Scipediacontent: Scipediacontent moved page Draft Content 454210423 to Ziu et al 2020a

2021-02-12T13:07:47Z

Scipediacontent moved page Draft Content 454210423 to Ziu et al 2020a

Scipediacontent: Created page with " == Abstract == The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring..."

2021-02-12T13:07:44Z

Created page with " == Abstract == The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring..."

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== Abstract ==

The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district&rsquo

s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district&rsquo

s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District&rsquo

s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator&rsquo

s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_454210423-beopen279-9954-document.pdf</pdf>

== Original document ==

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

* [http://dx.doi.org/10.3390/electronics9060945 http://dx.doi.org/10.3390/electronics9060945] under the license https://creativecommons.org/licenses/by

* [https://www.mdpi.com/2079-9292/9/6/945/pdf https://www.mdpi.com/2079-9292/9/6/945/pdf] under the license http://creativecommons.org/licenses/by/3.0/

* [https://www.mdpi.com/2079-9292/9/6/945 https://www.mdpi.com/2079-9292/9/6/945],
: [https://academic.microsoft.com/#/detail/3032957024 https://academic.microsoft.com/#/detail/3032957024] under the license cc-by

* [https://www.mdpi.com/2079-9292/9/6/945 https://www.mdpi.com/2079-9292/9/6/945],
: [https://doaj.org/toc/2079-9292 https://doaj.org/toc/2079-9292]

* [https://www.mdpi.com/2079-9292/9/6/945/pdf https://www.mdpi.com/2079-9292/9/6/945/pdf],
: [http://dx.doi.org/10.3390/electronics9060945 http://dx.doi.org/10.3390/electronics9060945]

under the license https://creativecommons.org/licenses/by/4.0/

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Scipediacontent: Scipediacontent moved page Draft Content 454210423 to Ziu et al 2020a

Scipediacontent: Created page with " == Abstract == The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring..."