Development of Predictive Methodology for Charging Load of Next Generation Electric Vehicle Using Probabilistic Method
- 주제(키워드) electric vehicle , EV charging load estimation
- 발행기관 고려대학교 대학원
- 지도교수 장길수
- 발행년도 2017
- 학위수여년월 2017. 8
- 학위구분 박사
- 학과 대학원 전자전기공학과
- 원문페이지 99 p
- 실제URI http://www.dcollection.net/handler/korea/000000076254
- 본문언어 영어
- 제출원본 000045915399
초록/요약
Upon recent uprising public interest and increased investment in the area relating to the environment, there are many ongoing changes underway in the energy field in various aspects. For example, in one aspect, efforts have been placed to reduce use of fossil fuels. Accordingly, while majority of energy is obtained from fossil fuels in current energy generation systems, the ratio of energy generated from environment-friendly new energy sources as represented by wind power and solar power is expected to gradually increase. In another aspect, efficient energy consumption has been contemplated as a significant issue. Indeed, many efforts are being made to develop technologies to reduce losses and improve efficiency in energy transportation and consumption processes. As a result, a new paradigm power system called “Smart Grid” is introduced. Among many energy consumption sectors, a radical and significant change in the transportation sector emerges in the automotive industry with the increase in electric vehicles. Unlike automobiles based on conventional fossil fuels, electric vehicles use electricity as an energy source, which leads to a major change in the proportion of final energy consumption, especially in the power sector. In this Dissertation, scenario-based probabilistic electric vehicle load forecast methodology considering characteristics of electric vehicles and their running pattern was developed. Unlike the existing first-generation electric vehicles, second-generation electric vehicles are in large-capacity and are capable of long-distance driving. Thus, this study considered such characteristics of the second-generation electric vehicles along with development of the fast charging technology as new factors when comparing with the existing electric vehicle charge load forecast methodology. In this dissertation, we also developed 1) a variety of scenarios that can be used in different situations, 2) an electric vehicle load forecasting methodology by a stochastic approach to charge capability according to a car usage pattern and charger supply conditions, and conducted simulations based on actual supply forecast data. Due to the development of battery technology and the subsequent falling of battery prices, second-generation electric vehicles are equipped with larger capacity batteries than previously used. Such increase in battery capacity not only affects the amount of charge load and charging time, but also can be a good flexibility resource when considering many electric vehicle batteries as virtual power sources. In future systems where more new and renewable energy sources arise, uncertainty and volatility are likely to increase as well. Thus, electric vehicles would provide some predictability in energy use, but concurrently may subject to some uncertainty along with use of such new and renewable energy sources. It is also expected that electric vehicles would play a role as a demand response source by a charge control, or a virtual plant upon using V2G technology, offsetting such uncertainties and volatility of renewable energy sources.
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TABLE OF CONTENTS
ABSTRACT I
TABLE OF CONTENTS IV
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER 1. INTRODUCTION 1
1.1 Motivations 1
1.2 Aim and Outline 5
1.3 Contributions to the Dissertation 6
CHAPTER 2. KEY INDICATORS OF ELECTRIC VEHICLES 8
2.1 Smart Grid Element 9
2.1.1 Renewables 9
2.1.2 Energy Storage 10
2.1.3 Electric Vehicle 11
2.1.4 Flexible Transmission System 11
2.2 Electric Vehicle Overview 12
2.2.1 Electric Vehicle Supply Outlook and Trneds 12
2.2.2 The Main Parameters of The Electric Vehicle Load Calculation 14
2.2.3 Charging Methods 16
2.2.4 Charging Time 18
2.2.5 Constraints by Number of Charger 18
2.3 Battery and Charging Characteristic 19
2.3.1 Battery Model 19
2.3.2 Charging Efficiency 20
2.3.3 Charging External Characteristic Parapetners of The Lithium-Ion Battery 21
2.4 Charging Modeling 24
2.4.1 Constant Current Model 25
2.4.2 Constant Voltage Model 26
2.4.3 Intermediate Point 28
2.4.4 Cut-off Point 29
CHAPTER 3 DECISION PROCESS AND METHOD OF MAJOR VARIABLES FOR DAILY ELECTRIC VEHICLE CHARGING LOAD FORECAST 31
3.1 Basic Assumptions 33
3.2 Background Information 35
3.2.1 Vehicle Information 35
3.2.2 Environment Information 38
3.2.3 Energy Efficiency Index According to Temeperature 39
3.3 Decision Process and Method of Major Variables for Daily Electric Vehicle Charging Load forecast 40
3.3.1 Charging Scenario 41
3.3.2 Periodic Charge 43
3.3.3 Generate Charging Signal 45
3.3.4 Set Charge Start Time 46
3.3.5 Charge Amount Calculation 48
3.3.6 Charging Time 48
CHAPTER 4 ELECTRIC VEHICLE CHARGING LOAD FORECAST METHODOLOGY 50
4.1 A Methodology of Total Annual Energy Consumption Calcualtion For Electirc Vehicles 51
4.2 Daily Electric Vehicle Charging Load Forecast Algorithm 52
CHAPTER 5. CASE STUDIES 59
5.1 Simulation Case 59
5.2 Simulation Result 64
5.2.1 Total Annual Energy Consumption 64
5.2.2 Daily Electric Vehicle Charging Load 65
CHAPTER 6. CONCLUSION 69
REFERENCES 71
APPENDIX A: Traffic Statistics Data 78
APPENDIX B: Daily Electric Vehicle Charging Load 81
KOREAN ABSTRACT 83
