Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system

Abstract Simulations, especially agent-based simulation, are able to facilitate the investigation of smart energy solutions and business models, and their impacts on the energy system and involved stakeholders. Technical details, alternatives, and multiple options for what-if scenarios influence sim...

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Main Authors: Kristoffer Christensen, Zheng Ma, Yves Demazeau, Bo Nørregaard Jørgensen
Format: Article
Language:English
Published: SpringerOpen 2021-09-01
Series:Energy Informatics
Subjects:
Online Access:https://doi.org/10.1186/s42162-021-00160-w
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spelling doaj-c140d1bd6aae4a53aa97e77bcf2515c92021-09-26T11:16:00ZengSpringerOpenEnergy Informatics2520-89422021-09-014S212310.1186/s42162-021-00160-wMethodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy systemKristoffer Christensen0Zheng Ma1Yves Demazeau2Bo Nørregaard Jørgensen3Center for Energy Informatics, Mærsk Mc-Kinney Møller Institute, University of Southern DenmarkCenter for Health Informatics and Technology, Mærsk Mc-Kinney Møller Institute, University of Southern DenmarkYves Demazeau, Laboratoire d’Informatique de Grenoble, Centre National de la Recherche ScientifiqueCenter for Energy Informatics, Mærsk Mc-Kinney Møller Institute, University of Southern DenmarkAbstract Simulations, especially agent-based simulation, are able to facilitate the investigation of smart energy solutions and business models, and their impacts on the energy system and involved stakeholders. Technical details, alternatives, and multiple options for what-if scenarios influence simulation quality, but no methodology available to support the investigation. This paper proposes a method for identifying technical details of smart energy solutions in the energy system and identifying research gaps in the smart grid context with EV solutions as an example. The method includes the investigation of the state-of-the-art EV solutions by scoping review and the allocation of the scoping review results into the Smart Grid Architecture Model framework with three dimensions (Domains, Zones, and interoperability layers). The quantitative scoping review results in a total number of 240 references and 10 references match the criteria based on the qualitative scoping review. The results show that the most popular EV use case within the targeted scope is the V2G concept, and 6 out of the 10 references discuss the EVs’ potentials to work as energy storage. Seventeen features are identified by mapping the EV use cases (solutions and business models) into the three dimensions (domain, zone, and interoperability layers) of the SGAM framework. The process at the Zone layer is the most popularly covered (mentioned 64 times), and enterprise at the Zone layer and communication in the interoperability layer are the least covered (mentioned 4 times each).https://doi.org/10.1186/s42162-021-00160-wElectric vehicleSmart energy solutionBusiness modelSmart gridMethodology
collection DOAJ
language English
format Article
sources DOAJ
author Kristoffer Christensen
Zheng Ma
Yves Demazeau
Bo Nørregaard Jørgensen
spellingShingle Kristoffer Christensen
Zheng Ma
Yves Demazeau
Bo Nørregaard Jørgensen
Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
Energy Informatics
Electric vehicle
Smart energy solution
Business model
Smart grid
Methodology
author_facet Kristoffer Christensen
Zheng Ma
Yves Demazeau
Bo Nørregaard Jørgensen
author_sort Kristoffer Christensen
title Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
title_short Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
title_full Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
title_fullStr Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
title_full_unstemmed Methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
title_sort methodology for identifying technical details of smart energy solutions and research gaps in smart grid: an example of electric vehicles in the energy system
publisher SpringerOpen
series Energy Informatics
issn 2520-8942
publishDate 2021-09-01
description Abstract Simulations, especially agent-based simulation, are able to facilitate the investigation of smart energy solutions and business models, and their impacts on the energy system and involved stakeholders. Technical details, alternatives, and multiple options for what-if scenarios influence simulation quality, but no methodology available to support the investigation. This paper proposes a method for identifying technical details of smart energy solutions in the energy system and identifying research gaps in the smart grid context with EV solutions as an example. The method includes the investigation of the state-of-the-art EV solutions by scoping review and the allocation of the scoping review results into the Smart Grid Architecture Model framework with three dimensions (Domains, Zones, and interoperability layers). The quantitative scoping review results in a total number of 240 references and 10 references match the criteria based on the qualitative scoping review. The results show that the most popular EV use case within the targeted scope is the V2G concept, and 6 out of the 10 references discuss the EVs’ potentials to work as energy storage. Seventeen features are identified by mapping the EV use cases (solutions and business models) into the three dimensions (domain, zone, and interoperability layers) of the SGAM framework. The process at the Zone layer is the most popularly covered (mentioned 64 times), and enterprise at the Zone layer and communication in the interoperability layer are the least covered (mentioned 4 times each).
topic Electric vehicle
Smart energy solution
Business model
Smart grid
Methodology
url https://doi.org/10.1186/s42162-021-00160-w
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