Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities

Applied Energy 255 (2019) 113855

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Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities Pei Huanga,b, Zhenjun Mac, Longzhu Xiaod, Yongjun Suna,e,

T

⁎

a

Division of Building Science and Technology, City University of Hong Kong, Kowloon, Hong Kong Department of Energy and Built Environment, Dalarna University, Falun, Sweden c Sustainable Buildings Research Centre, University of Wollongong, Australia d Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong e City University of Hong Kong Shenzhen Research Institute, Shenzhen, PR China b

H I GH L IG H T S

GIS techniques to collect building roof area for evaluating renewable potentials. • Use a GIS-assisted optimal design of renewable powered charging stations. • Develop locations and numbers to reduce cost under a required area coverage ratio. • Optimize • Reveal impacts of user-defined area coverage ratio on the optimal design solutions.

A R T I C LE I N FO

A B S T R A C T

Keywords: Renewable energy Charging station design Electric vehicle Geographic Information System

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel Geographic Information System (GIS) assisted optimal design method for renewable powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help highdensity cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities.

1. Introduction In high-density cities, due to the crowded urban environment and busy road traffic, the vehicle emissions cannot be easily dissipated, and they are trapped in the narrow space enclosed by the crowded buildings to form heavy and enduring air pollutions at street level [1]. Many governments and organizations have established strict regulations to control the street-level air pollutions. For instance, the Environmental Protection Department of Hong Kong has implemented a wide range of ⁎

measures to reduce the emissions from motor vehicles, e.g. phasing out pre-Euro IV diesel commercial vehicles, using roadside remote sensing equipment to strengthen emission control, setting up low emission zones, and tightening the emission standards [2]. The National Environment Agency of Singapore has set up high standards of exhaust emission and fuel quality for all vehicles [3]. Unfortunately, even with tremendous efforts from local governments, the roadside air pollutions in high-density cities still cannot be controlled at acceptable levels. In Hong Kong, the street-level NO2 concentration exceeded the required

Corresponding author at: Division of Building Science and Technology, City University of Hong Kong, Kowloon, Hong Kong. E-mail address: [email protected] (Y. Sun).

https://doi.org/10.1016/j.apenergy.2019.113855 Received 30 April 2019; Received in revised form 27 July 2019; Accepted 3 September 2019 0306-2619/ © 2019 Elsevier Ltd. All rights reserved.

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level by up to 134 ug/m3, and the respirable suspended particulates surpassed the allowed threshold by up to 10 ug/m3 [4]. The roadside air pollutions are proved to have diverse health damages to people. For instance, short-term exposures may lead to nose and throat irritations, shortness of breath, coughing and chest tightness [5]; while medium/ long-term exposures could result in more severe health impacts including risk increase of cancer [6]. Thus, the roadside air pollutions in high-density cities must be resolved. Electric vehicle (EV) is considered as a promising solution to the roadside air pollution and associated health damages since it directly cut off the pollutions from the source [7]. Many governments have made efforts in promoting its practical applications, such as tax reductions, one-for-one replacement scheme, and subsidies for EV purchase and EV licenses [8]. Unlike the conventional fuel-based vehicles, EV takes much longer time in its charging process. For instance, the regular charging takes 6–8 h, and even the fastest supercharging still takes about 30 min [9]. Due to the slow charging processes, significantly more charging stations will be needed for EVs if the equivalently convenient services as conventional gas stations are trying to achieve. Existing studies have developed some design methods of charging stations for improving the EV deployments. For instance, Wang et al. investigated the siting and sizing problem of fast charging stations in a highway network, where the budget constraint and the service capacities of charging spots are considered [10]. However, renewable energy integration was not considered. Luo et al. developed a comprehensive optimization model concerning the joint planning of distributed generators and electric vehicle charging stations, which involves spatially dispatchable characteristic at the planning stage of distribution systems and deploys power devices in a cost-effective way [11]. However, their method is not applicable to high-density cities, due to the limited spaces for installing the PV panels. There are three typical chargers for EVs, i.e. private charging piles [12], public charging stations [13], and mobilized chargers [14]. The EV owners can build a charging pile in their own garages. The EVs can be charged in the garages at night when the EV owners are off work at home. When the EV owners are not able to build their own charging piles, they can charge their vehicles in the public charging stations. To date, many public charging stations have been constructed worldwide and the number keeps growing [12]. Considering that the traffic jams and the limited existing charging stations may jointly cause EV users unable to charge conveniently, the Hong Kong Productivity Council developed a flexible mobilized EV charger, which offers multi-standard charging services and can fully recharge five to six EVs at the same time [14]. To promote the wide deployment of EVs, it is essential to provide EV users easy and convenient charging services. In high-density cities, developments of the private charging piles and the mobilized EV chargers may not be able to achieve such an objective due to the following reasons. In a high-density city, the limited garage space may not allow the wide installations of the private charging piles, and thus the EV users become more dependent of the public charging stations. A mobilized EV charger, in nature a vehicle with a slightly larger battery, may worsen the busy traffic that is already notorious in a high-density city. In addition, the limited battery capacity of the mobilized charger also prevents its wide applications in practice. For this reason, the development of the public charging stations become critical for EV deployment in the high-density cities. Currently, in most high-density cities, existing charging stations are limited, and they are far from sufficient to form a complete charging network with a high coverage ratio [15]. For instance, the total number of public charging stations in Hong Kong is merely 23, which can hardly cover the whole city and thus cannot provide EV users with convenient charging services. As a result, concerns and fears on being unable to find a charging port when needed have become a major hurdle to the growth of EV applications in high-density cities [14]. Meanwhile, existing charging stations still utilize high-polluting

