Imperial College London
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DrIoannisKonstantelos

Faculty of Engineering, Department of Electrical and Electronic Engineering

Research Fellow
 
 
 
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i.konstantelos

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Moreira:2017:10.1109/TPWRS.2017.2710637,
author = {Moreira, A and strbac, G and Moreno, R and Street, A and Konstantelos, I},
doi = {10.1109/TPWRS.2017.2710637},
journal = {IEEE Transactions on Power Systems},
pages = {486--501},
title = {A Five-Level MILP Model for Flexible Transmission Network Planning under Uncertainty: A Min-Max Regret Approach},
url = {http://dx.doi.org/10.1109/TPWRS.2017.2710637},
volume = {33},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The benefits of new transmission investment significantly depend on deployment patterns of renewable electricity generation that are characterized by severe uncertainty. In this context, this paper presents a novel methodology to solve the transmission expansion planning (TEP) problem under generation expansion uncertainty in a min-max regret fashion, when considering flexible network options and n    1 security criterion. To do so, we propose a five-level mixed integer linear programming (MILP) based model that comprises: (i) the optimal network investment plan (including phase shifters), (ii) the realization of generation expansion, (iii) the co-optimization of energy and reserves given transmission and generation expansions, (iv) the realization of system outages, and (v) the decision on optimal post-contingency corrective control. In order to solve the fivelevel model, we present a cutting plane algorithm that ultimately identifies the optimal min-max regret flexible transmission plan in a finite number of steps. The numerical studies carried out demonstrate: (a) the significant benefits associated with flexible network investment options to hedge transmission expansion plans against generation expansion uncertainty and system outages, (b) strategic planning-under-uncertainty uncovers the full benefit of flexible options which may remain undetected under deterministic, perfect information, methods and (c) the computational scalability of the proposed approach.
AU  - Moreira,A
AU  - strbac,G
AU  - Moreno,R
AU  - Street,A
AU  - Konstantelos,I
DO  - 10.1109/TPWRS.2017.2710637
EP  - 501
PY  - 2017///
SN  - 0885-8950
SP  - 486
TI  - A Five-Level MILP Model for Flexible Transmission Network Planning under Uncertainty: A Min-Max Regret Approach
T2  - IEEE Transactions on Power Systems
UR  - http://dx.doi.org/10.1109/TPWRS.2017.2710637
UR  - http://hdl.handle.net/10044/1/51791
VL  - 33
ER  -
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