Chance-constrained optimal power flow based on a linearized network model

Du, Xiao; Lin, Xingyu; Peng, Zhiyun; Peng, Sancheng; Tang, Junjie; Li, Wenyuan

doi:10.1016/j.ijepes.2021.106890

Cited by 13 publications

(2 citation statements)

References 38 publications

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“…In contrast, analytical approach to CC-OPF states the chance constraints using distributional information of the uncertainty and is the focus of this work. A substantial number of papers is devoted to designing convex/linear approximations to the chance-constraints problem [14,15,16,17,18]; however, most of these formulations are conservative and hence are unable to address a large change in power generation. Another line of research is focused on the nonlinear Polynomial chaos expansion for modeling uncertainties in power demand and generation [19,20].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Stochastic AC-OPF using Gaussian Processes

Mile¹,

Aleksandr²,

Vorobev³

et al. 2022

Preprint

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In recent years, electricity generation has been responsible for more than a quarter of the greenhouse gas emissions in the US. Integrating a significant amount of renewables into a power grid is probably the most accessible way to reduce carbon emissions from power grids and slow down climate change. Unfortunately, the most accessible renewable power sources, such as wind and solar, are highly fluctuating and thus bring a lot of uncertainty to power grid operations and challenge existing optimization and control policies. The chance-constrained alternating current (AC) optimal power flow (OPF) framework finds the minimum cost generation dispatch maintaining the power grid operations within security limits with a prescribed probability. Unfortunately, the AC-OPF problem's chance-constrained extension is non-convex, computationally challenging, and requires knowledge of system parameters and additional assumptions on the behavior of renewable distribution. Known linear and convex approximations to the above problems, though tractable, are too conservative for operational practice and do not consider uncertainty in system parameters. This paper presents an alternative data-driven approach based on Gaussian process (GP) regression to close this gap. The GP approach learns a simple yet non-convex data-driven approximation to the AC power flow equations that can incorporate uncertainty inputs. The latter is then used to determine the solution of CC-OPF efficiently, by accounting for both input and parameter uncertainty. The practical efficiency of the proposed approach using different approximations for GP-uncertainty propagation is illustrated over numerous IEEE test cases.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Stochastic AC-OPF using Gaussian Processes

Mile¹,

Aleksandr²,

Vorobev³

et al. 2022

Preprint

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“…25 The model presented by 26 introduces a new linear model for AC-OPF including active and reactive powers. The authors in 27 present a novel chance-constrained optimal power flow based on the linearized network considering voltage magnitude, reactive power and apparent power. The Monte-Carlo simulation, three-point estimation and Cornish-Fisher series expansion are applied to formulate the linearization.…”

mentioning

confidence: 99%

Hybrid renewable/nonrenewable/storage resources in electrical grid considering active‐reactive losses and depth of discharge

Hemmati

Mahdavi

2021

Int J Energy Res

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This paper presents a linear model for optimal operation of distributed generations (DGs) including renewable and nonrenewable DGs in the electrical grids.The accurate model of active-reactive losses is formed by two quadratic terms that are linearized by piecewise linear approximation of the quadratic curves.The DGs' technical/economic formulations are also presented based on the linear model. The proposed linear models are combined to implement linear optimal power flow (OPF) in the electrical grid integrated with hybrid diesel/wind/ solar/battery. The model is associated with uncertainties and expressed as stochastic mixed integer linear programming. Both the active and reactive powers of the grid and resources are incorporated. The model optimizes the following design variables: sitting and sizing of energy storage systems and DGs, depth of discharge, energy of battery, operation pattern of battery, operation pattern of non-renewables units, and investment/operational costs. The proposed model is tested on 69-bus distribution grid. The simulation results show that the depth of discharge is optimized on 30% to 95%. Active and reactive losses achieved by the proposed model are 72% and 47% more accurate compared to the other existing models. The voltage profile is improved by about 8%. The network losses are reduced by 24%. The peak shaving is performed at hours 18 to 24.

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Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study

Quan

Guo

et al. 2022

Applied Energy

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Chance-constrained optimal power flow based on a linearized network model

Cited by 13 publications

References 38 publications

Data-Driven Stochastic AC-OPF using Gaussian Processes

Data-Driven Stochastic AC-OPF using Gaussian Processes

Hybrid renewable/nonrenewable/storage resources in electrical grid considering active‐reactive losses and depth of discharge

Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study

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