| 摘要: | 由於全球氣候變遷，在國際能源總署所提出2050淨零碳排(Net zero emissions)的架構下，各國政府均依據階段性目標調整施政方針。目前我國交通運輸為化石燃料主要消費部門，儘管政府提出能耗標準、徵收碳費、購車補助等措施，但在大型車輛電動化的發展卻依然遲緩。由於貨車的能耗佔所有公路運具的29.27%、溫室氣體佔30.13%，並且登記數量遠高於大客車，若加速物流運輸產業電動化進程將有效降低台灣總碳排放量。然而電動貨車的價格為普通燃油貨車的3倍，且當前儲能技術較難滿足貨車高負重、長途運輸的車用需求；此外稀少的固定式充電站也讓充電時間產生很大不確定性，將影響貨物交期。因此本研究將透過搭配移動式充電的方式，彌補固定式充電站數量不足的問題，並消除物流業者的里程焦慮，建構一個專門為物流車隊補充電力的充電網路。
本研究為帶時間窗的車輛途程問題(VRP with Time Windows, VRPTW)，根據電動物流車規格與發車班次間隔，找出需求點的發生時間、位置與服務時間進行求解。研究目標為滿足所有需求點的前提下，求出所需要的移動式充電數量，並最小化所有移動式充電的總行駛成本。在安全充電百分比、物流車車速、充電效率等條件設定下，研究結果分別以2、3、4班車(16、24、32個需求點)的情境下呈現，包括所需移動式充電數量、總行駛距離與成本，及每台移動式充電的服務路徑、服務次數及營運時間。在敏感度分析中，儲能裝置的容量大小會使移動式充電數量存在最少限制，否則無法滿足所有需求點；此外儲能裝置容量與移動式充電數也並非愈多愈好，超過臨界值後總行駛成本便不會再降低。此外，充電器的功率(kW)也會影響單次服務時間與總行駛成本。因此物流業者若希望將物流車隊電動化，須根據車隊規模來決定應派遣多少台移動式充電，並謹慎計算儲能電池容量及搭載合適功率的充電器，才能在完成所有充電需求的同時避免不必要的花費。
;Due to global climate change, under the 2050 Net zero emissions framework proposed by the International Energy Agency, governments have adjusted their policies by phases. At present, Taiwan’s transportation department is the main consumer of fossil fuels. Although there are measures such as energy consumption standards, carbon tax, and subsidies for car purchase, the development of electrification of truck is still slow. The energy consumption of trucks accounts for 29.27% of all vehicles, greenhouse gases account for 30.13%, and the number of trucks (1,127,510) is much higher than buses (31,931). If the electrification process of logistics industry is accelerated, carbon emissions will be effectively reduced. For the logistics companies, the price of electric truck is three times than fuel truck, and current energy storage technology is still difficult to meet the needs of trucks with high load and long-distance moving. In addition, the scarcity of fixed charging stations also makes charging time greatly different, and will affect the delivery of goods. Therefore, this research will solve the problem of insufficient fixed charging stations by matching mobile charging stations (MCS), and eliminate range anxiety of truck drivers, to build a charging network specially designed for electric trucks power supplement.
This study is a vehicle routing problem with time windows (VRPTW). The problem is solved by using data such as the specifications of electric trucks, number of runs, the occurrence time, location and service time of demand points. The research goal is to find the required number of MCS on the premise of satisfying all demand points, and minimize the total transport cost of all MCS. The results are presented in the scenarios of 2, 3, and 4 runs of trucks (26, 24, and 32 demand points), respectively include the number of MCS required, total transport distance and cost, and the serving route, times of service and operating time of each MCS. In the sensitivity analysis, the capacity of the energy storage system will limit the number of MCS, otherwise it will not be able to meet all demand points. In addition, capacity will not make the total transport cost being reduced after the critical value is exceeded. The number of MCS also has a critical value, and the cost will not decrease after exceeds the certain value. Besides, the power (kW) of the charger will also affect the single service time and total transport cost. Therefore, if the logistics companies want to electrify their trucks, it is necessary to decide how many MCS should be dispatched according to the size of the fleets, and carefully calculate the capacity of battery and the charger with appropriate power rate, so that they can meet all the charging needs and avoid unnecessary cost. |
