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Energy
Article . 2019 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization

Authors: Matteo Zatti; Marco Gabba; Marco Freschini; Michele Rossi; Agostino Gambarotta; Mirko Morini; Emanuele Martelli;

k-MILP: A novel clustering approach to select typical and extreme days for multi-energy systems design optimization

Abstract

Abstract When optimizing the design of multi-energy systems, the operation strategy and the part-load behavior of the units must be considered in the optimization model, which therefore must be formulated as a two-stage problem. In order to guarantee computational tractability, the operation problem is solved for a limited set of typical and extreme periods. The selection of these periods is an important aspect of the design methodology, as the selection and sizing of the units is carried out on the basis of their optimal operation in the selected periods. This work proposes a novel Mixed Integer Linear Program clustering model, named k-MILP, devised to find at the same time the most representative days of the year and the extreme days. k-MILP allows controlling the features of the selected typical and extreme days and setting a maximum deviation tolerance on the integral of the load duration curves. The novel approach is tested on the design of two different multi-energy systems (a multiple-site university Campus and a single building) and compared with the two well-known clustering techniques k-means and k-medoids. Results show that k-MILP leads to a better representation of both typical and extreme operating conditions guiding towards more efficient and reliable designs.

Country
Italy
Keywords

670, 330, Design optimization, Typical days, 620, Multi-energy system, Extreme day, Design optimization; District energy systems; Extreme days; Multi-energy systems; Typical days, District energy system

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    citations
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    89
    popularity
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    Top 1%
    influence
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    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
89
Top 1%
Top 10%
Top 1%
Green