Physically based congestion management by powerflow superposition methods at optimal cost

Leveringhaus, Thomas; Breithaupt, Timo; Hofmann, Lutz

doi:10.1109/powercon.2012.6401373

Cited by 1 publication

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“…According to [6], the changes of the current magnitude can be estimated by adding a rotating operator of the negative current angles…”

Section: Derivation Of the Power Flow Decompositionmentioning

confidence: 99%

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Comparison of methods for state prediction: Power Flow Decomposition (PFD), AC Power Transfer Distribution factors (AC-PTDFs), and Power Transfer Distribution factors (PTDFs)

Leveringhaus¹,

Hofmann²

2014

2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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The precise prediction of changes in load flows, currents and voltage magnitudes due to changes in power is important for forecasting and managing grid congestions, voltage deviations and minimizing grid losses for example. This paper describes three different methods and further variants of those for state prediction and compares their approximations, neglects and quality of prediction. Since PTDFs and PFD modify the characteristics of the non-linear load flow equations by approximations and neglects, their qualities of prediction are less than those of the AC-PTDFs. To consider the way changings in grid losses are counteracted by the grid a new variant to consider secondary control reserve in the prediction is established. The AC-PTDFs deliver the highest quality of current and loss prediction, the most comprehensive mathematical approximation of the non-linear load flow equations, and the most potential for further development like optimized management of multiple congestions and Optimal Power Flow.

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“…According to [6], the changes of the current magnitude can be estimated by adding a rotating operator of the negative current angles…”

Section: Derivation Of the Power Flow Decompositionmentioning

confidence: 99%

“…The Power Flow Decomposition has been developed in the last years [6], [8], [9], [10], [16] and is working without neglects and sets itselfs apart with a fundamental different linearization approach compared to the other methods. Its approximations are described in chapter II.…”

Section: Introductionmentioning

confidence: 99%