Framework for estimating flexibility of commercial and industrial customers in Smart Grids

Wattjes, F. D.; Janssen, Sam; Slootweg, J.G.

doi:10.1109/isgteurope.2013.6695406

Cited by 11 publications

(10 citation statements)

References 4 publications

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“…According to Wattjes, et al [45], flexibility from a process point of view can be described in terms of: (1) flexibility speed, (2) flexibility duration, (3) flexibility of power. Generally, industrial loads include production (part of the production process) and support services (e.g., lighting) [2].…”

Section: Energy Consumption Sourcesmentioning

confidence: 99%

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Industrial Consumers’ Smart Grid Adoption: Influential Factors and Participation Phases

Jôrgensen

Asmussen

2018

Energies

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Abstract:The participation of industrial consumers in smart grid transition is important due to their consumption footprint, heavy energy use and complexity in the implementation of smart energy technologies. Active involvement of industrial consumers in the development of smart grid solutions is important to ensure the energy system transformation. Despite the importance of industrial consumers has been identified, the empirical studies on the smart grid still mainly address residential and commercial consumers. Therefore, based on four case studies with two industrial consumers, one energy consulting company and one electricity retailer, this paper investigates the factors that influence industrial consumers' acceptance of smart grid solutions, and how the influential factors are relevant to the smart grid adoption phases. Eleven influential factors are identified that impact on four stages for industrial consumers' adoption of smart grid solutions (inscription, translation, framing, and stabilization stages). The eleven influential factors are: awareness of multiple contexts, shared support, return-of-investment, ease of use, flexibility and dynamic pricing, liberalization and energy tariff structure, customer focus, solution integration, process improvement, service quality, and company's green image.

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Section: Energy Consumption Sourcesmentioning

confidence: 99%

“…The flexibility affects the customers' behavior by offering the monetary and social benefits [34]. Therefore, the flexibility activation also depends on consumers' potentials to shift the loads [34,36,45].…”

Section: Energy Consumption Sourcesmentioning

confidence: 99%

Industrial Consumers’ Smart Grid Adoption: Influential Factors and Participation Phases

Jôrgensen

Asmussen

2018

Energies

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“…In the latter spirit, Wattjes et al [14] propose a universal framework to estimate the flexibility of commercial and industrial customers, using a high level model largely based on estimates (rather than accurate measurements) of flexibility in terms of power, duration and speed of activating the flexibility (mainly from lighting and cooling). Abdisalaam et al [15] similarly estimate potential flexibility, and among others assess the potential benefit of interrupting EV charging -however, the EV data is a synthetic model based on typical car usage in The Netherlands from 2007.…”

Section: Related Workmentioning

confidence: 99%

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

Develder

Sadeghianpourhamami

Strobbe

et al. 2016

2016 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-The increasing adoption of electric vehicles (EVs) presents both challenges and opportunities for the power grid, especially for distribution system operators (DSOs). The demand represented by EVs can be significant, but on the other hand, sojourn times of EVs could be longer than the time required to charge their batteries to the desired level (e.g., to cover the next trip). The latter observation means that the electrical load from EVs is characterized by a certain level of flexibility, which could be exploited for example in demand response (DR) approaches (e.g., to balance generation from renewable energy sources).This paper analyzes two data sets, one from a charging-athome field trial in Flanders (about 8.5k charging sessions) and another from a large-scale EV public charging pole deployment in The Netherlands (more than 1M sessions). We rigorously analyze the collected data and quantify aforementioned flexibility:(1) we characterize the EV charging behavior by clustering the arrival and departure time combinations, identifying three behaviors (charging near home, charging near work, and park to charge), (2) we fit statistical models for the sojourn time, and flexibility (i.e., non-charging idle time) for each type of observed behavior, and (3) quantify the the potential of DR exploitation as the maximal load that could be achieved by coordinating EV charging for a given time of day t, continuously until t + ∆.

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“…Some of these methods are derived merely based on appliance energy usage patterns that are either obtained from sub-metering of household appliances [18] or assumed by studying the characteristics of the various appliances [19]. Analyzing the flexibility potential based on appliance energy usage patterns provides insights about the potential amount of deferrable energy of each appliance.…”

Section: State Of the Art In Residential Flexibility Assessmentmentioning

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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Framework for estimating flexibility of commercial and industrial customers in Smart Grids

Cited by 11 publications

References 4 publications

Industrial Consumers’ Smart Grid Adoption: Influential Factors and Participation Phases

Industrial Consumers’ Smart Grid Adoption: Influential Factors and Participation Phases

Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets

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

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