Response of smart residential buildings with energy management systems to price deviations

Kochanneck, Sebastian; Schmeck, Hartmut; Mauser, Ingo; Becker, Birger

doi:10.1109/isgt-asia.2015.7387186

Cited by 7 publications

(3 citation statements)

References 26 publications

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“…In the following, we refer to all kinds of third parties performing DSM, e.g. electric utilities (Gellings 1985) or dedicated regional EMSs (Kochanneck et al 2015), with the term demand side manager (DSMgr). For simplicity, we use the term building when referring to a provider of flexibility.…”

Section: Realization Patterns For Modeling Flexibilitymentioning

confidence: 99%

Modeling flexibility using artificial neural networks

et al. 2018

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The flexibility of distributed energy resources (DERs) can be modeled in various ways. Each model that can be used for creating feasible load profiles of a DER represents a potential model for the flexibility of that particular DER. Based on previous work, this paper presents generalized patterns for exploiting such models. Subsequently, the idea of using artificial neural networks in such patterns is evaluated. We studied different types and topologies of ANNs for the presented realization patterns and multiple device configurations, achieving a remarkably precise representation of the given devices in most of the cases. Overall, there was no single best ANN topology. Instead, a suitable individual topology had to be found for every pattern and device configuration. In addition to the best performing ANNs for each pattern and configuration that is presented in this paper all data from our experiments is published online. The paper is concluded with an evaluation of a classification based pattern using data of a real combined heat and power plant in a smart building.

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Section: Realization Patterns For Modeling Flexibilitymentioning

confidence: 99%

Modeling flexibility using artificial neural networks

et al. 2018

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“…The simulated buildings have been connected to a static, 1-phase LV grid model; some of them equipped with DG. This allows for static analyses of the effects of intelligent buildings within grids [4]. b) Simulation of LV Grids: While the so far described simulations focus on static scenarios of LV systems, dynamic simulations are needed for evaluation of fast acting entities: Static assessments allow to use more simple electric models, e. g. 1-phase instead of 3-phase load flow calculations, facilitating parameter studies on effects with high time constraints like virtual power plants.…”

Section: General Approachesmentioning

confidence: 99%

Establishing a hardware-in-the-loop research environment with a hybrid energy storage system

Kochanneck

Mauser

Bohnet³

et al. 2016

2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia)

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The KIT Energy Smart Home Lab is a smart residential building comprising building automation, metering systems, sensors, intelligent appliances, heating, ventilation, and airconditioning equipment, distributed generation, and energy storage systems. Currently, the lab is extended by a hybrid energy storage system and a linear voltage amplifier for real-time simulations, to facilitate fully functional power hardware-in-the-loop simulations and evaluations. This paper presents the setup of the lab, the hardwarein-the loop research environment, and first measurements when using a simulated artificial mains network.

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“…Building management systems and technologies enable to optimize energy consumption of appliances and devices [42]. Lighting has an energy-saving potential up to 40% by adopting control strategies (daylight harvesting, occupancy sensing, scheduling and load shedding) [3].…”

Section: Energy Flexibility Resources and Technologiesmentioning

confidence: 99%

Consumer Central Energy Flexibility in Office Buildings

Billanes¹,

Ma²,

Jôrgensen³

2017

JEPE

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Energy flexibility in buildings will play an important role in the smart energy system. Office buildings have more potentials to provide energy flexibility to the grid compared to other types of buildings, due to the existing building management, control systems and large energy consumption. Consumers in office buildings (building owners/managers and occupants) take a main role for adopting and engaging in building energy flexibility. This paper provides a systematic review of consumer central energy flexibility in office buildings with the discussion of social, technical and business aspects. This paper clarifies the correlations of consumers" concerns, external influential factors, energy flexibility resources and technology with eight hypotheses. This paper suggests that technical solutions with the integration of distributed energy resources, building management and control system can boost energy flexibility in the office buildings.

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Response of smart residential buildings with energy management systems to price deviations

Cited by 7 publications

References 26 publications

Modeling flexibility using artificial neural networks

Modeling flexibility using artificial neural networks

Establishing a hardware-in-the-loop research environment with a hybrid energy storage system

Consumer Central Energy Flexibility in Office Buildings

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