fossil fuel-based grid energy rather than cheaper and greener renewable energy. To further improve associated environmental performance, renewable energy (e.g. solar and wind) have been highly recommended to replace conventional grid energy for powering the charging stations [16]. The Department of Energy (DOE) has offered typical templates in utilizing diverse renewable sources to power charging stations [17]. Note that when solar energy is selected, the available area for photovoltaic (PV) panels is a major factor that influences the total renewable energy generation and consequently the system configuration in practice [18]. Currently, there are two typical types of system configurations of renewable powered charging stations, i.e. distributed family based one and centralized city based one. In the United States, house roof area is generally large enough to install PV panels to power distributed family-based EV charging port [19]. With an integrated battery, energy collected during the day is stored and made available for other periods, turning a house into a personal utility [19]. In the city of Yantai of China, taking advantage of its large open land in the suburban area, a centralized PV system, occupying 5000 m2, has been constructed to power the EV charging stations for all the electric buses in the city [20]. The charging stations are also connected with the grid in case of insufficient solar energy generation in cloudy or rainy days. The abovementioned two solar-powered charging system configurations are proved to be effective, but they are not suitable for highdensity cities due to the extremely limited land resource. In other words, in a high-density city, there is neither enough roof area for each family to install PV panels to power its own charging pile, nor enough open land for the city to install a centralized PV system to power the charging stations. This study, therefore, proposes a novel Geographic Information System (GIS) assisted optimal design method of solar powered charging stations for promoting EV applications in high-density cities. By selecting the optimal locations and optimal number of the renewable powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. To verify the proposed method, case studies were conducted in a real district of Hong Kong. The results were analysed and compared with other non-optimal options in terms of life cycle cost and district coverage ratio. Meanwhile, this study also investigated the impacts of user defined coverage ratio on the optimal design results. The proposed design method can be used in practice to help highdensity cities complete its public charging network with cost-effectiveness. Providing easy and convenient charging services for EV users, the public charging network will promote EV practical applications and thus can alleviate the roadside air pollutions in high-density cities. The contributions of the present study to the subject are briefly summarized as follows.

• The study adopts the Geographic Information System techniques for • •

convenient and quick collection of geographic information of massive buildings, and the information is then used to estimate the building roof solar energy potentials and help select proper locations of the renewable powered charging stations. The study proposes a GIS-assisted design method to optimize the charging station locations and number for minimized life cycle cost while satisfying a user-defined area coverage ratio. The study reveals the impacts of user-defined area coverage ratio on the optimal design results and the total life cycle costs.

The rest of this paper is organized as follows. Section 2 presents the details of the GIS-assisted optimal design method of renewable powered EV charging stations in high-density cities. Section 3 presents a case study by applying the proposed design method in the Kowloon district of Hong Kong. Section 4 investigates the impacts of user-defined coverage ratio on the performances of the designs. Conclusive remarks are given in Section 5. 2

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Fig. 1. Flowchart of the GIS assisted design method for renewable powered EV charging stations in high-density cities.

2. Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations

among individual charging stations, which is beneficial to achieving a defined area coverage with reduced cost. In Step 5, the optimal design alternative is identified by comparing the performance (i.e. maximum coverage ratio and life cycle cost) of the feasible design alternatives obtained from Step 4. The details of each step are introduced as follows.

This section introduces the developed optimal design method of renewable powered EV charging stations in high-density cities. Satisfying a user-defined coverage ratio, the proposed method aims to optimize the locations and number of renewable powered charging stations to minimize the life cycle cost. Fig. 1 presents the basic idea of the proposed design method. It consists of five steps. In Step 1, Geographic Information System (GIS) techniques are used for quick and convenient collection of the geographic information of massive buildings in a district or a whole city. Note that conventional techniques (e.g. survey and field measurement) are time-consuming or even impossible to collect the geographic information (e.g. roof area) of a large number of buildings. The studied district is first discretized by partitioning it into a number of equally sized grids. Then, the geographic information of each grid, including the location, aggregated roof area, and usable roof area ratio, are collected. In Step 2, the roof based solar energy potential in each grid is estimated using the usable roof area collected from Step 1 together with the local solar radiation. The solar energy potentials will be used to estimate the operating cost of the charging stations. In Step 3, a pool of possible design alternatives is generated by using an integer partition algorithm, and a simple rule-based filter is developed to rule out the unreasonable alternatives, thereby reducing the pool size. In Step 4, the performances (i.e. maximum coverage ratio and associated life cycle cost) of each design alternative are evaluated. The charging stations could locate anywhere on the map, leading to different coverage ratios and the associated life cycle costs. Genetic algorithm (GA) is applied for searching the optimal charging station locations that can maximize the area coverage. The associated life cycle cost is also estimated. Note that a maximized area coverage indicates a minimized coverage overlaps

2.1. Building geographical locations and roof areas obtained using Geographic Information System technique In this step, the geographic information of the studied districts is collected. Due to massive buildings considered, it is impractical to collect the data by conventional techniques (e.g. survey and field measurement). Thus, GIS techniques are used for the quick and convenient collection of the geographic information. The GIS is also prominent at capturing, storing and managing spatially referenced vector and raster data [21]. The processes of geographic information collection and compiling are briefly introduced as follows. First, the original map data of the studied district (e.g. boundaries, latitudes and longitudes of buildings, building area) are collected from the OpenStreetMap (OSM) database [22]. The OSM database is a comprehensive and complete map database with detailed building geographical information of many cities/ districts. To ensure the collected information can be easily and mathematically analyzed, the studied district is discretized into small grids [23]. Then, the information, including the coordinates of each grid and the aggregated building roof area in each grid, is calculated and stored in a two-dimensional matrix. The discretization of the studied districts into small grids and collection of the aggregated geographic information (i.e. locations and aggregated roof area) are automatically implemented in ArcGIS, which is a platform for compiling geographic data and analyzing mapping information [22]. After a series of automatic processing, including creating fishnet, intersecting, calculating 3

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geographic and spatial joining, the locations and aggregated roof area data of each grid can be obtained. More details of the associated GIS techniques can be found in [24]. Due to factors such as equipping roof-mounted cooling towers and green plants, in most cases not all the roof area can be used for installing PV panels. The area measurement tool in Google Earth will be used to assist the evaluation of the usable roof area ratios. It should be mentioned that the coordinates (indicating building geographical locations) and the aggregated usable building roof areas (indicating renewable potentials) will be used in the subsequent operating cost evaluation for search of the optimal design alternative. Note that the owner of roof area can be a second party, different from the charging network developer. To encourage the usage of roof area, the charging station owner/developer can provide economic incentives/subsidies to a second party for renting the space. Meanwhile, the local government can play a lead role in promoting roof-top renewable applications for a greener city. In a high-density city like Hong Kong, there are many public housing buildings directly owned and managed by the government. It will provide chances to install rooftop PV panels for the development of renewable based charging network.

