Energy Informatics

Goebel, Christoph; Jacobsen, Hans‐Arno; Razo, Victor del; Doblander, Christoph; Rivera, José; Ilg, Jens P.; Flath, Christoph M.; Schmeck, Hartmut; Weinhardt, Christof; Pathmaperuma, Daniel; Appelrath, Hans-Jürgen; Sonnenschein, Michael; Lehnhoff, Sebastian; Krämer, Oliver; Staake, Thorsten; Fleisch, Elgar; Neumann, Dirk; Strüker, Jens; Erek, Koray; Zarnekow, Rüdiger; Ziekow, Holger; Lässig, Jörg

doi:10.1007/s12599-013-0304-2

Cited by 58 publications

(23 citation statements)

References 18 publications

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“…It is in particular also considered how cross-realty concepts can be applied in the training of students and professionals in the operation of complex building systems. A good overview on energy informatics as a field is given by [4].…”

Section: Development In the Field Of Computational Sustainabilitymentioning

confidence: 99%

Sustainable Development and Computing—An Introduction

Lässig

2016

Computational Sustainability

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Computational Sustainability is the computer scientific branch of the interdisciplinary field of sustainability research, an applied science about the research in sustainable solutions and their implementation. This introductory chapter describes the origins and the development of common and current sustainability goals and the development of sustainability science as separate field of research. It points out the relevance of Computer Science in many fields and gives an overview of the state of the art research in Computational Sustainability as well as about the content of this edited volume and about the case studies that are addressed in subsequent chapters.

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Section: Development In the Field Of Computational Sustainabilitymentioning

confidence: 99%

Sustainable Development and Computing—An Introduction

Lässig

2016

Computational Sustainability

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“…The integration of the increasing decentralised renewable energy sources into the energy system is therefore one of the two most important research fields in energy informatics (Goebel et al 2014). High fluctuations in supply, as well as strong intra-day patterns e. g. in the case of solar energy, are challenges for a smooth integration (Denholm et al 2010).…”

Section: Introductionmentioning

confidence: 99%

A comprehensive modelling framework for demand side flexibility in smart grids

et al. 2017

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The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of flexibility to be successful. Although extensive research exists that describes, models and optimises various processes with flexible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised.In this paper, we develop a comprehensive modelling framework to mathematically describe demand side flexibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side flexibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program (MIP) solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional flexibility characteristics.

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“…Open questions are posed relating to, in particular, information and communication technology (ICT), such as the "trade-off between ICT deployment and the achieved benefits in economic terms" (Goebel et al, 2014). To close this research gap, this work contributes to the understanding of ICT costs within demand response systems as follows:…”

Section: Introductionmentioning

confidence: 99%

Value and granularity of ICT and smart meter data in demand response systems

2016

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The large-scale integration of intermittent resources of power generation leads to unprecedented fluctuations on the supply side. An electricity retailer can tackle these challenges by pursuing strategies of flexible load shifting -socalled demand response mechanisms. This work addresses the associated tradeoff between ICT deployment and economic benefits. The ICT design of a demand response system serves as the basis of a cost-value model, which incorporates all relevant cost components and compares them to the expected savings of an electricity retailer. Our analysis is based on a typical German electricity retailer to determine the optimal read-out frequency of smart meters. For our set of parameters, a positive information value of smart meter read-outs is achieved within an interval of 21 to 57 minutes regarding variable costs.Electricity retailers can achieve a profitable setting by restricting smart meter roll-out to large customers.

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Energy Informatics

Cited by 58 publications

References 18 publications

Sustainable Development and Computing—An Introduction

Sustainable Development and Computing—An Introduction

A comprehensive modelling framework for demand side flexibility in smart grids

Value and granularity of ICT and smart meter data in demand response systems

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