Determination of charging infrastructure location for electric vehicles

Available online at www.sciencedirect.com ScienceDirect Transportation Research Procedia 27 (2017) 768 775 www.elsevier.com/locate/procedia 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary Determination of charging infrastructure location for electric vehicles Bálint Csonka*, Csaba Csiszár Budapest University of Technology and Economics (BME), Faculty of Transportation Engineering and Vehicle Engineering (KJK), Department of Transport Technology and Economics, Műegyetem rkp. 3, Budapest 1111, Hungary Abstract The deployment of charging infrastructure is the prerequisite for the spread of electric vehicles. A well-established charging network increases vehicle miles using electricity, relieves range anxiety and reduces inconvenience concerning charging process. The research question was, where to install the charging stations to facilitate the longdistance travels and to meet the urban (local) demands considering both the existing stations and the installations are to be realized by legal regulations. We have elaborated weighted multi-criteria methods for both the national roads and the counties or districts. Several demographic, economic, environmental and transportation-related attributes, as well as the available services (points of interests) that influence the potential for charging station use, have been identified and their effects have been revealed in system approach. On the national roads point-oriented assessment, whereas in urban environment territorial unit-oriented assessments have been applied. On the national roads, the existing rest-places as prospective charging stations have been investigated. The strategic points (nearby border stations and capital city) as mandatory charging stations have been also designated. The methods have been applied to Hungary (on the level of national roads) and to Újbuda (11 th district of Budapest, on urban level). 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting. Keywords: electric vehicle; charging infrastructure; long-distance travel, local demand, multicriteria method * Corresponding author. Tel.: +36-20-446-4682. E-mail address: csonka.balint@mail.bme.hu 2352-1465 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 20th EURO Working Group on Transportation Meeting. 10.1016/j.trpro.2017.12.115

Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 769 1. Introduction In recent years, many European countries made efforts to raise the rate of battery electric vehicle (BEV) use. Financial subsidies can efficiently contribute to this aim, but it is of utmost importance to provide public electric charging network too. The main problem is that the refueling process of a charging vehicle extremely differs from a conventional vehicle. The charging time of a BEV is several times more than the refueling time of a conventional one and the drivers would not give up the comfort of a fast-refueling process. Furthermore, in the absence of mass market, the return of a charging station installation is not guaranteed. Hence, the key to success is to seamlessly suit the charging process to the current travel behavior and adjust the location of charging station to the charging demand. In this paper, the following questions were addressed: What are the main charging demand types? What are the main variables that influence the charging demand? How can the sites of charging stations be derived from the charging demand? Therefore, we determine the charging demand types and the main variables, such as travel behavior and electric vehicle (EV) characteristics, and how they influence the charging demand or the quality of time spent with charging. We develop two charging station location methods based on the variables and make a proposal for chargers at locations. The first users and beneficiaries of the methods should be the governments because we cannot expect the charging infrastructure developments to take place on a market basis in the early stage. The structure of the paper is the following: after a brief literature review in Section 2, we present the charging deployment method for long journeys and urban areas in Section 3. The application of the methods is provided in Section 4. Finally, the conclusion has been drawn. 2. Literature review Since a well-established charging station network is a key element of the spread of EVs, several studies focus on charging infrastructure development. There are two main viewpoints in charging infrastructure planning: energy network and travelers. From the traveler viewpoint, studies derive the charging station locations from travel behavior. We further categorized these studies into two groups: inter-city and intra-city charging infrastructure development. The general approaches of these groups are also different. Inter-city charging infrastructure developments usually use a flow-based approach (Hodgson, 1990), while intra-city developments use a node-based approach (Hakimi, 1964); however, there are exceptions as well. The reason for the difference is that the range of BEVs is enough for short trips in the metropolitan area, but not enough for a long trip. Thus, charging demand in the urban area occurs at the origin or at the destination of the trip, while inter-city charging demand arises during the trip. Node-based approaches assume point demands, which are specific for intra-city charging demands, while