Nmin =

2.3.2. Generation of possible design alternatives by using an integer partition algorithm In this step, all possible design alternatives are first generated by the integer partition algorithm. The number of charging stations in one location is in fact influenced by available renewable resource. When there is more renewable energy available in one location, multiple charging stations can be constructed. Considering that there can be multiple charging stations in one location, the integer partition algorithm will be used to identify all the possible design alternatives of charging stations. Integer partition examines how to represent a given integer n with the sum of one or more positive integers xi, i.e. n = x1 + x2 +…+ xm [30]. In such representation, the order of xi is of no consequence, and thus two partitions of an integer n are distinct only if they differ with respect to the xi they contain. Extensive studies have been conducted to investigate the integer partition problem. The theory of partition problem was introduced by [31]. Based on such theory, many integer partition algorithms have been developed, such as parallel algorithms [32], Tree traversal related algorithms [33], and loop-free algorithms [34]. Due to its simplicity and convenience, this study selects the algorithm developed in [30] for partitioning integers. For a given n, one way of integer partition is considered as 1 design alternative. For instance, there are 7 ways of partition when n equals 5, and thus there are 7 design alternatives (see Table 1). The partitioned integer n varies from Nmin to Nmax, as specified in Section 2.3.1. These design alternatives (when n varies from Nmin,…,Nmax) together form the space of all possible design alternatives. When the number of a charging station is 1, its coverage area is Scs (m2); Similarly, for a charging station of number n, its coverage area is n × Scs (m2).

Based on the collected geographic information by GIS techniques, the renewable energy generation in each grid is evaluated in this step. The renewable energy system considered in this study is the roofmounted PV panels. The annual renewable energy generations of the PV panel Es, ij (kW·h) is calculated by Eq. (1) [25]. The estimation of renewable energy generation will be conducted in the commonly used software, i.e. TRNSYS. 8760

∑ t=1

(τ × IAM , t × IT × η × (α × Sij ))

(2)

where γ is the user-defined coverage ratio that the charging network should meet, Stot (m2) is the total area of the districts, Scover _exist (m2) is the aggregated area of the district that is already covered by the existing charging stations, and Scs (m2) is the covered area of a charging station. The required number of charging stations will increase when the user-defined coverage ratio increases. Meanwhile, the total costs of the charging stations (including initial costs and operational costs) will also increase dramatically. Due to the concern of budget, a maximum required number of charging stations can be defined based on the available budgets.

2.2. Estimation of renewable generation potentials based on the collected geographic information

Epv, ij =

γ × Stot − Scover _exist Scs

(1)

where τ is the transmittance-absorptance product of the PV cover for solar radiation at a normal incidence angle, ranging from 0 to 1 [26]; IAM is the combined incidence angle modifier for the PV cover material, ranging from 0 to 1; IT (kW/m2) is the total amount of solar radiation incident on the PV collect surface; η is the overall efficiency of PV array; α is the available roof area ratio; Sij (m2) is the PV surface area, which equals to the aggregated roof area in the specified grid. The renewable energy system is connected with the power grid [27]. In the operation period, the surplus renewable energy of a charging station will be exported to the power grid; while when the renewable energy is insufficient, the charging station will use the grid energy to charge the EVs. Such process has similar mechanisms to the energy certificate systems [28] and power purchase agreements [29], which are both used for renewable energy generation and distribution on the power grid.

2.3.3. Rule-based filter of impractical alternatives Many design alternatives, generated by the integer partition algorithm from Section 2.3.2, are obviously not economic and thus they should be ruled out for the final optimal search. To improve the optimization efficiency through reducing the number of design alternatives, a rule-based screen is introduced to rule out the non-economic alternatives. In this study, one rules will be adopted as below to rule out the non-economic alternatives.

2.3. Generation of a feasible design alternative pool and reduction of the alternatives by a rule-based screen

Table 1 An example of design alternative pool using the integer partition algorithm.

In this step, a feasible design alternative pool of charging stations is generated by using an integer partition algorithm. Since not all the alternatives are able to produce the optimal performance, a simple rulebased screen is then conducted to filter the obvious unreasonable design alternatives.

Alternatives

1 2 3 4 5 6 7

2.3.1. Initialization of charging station number Based on a user-defined coverage ratio of the charge station network, the total number of designed charging stations will be initialized in this step. The minimum number of charging stations required is calculated by Eq. (2) 4

Number of charging stations in each location Location 1

Location 2

Location 3

Location 4

Location 5

5 4 3 3 2 2 1

— 1 2 1 2 1 1

— — — 1 1 1 1

— — — — — 1 1

— — — — — — 1

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The fitness function of the GA search is expressed by Eq. (4), which aims at maximizing the coverage ratio of the designed charging stations by searching the optimal X-Y coordinates.

Rule: The design alternatives kk , with the number of charging stations larger than a threshold value kmax, should be removed. This is because in comparison with distributed charging stations, placing too many charging stations in a same location could reduce the possibility of utilizing all renewable energy, thereby increasing operating costs as well as life cycle costs. ⇀

Jarea = max(γ )

γ is the coverage ratio of the designed charging stations under the X-Y coordinate trials, which is calculated by comparing the area covered by charging stations with the total district area considered, see Eq. (5).

