A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities
Nowadays, modeling tools are a crucial part of best practice in the elaboration and implementation of a decarbonization plan in any organization, city, or country. The present review analyzes the different modeling tools available to assess energy systems in smart cities. It creates an updated overv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Smart Cities |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-6511/4/4/75 |
| _version_ | 1827669614596718592 |
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| author | Fernando Martins Carlos Patrão Pedro Moura Aníbal T. de Almeida |
| author_facet | Fernando Martins Carlos Patrão Pedro Moura Aníbal T. de Almeida |
| author_sort | Fernando Martins |
| collection | DOAJ |
| description | Nowadays, modeling tools are a crucial part of best practice in the elaboration and implementation of a decarbonization plan in any organization, city, or country. The present review analyzes the different modeling tools available to assess energy systems in smart cities. It creates an updated overview of the modeling tools currently available, showing their capabilities and main potential outputs when considering the energy efficiency objective in the context of smart cities in Europe. A restricted set of 14 tools are identified which optimally fulfill the modeling mission of the energy sector, in a smart city context, for different time horizons. The selection considers the capability to include decarbonization assessments, namely, by considering the flexibility to use different external factors, energy policies, technologies, and mainly the implementation of Article 7 from the Energy Efficiency Directive and the “energy efficiency first” principle defined by the European Commission. The ELECTRE TRI method was used to implement a multi-criteria decision approach for sorting modeling tools, aiming at distributing the various alternatives by previously defined categories, and considering the performance criteria of each alternative modeling tool, the analysis suggests that the best options are the LEAP, MESSAGEix, and oemof tools. |
| first_indexed | 2024-03-10T03:05:41Z |
| format | Article |
| id | doaj.art-51f9ea6c5b6540aab5c5a6b224c20ec6 |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-10T03:05:41Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-51f9ea6c5b6540aab5c5a6b224c20ec62023-11-23T10:33:22ZengMDPI AGSmart Cities2624-65112021-11-01441420143610.3390/smartcities4040075A Review of Energy Modeling Tools for Energy Efficiency in Smart CitiesFernando Martins0Carlos Patrão1Pedro Moura2Aníbal T. de Almeida3Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalInstitute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalInstitute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalInstitute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, 3030-290 Coimbra, PortugalNowadays, modeling tools are a crucial part of best practice in the elaboration and implementation of a decarbonization plan in any organization, city, or country. The present review analyzes the different modeling tools available to assess energy systems in smart cities. It creates an updated overview of the modeling tools currently available, showing their capabilities and main potential outputs when considering the energy efficiency objective in the context of smart cities in Europe. A restricted set of 14 tools are identified which optimally fulfill the modeling mission of the energy sector, in a smart city context, for different time horizons. The selection considers the capability to include decarbonization assessments, namely, by considering the flexibility to use different external factors, energy policies, technologies, and mainly the implementation of Article 7 from the Energy Efficiency Directive and the “energy efficiency first” principle defined by the European Commission. The ELECTRE TRI method was used to implement a multi-criteria decision approach for sorting modeling tools, aiming at distributing the various alternatives by previously defined categories, and considering the performance criteria of each alternative modeling tool, the analysis suggests that the best options are the LEAP, MESSAGEix, and oemof tools.https://www.mdpi.com/2624-6511/4/4/75modeling toolssmart citiesdecarbonizationelectrificationenergy efficiency |
| spellingShingle | Fernando Martins Carlos Patrão Pedro Moura Aníbal T. de Almeida A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities Smart Cities modeling tools smart cities decarbonization electrification energy efficiency |
| title | A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities |
| title_full | A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities |
| title_fullStr | A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities |
| title_full_unstemmed | A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities |
| title_short | A Review of Energy Modeling Tools for Energy Efficiency in Smart Cities |
| title_sort | review of energy modeling tools for energy efficiency in smart cities |
| topic | modeling tools smart cities decarbonization electrification energy efficiency |
| url | https://www.mdpi.com/2624-6511/4/4/75 |
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