電動車共享系統車隊布署最佳化模式

本研究發展數學規劃模式，在給定之車隊規模下，求解遊憩式電動車共享系統各租賃站之最佳車輛配置數，目標為最大化系統營運業者之利潤。本研究建立電動車車流時空網路，描述電動車在時空維度之移動，並以此為基礎發展公共電動車系統車隊布署模型。此外，此模式亦考量充電式（plug-in）電動車之營運特性，包括電動車電量消耗、電量限制與充電需求。為了測試本研究所發展的公共電動車系統車隊布署模式應用於不同問題情境之結果，本研究以日月潭風景區電動車共享系統為參考對象，將其延伸成不同租賃站數與車隊規模之各種測試情境，包括依照目前現況設計之情境1（2個租賃站、19輛電動車）、未來可能新增租賃站及擴大車隊規模之情境2（3個租賃站、30輛電動車）。本研究利用最佳化問題求解軟體CPLEX求解所設計之測試例題，並於每一情境下針對顧客需求進行敏感度分析，探討此模式在不同需求水準下所得之結果，並提出相關之建議。 This study develops a mathematical programming model to determine optimal deployment of a given fleet of plug-in electric vehicles (EVs) to the stations of a leisure-oriented EV-sharing system. The objective is to maximize the profit of the system operator: The proposed model is developed based on an EV-flow time-space network that describes the movements of EVs in the spatial and temporal dimensions. In addition, the model takes into account the operational characters of plug-in EVs, including energy consumption, battery capacity and charging requirement. To examine the performance of the proposed model, this study conducts a set of computational experiments on two problem instances (or scenarios) of different sizes (in terms of numbers of stations and EVs) generated based on the EV-Sharing system deployed in Sun Moon Lake national park in Nantou, Taiwan. The base case scenario (scenario 1) is designed according to the current status of the system, including 2 stations and 19 EVs. Considering that the system will be expanded in the near future, the other scenario with more stations and EVs is also generated; scenario 2 has 3 stations and 30 EVs. These two problem instances of the model are solved using CPLEX. The results show that the proposed model is effective in obtaining optimal fleet allocations for the EV-Sharing system. We also conduct a sensitivity analysis to examine the impact of different demand levels of on the fleet allocation results of the model. The results of the sensitivity analysis are discussed and suggestions for dealing with unmet demand, such as real-time dispatching or reallocation of EVs and increasing the fleet size, are presented. The fleet allocation results obtained by solving the model can be used as a reference for the system operator to improve the overall operational efficiency and service level of the system.

This study develops a mathematical programming model to determine optimal deployment of a given fleet of plug-in electric vehicles (EVs) to the stations of a leisure-oriented EV-sharing system. The objective is to maximize the profit of the system operator: The proposed model is developed based on an EV-flow time-space network that describes the movements of EVs in the spatial and temporal dimensions. In addition, the model takes into account the operational characters of plug-in EVs, including energy consumption, battery capacity and charging requirement. To examine the performance of the proposed model, this study conducts a set of computational experiments on two problem instances (or scenarios) of different sizes (in terms of numbers of stations and EVs) generated based on the EV-Sharing system deployed in Sun Moon Lake national park in Nantou, Taiwan. The base case scenario (scenario 1) is designed according to the current status of the system, including 2 stations and 19 EVs. Considering that the system will be expanded in the near future, the other scenario with more stations and EVs is also generated; scenario 2 has 3 stations and 30 EVs. These two problem instances of the model are solved using CPLEX. The results show that the proposed model is effective in obtaining optimal fleet allocations for the EV-Sharing system. We also conduct a sensitivity analysis to examine the impact of different demand levels of on the fleet allocation results of the model. The results of the sensitivity analysis are discussed and suggestions for dealing with unmet demand, such as real-time dispatching or reallocation of EVs and increasing the fleet size, are presented. The fleet allocation results obtained by solving the model can be used as a reference for the system operator to improve the overall operational efficiency and service level of the system.