⇀

(3)

Removekk , ifmax(kk) ≥ kmax

(4)

The threshold value kmax is determined by the radius of the largest circle that can be put inside the studied district. For any design alternative, its maximum number of charging stations at a same location should not be larger than kmax. Otherwise, the coverage area of the charging stations will exceed the district, leading to useless area coverage and increased operating cost. After applying the rule-based filter, the feasible design alternatives are selected out. Their performance will then be evaluated in the next step.

γ=

Scover _exist + Scover _new Stot 2

(5) 2

Scover _exist (m ) and Scover _new (m ) are the coverage area of the existing charging stations and the newly designed charging stations, respectively. Stot (m2) is the area of the whole district. The coverage area (Scover (m2)) of the charging stations in one location is determined by the number of charging stations n. Scover = n × Scs

(6)

πR2

(7)

Scs = 2.4. Performance evaluation of the feasible design alternatives

where R (m) is the coverage radius of one charging station. This study assumed that the EVs are evenly distributed in the considered district. Under such an assumption, the coverage area is simply determined through dividing the number of EVs that a charging station can accommodate by the statistical EV distribution density. Please note that the proposed method can also consider more complex EV distributions and then search for the corresponding optimal number and locations of the charging stations. The X-Y coordinates should meet the following constraints. The x1,x2,…,xm should be within the range [xmin,xmax], and y1,y2,…,ym should be within the range [ymin,ymax].

This step evaluates the performances of the screened feasible design alternatives in terms of the maximum coverage area and total costs. For a design alternative, its life cycle cost will approach the minimum when it has the largest area coverage ratio. This is because once the number of charging stations is determined, the initial cost is nearly fixed, and thus the life cycle costs is mainly determined by the operating cost. With the increased coverage ratio, the coverage overlaps among individual charging stations will decrease. As a result, the operational cost, mainly caused by the grid energy imports, will decrease when there is insufficient renewable generation. Thus, in the genetic algorithm, maximizing the coverage area is directly used as the fitness function. The locations of charging stations are the variables to be optimized. Based on the searched locations, the overlaps of charging station coverages are determined, and then the operating cost is estimated with the available renewable energy. Last, the life cycle cost is calculated.

x min ≤ x1, x2 , ⋯, x m ≤ x max , ymin ≤ y1 , y2 , ⋯, ym ≤ ymax

(8)

Note that, the overlapped area should not be repeatedly considered when there are overlaps between the coverage of different charging stations. This is conducted by the following process: (1) Based on the original renewable production matrix (i.e. [E 0 pv, ij]I × J obtained in Section 2.2), the renewable production in the coverage of the 1st charging station is calculated by aggregating the production in each grid (i.e. represented by index i and j) within its coverage. (2) After calculation for the first charging station, the renewable production in the calculated grids are reset to 0. The original renewable production matrix is updated to be [E1 pv, ij]I × J . (3) Based on the updated renewable production matrix (i.e. [E1 pv, ij]I × J ), the renewable production in the coverage of the 2nd charging station is calculated by aggregating the production in each grid within its coverage. The process is repeated until all the charging stations are calculated. By resetting the renewable production of the calculated grids to 0 and updating the renewable production matrix, the repeated calculations of the overlapped areas among different charging stations are avoided.

2.4.1. Search of the maximum coverage area by genetic algorithm When charging stations are placed in different locations, their effective coverage area can be different. Since genetic algorithm (GA) is effective in searching the global or near optimal solutions [35], it was used for identifying the maximum coverage area. For each alternative ⇀

(e.g. alternative kk with N charging stations placed in m locations), the GA algorithm searches the optimal X-Y coordinates (e.g. [x1, x2,…,xm; y1,y2,…,ym]) of the designed charging stations that maximize the coverage ratio. In each generation of GA, the trials of the X-Y coordinates (e.g. [x1,x2,…,xm; y1,y2,…,ym]) are generated by the GA optimizer to evaluate the effective coverage ratio. Fig. 2 presents the chromosomes in the GA optimization. There are 2 × m variables to be optimized in each GA optimization. xi is within the range [0,233], and yi is within the range [0,163]. Both xi and yi can be represented by an 8-bit binary number. In total, the chromosome has 16 × m bits. More details about the GA can be found in [36].

2.4.2. Analysis of life cycle cost The considered costs of charging stations consists of two parts, including initial cost (Costinitial (USD)) for installing charging stations and renewable energy systems (i.e. PV panels), and operational cost

Fig. 2. Illustration of the chromosomes used in the GA optimization. 5

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the estimated operating cost and the actual one. To reduce such differences, a proper sized storage unit can be installed.

Table 2 Parameter values used for cost calculation. ID

Parameter

Definition

Value

Reference

1

α

0.98

[38]

2 3

ρ Costpv, unit

Discount rate due to increase of charging station scales Electricity prices (USD/(kW·h)) Unit price of PV panels (USD/(m2))

0.2 150

[42] [39] 4

4

Costcharge, unit

Unit price of charging stations (USD)

2 × 10

[43]

5

T

Considered life cycles (year)

30

[39]

2.5. Search for the optimal design alternative by comparing the obtained performance After performance evaluation of each feasible design alternative, the optimal design that meets the user-defined coverage ratio is identified in this step. The search consists of two sub-steps. First, the feasible design alternatives that meet the user-defined coverage ratio are selected. Then, these selected design alternatives are sorted by their life cycle costs. The one with the minimal costs is selected as the optimal design and will be finally used for the design of EV charging stations. The outputs of the proposed design methods consist of the number (m) of locations with charging stations constructed, the coordinates of each location ([x1, x2,…,xm; y1, y2,…,ym]), the number of charging stations ([n1, n2,…,nm]) in each location, the coverage ratio (γ ) and the total costs (C).

(Costoperation (USD)) due to insufficient renewable energy supply during the considered life cycles (T (years)), as described by Eq. (9).

