Abstract
This thesis aims to construct a systematic framework for integrating Electric Vehicles (EVs) into Low Voltage (LV) distribution grids. The ultimate goal is to develop a multi-functional, flexible and reliable smart charging (SC) algorithm enabling EV mass deployment in LV distribution grids. The framework for achieving the main research objective is segmented into several key parts:
- Conducting a thorough study on the EV mass deployment in distribution grids through grid load flow analysis.
- Performing a comparative investigation of representative heuristic EV charging tactics to establish a foundation for a smart charging algorithm.
- Developing a Power Transfer Distribution Factors (PTDF) based grid congestion prevention mechanism from the Distribution System Operator (DSO) perspective in anticipation of widespread EV connections.
- Designing, refining and validating a flexible, efficient and reliable hierarchical mixed integer programming (MIP) EV smart charging algorithm.
a. The developed algorithm is equipped with a passive stochasticity processing function and considers practical constraints in protocols such as IEC/ISO 15118 and IEC 61851-1. It is verified and assessed in a Power Hardware-In-the-Loop (PHIL) testbed.
b. Based on the experimental results, the algorithm's effectiveness is further enhanced in: charging current command levelling for a steadier charging process, upgrading grid balancing services, and acquiring a higher level of proximity to optimality. The stochasticity managing function is also upgraded for ad hoc admittance of (future) erratic charging events and self-correction of charging parameters.
c. The advanced EV smart charging algorithm is then assessed by comparing with uncontrolled and one heuristic charging method presented in part 2 above.
- Conducting a thorough study on the EV mass deployment in distribution grids through grid load flow analysis.
- Performing a comparative investigation of representative heuristic EV charging tactics to establish a foundation for a smart charging algorithm.
- Developing a Power Transfer Distribution Factors (PTDF) based grid congestion prevention mechanism from the Distribution System Operator (DSO) perspective in anticipation of widespread EV connections.
- Designing, refining and validating a flexible, efficient and reliable hierarchical mixed integer programming (MIP) EV smart charging algorithm.
a. The developed algorithm is equipped with a passive stochasticity processing function and considers practical constraints in protocols such as IEC/ISO 15118 and IEC 61851-1. It is verified and assessed in a Power Hardware-In-the-Loop (PHIL) testbed.
b. Based on the experimental results, the algorithm's effectiveness is further enhanced in: charging current command levelling for a steadier charging process, upgrading grid balancing services, and acquiring a higher level of proximity to optimality. The stochasticity managing function is also upgraded for ad hoc admittance of (future) erratic charging events and self-correction of charging parameters.
c. The advanced EV smart charging algorithm is then assessed by comparing with uncontrolled and one heuristic charging method presented in part 2 above.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 24 Oct 2024 |
Print ISBNs | 978-94-6384-651-6 |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Though not part of the committee, Dr. ir. A. Shekhar of Delft University of Technology has contributed greatly to the preparation of this dissertation.Keywords
- EV smart charging
- EV-grid integration
- Distribution grid
- Grid congestion management
- Hardware-In-the-Loop
- Energy system optimisation
- Power market