A multi-criteria decision method for performance evaluation of public charging service quality

Journal Pre-proof A multi-criteria decision method for performance evaluation of public charging service quality

Lihui Zhang, Zhenli Zhao, Meng Yang, Songrui Li PII:

S0360-5442(20)30065-7

DOI:

https://doi.org/10.1016/j.energy.2020.116958

Reference:

EGY 116958

To appear in:

Energy

Received Date:

07 May 2019

Accepted Date:

11 January 2020

Please cite this article as: Lihui Zhang, Zhenli Zhao, Meng Yang, Songrui Li, A multi-criteria decision method for performance evaluation of public charging service quality, Energy (2020), https://doi.org/10.1016/j.energy.2020.116958

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof A multi-criteria decision method for performance evaluation of public charging service quality Lihui Zhanga,b, Zhenli Zhaoa,b,*, Meng Yang a,b, and Songrui Li a,b

aSchool bBeijing

of Economics and Management, Beijing, China Key Laboratory of New Energy and Low-Carbon Development

(North China Electric Power University), Changping, Beijing, 102206, China *[email protected]

Journal Pre-proof A multi-criteria decision method for performance evaluation of public charging service quality Abstract: Public electric vehicle charging infrastructure provides an essential support for sustainable electric transportation systems. However, the current development model for such infrastructure tends to emphasize quantity over quality and cannot meet the charging needs of electric vehicle users. Addressing this situation requires further guidance from governments, which should be based on performance evaluation systems. This study therefore developed a multi-criteria evaluation framework to assess the performance of public charging infrastructure in terms of planning rationality, operational efficiency, service capacity, charging safety, and sustainable development. After defining individual charging station attributes through numerical data and user/expert input, a modified Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model was used, with vague sets to standardize, weight, and process the data. The assessed stations were subsequently ranked. The model was applied to three public charging stations in Beijing, China to verify its effectiveness and robustness. The resulting rankings can facilitate regulatory assessment of these stations’ performance and guide improvements in the quality of their charging services. The results further indicated that the sustainable development value of charging facilities is often undervalued, and relevant incentive strategies should thus be implemented by policymakers. Keywords: public charging infrastructure; service quality; sustainable development, TOPSIS model 1. Introduction Global economic growth has drawn attention to the energy consumption of, and environmental pollution caused by, the transportation sector [1]. The use of zero-emission electric vehicles (EVs) has become a significant approach to reducing greenhouse gas emissions [2,3]. Various incentives have been proposed to expand EV use [4]. In JanuarySeptember 2018, global EV sales reached 1.3 million, a 68% year-on-year growth. China has become the largest EV market in the world, with 860,000 sales in 2018 [5]. In China, EVs are regarded as a strategic sector, and various industrial and economic policies have been introduced to increase their market share. Importantly, proper charging infrastructure facilitates the sustainable use of EVs. Significant research and planning have been undertaken regarding the expansion of charging facilities. For example, the United States planned to invest USD 2 billion in the

Journal Pre-proof construction of charging stations from 2017–2027, and Germany planned to invest USD 338 million to build 15,000 charging stations by 2020. China planned to build 12,000 centralized power stations and 4.8 million distributed charging stations by 2020 [6]. The Tokyo metropolitan government aimed to boost the distribution of EVs by supporting the free installation of charging stations at residences [7]. Private EV charging facilities dominate the available supply of charging stations, and 3 million units were installed in homes and workplaces globally by the end of 2017. In Europe and the United States, car owners with private garages or driveways prefer to charge their EVs at home at night. However, not all EV users have access to such facilities, especially in densely populated areas such as the Netherlands, Beijing, or Tokyo, where more than half of the population lives in multi-family housing. In such areas, insufficient parking and limited grid capacity necessitate the use of public charging infrastructure (PCI) [8]. Thus, the availability of PCI and its ability to meet user requirements have become the focus for many governments. The Chinese government has reduced PCI construction costs through subsidies while encouraging investment through social capital. This has formed an EV charging market dominated by distributors, oil companies, equipment companies, and automobile manufacturers [9]. As of December 2018, the total number of public charging facilities in China reached 300,000, increasing by 44.93% over 2017 (Fig. 1) and alleviating a previously insufficient supply [10]. However, a lack of operational management experience has led to a “high quantity low quality” situation because of unreasonable locations, damaged facilities, and occupancy by conventional vehicles, among other problems. This focus on quantity over quality has lowered the value of PCI and reduced consumer willingness to adopt EVs, requiring changes to policies guiding PCI operation and management to improve the efficiency of charging services. For example, in October 2018, Beijing issued a detailed implementation guide for the operation and assessment of PCI, including average charging price, data change push timing rate, utilization rate and other operating indicators. This required operators to improve the quality of their charging services and provided subsidies to well-operated stations.

350000

600%

300000

500%

250000

400%

200000 300% 150000 200%

100000

Increase rate [%]

Journal Pre-proof

100%

50000 0 2010

2011

2012

2013

2014

2015

2016

2017

0% 2018 Year

Fig. 1. Number of public charging facilities and its annual increase in China ([11])

Improving PCI service and efficiency at a national level is an important step toward promoting the sustainable development of EVs. Moreover, establishing a scientific performance evaluation system is required for improving charging service quality, e.g. to guide the proper allocation of charging resources [12,13]. Many researchers have argued that optimizing charging facilities is vital to improving the penetration rate of EVs [14], and previous studies have focused on charging station characteristics such as location, capacity optimization, and grid impact [15]. Guo and Zhao [16] established a site selection index system from the perspective of sustainable development based on economic, social development, and environmental considerations. Hosseini and Sarder [17] further proposed a Bayesian network model for optimal site selection. Tao et al. [16] used driving data to establish a model for optimizing PCI distribution, and Wang et al. [19] investigated issues in the siting and sizing of fast charging stations within a highway network. Li et al. [20] showed that geographic distribution, vehicle range, and deviation choice are significant factors in proper PCI allocation. Zhang et al. [21] focused on charging price, an important factor in PCI usage, but did not address comprehensive improvements to PCI. Philipsen et al. [22] showed that highway service centers, shopping areas, and fueling stations had the highest potential as fast-charging station locations, and dual use (availability of both fuel and electricity), reliability, and accessibility were viewed as important assessment criteria for PCI. Zenginis et al. [23] pointed out that fast-charging stations providing higher-quality service can facilitate EV penetration, but did not assess factors influencing the service level of such stations. Additional electrical load on the power grid caused by EVs could

