基于云边端协同的电动汽车多目标优化调度

Multi-objective optimal scheduling of electric vehicles based on cloud edge end cooperation

随着再电气化进程不断加快、新能源高比例接入,电网电力平衡压力显著加大。因此,迫切需要挖掘电动汽车等灵活负荷参与调控潜力,调动其主动参与电网调控运行。建立基于云边端协同的分布式控制架构,从用户侧和电网侧的角度实现对电动汽车多目标优化调度策略。第一阶段在云端考虑电动汽车参与电网调度,达到电网削峰填谷的需求;第二阶段考虑用户需求,以用户充放电费用及满意度为目标函数建立模型,同时引入分时电价,合理引导电动汽车有序充放电;最后,以某区域配电网中电动汽车负荷数据为例,采用多目标优化遗传算法求解,结果验证了所提出的充放电优化调度策略可以有效降低用户成本,减小负荷峰谷差,平抑电网波动,达到云边协同、边端自治的台区用能精细化调控目的。

With the acceleration of re-electrification process and the high proportion of new energy access,the power balance pressure of the grid increases significantly.Therefore,it is urgent to tap the potential of flexible load participation in regulation such as electric vehicles,and mobilize them to actively participate in power grid regulation operation.A distributed control architecture based on cloud side-end collaboration is established to optimize the multi-objective scheduling strategy for electric vehicles from the perspectives of users and power grids.In the first stage,the cloud considers the participation of electric vehicles in power grid scheduling to meet the demand of power grid peak cutting and valley filling.In the second stage,the user demand of charging and discharging cost and satisfaction of users are taken as the objective function to establish the model,At the same time,time of use tariff is introduced to guide the orderly charging and discharging of electric vehicles.Finally,a typical regional distribution network load data is taken as an example,and multi-objective optimization genetic algorithm is used to solve the problem.The results verify that the charge-discharge optimization scheduling strategy proposed in this paper can effectively reduce the user cost,reduce the load peak-valley difference,suppress the fluctuation of the power network,and to achieve cloud-side coordination,side-end autonomy of the platform area energy fine regulation.