flow-based models assume that the demands are given as origin destination (O D) flows and the aim is to serve as many O-D flows as possible. Hence, flow-based models are better suited to tackle charging station optimization at the state scale (Upchurch and Kuby, 2010). The base of papers dealing with inter-city charging demands is a flow-based or also known as flow capturing location model (FCLM); however, the application of the models and the set of variables may differ. Sathaye and Kelley (2013) determine the locations of charging stations mainly considering the characteristics of traffic flows. In addition, the demographics were also taken into account. Tan and Lin (2014) provide a stochastic FCLM that takes into account the stochastic user demands along the routes as well as the installation cost and the service quality of a charging station. However, the sole use of stochastic model provides a lower service coverage rate than the deterministic model, because of the statistical fluctuation. Lin and Hua (2015) provide a particle swarm optimization model based on FCLM for establishing an optimal selection of charging station location. The optimization is performed according to the installation cost, the service area of a charging station and volume of traffic flow. Kuby and Lim (2005) adjust FCLM to alternative-fuel vehicles to determine the optimal location of refueling facilities. The FCLM was extended with a refueling-logic, where a flow is captured only if the vehicle never runs out of fuel. The location of fuel stations depends on the vehicle range, the arc length, and the node spacing of the transportation network. Later they extend the flow refueling location model by adding candidate sites along the arcs (Kuby and Lim, 2007) and propose efficient heuristic algorithms for siting fuel stations (Lim and Kuby, 2010). Davidov and Pantoš (2017) also determine the candidate sites and driving routes, then use a discrete set modeling approach to reduce the complexity of location modeling.

770 Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 The papers dealing with intra-city demands usually focus on the origin and destination points, but there are several exceptions too. Xi et al. (2013) determine the charging station locations according to O-D flows and probability of EV use. They forecast the rate of EV use considering the demographics and economic trends, e.g. education, age, income or number of vehicles per households. De Gennaro et al. (2015) consider parking behavior. Their model deploys charging stations in those areas where the parking demand is concentrated. According to their study, the most appropriate places are gas stations, airports, shopping centers and parking lots. The drawback of the method is the vast amount of area specific input data. Andrenacci et al. (2016) determine the charging station locations also from the parking behavior point of view. They analyze the routes of vehicles, cluster the most frequently visited places and propose charger deployment close to them. Both Cai et al. (2014) and Shahraki et al. (2015) analyze the routes of cabs in Beijing. The aim was to cover the busiest nodes in the city with the service areas of charging station. The service area was a round with a 2-mile diameter. Gas stations and parking lots were the candidate sites in general. Xydas et al. (2016) and Morrissey et al. (2016) derive the charging station locations from the current EV use. Xydas et al. (2016) analyze the correlation between the number of charging processes and traffic volume. They determine that forenoon is the most frequent charging time at public chargers because public chargers are expected to be used for charging when EV owners are at their work or when they do shopping. Morrissey et al. (2016) determine that charging at home is the most frequent followed by charging at parking lots and gas stations. The drawback of the analysis of current EV use and charging behavior is that the current users do not represent the future EV users. Finally, there are studies that deal with the deployment of charging infrastructure in general. Philipsen et al. (2015) made a survey to determine the relation between the possibility of EV use and habitats. According to their study, the most important charging station attributes that influence the use of an EV are additional services, accessibility, and reliability. Soylu et al. (2016) determine the charging demands at public chargers in general. They consider fleet composition, availability of private parking at home and availability of on-street charging infrastructure as the three major factors that influence the charging infrastructure developments. 3. Charging station deployment method According to the literature review and our experiences, we identify two distinct charging demand types and we define a charging station deployment method for each demand type: inter-city charging demand: the charging process interrupts the journey, and intra-city charging demand: the charging process is performed at the end of the journey. 