1. Introduction
The rapid speed of urbanization leads to many environmental problems [
1]. The sustainable development of cities has become an increasing concern in recent years [
2]. One of the goals of the United Nations (UN) 2030 agenda for sustainable development is “Sustainable cities and communities”, aiming to make cities and human settlements safe, resilient, and sustainable [
3]. Sustainability in cities involves using technological innovations and knowledge from various scientific fields for ensuring urban residents’ quality of life that can be sustained over a long period [
4]. A sustainable city is defined as one in which people and businesses continuously endeavor to improve their natural, built, and cultural environment [
5]. In other words, sustainability in cities aims to sustain the quality of life for urban residents while a sustainable city is envisaged as a balance of natural, built, and cultural elements. Sustainable transport is a significant part of sustainable cities [
6]. Greenhouse gas (GHG) emissions are concentrated in urban areas [
7], and carbon dioxide emitted by vehicles accounts for about 40% of the total urban carbon emissions [
8]. Meanwhile, persistent pollutants, heavy metals, and particulate matter produced by transportation have adverse effects on human health and are becoming some major concerns for people who live and work in urban areas [
9,
10,
11]. Consequently, the introduction of more environmentally sustainable alternative transport solutions is essential [
12].
Electric vehicles (EVs) have great potential to increase energy efficiency, reduce greenhouse gas emissions, and diversify energy resources for more sustainable transport [
13,
14,
15]. Therefore, promoting the development of EVs is critical to deal with climate change challenges and achieve sustainable transport [
16,
17]. As an important guarantee for the development of EVs, the development level of electric vehicle charging stations (EVCS) has a direct impact on the development speed and quality of the electric vehicle industry [
18,
19,
20]. However, the shortage of EVCS has become one of the important factors hindering the development of the electric vehicle industry [
21]. Consequently, finding reasonable locations of EVCS has become a key issue to promote the development of the electric vehicle industry.
Smart cities, which rely on the deployment of information and communication technology [
22], provide a new opportunity for the development of EVs that can be applied for sustainability in cities [
23]. Smart cities emphasize the effective integration and utilization of resources for sustainable development [
24]. As an important part of EV planning in smart cities, EVCS is crucial for the development of sustainable cities.
Figure 1 describes the relationship between EVCS and sustainable cities. With the increase in the scale of use of EVs, the optimal location of EVCS has become a hot topic and focus of research. Many scholars have analyzed and provided solutions to the EVCS location from different perspectives and have achieved many objectives, mainly based on the following topics: (1) the coverage problem [
25,
26,
27,
28,
29,
30], (2) the heuristic electric vehicle charging placement [
31,
32,
33], (3) the flow-capturing approach [
34,
35,
36,
37,
38,
39,
40], and (4) the traffic network equilibrium problem [
41,
42,
43,
44,
45]. These works provide a lot of valuable references for us to explore the EVCS location problem. The purpose of charging stations is to maximize the net social benefit in some countries (e.g., China) as EVs are now in the promotional phase for these countries [
25,
28,
29]. Long charging queues result in inconvenience and high social cost [
46]. With the rapid development of the Internet, a reservation service has become popular as it allows consumers to choose suitable charging stations and times according to their demand, avoiding the long waiting time and enhancing customer convenience. In other words, a reservation service can not only improve system efficiency, but also increase customer satisfaction, promoting the development of the industry. Therefore, this paper aims to minimize the total social cost of EVCS location planning, allowing for the impact of the reservation service, and seeks to find the optimal locations of charging stations, providing a reference for sustainable urban planning and development.
In this paper, an EVCS location model, which is based on location theory involving the maximal covering location problem (MCLP) and queuing theory, is proposed to improve the resource utilization of electric vehicles for sustainable cities [
47,
48]. Then, the genetic algorithm (GA) is applied to solve this model. Moreover, analyses of results obtained are performed to provide implications and recommendations. These analyses provide insights into the effects of a reservation service and tolerable waiting time on the total social cost of the EVCS locations. The remainder of this paper is organized as follows.
Section 2 first provides a formal description of the EVCS location and then proposes a model for the EVCS location. In
Section 3, a case from Chengdu, China is given to show the practicality and effectiveness of the proposed model, and the results are computed by the GA.
Section 4 contains further analysis and discussions. Finally, the conclusions, limitations of the study, and future directions are presented in
Section 5.
3. Results
To verify the feasibility and effectiveness of the proposed method in this paper, the actual road system in the urban area of Wenjiang, Chengdu, China, was used. This area is approximately 36 km
2, and the number of cars is about 18,061. By analyzing the distribution of cars and the road system, 53 demand nodes were identified and the number of cars at each demand node was estimated, as shown in
Table 2. Parking lots could be transformed into centralized quick charging stations. Therefore, 21 parking lots in this area were taken as alternative charging stations, taking natural environment, geographical space, and other factors into consideration. The map is shown in
Figure 3. The distances between demand nodes and alternative charging stations are the shortest traveling distances measured by the Baidu map tool, as shown in
Table A1 of
Appendix A.
