AI SearchOptimal scheduling of the EV charging for the mitigation of overload and unbalanced three-phase load conditions on the distribution transformer
Call Number: AIT Thesis no.SE-24-02 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. SE-24-02Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2024Description: 182 leaves ; illSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 2024 Summary: The global trend of EVs is increasing, as well as in Thailand. As EV penetration increases, single-phase residential chargers continue to account for most charging infrastructure. The additional load from residential EVs could generate peak demand that might affect the overload on the distribution transformer. In addition, the unplanned distributed single-phase chargers could increase the three-phase unbalanced load. Firstly, this research aims to study the impact of EV charging on the distribution transformer and Three-phase unbalanced load in the case of Overload. Then, EV charging scheduling techniques are developed to avoid overload of the parallel distribution transformer to mitigate three-phase load imbalances. To make the outcome more realistic, the model is simulated based on the actual low voltage distribution grid of the Provincial Electricity Authority (PEA) in Thailand, and The EV charging profile would be generated by a stochastic process based on data from National Household Travel Survey (NTHS 2017). The EV scheduling program is established by two-step optimization developed by GAMS software. The first objective is phase unbalance mitigation. Then, the EV charging power was scheduled in step two optimization based on the priced-based objective. The individual electric vehicle charging time from the scheduling program was simulated in the system by coordinating Python API and DIgSILENT power factory software. In conclusion, the outcomes from the simulation show the overload on the distribution transformer and the increase of three-phase unbalanced factors in a random charging scheme. After implementing the scheduling program, the simulation indicates no overload on the distribution transformer along the simulation. In terms of imbalance, the fluctuation of three-phase imbalance factors has been improved. As a result, the overall losses in the system with an EV scheduling program are lower than EVs charging scheme without a scheduling program.
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Asian Institute of Technology Library Archives | AIT Thesis no.SE-24-02 (Browse shelf(Opens below)) | Not for loan |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Sustainable Energy Transition
Thesis (M. Eng.) - Asian Institute of Technology, 2024
The global trend of EVs is increasing, as well as in Thailand. As EV penetration increases, single-phase residential chargers continue to account for most charging infrastructure. The additional load from residential EVs could generate peak demand that might affect the overload on the distribution transformer. In addition, the unplanned distributed single-phase chargers could increase the three-phase unbalanced load. Firstly, this research aims to study the impact of EV charging on the distribution transformer and Three-phase unbalanced load in the case of Overload. Then, EV charging scheduling techniques are developed to avoid overload of the parallel distribution transformer to mitigate three-phase load imbalances. To make the outcome more realistic, the model is simulated based on the actual low voltage distribution grid of the Provincial Electricity Authority (PEA) in Thailand, and The EV charging profile would be generated by a stochastic process based on data from National Household Travel Survey (NTHS 2017). The EV scheduling program is established by two-step optimization developed by GAMS software. The first objective is phase unbalance mitigation. Then, the EV charging power was scheduled in step two optimization based on the priced-based objective. The individual electric vehicle charging time from the scheduling program was simulated in the system by coordinating Python API and DIgSILENT power factory software. In conclusion, the outcomes from the simulation show the overload on the distribution transformer and the increase of three-phase unbalanced factors in a random charging scheme. After implementing the scheduling program, the simulation indicates no overload on the distribution transformer along the simulation. In terms of imbalance, the fluctuation of three-phase imbalance factors has been improved. As a result, the overall losses in the system with an EV scheduling program are lower than EVs charging scheme without a scheduling program.
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