C = Costinitial + Costoperation × T

(9)

The initial cost (Costinitial (USD)) includes the charging station cost (Coststation (USD)), and renewable energy systems cost (Costpv (USD)), which are calculated by Eqs. (11) and (12), respectively. m

∑k =1 Coststation,k + Costpv

(10)

Coststation, k = α nk × Costcharge, unit × nk

(11)

Costinitial =

3. Application of the proposed design method in Hong Kong To illustrate the proposed method, case studies were conducted in the Kowloon District of Hong Kong. The radius of the coverage area was set to be 750 m as an example. Note that in practice, this radius of coverage should be determined and adjusted based on the statistical analysis of EV density. The threshold of renewable insufficiency Eth, unit was set to 5 × 107 kW·h with reference to the density of EVs in Hong Kong [41]. Table 2 summarizes the parameters related to the cost evaluation of solar-powered EV charging stations. The user-defined coverage ratio of EV charging stations was set as 40% as an example in this section. Different user-defined coverage ratios have been further discussed in Section 4.

Usually, the initial cost of charging stations will gradually increase with the increased number of charging stations, but the increase is not in a simple linear relationship, since a large scale charging station (e.g. two centralized charging stations) can save some costs in electricity cables, installation costs, land area, etc. as compared with several smallscale charging stations which have the same aggregated number (e.g. two distributed charging stations). With reference to [37] and [38], this study considered a discount coefficient α in the evaluation of charging stations costs. In Eq. (11), Costcharge, unit (USD) indicates the unit initial cost of a charging station, and nk indicates the number of charging stations in the kth (k = 1,…,m) location.

Costpv = Costpv, unit × Spv

3.1. Geographic information collected by Geographic Information System

(12)

The geographic information of the Kowloon district was collected by GIS techniques [22]. Fig. 3 presents the collected geographic information. The red curve indicates the boundary of the studied district, and the area with dark gray color indicates the building roofs. The whole district was discretized by partitioning it into 50 m × 50 m grids, i.e. the length and width of each grid equals 50 m. In total, there were 37,979 (X-coordinate 233 × Y-coordinate 163) grids. Using the GIS technique, the building area in each grid was also obtained and stored in a matrix with dimensions of 233 × 163. Due to factors such as equipping roof-mounted cooling towers (e.g. see Fig. 4(a)) and green plants, in most cases not all the roof area can be used for installing PV panels. To investigate the available roof area ratio, 500 buildings (consisting of commercial buildings, residential buildings and factories) were selected in different locations of Kowloon, and their available roof areas were estimated by Google Earth. An example is presented in Fig. 4(a). Fig. 4(b) presents the statistical analysis results of the available roof area ratio based on the estimated ratio of these 500 buildings. The mean of available roof area ratio was 70%. This ratio was used to evaluate the renewable energy generations (see Eq. (1)) in the following steps.

The initial cost of PV panels (Costpv (USD)) is considered to be proportional to the area of the PV panels [39]. Costpv, unit (USD/m2) is the unit price for installing PV panels, and Spv (m2) indicates the area of the installed PV panels. The operation cost (Costoperation (USD)) of the solar-powered EV charging stations is considered since the renewable generations in some areas may not be enough to meet the EV electricity demand, and thus the power grid electricity is needed. The operational cost is determined based on the renewable energy shortage, as calculated by Eq. (13) [40].

Costoperation = ρ ×

m

∑k =1 (−ΔEk )

(13)

where ρ (USD/(kW·h)) is the electricity price in the local market, and ΔEk (kW·h) is the renewable energy shortage in the kth location, which is calculated by Eq. (14).

ΔEk =

⎧ Epv, k − Eth, unit × nk , if Epv, k < Eth, unit × nk , ⎨ 0, if Epv, k > Eth, unit × nk ⎩

(14)

where Epv, k (kW·h) is the annual renewable energy production by the PV panels in the kth location, which is calculated by Eq. (1).nk represents the number of charging station in the kth location. Eth, unit (kW·h) indicates the threshold of annual energy demand required by the EVs, which should be set based on the statistical analysis of the local EV density. Note that when there is renewable shortage, the insufficient electricity will be imported from the power grid. Note that the proposed method can calculate the operating cost with smaller time intervals (e.g. minute interval and second interval) if the provided inputs can achieve such high time resolutions. A smaller time interval can help improve the accuracy of the operating cost estimation. In practical applications, high time-resolution data (e.g. solar radiation) could be difficult to be attained and thus cause the differences between

3.2. Renewable energy generation evaluation results After obtaining the geographic information by GIS techniques, the renewable energy generations in each grid was evaluated. Using the weather data in Hong Kong, the annual renewable energy generations was calculated in TRNSYS. Fig. 5 presents the renewable energy generations in each grid. The blue dotted curve is the boundary of Kowloon district. In some grids, due to the high density of buildings, the annual renewable generations can be as high as 3.4 × 105 kW·h. While in some grids, due to the low density of buildings, the annual renewable 6

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Fig. 3. Collected building roof area information from GIS (the dark color indicates buildings).

districts. The locations of the existing charging stations as well as the renewable energy generation information were used as the inputs in the following design optimization.

generation can be very small. In some mountain districts, there are no buildings constructed, and thus the renewable energy generation is equal to 0. According to the Tesla Hong Kong website [12], currently there are 6 public charging stations located in the Kowloon District. The geographic information (i.e. latitude and longitude) of these six existing charging stations are summarized in Table 3. Then, these latitude and longitude coordinates were converted to the coordinates in the grid matrix obtained in Section 3.1 for further mathematical analysis. The scale of these charging stations was considered to be 1. Fig. 6 highlights the locations of the six existing charging stations (i.e. the blue filled dots) and their coverage area (i.e. the blue circles) in the Kowloon

