An automated residential demand response pilot experiment, based on day-ahead dynamic pricing

Vanthournout, Koen; Dupont, Benjamin; Foubert, Wim; Stuckens, Catherine; Claessens, Sven

doi:10.1016/j.apenergy.2015.05.100

Cited by 81 publications

(35 citation statements)

References 28 publications

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“…Dynamic pricing is the key to utilising demand flexibility, and this mechanism has been used in various pilot projects, such as PowerMatcher [27] and Your Energy Moment [16] in the Netherlands, the EcoGrid EU demonstration in Denmark [19] and the Olympic Peninsula Project in the U.S. [28]. A study on the performance of all types of smart white good appliances under dynamic pricing demand response scheme was conducted in a Belgium pilot project, Linear [29] with 58 households participated. A significant shift of flexible electricity consumption to lower price periods was observed.…”

Section: Introductionmentioning

confidence: 99%

Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands

Dane

Finck

et al. 2017

Applied Energy

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h i g h l i g h t sAwareness of smart grids is the highest among respondents aged 20-29 years old. Willingness to use smart technologies and change energy behaviour are interdependent. Potential flexible building users were found to be 11% of the respondents. Building energy flexibility might play a crucial role in demand side management for integrating intermittent renewables into smart grids. The potential of building energy flexibility depends not only on the physical characteristics of a building but also on occupant behaviour in the building. Building users will have to adopt smart technologies and to change their daily energy use behaviours or routines, if energy flexibility is to be achieved. The willingness of users to make changes will determine how much demand flexibility can be achieved in buildings and whether energy flexible buildings can be realized. This will have a considerable impact on the transition to smart grids. This study is thus to assess the perception of smart grids and energy flexible buildings by building users, and their readiness for them on a large scale. We attempted to identify the key characteristics of the ideal user of flexible buildings. A questionnaire was designed and administered as an online survey in the Netherlands. The questionnaire consisted of questions about the sociodemographic characteristics of the current users, house type, household composition, current energy use behaviour, willingness to use smart technologies, and willingness to change energy use behaviour. The survey was completed by 835 respondents, of which 785 (94%) were considered to have provided a genuine response. Our analysis showed that the concept of smart grids is an unfamiliar one, as more than 60% of the respondents had never heard of smart grids. However, unfamiliarity with smart grids increased with age, and half of the respondents aged 20-29 years old were aware of the concept. Monetary incentives were identified as the biggest motivating factor for adoption of smart grid technologies. It was also found that people would be most in favour of acquiring smart dishwashers (65% of the respondents) and refrigerator/freezers (60%). Statistical analysis shows that people who are willing to use smart technologies are also willing to change their behaviour, and can thus be categorised as potentially flexible building users. Given certain assumptions, 11% of the respondents were found to be potentially flexible building users. To encourage people to be prepared for energy flexible buildings, awareness of smart grids will have to be increased, and the adoption of smart technologies may have to be promoted by providing incentives such as financial rewards. a r t i c l e i n f o

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Section: Introductionmentioning

confidence: 99%

Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands

Dane

Finck

et al. 2017

Applied Energy

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“…Note that our first model is proposed specifically for modeling customer behavior that exhibits clear deadlines. We observed this behavior in dishwasher usage, an appliance that has been attributed a high demand response potential [21,27,28]. Our second model is a more general approach, which we found to be suitable for modeling all types of studied white goods (i.e., washing machine, tumble dryer and dishwasher).…”

Section: Methodsmentioning

confidence: 93%

“…The flexibility profile is defined for a given customer and a particular appliance, and is a model for his/her usage of that appliance. 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).…”

Section: Quantitive Specification Of Flexibilitymentioning

confidence: 99%

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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“…Recent experience shows, however, that real-time prices work best with automated control and may be less applicable to manual response [16,17]. Comparisons of dynamic pricing studies also reveal that higher elasticities are achieved under critical event pricing or time-of-use schemes than under real-time pricing [18].…”

Section: Introductionmentioning

confidence: 96%

Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system

Katz

Andersen

Morthorst

2016

Energy

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An automated residential demand response pilot experiment, based on day-ahead dynamic pricing

Cited by 81 publications

References 28 publications

Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands

Are building users prepared for energy flexible buildings?—A large-scale survey in the Netherlands

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

Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system

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