3.1. Inter-city charging station deployment method Charging stations along national corridors mostly serve the inter-city charging demands during long journeys. Since the charging process interrupts the journey, only fast chargers are considered. We have elaborated a weighted multicriteria method to support long journeys. We chose the multicriteria method because it takes a large amount of data into consideration as well as quantifiable and non-quantifiable factors can be both evaluated. Figure 1 displays the steps of inter-city charging station deployment method. Fig. 1. Steps of inter-city charging station deployment method 4. Selection of installation sites 1. Determination of strategically important sites 2. Determination of candidate sites Legend: CS: candidate site IP: Installation potential 3. Evaluation of candidate sites 4.1. Calculate IP 4.2. Select CS where MAX(IP) 4.3. Deployment requirement not met Set of installation sites The method proposes the installation of a charging station at a candidate site according to the installation potential (IP). The result of the method is the set of recommended installation sites. The user of the method defines a deployment requirement. The deployment requirement should be to deploy a specific number of chargers or cover the road with chargers, where the distance between two neighboring chargers does not exceed a predefined limit. The distance should be derived from the average range of BEVs. When the deployment requirement is met, the process is done. met

Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 771 Determination of strategically important sites: to benefit from the local knowledge we dedicated a step to select the strategically important sites, where charging point installation is justified. Since we consider the specialties of an area, we could use some simplification in the method to ensure the general applicability. This step is optional. The method in the further steps considers the strategically important sites as installation sites. Determination of candidate sites: since the utilization of built infrastructure reduces the installation cost of a charging station, we consider only the rest-places as candidate sites (CS) not farther than 250 meters from the national corridors. Furthermore, the average distance between neighboring rest places is lower than the average range of BEVs. Available services at CSs raise the usefulness of time spent with charging for EV users. We categorized the CSs into quality groups according to it. The quality categories are the following: Basic rest-place: parking lot, restroom. Minimum rest-place: basic rest-place services, small shop. Medium rest-place: minimum rest-place services, dining options (e.g. restaurant, buffet) and additional services (e.g. pharmacy, supermarket). Superior rest-place: medium rest-place services and accommodation (e.g. hotel). We propose that minimum rest-place services should be available at each charging station. Evaluation of candidate sites: we evaluate the CSs on the base of IP according to (1). IP a x a x a x (1) j 1 1, j 2 2, j 3 3, j Where: IP j: installation potential of candidate site j. x 1,j: evaluation value of traffic volume on near roads at candidate site j. x 2,j: evaluation value of service level of candidate site at candidate site j. x 3,j: evaluation value of negative effect of near fast charging stations at candidate site j. a 1, a 2, a 3: weights of variables. We consider the installation cost of a charging station at a candidate site indirectly because we take into account the service level at sites, which is in correlation with the level of infrastructure and free electric capacity. We use the same 0-to-5 scale for each evaluation variables. We define the value of evaluation variables in the following way: x 1 traffic volume: we define the value of x 1 on the base of the sum of average daily traffic volumes on national corridors not farther than 250 meters from the CS (N j) [vehicle/day]. We consider the traffic volume of the directions from where the rest-place is accessible. The value of x 1 is calculated according to (2). 0, if N j 5000 N j 5000 x1, j 5, if 5000 N j 20000 15000 5, if N j 20000 We defined the limit values (20000 and 5000) according to the characteristic of the Hungarian traffic. Thus, the user of the method should adjust the limit values to the investigated area if it differs. x 2 service level of candidate site: we assign a value to each service category (Table 1). Table 1. Service level categories Service level categories x 2 Basic rest-place 0 Minimum rest-place 1 Medium rest-place 3 Superior rest-place 5 x 3 negative effect of the nearest fast charging station: we propose that the favorable distance between chargers along national roads is 50 km and assume that a fast charger closer than 50 km reduces the utilization of another charger at CS. We define the negative effect as a function of distance according to (3). We consider only the nearest fast charger, thus the most suitable place for a new charging station between two neighboring charging stations is on the halfway. (2)