Journal Pre-proof be accommodated by managing charging time and implementing battery swapping [24]. Moreover, the energy storage potential of EVs is key to promoting the development of renewable energy sources (such as wind and photovoltaic power) and effectively transitioning to green power generation [25]. Domínguez-Navarro et al. [26] designed a fast-charging station integrating renewable energy and storage systems, showing how this system can improve economic benefits and reduce high energy demand from the power grid. Liu et al. [27] focused on the allocation optimization of charging stations by considering the influence of factors such as charging satisfaction and distributed renewables integration. An increasing number of researchers have recognized that improving public charging facilities plays an important role in promoting EV development [28]. The abovementioned studies provide a practical basis for optimized site selection and evaluation of charging stations to promote convenient charging services and reduce the impact of disorganized EV charging on the power grid. However, most previous research did not consider specific preference evaluation criteria to assess the operational efficiency and service quality of charging facilities. Although Helmus and Van den Hoed [29] provided some key performance evaluation indicators, they did not develop a complete performance assessment system. Service quality is a central problem for PCI. EV users are sensitive to the various aspects of service quality, such as charging fees, waiting time, and reliability of charging. The service quality of charging facilities can be assessed by whether they can provide safe, reliable and stable charging services for EV users. The purpose of this study is to establish a performance evaluation system for PCI. Such a system could (1) help the government to adjust incentive policies and establish a fair and reasonable PCI performance evaluation and reward mechanism; (2) supervise operators to optimize charging resources, improve charging efficiency, and ensure both the quality and quantity of charging services for EV users; and (3) alleviate concerns of existing EV owners, stimulate consumer willingness to purchase EVs, and promote the sustainable development of charging facilities and EVs. In general, performance assessment is considered a multi-criterion decision making (MCDM) problem which includes various requirements under uncertain conditions [30]. MCDM methods have been widely used and developed, including the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) [31], the Elimination and Choice Translating Reality [32], VlseKriterijumska Optimizacija I

Journal Pre-proof Kompromisno Resenje (VIKOR) [33], and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) [34]. TOPSIS has outstanding performance in solving performance evaluation problems. Solangi et al. [35] evaluated and ranked energy strategies in Pakistan, and Dong and Shi [36] studied regional differences in renewable energy performance, both using the TOPSIS method. Bai and Sarkis [37] combined TOPSIS and VIKOR method to evaluate and rank supplier candidates. Yue [38] evaluated software quality based on the TOPSIS method. Therefore, this method is effective in dealing with performance evaluation issues. The assessment of PCI performance is complex, and imprecise information may occur in the evaluation process. TOPSIS assesses performance, ranks alternatives and obtains the best solutions, which are needed for PCI evaluation. Therefore, this study used TOPSIS to evaluate the performance of public charging stations in China. This paper is organized as follows: Section 2 describes the PCI performance evaluation criteria, Section 3 presents an extended TOPSIS model, Section 4 reports on a case study based on analytical and simulation results, and Section 5 concludes the paper. 2. Multi-criterion PCI evaluation system To assess the performance of PCI, a comprehensive performance evaluation index system was developed, consisting of 5 criteria and 20 sub-criteria (Fig. 2). Performance evaluation of public charge infrastructure

Goal

PR

OE

SC

CS

SD

Planning rationality

Operational efficiency

Service capability

Charging safety

Sustainable development

Safety management system

Renewable energy supply ratio

Criteria

Location rationality

Average charge fee

Site accessibility

Utilization

Sub-criteria

Average waiting time Proportion of equipment connect to public platform Data push accuracy

Charging capacity

Equipment intelligence level Site accident rate Demand response ability

Turnover rate Types of payment

Ratio of fast charge piles

Malfunction rate

Complaint and feedback processing

Rectification rate

Non-charging business benefit

Fig. 2. Performance evaluation index system for public charging infrastructure

2.1. Planning rationality (PR) This criterion reflects the need to consider the surrounding geographical environment and adjacent users' charging needs during PCI site selection and

Journal Pre-proof configuration. Location rationality (C1) includes characteristics of the surrounding geography, traffic density, distribution of charging demand, and local power grid. Site accessibility (C2) involves locations’ relevance to users' travel route or destination; sites should be convenient to find and reach [22]. Charging capacity (C3) entails the ability to provide charging services and is calculated using Eq. (1): 𝑛

𝐸𝜂 = ∑𝑖 𝑛𝑖𝑝𝑖

(1)

where 𝑝𝑖 is the charging power of equipment type 𝑖 and 𝑛𝑖 is the number of equipment type 𝑖. Fast-charge ratio (C4) is the ratio of the number of fast-charging stations to the total number of stations [39]. EV users prefer using fast-charging facilities to save time, and a PCI location with a higher fast charge ratio is thus more likely to provide efficient charging services. 2.2. Operational efficiency (OE) This criterion reflects the actual operation status of the public charging station, including charging fees and infrastructure operation. Average charging fee (C5) influences users’ enthusiasm for using the facility and affects the promotion of EVs [40]. It is calculated as follows: 24

V𝜁 =

∑𝑖 (𝑃𝐸𝑖 + 𝑃𝑆𝑖) T

(2)

where 𝑃𝐸𝑖 is primary electric price per hour, 𝑃𝑆𝑖 is charging service fee per hour, and T is the working time. Utilization (C6) is the ratio of charging time per station to total use time per station, reflecting the efficiency of station use as affected by the location, capacity, planning, and management of the site. Turnover rate (C7) involves the average use frequency per station, indicating the intensity of usage. Malfunction rate (C8) reflects charging facilities not in operation. 2.3. Service capability (SC) This criterion reflects the ability of PCIs to provide an efficient user experience [41]. Average waiting time (C9) is the total time spent at a public charging station minus the charging time. Connectivity to public platforms (C10) is the ratio of total charging facilities

Journal Pre-proof connected to the public intelligent charging platform to the total number of charging facilities. Data accuracy (C11) comprises charging applications that help EV users find charging points, assess their status, and pay for charging. It assesses the accuracy of the information provided by a location. Payment type (C12) refers to the payment method (e.g., cash, power grid card, prepaid card, credit card, Alipay, or WeChat); more diverse payment types increase user convenience and draws more consumers. Feedback processing (C13) assesses stations’ establishment of a complaint/ feedback acceptance agency and contact details. Rapid processing of and response to, complaints can improve user satisfaction. 2.4. Charging safety (CS) Public charging points are usually located in densely populated transportation hubs and/or economically developed areas, making user safety critical [42]. Safety management system (C14) is the complete daily safety management of, and emergency plans for, charging points, including clear instructions, safety warnings, and security alarms. Site accident rate (C15) is the ratio of major incidents at the site to the number of incidents in the entire region. Rectification rate (C16) is the ratio of rectified safety hazards to identified safety hazards. 2.5. Sustainable development (SD) PCI operators can improve the sustainability of their facilities by configuring renewable energy power systems, energy storage systems, and demand side responses. Given the ongoing development of new energy generation, storage, and vehicle-to-grid (V2G) technologies, the potential of EVs serving as mobile power banks by helping grids to balance peaks and valleys while promoting renewable energy generation is receiving increasing attention [43]. Renewable energy supply (C17) refers to the proportion of electricity supplied by the charging station generated from renewable energy [14,44]. Equipment intelligence (C18) involves the technological integration and collaborative control abilities of the charging equipment with regard to new energy systems and power grids. For example, intelligent devices should be able to monitor the charging and discharging status in real time. Facilities with V2G functionality can help

Journal Pre-proof to balance grid demand and emergency power supply while improving grid stability. Demand response ability (C19) is the capability of PCI to use pricing mechanisms (i.e., peak and valley time-of-use electricity pricing) to guide users toward improved management of peak loads [45]. Alternate revenue sources (C20) involve the capability of a location to profit from non-power sales such as advertising, participation in grid dispatching, and renewable energy generation. 3. Methodology Performance evaluations should consider various requirements under uncertain environments [46]. Many multi-criterion decision-making methods have been developed [47]. The TOPSIS method developed by Hwang and Yoon [47] is a simple and effective approach to approximate the ideal solution. While the processing of criterion attribute values is an important part of this method, incomplete data and limited expert experience can result in imprecise attribute values [48]. Step1: Select evaluation indicators

