MILP‐based customer‐oriented E‐Fleet charging scheduling platform
Abstract Conventional road transport is a significant contributor to greenhouse gas emissions and air pollution. Electric mobile assets (E‐buses, E‐trucks, E‐taxis, and even E‐ferries) are an efficient, low noise, low emission alternative which could—if operated alongside significant renewable energ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/stg2.12034 |
| _version_ | 1811218874768556032 |
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| author | Parisa Akaber Timothy Hughes Sergey Sobolev |
| author_facet | Parisa Akaber Timothy Hughes Sergey Sobolev |
| author_sort | Parisa Akaber |
| collection | DOAJ |
| description | Abstract Conventional road transport is a significant contributor to greenhouse gas emissions and air pollution. Electric mobile assets (E‐buses, E‐trucks, E‐taxis, and even E‐ferries) are an efficient, low noise, low emission alternative which could—if operated alongside significant renewable energy capacity—help to improve air quality and avoid catastrophic global warming. In this study, a Mixed Integer Linear Programming‐based solution for operating a fleet of E‐mobile assets has been proposed, considering technical and operational characteristics of different fleet elements. It takes into account customer (E‐fleet operator) objectives, such as load balancing and charging cost minimization. Furthermore, the E‐depot charging capacity increase achieved through integrating an energy storage system has been studied and discussed. The results of the optimization have been discussed and reported using a case study (demonstrating peak reduction of up to 50% and total charging reduction of 27%) and the benefits of operating an E‐fleet using the proposed solution have been discussed from economic and technical perspectives. |
| first_indexed | 2024-04-12T07:16:51Z |
| format | Article |
| id | doaj.art-3d46682ce8c34595ad7019ac3f38205e |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-12T07:16:51Z |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-3d46682ce8c34595ad7019ac3f38205e2022-12-22T03:42:25ZengWileyIET Smart Grid2515-29472021-04-014221522310.1049/stg2.12034MILP‐based customer‐oriented E‐Fleet charging scheduling platformParisa Akaber0Timothy Hughes1Sergey Sobolev2Smart Infrastructure Siemens plc Newcastle upon Tyne, England UKSmart Infrastructure Siemens plc Newcastle upon Tyne, England UKSmart Infrastructure Siemens plc Newcastle upon Tyne, England UKAbstract Conventional road transport is a significant contributor to greenhouse gas emissions and air pollution. Electric mobile assets (E‐buses, E‐trucks, E‐taxis, and even E‐ferries) are an efficient, low noise, low emission alternative which could—if operated alongside significant renewable energy capacity—help to improve air quality and avoid catastrophic global warming. In this study, a Mixed Integer Linear Programming‐based solution for operating a fleet of E‐mobile assets has been proposed, considering technical and operational characteristics of different fleet elements. It takes into account customer (E‐fleet operator) objectives, such as load balancing and charging cost minimization. Furthermore, the E‐depot charging capacity increase achieved through integrating an energy storage system has been studied and discussed. The results of the optimization have been discussed and reported using a case study (demonstrating peak reduction of up to 50% and total charging reduction of 27%) and the benefits of operating an E‐fleet using the proposed solution have been discussed from economic and technical perspectives.https://doi.org/10.1049/stg2.12034air pollutionair pollution controlbattery powered vehiclesinteger programminglinear programmingrenewable energy sources |
| spellingShingle | Parisa Akaber Timothy Hughes Sergey Sobolev MILP‐based customer‐oriented E‐Fleet charging scheduling platform IET Smart Grid air pollution air pollution control battery powered vehicles integer programming linear programming renewable energy sources |
| title | MILP‐based customer‐oriented E‐Fleet charging scheduling platform |
| title_full | MILP‐based customer‐oriented E‐Fleet charging scheduling platform |
| title_fullStr | MILP‐based customer‐oriented E‐Fleet charging scheduling platform |
| title_full_unstemmed | MILP‐based customer‐oriented E‐Fleet charging scheduling platform |
| title_short | MILP‐based customer‐oriented E‐Fleet charging scheduling platform |
| title_sort | milp based customer oriented e fleet charging scheduling platform |
| topic | air pollution air pollution control battery powered vehicles integer programming linear programming renewable energy sources |
| url | https://doi.org/10.1049/stg2.12034 |
| work_keys_str_mv | AT parisaakaber milpbasedcustomerorientedefleetchargingschedulingplatform AT timothyhughes milpbasedcustomerorientedefleetchargingschedulingplatform AT sergeysobolev milpbasedcustomerorientedefleetchargingschedulingplatform |