34, No. 2, 8516306, 2019, p. 1005-1020.

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Rostampour, Vahab ; Ter Haar, Ole ; Keviczky, Tamas. / Distributed stochastic reserve scheduling in AC power systems with uncertain generation. In: IEEE Transactions on Power Systems. 2019 ; Vol. 34, No. 2. pp. 1005-1020.

BibTeX

@article{86c4cfe54b6641c1809586578d3435d4,
title = "Distributed stochastic reserve scheduling in AC power systems with uncertain generation",
abstract = "This paper presents a framework to carry out multi-area stochastic reserve scheduling (RS) based on an AC optimal power flow (OPF) model with high penetration of wind power using distributed consensus and the alternating direction method of multipliers (ADMM). We first formulate the OPF-RS problem using semidefinite programming (SDP) in infinite dimensional spaces that are in general computationally intractable. Using a novel affine policy, we develop an approximation of the infinite dimensional SDP as a tractable finite dimensional SDP, and explicitly quantify the performance of the approximation. To this end, we adopt the recent developments in randomized optimization that allow a priori probabilistic feasibility guarantees to optimally schedule generating units while simultaneously determining the required reserve. We then use the geographical pattern of the power system to decompose the large-scale system into a multi-area power network, and provide a consensus ADMM algorithm to find a feasible solution for both local and overall multi-area network. Using our distributed stochastic framework, each area can use its own wind information to achieve local feasibility certificates, while ensuring overall feasibility of the multi-area power network under mild conditions. We provide numerical comparisons with a new benchmark formulation, the so-called converted DC (CDC) power flow model, using Monte Carlo simulations for two different IEEE case studies.",
keywords = "Generators, Optimization, Power systems, Probabilistic logic, Stochastic processes, Uncertainty, Wind power generation",
author = "Vahab Rostampour and {Ter Haar}, Ole and Tamas Keviczky",
year = "2019",
doi = "10.1109/TPWRS.2018.2878888",
language = "English",
volume = "34",
pages = "1005--1020",
journal = "IEEE Transactions on Power Systems",
issn = "0885-8950",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS

TY - JOUR

T1 - Distributed stochastic reserve scheduling in AC power systems with uncertain generation

AU - Rostampour, Vahab

AU - Ter Haar, Ole

AU - Keviczky, Tamas

PY - 2019

Y1 - 2019

N2 - This paper presents a framework to carry out multi-area stochastic reserve scheduling (RS) based on an AC optimal power flow (OPF) model with high penetration of wind power using distributed consensus and the alternating direction method of multipliers (ADMM). We first formulate the OPF-RS problem using semidefinite programming (SDP) in infinite dimensional spaces that are in general computationally intractable. Using a novel affine policy, we develop an approximation of the infinite dimensional SDP as a tractable finite dimensional SDP, and explicitly quantify the performance of the approximation. To this end, we adopt the recent developments in randomized optimization that allow a priori probabilistic feasibility guarantees to optimally schedule generating units while simultaneously determining the required reserve. We then use the geographical pattern of the power system to decompose the large-scale system into a multi-area power network, and provide a consensus ADMM algorithm to find a feasible solution for both local and overall multi-area network. Using our distributed stochastic framework, each area can use its own wind information to achieve local feasibility certificates, while ensuring overall feasibility of the multi-area power network under mild conditions. We provide numerical comparisons with a new benchmark formulation, the so-called converted DC (CDC) power flow model, using Monte Carlo simulations for two different IEEE case studies.

AB - This paper presents a framework to carry out multi-area stochastic reserve scheduling (RS) based on an AC optimal power flow (OPF) model with high penetration of wind power using distributed consensus and the alternating direction method of multipliers (ADMM). We first formulate the OPF-RS problem using semidefinite programming (SDP) in infinite dimensional spaces that are in general computationally intractable. Using a novel affine policy, we develop an approximation of the infinite dimensional SDP as a tractable finite dimensional SDP, and explicitly quantify the performance of the approximation. To this end, we adopt the recent developments in randomized optimization that allow a priori probabilistic feasibility guarantees to optimally schedule generating units while simultaneously determining the required reserve. We then use the geographical pattern of the power system to decompose the large-scale system into a multi-area power network, and provide a consensus ADMM algorithm to find a feasible solution for both local and overall multi-area network. Using our distributed stochastic framework, each area can use its own wind information to achieve local feasibility certificates, while ensuring overall feasibility of the multi-area power network under mild conditions. We provide numerical comparisons with a new benchmark formulation, the so-called converted DC (CDC) power flow model, using Monte Carlo simulations for two different IEEE case studies.

KW - Generators

KW - Optimization

KW - Power systems

KW - Probabilistic logic

KW - Stochastic processes

KW - Uncertainty

KW - Wind power generation

UR - http://www.scopus.com/inward/record.url?scp=85055868570&partnerID=8YFLogxK

U2 - 10.1109/TPWRS.2018.2878888

DO - 10.1109/TPWRS.2018.2878888

M3 - Article

VL - 34

SP - 1005

EP - 1020

JO - IEEE Transactions on Power Systems

T2 - IEEE Transactions on Power Systems

JF - IEEE Transactions on Power Systems

SN - 0885-8950

IS - 2

M1 - 8516306

ER -

ID: 47478844