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Joint Economic Optimization of AGV Logistics Scheduling and Orderly Charging in a Low-Carbon Automated Terminal
WANG Xuan, WANG Bao, CHEN Yanping, LIU Hong, MA Xiaohui
Journal of Shanghai Jiao Tong University    2024, 58 (9): 1370-1380.   DOI: 10.16183/j.cnki.jsjtu.2023.027
Abstract   (2058 HTML8 PDF(pc) (3702KB)(557)  

To improve the current automated guided vehicle (AGV) charging strategy at automated terminals, which is not fully coordinated with the distributed power supply, a joint optimization method of AGV logistics scheduling and orderly charging is proposed. First, the synergetic relationship between AGV logistics scheduling and charging scheduling is analyzed, and a joint optimization framework is built. Then, a method to calculate the distance traveled by AGVs while considering the segregation requirements of trucks inside and outside the terminal is proposed. Afterwards, for the AGV charging module, the judgment conditions of AGV charging status and the pile selection method are defined. Furthermore, to minimize the cost of purchasing electricity at the terminal, a joint optimization model of logistics scheduling and orderly charging is constructed by considering time-of-use tariff, distributed power feed-in tariff, power balance constraint, state of charge constraint at the termination moment, upper and lower bound constraints of decision variables, and logistics scheduling constraint. Finally, a fast solution method based on improved particle swarm optimization algorithm is proposed, of which the effectiveness and economic efficiency are verified by an actual case of a terminal.


Fig.4 Time series of wind PV power and base load
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AGV初始时刻默认均匀分布在堆场区域,无充电状态,且所在位置满足内外集卡隔离要求.风光出力和基准负荷的时序曲线如图4所示.
由图可见,以上4种策略的整日购电费用依次为 8 554、7 185、7 745 和 6 819 元.受起始电量影响,4种策略在起始时段00:00—04:15均处于充电状态,策略1仅与SOC电量有关;策略2将16:45—21:00间充电转移到平时段12:00—15:15;策略3将部分电量转移到03:15—05:15和14:30—15:00风光出力高峰时段;策略4综合考虑分时电价与风光出力.4种策略在净发电量大于零时段的充电量占比分别为60.1%、80.4%、80.3%和89.5%,说明所提联合优化方法的经济性最优.
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