全球暖化所帶來的影響不僅僅是溫度上升，更進一步影響地球生態環境，甚至造成物種滅絕等不可逆影響。因此，如何有效減少溫室氣體排放已成為一大重要議題。要減緩全球暖化，再生能源的使用是不可或缺的。而現今主要的再生能源包括太陽能、風力發電等，這些再生能源非常依賴天候因素，造成其能源提供的穩定性不佳。對於供給不穩定的能源，可以透過儲能的方式將其儲存起來，將離峰時期的過多的能源轉移至尖峰時期使用。在有需求時能及時運送至需求點，不僅能解決能源不足的問題，也同時能提高再生能源的使用率。綜上所述，本研究將針對如何將移動式電池運送至需求點進行相關研究，並以最大化所能滿足的電力需求為目標。而因應現實情況考量，在各電力需求點加上時間窗來限制其所能運送的時間，並針對運送載具的容量及運送距離也進行了限制。基於以上，透過網狀路徑圖來模擬現實情境設置需求點，且問題本身也屬於路徑構成之VRP問題，路徑長短也為考量之因素，所以採用調整型螞蟻演算法來進行求解。改良演算法參照了螞蟻演算法之基礎並進行對問題特性之改良。透過不同類型問題之實驗對演算法之可行性及參數組合進行調整，驗證改良演算法對問題求解的結果，並選擇出較佳之參數組合，實驗結果證明改良演算法可以有效對本研究問題之求解過程及結果有較好的目標值。;The impacts of global warming extend beyond the rise in temperatures, further affecting the Earth′s ecological environment and even causing irreversible effects such as species extinction. Therefore, effectively reducing greenhouse gas emissions has become a significant issue. To mitigate global warming, the use of renewable energy is indispensable. Currently, the main renewable energy sources include solar and wind power. These renewable energy sources are highly dependent on weather conditions, leading to instability in their energy supply. For the unstable energy supply, energy can be stored during off-peak periods and transferred to peak periods for use. This method allows energy to be delivered to demand points when needed, solving the problem of energy shortages and simultaneously increasing the utilization rate of renewable energy. In summary, this study focuses on researching how to transport mobile batteries to demand points with the goal of maximizing the power demand that can be met. Considering real-world situations, time windows are added to each power demand point to restrict the delivery time, and limitations are also set on the capacity and delivery distance of the transport vehicles. Based on the above, a mesh path map is used to simulate the real-world setting of demand points. The problem itself belongs to the VRP (Vehicle Routing Problem) formed by paths, where the length of the paths is also a factor to consider. Therefore, an adaptive ant colony algorithm is used to solve the problem. The improved algorithm is based on the fundamental ant colony algorithm and is adjusted according to the characteristics of the problem. Through experiments on different types of problems, the feasibility and parameter combinations of the algorithm are adjusted, verifying that the improved algorithm provides better objective values in solving the problem of this study.
