From residential electric load profiles to flexibility profiles – A stochastic bottom-up approach

Fischer, David; Surmann, Arne; Biener, Wolfgang; Selinger-Lutz, Oliver

doi:10.1016/j.enbuild.2020.110133

Cited by 29 publications

(16 citation statements)

References 15 publications

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“…2), and appliances electricity demand aggregated for the second. Fischer et al [23] implemented their own model synPRO [24]. These "Bottom-Up" methods will be described in more detail in section 3.…”

Section: Stochastic Methodsmentioning

confidence: 99%

“…The major lock to the realistic determination of specific electricity demand lies in the simulation of the real behavior of the users which results in variable and random load curves. Several strategies can be carried out to build these electricity load curves: experimental strategies [3], statistical (and / or normative) strategies [4][5][6][7][8][9], stochastic strategies [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] or black box models [10] (artificial neural networks [27,28] or multi agent systems). However, there is a lack of data on yearly high resolution electricity load curves in the literature [10] and more precisely in the European context.…”

Section: Introductionmentioning

confidence: 99%

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High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications

Bouvenot

Latour

Flament

et al. 2020

Energy and Buildings

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This work relates to a new stochastic and probabilistic tool allowing to model dwellings specific electricity load profiles (in the European context) for 39 different electrical appliances with a high temporal resolution (2-min time step). This tool was developed by using probabilistic (Monte Carlo) and statistic (Time Of Use curves) methods as well as transition probabilities (Markov chains), seasonal, daily and hourly effects coefficients to obtain the more realistic as possible electricity load curves. The tool distinguishes the flexible part which can be shifted during power self-production operation (micro combined heat and power µCHP or photovoltaic panels PV) periods and the no flexible part (lighting for example). A sensitivity analysis shows the impact of the time step and the repeatability level of the tool on a µCHP system coupled with a building to prove the need to use high resolution electricity demand load curves at low scale (a building) in comparison with hourly data which can overestimate µCHP electrical generation self-consumption rate up to 20 %. Time step of maximum 2 min is required for the electricity demand profiles to obtain reliable results on energetic indicators. Finally, a validation procedure based on literature review profiles, high resolution in situ measurements and expansion national load curves is carried out.

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Section: Stochastic Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications

Bouvenot

Latour

Flament

et al. 2020

Energy and Buildings

View full text Add to dashboard Cite

show abstract

“…With socio-economic, normative, institutional, and technical factors influencing electricity demand and behaviour, the question arises to what extent spatial factors can have an influence. Here, first results analysing electricity demand for charging of electric vehicles show that individual charging behaviour differs between home and workplace regarding charging times and therefore influences the occurrence of daily load peaks [34]. This paper ties to this strand of literature and investigates the influence of three combined factors on energy consumption behaviour with the spatial component distinguishing between urban and rural places of residence, the institutional factor of being a (co-)owner of RE infrastructure, and the socio-economic factor of having the possibility to sell the (self-)produced energy.…”

Section: Literature Reviewmentioning

confidence: 99%

An Empirical Approach to Differences in Flexible Electricity Consumption Behaviour of Urban and Rural Populations—Lessons Learned in Germany

2021

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This article analyses two major trends of the 21st century. Firstly, the transition from fossil fuel-based energy production to renewable energy sources. Secondly, the inexorable urbanisation which can be witnessed all over the globe. The most promising renewable energy production technologies for the near future, i.e., wind and solar energy, are volatile by nature which makes matching supply and demand essential for a successful transition. Therefore, the aspects that determine the willingness of consumers to flexibilise their demand has gained growing attention. Initial research shows that different settings for (co-)ownership in terms of available prosumption options and used production technologies have a varying impact on demand flexibility. However, existing research has analysed flexibility drivers solely for the general population as an aggregate without any distinction regarding spatial, economic, or social factors. In this article, the authors go one step further and analyse whether those drivers for flexible consumption behaviour differ in rural or urban areas acknowledging differences in day-to-day life in both cases. This study is based on 2074 completed questionnaires from German consumers which were analysed using propensity score matching. The results show that people from rural and urban areas do not significantly differ in their willingness to be demand flexible in general. However, (co-)owners of RE installations from rural areas are generally significantly more demand flexible than (co-)owners of RE installations from urban areas. Further, when looking at different RE technologies, the results show that (co-)owners of solar installations are significantly more demand flexible if they are from rural areas. Lastly, when looking at usage options, people who solely consume produced electricity are more demand flexible if they are from rural areas as well.

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“…However, the availability of demand data is only given for partial areas. Therefore, model coupling with bottom-up demand simulation models such as MAED-2 [142], HERMES [143], SynPro [144,145] or Forecast [49] are often used to calculate heating or industrial loads depending on influencing parameters such as weather or GDP and different user behaviour. User behaviour also depends on other factors such as context, lifestyle, social milieu, attitudes, norms, demography, etc.…”

Section: Model Integrationmentioning

confidence: 99%

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

et al. 2020

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The transformation of the energy system is a highly complex process involving many dimensions. Energy system models help to understand the process and to define either target systems or policy measures. Insights derived from the social sciences are not sufficiently represented in energy system models, but address crucial aspects of the transformation process. It is, therefore, necessary to develop approaches to integrate results from social science studies into energy system models. Hence, as a result of an interdisciplinary discourse among energy system modellers, social scientists, psychologists, economists and political scientists, this article explains which aspects should be considered in the models, how the respective results can be collected and which aspects of integration into energy system models are conceivable to provide an overview for other modellers. As a result of the discourse, five facets are examined: Investment behaviour (market acceptance), user behaviour, local acceptance, technology innovation and socio-political acceptance. Finally, an approach is presented that introduces a compound of energy system models (with a focus on the macro and micro-perspective) as well as submodels on technology genesis and socio-political acceptance, which serves to gain a more fundamental knowledge of the transformation process.

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From residential electric load profiles to flexibility profiles – A stochastic bottom-up approach

Cited by 29 publications

References 15 publications

High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications

High resolution stochastic generator of European household specific electricity demand load curves for decentralized power self-production applications

An Empirical Approach to Differences in Flexible Electricity Consumption Behaviour of Urban and Rural Populations—Lessons Learned in Germany

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

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