Step2:Determine attribute values

Literature review and expert judgment

Actual situation and experts’ linguistic evaluation

Step3:Translate attribute values into vague values Vague set Step4: Normalize the processing of criteria attribute values

Step5: Determine criteria weighting

The order relation analysis (G1) method

Step6: Measure the distance between the positive and negative ideal solution

Extended TOPSIS method

Compute the closeness coefficient Step7 Rank the alternatives

Fig. 3. Computational flow chart of extended TOPSIS-based method

To reduce such ambiguity and improve results, we modified the TOPSIS method using the vague set approach proposed by Wen and Buehrer [49] to simulate human decision-making processes and more effectively process incomplete and/or inaccurate data (Fig.3). By translating language-based expert knowledge into vague values (using

Journal Pre-proof interval number forms to represent the membership degree), this method can effectively reduce the data loss caused by ambiguous attribute values and improve the accuracy of information processing. An expanded TOPSIS method was developed involving the calculation procedure described below. Step 1: Select evaluation indicators Based on a literature review, we identified and selected PCI performance evaluation factors, including the abovementioned 5 criteria and 20 sub-criteria. The sub-criteria identified were divided into benefits and costs, and their values were numerically or linguistically categorized. Step 2: Determine attribute values We collected original data and invited experts to qualitatively evaluate the performance of the qualitative criteria. The corresponding variables’ values were clear phrases such as “very good”, “good”, or “fairly good” (Table 1). Step 3: Translate attribute values into vague values The criterion values into vague values to eliminate the effect of criterion dimensions [50]. If U was defined as the domain and x as any element, then A was the vague set in U and was represented by 𝑡𝐴 and 𝜈𝐴 (true membership and non-membership degrees, respectively). Furthermore 𝜋𝐴 was the hesitancy degree, defined as 𝜋𝐴 = 1 ― 𝑡𝐴 ― 𝜈𝐴. The numerical and linguistic variables were thus translated using the following procedures: For numerical values: if there were m alternatives 𝐴𝑖(𝑖 = 1, 2, …, 𝑚) and n criterion = 𝐶𝑗(𝑗 = 1,2,⋯,𝑛), and the initial criterion values were represented by 𝑥𝑖𝑗, then 𝑥𝑚𝑎𝑥 𝑗 and 𝑥𝑁𝐼𝑆 were the optimal and worst 𝑚𝑎𝑥 {𝑥𝑖𝑗} and 𝑥𝑚𝑖𝑛 = min {𝑥𝑖𝑗}, where 𝑥𝑃𝐼𝑆 𝑗 𝑗 𝑗

1≤𝑖 ≤𝑚

1≤𝑖 ≤𝑚

solutions of criterion 𝑗, respectively. If 𝑥𝑖𝑗 was translated into the vague value 𝑟𝑖𝑗 according to the nature of the 𝑗𝑡ℎ criterion, then the vague value 𝑟𝑖𝑗 of 𝑥𝑖𝑗 for benefit criteria could be determined using Eq. (3): 𝑟𝑖𝑗 =

[

2

(𝑥𝑖𝑗 ― 𝑥𝑚𝑖𝑛 𝑗 )

2

,1 ―

𝑁𝐼𝑆 2 (𝑥𝑃𝐼𝑆 𝑗 ― 𝑥𝑗 )

(𝑥𝑖𝑗 ― 𝑥𝑚𝑎𝑥 ) 𝑗

]

𝑁𝐼𝑆 2 (𝑥𝑃𝐼𝑆 𝑗 ― 𝑥𝑗 )

(3)

For cost criteria, the vague value 𝑟𝑖𝑗 of 𝑥𝑖𝑗 could be determined using Eq. (4): 𝑟𝑖𝑗 =

[

2

2

]

(𝑥𝑖𝑗 ― 𝑥𝑚𝑎𝑥 ) 𝑗

(𝑥𝑖𝑗 ― 𝑥𝑚𝑖𝑛 𝑗 )

(𝑥𝑃𝐼𝑆 𝑗 ― 𝑥𝑗 )

𝑁𝐼𝑆 2 (𝑥𝑃𝐼𝑆 𝑗 ― 𝑥𝑗 )

,1 ― 𝑁𝐼𝑆 2

(4)

Qualitative variable criteria as defined by the experts were translated into vague values using the 7-level rules documented in Table 1.

Journal Pre-proof Table 1. The 7-level transformation rules of linguistic variables provided by experts Linguistic variables

Corresponding vague value

Abstentions

Very good（VG）

[0.9,1]

0.1

Good（G）

[0.8,0.9]

0.1

Fairly good（FG）

[0.6,0.8]

0.2

Medium (M)

[0.5,0.5]

0

Fairly poor（FP）

[0.3,0.5]

0.2

Poor (P)

[0.2,0.3]

0.1

Very poor（VP）

[0.1,0.2]

0.1

Step 4: Normalize processing of criteria attribute values The criterion attribute values are normalized as follows:

(

𝑟𝑖𝑗 = (𝑡𝑖𝑗,𝜈𝑖𝑗,𝜋𝑖𝑗) = 𝜆1𝑟1𝑖𝑗⨁𝜆2𝑟2𝑖𝑗⨁⋯⨁𝜆𝑠𝑟𝑠𝑖𝑗⨁⋯⨁𝜆𝑝𝑟𝑝𝑖𝑗 = 𝑝 𝑝 𝑝 𝜆 𝜆 𝜆 (5) 1 ― ∏𝑠 = 1(1 ― 𝑡𝑠𝑖𝑗) 𝑠,∏𝑠 = 1(𝜈𝑠𝑖𝑗) 𝑠,∏𝑠 = 1(𝜋𝑠𝑖𝑗) 𝑠

)

Step 5: Determine criteria weighting To reduce computational complexity, we used the order relation analysis (G1) method to determine the index weights. This is a typical approach that does not consider criterion data. If criterion 𝑥𝑖 was as or more important than criterion 𝑥𝑗, then 𝑥𝑖 ≻ 𝑥𝑗. The relative importance of criterion k (𝑟𝑘) with respect to adjacent criteria 𝑥𝑘 ― 1 and 𝑥𝑘 was determined according to Table 2 and Eq. (6): 𝑟𝑘 =

𝑤𝜌𝑘 ― 1 𝑤𝜌𝑘

(𝑘 = 𝑚,𝑚 ―1,⋯,2)

(6)

where 𝜌 indicates the number of decision makers. Table 2. Value of relative importance 𝑟𝑘 of criteria 𝑟𝑘

Description

1.0

𝑥𝑘 ― 1 is same as 𝑥𝑘

1.2

𝑥𝑘 ― 1 is slightly more important than 𝑥𝑘

1.4

𝑥𝑘 ― 1 is more important than 𝑥𝑘

1.6

𝑥𝑘 ― 1 is strongly more important than 𝑥𝑘

1.8

𝑥𝑘 ― 1 is extremely more important than 𝑥𝑘

Next, the criterion weights could be obtained using Eqs. (7) and (8):

(

𝑚

𝑚

)

𝑤𝜌𝑚 = 1 + ∑𝑘 = 2∏𝑖 = 𝑘𝑟𝑖 𝑤𝜌𝑚 ― 1 = 𝑟𝑘𝑤𝜌𝑘

―1

(7) (8)

