Date of Award

2022

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering (PhD)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Bo Chen

Committee Member 1

Zhaohui Wang

Committee Member 2

Lan Zhang

Committee Member 3

Ye Sun

Abstract

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI). This dissertation studies the optimal EV charging control and energy management of VGI system to address the economic benefits of EV charging customers and operation stability of distribution power grids. First of all, a centralized EV charging control method is developed under the transactive energy environment to minimize the EV charging cost and avoid the distribution power grid overloading. The quadratic programming is used to obtain optimized charging control actions. Case studies with hundreds of EVs are conducted and analyzed. Secondly, to explore a more effective and securer VGI system, an advanced distributed algorithm is developed for solving the large-scale EV charging scheduling problem, including the perspectives at an individual EV level, the distribution node level, and the distribution network level. A clearing electricity price is attained by a negotiation method among the distribution system operator and the EV aggregators (EVAs). This mechanism provides incentive for EV charging customers to improve network operation performance. Thus, EVAs and EVs can make their charging scheduling autonomously with the clearing price signals. Thirdly, to realistically estimate the charging power demand of extreme fast charging (XFC) stations, a Monte Carlo (MC) simulation tool is developed based on the EV arrival time and state of charge (SOC) distributions obtained from vehicle travel survey dataset. To reduce the investment and operation costs of XFC stations and avoid overloading the grid due to XFC events, an optimal configuration method is presented for the multiple XFC stations in a distribution network to determine the optimal energy capacity of energy storage system (ESS), ESS rated power, and the size of photovoltaic (PV) panels, which are integrated with XFC stations. The MC simulation tool is valuable since little XFC charging dataset is available at the current stage and the optimal configuration method can reduce the investment and operation costs of XFC stations while meeting the charging demand and the operational constraints of the distribution network, XFC, ESS, and PV panels. Finally, a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies is presented. This test platform considers the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. The modular open systems design approach of the platform allows the integration of EV charging control algorithms and hardware charging systems for performance evaluation and interoperability testing.

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