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An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations

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  • Erdogan, Nuh
  • Pamucar, Dragan
  • Kucuksari, Sadik
  • Deveci, Muhammet

Abstract

This study addresses the optimal planning of electric vehicle charging infrastructure at workplaces. As the optimal planning for a given workplace can involve various criteria that comprise conflicting single objectives, this study proposes a new integrated multi-objective optimization and multi-criteria decision-making (MCDM) model for determining the most suitable electric vehicle supply equipment (EVSE) configuration. This approach combines the advantage of multi-objective optimization, which yields Pareto solutions, with an improved MCDM model. The latter is used to evaluate the Pareto frontier to find the best performing solution by enabling the station owners to use linguistic variables for weighting the decision-making variables. The conventional weighted aggregated sum product assessment (WASPAS) method is improved by introducing the Dombi Bonferroni functions in the proposed model making it more flexible as compared to its counterparts. In the final step, the selected solutions are ranked by reapplying the MCDM model. A case study is performed based on collected charging data from a workplace. To validate the proposed model, a comparison against four alternative MCDM models is performed. It is demonstrated that the proposed model yields very close ranking order as the alternative approaches. Among five EVSE options, DC fast charging is found to be the best while AC Level-2 EVSE (19.2/22 kW) is found to be the least attractive option. Sensitivity analysis shows the robustness of the ranking results in response to changing weightings of the model coefficients.

Suggested Citation

  • Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Deveci, Muhammet, 2021. "An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011880
    DOI: 10.1016/j.apenergy.2021.117866
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    References listed on IDEAS

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    2. Halil İbrahim Koruca & Murat Serdar Emek & Esra Gulmez, 2023. "Development of a new personalized staff-scheduling method with a work-life balance perspective: case of a hospital," Annals of Operations Research, Springer, vol. 328(1), pages 793-820, September.
    3. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    4. Sami M. Alshareef & Ahmed Fathy, 2023. "Efficient Red Kite Optimization Algorithm for Integrating the Renewable Sources and Electric Vehicle Fast Charging Stations in Radial Distribution Networks," Mathematics, MDPI, vol. 11(15), pages 1-30, July.
    5. Nagababu, Garlapati & Puppala, Harish & Pritam, Kocherlakota & Kantipudi, MVV Prasad, 2022. "Two-stage GIS-MCDM based algorithm to identify plausible regions at micro level to install wind farms: A case study of India," Energy, Elsevier, vol. 248(C).
    6. Deveci, Muhammet & Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Cali, Umit, 2023. "A rough Dombi Bonferroni based approach for public charging station type selection," Applied Energy, Elsevier, vol. 345(C).
    7. Fanshun Zhang & Zhuorui Zhang & Quanquan Zhang & Xiaochun Zhu, 2023. "Dynamic Evaluation of Product Innovation Knowledge Concerning the Interactive Relationship between Innovative Subjects: A Multi-Objective Optimization Approach," Mathematics, MDPI, vol. 11(9), pages 1-33, April.

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