Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Engineering Science

Program

Electrical and Computer Engineering

Supervisor

Badrkhani Ajaei, Firouz.

Abstract

This thesis presents a methodology for optimal Battery Energy Storage System (BESS) sizing in DC Fast Charging stations (DCFCs) using a Mixed-Integer Linear Programming (MILP) framework. The approach minimizes electricity costs, reduces grid dependence, and enhances system resilience, while accounting for battery aging and the value of unserved energy. The methodology is evaluated by comparing its performance with a commercial software. A novel energy management approach is developed based on managed charging and load curtailment and Model Predictive Control (MPC). This energy management algorithm utilizes MILP formulation with a rolling horizon to maximize profit and address challenges such as limitation in the power exchanged with the grid, improving system reliability, and maintaining power balance of the fast-charging station. Compared with a rule-based energy management method as a baseline, it demonstrates superior performance in cost reduction and efficiency. A tailored load estimation technique is also introduced to improve load prediction accuracy to use in both battery sizing and energy management approach.

Summary for Lay Audience

The increasing adoption of Electric Vehicles (EVs) has increased the demand for Fast Charging Stations (FCSs). However, managing the energy consumed by these charging stations, especially in terms of electricity costs, grid dependence, and system resiliency presents significant challenges. This thesis aims to reduce the investment, operation and maintenance costs of FCSs by introducing a new approach for BESS sizing and an effective Energy Management algorithm. The proposed BESS sizing approach utilizes a Mixed-Integer Linear Programming (MILP) optimization which enables the FCS to minimize its electricity costs and improve its resiliency under power outages, while also considering battery aging and degradation. The developed BESS sizing algorithm is validated using a detailed prediction of the demand which is based on real-world charging patterns. The proposed energy management approach utilizes a Model Predictive Control (MPC)-based, rolling horizon optimization with MILP formulation to maximize profit, optimize energy generation, storage, and consumption in the FCS microgrid to provide cost-effective and reliable charging while accounting for operational limits. The developed energy management approach incorporates strategies such as managed charging and load curtailment, which help power balance, lower electricity costs, and prevent overloading of the grid’s components. This work highlights the importance of energy storage and smart charging strategies in designing efficient and sustainable EV charging stations. The findings are valuable for EV charging station owners and operators looking to reduce costs, improve energy efficiency, and ensure reliable service for drivers.

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