Research on location algorithm of new energy vehicle charging station based on multi-objective decision

Chaojie Zhang

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Detalles de la revista

Formato: Revista
eISSN: 2444-8656
Primera edición: 01 Jan 2016
Calendario de la edición: 2 veces al año
Idiomas: Inglés

With the rapid changes of the times, the popularization rate of automobiles has increased rapidly. Automobiles have brought great convenience to people’s lives, at the same time, the exhaust gas from automobiles has become the main source of urban air pollution [1, 2]. With the increasing emphasis on environmental pollution and energy shortage, governments have taken various measures to reduce automobile exhaust emissions. The vigorous promotion of new energy vehicles has become a common choice for many countries [3–5]. At present, it is superior to traditional fuel vehicles in pollutant emissions and energy consumption. However, the popularization and development of it are subject to the development of charging facilities and equipment and the layout of charging stations. Therefore, the study of the charging station planning location problem is very important for its wide application.

Scholars have conducted many studies on this issue. Based on the computational geometry method and considering the operation cost and construction cost, reference established the location model of the station [6]. Taking the traffic flow as the constraint, the charging station revenue maximization as the goal, reference established the location model of the station [7]. Based on SA, considering the investment and operating costs, reference established the charging station layout optimization model [8]. Considering the investment cost, annual income and operation cost, reference established the location model of the station [9]. Considering the total cost of charging station construction, reference established an integer programming model of the station [10]. Considering the total cost of the stations and the loss cost of power grid, reference established the location model of the station [11]. At present, the location problem of the station has a single goal, and there are few studies on the location of the station under various factors.

Firstly, this paper introduces the multi-objective optimization problem, and then analyzes various charging station location factors, and determines the consideration form of various factors in the model. Finally, based on multi-objective decision-making method, considering the impact of economy, environmental protection and convenience, a new energy vehicle charging station location optimization model is established.

principles and methods

2.1

Multi-objective optimization theory

Multi-objective optimization means that two or more objective functions need to be solved under the same constraint conditions. The composition of multi-objective optimization mainly includes decision variables, objective functions and constraint conditions. The objective function can represent ${(f_{1} (x), f_{2} (x), \dots, f_{m} (x))}^{T}$ , where m represents the number of objective functions, and m ≥ 2; Constraints are generally composed of multiple equality or inequality equations, and their general forms are shown in Formula 1 and Formula 2.

1 $\begin{aligned} min_{x \in R^{n}} {f_{1} (x), f_{2} (x), \dots, f_{m} (x)} \end{aligned}$ $$\matrix{ {\mathop {{\rm{min}}}\limits_{x \in {R^n}} \{ {f_1}(x),{f_2}(x), \cdots ,{f_m}(x)\} } \cr } $$ 2 $\begin{aligned} s . t . {\begin{cases} g_{i} (x) \leq 0 & j = 1, 2, \dots, a; \\ h_{k} (x) = 0 & k = 1, 2, \dots, b; \end{cases} \end{aligned}$ $$s.t.\left\{ {\matrix{ {{g_i}(x) \le 0} \ \ \ {j = 1,2, \cdots ,a;} \cr {{h_k}(x) = 0} \ \ \ {k = 1,2, \cdots ,b;} \cr } } \right.$$

This problem is a multi-objective question, and there may be contradictions among the sub-objectives. When solving this problem, it is necessary to coordinate the relationships among sub-objectives as reasonably as possible, so that each sub-objective function is as close to the optimal solution as possible.

The reasonable determination of objectives is the key to the credibility of multi-objective optimization decision. In this paper, when making multi-objective decision, the AHP is used to weight the targets. When we encounter complex decision-making problems in AHP, we often have no idea, on the premise of in-depth exploration of influencing factors, through limited information can make decision-making into mathematical problems, so that the problem becomes more simple.

2.2

Chicken Swarm Optimization Algorithm

Chicken colony optimization algorithm has the advantages of fast convergence and high optimization accuracy. It simulates the hierarchical structure of chickens and the foraging behavior of different individuals to show the foraging ability of chickens [12–14].