Finally, for problems with multiple decision makers, we aggregated the final weights according to the calculation results, and the weights of the decision makers were calculated as follows:

Journal Pre-proof 𝜌2 𝜌𝑠 𝑤𝑚 = 𝜆1𝑤𝜌1 𝑚 + 𝜆2𝑤𝑚 +⋯ + 𝜆𝑠𝑤𝑚

(9)

where 𝜆𝑠 is the weight of decision maker 𝑠. Step 6: Measure the distance between the positive and negative ideal solution We identified the positive-ideal solution (PIS) 𝐴 + and negative-ideal solution (NIS) 𝐴 ― and calculated the distances between these two solutions using the Hamming distance method: 𝑛

= ∑𝑖 = 1𝑤𝑗𝑑(𝐴𝑖,𝐴 + ) = 𝑑𝑃𝐼𝑆 𝑖 𝑛

∑𝑖 = 1𝑤𝑗(|𝑡(𝑥𝑖) ― 𝑡(𝑥 + )| + |𝑣(𝑥𝑖) ― 𝑣(𝑥 + )| + |𝜋(𝑥𝑖) ― 𝜋(𝑥 + )|)

(10) 𝑛

= ∑𝑖 = 1𝑤𝑗𝑑(𝐴𝑖,𝐴 ― ) = 𝑑𝑁𝐼𝑆 𝑖 𝑛

∑𝑖 = 1𝑤𝑗(|𝑡(𝑥𝑖) ― 𝑡(𝑥 ― )| + |𝑣(𝑥𝑖) ― 𝑣(𝑥 ― )| + |𝜋(𝑥𝑖) ― 𝜋(𝑥 ― )|)

(11)

where 𝑤𝑗 is the weight of criterion 𝐶𝑗, 𝐴 + = 𝑚𝑎𝑥 𝐴𝑖, and 𝐴 ― = 𝑚𝑖𝑛 𝐴𝑖. 1≤𝑖≤𝑚

1≤𝑖≤𝑚

Step 7: Compute the closeness coefficient 𝑫𝒊 of each criterion and rank the alternatives The closeness coefficient 𝐷𝑖 was calculated as follows: 𝑑𝑁𝐼𝑆 𝑖

(12)

𝐷𝑖 = 𝑑𝑁𝐼𝑆 + 𝑑𝑃𝐼𝑆 𝑖

𝑖

The alternatives were ranked by the closeness coefficient, in which a higher value meant a better performance for the alternative. The highest value of 𝐷𝑖 was thus the optimal situation. 4. Case study 4.1. Background information To test this approach, three public charging stations (PCI1, PCI2, and PCI3) in Beijing are assessed in this case study. The initial data for each station charging stations are collected, along with additional background information (Table 3). Table 3. Background information of three public charging stations (PCI1. PCI2 and PCI3) PCI1

PCI2

PCI3

five 37.5kW fast charging piles, and

ten 37.5kW fast charging piles

three 45kW fast charging piles,

seventeen 7kW slow charging piles

two 22kW fast charging piles, and three 7kW slow charging piles

24-h service

12-h service (7:00am-21:00

24-h service

pm) 0.53yuan/kWh+0.8yuan/kWh

1.0044yuan/kWh(10:00-

1.0044yuan/kWh(10:00-

15:00,18:00-19:00)

15:00,18:00-

Journal Pre-proof +0.6950yuan/kWh(7:00-

21:00)+0.6950yuan/kWh(7:00-

10:00,15:00-

10:00,15:00-18:00,21:00-

18:00)+0.8yuan/kWh

23:00)+0.3946(23:007:00am)+0.8yuankW/h

12 charging piles are connected to

9 charging piles are connected

6 charging piles are connected

the charging platform

to the charging platform

to the charging platform

All charging piles can be used

One charging pile is paralyzed

All charging piles can be used

normally

normally

The average wait is 2 minutes

The average wait is 10 minutes

The average wait is 4 minutes

Pay by credit card, WeChat and

Pay by State grid's charge card

Pay by charging card

No new energy for power

Equipped with photovoltaic

generation

power generation, and it can

AliPay No new energy for power generation

generate 30% of the electricity per day

4.2. Evaluation process and results We formed an expert group for qualitative evaluation that included an office worker of the Beijing municipal commission of urban management (E1), a marking director at a charging facility company (E2), an academic researcher with extensive operational experience in public charging facilities (E3), and a frequent, long-time user of EVs (E4). These experts contributed their observations of real-world situations and assessed the qualitative variables accordingly. Eight qualitative criteria were identified along with numerical evaluations. Based on Eq. (1), the charging capacities of PCI1, PCI2, and PCI3 were 341.5, 375, and 110kW, respectively, and their average charging fees were 1.3300, 1.6497, and 1.4980 yuan/kWh, respectively. The initial data for the three stations are given in Table 4. Table 4. Initial criterion values of three public charging stations in Beijing (PCI1–PCI3) PCI1

PCI2

PCI3

E1

E2

E3

E4

E1

E2

E3

E4

E1

E2

E3

E4

C1

G

VG

FG

FG

G

FG

FG

G

FG

G

VG

G

C2

VG

VG

G

FG

FG

VG

VG

VG

VG

G

FG

FG

C3

341.5

375

110

C4

22.73%

100%

50%

C5

1.3300

1.6497

1.4980

C6

18.52%

60%

16.67%

C7

1.4

4.3

1.2

C8

0

10%

0

C9

2

10

4

C10

54.55%

90%

100%

Journal Pre-proof C11

100%

100%

98%

C12

VG

G

G

VG

VG

G

FG

VG

FG

FG

G

FG

C13

VG

VG

G

G

FG

G

VG

VG

VG

G

FG

FG

C14

VG

G

G

FG

FG

FG

G

VG

VG

VG

VG

VG

C15

0

0

0

C16

100%

100%

100%

C17

0

C18

M

M

M

M

M

FG

M

M

FG

FG

G

FG

C19

M

M

M

FG

FP

M

M

FP

FG

FG

FG

M

C20

FP

M

FP

FP

FP

FP

FP

FP

FG

FG

M

M

0

30%

Step 1: Process the evaluation values According to Eqs. (3)-(5), quantitative criteria are normalized, and the qualitative evaluation variables are translated into vague values based on the rules presented in Table 1, and the criterion attribute values are normalized, as shown in Table. 5. Table 5. Normalized vague values of three public charging stations in Beijing (PCI1–PCI3) PCI1

PCI2

PCI3

C1

(0.8318,0.8732,0.1395)

(0.7172,0.8730,0.1414)

(0.7972,0.8951,0.1414)

C2

(0.8827,0.9253,0.1173)

(0.8794,0.9415,0.1203)

(0.8605,0.9500,0.1295)

C3

(0.7632,0.9840)

(1,1)

(0,0)

C4

(0,0)

(1,1)

(0.1246,0.5813)

C5

(1,1)

(0,0)

(0.2252,0.7239)

C6

(0.0018,0.0034)

(1,1)

(0,0)

C7

(0.0042,0.1249)

(1,1)

(0,0)

C8

(1,1)

(0,0)

(1,1)

C9

(1,1)

(0,0)

(0.5625,0.9375)

C10

(0.0)

