Towards Flexibility Detection in Device-Level Energy Consumption

Neupane, Bijay; Pedersen, Torben Bach; Thiesson, Bo

doi:10.1007/978-3-319-13290-7_1

Cited by 19 publications

(13 citation statements)

References 5 publications

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“…In this section, we summarize the experimental results from our previous papers with a focus on quantifying flexibility potential of household TLC and non-TLC devices, evaluating accuracy of FOs generation method, scalability of the FOs aggregation, and the financial benefits of FOs. 1) Flexibility Potential: In [9], we analyze device-level energy consumption data from 6 different households [7]. A data aggregation based on device flexibility types, i.e., fully-flexible (e.g., EV, heat-pump, dishwasher), semi-flexible (e.g., oven), and non-flexible (e.g., lights) devices, demonstrate that on 1 www.nordpoolspot.com 2) Flex-offer Generation: In [11], we analyze the performance of the FO generation model in terms of quantity and quality of extracted flexibilities and generated FOs.…”

Section: Resultsmentioning

confidence: 99%

Modeling and Managing Energy Flexibility Using FlexOffers

Pedersen

Šikšnys

Neupane

2018

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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The recent spread of distributed renewable energy sources and smart IoT devices offer exciting new possibilities for the use of energy flexibility, opening a new era of the socalled bottom-up or cellular energy systems. In order to harness the full potential of flexibility, flexibility has to be modeled and represented in a manner that can be efficiently managed, manipulated, and traded on a market. In this paper, we provide a comprehensive overview of the FlexOffer concept, which offers an effective way of modeling and managing energy demand and supply flexibilities from a wide range of flexible resources and their aggregates. First, we define the basic concept and present the different phases of the FlexOffer life-cycle. Then, we discuss more advanced internal FlexOffer constraints as well as algorithms for FlexOffer generation, aggregation, disaggregation, and pricing that can significantly reduce energy management and trading complexities and increase overall efficiency. Finally, we present a general decentralized system architecture for trading flexibility (FlexOffers) in existing and new markets. Our experimental results show that (1) FlexOffers can be extracted with up to 98% accuracy, (2) aggregation and disaggregation can scale to 1000K FlexOffers and more, and (3) flexibility can be traded in the NordPool flexi order market while providing up to 89.9% (of optimal) reduction in the energy cost.

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Section: Resultsmentioning

confidence: 99%

Modeling and Managing Energy Flexibility Using FlexOffers

Pedersen

Šikšnys

Neupane

2018

2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

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“…Using arbitrary set thresholds [33] does not perform satisfactorily in cases where the baseline consumption is above and will not scale with the diversity of appliances and baseline consumption profiles. In the case of circuit-level at the room level, the energy used by consumer electronics in standby mode adds up and will vary from one household to the other, making the definition of a threshold difficult to scale on a large set of households.…”

Section: User Engagement and Motivationmentioning

confidence: 96%

A collaborative framework for annotating energy datasets

Cao

Wijaya

Aberer

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Targeting human activities responsible for the energy consumption instead of focusing solely on single appliance feedback for achieving energy efficiency in residential homes would link human behaviors to the resulting energy consumption. To this end, learning when appliances are in an active or idle state and the related user activity is crucial. Until smart appliances become widespread and can communicate their internal state, identifying when the residents interact with the appliances has to be determined from the available information that can be recorded from these devices. Developing and validating learning models require ground truth in the form of annotations to indicate when an appliance is active or idle. Launching data collection campaigns to incorporate these missing ground truth data involves careful planning before the roll-out of the experiment. Prohibitive costs for the hardware and time investment to monitor the deployed equipment are necessary for quality data. As such, publicly released datasets containing appliance-level data offer a basis for most researchers. This paper addresses these challenges by providing a collaborative web-based framework to retrofit labeling on existing datasets. The platform is publicly available, applies the wisdom of the crowd in the realm of energy research and leverages gamification techniques to encourage users' active contribution. The access to the platform and furthermore to the expert manually labeled dataset intends to enable future research and foster more collaboration in this area.

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“…The vision behind the TotalFlex is to analyse past usage patterns, operation correlations, and energy profiles of individual devices, and then predict available flexibilities. The potential of extracting flexibility from the usage of devices in a household is demonstrated in [10]. Still, this flexibility in demand depends on the consumers and their willingness to contribute to it.…”

Section: Flexibilitymentioning

confidence: 98%

Evaluating the Value of Flexibility in Energy Regulation Markets

Neupane

Pedersen

Thiesson

2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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In this paper, we perform an econometric analysis on the benefits of introducing flexibility in the Danish/Nordic regulating power market. The paper investigates the relationships between market power prices and regulation volumes, in order to quantify the effects of flexibility on regulating power prices. Further, we analyze the benefit for various types of flexibility and market objectives, to detect the type of energy flexibility that maximizes the benefits. Results show that if 3.87% of total demand is flexible, the market can reduce the regulation cost by 49% and the regulation volume by 29.4%.

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Towards Flexibility Detection in Device-Level Energy Consumption

Cited by 19 publications

References 5 publications

Modeling and Managing Energy Flexibility Using FlexOffers

Modeling and Managing Energy Flexibility Using FlexOffers

A collaborative framework for annotating energy datasets

Evaluating the Value of Flexibility in Energy Regulation Markets

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