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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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 |
work_keys_str_mv |
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