Energy services for the smart grid city

Karnouskos, Stamatis; Silva, Per Goncalves da; Ilić, Dejan

doi:10.1109/dest.2012.6227925

Cited by 47 publications

(22 citation statements)

References 5 publications

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“…These datasets for wind and solar refer to a single location in Spain as collected by the NOBEL project platform [12]. For solar we arbitrary selected the production data of the 06-Jun-2012.…”

Section: B Experimental Settingmentioning

confidence: 99%

Charging optimization of enterprise electric vehicles for participation in demand response

Detzler

Eichhorn

Karnouskos

2015

2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST)

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Abstract-In the emerging smart grid, using flexible infrastructures to realize better energy management via demand response scenarios is at its core. The potential of electric vehicles used to realize such flexibility is widely experimented with. In this work, we take a closer look at a specific case of enterpriseowned electric vehicles, parked at enterprise premises, and how their charging can be optimized in order to both adhere to the enterprise operational constraints, as well as consider dynamic changes stemming from other grid stakeholders. A simplified optimization using an evolutionary algorithm is realized, and the approach is evaluated under two scenarios of interest. Of specific interest to the many smart grid stakeholders are the DR scenarios [4], as these can yield additional (usually monetary) benefits to the involved stakeholders, while in parallel tackling key problems in the grid due to highly dynamic energy production stemming from Renewable Energy Sources (RES). The role of the Electric Vehicles (EVs) in smart grid is increasingly investigated [6], including their utilization as dynamic storage [7], since, if a critical mass of them is reached, they can have a significant energy impact on existing infrastructure, future planning and naturally in any energy optimization scenario. Index Terms-EnergyEVs can be an active participant in DR, since they provide flexibility during longer standing times. This is especially of interest when larger numbers of them are available, which is the case e.g., for EV fleets. Several uncertainties are coupled with individual EVs including, their presence, the authorization to centrally control charging, the acceptance by the consumer (EV owner), the impact on the EV battery, etc. However, many of these considerations, can be set aside in specific cases, such as those involving enterprise-owned cars.In this work we focus on this area and introduce two DR scenarios (a price-based and an incentive-based one) that are attractive for enterprise fleets of electric vehicles. The latter can react to DR events, with the flexibility given by long and predictable parking times without interfering with their operational plan. We introduce an optimization approach that allows operators of such EV fleets to react to two different types of DR events. Finally we evaluate these two scenarios and the optimization realized, with real world data both for available RES and enterprise EV fleet. II. DEMAND RESPONSE FOR DSO AND SUPPLIERSIn the context of enterprise-owned EVs, different motivations exist for shifting electrical loads over time. From a local point of view, such load shifts can help reducing consumption (due to EV charging) at times where electricity is expensive in order to reduce overall enterprise costs. In addition, EVs can also be used to prevent the overall power draw from exceeding technical or contractual limitations which could lead to physical damages or penalty fees. DR allows extending this concept from local boundary conditions to more global aspects, as due t...

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Section: B Experimental Settingmentioning

confidence: 99%

Charging optimization of enterprise electric vehicles for participation in demand response

Detzler

Eichhorn

Karnouskos

2015

2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST)

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“…This generation datasets for wind refer to a single location in Spain as collected by the NOBEL project [10]. To calculate the energy production from the wind data, we used the model for the "Tornado 1 kW" turbine.…”

Section: Grid Energy Management System (Ems)mentioning

confidence: 99%

A model and an evolutionary algorithmic approach towards optimization of Electric Vehicle fleet charging

Detzler

Karnouskos

2015

2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST)

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Abstract-The prevalence of the Smart Grid and its capabilities, has enabled sophisticated energy management that can be realized as a multi-constraint optimization problem and tailored to the specific scenario needs. In conjunction with the increasing introduction of Electric Vehicles (EVs), energy management tools can now consider expanded conditions including grid balance, cost optimization, EV characteristics, asset utilization, operational goals etc. In this work we analyze such a scenario and demonstrate how an EV fleet charging can be optimized in a timely manner while taking into consideration local conditions e.g., individual EV needs as well as global ones e.g., grid limits and energy price. We formalize a model that reflects the EV restrictions, and use it to assess an algorithmic approach that solves this non-linear optimization problem.

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“…The module is especially indicated to support continuous monitoring during the implantation of energy management procedures as ISO50001 or ISO 50006:2014 (energy performance) and also can be used to support audits (EN16247-1, ISO50002) and performance measure and verification protocols (IPMVP, ISO50015:2014) [Directive 2012/27/EU]. The module has been built using web service technology and consequently their functionalities can easily be integrated in other modules or applications [Karnouskos, 2012].…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis web service module for energy monitoring in buildings

et al. 2018

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This work presents the development of a web service module for monitoring energy consumption data recorded in buildings. This software is based on the application of Multivariate Principal Component Analysis (MPCA) to implement a fault detection and diagnosis tool that detects anomalies or misbehaviors in energy consumptions. The result can be integrated into an architecture to facilitate the access and management of data coming from different energy subsystems (and other sources of data as meteorological information) involved in the operation of a building.

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Energy services for the smart grid city

Cited by 47 publications

References 5 publications

Charging optimization of enterprise electric vehicles for participation in demand response

Charging optimization of enterprise electric vehicles for participation in demand response

A model and an evolutionary algorithmic approach towards optimization of Electric Vehicle fleet charging

Fault detection and diagnosis web service module for energy monitoring in buildings

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