First Advisor

Robert Bass

Term of Graduation

Spring 2021

Date of Publication

4-20-2021

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Language

English

Subjects

Smart power grids, Electric power distribution -- Evaluation, Electric vehicles -- Energy consumption

Physical Description

1 online resource (xii, 104 pages)

Abstract

Growing Electric Vehicle penetration presents unwanted problems to grid reliability. For nearly every EV there is corresponding household charging. High penetration of Electric Vehicle Service Equipment can lead to over loading of assets and under voltage conditions. In order to understand the effects EV have on a distribution system, studies have to be done for EVSE to understand how the distribution system is affected. Using advanced Power Engineering Simulation Software is often the best way to model systems due to the credibility of their software modules. For this thesis, I developed a suite of distribution system analysis tools using CYME 7.1 and Python 2.7 for evaluating the impacts from EV penetration, particularly overloading and under-voltage events. EV penetration is the percentage of electric vehicles among total vehicles. Two of these tools apply new loads to and create intentional spotloads on the provided system. Another two tools incorporate time series demands for EV loads and provide load growth on the system. The final tool covers data collection for over loading and under voltage events. Through use of this work's EV Evaluation Tools, users can study how a distribution system may be impacted due to EV load growth and stochastic EV placement. These tools allow for a representation of how the system changes with increased EV penetration, at the years the penetration are projected to increase.

Rights

© 2021 Jacob Sheeran

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Comments

The dataset associated with this thesis can be accessed at https://archives.pdx.edu/ds/psu/34878.

Persistent Identifier

https://archives.pdx.edu/ds/psu/35666

definitions.py (1 kB)
Python Code: Definitions

function_study_analysis.py (2 kB)
Python Code: Function Study Analysis

LoadFlowOverload.py (114 kB)
Python Code: Load Flow Overload

lookup.py (2 kB)
Python Code: Lookup

main.py (16 kB)
Python Code: Main

ModifySpotLoad.py (38 kB)
Python Code: Modify Spot Load

UserInput.py (8 kB)
Python Code: User Input

CarsFromLoad.csv (3 kB)
Input CSVs: Cars from Load

PenetrationVsYear.csv (1 kB)
Input CSVs: Penetration vs Year

PEV-Profiles-L1.csv (44862 kB)
Input CSVs: PEV Profiles L1

PEV-Profiles-L2.csv (38965 kB)
Input CSVs: PEV Profiles L2

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