3.3. Initialization of design alternatives After obtaining the renewable energy generation and the locations of the existing charging stations, the possible design alternatives were generated by the integer partition algorithm. Calculated by Eq. (2), the minimum number of required charging stations was 5 for a user-defined coverage ratio of 40%. Considering budget limit, the maximum number of designed charging stations was set as 20. When N varied from 5 to

(a) Illustration of the available and unavailable are in a case building

(b) Statistical analysis of the available roof area ratio from 100 typical buildings

Mean value Available area

Unavailable area

Fig. 4. Estimation of the available roof area ratio for installing PV panels. 7

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Renewable energy generation results Kowloon Boundary Renewable energy generation in each grid (50m 50m dimension)

kW·h per year

Coverage of the existing charging stations

Fig. 5. Evaluation results of renewable energy generations in each microgrid in Kowloon.

locations of the existing charging stations, and the blue circles represent their coverage areas. The orange dots indicate the locations of the newly designed charging stations, and the orange circles represent their coverage ranges. n indicates the number of charging stations in one location. As depicted by Fig. 6, adding 6 more charging stations could meet the user-defined coverage ratio. The optimal design has two features. First, there were very limited overlaps between the designed charging stations. Reducing the overlaps between the coverage ranges of different charging stations can help increase the utilization of renewable energy, and thereby lowering down the operational cost. Another feature is that most of the charging stations were installed in the locations where there were sufficient renewable energy generations. This is because increased renewable generations can help reduce the operational cost. To better explain the performances, the optimal design was compared with four near-optimal designs, in which the total number of charging stations was the same (i.e. 6). In Fig. 7, ΔE (106 kW∙h) indicates the amount of renewables shortage in each location (calculated by Eq. (15)), Cin , Cop , Ctot (kUSD) represents the initial costs, operational costs due to renewables shortage, and total costs of the design alternatives, respectively, and γ is the coverage ratio of the design alternative. Three different regions were selected for detailed analysis. In Region a, the renewable energy generations can sufficiently supply up to two centralized charging stations (i.e. Figs. 7.1, 7.4 and 7.5). When more than two charging stations are installed (i.e. Fig. 7.3) or distributed charging stations are installed (i.e. Fig. 7.2), there will be renewable energy shortages. This is because the layout of two centralized charging stations fits the distribution of renewable energy generations better in this region. For instance, as shown in Fig. 7.2, there was a 3 × 106 kW∙h renewable energy shortage when installing two distributed charging stations. The renewable energy shortage will increase to 20 × 106 kW∙h when installing 3 charging stations, as shown in Fig. 7.3. Consequently, the increased renewable energy shortage will lead to increased operational costs. In Region b, the renewable energy generations can sufficiently supply up to three distributed charging stations (i.e. Figs. 7.1 and 7.5).

Table 3 Survey of existing public charging stations. ID

1 2 3 4 5 6

Name

Festival Walk Supercharger Mikiki Supercharger E-Max Supercharger Olympian City Supercharger Elements Supercharger Kai Tak Supercharger

Original coordinates

Converted coordinates

Latitude

Longitude

X

Y

22.33757 22.33359 22.32396 22.31802

114.1742 114.1969 114.2042 114.1584

85 132 147 52

116 107 86 73

22.3068 22.30611

114.1615 114.2132

59 165

48 46

20, the total number of possible partition ways was 2702 (see Fig. 8). The maximum number of charging stations in one location was set to 8, with reference to the size of the Kowloon district on the map. After applying the rules-based filter, the number of feasible design alternatives was reduced to 1826 (see Fig. 8). Table 4 summarizes the counting results of the removed and remained design alternatives under different number of charging stations. 3.4. Performance evaluation and search results of the optimal design After obtaining the feasible design alternatives, the cost performance and the maximum coverage ratio of each alternative were evaluated. To guarantee that the GA algorithm terminated by reaching the fitness limit, the GA parameters were tuned using the trial and error method. The number of generations was set as 100, the population size was set as 500, and the termination tolerance was set as 1e−6. Based on the user-defined coverage ratio (i.e. 40%), 1814 design alternatives, which had a coverage ratio over 40%, were selected and sorted. The one with the minimal cost was selected as the optimal design. The coverage ratio of the optimal design was 43.2%, and the total cost was about 584 kUSD. Fig. 6 displays the locations and number of charging stations in the optimal design alternative on the map. The blue dots indicate the 8

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Kowloon Boundary

kW·h per year

Coverage of the planned new charging stations

Coverage of the existing charging stations

Fig. 6. Optimized location and number of charging stations when the required coverage ratio was 40%.

operational cost was the smallest. Overall, the optimal design showed the best economic performance. The optimal design also had the largest coverage ratio (i.e. 43.2%) compared with the four near-optimal designs which had the same total number of charging stations. Even though the aggregated area of the circles was similar in different alternatives, their effective coverages of occupancy area were different, since the buildings were not evenly distributed in the Kowloon district.

Table 4 Counting of design alternatives under different number of charging stations. Number of charging stations

5

Number of partitions 7 Number of removed alternatives 1 Number of remained alternatives 6 Number of charging stations 13 Number of partitions 101 Number of removed alternatives 19 Number of remained alternatives 82 Total number of alternatives Total number of remained alternatives

