== Abstract ==

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   This study determines optimum aggregation areas for a given distribution network considering spatial distribution of loads and costs of aggregation. An elitist genetic algorithm combined with a hierarchical clustering and a Thevenin network reduction is implemented to compute strategic locations and aggregate demand within each area. The aggregation reduces large distribution networks having thousands of nodes to an equivalent network with few aggregated loads, thereby significantly reducing the computational burden. Furthermore, it not only helps distribution system operators in making faster operational decisions by understanding during which time of the day will be in need of flexibility, from which specific area, and in which amount, but also enables the flexibilities stemming from small distributed resources to be traded in various power/energy markets. A combination of central and local aggregation scheme where a central aggregator enables market participation, while local aggregators materialize the accepted bids, is implemented to realize this concept. The effectiveness of the proposed method is evaluated by comparing network performances with and without aggregation. For a given network configuration, steady-state performance of aggregated network is significantly accurate (    ≈  ±    1.5% error) compared to very high errors associated with forecast of individual consumer demand.

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== Original document ==

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The different versions of the original document can be found in:

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* [https://www.osti.gov/biblio/1375244 https://www.osti.gov/biblio/1375244]

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* [https://api.elsevier.com/content/article/PII:S0142061516325601?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0142061516325601?httpAccept=text/xml],

: [https://api.elsevier.com/content/article/PII:S0142061516325601?httpAccept=text/plain https://api.elsevier.com/content/article/PII:S0142061516325601?httpAccept=text/plain],

: [http://dx.doi.org/10.1016/j.ijepes.2017.05.005 http://dx.doi.org/10.1016/j.ijepes.2017.05.005] under the license https://www.elsevier.com/tdm/userlicense/1.0/

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* [https://vbn.aau.dk/da/publications/043faad3-660f-4bcb-9b8d-7577d7457b59 https://vbn.aau.dk/da/publications/043faad3-660f-4bcb-9b8d-7577d7457b59],

: [https://doi.org/10.1016/j.ijepes.2017.05.005 https://doi.org/10.1016/j.ijepes.2017.05.005]

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* [https://www.sciencedirect.com/science/article/pii/S0142061516325601 https://www.sciencedirect.com/science/article/pii/S0142061516325601],

: [https://core.ac.uk/display/84876707 https://core.ac.uk/display/84876707],

: [http://www.sciencedirect.com/science/article/pii/S0142061516325601 http://www.sciencedirect.com/science/article/pii/S0142061516325601],

: [https://academic.microsoft.com/#/detail/2616013806 https://academic.microsoft.com/#/detail/2616013806]

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