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|Title:||An optimal scheduling scheme for electric vehicles in smarts grids||Authors:||Teh, Xiu Yi||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2020||Publisher:||Nanyang Technological University||Project:||A1146-191||Abstract:||Over the past ten years, the use of electric cars has risen rapidly. While charging a huge amount of electric vehicles at the same time or during peak hours might affect the electrical grid because of the sudden surge of electric demand. However, if the electric vehicles are being charged using smart charging solutions, the electrical grid will be stable even with a significant increase in the number of electric vehicles. Therefore, in this project, two techniques of optimization will be performed to find the optimal electric vehicle charging scheduling. The comparison will be done on the charging of electric vehicles without scheduling, global optimization with a centralized controller, and local optimization with a local controller using the sliding window method. First, a global optimization scheduling scheme is introduced as it helps to reduce the total cost price of charging electric vehicles and it is able to control the charging schedule of all electric vehicles by a central controller as it reshapes the base load of the grid. However, it is an impractical scheme as the future base load of energy demand, arrival time and charging period of the electric vehicles are unknown. Hence, a local optimization scheme is then introduced to tackle those problems. Local optimization scheme also aims to minimize the total cost price of charging the electric vehicle but instead of using the central controller, it focuses on controlling the charging schedule of the current ongoing electric vehicle set in a local group by a local controller. In this way, the scheme is able to work with a dynamic electric vehicle’s arrival times and able to handle a large population of vehicles. The simulation will be done by using MATLAB to create mathematical programming models together with Gurobi, an optimization problem solver. We will be able to see that both optimization scheme can control the charging schedule of electric vehicles and minimize the total cost price as compared to the non-scheduling of the charging load. Also, a local optimization scheduling scheme can achieve a more accurate result as compared to a global optimization scheduling scheme.||URI:||https://hdl.handle.net/10356/141881||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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