772 Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 3 d ji 5 1 3 x 3, j 50, if d 50 km, (3) 0, if d 50 km. Where x 3,j is the negative effect of the nearest charger on candidate site j, and d j is the distance between the fast charger and the candidate site [km]. We use the third power of distance to significantly reduce the IP close to another fast charger. The weights of variables are derived from the charging station development plan. Hence, the high IP means that the candidate site is close to the development plan requirements. Selection of installation sites: the steps of installation site selection are the following: 4.1. We calculate IP for each CS. We consider installation sites as existing charging stations because it influences the value of variable x 3. 4.2. We select the candidate site where IP is the highest and add to the set of installation sites. 4.3. We check the deployment requirement. If the deployment requirement is met, the process is done, unless go to step 1. 3.2. Intra-city charging station deployment method Intra-city charging demand may be served when the vehicles are parking. The number of parking vehicles besides the number of parking places is mainly influenced by two variables: available services (daytime parking) and population (nighttime parking). To determine the suitable places for charging stations in urban areas we parcel the area to hexagons and consider the aforementioned variables and existing chargers. We assume that an existing charger close to the hexagon decreases the utilization regardless of its capacity. The distance between two parallel sides of a hexagon is 250 meters. We propose an approximate place for a charging station based on the local characteristics, because several other factors, such as the free capacity of electric network, can influence the exact position. We calculate the installation potential of a hexagon (W q) according to (4), (5) and Table 2. The higher the value of W q is, the higher is the estimated utilization of a charging station inside the hexagon. In urban areas we favor the economic normal chargers (AC, approximately 22kW) due to the lower price and electric capacity demand, however, the charging time is more than 1 hour. Hence, we emphasize areas where the parking time is longer. W b V b V b v (4) n q 1 q 2 q 3 q Table 2. Negative effect of charging stations in near hexagons Near charging station v q In the neighboring hexagon 1.5 One hexagon away 1 Two hexagons away 0.5 Three or more hexagons away 0 S Pq V q c 5 c 5 MAX ( P ) (5) q 1 MAX S 2 q Where: W q: installation potential of hexagon q. V q: evaluation value of hexagon q. It is calculated based on (5). n V :average evaluation value of neighboring hexagons. q q Table 3. Evaluation values of average parking time at service types Average parking time at a service point s 0-15 min 0 15-30 min 1 30-45 min 2 46-60 min 3 1 2 hour 4 >2 hour 5

Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 773 v q: evaluation value (Table 2) of the negative effect of charging stations in near hexagons. b 1, b 2, b 3, c 1, c 2: weights of variables. S q: each service in a hexagon has a value (s) based on the time spent with parking (Table 3). The sum of s values in hexagon q is S q. MAX(S q): the highest S q value in the study area. If MAX(S q) is 0, then c 1 also equals to 0. P q: total population of hexagon q [inhabitant]. MAX(P q): the highest P q value in the study area. The weights of variables are to be derived using the local knowledge. Our method does not propose the type of charger, it is to be derived from the parking behavior at the proposed site. The optimal number of chargers in an area could be derived from the number of EVs in the area. 4. Application of the methods 4.1. Inter-city charging station deployment method We apply the inter-city charging station deployment method for highway M3 which connects Budapest to Nyíregyháza. The length of the investigated section is 215 kilometers. We found 34 candidate sites along the highway. The CSs are numbered from 1 to 34, in ascending order by the distance from Budapest. Some of them are accessible from both directions. We aggregated the traffic volume of directions if the rest-place is accessible from both directions. Currently, there is not any charging station along the highway. The general aim of our development plan was to serve the travelers along the highway with fast chargers at highquality rest places. Because of that, x 1 and x 3 variables had heavy weights (a 1=0,7, a 2=0,3, a 3=1). Our deployment requirement was that the distance between the neighboring charging stations must not exceed 60 kilometers. We evaluated the CSs according to (1), (2), (3) and Table 2. The investigated CSs are summarized in Appendix A. The traffic volume at CSs was given by the main Hungarian road operator. 1 12/ 13 20/ 21 24/ 25 28 Fig. 2. The investigated highway section and the proposed installation sites The method selects 5 rest-places per directions. Since 2 rest-places are accessible from both direction, only 8 charging stations are needed to fulfill the deployment requirement. Figure 2 displays the result. The maximum distance between two neighboring charging stations is 50 kilometers. The method did not deploy charging stations at basic rest-places. 4.2. Intra-city charging station deployment method We apply the intra-city charging station deployment method for the district 11 th (Újbuda) of Budapest. We calculate W q for each hexagon according to (4) and (5). There are few existing chargers and some others in development. We do not evaluate the hexagons with existing or in development chargers. Our aim is to cover new parts of the district; thus, b 1=0.8, b 2=0.2, b 3=1. According to our experience, the service type is slightly more important than the population of a hexagon; thus, c 1=0.6 and c 2=0.4. Figure 3 displays the 10 most favorable hexagons for chargers according to W q. The hexagons are the proposed sites for charging stations. Each station is in a high-density residential area or close to services. Furthermore, each proposed site has an easy access on road.