(0.6278,0.9569)

(1,1)

C11

(1,1)

(1,1)

(0,0)

C12

(0.8586,0.9487,0.1)

(0.8586,0.9212,0.1189)

(0.6636,0.8239.0.1682)

C13

(0.8605,0.9507,0.1)

(0.8349,0.9171,0.1206)

(0.7687,0.8751,0.1395)

C14

(0.8055,0.9012,0.1173)

(0.7555,0.8673,0.1434)

(0.9,1,0.1)

C15

(1,1)

(1,1)

(1,1)

C16

(1,1)

(1,1)

(1,1)

C17

(0,0)

(0,0)

(1,1)

C18

(0.5,0.5,0)

(0.5271,0.5623,0)

(0.6636,0.8239,0.1682)

C19

(0.5250,0.5571,0)

(0.4084,0.5,0)

(0.5789,0.7180,0)

C20

(0.3565,0.5,0)

(0.3,0.5,0.2)

(0.5545,0.6384,0)

Step 2: Obtain the weights of evaluation criteria To calculate the criterion weights, we consulted three decision makers: the government (DM1), a charging company (DM2), and a consumer (DM3). The latter had five years’ experience with public EV charging. These experts defined the following

Journal Pre-proof weighting orders for the 5 criteria and 20 sub-criteria: DM1: PR ≻ CS ≻ OE ≻ SC ≻ SD. C1 ≻ C2 ≻ C3 ≻ C4, C6 ≻ C8 ≻ C5 ≻ C7, C10 ≻ C13 ≻ C9 ≻ C11 ≻ C12, C15 ≻ C14 ≻ C16, C17 ≻ C19 ≻ C18 ≻ C20. DM2: OR ≻ SC ≻ SD ≻ PR ≻ CS. C3 ≻ C4 ≻ C1 ≻ C2, C6 ≻ C7 ≻ C5 ≻ C8, C10 ≻ C9 ≻ C11 ≻ C13 ≻ C12, C14 ≻ C15 ≻ C16, C20 ≻ C17 ≻ C18 = C19. DM3: SC ≻ OE ≻ PR ≻ CS ≻ SD. C1 ≻ C2 ≻ C4 ≻ C3, C5 ≻ C8 ≻ C7 ≻ C6, C9 ≻ C11 ≻ C12 ≻ C13 ≻ C10, C15 ≻ C16 ≻ C14, C17 ≻ C20 ≻ C18 ≻ C19. 1

DM1-DM3 are treated equally (𝜆𝑠 = 3, 𝑠 = 3); the final weights are given in Table 6 and Figs. 4 and 5. SD, 13.3%

PR, 20.3%

CS, 15.6%

SC, 25.5% OE, 25.3%

Fig. 4. Weights of the five major criteria (from Table 5) 0.080 0.070 0.060 0.050 0.040 0.030 0.020 0.010 0.000 C5 C6 C15 C9 C1 C8 C10 C7 C11 C2

Fig. 5. Ranking of top ten sub-criteria. Table 6. Weights of all criteria Criteria

Weight

Sub-criteria

Weight

Rank

PR

0.203

C1

0.062

5

C2

0.051

10

C3

0.045

12

C4

0.045

13

C5

0.071

1

OE

0.253

Journal Pre-proof

SC

0.255

CS

0.156

SD

0.133

C6

0.069

2

C7

0.055

8

C8

0.057

6

C9

0.065

4

C10

0.056

7

C11

0.052

9

C12

0.038

17

C13

0.045

14

C14

0.047

11

C15

0.068

3

C16

0.042

16

C17

0.043

15

C18

0.028

20

C19

0.029

19

C20

0.033

18

Figs 4-5 show that service capacity (SC) and operational efficiency (OE) were the top two criteria. The top 10 sub-criteria were average charging fee (C5), utilization (C6), site accident rate (C15), average waiting time (C9), location rationality (C1), malfunction rate (C8), connectivity to public platforms (C10), turnover rate (C7), data accuracy (C11), and site accessibility (C2) (Fig. 5). We also identified the following performance factors for charging facilities based on previous studies: service costs [29], utilization [51], waiting time [23], location [20], and safety performance [52].. Step 3: Measure the distance, the closeness coefficient and rank three alternatives based on the proposed TOPSIS method The distances 𝑑𝑃𝐼𝑆 and 𝑑𝑁𝐼𝑆 and the closeness coefficients of the three charging 𝑖 𝑖 stations are obtained using Eqs. (10)-(12) (Table. 7). 𝑁𝐼𝑆 Table 7. Values 𝑑𝑃𝐼𝑆 and 𝐷𝑖 of three alternatives 𝑖 , 𝑑𝑖

𝑑𝑃𝐼𝑆 𝑖

𝑑𝑁𝐼𝑆 𝑖

𝐷𝑖

Rank

PCI1

0.6024

0.6937

0.5352

2

PCI2

0.5852

0.7502

0.5618

1

PCI3

0.7442

0.5649

0.4315

3

Since 𝐷2 was the highest value, PCI2 was the optimal alternative based on the rules defined in Section 3. Furthermore, based on 𝐷𝑖, the stations ranked PCI2>PCI1>PCI3. 4.3. Discussion 4.3.1. Sensitivity analysis To verify the feasibility and effectiveness of the proposed method’s focus on PCI preference criteria, a sensitivity analysis was conducted with respect to the effect of expert

Journal Pre-proof weighting (Fig. 6) and criterion weighting (Fig.7). The expert weights were classified into four groups: Group A used the expert weights of mean values, Group B increased the expert weight of the government by 10%, Group C increased the expert weight of the charging company by 10%, and Group D increased the expert weight of the consumer experts by 10%. The weights of the other experts were reduced proportionally such that the sum of all weights remained at 1. the performance ranking order of the three public charging stations were determined and shown in Fig. 6. 0.6

0.8 0.7

0.5

0.6 0.4 0.5 0.3

0.4 A

PIS1 NIS2

B

NIS1 PIS3

C

PIS2 NIS3

A

D

a

B D1

C D2

Fig. 6. Sensitivity analysis results for weighting Groups A-D as defined in the text: (a) “PIS1”, “NIS1”, “PIS2”, “NIS2”, “PIS3”, and “NIS3” represent the values of 𝑑𝑃𝐼𝑆 and 𝑑𝑁𝐼𝑆 for PCI1, PCI2, and PCI3; (b) 𝑖 𝑖 closeness coefficients for PCI1, PCI2, and PCI3.

The closeness coefficients of PCI2 were highest in Groups A-D, followed by PCI1 and PCI3 (Fig. 6). The distances for PCI2 from both the positive and negative ideal solutions were greatest. Regardless of changes in expert weightings, the three alternatives retained the ranking PCI2>PCI1>PCI3, indicating that the vague TOPSIS model was robust and effective. However, changing the consumer weight significantly altered the closeness coefficients of PCI1, suggesting that selecting an experienced EV user for such assessments is particularly important to ensure the correctness and objectivity of the final evaluation results.