Most charging events start with a 40%–50% SOC [
52]. In this case study, we assume EV drivers start charging at a 40% SOC. Each vehicle travels 30 miles a day on average, the electricity consumption rate is 0.3 kWh/miles, and the battery capacity is 30 kWh [
31]. The fast charger power is 60 kW [
58], and we assume EV drivers leave the charging station at a 100% SOC, as discussed in
Section 2. Then, according to Equations (1) and (2), we can calculate that each EV will be recharged once every two days (i.e.,
σ = 0.5), and the charging time will be about 18 min (i.e.,
tcharging = 18 min). Considering that a series of actions during the charging service will take about 2 min [
59], we assume that it takes drivers an average of 20 min from when they start receiving the service to when they leave the charging station, so the service rate is
μ1 = 3. Then, to provide a buffer time for the users who have a reservation, the service rate is assumed as
μ2 = 2.5.
The construction of charging stations in the urban area of Wenjiang to provide enough electric power for EVs requires a reasonable selection of some necessary parameters. The parameters were set based on previous research [
54,
55,
60], and the specific values are shown in
Table 3.
To reduce the solution difficulty and avoid a situation where there is only one charging pile in a charging station, this study assumed that six charging piles could be constructed at each EVCS. The proposed model was solved by the genetic algorithm (GA), and the algorithm was coded in Matlab 2017b. The obtained optimal solution is shown in
Table 4 and
Table 5 and
Figure 4. Under the assumptions of the model, it can be seen from
Table 3 that when
τ = 0.1,
a = 0.1, and
m = 6, five charging stations need to be built in this area at a total annual cost of 6,761,684 CNY.
4. Discussion
4.1. Benefit of the Reservation Service
To explore the effect of the reservation service on the optimal EVCS location, the proposed model was solved by the GA with a changing number of charging piles for a reservation service under different penetration rates. To analyze the advantages of the reservation service, the cases where the reservation service is not provided are also considered, i.e., a = 0.
As shown in
Figure 5, when the penetration rate is the same, the cost without considering the reservation service is always higher than the cost considering the reservation service, which means that the introduction of the reservation service has a positive impact on the EVCS location problem. Meanwhile, with the increase in the number of charging piles providing the reservation service, the total cost gradually decreases. The total cost always increases with the increase of penetration rate, regardless of the number of charging piles for the reservation service. This is consistent with reality, as the total number of chargers will increase to meet the increasing charging demand due to the increase in the total number of EVs. The construction cost is the main component in the cost structure.
Figure 6 shows that the introduction of reservation services may reduce the number of EVCS needed. It can be seen that when
τ = 0.1,
τ = 0.15, or
τ = 0.2, the introduction of the reservation service will reduce the number of EVCS required but not when
τ = 0.05. It is also consistent with reality, because the introduction of the reservation service mainly affects the waiting time constraint, and when the penetration rate is low, the waiting time constraint is weak. When the penetration rate increases, the waiting time constraint is stronger, and the reservation service is more effective. The above analysis indicates that the introduction of the reservation service plays a positive role in reducing the total cost and improving social benefits. Meanwhile, the greater the penetration rate is, the more obvious the advantages of the reservation service will be.
To understand how the reservation service affects the total cost,
Figure 7,
Figure 8,
Figure 9 and
Figure 10 show the cost structure with a changing number of charging piles for the reservation service under different penetration rates. It can be seen that the construction cost curve is always parallel to the operating cost curve, because in this paper, there is a linear conversion factor between the construction cost and the operating cost. As shown in
Figure 7, when
τ = 0.05, with the increase in the number of charging piles providing reservation services, the waiting time significantly decreases, and the driving cost also slightly decreases. However, when
τ = 0.1, the number of charging piles providing the reservation service increases from 2 to 3, and the cost of waiting time and driving increases. A similar situation happens when
τ = 0.15 and
τ = 0.2. This seems unreasonable because drivers who have a reservation do not need to wait. However,
Figure 6 shows that this unusual situation can be clearly explained. When
τ = 0.1 and
a = 3, the number of EVCS planned to be built is less than that under
τ = 0.1 and
a = 2, and as the number of EVCS decrease, the overall service capacity of the region decreases, resulting in an increase in waiting time.
In theoretical research, the impact of reservation services has been considered in many queuing scenarios [
61,
62]. Long queues for charging have also been one of the important factors hindering the development of the electric car industry. However, in the area of EVCS location, few people have considered the impact of a reservation service. Therefore, this paper introduces a reservation service into the EVCS location problem. Through a comparative analysis, it can be found that the introduction of a reservation service has a positive impact, as it can reduce the total cost compared with no reservation service. The more charging piles for the reservation service, the less it costs. Meanwhile, the larger the EV penetration rate, the more obvious the advantages of the reservation service.
