An optimal scheduling scheme for electric vehicles in smart grids
Electric Vehicles (EV) have become significantly popular in recent years due to its energy efficiency, quiet driving experience, elimination of harmful CO2 emission and, a cheaper alternative to their gasoline-powered counterparts. The increase in EV population will have a serious effect on the elec...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77777 |
| _version_ | 1826113777613930496 |
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| author | Chua, Kenneth Dian Chao |
| author2 | Soh Cheong Boon |
| author_facet | Soh Cheong Boon Chua, Kenneth Dian Chao |
| author_sort | Chua, Kenneth Dian Chao |
| collection | NTU |
| description | Electric Vehicles (EV) have become significantly popular in recent years due to its energy efficiency, quiet driving experience, elimination of harmful CO2 emission and, a cheaper alternative to their gasoline-powered counterparts. The increase in EV population will have a serious effect on the electrical grid with a surge in electrical demand. Two optimisation methods will be done in this project and compared to find the best method which can improve the charging and discharging of EVs. Global scheduling optimisation is one of the methods which optimises the charging powers to minimise the full cost of all the EVs performing both charge and discharge in a day. Another method is the locally optimised scheduling scheme, which aims to minimise the total cost of a local group of the EVs. Nonetheless, the global optimisation scheme is not practical as it needs the future base load data, arrival and departure times, and the future EV charging periods times of the day. The local optimisation scheme is a better option as it works with dynamic EV arrival times and it is also adaptable to a larger population of EVs. MATLAB will be used where mathematical programming models are formulated along with CVX, a solver for optimisation problems. Through simulations will the results be compared and shown that the local optimisation scheduling scheme can achieve a more accurate result than the global optimisation scheduling scheme. |
| first_indexed | 2024-10-01T03:28:47Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77777 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:28:47Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/777772023-07-07T16:44:33Z An optimal scheduling scheme for electric vehicles in smart grids Chua, Kenneth Dian Chao Soh Cheong Boon Liu Xiaochuan School of Electrical and Electronic Engineering DRNTU::Science::Mathematics::Applied mathematics::Optimization DRNTU::Engineering::Electrical and electronic engineering Electric Vehicles (EV) have become significantly popular in recent years due to its energy efficiency, quiet driving experience, elimination of harmful CO2 emission and, a cheaper alternative to their gasoline-powered counterparts. The increase in EV population will have a serious effect on the electrical grid with a surge in electrical demand. Two optimisation methods will be done in this project and compared to find the best method which can improve the charging and discharging of EVs. Global scheduling optimisation is one of the methods which optimises the charging powers to minimise the full cost of all the EVs performing both charge and discharge in a day. Another method is the locally optimised scheduling scheme, which aims to minimise the total cost of a local group of the EVs. Nonetheless, the global optimisation scheme is not practical as it needs the future base load data, arrival and departure times, and the future EV charging periods times of the day. The local optimisation scheme is a better option as it works with dynamic EV arrival times and it is also adaptable to a larger population of EVs. MATLAB will be used where mathematical programming models are formulated along with CVX, a solver for optimisation problems. Through simulations will the results be compared and shown that the local optimisation scheduling scheme can achieve a more accurate result than the global optimisation scheduling scheme. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-06T05:52:25Z 2019-06-06T05:52:25Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77777 en Nanyang Technological University 59 p. application/pdf |
| spellingShingle | DRNTU::Science::Mathematics::Applied mathematics::Optimization DRNTU::Engineering::Electrical and electronic engineering Chua, Kenneth Dian Chao An optimal scheduling scheme for electric vehicles in smart grids |
| title | An optimal scheduling scheme for electric vehicles in smart grids |
| title_full | An optimal scheduling scheme for electric vehicles in smart grids |
| title_fullStr | An optimal scheduling scheme for electric vehicles in smart grids |
| title_full_unstemmed | An optimal scheduling scheme for electric vehicles in smart grids |
| title_short | An optimal scheduling scheme for electric vehicles in smart grids |
| title_sort | optimal scheduling scheme for electric vehicles in smart grids |
| topic | DRNTU::Science::Mathematics::Applied mathematics::Optimization DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/77777 |
| work_keys_str_mv | AT chuakennethdianchao anoptimalschedulingschemeforelectricvehiclesinsmartgrids AT chuakennethdianchao optimalschedulingschemeforelectricvehiclesinsmartgrids |