Towards Price Based Demand Side Management Using Machine Learning

Resource type
Mischa Ahrens, Jan Müller, Hartmut Schmeck
Book title
Abstracts from the 8th DACH+ Conference on Energy Informatics
In demand side management, variable electricity pricing is often used to shape the load of electricity consumers and producers. The task of finding the right price profile to realize a target load profie 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 simplifications and often consider only specific device types or load shaping methods. Simple pricing schemes often fail to induce specific target load profiles due to effects like load synchronization. This poster abstract extends a machine learning based electricity pricing scheme proposed in previous work. Its objective is to generate price profiles basedon knowledge about the behavior of energy resources in response to different price profiles and in various situations. Principally, the presented pricing scheme can be used for any device configuration under the assumption that it offers exploitable flexibility and is governed by an automated energy management system aimed at minimizing energy costs.
Research focus
Energy Management
C/sells, OCTIKT
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Published by
Mischa Ahrens