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1096. Robust Optimization for Long-term Uncertainty in DER Sizing and Placement in Distribution Networks

Invited abstract in session WD-19: OR in Energy III, stream OR in Energy.

Wednesday, 14:30-16:00
Room: 44 (building: 116)

Authors (first author is the speaker)

1. Fernando García
USACH
2. Sebastián Dávila
Industrial Engineering, Universidad de Santiago de Chile
3. Franco Quezada
Industrial Engineering Department, University of Santiago of Chile
4. Cristian DURAN MATELUNA
UMA, ENSTA, Institut Polytechnique de Paris

Abstract

This study comprehensively compares stochastic programming and robust optimization approaches for handling long-term uncertainty in the optimal sizing and placement of distributed energy resources (DERs) in LV distribution networks. The study covers various optimization models, ranging from a deterministic model to a two-stage stochastic programming approach, single-stage robust optimization, adaptive robust optimization (ARO), and a hybrid variant of stochastic robust optimization, considering the electricity load and power generated by PV systems as random variables. A decomposition method approach has been used to address the three levels raised by robust optimization using primal and dual cuts. The models have been tested using a modified version of the IEEE 33 bus system, comparing mainly the ARO with the hybrid models because the hybrid model allows the handling of PV generation through a stochastic approach and the electricity demand by a robust method, which could be more suitable to represent their uncertainty in long-term scenarios. Thus, the results show that the hybrid approach i) reduces the convergences times, ii) reduces around 80\% on average the number of iterations regarding the ARO for different Budget of Uncertainty (BoU) levels, and iii) delivers a notably less conservative solution than the ARO.

Keywords

Status: accepted

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