The total number of individuals in the chicken group is set as N, and D is the dimension of solving the problem. The position of each individual at any time can be expressed as x_ij(t), in which $i = 1, 2, \dots$ , N, $j = 1, 2, \dots, D$ , the numbers of roosters, hens, mother hen and chickens are respectively expressed as RN, HN, MN, CN.

The whole population includes several small groups, each group contains a rooster, several hens and chicks, some of which are chicks’ mothers;

The hierarchy of chickens is judged according to the fitness value which corresponds to the foraging ability. The cock has the strongest foraging ability, followed by the hen, and the worst is the chick. The kinship between hen and chick can be determined by random combination;

In every G iteration, the relationships among the groups will remain unchanged. After G iterations, all the roles of the chickens will be updated according to the new fitness value;

Rooster is the center of the whole group, and other hens will feed around rooster, but they may also have the ability to compete for foraging territory with other groups of chickens. For chicks, the individual’s foraging ability is weak, and they will only surround their mothers.

The location update for each type of individual is as follows.

The location update mode of rooster is shown in Formula 3-5.

3 $\begin{aligned} x_{i j} (t + 1) = x_{i j} (t + 1) \cdot [1 + R a n d n (0, α^{2})] \end{aligned}$ 4 $\begin{aligned} α^{2} = {\begin{cases} 1, & f_{i} \leq f_{q} \\ \exp (\frac{f_{q} - f_{i}}{| f | + τ}) = 0, & e l s e \end{cases} \end{aligned}$ $$\matrix{ {{\alpha ^2} = \left\{ {\matrix{ {1,} & {{f_i} \le {f_q}} \cr {\exp ({{{f_q} - {f_i}} \over {|f| + \tau }}) = 0,} & {else} \cr } } \right.} \cr } $$ 5 $\begin{aligned} q \in [1, R N] q \neq i, i = 1, 2, \dots, R N \end{aligned}$ $$\matrix{ {q \in [1,RN]\quad q \ne i,\>\,i\> = 1,2, \cdots ,RN} \cr } $$

Among them, $R a n d n (0, α^{2})$ represents a uniform random number whose size is in the interval [0,1]; τ represents a minimal positive number; Q represents the index of cock.

The location update mode of hens is shown in Formula 6–8.

6 $\begin{aligned} x_{i j} (t + 1) = x_{i j} (t) + V_{1} \cdot R a n d \cdot [x_{a_{1} j} (t) - x_{i j} (t)] + V_{2} \cdot R a n d \cdot [x_{a_{2} j} (t) - x_{i j} (t)] \end{aligned}$ 7 $\begin{aligned} V_{1} = \exp {(f_{i} - f_{a 1}) / [| f_{i} | + τ]} \end{aligned}$ 8 $\begin{aligned} V_{2} = \exp (f_{a_{2}} - f_{i}), i = 1, 2, \dots, H N \end{aligned}$

Among them, Rand represents the uniform random number in the interval [0,1]; a₁ represents the cock in the corresponding group of the i-th hen; a₂ represents other chickens selected randomly, and $a_{1} \neq a_{2}$ .

The way to update the position of the chicken is shown in Formula 9.

9 $\begin{aligned} x_{i j} (t + 1) = x_{i j} (t) + H \cdot [x_{m i} (t) - x_{i j} (t)], i = 1, 2, \dots, C N \end{aligned}$

Among them, the position of the i-th mother hen can be expressed as x_mi(t); H is a parameter belonging to the interval [0,2].

When using the chicken swarm optimization algorithm for site selection, the specific implementation steps are shown in Figure 1.

Specific steps of chicken swarm algorithm

Analysis of influencing factors of site selection

3.1

Charging demand

Charging demand is largely affected by the popularity of new energy vehicles. The increasing demand for charging brings greater challenges to the design of charging stations. When the number of cars continues to increase, the construction of charging stations also needs to increase correspondingly. It is essential not only to ensure the demand within the service life, but also to avoid excessive advance construction.

3.2

Economic factors

In the process of planning the charging infrastructure of new energy vehicles, it is necessary to consider the economy in its operation. This study first summarizes the cost-benefit composition of the charging station, as shown in Figure 2.

