泛在电力物联网下电动汽车充电服务费定价模型

针对现有充电服务难以定量定价问题，提出了一种基于系统动力学的充电服务费定价模型。首先分析了充电服务网在泛在电力物联网定位及多主体关系，然后综合考虑社会发展、人口增长、电动汽车规模、科学水平、充电桩数量、建设成本等多维因素，量化多维因素之间的因果关系，由此建立充电服务费定价的系统动力学模型，得到充电服务费价格未来的变化趋势，并分析不同因素对充电服务费价格的影响。仿真结果发现：在算例场景设置条件下，电动汽车充电服务费的变化曲线呈现V型，前期阶段充电服务费价格逐渐降低至最低，主要受到政府补贴政策的影响；中间阶段充电服务费缓慢增加，原因是政府补贴的不断衰减、充电服务网络进一步完善，新增充电设施增加了建设成本；后期充电服务费变化逐渐稳定，基础设施的建设成本成为影响充电服务费最主要的因素，充电量的变化在一定程度上也会影响充电服务费的定价。本文为充电服务定价问题给出了一种具有可操作性的宏观动力学定量分析模型，为未来充电服务网运营与规划提供了参考。

Pricing Model of Electric Vehicle Charging in Electrical Internet of Things

In order to address the issue that the charging service is not difficult to quantitative pricing, the pricing model of electric vehicle charging service based on system dynamics was proposed. This paper first analyzed the location of charging service network in the ubiquitous power internet of things and the relationship between multiple subjects. Then other multi-dimension factors were took into account, such as social development, population growth, the scale of the electric vehicle, scientific level, the quantity of charging piles, construction costs, and the causality between multi-dimensional factors based on historical data was quantified. Finally a system dynamics simulation model of charging service price was established, then the charging service price was obtained from the model and the impact of various factors on charging service price was analyzed. The simulation results showed that the change curve of electric vehicle charging service price appeared to be V-shaped under the setting conditions in the example scenario. In the early stage, the price of charging service decreases gradually, which is mainly affected by the government subsidy policy. In the middle stage, the price will increase slightly. The reason is that the government subsidy policy has been continuously weakened, and the charging service network has been further improved, and the newly-built charging facilities have led to an increase in the construction cost. In the later period, the charging service price will gradually stabilize and fluctuate at the peak. The charging service price was mainly affected by construction cost. This paper provides an operational macro dynamic quantitative analysis model for charging service price, which provides a reference for the operation and planning of charging service network in the future.