Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers
Based on the global greenhouse gas (GHG) emissions targets, governments all over the world are speeding up the adoption of electric vehicles (EVs). However, one of the key challenges in designing the novel EV system is to forecast the accurate time for the replacement of conventional vehicles and op...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9963948/ |
| _version_ | 1811214303234097152 |
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| author | Heba M. Abdullah Adel Gastli Lazhar Ben-Brahim Semira O. Mohammed |
| author_facet | Heba M. Abdullah Adel Gastli Lazhar Ben-Brahim Semira O. Mohammed |
| author_sort | Heba M. Abdullah |
| collection | DOAJ |
| description | Based on the global greenhouse gas (GHG) emissions targets, governments all over the world are speeding up the adoption of electric vehicles (EVs). However, one of the key challenges in designing the novel EV system is to forecast the accurate time for the replacement of conventional vehicles and optimization of charging vehicles. Designing the charging infrastructure for EVs has many impacts such as stress on the power network, increase in traffic flow, and change in driving behaviors. Therefore, the optimal placement of charging stations is one of the most important issues to address to increase the use of electric vehicles. In this regard, the purpose of this study is to present an optimization method for choosing optimal locations for electric car charging stations for Campus charging over long-term planning. The charger placement problem is formulated as a complex Multi-Criteria Decision Making (MCDM) which combines spatial analysis techniques, power network load flow, traffic flow models, and constrained procedures. The Analytic Hierarchy Process (AHP) approach is used to determine the optimal weights of the criteria, while the mean is used to determine the distinct weights for each criterion using the AHP in terms of accessibility, environmental effect, power network indices, and traffic flow impacts. To evaluate the effectiveness of the proposed method, it is applied to a real case study of Qatar University with collected certain attributes data and relevant decision makers as the inputs to the linguistic assessments and MCDM model. The Ranking of the optimal locations is done by aggregating four techniques: Simple Additive Weighting Method (SAW, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II). A long-term impact analysis is a secondary output of this study that allows decision-makers to evaluate their policy impacts. The findings demonstrate that the proposed framework can locate optimal charging station sites. These findings could also help administrators and policymakers make effective choices for future planning and strategy. |
| first_indexed | 2024-04-12T06:00:59Z |
| format | Article |
| id | doaj.art-e76215557020459da16ba522a6bcf85c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T06:00:59Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e76215557020459da16ba522a6bcf85c2022-12-22T03:45:02ZengIEEEIEEE Access2169-35362022-01-011012345212347310.1109/ACCESS.2022.32247969963948Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle ChargersHeba M. Abdullah0https://orcid.org/0000-0002-0063-4957Adel Gastli1https://orcid.org/0000-0002-6818-3320Lazhar Ben-Brahim2https://orcid.org/0000-0003-4510-8544Semira O. Mohammed3https://orcid.org/0000-0003-2834-8976Electrical Engineering Department, College of Engineering, Qatar University, Doha, QatarElectrical Engineering Department, College of Engineering, Qatar University, Doha, QatarElectrical Engineering Department, College of Engineering, Qatar University, Doha, QatarQatar Transportation and Traffic Safety Center, College of Engineering, Qatar University, Doha, QatarBased on the global greenhouse gas (GHG) emissions targets, governments all over the world are speeding up the adoption of electric vehicles (EVs). However, one of the key challenges in designing the novel EV system is to forecast the accurate time for the replacement of conventional vehicles and optimization of charging vehicles. Designing the charging infrastructure for EVs has many impacts such as stress on the power network, increase in traffic flow, and change in driving behaviors. Therefore, the optimal placement of charging stations is one of the most important issues to address to increase the use of electric vehicles. In this regard, the purpose of this study is to present an optimization method for choosing optimal locations for electric car charging stations for Campus charging over long-term planning. The charger placement problem is formulated as a complex Multi-Criteria Decision Making (MCDM) which combines spatial analysis techniques, power network load flow, traffic flow models, and constrained procedures. The Analytic Hierarchy Process (AHP) approach is used to determine the optimal weights of the criteria, while the mean is used to determine the distinct weights for each criterion using the AHP in terms of accessibility, environmental effect, power network indices, and traffic flow impacts. To evaluate the effectiveness of the proposed method, it is applied to a real case study of Qatar University with collected certain attributes data and relevant decision makers as the inputs to the linguistic assessments and MCDM model. The Ranking of the optimal locations is done by aggregating four techniques: Simple Additive Weighting Method (SAW, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Grey Relational Analysis (GRA), and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-II). A long-term impact analysis is a secondary output of this study that allows decision-makers to evaluate their policy impacts. The findings demonstrate that the proposed framework can locate optimal charging station sites. These findings could also help administrators and policymakers make effective choices for future planning and strategy.https://ieeexplore.ieee.org/document/9963948/Analytic hierarchy processchargerelectric vehicleload flow multi-criteria decision making |
| spellingShingle | Heba M. Abdullah Adel Gastli Lazhar Ben-Brahim Semira O. Mohammed Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers IEEE Access Analytic hierarchy process charger electric vehicle load flow multi-criteria decision making |
| title | Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers |
| title_full | Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers |
| title_fullStr | Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers |
| title_full_unstemmed | Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers |
| title_short | Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers |
| title_sort | integrated multi criteria model for long term placement of electric vehicle chargers |
| topic | Analytic hierarchy process charger electric vehicle load flow multi-criteria decision making |
| url | https://ieeexplore.ieee.org/document/9963948/ |
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