For the impact of economic factors on site selection, first assume that $C = [C_{1}, C_{2}, \dots, C_{n}]$ represents the cost set, usually expressed as land cost, construction cost, operating cost, etc., where n is the number of cost conditions under study; Set up $E = [E_{1}, E_{2}, \dots, E_{m}]$ to benefit set, which is usually expressed as charging service income, environmental benefits, etc., where m represents the number of benefit conditions in this study; Set up $S = [s_{1}, s_{2}, \dots, s_{h}]^{T}$ , where h represents the constraint conditions in the model except for cost and benefit. In addition, it is also necessary to set the charging demand as D [15]. When the model is established, there are mainly the following considerations for economic factors in the charging station location model.

On the premise that all charging requirements are met, the total cost of should be guaranteed to be the smallest.

10 $\begin{aligned} M i n \sum_{i = 1}^{n} C_{i} \end{aligned}$ 11 $\begin{aligned} s . t . D \geq D_{max} \end{aligned}$ $$\matrix{ {s.t.D \ge {D_{\max }}} \cr } $$ 12 $\begin{aligned} S = [s_{1}, s_{2}, \dots>, s_{h}]^{T} \end{aligned}$

On the premise of the given input cost limit, it is required to realize the maximum demand for charging service.

13 $\begin{aligned} M i n D \end{aligned}$ 14 $\begin{aligned} s . t . \sum_{i = 1}^{n} C_{i} \leq C_{max} \end{aligned}$ $$\matrix{ {s.t.\mathop \sum \limits_{i = 1}^n {C_i} \le {C_{\max }}} \cr } $$ 15 $\begin{aligned} S = [s_{1}, s_{2}, \dots, s_{h}]^{T} \end{aligned}$

Under the premise of realizing the maximum charging service demand, the maximum benefit should be realized.

16 $\begin{aligned} M i n (\sum_{k = 1}^{m} E_{k} - \sum_{i = 1}^{n} C_{i}) \end{aligned}$ $$\matrix{ {Min\left( {\mathop \sum \limits_{k = 1}^m {E_k} - \mathop \sum \limits_{i = 1}^n {C_i}} \right)} \cr } $$ 17 $\begin{aligned} s . t . D \geq D_{max} \end{aligned}$ $$\matrix{ {s.t.D \ge {D_{\max }}} \cr } $$ 18 $\begin{aligned} S = [s_{1}, s_{2}, \dots, s_{h}]^{T} \end{aligned}$

3.3

Regional development

Regional development refers to the future demand growth potential in the region. Since the charging station as a period from planning and construction to the end of service life,, it is of certain significance to judge the growth potential of future charging demand, which has become one of the important factors that need to be considered in the research. The judgment of the influencing factors in this study should first consider the population, purchasing power and consumption habits.

There is a certain relationship between the population of the area and the number of customers with charging demand. The more populated the area, the more electric vehicle users with charging needs, and the more car owners with potential charging needs. From the perspective of probability, as long as the population is huge, charging demand will also have greater development potential. Therefore, Therefore, the population of a region should be an important indicator for future development.

The future growth potential of charging demand is also affected by the purchasing power and consumption habits of residents in the region. The greater the purchasing power, the more likely it is that the demand for charging will increase. With the large-scale promotion of new energy vehicles, it will be favored by consumers with future car purchase demand, which will increase the charging demand in the region and affect the location of charging stations in the region. At the same time, consumption habits also have an impact on charging demand. Compared with the suburbs, residents in areas with developed commerce and dense offices will have a higher probability of purchasing new energy vehicles.

3.4

Block characteristics

The analysis of block characteristics needs to divide the cities to be planned into different blocks according to certain rules. The characteristics of a block first reflect the characteristics of landmark buildings in the block, such as residential areas, schools, hospitals, large shopping malls, office areas, scenic spots, etc. Different landmark buildings correspond to different travel destinations, and ultimately correspond to different charging needs; Secondly, the opening degree of the parking lot in the block grid reflects the influence of the block grid on the capturing capacity of road nodes traffic flow [16]. Generally speaking, the higher the opening degree of the parking lot, the stronger the ability of its grid to capture the traffic flow of road nodes. Among them, social public parking lots have the highest degree of opening to the outside world, there may be a shortage of parking spaces, and the bow value will be larger. The bow values under different block characteristics are shown below.

