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博碩士論文 etd-0715116-172555 詳細資訊
Title page for etd-0715116-172555
論文名稱
Title
在智能電網中運用負載預測於電動車充電排程之研究
Study on Charging Scheduling of Electric Vehicles with Load Forecast Information in Smart Grid
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
66
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-07-18
繳交日期
Date of Submission
2016-08-15
關鍵字
Keywords
電動車、帕雷托最佳解、線上演算法、充電排程、智慧電網
Electric vehicles, online algorithm, Pareto-optimal, scheduling, smart grid
統計
Statistics
本論文已被瀏覽 5715 次,被下載 33
The thesis/dissertation has been browsed 5715 times, has been downloaded 33 times.
中文摘要
在本篇論文中,我們利用一個中央控制站對微電網(學校或是城鎮)中的電動車進行充電排程的研究,利用一個中央控制站收集所有電動車的資料之後,針對充電費用以及用戶滿意度進行最佳化,因此提出一個雙目標函數的最佳化問題,然而同時達到兩個目標的計算複雜度非常高,所以我們提出一個演算法可以同時達到兩個目標但是不需要直接對雙目標函數找到最佳解,我們針對其中一個目標函數找到最佳解,根據帕雷托最佳解(Pareto optimality),可以找到對另一目標函數的接近最佳解,於此同時我們利用一個更有效的演算法來預測電網未來的基礎附載,使得我們演算法有更高的穩定度。在模擬結果中,相較於文獻提出的演算法,我們可以有效地降低電動車的充電時間以及充電時對電網的影響,同時增加用戶滿意度。
Abstract
This thesis studies the charge scheduling problem of electric vehicles (EVs) in
the scale of a microgrid (e.g., a university or town), where a set of charging stations
are controlled with a central aggregator. A bi-objective optimization problem is
formulated to jointly optimize total charging cost and user convenience. A close-tooptimal
online scheduling algorithm is then proposed as a solution. The algorithm
achieves optimal charging cost and is near optimal in terms of user convenience. The
proposed method also applies an efficient load forecasting technique to obtain future
load information. The algorithm is assessed through simulation and compared with
those in previous studies. Results reveal that our method improves previous alternative
methods in terms of the Pareto-optimal solution of the bi-objective optimization
problem, and provides a close approximation for load forecasting.
目次 Table of Contents
Thesis Validation i
Chinese Acknowledgement ii
English Acknowledgement iii
Chinese Abstract iv
English Abstract v
Contents vi
List of Figures vii
List of Tables ix
Glossary xi
Notations xii
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Related Work 6
2.1 Service Provider . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 EV Owners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Joint Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Load Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 System Model 10
4 Problem Formulation 13
4.1 Charging Cost . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.2 User Convenience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 EV Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.4 Power Grid Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.5 Dynamic Equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.6 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5 Solution 20
5.1 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.2 Scheduling Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5.3 Load Forecasting Method . . . . . . . . . . . . . . . . . . . . . . . . 24
6 Simulation 30
6.1 Simulation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
6.2.1 Results for Load Forecasting . . . . . . . . . . . . . . . . . . . 32
6.2.2 Evaluation Under Di erent Number of EVs in Residential Area 35
6.2.3 Evaluation Under Di erent Number of EVs in Commercial Area 41
7 Conclusion 48
Bibliography 50
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