LINEAR breakthrough project: Large-scale implementation of smart grid technologies in distribution grids

Dupont, Benjamin; Vingerhoets, Pieter; Tant, Peter; Vanthournout, Koen; Cardinaels, Wim; Rybel, Tom De; Peeters, Eefje; Belmans, Ronnie

doi:10.1109/isgteurope.2012.6465708

Cited by 57 publications

(53 citation statements)

References 31 publications

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“…We have applied our methodologies on the data from year-long measurements in the LINEAR pilot project [26] for 3 types of white goods: dishwasher, washing machine and tumble dryer. The flexibility data associated with each appliance is obtained from smart meters.…”

Section: Resultsmentioning

confidence: 99%

“…Our analysis is based on real-world smart appliance usage (washing machines, dishwashers and tumble dryers) as collected in the LINEAR pilot project [26] and offers these contributions:…”

Section: Motivation and Contributionsmentioning

confidence: 99%

“…The probabilistic analysis of time of configuration as well as the duration of provided flexibility is performed for various appliances. Their analysis is based on the LINEAR pilot project [26] in which 15 minute based measurements were collected from 418 appliances in 186 Belgian households. The analysis in [25] was independent of the underlying DR scheme that harnessed the flexibility potential, hence inferring a better estimation.…”

Section: State Of the Art In Residential Flexibility Assessmentmentioning

confidence: 99%

“…Note that we do not parameterize flexibility by the amount of shiftable power, since this attribute has already been analyzed extensively in the literature (e.g., [28]). The flexibility data for each customer and each appliance is obtained from year-long measurements in the LINEAR pilot project [26] (see further, Section 3). Figure 1 depicts flexibility profiles of four selected customers for their dishwasher.…”

Section: Quantitive Specification Of Flexibilitymentioning

confidence: 99%

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Modeling and analysis of residential flexibility: Timing of white good usage

et al. 2016

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Challenges that smart grids aim to address include the increasing fraction of supply by renewable energy sources, as well as plain rise of demand, e.g., by increased electrification of transportation. Part of the solution to these challenges lies in exploiting the opportunity to steer residential electricity consumption (e.g., for flattening the peak load or balancing the supply and demand in presence of the renewable energy production). To optimally exploit this opportunity, it is crucial to have insights on how flexible the residential demand is. Load flexibility is characterized by the amount of power, time of availability and duration of deferrable consumption. Residential flexibility however, is challenging to exploit due to the variation in types of customer loads and differences in appliance usage habits from one household to the other. Existing analyses of individual customer flexibility behavior in terms of timing are often based on inferences from surveys or customer load patterns (e.g., as observed through smart meter data): there is a high level of uncertainty about customer habits in offering the flexibility. Even though some of these studies rely on real world data, only few of them have quantitative data on actual flexible appliance usage, and none of them characterizes individual user behavior. In this paper, we address this gap and contribute with: (1) a new quantitative specification of flexibility, (2) two systematic methodologies for modeling individual customer behavior, (3) evaluation of the proposed models in terms of how accurately the data they generate corresponds with real world customer behavior, and (4) a basic analysis of factors influencing the flexibility behavior based on statistical tests. Experimental results for (2)-(4) are based on a unique dataset from a real-life field trial.

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Section: Resultsmentioning

confidence: 99%

“…Our analysis is based on real-world smart appliance usage (washing machines, dishwashers and tumble dryers) as collected in the LINEAR pilot project [26] and offers these contributions:…”

Section: Motivation and Contributionsmentioning

confidence: 99%

Section: State Of the Art In Residential Flexibility Assessmentmentioning

confidence: 99%

Section: Quantitive Specification Of Flexibilitymentioning

confidence: 99%

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Modeling and analysis of residential flexibility: Timing of white good usage

et al. 2016

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“…This project focuses on questions such as which information should be available for each actor or which reference consumption to choose. Other projects in Europe rely on the deployment of demand side management such as GREDOR [18], iPower [19], EvolvDSO [20], LINEAR [21], ADINE [22], Local Load Management [23], Nice Grid [24] and Optimate [25]. The testbed introduced in this paper is a convenient tool to test the ideas proposed in the previous projects.…”

Section: Introductionmentioning

confidence: 99%

DSIMA: A testbed for the quantitative analysis of interaction models within distribution networks

Mathieu

Louveaux

Ernst

et al. 2016

Sustainable Energy, Grids and Networks

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a b s t r a c tThis article proposes an open-source testbed to simulate interaction models governing the exchange of flexibility services located within a distribution network. The testbed is an agent-based system in which the distribution system operator, the transmission system operator, producers and retailers make their decisions based on mixed-integer linear programs. This testbed helps to highlight the characteristics of an interaction model, the benefits for the agents and eases the detection of unwanted or abusive behaviors which decreases the welfare. The testbed is implemented in Python and the optimization problems are encoded in the modeling language ZIMPL. A web interface is coupled with an instance generator using macroscopic parameters of the system such as the total power production. This testbed is, therefore, well suited to test the implemented interaction models on various scenarios and to extend the implementation to other models. Five interaction models developed with industrial partners are simulated over a year on a 75-bus test system. Simulations show that interaction models relying on active network management, as they have been developed, lead to substantial welfare even though they suffer from a lack of coordination between the DSO and the TSO. A conservative interaction model restricting grid users to an access range that is computed ahead of time to prevent any congestion, avoids shedding distributed generation but considerably restrains the amount of distributed production.

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