Table 1

Values under different block characteristics

Block building	Bow value
Public parking lot	0.95
megastore	0.7
Venue	0.6
Office area	0.4
Hospital medical system	0.3
Educational scientific research system	0.2
Residential quarters	0.1

3.5

Traffic conditions

The choice of new energy vehicle charging station site is affected by traffic convenience. The more lanes in a specific area and the greater their importance, the higher the smoothness of the road, indicating that the area has higher traffic convenience and is more suitable as an alternative point for new energy vehicle charging stations.

3.6

Impact on power grid

When planning site selection, it is necessary to consider the impact of site selection scheme on power grid security [17]. Once the location scheme of the planned area is determined, it will bring huge electricity consumption to the area and threaten the operation safety of the power grid.

Due to the rapid growth of electricity consumption, there may be a need to transform or add transmission and distribution networks, even by upgrading old units or new power plants to meet load growth.

Study on site selection optimization of new energy vehicle charging station

4.1

Basic assumptions

When making the planning objectives, this paper gives consideration to economy, environmental protection and convenience, and can formulate fixed-point planning schemes for different cities with planning needs, as follows:

For economically underdeveloped cities, economy can be considered as the focus of their planning in the initial construction stage;

For cities with large environmental burdens, environmental protection can be taken as the focus of their planning;

For cities with developed economy and high requirements for urban convenience, convenience can be taken as the focus of their planning.

In this paper, considering that new energy vehicle travelers have certain time and space constancy, their driving behaviors are mostly concentrated in a certain short period and a certain area, with obvious rules. The model will consider a fixed area and divide it into many small areas, such as business area, office area, education and research area, schools and hospitals. Some basic assumptions are given below.

The new energy vehicles considered in this are all pure electric vehicles of the same type, regardless of the case of hybrid electric vehicles;

New energy vehicle users with fast charging demand are more likely to choose a charging station closer to themselves to charge.

4.2

Objective function

4.2.1

Minimizing annual cost of construction operation and maintenance

From the perspective of the whole life cycle of charging station, its construction cost and management and maintenance cost are very important indicators.

19 $\begin{aligned} min f_{1} = C_{\cos t} \end{aligned}$ $$\matrix{ {\min {f_1} = {C_{\cos t}}} \cr } $$ 20 $\begin{aligned} C_{\cos t} = \sum_{i = 1}^{I_{C S}} (C_{C C, i} + C_{O C, i}) \end{aligned}$

Among them, C_{cos t} is the annual construction and operation cost of the station i; C_CC,i is the annual construction cost of the station i; C_CC,i is the annual operation cost of the station i; I_CS is the collection of the station.

Construction cost

Construction cost refers to the investment cost of infrastructure and equipment, which includes the construction cost of charging station and other supporting facilities. Therefore, the annual construction cost C_CC,i of station i can be written as a variable function of N_i. The construction cost is a one-time investment in the construction year, which is huge and the impact on time will be amplified. Therefore, in the calculation of construction costs, the impact of time will need to be measured and the cost will be shared for each year of the use period. The calculation formula of annual construction cost is shown in 21.

21 $\begin{aligned} C_{C C, i} = f_{i} (N_{i}) \frac{r {(1 + r)}^{m}}{{(1 + r)}^{m} - 1} \end{aligned}$ f_i(N_i) is the initial investment of station i, and is the function of N_i, which is used for the quadratic polynomial with the number of chargers as variable. The expression is shown in Formula 22.

22 $\begin{aligned} f_{i} (N_{i}) = A_{i} (\partial_{1} + \partial_{2} N_{i} + \partial_{3} N_{i}^{2}) \end{aligned}$

Operating cost

Operation cost mainly covers labor cost, equipment consumption, maintenance and repair cost, etc. To make the problem easier, the initial investment of station i is taken as the base and extracted according to a certain proportion.

23 $\begin{aligned} C_{O C, i} = δ^{*} C_{C C, i} \end{aligned}$

4.2.2

Minimize the extra carbon emissions caused by users of new energy vehicles driving to charging stations

24 $\begin{aligned} min f_{2} = F_{{co}_{2}} \end{aligned}$ $$\matrix{ {\min {f_2} = {F_{c{o_2}}}} \cr } $$

The carbon emissions caused by the new energy vehicles with charging demand on the way to the station can be calculated by Formulas 25.

