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系統識別號 U0026-2607201714543800
論文名稱(中文) 加氫站設置區位與數量之策略模擬
論文名稱(英文) Hydrogen filling station allocation strategies
校院名稱 成功大學
系所名稱(中) 都市計劃學系
系所名稱(英) Department of Urban Planning
學年度 105
學期 2
出版年 106
研究生(中文) 蔡仲穎
研究生(英文) Jong-Ying Tsai
學號 P26014127
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 口試委員-陳彥仲
口試委員-石豐宇
口試委員-黃泰霖
指導教授-李子璋
中文關鍵字 加氫站  區位模型  P中位模型  蒙地卡羅模擬 
英文關鍵字 Hydrogen filling station  Allocation  The P-median model  Monte-Carlo simulation  Google Map distance matrix API 
學科別分類
中文摘要 氫能為未來極具發展潛力的能源戰略選項,其具有燃燒時無溫室氣體及有害氣體的排放,並能在地生產等優勢;作為車輛動力來源時,則擁有短時間補充燃料與長行駛距離等優點,是取代化石燃料的重點發展能源之一。
在未來推廣氫能時,提供貼近民眾的加氫站配置是一項重要關鍵。若在推廣能源的初期無法有效控管加氫站布點,將影響民眾購買氫能車輛意願,間接影響日後新能源的推廣。能源策略與政府政策息息相關,如何找出有效率的加氫站數量與區位分布,並透過補貼、與私部門合作等相關政策支援,以形成適當的加氫站網絡,為本研究的主要目的。
本研究選擇以P中位模型創造加氫站布點配置,並以台南市作為研究範圍。P中位模型能夠提供一套具彈性的流程以形成不同政策下的情境,模型建構所需的人口統計資訊也較容易收集,未來亦能套用到其他縣市幫助形成未來加氫站網絡。
研究方法上,P中位模型概念為配置一數量的加氫站情況下,找出加氫站位置分布能使各里民眾至最近加氫站加權距離總和最小化。本研究假設加氫站是以現有加油站升級而來,若一加油站空間及規範符合我國「加氣站設置管理規則」,則該加油站可作為未來加氫站候選點位;在計算全市167座加氫站候選點位到752個里之間的距離上,本研究透過程式撰寫向Google Map Distance Matrix API取得行車距離與時間,此種方法相較傳統最短距離計算等方法,能提供更精確的行車時間與距離計算。計算結果分別儲存於行車時間、行車距離兩矩陣中;在權重上,本研究以各里之家戶數作為第一種權重。此外並測試各里的活動量作為第二種權重,活動量在本研究中定義為該里所產生與結束的小汽車旅次數量;將行車時間、距離矩陣與家戶數、活動量二權重分別相乘,可得到四種組合矩陣;本研究再以程式撰寫蒙地卡羅模擬,分別找出四個矩陣中的最小化行車時間(距離),蒙地卡羅模擬能夠提供相較於傳統數學規劃更有效率與彈性的流程設計。
研究結果上,整個流程能夠提供不同加氫站數量下的點位配置模擬,包含台南市的加氫站配置點位分布、加氫站數量與行車時間(距離)間的關係與效用曲線、以及其他情境下加氫站配置策略建構。研究結果發現目前民眾抵達最近加油站的平均駕駛時間約為4分鐘,行駛距離約1.8公里;若現有加油站的10%未來提供加氫服務,在最佳配置情形下,前往最近加氫站的平均開車時間約為8分鐘,行駛距離約為3.6公里;若18%現有加油站提供加氫服務,開車到最近加氫站的時間約為6分鐘,行駛距離約為2.8公里;若30%現有加油站提供加氫服務,找尋到最近加氫站的時間約為5分鐘,行駛距離約2.3公里。本研究並比較使用行駛時間及行駛距離上的產生的差異、兩種權重之間的差異、台南市與美國加州沙加緬度郡效用曲線的差異。另外本研究亦模擬限制以中油直營站作為加氫站的情境、限制以特定站點作為加氫站的情境等,以提供相關單位在未來加氫站布點配置模擬上可能的策略。
英文摘要 The study is aimed toward determining the most efficient deployment of hydrogen filling stations to develop strategies for promoting hydrogen energy. Hydrogen is an available, clean form of energy for hydrogen vehicles and has the advantage of reducing negative impacts on the environment due to being free of greenhouse gases and other harmful emissions. Hydrogen vehicles take three to five minutes to refuel and have a long travel range on a single tank of fuel. Hydrogen is considered to be one of the best potential energy forms to replace gasoline for vehicles in the future.
