Stochastic Energy Management of Electric Vehicles in Smart Grid

• Author / Creator

  Liu, Yuan

• Along with the growing environmental concerns, it is envisioned that electric vehicles (EVs) will play a significant role in transportation systems in the near future. Due to the ever-increasing penetration rate of EVs, new opportunities and challenges come one after another. On one hand, the increasing concentrated charging load of EVs potentially affects the power quality in the distribution system. On the other hand, based on the vehicle-to-grid (V2G) mechanism, EVs can also provide ancillary services by optimizing the charging/discharging schedules according to the requirement of the distribution systems. Furthermore, by involving the EV aggregators (AGGs) to manage all the EVs’ charging/discharging processes in a certain region, the EVs’ ancillary service capacity can be boosted. In order to optimize EV energy management, the randomness of individual EV mobility, electricity market, and power system should be taken into account.As a special category of EVs, electric buses (EBs) also attracted extensive attention in recent years because of their significance in the future public transportation systems. Compared with general EVs, EBs have large charging demand, considerable battery size, regular operation schedule, and high controllability in the charging schedule. Thus, the impact of increasing the EBs’ charging load on the distribution system should be evaluated. On the other hand, by effectively managing the energy flows in the EB transit center (EBTC) in coordination with the distribution system operator (DSO), the EBTC can become a valuable source of ancillary services.In this research, stochastic energy management of EVs and EBs in smart grid is investigated to mitigate the negative impact on power distribution systems and provide ancillary services. Four main research topics have been investigated. Firstly, the stochastic EV charging station operation in the smart grid is investigated. Secondly, the voltage regulation (VR) auction mechanism in the distribution system involving EV AGGs is studied. Thirdly, a data-driven approach for EB energy consumption estimation is proposed. Fourthly, a three-layer stochastic energy management approach is proposed for EBTCs to reduce the operation cost while maintaining local voltage quality.

• Subjects / Keywords

  • Electric Vehicle
  • Smart Grid
  • Stochastic optimization

• Graduation date

  Spring 2021

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-gxdy-aj29

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.