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|Abstract=In demand side management, variable electricity pricing is often used to shape the load of electricity consumers and producers. The task of �nding the right price pro�le to realize a target load pro�le is a bilevel optimization problem that varies in complexity depending on the considered distributed energy resources. Solutions to this problem proposed in the literature usually rely on extensive simpli�cations and often consider only speci�c device types or load shaping methods. Simple pricing schemes often fail to induce speci�c target load pro�les due to e�ects like load synchronization. This poster abstract extends a machine learning based electricity pricing scheme proposed in previous work. Its objective is to generate price pro�les based on knowledge about the behavior of energy resources in response to di�erent price pro�les and in various situations. Principally, the presented pricing scheme can be used for any device con�guration under the assumption that it o�ers exploitable  exibility and is governed by an automated energy management system aimed at minimizing energy costs.
 
|Abstract=In demand side management, variable electricity pricing is often used to shape the load of electricity consumers and producers. The task of �nding the right price pro�le to realize a target load pro�le is a bilevel optimization problem that varies in complexity depending on the considered distributed energy resources. Solutions to this problem proposed in the literature usually rely on extensive simpli�cations and often consider only speci�c device types or load shaping methods. Simple pricing schemes often fail to induce speci�c target load pro�les due to e�ects like load synchronization. This poster abstract extends a machine learning based electricity pricing scheme proposed in previous work. Its objective is to generate price pro�les based on knowledge about the behavior of energy resources in response to di�erent price pro�les and in various situations. Principally, the presented pricing scheme can be used for any device con�guration under the assumption that it o�ers exploitable  exibility and is governed by an automated energy management system aimed at minimizing energy costs.
|Forschungsgruppe=Angewandte Technisch-Kognitive Systeme
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|Forschungsgruppe=Effiziente Algorithmen/en
 
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{{Forschungsgebiet Auswahl
 
{{Forschungsgebiet Auswahl
 
|Forschungsgebiet=Energieinformatik
 
|Forschungsgebiet=Energieinformatik
 
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Aktuelle Version vom 24. August 2021, 19:53 Uhr


Towards Price Based Demand Side Management Using Machine Learning


Towards Price Based Demand Side Management Using Machine Learning



Published: 2019 September

Buchtitel: Abstracts from the 8th DACH+ Conference on Energy Informatics
Ausgabe: 2
Nummer: 31
Reihe: Energy Informatics
Seiten: 5-8
Verlag: SpringerOpen

Referierte Veröffentlichung

BibTeX

Kurzfassung
In demand side management, variable electricity pricing is often used to shape the load of electricity consumers and producers. The task of �nding the right price pro�le to realize a target load pro�le is a bilevel optimization problem that varies in complexity depending on the considered distributed energy resources. Solutions to this problem proposed in the literature usually rely on extensive simpli�cations and often consider only speci�c device types or load shaping methods. Simple pricing schemes often fail to induce speci�c target load pro�les due to e�ects like load synchronization. This poster abstract extends a machine learning based electricity pricing scheme proposed in previous work. Its objective is to generate price pro�les based on knowledge about the behavior of energy resources in response to di�erent price pro�les and in various situations. Principally, the presented pricing scheme can be used for any device con�guration under the assumption that it o�ers exploitable exibility and is governed by an automated energy management system aimed at minimizing energy costs.



Forschungsgruppe

Effiziente Algorithmen/en


Forschungsgebiet

Energieinformatik