In reality, the introduction of a reservation service also conforms to the development of a smart city. The drivers can make a reservation in advance, so that they can better plan their trips and make them more comfortable and convenient. Meanwhile, EVs are at the promotion stage, and there are few EVCSs. Some stations may only be known by a small number of drivers. By introducing the reservation service, the driver will know the location of the EVCS and the number of available charging piles, and will generally choose the charging station with free charging piles within an acceptable distance, so as to improve the utilization rate of resources.
The goal of the government is to minimize the total social cost of EVCS locations, as EVs are in the promotion stage in China. However, in some scenarios, considering the impact of reservation services when selecting EVCS sites can lead to a longer time for drivers traveling to charging stations, because sometimes the increase in reservation service capacity reduces the number of EVCS that need to be built but increases the time cost. Therefore, in reality, there is a need to balance the construction cost and drivers’ tolerable waiting time.
4.2. Effect of Tolerable Waiting Time
The effect of different tolerable waiting times was examined in this study. The tolerable waiting time (i.e.
tθ,) was assumed to be 15 min in the above discussions. In addition to the case for 15 min, the cases for 1, 5, and 10 min when
τ = 0.1,
a = 1, or
a = 0 respectively, are also discussed here. It can be seen from
Figure 11 that with a decrease in tolerable time, the total cost increases. In addition, when the waiting time is reduced from 5 to 1 min, the suggested number of EVCS to be built increases by one. Reduced tolerable waiting times mean fewer EVs can be served at each EVCS, which will increase the number of EVCS required. Meanwhile,
Figure 11 shows that when the tolerable time is the same, the total cost with the reservation service is always less than that without the reservation service.
Figure 12 shows the changing cost structure with the change in the tolerable time under
τ = 0.1,
a = 1, and
a = 0. The construction cost and operating cost of
a = 1 are equal to that of
a = 0, because the number of EVCS required is the same, which can be seen from
Figure 11. It can be seen that, with a decrease in tolerable time, both time cost and travel cost increase. In addition, as the tolerable time decreases, the difference between the time cost curve with the reservation service and the time cost curve without the reservation service also increases, which means that the more that people are averse to waiting, the more effective the reservation service will be. Compared with the traditional car, the longer charging time of electric vehicles is one of the factors that hinder the development of EVs. However, if there is congestion in the charging station, drivers in the queue must wait at least 20 min under the assumption in this paper, which will undoubtedly further reduce the acceptance of EVs by drivers, thus further hindering the development of the EV industry. Therefore, the introduction of a reservation service can reduce the waiting time of the driver who made the reservation to 0, so that drivers can have a better, more convenient plan, and feel more comfortable, which will improve drivers’ satisfaction and promote the development of EVs.
Based on existing related literature, this study introduces a reservation service into the EVCS location problem. The analysis above shows that the introduction of a reservation service has a positive impact on this problem, as it can reduce the total social cost and enhance customer convenience by reducing the waiting time compared with no reservation service.
5. Conclusions
Based on the development of the Internet, this paper introduced a reservation service into the EVCS location problem for the development of sustainable cities. Meanwhile, considering the long charging time of EVs, there will be congestion during the peak periods and idle equipment during the off-peak periods. Therefore, this paper introduced the constraint of average queuing time during the peak periods and idle rate during the off-peak periods to balance the service and improve resource utilization. Finally, a case from Chengdu, China was considered to test the feasibility of the proposed model. The results show that the introduction of a reservation service can not only reduce the total social cost, but also lower the waiting time of users, resulting in increased convenience for customers.
The contributions of this study can be summarized as follows. First, a reservation service is introduced into the EVCS location problem for reducing waiting times at the charging stations. Second, a model for the location problem allowing for a reservation service is proposed to minimize the total social cost. Moreover, analyses of results obtained using the proposed model are performed. These analyses shed light on the effects of different penetration rates and tolerable waiting times on the EVCS location planning. Compared to previous literature without a reservation service, the main merit of this study is that the introduction of the reservation service can not only benefit the drivers by reducing waiting time cost, but also benefit other stakeholders by decreasing the total social cost, which is of positive significance for the promotion of EVs towards sustainable cities for healthier lives.
There are a few limitations of this study. First, the characteristics of user charging behavior were not fully considered. Moreover, this paper considers technology-related parameters (e.g., service rates) as fixed values, but technologies may be improved in the future. Future research will focus on the following three directions: (1) fully considering the characteristics of user charging behavior with regard to the EVCS location problem, (2) integrating management optimization and technical innovation to improve the EVCS location, and (3) developing comprehensive planning for sustainable EVCS management systems.