D D3

b

Journal Pre-proof PCI1

22 21 20

23

PCI2 1 24 0.7

15

2

0.6 0.5 0.4 0.3 0.2 0.1 0

19 18 17 16

PCI3 3

6

46 45 44

7

43

8

42 41 40

4 5

14

12

11

9 10

75 74 73 72 71 70 69 68

76

67

PCI2 49

0.7 77 78 0.6 0.5 0.4 0.3 0.2 0.1 0

66 65

63 62

39

52

61

PCI2 25 48 0.7

95 94 93

35

b

PCI2 79 0.6 0.5 0.4 0.3 0.2 0.1 0

PCI3 80 81 82 83 84 85

90

86 89

87 88

PCI2 97

120 0.6 0.5 0.4 0.3 0.2 0.1 0

d

PCI3 98

99

100 101 102 103

114 113 112 111

36

32 33 34

91

PCI1

115

28 29 30

92

c

116

27

31

38

PCI1

53 54 55 56 57 58 59 60

119 118 117

26

0.6 0.5 0.4 0.3 0.2 0.1 0

96

64

PCI3

37

PCI3 50 51

47

a

13 PCI1

PCI1

104

110

108 109

105 106 107

e

Fig. 7. Sensitivity analysis results of C1-C20 criteria for five groups: (a) C1-C4 in PR group; (b) C5-C8 in OE group; (c) C9-C13 in SC group; (d) C14-C16 in CS group; (e) C17-C20 in SD group

We recalculated the closeness coefficients of the three stations based on weights 10%, 20%, and 30% lighter and 10%, 20%, and 30% heavier than the base weight (Fig.7ae). For the 20 sub-criteria, 120 experiments were conducted, and the sensitivity analysis results were divided into five groups: namely PR (Fig.7a), OE (Fig.7b), SC (Fig.7c), SC (in Fig.7d), and SD (Fig.7e). In experiments 1–24 (Fig.7a), the assessment results were obtained using the weight fluctuation of criteria C1-C4 in the PR group. PCI2 performed best and the ranking order

Journal Pre-proof remained PCI2>PCI1>PCI3. In experiments 25–48 (Fig.7b), the results were obtained using the weight fluctuation of criteria C5-C8 in the OE group. Here, the performance of PCI1 in terms of criteria 5, 6, and 9 was better than PCI2 in experiments 30, 31, and 48. Nevertheless, PCI2 maintained stable scores and outperformed the others overall. In experiments 49–120, the ranking order remained PCI2>PCI1>PCI3, although the closeness coefficients of the stations changed. The evaluation results were more sensitive to criteria C5, C6, and C9 (Fig. 7). Therefore, PCI2 was the best-performing charging station in 117 experiments (97.5%), demonstrating that PCI performance evaluation using the proposed TOPSIS technique was robust and reliable. 4.3.2. Interpretation of results Comparing the studied stations by the five main criteria (Fig. 8) shows that topranked PCI2 performed best in terms of PR and slightly poorer in SC, OE, and SD, so should therefore emphasize improvements to those three factors. Second-ranked PCI1 performed best in terms of OE and SC, but poorly in PR and very poorly in SD, so should seek to improve those two factors. Although PCI3 performed similarly to the others in terms of CS and was far superior in SD, it performed poorly in PR, OE, and SC, so should focus on improving those three factors. PCI1 1.00

PCI2

0.80

PCI3

0.60 0.40 0.20 0.00 PCI1

PR 0.472

OE 0.523

SC 0.733

CS 0.499

SD 0.080

PCI2 PCI3

0.956 0.179

0.491 0.270

0.564 0.560

0.498 0.502

0.412 1.000

PCI3 PCI2 PCI1

Fig. 8. Preference ranking of three public charging stations (PCI1-PCI3) by major criteria: CS: charging safety; OE: operational efficiency; PR: planning rationality; SC: service capacity; SD: sustainable development.

The three stations’ relative performance in terms of specific sub-criteria were further assessed. PCI2 scored the highest in terms of C3 and C4 (Fig. 9). The fast-charge ratio is

Journal Pre-proof a critical consideration for users when selecting public charging facilities. Common capacities in the market are 45, 60, and 75 kW. Under normal circumstances, EVs can be charged to 80% in 30 min, meeting the basic needs of EV owners. However, the capacity of a slow-charging device is only 7.5 kW, requiring 6–8 h to recharge a 50 kW EV. In China, charging cost is billed according to charging power, and EV users generally prefer a fast-charging station for a given waiting time. Therefore, operators should increase their proportion of fast-charging stations when investing in charging facilities.

PCI1

0.8060

0 0

0.6744

PCI2

0

1 1

0.6361

0 0

PCI3

1 1

0.2873 0.2

0.4 C1

0.6 C2

C3

0.8

1

C4

Fig. 9. Preference ranking of three public charging stations by sub-criteria C1-C4 (planning rationality).

For C5-C8, PCI2 performed significantly better than PCI1 and PCI3 for C6 and C7, scoring the opposite of PCI1 for C5 and C8 (Fig. 10). The occupancy of charging stations by conventional vehicles and fully charged EVs that do not leave quickly can seriously affect the utilization and turnover rate of charging equipment. Therefore, operators should (1) increase the number of on-site service personnel to better control such behavior, (2) improve the intelligence level of charging stations, allowing the real-time monitoring of charging status through mobile devices, and (3) introduce parking fees for the time that EVs occupy a station after being fully charged.

Journal Pre-proof

0.0034

PCI1

1 0.1114

1

0

1 1

PCI2 0

0 0

PCI3

0

0.1708 1 0.2

0.4 C5

0.6 C6

C7

0.8

1

C8

Fig. 10. Preference ranking of three public charging stations (PCI1-PCI3) by sub-criteria C5-C8 (operational efficiency).

For C9-C13, PCI1 outperformed the others in all criteria but C10 (Fig.11). In general, EV users have increased charging times at unfamiliar stations, and charging operators should therefore increase the number of on-site service staff to assist users or clarify the charging guidelines and facility layout to shorten charging time. As for payment ability, PCI1 accepts payments from Alipay, WeChat, and E-charging applications, PCI2 accepts E-charging and grid cards issued by the State Grid Corporation, and PCI3 accepts payments from grid card and E-charging applications. More payment methods increase the convenience for EV users, who often prefer using mobile device-based payments. Linking charging stations with mobile devices also allows the real-time monitoring of charging status, such that EV users can both end charging and pay remotely. The 22 charging stations at PCI1 are not fully connected to the public platform, which hampers searches and affects the facility’s performance. Therefore, all charging facilities should be encouraged to be fully connected, allowing users to more easily find charging locations, control charging status, and complete payment remotely.

Journal Pre-proof

1

0

PCI1

1 1 1

0

0.7198

PCI2

1

0.8804

0.6143 0.6832 PCI3

1

0 0 0 0

0.2

0.4 C9

C10

0.6 C11

C12

0.8

1

C13

Fig. 11. Preference ranking of three public charging stations (PCI1-PCI3) by sub-criteria C9-C13 (service capability).

All three public charging stations performed well in terms of C15 and C16, whereas the performance in terms of C14 was more variable (Fig. 12), showing that operators’ awareness of safety management is good overall. 0.3542 PCI1

0.5 0.5

PCI2

0.5 0.5

PCI3

0.5 0.5

1

1

0

0.2

0.4 C14

0.6 C15

0.8

1

C16

Fig. 12. Preference ranking of three public charging stations (PCI1–PCI3) by sub-criteria C14-C16 (charging safety).

