Imperial College London
Alternate

DrDannyPudjianto

Faculty of Engineering, Department of Electrical and Electronic Engineering

Advanced Research Fellow
 
 
 
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Contact

 

+44 (0)7989 443 398d.pudjianto Website

 
 
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Location

 

1106Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Sun:2019:10.1109/TPWRS.2019.2892619,
author = {Sun, M and Teng, F and Zhang, X and Strbac, G and Pudjianto, D},
doi = {10.1109/TPWRS.2019.2892619},
journal = {IEEE Transactions on Power Systems},
pages = {2925--2936},
title = {Data-driven representative day selection for investment decisions: a cost-oriented approach},
url = {http://dx.doi.org/10.1109/TPWRS.2019.2892619},
volume = {34},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Power system investment planning problems become intractable due to the vast variability that characterizes system operation and the increasing complexity of the optimization model to capture the characteristics of renewable energy sources (RES). In this context, making optimal investment decisions by considering every operating period is unrealistic and inefficient. The conventional solution to address this computational issue is to select a limited number of representative operating periods by clustering the input demand-generation patterns while preserving the key statistical features of the original population. However, for an investment model that contains highly complex nonlinear relationship between input data and optimal investment decisions, selecting representative periods by relying on only input data becomes inefficient. This paper proposes a novel investment costoriented representative day selection framework for large scale multi-spacial investment problems, which performs clustering directly based on the investment decisions for each generation technology at each location associated with each individual day. Additionally, dimensionality reduction is performed to ensure that the proposed method is feasible for large-scale power systems and high-resolution input data. The superior performance of the proposed method is demonstrated through a series of case studies with different levels of modeling complexity.
AU  - Sun,M
AU  - Teng,F
AU  - Zhang,X
AU  - Strbac,G
AU  - Pudjianto,D
DO  - 10.1109/TPWRS.2019.2892619
EP  - 2936
PY  - 2019///
SN  - 0885-8950
SP  - 2925
TI  - Data-driven representative day selection for investment decisions: a cost-oriented approach
T2  - IEEE Transactions on Power Systems
UR  - http://dx.doi.org/10.1109/TPWRS.2019.2892619
UR  - http://hdl.handle.net/10044/1/65482
VL  - 34
ER  -
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