考虑物流特性用于削峰填谷的纯电重卡双层协调优化调度方法

A Two-layer Coordinated Optimization Dispatch Method of Electric Trucks for Peak Shaving and Valley Filling Considering Logistics Characteristics

针对纯电重卡大规模充电给电网带来的负荷峰值大、运行成本高等问题,提出了用于削峰填谷的双层协调优化调度方法。首先,考虑物流特性对纯电重卡进行负荷预测,并获取纯电重卡的行驶行为,得到充电负荷曲线,同时利用模糊C均值聚类将充电负荷分为可调度与不可调度两类,计算其可调度潜力;其次,以总负荷方差最小为目标函数,构造上层优化模型,制定纯电重卡日充电方案;然后,将上层寻优的日充电方案传递到下层,考虑柔性时间窗约束,以纯电重卡运行成本最低为目标函数,构造下层优化模型,实现考虑充电负荷方差与运行成本的双层优化调度;最后,在主从博弈框架下利用自适应粒子群算法求解双层协调优化调度模型,采用某物流区域实际纯电重卡数据进行仿真验证。结果表明,提出的双层优化调度方法能够在有效平滑负荷曲线的同时,降低纯电重卡的运行成本。

To address the problems of large load peak and high operating costs brought by the large-scale charging of electric trucks to the power grid,a two-layer coordinated optimal dispatch method is proposed for peak shaving and valley filling.Firstly,load prediction of electric trucks is carried out by considering logistic characteristics,acquiring the driving behaviour of electric trucks,and obtaining charging load curves.Fuzzy C-mean clustering is used to divide the charging loads into dispatchable and non-dispatchable categories,and the schedulable potential is measured.Secondly,the minimum variance of total load is selected as the objective function.The upper layer optimization model is constructed,and the daily charging scheme of electric trucks is formulated.Then,the optimal daily charging scheme of the upper layer is transferred to the lower layer,and the lower layer optimization model with the lowest operating cost of the electric trucks as the objective function is constructed by considering the flexible time window constraints.The twolayer optimization dispatch is implemented with the variance of charging loads and the operating cost.Finally,the two-layer optimization model is solved by an adaptive particle swarm algorithm under the master-slave game framework,and the simulation verification is carried out using actual electric truck data in a logistics park.The results show that the proposed twolayer optimization dispatch method can effectively achievesmoothing the load profile of the grid while reducing the operating costs of electric trucks.