774 Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 Fig. 3. The 10 most favorable hexagons for chargers in Újbuda 5. Conclusion The specialty of the charging of EVs makes imperative a novel approach in refueling facility network development. Since some of the methods in the literature review require a huge amount of data, that hardens the utilization, and others do not consider the existing infrastructure, we have elaborated a charging station deployment method both for inter- and intra-city charging demand, which is the main contribution of the paper. The key findings of the paper are: The locating of charging stations to support long journeys and charging stations in urban areas require different approaches according to the dissimilarities. Traffic volume, distance to the nearest fast charging station and the range of available services are the main variables that influence the utilization of a charging station along national roads. In an urban area, the favorable location for a charging station where vehicles are frequently parking for a longer term. The existing refueling network and rest-places along the highways are suitable for the charging stations to serve the inter-city charging demand. Usually, there is no need to build new rest-places. According to population density and parking behavior, the gas stations are not suitable to serve the intra-city charging demand. The main problem is the lack of services at gas stations. Only a few fast chargers are recommended in urban areas because there are several service points where drivers stay long enough to use a normal charger. The further research directions: Consider the seasonal effects in traffic volume and the population along national corridors. Development of information services to support driver decision and operation considering real-time data from charging network and traffic control system. Charging optimization of plug-in EVs connected to the smart grid from the operator and driver point of view. Development of charging demand model to determine the necessary number of charging points at charging stations.

Bálint Csonka et al. / Transportation Research Procedia 27 (2017) 768 775 775 Appendix A. Attributes of the investigated rest-places along highway M3 j 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 N NY [10 3 veh/day] 37.1 23.8 19.5 n.a. 22.8 22.8 19.9 19.9 n.a. 19.9 16.6 16.6 n.a. 14.4 n.a. 14.4 n.a. N B [10 3 veh/day] 37.1 23.8 n.a. 19.5 n.a. 22.8 19.9 n.a. 19.9 19.9 16.6 n.a. 16.6 n.a. 14.4 n.a. 14.4 d B [km] 1 10 16 16 24 29 32 36 36 43 46 51 51 59 59 66 66 x 1 3 3 1 1 1 3 1 0 0 3 1 5 5 1 1 0 0 x 2 5 5 4.8 4.8 5 5 5 5 5 5 5 3.9 3.9 3.1 3.1 3.1 3.1 j 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 N NY [10 3 veh/day] 14.5 n.a. 13.9 n.a. 12.4 n.a. 12.4 n.a. 9.5 n.a. 8.8 8.8 n.a. 4.6 n.a. 4.6 n.a. N B [10 3 veh/day] n.a. 14.5 n.a. 13.9 n.a. 12.4 n.a. 12.4 n.a. 9.5 8.8 n.a. 8.8 n.a. 4.6 n.a. 4.6 d B [km] 69 69 98 98 114 114 129 129 142 142 166 174 174 185 185 205 205 x 1 1 1 1 1 0 0 1 1 0 0 3 1 1 0 0 1 1 x 2 3.2 3.2 3 3 2.5 2.5 2.5 2.5 1.5 1.5 4.2 1.3 1.3 1 1 1 1 Legend: N NY: average daily traffic volume from Budapest to Nyíregyháza, N B: average daily traffic volume from Nyíregyháza to Budapest, d B: the distance between Budapest and the rest-place on highway, n.a.: rest place is not accessible from that direction. The traffic volumes are same in both direction because we divided the aggregated traffic volume by 2. References Andrenacci, N., Ragona, R., Valenti, G., 2016. A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas. Applied Energy 182, 39-46. DOI: http://dx.doi.org/10.1016/j.apenergy.2016.07.137 Cai, H., Jia, X., Chiu, A. S.F., Hu, X., Xu, M., 2014. 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