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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 |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
66 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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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 |
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統計 Statistics |
本論文已被瀏覽 5715 次,被下載 33 次 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 |
參考文獻 References |
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