25 $\begin{aligned} F_{{co}_{2}} = K_{{co}_{2}} \sum_{i = 1}^{I_{C S}} \sum_{j = 1}^{J_{q}} A_{i} P n_{j} Z_{i j} d_{i j} μ_{i j} \end{aligned}$

The regional traffic congestion coefficient can reflect the traffic congestion degree in the region, and to some extent, it also reflects the charging demand of new energy vehicle users. The larger the traffic congestion index, the greater the possibility of high charging demand. The values of μ_ij in this study refer to the traffic index as shown in Table 2.

Table 2

Traffic index value

Traffic index value	0-2	2-4	4-6	6-8	8-10
Meaning	unobstructed	Basically unobstructed	Mild congestion	Moderate congestion	Serious congestion
Time-consuming congestion/smoothness	1	1.2–1.5	1.5–1.8	1.8–2.1	>2.1

The specific calculation of k_e is shown in Formula 26 and Formula 27.

26 $\begin{aligned} K_{{co}_{2}} = k_{e} \times \frac{E_{100 k m}}{η_{e} \times 100 \times (1 - θ)} \end{aligned}$ 27 $\begin{aligned} k_{e} = \sum_{g \in G} (E F_{g} \times \frac{E Q_{g}}{\sum_{g \in G} E Q_{g}}) \end{aligned}$ $$\matrix{ {{k_e} = \mathop \sum \limits_{g \in G} \left( {E{F_g} \times {{E{Q_g}} \over {\mathop \sum \limits_{g \in G} E{Q_g}}}} \right)} \cr } $$

Compared with fuel vehicles, new energy vehicles consume less fuel and emit less exhaust gas. However, since the energy supply is electricity, it belongs to secondary energy. Its environmental benefits depend on the carbon intensity of the national energy structure, and whether the energy consumption in the upstream power production stage is clean or not will have an important impact on its emission reduction effect. The carbon emission of unit thermal power in China is 1045 $g C O_{2} e / k W h$ i. Considering from the whole life cycle, clean energy will also produce a small amount of carbon emissions. The emission factor data of each process in the power supply chain are shown in Table 3.

Table 3

Greenhouse gas emission values under different power generation types

Power generation form	Carbon dioxide emission
Thermal power generation	1045
Photovoltaic power generation	53 2.65
Wind power generation	28.6 3.2
Nuclear energy power generation	12.4 1.5
Water conservancy power generation	3.5 0.4

4.2.3

Maximize the service capacity

28 $\begin{aligned} min f_{3} = R \end{aligned}$ $$\matrix{ {\min {f_3} = R} \cr } $$

A charging service capability evaluation model is constructed to evaluate the charging station’s service capability for charging demand points. The model first calculates the service capacity that all alternative charging stations can provide for a single demand point, and then adds the service capacity that all charging demand points can obtain to obtain the overall service capacity value that the charging station can provide to all charging demand points. The expression is shown in Formula 29.

29 $\begin{aligned} R = \sum_{j = 1}^{J_{q}} R_{j} v_{j} \end{aligned}$

Because the demand of different demand points is different, the weight of service capacity that can be received by all charging demand points should be related to the number of new energy vehicles with fast charging demand at that demand point. The expression is shown in Formula 30.

30 $\begin{aligned} v_{j} = \frac{n_{j} P}{\sum_{j = 1}^{J_{q}} n_{j} P} \end{aligned}$ $$\matrix{ {{v_j} = {{{n_j}P} \over {\mathop \sum \limits_{j = 1}^{{J_q}} {n_j}P}}} \cr } $$

In formula 30, R_j represents the service capability obtained by demand point j. The more charging piles, the closer to the demand point and the smoother the road and the less electric vehicles with fast charging demand in the region, the ability to accept the service provided by the station will be greater for any electric vehicle with fast charging requirements within the demand point. This allows you to evaluate the quality of charge service available at this requirement point, as shown in Formula 31.

