Electric vehicles (EVs) have been considered as a feasible solution to deal with high fuel consumption and greenhouse gas emissions [1
]. The global stock of EVs increased by 68% with respect to the previous year, and it targets a 30% sales share for EVs by 2030 [3
]. Therefore, the charging load will become one of the important loads of the distribution network. However, the uncontrolled nature of charging may have adverse effects on the distribution network and road network [4
At present, the research mainly focuses on the impact assessment of electric vehicles on the distribution network. Wang, H. [7
] proposed a comprehensive risk assessment method for distribution network operation, considering multiple risk factors in view of the security, economic, and other operational risks caused by EVs’ access to the distribution network. The principal component analysis method was used to reduce the dimensions of the risk assessment matrix and calculate the objective weight coefficient, and the acceptable number of electric vehicles in the region was estimated. Chen, W. [8
] proposed the evaluation index system of distribution network carrying capacity, and evaluated the carrying capacity of the distribution network from the aspects of technical rationality, safety, reliability, and economy. Wang, J. [9
] evaluated the economic performance of fast charging stations (FCSs) from the aspects of investment costs and operating benefits.
In order to reduce the adverse impact of a charging load on the power system, Li, D. [10
] proposed a charging guidance strategy based on the real-time demand of the market. Through the application of the strategy, EVs can obtain subsidies for charging or discharging. However, the author did not consider the negative impact of a large number of EVs charging on the power system. In order to improve the consumption level of renewable energy generation, Ding, Z. [11
] proposed a charging guidance strategy for the operation of highway FCSs. However, the operation status of the power system mentioned in [11
] is relatively fixed. In order to reasonably guide EVs to the FCSs, T. Zhao [12
] proposed a charging price formulation strategy based on the Stackelberg game. EVs can be guided to the appropriate FCSs to complete charging according to the change in price. X. Sun [13
] proposed a voltage control strategy for the distribution network based on charging navigation. The operation level of the distribution network has been effectively improved. In order to reduce the travel cost and improve the operation level of the distribution network, H. Yang [14
] proposed the road speed matrix acquisition and recovery algorithm to achieve the orderly charge and discharge of EVs, and improve the load operation level of the distribution network.
At the same time, some scholars also began to study how to mitigate the impact of EVs on the power system and road network. In order to meet the charging demand of EVs and reduce the traffic burden, Li, X. [15
] proposed a charging navigation strategy for EVs based on price incentives. In order to relieve the charging problem at peak hours, J. Tan [16
] proposed a charging guidance strategy based on a hierarchical game. A non-cooperative game is used to simulate competition between FCSs. The evolutionary game is used to realize the decision-making and selection of EVs. The reliability of the distribution network and the utilization efficiency of FCSs were improved. Z. Moghaddam [17
] proposed an intelligent charging guidance strategy based on the EV network. The charging time, driving time, and charging cost were considered. Shi, X. [18
] proposed an electric EV charging guidance strategy based on locational marginal prices (LMP). The congestion problem of traffic and power lines was alleviated. Z. Jin [19
] proposed a charging guidance strategy based on the alliance blockchain for the taxi charging system to maximize passenger satisfaction and the service efficiency of operators. Qian, T. [20
] proposed an EV charging guidance strategy based on deep reinforcement learning. However, the proposed strategy did not analyze the impact on the power system and road network.
Furthermore, Zenginis I [1
] proposed a charging price strategy based on the daily profits of FCSs and charging waiting time which effectively avoided the congestion in FCSs. Y. Xiang [21
] studied the interaction of EVs in the process of path planning, and proposed an optimization method of the EV charging path based on an event-driven pricing strategy.
Although the above article alleviates the impact of EV charging on the power system and road network, it does not offer a quantitative analysis of the network connection capability of FCSs. The main contributions of this article are as follows:
(1) A hierarchical comprehensive evaluation architecture for the network-related capability of fast charging stations is proposed. The architecture is mainly divided into three layers which comprehensively evaluate the operation status of the system under different guidance strategies. From the three dimensions of the index layer, the rule layer, and the target layer, the network-related capability of FCSs can be evaluated quantitatively.
(2) A hierarchical comprehensive evaluation method is proposed. In the index layer, a combination weighting method based on the entropy weight method (EWM) and analytic hierarchy process (AHP) is proposed to allocate the weight of indicators. In addition, the fuzzy comprehensive evaluation method (FCEM) is applied to evaluate the network-related capability of FCSs.
(3) A charging guidance strategy is proposed based on the comprehensive evaluation results. By the application of the proposed strategy, the EVs will be guided to the FCSs which have the highest comprehensive evaluation score. The application of the proposed strategy avoids the occurrence of the extreme operation state in power systems, road networks and FCSs. Compared with the other three strategies, the network-related capability of FCSs is improved.
In the process of strategy development, the proposed strategy is realized based on the operation state of the distribution network, road network, and FCSs. The EVs will be guided to the FCS which has the least impact on the operation of the coupling system. The advantages of the proposed strategy are shown in Table 1
. Compared with the strategy proposed in [22
], the operation level of the distribution network is improved by the application of the proposed strategy (e.g., voltage violation rate). Compared with the strategy proposed in [23
], on the basis of ensuring that the road network is at a high operation level, the operation level of the distribution network is significantly improved by the application of the strategy proposed in this paper. Compared with the strategy proposed in [24
], on the basis of ensuring that the road network is at a high operation level, the operation levels of the road network and FCSs are significantly improved by the application of the strategy proposed in this paper (e.g., travel flow, charging waiting time, and utilization rate of FCSs).
The chapter arrangement and relationship of this paper are shown in the Figure 1
In this paper, the evaluation system for the network-related capacity of fast charging stations will be constructed in Section 2
, and the selection principles and calculation methods of evaluation indexes will also be given in Section 2
. Section 3
will discuss the network-related capacity evaluation of the fast charging station at different layers. In Section 4
, the charging guidance strategy is proposed based on the network-related capacity of FCSs, and the proposed strategy is verified in Section 5