Critical review of recent advances and further developments needed in AC optimal power flow

Capitanescu, Florin

doi:10.1016/j.epsr.2016.02.008

Cited by 231 publications

(145 citation statements)

References 58 publications

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“…According to the nomenclature, the decision for investment in CVC, DSR, and SOP is denoted by the binary variables , , , and , respectively. In addition, a non-linear ACOPF formulation has been adopted to capture both thermal and voltage constraints [18].…”

Section: Mathematical Formulationmentioning

confidence: 99%

Strategic valuation of smart grid technology options in distribution networks

Konstantelos

Giannelos

Štrbac

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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-The increasing penetration of renewable distributed generation (DG) sources in distribution networks can lead to violations of network constraints. Thus, significant network reinforcements may be required to ensure that DG output is not constrained. However, the uncertainty around the magnitude, location and timing of future DG capacity renders planners unable to take fully-informed decisions and integrate DG at a minimum cost. In this paper we propose a novel stochastic planning model that considers investment in conventional assets as well as smart grid assets such as demandside response, coordinated voltage control and soft open points (SOPs). The model also considers the possibility of active power generation curtailment of the DG units. A node-variable formulation has been adopted to relieve the substantial computational burden of the resulting mixed integer non-linear programming (MINLP) problem. A case study shows that smart technologies can possess significant strategic value due to their inherent flexibility in dealing with different system evolution trajectories. This latent benefit remains undetected under traditional deterministic planning approaches which may hinder the transition to the smart grid.

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Section: Mathematical Formulationmentioning

confidence: 99%

Strategic valuation of smart grid technology options in distribution networks

Konstantelos

Giannelos

Štrbac

2017

2017 IEEE Power &Amp; Energy Society General Meeting

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“…(For a recent survey on SCOPF see [10].) a) Day-ahead operational planning proxy: Available control actions are DA generator startup, shutdown and rescheduling, as well as provisional RT corrective rescheduling to be applied following each N − 1 contingency, the latter computed in anticipation of the forecast conditions for each hour in the next day.…”

Section: B Implementation Detailsmentioning

confidence: 99%

A computational model of mid-term outage scheduling for long-term system studies

Marín

Karangelos

Wehenkel

2017

2017 IEEE Manchester PowerTech

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Abstract-This paper presents a computational model of the mid-term outage scheduling process of electric power transmission assets, to be used in long-term studies such as maintenance policy assessments and system development studies, while accounting for the impact of outage schedules on short-term system operation. We propose a greedy algorithm that schedules the outages one by one according to their impact on system operation estimated via Monte-Carlo simulations. The model is implemented in JULIA and applied to the IEEE RTS-96.

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“…In practice, this is expressed by the N-1 criterion, and reliability management in real-time can be modelled suitably by a so-called 'Security Constrained Optimal Power Flow Problem' (SCOPF) [1]. In the literature, probabilistic versions of this approach have as well been proposed, leading to a 'Probabilistic-SCOPF' formulation, where the covered list of contingencies is dynamically adjusted by taking into account the weather dependent probabilities of failures and the costs of potential service interruptions [2].…”

Section: Introductionmentioning

confidence: 99%

Machine learning of real-time power systems reliability management response

Duchesne

Karangelos

Wehenkel

2017

2017 IEEE Manchester PowerTech

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Abstract-In this paper we study how supervised machine learning could be applied to build simplified models of realtime (RT) reliability management response to the realization of uncertainties. The final objective is to import these models into look-ahead operation planning under uncertainties. Our response models predict in particular the real-time reliability management costs and the resulting reliability level of the system. We tested our methodology on the IEEE-RTS96 benchmark.Among the supervised learning algorithms tested, extremely randomized trees, kernel ridge regression and neural networks appear to be the best methods for this application. Furthermore, by using feature "importances" computed by tree-based ensemble methods, we were able to extract the most relevant variables to predict the response of real-time reliability management, and thus obtain a better understanding of the system properties.

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Critical review of recent advances and further developments needed in AC optimal power flow

Cited by 231 publications

References 58 publications

Strategic valuation of smart grid technology options in distribution networks

Strategic valuation of smart grid technology options in distribution networks

A computational model of mid-term outage scheduling for long-term system studies

Machine learning of real-time power systems reliability management response

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