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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 Pub Date : 2021-09-24 , DOI: 10.1186/s42162-021-00160-w
Kristoffer Christensen 1 , Bo Nørregaard Jørgensen 1 , Zheng Ma 2 , Yves Demazeau 3
Affiliation  

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).

中文翻译:

识别智能能源解决方案技术细节和智能电网研究差距的方法:以能源系统中的电动汽车为例

模拟,尤其是基于代理的模拟,能够促进智能能源解决方案和商业模式的调查,以及它们对能源系统和相关利益相关者的影响。假设情景的技术细节、替代方案和多个选项会影响模拟质量,但没有可用的方法来支持调查。本文以电动汽车解决方案为例,提出了一种识别能源系统中智能能源解决方案技术细节和识别智能电网背景下研究空白的方法。该方法包括通过范围审查和将范围审查结果分配到具有三个维度(域、区域和互操作性层)的智能电网架构模型框架中来研究最先进的 EV 解决方案。定量范围审查结果共有 240 篇参考文献,其中 10 篇参考文献符合基于定性范围审查的标准。结果表明,目标范围内最受欢迎的电动汽车用例是 V2G 概念,10 篇参考文献中有 6 篇讨论了电动汽车作为储能的潜力。通过将 EV 用例(解决方案和业务模型)映射到 SGAM 框架的三个维度(域、区域和互操作性层)中,确定了 17 个特性。其中Zone层的流程覆盖最广(提及64次),Zone层的企业和互操作层的通信覆盖最少(各提及4次)。结果表明,目标范围内最受欢迎的电动汽车用例是 V2G 概念,10 篇参考文献中有 6 篇讨论了电动汽车作为储能的潜力。通过将 EV 用例(解决方案和业务模型)映射到 SGAM 框架的三个维度(域、区域和互操作性层)中,确定了 17 个特性。其中Zone层的流程覆盖最广(提及64次),Zone层的企业和互操作层的通信覆盖最少(各提及4次)。结果表明,目标范围内最受欢迎的电动汽车用例是 V2G 概念,10 篇参考文献中有 6 篇讨论了电动汽车作为储能的潜力。通过将 EV 用例(解决方案和业务模型)映射到 SGAM 框架的三个维度(域、区域和互操作性层)中,确定了 17 个特性。其中Zone层的流程覆盖最广(提及64次),Zone层的企业和互操作层的通信覆盖最少(各提及4次)。通过将 EV 用例(解决方案和业务模型)映射到 SGAM 框架的三个维度(域、区域和互操作性层)中,确定了 17 个特性。其中Zone层的流程覆盖最广(提及64次),Zone层的企业和互操作层的通信覆盖最少(各提及4次)。通过将 EV 用例(解决方案和业务模型)映射到 SGAM 框架的三个维度(域、区域和互操作性层)中,确定了 17 个特性。其中Zone层的流程覆盖最广(提及64次),Zone层的企业和互操作层的通信覆盖最少(各提及4次)。
更新日期:2021-09-24
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