To promote this type of energy, widespread availability of such a new energy is critical. However, there is no hydrogen filling station in Taiwan nowadays. The high cost of first hydrogen filling stations cannot be initially balanced by their returns. If there are no adequate hydrogen filling stations available initially, few customers will purchase hydrogen vehicles, which will lead to a vicious circle. Thus, it is critical to have efficient deployment of hydrogen filling station in the initial phrase.
Previous studies showed that the application of both the P-median models and the flow location models could create hydrogen filling station deployment scenarios. To fit the data requirement and future applications, and to develop a more flexible procedure, this study apply the P-median model to depict future hydrogen filling station deployment.
The P-median model was applied to minimize the total weighted driving time or distance from the centers of administrative neighborhoods to their nearest hydrogen fuel filling candidate sites. These candidate sites were filtered by testing whether existing gasoline filling stations meet the Regulations Governing Gas filling Stations criteria. Driving attribute were acquired through the Google Map Distance Matrix Application Programming Interface, which affords a more accurate driving information based on its comprehensive recommended routes compared to the conventional shortest route methods. The results were saved in two matrices, driving time and driving distance matrix, each of them contained driving data from 752 administrative neighborhoods to 167 current gasoline filling stations in Tainan City. The number of households and the activity-level were tested as the weights in the study. Then, the Monte-Carlo simulation, then, was applied to approach sets of combination of stations with the minimal total weighted attributes within each matrix. By testing the different scenarios, the optimization results of each matrix could be found.

This study provides a discussion of the relation between driving attribute and the number of hydrogen filling stations. The model suggests that around 10% of current gasoline filling stations only doubles the current driving time, or driving distance, to the nearest gasoline filling stations. When using 18% of the current gasoline filling stations as hydrogen filling stations, driving to hydrogen filling stations takes 2 minutes more or 1 km more than driving to gasoline filling stations. When 30% of the current gasoline filling stations are hydrogen filling stations, only 1 minute more or 0.5 km more is required to drive to hydrogen filling stations as compared to driving to gasoline filling stations. The simulation results verify that the obtained LOS of 10-30% of the gasoline filling stations is close to the LOS for gasoline filling stations. In addition to the quantity, the specific station locations can be obtained through the study process. This study also examined the difference in driving time and driving distance, and difference between weights, and compared the results between Tainan City, Taiwan and the Sacramento County, the U.S. Finally, the procedure of this study help create different future scenario strategies, such as CPC station scenarios and specific stations scenarios. By applying this process, policy makers can develop different strategies for allocating hydrogen filling stations in the future.
論文目次 Terminology...1
Chapter 1. Introduction...2
1.1. Background...2
1.2. Methodology and Contributions...2
1.3. Research objectives...3
Chapter 2. Literature review...4
2.1. Future alternative energy- hydrogen...4
2.2. Regulations of hydrogen filling stations...10
2.3. Method for allocation of hydrogen filling stations: location models...12
Chapter 3. Methodology...19
3.1. Research design...20
3.2. The P-median model description: attributes and weights...21
3.3. Programming of the P-Median model...24
3.4. The Monte-Carlo simulation...24
3.5. Adding or subtracting stations...28
3.6. Constraints...28
Chapter 4. Results...29
4.1. Optimization results...29
4.2. Applications...41
Chapter 5. Conclusions...44
5.1. Future application of this study...45
5.2. Future works...46
Reference list...47
Appendix...50
Appendix 1. Regulations Governing Gas filling Stations (in Chinese)...50
Appendix 2. Enforcement Rules of Urban Planning Law (in Chinese)...52
Appendix 3. Regulations Governing Gasoline filling Stations (in Chinese)...53
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