31 $\begin{aligned} v_{j} = \frac{n_{j} P}{\sum_{j = 1}^{J_{q}} n_{j} P} \end{aligned}$ $$\matrix{ {{v_j} = {{{n_j}P} \over {\mathop \sum \limits_{j = 1}^{{J_q}} {n_j}P}}} \cr } $$

Among them, P is the probability of new energy vehicles with fast charging demand. In order to simplify the model, this study assumes that electric vehicle users with fast charging demand will prefer charging stations closer to themselves. Therefore, The smaller the distance d_ij between station i and demand point j, the greater the probability that the station i can provide services to demand point j. Therefore, the probability of charging station i serving demand point j is P_ij, and the expression is shown in Formula 32. The smaller the distance d_ij between station i and demand point j, the greater the probability that the station i can provide services to demand point j.

32 $\begin{aligned} v_{j} = \frac{n_{j} P}{\sum_{j = 1}^{J_{q}} n_{j} P} \end{aligned}$ $$\matrix{ {{v_j} = {{{n_j}P} \over {\mathop \sum \limits_{j = 1}^{{J_q}} {n_j}P}}} \cr } $$

4.3

Constraints

4.3.1

Quantity constraint of charging pile construction

The number of charging piles will not only affect the investment cost of builders, but also affect the charging service experience and waiting time of users.

33 $\begin{aligned} A_{i} N_{i, min} \leq N_{i} \leq A_{i} N_{i, max} \end{aligned}$ $$\matrix{ {{A_i}{N_{i,\min }} \le {N_i} \le {A_i}{N_{i,\max }}} \cr } $$

4.3.2

Constraints of charging equipment construction

34 $\begin{aligned} N_{i} \leq β A_{i} \end{aligned}$

Where, β is a very large number, and the constraint is to ensure that the charging equipment can be built in charging station i.

4.3.3

Distance constraints of charging stations

This constraint is to prevent the layout of sites from being too dense. Therefore, the constraint of mutual distance between sites is set.

35 $\begin{aligned} ξ D_{c, i i^{'}} \geq D_{c, min} i, i^{'} \in I; i \neq i^{'} \end{aligned}$ $$\matrix{ {\xi {D_{c,i{i^\prime }}} \ge {D_{c,\min }}\quad i,\,\,{i^\prime } \in I;\>i \ne {i^\prime }} \cr } $$

4.3.4

Environmental constraints of charging station

36 $\begin{aligned} \sum_{i \in I_{C S}} c \times N_{i} \times A_{i} \leq φ_{c} \times R \times m \end{aligned}$

The carbon emission reduction ratio φ_c is set as a fixed value. Assuming that the average governance cost of carbon emissions of fuel vehicles in a city for one year is R, and the site can be used for m years at the current technical level, then the governance cost saved due to the emission reduction characteristics of electric vehicles can be used to maintain the charger as the environmental protection constraint of the charging station.

Conclusion

In this study, the factors affecting the location of charging facilities are studied, regional development, block characteristics, traffic conditions, and the impact on the power grid. The model takes the minimization of the annual value of the construction and operation cost of the charging station, the minimization of the additional carbon emissions caused by the charging, and the maximization of the service capacity of the station as the comprehensive optimization objective. Complex problems are simplified and solved by chicken swarm algorithm. The AHP is used to determine the development of a specific city in economy, environmental protection and convenience, and a model suitable for regional development is established.

Figuras y tablas

Values under different block characteristics

Block building	Bow value
Public parking lot	0.95
megastore	0.7
Venue	0.6
Office area	0.4
Hospital medical system	0.3
Educational scientific research system	0.2
Residential quarters	0.1

Greenhouse gas emission values under different power generation types

Power generation form	Carbon dioxide emission
Thermal power generation	1045
Photovoltaic power generation	53 2.65
Wind power generation	28.6 3.2
Nuclear energy power generation	12.4 1.5
Water conservancy power generation	3.5 0.4

Traffic index value

Traffic index value	0-2	2-4	4-6	6-8	8-10
Meaning	unobstructed	Basically unobstructed	Mild congestion	Moderate congestion	Serious congestion
Time-consuming congestion/smoothness	1	1.2–1.5	1.5–1.8	1.8–2.1	>2.1

