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Abstract
To address the uncertain output of distributed generators (DGs) for reactive power optimization in active distribution networks, the stochastic programming model is widely used. The model is employed to find an optimal control strategy with minimum expected network loss while satisfying all the physical constraints. Therein, the probability distribution of uncertainties in the stochastic model is always predefined by the historical data. However, the empirical distribution can be biased due to a limited amount of historical data and thus result in a suboptimal control decision. Therefore, in this paper, a datadriven modeling approach is introduced to assume that the probability distribution from the historical data is uncertain within a confidence set. Furthermore, a datadriven stochastic programming model is formulated as a twostage problem, where the firststage variables find the optimal control for discrete reactive power compensation equipment under the worst probability distribution of the second stage recourse. The secondstage variables are adjusted to uncertain probability distribution. In particular, this twostage problem has a special structure so that the secondstage problem can be directly decomposed into several smallscale subproblems, which can be handled in parallel without the information of dual problems. Numerical study on two distribution systems has been performed. Comparisons with the twostage stochastic and robust approaches demonstrate the effectiveness of the proposal.
Original language  English 

Article number  7869401 
Journal  IEEE Transactions on Smart Grid 
Volume  9 
Issue number  5 
Pages (fromto)  49945004 
Number of pages  11 
ISSN  19493053 
DOIs  
Publication status  Published  Sep 2018 
Keywords
 Stochastic optimization
 Reactive power optimization
 Columnandconstraint generation algorithm
 Active distribution network
 Distributed generation
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 1 Finished

PV2GRID: A NextGeneration Grid Side Converter with Advanced Control and Power Quality Capabilities
Blaabjerg, F., Yang, Y. & Sangwongwanich, A.
01/01/2015 → 30/09/2017
Project: Research