1. Introduction
Fossil-fuel-based internal combustion engines (ICEs) are one of the key factors which account for 50% of environmental pollution [
1]. Developing countries suffer more because of old and inefficient engines used in their transportation network which are the cause of transport-generated pollution, particularly in Asia, Africa, and the Middle East, ranging from 12 to 70% [
2,
3]. The challenge of transportation pollution can only be overcome by changing the transport fleet from ICE to plug-in electric vehicles (PEVs) [
3]. To encourage maximum PEV penetration, there must be a coordinated network of fast charging stations available publicly with private parties involved to also enable the rapid market penetration of PEVs. In recent years, many researchers have focused on the optimal placement of charging stations by continuing to study areas such as the environment, commerce, self-sustainability, etc. [
4,
5,
6,
7].
Presently, Pakistan lacks a PEV charging infrastructure plan to facilitate the adoption of PEVs on a wide scale in the country. To solve this problem robustly, a similar approach as discussed in [
7] is adopted with slight improvements in a model for the optimal placement of direct current fast charging (DCFC) stations based on the flow calculation by using the dataset provided by the National Highways and Motorways authority. The considered networks for this contextual analysis are the Motorway 2 (M2) and National Highway 5 (N5) networks from Lahore to Islamabad. These routes are more active traffic routes than the rest of the road networks in the country, and also, the region covering these routes is among the most densely populated areas of the country. Moreover, the study is focused on proposing an optimal PEV charging station plan for intercity routes to ensure long-range, anxiety-free traveling in the future.
2. Electrical Charging Stations Locality Deployment Model
To maximize PEVs’ market share, a coordinated charging station (CS) network along highways and motorways is suggested. In this study, all vehicles were considered as cars, and heavy-duty traffic was not considered. Charging time was assumed to be 30 min for standardization, and the charger electricity consumption was 50 KW. The PEV charging port and CS charging port adopted the same type of standardization for the convenience of installation purposes. The tariff was assumed to be 35 rupees for dedicated load EV charging by the distribution companies and an annual 10% rupee devaluation. As the charging process interrupts the journey, only DCFC chargers were considered. To determine CS sites, we only considered rest-places with basic rest-place facilities as candidate sites. These facilities are available on the candidate site and also no farther than 250 m from it and are categorized as: (i)
basic facility location: parking, small shops, and prayer provision (ii)
medium facility location: supermarket, dining court, and minimum rest-place facility (iii)
superior facility location: High-end rest and accommodation facility, food courts, and additional facilities such as a pharmacy, etc. By considering these facilities, the potential location of CSs could be selected based on the re-defined equation detailed in [
7] for each nominated site, and the process is illustrated in
Figure 1.
where
PLi = potential location of candidate site, ‘
i’,
x1,i = security level on nearby roads at the candidate site, ‘
i’,
x2,i = evaluation value of traffic volume on nearby roads at the candidate site, ‘
i’,
x3,i = evaluation value of service level of the candidate site, ‘
i’,
x4,i = evaluation value of the distance between two candidate sites, ‘
i’,
x5,i = electricity availability at the candidate site ‘
i’, while
a1,
a2,
a3,
a4, and
a5 are the weights of variables.
The parameters in (1) require exploration for the precise determination of optimal CS spots. In (1)
x1,i is the security factor for the CSs as well as for the nearby roads. The value of ‘
x2,i’ is the sum of average daily traffic volume that passes through national highways and motorways within the range of 20–100 km (Km) from the CS (
Ni) (vehicle/day) location. We considered the traffic volume of the directions from where the rest-place is accessible. The value of
x1 is calculated according to the equation below [
7]:
where
= number of vehicle flow, and
= maximum vehicle flow
= minimum vehicle flow. We defined the limit values according to the calculations by using the dataset. In terms of service level,
, a basic service facility is ranked as 1, medium is ranked as 2, while a superior service level at CS locations is given a rank of 3.
is assumed to be constant as the distance between two candidate sites on the motorway network is fixed (service areas also have a fixed location), while on the N5 network, a supposition is made that there must be a charging station after every 40 km. Additionally,
factor ensures the availability of national power grids, transmission, and distribution networks for PEV CS integration at each candidate site.
3. Results and Discussions
To determine the optimal charging station locations based on the dataset, vehicle flow was calculated at N5 north, from Lahore to Islamabad, and at motorway M2 from Islamabad to Lahore. The dataset consisted of data of vehicle flow for April 2019 as depicted in
Figure 2a,b, and for March 11 to the April 14 of the year 2020, respectively, as shown in
Figure 2c. This particular dataset is important because it covered the pre-COVID-19 (2019) as well as the post-COVID-19 (2020) period. So, in this way, we gained the regular maximum vehicle flow data as well as the minimum vehicle flow data. Due to the availability of minimum vehicle flow data, different case scenarios could be developed, and we also learned the minimum amount of the traffic that would flow in any bad scenario.
Considering the provision of facilities, the study area was divided into different zones on M2 and N5, as shown in
Figure 3. The zones were divided according to the traffic data and the nature of the facilities available. The PEV population was distributed between these zones. Considering the zones, proposed locations with distances in-between the two CSs are enlisted in
Table 1. Further, battery size and the mileage range of different models of cars were also considered for this investigation (see
Table 2 [
8]). From the dataset, the average vehicle flow was calculated in the normal period as well as during the COVID-19 period (see
Table 3 and
Table 4) by adopting approach detailed in [
9,
10]. By assuming the differences ranged from 1% to 10%, we developed the scenarios as listed in
Table 5 and
Table 6 for the N5 and M2 highways, respectively. In this way, the goal of the research effort to establish a certain number of priority CSs was accomplished by maximizing the service of charging stations. It is to be noted that when calculating the distance from the demand point to the candidate point, the mathematical model mentioned in (1) and (2) and the after-mentioned principles were adhered to for the optimal placement of PEV CSs. The finalized scenario including transmission network infrastructure and the proposed potential charging station candidates for the M2 and N5 routes are depicted in
Figure 4.
Further, economic analysis about the investment and payback period was also taken into account for a feasibility analysis of the proposed model. For this, we considered the initial cost of investment, variable cost, operational cost, etc., as listed in
Table 7.
The charger mentioned above is the DCFC, with two ports for charging at one time. The installation cost included the labor cost, material cost, and other such parameters; the new connection cost was the cost of the regulator, and in the case of the transformer, there was a minimum cost both for the regulator and transformer. The operational and maintenance cost was taken as the 10% annual cost. The electricity and taxes costs were obtained from the provider, while we had to consider the rupee devaluation for investment and some miscellaneous charges, as this is the new technology, and there will inevitably be some unknown annual charges. Even during the period of strict lockdown during the COVID-19 pandemic, the minimum EV flow was 3 at each point in 1 h. So, at least two chargers are needed at one optimal location point. If the charging cost is assumed to be 0.31 USD/KWh and the installed charger worked for 24 h, then:
Total 1 day selling cost = 0.31 * (24 * 2) * 50 KW = 744 USD/KWh; Total 30 days selling cost = 22,320 USD/KWh, while:
Total 30 days actual electric cost is = 18,144 USD/KWh. Profit for 30 days = 4176 USDKWh.
Total investment recovery time = 29,400/4176 = 7 months.
So, in almost 7 months, the total investment will be recovered, even when the devaluation (or, if we remain in dollars, considering the interest rate at 10%) is also considered.