6

7

8

9

10

11

12

11 1 10 14 135 30 105

15 1 14 15 176 45 131

22 1 21 16 231 67 164

30 2 28 17 297 96 201

42 4 38 18 385 137 248 2702 1826

56 7 49 19 490 190 300

77 12 65 20 627 263 364

4. Discussions The user-defined coverage ratio has significant impacts on the optimal design of renewable powered EV charging stations. This section analyzed the performance of all the design alternatives and investigated the relationship between the required coverage ratios and the costs. Fig. 8 presents the coverage ratio and cost performance of all the design alternatives, including the removed design alternatives by the pre-set two rules in Section 2.3.3 (i.e. the ‘hollow’ marks) and the remaining feasible design alternatives (i.e. the ‘filled’ marks). As can be seen, the rule-based filter was effective in selecting most of the nonoptimal design alternatives, which had low coverage ratios but high total costs (see the region within the green dashed curve). N indicates the total number of charging stations, and the different values were highlighted by the different marks and colors. Both the average total costs and average coverage ratio gradually increased with the increase of charging station total number. The optimal design under a set of user-defined coverage ratios, ranging from 0.4 to 0.9 with an interval of 0.1, were searched and marked by black pentagrams in Fig. 8. The total costs of the optimal design increased in an exponential relationship when increasing the required coverage ratio. The number of possible design alternatives will increase with Nmin, leading to increased computational load. There are two means to improve the computational efficiency. First, a large district can be portioned into several small districts, which break downs a complex optimization problem into several simpler ones, thereby reducing the complexity and computation load. Second, since searching of the optimal locations for each alternative are independent, parallel computing techniques can be employed to reduce search duration. By district

When more than three charging stations are installed (i.e. Fig. 7.2) or centralized charging stations are installed (i.e. Figs. 7.3 and 7.4), there will be renewable energy shortages. This is because the layout of three distributed charging stations can better fit the distribution of renewable energy generations in this region. For instance, as shown in Figs. 7.3 and 7.4, there was a 7 × 106 kW∙h renewable energy shortage when installing three centralized charging stations. The renewable energy shortage will increase to as high as 11 × 106 kW∙h when installing four charging stations, as shown in Fig. 7.2. In Region c, the renewable energy generations can sufficiently supply up to one charging station (i.e. Fig. 7. 1). The renewable energy shortages will be resulted when more than one charging stations were installed (i.e. Figs. 7.4 and 7.5). This is due to the limited renewable energy available in this region. For instance, as shown in Figs. 7.4 and 7.5, there was a 10 × 106 kW∙h renewable energy shortage when installing two centralized charging stations. With the increase of renewable energy shortage, the operational costs of design alternatives will gradually increase. With the centralization of charging stations, the initial costs of design alternatives will gradually decrease. Compared with the four near-optimal designs, the initial cost of the optimal design (i.e. Fig. 7. 1) was the largest due to the dispersion of charging stations. However, because of the proper design of charging station locations and numbers, the renewable energy shortage of the optimal design was well controlled at 0, and thus its 9

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(1)

a The renewables in this region can only satisfy a maximum of 2 concentric charging station (i.e., Figs. (1),(4) and (5)). Otherwise, there will be a shortage of renewables (i.e., Figs. (2) and (3)). More grid energy will be needed, leading to increase of operational costs.

c b

a

b

The renewables in this region can only satisfy a maximum of 3 dispersed charging station (i.e., Fig. (1) and (5)). Otherwise, there will be a shortage of renewables (i.e., Figs. (2),(3) and (4)).

c

Near-optimal designs

The renewables in this region can only satisfy 1 charging station (i.e., Fig. (1)). Otherwise, there will be a shortage of renewables (i.e., Figs. (3) and (4)).

(3)

(2)

b

a

b

a

b

a

(5)

(4)

c

c b

a

Fig. 7. Performances comparison of the proposed design with four near-optimal designs.

5. Conclusion

partitioning and parallel computing, the computing time can be significantly reduced. The layouts of the six selected optimal designs, which were determined under the required coverage ratios ranging from 0.4 to 0.9, are presented in Fig. 9. It can be seen that the overlaps between the area coverages of different charging stations increased with the increase of the required coverage ratios. When the required coverage ratio γ was larger than 0.7, there was a dramatic increase in the operational costs. This was because the overlaps between the area coverages of different charging stations would increase inevitably and significantly for high required coverage ratios. Due to the increased overlaps of coverage area and increased number of charging stations, the total costs will increase dramatically when the required coverage ratio is large. The developed design method is effective in optimizing the number and locations of charging stations in high-density cities under different user-required coverage ratios.

This study has proposed a novel Geographic Information System (GIS) assisted optimal design method of renewable powered electric vehicle charging stations for high-density cities. By selecting the optimal locations and number of renewable powered charging stations considering the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. The developed method consists of the five steps. First, the geographic information of massive buildings in the studied district is collected by GIS techniques. The studied district is discretized into equally sized grids, and the geographic information of each grid (i.e. locations, aggregated roof area, and usable roof area ratio) are collected. Then, Using the collected usable roof area together with the local solar information, the roof based solar energy potential in each grid is estimated. Next, a pool of feasible design alternatives is generated by using an integer partition 10

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Total costs (kUSD)

Optimal design ( =0.9) Optimal design ( =0.8) Optimal design ( =0.7) Optimal design ( =0.6) Optimal design ( =0.5) Optimal design ( =0.4)

Fig. 8. Performances of all the design alternatives and relationship between the minimum required investments and the required coverage ratio.

(1)

(3)

(2)

C=2126kUSD

(4)

(5)

(6)

Fig. 9. Optimized location and number of charging stations under different required coverage ratio. 11

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algorithm, and a rule-based screen is applied for reducing the number of alternatives. After that, the performances (i.e. maximum coverage ratio and minimal life cycle cost) of all the selected feasible design alternatives are evaluated. Finally, the optimal design is identified by comparing the evaluated performance. Case studies have been conducted in the Kowloon district of Hong Kong. For validation purpose, the coverage ratio and cost performance of the optimal design have been compared with several near-optimal designs, and the performance improvements have been analysed. The impacts of the required coverage ratio on the optimal design have also been investigated. The major findings are summarized as follows.