For C17-C20, PCI3 outperformed the other stations in terms of SD (Fig. 13). Only PCI3 is equipped with a distributed photovoltaic power system, but its actual operational effect is not ideal. Actual renewable energy generation is not high, and equipment usage is low. In addition, C19 and C20 were lower than expected for PCI1 and PCI2. There are four reasons for this: (1) insufficient EV market share leads to low equipment utilization, such that potential alternative revenue streams (e.g., advertising revenue and demand

Journal Pre-proof response) could not be fully exploited; (2) limited capacity of installed renewable energy systems means that power generation is disrupted by weather; (3) the emission-reduction effect of PCI3 was not obvious given the uninstalled energy storage system; and (4) intelligent charging facilities need high investment costs but have poor economic returns, undercutting economic efficiency and maintenance. 0 0

PCI1

PCI2

0.2742

0.4339 0.5

0.2014

0 0

1 1 1 1

PCI3

0

0.2

0.4 C17

C18

0.6 C19

0.8

1

C20

Fig. 13. Preference ranking of three public charging stations (PCI1-PCI3) by sub-criteria C17-C20 (sustainable development)

Given these issues, relevant measures should be taken to increase the awareness of operators and the public regarding the SD value of PCI by (1) increasing subsidies for purchasing EVs to reduce overall costs and increase EV market share, (2) funding research and development related to improved charging equipment (including intelligence level), and (3) subsidizing charging stations powered by renewable energy systems to reduce investment costs. Appropriate government policies are necessary to encourage charging operators to improve distributed energy generation, demand side response, V2G, and other services to increase the SD value of charging facilities. 5. Conclusions With the development of the EV industry, the operational service level of PCI has become an important factor affecting whether users purchase and travel with EVs. To empirically evaluate PCI performance level, this study developed a performance evaluation index system based on an improved TOPSIS model using 5 criteria and 20 sub-criteria, and conducted a case study comparing three public charging stations in Beijing. This research will be of interest to regulators and policy makers, providing a broader view of charging service quality than would be possible by considering individual

Journal Pre-proof indicators. The following measures to improve PCI performance were suggested: (1) Increase the proportion of fast-charging stations, (2) Set up guideboards to reduce space occupancy by internal combustion vehicles and reduce average waiting time, (3) Connect charging equipment to public platforms and enable ability to mobile devices for increased charging and payment convenience, and (4) Emphasize the SD value of charging facilities. Although two of the three assessed public charging facilities did not perform well in terms of SD, advances in energy generation and storage as well as V2G technologies can increase the value of charging facilities and improve their integration into power grids. Future research should focus on improving the SD value of PCI to take advantage of EVs’ ability to serve as interruptible mobile loads. Acknowledgements This work was supported by the 2017 Special Project of Cultivation and Development of Innovation Base (No. Z171100002217024). Declaration of interest The authors declare no conflicts of interest. Reference [1] De G, Tan Z, Li M, Huang L, Wang Q, Li H. A credit risk evaluation based on intuitionistic fuzzy set theory for the sustainable development of electricity retailing companies in China. Energy Sci Eng 2019:ese3.464. https://doi.org/10.1002/ese3.464. [2] Erbaş M, Kabak M, Özceylan E, Çetinkaya C. Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis. Energy 2018;163:1017–31. https://doi.org/10.1016/j.energy.2018.08.140. [3] Ren X, Zhang H, Hu R, Qiu Y. Location of electric vehicle charging stations: A perspective using the grey decision-making model. Energy 2019;173:548–53. https://doi.org/10.1016/j.energy.2019.02.015. [4] Zhang L, Zhao Z, Xin H, Chai J, Wang G. Charge pricing model for electric vehicle charging infrastructure public-private partnership projects in China: A system dynamics analysis. J Clean Prod 2018;199:321–33. https://doi.org/10.1016/j.jclepro.2018.07.169. [5] Global sales of electric vehicles exceeded 2 million in 2018 n.d. http://www.cinic.org.cn/xw/tjsj/473468.html (accessed October 2, 2019). [6] China-daily. http://www.chinadaily.com.cn/a/201801/19/WS5a613492a3106e7dcc1352f8.html (accessed April 10, 2019). [7] The Nation. http://www.nationmultimedia.com/detail/Startup_and_IT/30335929 (accessed April 14, 2019). [8] Hardman S, Jenn A, Tal G, Axsen J, Beard G, Daina N, et al. A review of consumer preferences of and interactions with electric vehicle charging infrastructure. Transp Res Part Transp Environ 2018;62:508–23. https://doi.org/10.1016/j.trd.2018.04.002.

Journal Pre-proof [9] Zhu L, Zhang Q, Lu H, Li H, Li Y, McLellan B, et al. Study on crowdfunding’s promoting effect on the expansion of electric vehicle charging piles based on game theory analysis. Appl Energy 2017;196:238–48. https://doi.org/10.1016/j.apenergy.2016.11.060. [10] EVCIPA. https://www.d1ev.com/news/shuju/80557 (accessed December 25, 2018). [11] China electric charging infrastructure promotion alliance released charging pile operation data. http://k.sina.com.cn/article_1001961051_p3bb8b65b02700gumj.html (accessed September 18, 2019). [12] Helmus J, van den Hoed R. Key Performance Indicators of Charging infrastructure. World Electr Veh J 2016;8:733–41. https://doi.org/10.3390/wevj8040733. [13] Neaimeh M, Salisbury SD, Hill GA, Blythe PT, Scoffield DR, Francfort JE. Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles. Energy Policy 2017;108:474–86. https://doi.org/10.1016/j.enpol.2017.06.033. [14] Abdalrahman A, Zhuang W. A Survey on PEV Charging Infrastructure: Impact Assessment and Planning. Energies 2017;10:1650. https://doi.org/10.3390/en10101650. [15] Serradilla J, Wardle J, Blythe P, Gibbon J. An evidence-based approach for investment in rapid-charging infrastructure. Energy Policy 2017;106:514–24. https://doi.org/10.1016/j.enpol.2017.04.007. [16] Guo S, Zhao H. Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective. Appl Energy 2015;158:390–402. https://doi.org/10.1016/j.apenergy.2015.08.082. [17] Hosseini S, Sarder M. Development of a Bayesian network model for optimal site selection of electric vehicle charging station. Int J Electr Power Energy Syst 2019;105:110–22. https://doi.org/10.1016/j.ijepes.2018.08.011. [18] Tao Y, Huang M, Yang L. Data-driven optimized layout of battery electric vehicle charging infrastructure. Energy 2018;150:735–44. https://doi.org/10.1016/j.energy.2018.03.018. [19] 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. https://doi.org/10.1016/j.apenergy.2018.07.025. [20] Li S, Huang Y, Mason SJ. A multi-period optimization model for the deployment of public electric vehicle charging stations on network. Transp Res Part C Emerg Technol 2016;65:128–43. https://doi.org/10.1016/j.trc.2016.01.008. [21] Zhang Q, Li H, Zhu L, Campana PE, Lu H, Wallin F, et al. Factors influencing the economics of public charging infrastructures for EV – A review. Renew Sustain Energy Rev 2018;94:500–9. https://doi.org/10.1016/j.rser.2018.06.022. [22] Philipsen R, Schmidt T, van Heek J, Ziefle M. Fast-charging station here, please! User criteria for electric vehicle fast-charging locations. Transp Res Part F Traffic Psychol Behav 2016;40:119–29. https://doi.org/10.1016/j.trf.2016.04.013. [23] Zenginis I, Vardakas JS, Zorba N, Verikoukis CV. Analysis and quality of service evaluation of a fast charging station for electric vehicles. Energy 2016;112:669–78. https://doi.org/10.1016/j.energy.2016.06.066. [24] Liu J. Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing. Energy Policy 2012;51:544–57. https://doi.org/10.1016/j.enpol.2012.08.074.