Referencias

[1]Lin B, Wu W. The impact of electric vehicle penetration: A recursive dynamic CGE analysis of China[J]. Energy economics, 2021, 94:105086. Lin B, Wu W. The impact of electric vehicle penetration: A recursive dynamic CGE analysis of China[J]. Energy economics, 2021, 94:105086.10.1016/j.eneco.2020.105086Search in Google Scholar

[2]Lin Luo. Development Status of New Energy Vehicles in Foreign Countries[J]. Business Observation, 2019 (05): 35-36. (in Chinese) Lin Luo. Development Status of New Energy Vehicles in Foreign Countries[J]. Business Observation, 2019 (05): 35-36. (in Chinese)Search in Google Scholar

[3]Xiong Hu, Xiang Tieyuan, Zhu Yonggang, et al. Optimal planning of electric vehicle public charging station layout[J]. Power system Automation, 2012, 36(23): 65-70. (in Chinese) Xiong Hu, Xiang Tieyuan, Zhu Yonggang, Optimal planning of electric vehicle public charging station layout[J]. Power system Automation, 2012, 36(23): 65-70. (in Chinese)Search in Google Scholar

[4]Zeng D, Dong Y, Cao H, et al. Are the electric vehicles more sustainable than the conventional ones influences of the assumptions and modeling approaches in the case of typical cars in China[J]. Resources, Conservation and Recycling, 2021, 167: 105210. Zeng D, Dong Y, Cao H, Are the electric vehicles more sustainable than the conventional ones influences of the assumptions and modeling approaches in the case of typical cars in China[J]. Resources, Conservation and Recycling, 2021, 167: 105210.10.1016/j.resconrec.2020.105210Search in Google Scholar

[5]Choi H, Koo Y. Effectiveness of battery electric vehicle promotion on particulate matter emissions reduction[J]. Transportation Research Part D: Transport and Environment, 2021, 93: 102758. Choi H, Koo Y. Effectiveness of battery electric vehicle promotion on particulate matter emissions reduction[J]. Transportation Research Part D: Transport and Environment, 2021, 93: 102758.10.1016/j.trd.2021.102758Search in Google Scholar

[6]Tang Xiangang, Liu Junyong, Liu Youbo, et al. Electric Vehicle Charging Station Planning Based on Computational Geometry[J]. Power System Automation, 2012, 36(8): 24-30. (in Chinese) Tang Xiangang, Liu Junyong, Liu Youbo, Electric Vehicle Charging Station Planning Based on Computational Geometry[J]. Power System Automation, 2012, 36(8): 24-30. (in Chinese)Search in Google Scholar

[7]Jia Heping, Xie Shengli, Shao Xiang, et al. Optimal layout of urban electric vehicle charging stations based on simulated annealing algorithm[J]. Shanxi Electronic Technology, 2013(3): 26-27. (in Chinese) Jia Heping, Xie Shengli, Shao Xiang, Optimal layout of urban electric vehicle charging stations based on simulated annealing algorithm[J]. Shanxi Electronic Technology, 2013(3): 26-27. (in Chinese)Search in Google Scholar

[8]Chen Ting, Wei Zhinong, Wu Shuang, et al. Distribution network planning considering the location and capacity of electric vehicle charging station[J]. Power System and its Acta Automatica Sinica, 2013, 25 (3): 1-7. (in Chinese) Chen Ting, Wei Zhinong, Wu Shuang, Distribution network planning considering the location and capacity of electric vehicle charging station[J]. Power System and its Acta Automatica Sinica, 2013, 25 (3): 1-7. (in Chinese)Search in Google Scholar

[9] Meng Zijie. Electric Vehicle Charging Station Planning and Mathematical Model Research[J]. Guangdong Science and Technology, 2013(24): 224-225. (in Chinese) Meng Zijie. Electric Vehicle Charging Station Planning and Mathematical Model Research[J]. Guangdong Science and Technology, 2013(24): 224-225. (in Chinese)Search in Google Scholar

[10]Huang Zhensen, Yang Skirt. Location of charging station considering service capacity[J]. Industrial Engineering and Management, 2015, 20(5): 111-118. (in Chinese) Huang Zhensen, Yang Skirt. Location of charging station considering service capacity[J]. Industrial Engineering and Management, 2015, 20(5): 111-118. (in Chinese)Search in Google Scholar

