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High share of variable renewable energy is challenging to the traditional power system technically and economically. This calls for a significant increase to the system flexibility, which might result in the costs associated with energy storage and costly upgrades to the traditional transmission and distribution system. This paper presents a multi-stage battery transportation and logistics optimization method to increase the renewable energy consumptions, economics, and mobilities of the battery utilization. A new approach is proposed in which the batteries are charged in the renewable power plants and transported back and forth by railways between the renewable power plants and cities. Based on the forecasts of battery supplies/demands, multiple optimization stages (full train transport and carpooling) are designed by the branch-and-bound algorithm and genetic algorithm respectively. The proposed battery transportation and logistics concept and model are performed using the Beijing-Tianjin-Hebei region in China as an example. The results show that the levelized cost of energy of the battery transportation and logistics model is $0.045/kWh averagely. Also, by the use of mobilized batteries, the proposed battery transportation and logistics model increases the system flexibilities and renewable energy deliveries to the end users without the reinforcement of transmission and distribution system and any constraint from a highly penetrated power system.