以混合整數規劃優化移動式充電樁存放位置;Using Mixed-integer Programming to Optimize the Selection of Parking Location for Mobile EV Chargers

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/94571

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/94571

Title:	以混合整數規劃優化移動式充電樁存放位置;Using Mixed-integer Programming to Optimize the Selection of Parking Location for Mobile EV Chargers
Authors:	黃柏翰;Huang, Po-Han
Contributors:	工業管理研究所
Keywords:	永續發展;淨零碳排;再生能源;電動車;充電選址;混合整數規劃;Sustainable Development;Net-Zero Carbon Emissions;Renewable Energy;Electric Vehicles;Charging Station Site Selection;Mixed Integer Programming
Date:	2024-07-23
Issue Date:	2024-10-09 15:17:05 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年來，氣候變遷問題日益嚴重，各國積極推動減碳行動。台灣也提出了2040年小客車全面電動化的政策目標。本研究旨在探討電動車對環境的影響及其減少碳排的重要性，並以優化移動式電動車充電樁的設置來解決充電基礎設施不足的問題。考慮到現有固定式充電站在土地資源有限的城市地區設置面臨的挑戰，本研究以台北市為案例，提出了一個兩階段的混合整數規劃（MIP）模型。第一階段的MIP模型選擇最大化覆蓋分數候選站點組合。第二階段的MIP模型則以成本最小化為目標，選擇最優的存放站設置位置。　　研究結果顯示，本研究篩選出104個站點，並在第一階段選擇了20個候選站點。在這20個站點中，無任何兩站點距離小於500公尺，並且有30筆資料已選站點與未選站間間距離小於500公尺，達到了最大化覆蓋分數的目標。第二階段最終選定汎美皮膚科診所作為存放站設置位置，總成本為105,305,512元，其中派遣成本為10,366,584元，電費成本為42,110,654元。優化移動式充電樁的設置能顯著提高充電基礎設施的覆蓋範圍，特別在城市土地資源有限的情況下，提供了一種可行且有效的替代方案。考慮到不同地區的特殊情況和需求，本研究模型具有適應性和擴展性，並通過引入綠能發電來減少碳排放和降低運營成本。未來的研究可進一步擴展模型應用，結合最新的技術發展如5G通信和物聯網技術，提升系統的智能化和自動化水平，實現更加可持續和綠色的交通系統，為全球減碳目標做出貢獻。 ;In recent years, the issue of climate change has become increasingly severe, prompting countries worldwide to actively promote carbon reduction initiatives. Taiwan has also set a policy goal of fully electrifying passenger cars by 2040. This study aims to explore the environmental impact of electric vehicles (EVs) and their significance in reducing carbon emissions. To address the issue of insufficient charging infrastructure, we propose optimizing the deployment of mobile EV charging stations. Considering the challenges faced by fixed charging stations in urban areas with limited land resources, this study uses Taipei City as a case study and proposes a two-stage Mixed Integer Programming (MIP) model. 　　In the first stage, the MIP model selects the combination of candidate sites that maximizes the coverage score. In the second stage, the MIP model aims to minimize costs by selecting the optimal locations for storage stations. The research results indicate that out of the 104 screened sites, 20 candidate sites were selected in the first stage. None of these 20 sites are within 500 meters of each other, and there are 30 data points where the selected sites and unselected sites are within 500 meters, achieving the goal of maximizing the coverage score. In the second stage, the Pan America Dermatology Clinic was ultimately chosen as the storage station location, with a total cost of NT$105,305,512, including dispatch costs of NT$10,366,584 and electricity costs of NT$42,110,654. 　　Optimizing the deployment of mobile charging stations can significantly improve the coverage of charging infrastructure, providing a feasible and effective alternative, especially in urban areas with limited land resources. Considering the unique circumstances and needs of different regions, the proposed model is adaptable and scalable. By incorporating green energy generation, the model can also reduce carbon emissions and operational costs. Future research can further expand the application of the model by integrating the latest technological developments, such as 5G communication and the Internet of Things (IoT), to enhance the intelligence and automation of the system. This would contribute to a more sustainable and green transportation system, aiding global carbon reduction goals.
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