[11]Liu Zhipeng, Wen Fushuan, Xue Yusheng, et al. Optimal location and constant capacity of electric vehicle charging station[J]. Power System Automation, 2012, 36 (3): 54-59. (in Chinese) Liu Zhipeng, Wen Fushuan, Xue Yusheng, Optimal location and constant capacity of electric vehicle charging station[J]. Power System Automation, 2012, 36 (3): 54-59. (in Chinese)Search in Google Scholar

[12]Bi Shuting, Han Yi, Sean. Brief introduction of chicken colony algorithm and related research[J]. Modern Marketing, 2019(4): 92. (in Chinese) Bi Shuting, Han Yi, Sean. Brief introduction of chicken colony algorithm and related research[J]. Modern Marketing, 2019(4): 92. (in Chinese)Search in Google Scholar

[13]Wu Jiawu, Qiu Xiaoyan, Pan Yinji, etc. Research on the Orderly Charging Strategy of Electric Vehicles Based on Improved Chicken Swarm Algorithm[J]. Electrical Measurement and Instrument, 2019, 56(09): 97-103. (in Chinese) Wu Jiawu, Qiu Xiaoyan, Pan Yinji, etc. Research on the Orderly Charging Strategy of Electric Vehicles Based on Improved Chicken Swarm Algorithm[J]. Electrical Measurement and Instrument, 2019, 56(09): 97-103. (in Chinese)Search in Google Scholar

[14]Wang Lu. Study on the optimization model of layout and location of urban pure electric vehicle rapid charging facilities[D]: Beijing Jiaotong University, 2016. (in Chinese) Wang Lu. Study on the optimization model of layout and location of urban pure electric vehicle rapid charging facilities[D]: Beijing Jiaotong University, 2016. (in Chinese)Search in Google Scholar

[15]Zhang Yihan, Xu Jing, Li Qiuyan, etc. Location and capacity determination method of electric vehicle charging station based on density peak clustering[J]. Power System Protection and Control, 2021, 49(05): 132-139. (in Chinese) Zhang Yihan, Xu Jing, Li Qiuyan, etc. Location and capacity determination method of electric vehicle charging station based on density peak clustering[J]. Power System Protection and Control, 2021, 49(05): 132-139. (in Chinese)Search in Google Scholar

[16]Zhang Zhiqiang, Yu Mingxuan. Discount rate and capital cost: how to determine the discount rate[J]. Research on financial issues, 2014(12): 11-17. (in Chinese) Zhang Zhiqiang, Yu Mingxuan. Discount rate and capital cost: how to determine the discount rate[J]. Research on financial issues, 2014(12): 11-17. (in Chinese)Search in Google Scholar

[17]Xiong Hu, Xiang Tieyuan, Zhu Yonggang, et al. Optimal planning of electric vehicle public charging station layout[J]. Power System Automation, 2012, 36(23): 65-70. Xiong Hu, Xiang Tieyuan, Zhu Yonggang, Optimal planning of electric vehicle public charging station layout[J]. Power System Automation, 2012, 36(23): 65-70.Search in Google Scholar

Applied Mathematics and Nonlinear Sciences

Research on location algorithm of new energy vehicle charging station based on multi-objective decision

Chaojie Zhang
Chaojie Zhang

Publicado en línea: 23 Dec 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 24 Apr 2022

Aceptado: 14 Jul 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Vista previa del PDF

Artículo

Fig. 1

Fig. 2

Figuras y tablas

Fig. 1

Fig. 2

Values under different block characteristics

Greenhouse gas emission values under different power generation types

Traffic index value

Referencias

Artículos Recientes

Planifique su conferencia remota con Sciendo

Applied Mathematics and Nonlinear Sciences

Research on location algorithm of new energy vehicle charging station based on multi-objective decision

Chaojie ZhangChaojie Zhang

Publicado en línea: 23 Dec 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 24 Apr 2022

Aceptado: 14 Jul 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Vista previa del PDF

Artículo

Fig. 1

Fig. 2

Figuras y tablas

Fig. 1

Fig. 2

Values under different block characteristics

Greenhouse gas emission values under different power generation types

Traffic index value

Referencias

Artículos Recientes

Planifique su conferencia remota con Sciendo

Chaojie Zhang
Chaojie Zhang