Modeling and Managing Energy Flexibility Using FlexOffers

Pedersen, Torben Bach; Šikšnys, Laurynas; Neupane, Bijay

doi:10.1109/smartgridcomm.2018.8587605

Cited by 20 publications

(19 citation statements)

References 15 publications

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“…The results show the better integration of RES and peak demand reduction [20]. Pederson et al [21] provide a good overview of the principle of FlexOffer. Basically, FlexOffers are divided into time and energy flexibility, and the application takes place as follows: (1) collection of the data of the flexible loads; (2) preparation of the data (elimination of "outliers", and filling of data gaps); (3) flexibility forecasting using a model; (4) generation of the FlexOffers-time and energy flexibility;…”

Section: Charging Flexibilitymentioning

confidence: 97%

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Barthel

Schlund

Landes³

et al. 2021

Energies

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A successful transformation of the energy and transportation sector is one of the main targets for our society today. Battery electric vehicles can play a key role in future renewable-based energy supply systems because of their ability to store electrical power. Additionally, they provide significant charging flexibility due to the long parking durations. In this paper, we provide insights into the temporal and power-specific flexibility behavior of three different vehicle fleets. These fleets are pool vehicles of office employees, a public authority, and a logistics company. Several parameters, such as the average charging power per charging event or the average plug-in duration per charging event, are discussed. Additionally, we investigate different charging rates and their impact on the temporal flexibility of the charging events. The data analysis shows that the logistics site has the most homogeneous charging profile as well as high charging flexibility, in contrast to the office and public agency site. The results are of significant importance for future applications in the field of smart charging and ancillary services provision.

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Section: Charging Flexibilitymentioning

confidence: 97%

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Barthel

Schlund

Landes³

et al. 2021

Energies

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“…However, their production fluctuates as, e.g., wind turbines only produce energy when there is wind and solar panels only produce energy when there is daylight. To enable the Green Transition to a low-carbon society based on intermittent RES, the FED 1 project brings together different Danish research institutions, companies from the energy technology sector (referred to as EnergyTech companies), and owners of reallife physical environments where energy usage (and other relevant measures, e.g., indoor climate) can be monitored and experimented with. The latter are called Living Labs (LLs) and represent different parts of the Danish society.…”

Section: Introductionmentioning

confidence: 99%

“…To enable scalable and effective use of all flexibility, it is important to capture flexibility in a uniform and powerful format. In FED, we use the FlexOffer (FO) format developed in a range of large EU and Danish projects [1]. Consider the EV charging scenario from above.…”

Section: Introductionmentioning

confidence: 99%

“…These novel tools and solutions using machine learning and artificial intelligence enable DSOlab to determine the ways to reduce the load on the electricity grid during peak periods. Tools such as FlexOffers (FO) [1] capture flexibility that can be used to respond to the flexibility challenges. For instance, a FO schedules the charging cycle for the vehicle based on the analysis of historical user behaviour and prediction of future energy demand.…”

Section: Introductionmentioning

confidence: 99%

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The Danish National Energy Data Lake: Requirements, Technical Architecture, and Tool Selection

Hamadou

Pedersen

Thomsen

2020

2020 IEEE International Conference on Big Data (Big Data)

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Renewable Energy Sources such as wind and solar do not emit CO2 but their production vary considerably depending on time and weather. Thus, it is important to use the flexibility in device loads to shift energy consumption to follow the production. For example, an Electrical Vehicle (EV) can be charged very flexibly between arriving home at 5PM and leaving again at 7AM. Utilizing all available energy flexibility requires applying machine learning and AI on massive amounts of Big Data from many different actors and devices, ranging from private consumers, over companies, to energy network operators, and using this to create digital solutions to enable and exploit flexibility. The project Flexible Energy Denmark (FED) is building the foundation for this for the entire Danish society. Specifically, FED collects data from a number of Living Labs (LLs) in representative real-life physical environments. The data is stored in the Danish National Energy Data, called FED Data Lake (FEDDL) to enable efficient and advanced analysis. FEDDL is built using only open source tools which can run both on-premise and in cloud settings. In this paper, we describe the requirements for FEDDL based on a representative LL case study, present its technical architecture, and provide a comparison of relevant tools along with the arguments for which ones we selected.

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“…FlexOffer aggregation and disaggregation[29].balancing capacities are much higher. At the same time, managing large numbers of individual FOs is tedious and complex.…”

mentioning

confidence: 99%

Flexibility Modeling, Management, and Trading in Bottom-up Cellular Energy Systems

Šikšnys

Pedersen

Aftab

et al. 2019

Proceedings of the Tenth ACM International Conference on Future Energy Systems

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Accelerated local deployments of renewable energy sources and energy storage units, as well as increased overall flexibility in local demand and supply through active user involvement and smart energy solutions, open up new opportunities (e.g., self-sufficiency and CO 2 neutrality through local renewables) and yet pose new challenges (e.g., how to maintain the security of supply and get the best yield) to market players in the lower parts of the energy system (including prosumers, energy communities, aggregators, and distribution system operators (DSOs)). One way to cope with the challenges requires "logical" reorganization of the energy system bottom-up as a number of nested (maximally) self-sufficient and interacting cells with their own local (i.e. within a cell) energy management and trading capabilities. This change necessitates effective IT-based solutions. Towards this goal, we propose a unified Flexibility Modeling, Management, and Trading System (FMTS) that generalizes flexibility modeling, management, and intra-cell trading in such cellular energy systems. Our system offers different flexibility provisioning options (Machine Learning based, and Model Predictive Control based), activation mechanisms (indirect and direct device-control), and trading schemes (e.g. flexibility contracts, market-based trading) and suits different cellular system use-cases. In this paper, we introduce the FMTS, overview its core functionality and components, and explain how it practically manages, prices, and trades flexibility from a diverse variety of loads. We then introduce the real-world FMTS instances developed in the GOFLEX project 1 and present experimental results that demonstrate significantly increased flexibility capacities, user gains, and balance between demand and supply when an FMTS instance is used in the simulated cellular energy system setting.

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Modeling and Managing Energy Flexibility Using FlexOffers

Cited by 20 publications

References 15 publications

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

The Danish National Energy Data Lake: Requirements, Technical Architecture, and Tool Selection

Flexibility Modeling, Management, and Trading in Bottom-up Cellular Energy Systems

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