The charging demand and load distributing of massive electric vehicles(EVs)should obey some regularity. From the perspective of cluster response

the dimensions of the EV scheduling problem will be greatly reduced and the efficiency of optimization calculations will be improved. An optimization model of local agents for minimizing peak-valley difference is established based on the cluster response characteristics of EVs. Moreover

a real-time pricing model for EVs cluster response is built according to EVs' sensitivity to charging price. The charging behavior of EVs is guided through charging price so as to realize the strategy of power grid peak shaving. By taking typical distribution network load data of a certain area as a test case

the effectiveness of the charging load regulation method is verified. Finally

the EVs' sensitivity to real-time pricing response and cost savings of EVs and local agents of different sensitivity are discussed. This work is supported by National Natural Science Foundation of China(No. 51577115)and National Key Research and Development Program of China(No. 2016YFB0901304).