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研究生： 張荐豪
Chang, Chien-Hao
論文名稱： 以排隊理論為基礎之電動車充電站最佳化電力排程
Queuing Theory-based Power Scheduling for an EV Charging Station
指導教授： 楊宏澤
Yang, Hong-Tzer
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 78
中文關鍵詞： 電動車 、分散式架構 、最佳化充放電排程 、排隊模型 、非齊性卜瓦松過程 、輔助服務
外文關鍵詞： Electric vehicle (EV), decentralized architecture, optimal scheduling of charging and discharging, queuing model, non-homogeneous Poisson process, ancillary service
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    • 由於各國採取的綠能政策，電動車將被大量加入未來的智慧電網中，公共充電站也將變得越來越普遍。然而，大量電動車的不協調充電將會嚴重影響電網穩定與安全。相反地，若能經過整合與正確管理電動車，透過車輛到電網（V2G）系統不但可以在能源市場中進行套利，還能提供電網各項的輔助服務，例如頻率調節、削峰填谷，並有助於可再生能源的整合。
    本文旨於提出一應用於電動車充電站之最佳化充/放電排程策略，在有限的資源下，根據各台電動車所計算出的動態優先權，以分散式架構下進行有效的管理充電站，並參與升/降載輔助服務市場。此外，本文亦考慮排隊模型以符合現實中電動車用戶的動態特性與充電站有限的容量限制，其排隊模型將電動車的到達視為一天中不同抵達率的非齊性卜瓦松過程，而服務時間則根據電池充電行為與停留時間進行建模。在模擬結果中顯示充電站業者透過整合大量車輛參與輔助服務市場確實可從中獲利。

    The adoption of renewable energy policies by various countries indicates that electric vehicles (EV) will be added to future smart grids in large numbers, and public charging stations will become increasingly common. However, the uncoordinated charging of a large number of EVs may seriously affect the stability and safety of the power grid. On the contrary, if the EVs can be integrated and properly managed, the vehicle-to-grid (V2G) system will not only facilitate arbitrage in the energy market but also provide various ancillary services to the grid, such as frequency regulation and peak load balancing, and contribute to the integration of renewable energy.
    This thesis aims to propose an optimal charge/discharge scheduling strategy for EVs in the charging stations. As the resources are limited, the dynamic priority of each EV is calculated and a decentralized architecture is used for the EV charging stations to ensure effective management and to participate in the ramping up and down of the ancillary services market. In addition, this thesis considers the queuing model to meet the real-time dynamic characteristics of EVs and the limited capacity of charging stations. The queuing model regards the arrival of EVs as a non-homogeneous Poisson process with different arrival rates over the day, while the service time is modeled based on battery charging behavior and parking time. The simulation results show that the charging station operator can benefit from integrating a large number of vehicles by participating in the ancillary service market.

    摘 要 I ABSTRACT II 誌 謝 IV CONTENTS VI LIST OF FIGURES IX LIST OF TABLES XI CHAPTER 1 INTRODUCTION 1 1.1 Background and Motivation 1 1.2 Literature Review 2 1.3 Research Objective and Contributions 5 1.4 Organization of the Thesis 6 CHAPTER 2 SYSTEM DESCRIPTION AND MATHEMATICAL MODELS 7 2.1 Introduction 7 2.2 Overall System Architecture 7 2.3 Function of Components 9 2.3.1 Transmission and Distribution System Operator 9 2.3.2 Charging Station Operator 9 2.3.3 Electric Vehicle Charging Station 10 2.3.4 Distributed Energy Resource 10 2.4 EV Charger 10 2.5 Market Mechanism 12 2.5.1 Background 12 2.5.2 Flexible Ramping Market 13 2.5.3 Charging Fee for EV Owners 15 2.6 Battery Charging Behavior (BCB) 16 2.7 Queuing Theory 19 2.7.1 Little's Law 19 2.7.2 M/G/S/K Queuing Model 19 CHAPTER 3 THE PROPOSED METHOD AND OPTIMIZED SCHEDULING ALGORITHM 25 3.1 Introduction 25 3.2 Procedure of the Proposed Method 25 3.3 Load Forecasting 28 3.4 Dynamic Characteristic Processing 31 3.4.1 Time-to-Interval Mapping 31 3.4.2 Sliding Time Window 32 3.5 Battery Wear Cost Model 33 3.6 Description of the Problem for EVCS 37 3.6.1 Objective Function 37 3.6.2 Resource Allocation Strategy 41 3.6.3 Constraints 43 3.7 Differential Evolution Algorithm 47 CHAPTER 4 SIMULATION RESULTS 51 4.1 Introduction 51 4.2 Simulated Systems 51 4.2.1 Charging Station System Parameters 51 4.2.2 EV Parameters 52 4.2.3 Base Load and PV System 57 4.2.4 California Electricity Market 58 4.3 Simulation Results and Analysis 59 4.3.1 Comparison of Different Average Number of EV 59 4.3.2 Participation in Ancillary Service Market 65 4.3.3 Impact of Different Contract Capacities 67 4.3.4 Sensitivity Analysis of Number of Chargers 71 CHAPTER 5 CONCLUSIONS 72 5.1 Conclusions 72 5.2 Future Work 73 REFERENCES 74

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