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研究生： 陳俊志
Chen, Jyun-Jhih
論文名稱： 電動車停車場參與輔助服務市場之電能管理系統
Energy Management System of EV Charging Station for Participation in Ancillary Service Market
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： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 98
中文關鍵詞： 輔助服務 、電動車 、再生能源 、隨機規劃 、兩階段電能管理系統
外文關鍵詞： ancillary service (AS), electric vehicle (EV), renewable energy sources (RES), stochastic programming, two-stage energy management system
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    • 隨著全球環保意識提高，各國採取綠能政策下，大量再生能源與電動車併入電網。由於兩種資源與設備都具有高度不確定性，將帶給電力系統運轉嚴峻的挑戰。因此須透過電能管理系統有效的協調整合再生能源與電動車，以提高電網安全性與穩定性，並增加相關運營商利潤。本文旨在提出一應用於電動車充電站的兩階段最佳化排程策略，其中使用隨機規劃方法處理太陽能發電與電動車的不確定性，考慮儲能系統操作成本與電動車使用者收入下，參與需量反應負載管理措施等輔助服務市場，有效操作系統內分散式能源，使充電站總成本最小化。本文藉由模擬一座含有100具充電樁之充電站以驗證所提出方法之可行性，結果顯示所提方法考慮不同需量反應負載管理措施與輔助服務機制對最佳化結果之影響，進行充電站業者整體成本比較，可解決大量電動車與再生能源加入電網後，所造成的區域微電網即時供需不平衡問題。結果亦顯示充電站參與即時備轉輔助服務可獲得最大收益外，方法中考慮再生能源與電動車之不確定性有其重要性。

    As environmental awareness increases worldwide and countries adopt renewable energy policies, numerous renewable energy sources (RES) and electric vehicles (EVs) have been integrated into the power grid. However, therein challenges the stability of the power system. The effective and coordinated integration of RES and EVs through energy management systems (EMSs) can go beyond improving the safety and stability of the power system to increase operator profit.
    This thesis proposes a two-stage optimization scheduling strategy for use in EV charging stations. This strategy uses stochastic programming to manage the uncertainties of photovoltaic power generation and EVs and to account for the operating cost of the energy storage system (ESS) and revenue from EVs. The charging station is involved in the demand response load management program or ancillary service (AS) market. Moreover, it effectively allocates distributed energy resources (DERs) to minimize its total system costs. A simulation of a charging station with 100 EV chargers was conducted to verify the feasibility of the proposed method in resolving the supply-demand imbalance in regional microgrid caused by the addition of several EVs and RES to the grid. In the results, various demand response load management and AS mechanisms were compared concerning the correspondence between the optimization results and the operator cost. It is not only observed that the charging station obtain the maximal benefits for participating in the spinning reserve service of AS market but also shows that uncertainties are highly influential.

    摘要 I ABSTRACT II 誌謝 IV CONTENTS V LIST OF FIGURES VIII LIST OF TABLES XI CHAPTER 1 INTRODUCTION 1 1.1 Background and Motivation 1 1.2 Review of Literature 2 1.3 Research Objective and Contributions 4 1.4 Organization of the Thesis 6 CHAPTER 2 SYSTEM DESCRIPTION AND MODELING 7 2.1 Introduction 7 2.2 Overall System Framework 7 2.3 Function of Roles 8 2.3.1 Transmission and Distribution System Operator 8 2.3.2 Charging Point Operator 9 2.3.3 Distribution Energy Resources 9 2.3.4 Electric Vehicle Charging Station 9 2.4 EV Charging Methods and EV Chargers 10 2.5 Market Mechanisms 12 2.5.1 Charging Fees for EV Users 12 2.5.2 Demand Response Load Management Program 13 2.5.3 Ancillary Services 16 CHAPTER 3 THE PROPOSED METHOD AND OPTIMIZATION SCHEDULING ALGORITHM 20 3.1 Introduction 20 3.2 Problem Formulation and Proposed Solution 20 3.2.1 Objective Function of Day-Ahead Energy Planning 20 3.2.2 Constraints of Day-Ahead Energy Planning 25 3.2.3 Objective Function of Real-Time Operation 33 3.2.4 Constraints of Real-Time Operation 37 3.3 Battery Degradation Model 49 3.4 Procedure of Two-Stage Energy Management System of EVCS 50 3.5 Dynamic Characteristic Processing 55 3.5.1 Time-to-Interval Mapping 55 3.5.2 Sliding-Time Window 56 3.6 Stochastic Programming Method 57 3.7 Linear Programming 59 CHAPTER 4 SIMULATION RESULTS 60 4.1 Introduction 60 4.2 Simulation System Parameters 60 4.2.1 Charging Station System Parameters 60 4.2.2 EV Parameters 61 4.2.3 Baseload and PV Systems 65 4.2.4 Electricity Price Signals 68 4.3 Simulation Results and Analysis 69 4.3.1 Case 1: Cost Analysis of Participation in Different Service Markets 69 4.3.2 Case 2: Sensitivity Analysis of ESS Specifications and Contract Capacities 80 4.3.3 Case 3: Cost Analysis of Only EVs Participation in the SpR Service 85 4.3.4 Case 4: With/Without Consideration of PV and EV Uncertainties 88 CHAPTER 5 CONCLUSION AND FUTURE PROSPECTS 90 5.1 Conclusion 90 5.2 Future Prospects 91 REFERENCES 92

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