随着电动汽车数量井喷式增长，如何减小电动汽车充换电需求的预测误差、以及如何满足该需求的多样性，成为越来越有现实意义的问题。为减小电动汽车充换电需求的预测误差，提出了一种预约入网的充换电模式，从而较为精准地获知电动汽车负荷的充换电需求；为满足充换电需求的多样性，提出了一种多类型充换电设备的优化配置方法，实现了不同额定功率的充换电设备的最优化组合，满足了电动汽车用户个性化的充换电需求。所构建的数学模型能够采用二阶锥松弛（second-order cone relaxation, SOCR）方法进行有效求解。最后，以杭州市钱塘区实际地理信息和交通信息为例，耦合了IEEE 33节点配电网系统和分布式光伏进行测试与仿真，验证了电动汽车预约入网的充换电模式参与多类型充换电设备优化配置的可行性和有效性。

With explosive growth of the number of electric vehicles(EVs), how to effectively meet the autonomy and diversity of the users' charging and swapping needs has become a realistic issue. To meet the autonomy of charging and swapping needs, a reserved charging and swapping mode was proposed to connect EVs to the network. EV users can reduce their queue time and costs by making reservations, which improves the comfort of charging and swapping behavior. To meet the diversity of charging and swapping needs, an optimal configuration method of multiple-type charging and swapping equipment was proposed, which realizes the optimal combination of charging and swapping equipment with different rated powers to meet the personalized needs of EV users. By using the second-order cone relaxation (SOCR) method, the proposed mathematical model can be transformed into a mixed-integer SOCR model for efficient solution. Finally, taking the actual geographic and traffic information within Hangzhou City as an example, the IEEE 33-node distribution network was coupled for simulation, the results verified the rationality and effectiveness of this proposed model.