Journal Pre-proof [25] Goebel C. On the business value of ICT-controlled plug-in electric vehicle charging in California. Energy Policy 2013;53:1–10. https://doi.org/10.1016/j.enpol.2012.06.053. [26] Domínguez-Navarro JA, Dufo-López R, Yusta-Loyo JM, Artal-Sevil JS, BernalAgustín JL. Design of an electric vehicle fast-charging station with integration of renewable energy and storage systems. Int J Electr Power Energy Syst 2019;105:46– 58. https://doi.org/10.1016/j.ijepes.2018.08.001. [27] Liu J, Zhang T, Zhu J, Ma T. Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration. Energy 2018;164:560–74. https://doi.org/10.1016/j.energy.2018.09.028. [28] Levinson RS, West TH. Impact of public electric vehicle charging infrastructure. Transp Res Part Transp Environ 2018;64:158–77. https://doi.org/10.1016/j.trd.2017.10.006. [29] Helmus J, van den Hoed R. Key Performance Indicators of Charging infrastructure. World Electr Veh J 2016;8:733–41. https://doi.org/10.3390/wevj8040733. [30] Mohammadzadeh Bina S, Jalilinasrabady S, Fujii H, Farabi-Asl H. A comprehensive approach for wind power plant potential assessment, application to northwestern Iran. Energy 2018;164:344–58. https://doi.org/10.1016/j.energy.2018.08.211. [31] de Melo Ribeiro FH, Naval LP. Reuse alternatives for effluents from the fish processing industry through multi-criteria analysis. J Clean Prod 2019;227:336–45. https://doi.org/10.1016/j.jclepro.2019.04.110. [32] Petrović M, Bojković N, Stamenković M, Anić I. Supporting performance appraisal in ELECTRE based stepwise benchmarking model. Omega 2018;78:237–51. https://doi.org/10.1016/j.omega.2017.07.002. [33] Xu F, Liu J, Lin S, Yuan J. A VIKOR-based approach for assessing the service performance of electric vehicle sharing programs: A case study in Beijing. J Clean Prod 2017;148:254–67. https://doi.org/10.1016/j.jclepro.2017.01.162. [34] Long R, Lai X, Liu Z, Liu W. Pressure retarded osmosis: Operating in a compromise between power density and energy efficiency. Energy 2019;172:592–8. https://doi.org/10.1016/j.energy.2019.01.169. [35] Solangi YA, Tan Q, Mirjat NH, Ali S. Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. J Clean Prod 2019;236:117655. https://doi.org/10.1016/j.jclepro.2019.117655. [36] Dong F, Shi L. Regional differences study of renewable energy performance: A case of wind power in China. J Clean Prod 2019;233:490–500. https://doi.org/10.1016/j.jclepro.2019.06.098. [37] Bai C, Sarkis J. Integrating and extending data and decision tools for sustainable third-party reverse logistics provider selection. Comput Oper Res 2019;110:188– 207. https://doi.org/10.1016/j.cor.2018.06.005. [38] Yue C. A projection-based approach to software quality evaluation from the users’ perspectives. Int J Mach Learn Cybern 2019;10:2341–53. https://doi.org/10.1007/s13042-018-0873-y. [39] Carbon C-C, Gebauer F. The Safe-Range-Inventory (SRI): An assistance tool for optimizing the charging infrastructure for electric vehicles. Transp Res Part F Traffic Psychol Behav 2017;47:101–13. https://doi.org/10.1016/j.trf.2017.04.011. [40] Motoaki Y, Shirk MG. Consumer behavioral adaption in EV fast charging through pricing. Energy Policy 2017;108:178–83. https://doi.org/10.1016/j.enpol.2017.05.051.

Journal Pre-proof [41] Philipsen R, Schmidt T, Ziefle M. A Charging Place to Be - Users’ Evaluation Criteria for the Positioning of Fast-charging Infrastructure for Electro Mobility. Procedia Manuf 2015;3:2792–9. https://doi.org/10.1016/j.promfg.2015.07.742. [42] Campíñez-Romero S, Colmenar-Santos A, Pérez-Molina C, Mur-Pérez F. A hydrogen refuelling stations infrastructure deployment for cities supported on fuel cell taxi roll-out. Energy 2018;148:1018–31. https://doi.org/10.1016/j.energy.2018.02.009. [43] Hernández JC, Ruiz-Rodriguez FJ, Jurado F. Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems. Energy 2017;141:316–32. https://doi.org/10.1016/j.energy.2017.09.025. [44] Yang L, Ribberink H. Investigation of the potential to improve DC fast charging station economics by integrating photovoltaic power generation and/or local battery energy storage system. Energy 2019;167:246–59. https://doi.org/10.1016/j.energy.2018.10.147. [45] Ji Z, Huang X. Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges. Renew Sustain Energy Rev 2018;90:710–27. https://doi.org/10.1016/j.rser.2018.04.011. [46] Wu Y, Xu C, Zhang B, Tao Y, Li X, Chu H, et al. Sustainability performance assessment of wind power coupling hydrogen storage projects using a hybrid evaluation technique based on interval type-2 fuzzy set. Energy 2019;179:1176–90. https://doi.org/10.1016/j.energy.2019.04.195. [47] Barbosa A de S, Shayani RA, Oliveira MAG de. A multi-criteria decision analysis method for regulatory evaluation of electricity distribution service quality. Util Policy 2018;53:38–48. https://doi.org/10.1016/j.jup.2018.06.002. [48] Yong D. Plant location selection based on fuzzy TOPSIS. Int J Adv Manuf Technol 2006;28:839–44. https://doi.org/10.1007/s00170-004-2436-5. [49] Wen LG, Buehrer DJ. Vague sets. IEEE Trans Acti Sys T Ems Man Cybern Eti Cs 1993;2:610–4. [50] Liu P. An approach to group decision making based on 2-dimension uncertain linguistic information. Technol Econ Dev Econ 2012;18:424–37. https://doi.org/10.3846/20294913.2012.702139. [51] Notice of interim measures for assessment and reward of electric vehicle public charging facilities in Beijing n.d. http://csglw.beijing.gov.cn/zwxx/zcwj/hygfxwj/201810/t20181008_46993.html (accessed October 2, 2019). [52] Li Q, Xiao Y, Zhao S, Zhu X, Wang Z, Liu Z, et al. Performance Status Evaluation of an Electric Vehicle Charging Infrastructure Based on the Fuzzy Comprehensive Evaluation Method. World Electr Veh J 2019;10:35. https://doi.org/10.3390/wevj10020035.

Journal Pre-proof

HIGHLIGHTS 

A new performance evaluation framework of public charging infrastructure (PCI) is developed.



The quantity of public charging infrastructure is overemphasized.



Service capacity and operational efficiency are two critical dimensions for performance evaluation.



The sustainable development ability is considered.



The fast-charge pile is the trend of future construction in PCI sector.