Towards a Taxonomy of Constraints in Demand-Side-Management-Methods for a Residential Context

Behrens, Dennis; Schoormann, Thorsten; Knackstedt, Ralf

doi:10.1007/978-3-319-59336-4_20

Cited by 4 publications

(8 citation statements)

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“…DR methods can be classified by two dimensions: Place of decision-making and used communication (Kosek et al 2013). The loads addressed and constraints met must also be determined, as, for example, methods that do not consider the same constraints cannot be compared (c.f., (Behrens et al 2017)). Every method must be comparable with other methods from the same "class" and suitable for the scenario.…”

Section: Soc4-add Metadata To the Methods And Form Groupsmentioning

confidence: 99%

“…Every method must be comparable with other methods from the same "class" and suitable for the scenario. Therefore, we add additional data (metadata) based on the DR method's classification (Kosek et al 2013), the compliances addressed and the constraints considered (Behrens et al 2017). The methods themselves must be available in a repository, either locally on the HEMS or in the cloud.…”

Section: Soc4-add Metadata To the Methods And Form Groupsmentioning

confidence: 99%

“…This section presents seven REQs for a decision support framework grounded in general benchmarking approaches (that follow a plan-collect-analyze-improve cycle (Lugauer et al 2012)), a literature analysis, a review of several DR methods (e.g., found in (Balijepalli et al 2011;Al-Sumaiti et al 2014;Gerwig et al 2015; Barbato and Capone 2014)), and expert interviews (Behrens et al 2017).…”

Section: Gathering Requirementsmentioning

confidence: 99%

“…We need an adjustment between these two phases, for example, to identify the possible using intervals of appliances (e.g., (Behrens et al 2017)). This can be done automatically (e.g., by predicting user's behavior and needs) or manually by the user.…”

Section: Gathering Requirementsmentioning

confidence: 99%

“…However, not all DR methods have directly comparable characteristics and therefore additional demarcation is needed. Different characteristics, regarding their functionality and the considered appliances, infrastructure and constraints (Kosek et al 2013;Gerwig et al 2015;Behrens et al 2017) need to be addressed. Benchmarking must guarantee that the only methods that are compared are those that, for example, fulfill the same constraints (Behrens et al 2017).…”

Section: Gathering Requirementsmentioning

confidence: 99%

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Empowering the selection of demand response methods in smart homes: development of a decision support framework

et al. 2018

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Demand Response (DR) facilitates the monitoring and management of appliances in energy grids by employing methods that, for example, increase the reliability of energy grids and reduce users' cost. Within energy grids, Smart Home scenarios can be characterized by a unique combination of appliances and user preferences. To increase their impact, a scenario-specific selection of the best performing DR methods is necessary. As the user faces a multitude of heterogeneous DR methods to choose from, a complex decision problem is present. The primary goal of this study is to develop a decision support framework that can determine the bestperforming DR methods. Building on literature analyses, expert workshops and expert interviews, we identify seven requirements, derive solution concepts addressing these requirements, and develop the framework by combining the concepts using a benchmarking process as a template. To demonstrate the framework's applicability, we conduct a simulation study that uses artificial (simulated) data for seven types of households. Within this study, we employ four DR methods, assume changing appliances over time and cost minimization as primary objective. The study indicates, that by using the framework and thus by identifying and using the best DR method for each scenario, the users can achieve further cost benefits. The application of the framework allows practitioners to increase the efficiency of the DR method selection process and to further enhance DR-related benefits, such as cost minimization, load profile flattening, and peak load reduction. Researchers benefit from guidance for benchmarking and evaluating DR methods.

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Section: Soc4-add Metadata To the Methods And Form Groupsmentioning

confidence: 99%

Section: Soc4-add Metadata To the Methods And Form Groupsmentioning

confidence: 99%