[5] Department EP. An overview on air quality and air pollution control in Hong Kong; 2018. Accessed on. Available at https://www.epd.gov.hk/epd/english/ environmentinhk/air/air_maincontent.html. [6] Protection CfH. The health effects of air pollution; 2017. Accessed on. Available at https://www.chp.gov.hk/en/healthtopics/content/460/3557.html. [7] Seddig K, Jochem P, Fichtner W. Two-stage stochastic optimization for cost-minimal charging of electric vehicles at public charging stations with photovoltaics. Appl Energy 2019;242:769–81. [8] Department EP. Promotion of Electric Vehicles in Hong Kong; 2019. [9] Scientists UoC. Electric vehicle charging: types, time, cost and savings; 2018. Accessed on. Available at https://www.ucsusa.org/clean-vehicles/electric-vehicles/ car-charging-time-type-cost. [10] Wang Y, Shi J, Wang R, Liu Z, Wang L. Siting and sizing of fast charging stations in highway network with budget constraint. Appl Energy 2018;228:1255–71. [11] Luo L, Gu W, Wu Z, Zhou S. Joint planning of distributed generation and electric vehicle charging stations considering real-time charging navigation. Appl Energy 2019;242:1274–84. [12] Tesla. Accessed in 2019. Search of superchargers. Accessed on. Available at https:// www.tesla.com/zh_HK/supercharger. [13] Luo L, Gu W, Zhou S, Huang H, Gao S, Han J, et al. Optimal planning of electric vehicle charging stations comprising multi-types of charging facilities. Appl Energy 2018;226:1087–99. [14] Council HKP. Multi-standard Mobilized EV Smart Charger – A Flexible EV Charging Solution; 2018. Accessed on. Available at https://www.hkpc.org/en/industrysupport-services/latest-information/6506-auto-charger-urgent. [15] Sadeghi-Barzani P, Rajabi-Ghahnavieh A, Kazemi-Karegar H. Optimal fast charging station placing and sizing. Appl Energy 2014;125:289–99. [16] Abbasi MH, Taki M, Rajabi A, Li L, Zhang J. 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J Comput Civil Eng 1999;13:144–52. [22] OpenStreetMap. Accessed December 18, 2018. Map data. Accessed on. Available at https://extract.bbbike.org. [23] Wang W, Hong T, Li N, Wang RQ, Chen J. Linking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification. Appl Energy 2019;236:55–69. [24] ESRI. ArcGIS tutorials; 2019. Accessed on. Available at http://desktop.arcgis.com/ en/arcmap/latest/get-started/introduction/arcgis-tutorials.htm. [25] Huang P, Huang G, Sun Y. Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements. Appl Energy 2018;213:486–98. [26] Huang P, Sun Y. A clustering based grouping method of nearly zero energy buildings for performance improvements. Appl Energy 2019;235:43–55. [27] Huang P, Sun YJE. A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level. 2019;174:911–21. [28] Ltd. GS. 2014. Energy Certificate Systems. Accessed on May, 2019. Available at https://grexel.com/sites/grexel.com/files/energycertificates.pdf. [29] Bruck M, Sandborn P, Goudarzi N. A Levelized Cost of Energy (LCOE) model for wind farms that include Power Purchase Agreements (PPAs). Renew Energy 2018;122:131–9. [30] Stojmenović I, Zoghbi A. Fast algorithms for genegrating integer partitions. Int J Comput Math 1998;70:319–32. [31] Andrews GE. Encyclopedia of mathematics and its applications, vol. 2, The Theory of Partitions. Addison-Wesley, Reading, MA; 1976. [32] Akl G. Parallel algorithms for generating integer partitions and compositions. J Combinat Math Combinat Comput 1993;13:107–20. [33] Fenner TI, Loizou G. Tree traversal related algorithms for generating integer partitions. SIAM J Comput 1983;12:551–64. [34] Fenner TI, George L. An analysis of two related loop-free algorithms for generating integer partitions. Acta Inform 1981;16:237–52. [35] Huang P, Wu H, Huang G, Sun Y. 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• With the assistance of GIS techniques, the proposed design method •

• •

is able to conveniently and quickly collect the geographic information of massive buildings, which is then used to estimate the building roof solar energy potentials and help select proper locations of the renewable powered charging stations. By locating the charging stations in sites with large solar power potential and minimizing the overlaps between different charging stations’ coverage, the proposed method is effective in minimizing the life cycle cost of charging stations while satisfying a user-defined area coverage ratio. In the case studies, the coverage ratio of the optimal design was 43.2%, which was much higher than the ratios of the four near-optimal designs (i.e. 37.2–40.7%). The life cycle cost of the optimal design was 584 kUSD, which was much lower than the costs of the near-optimal designs (i.e. 779–1101 kUSD). The rule-based filter can effectively reduce the considered number of design alternatives, thereby largely improving the computational efficiency. The life cycle cost of the renewable powered charging stations increases in an exponential relationship with the increase of the required coverage ratio. There was a dramatic increase in total cost when the required coverage ratio is large, since the operational cost will increase inevitably and dramatically due to the increased overlaps between the coverage range of different charging stations.

It should be mentioned that one limitation of this study is that the 3D information (e.g. height and shape) of the buildings and their impacts on the solar radiation capture of the adjacent buildings were not considered due to lack of proper computation-efficient modelling tools. This may cause differences between the predicted renewable potentials and actual renewable generations. In the future, with more advanced district-/city-level solar radiation modelling tools developed, the PV power generations can be more accurately simulated, thus improving the performance of the developed method. The density of vehicles is an important factor that may affect the design of charging network. Future work will take account of such factor with reference to the practical data. The renewable energy generations may be affected by uncertain factors such as weather conditions. Future work will also take such uncertain factors into consideration. Acknowledgements The research work presented in this paper is jointly supported by the Early Career Scheme of the Hong Kong Special Administrative Region, China (CityU 21207915) and National Natural Science Foundation of China (Project No. 51608463). References [1] Committee TA. Report on Study of Road Traffic Congestion in Hong Kong; 2014. [2] Department EP. 2018. Air Pollution Control Strategies. Accessed on. Available at https://www.epd.gov.hk/epd/english/environmentinhk/air/prob_solutions/ strategies_apc.html. [3] Government NEAoS. Air pollution regulations; 2019. [4] Group ARW. Roadside Air Quality and the Vehicle Emission Control Measures; 2016.

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