Imperial College London
Alternate

DrIoannisKonstantelos

Faculty of Engineering, Department of Electrical and Electronic Engineering

Research Fellow
 
 
 
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Contact

 

i.konstantelos

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Cremer:2018:10.1109/PMAPS.2018.8440373,
author = {Cremer, JL and Konstantelos, I and Strbac, G and Tindemans, SH},
doi = {10.1109/PMAPS.2018.8440373},
publisher = {IEEE},
title = {Sample-derived disjunctive rules for secure power system operation},
url = {http://dx.doi.org/10.1109/PMAPS.2018.8440373},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the task of prediction and propose a comprehensive approach to use predictors, such as Decision Trees (DT), within a standard optimization framework for pre- and post-fault control purposes. In particular, we present a generalizable method for embedding rules derived from DTs in an operation decision-making model. We begin by pointing out the specific challenges entailed when moving from a prediction to a control framework. We proceed with introducing the solution strategy based on generalized disjunctive programming (GDP) as well as a two-step search method for identifying optimal hyper-parameters for balancing cost and control accuracy. We showcase how the proposed approach constructs security proxies that cover multiple contingencies while facing high-dimensional uncertainty with respect to operating conditions with the use of a case study on the IEEE 39-bus system. The method is shown to achieve efficient system control at a marginal increase in system price compared to an oracle model.
AU  - Cremer,JL
AU  - Konstantelos,I
AU  - Strbac,G
AU  - Tindemans,SH
DO  - 10.1109/PMAPS.2018.8440373
PB  - IEEE
PY  - 2018///
TI  - Sample-derived disjunctive rules for secure power system operation
UR  - http://dx.doi.org/10.1109/PMAPS.2018.8440373
UR  - http://hdl.handle.net/10044/1/62576
ER  -
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