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.

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

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.

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 _{ij}

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.

Among them,

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

Among them, _{1} represents the cock in the corresponding group of the i-th hen; _{2} represents other chickens selected randomly, and

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

Among them, the position of the i-th mother hen can be expressed as _{mi}

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

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.

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

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

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

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

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.

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.

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 |

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.

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.

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.

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

Among them, _{cos t} is the annual construction and operation cost of the station i; _{CC,i}_{CC,i}_{CS}

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 _{CC,i}_{i}

_{i}_{i}_{i}

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.

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

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 _{ij}

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 _{e}

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

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 |

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.

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.

In formula 30, _{j}

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 _{ij}_{ij}_{ij}

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.

Where,

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

The carbon emission reduction ratio _{c}

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.

#### 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 |

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management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Study on the influence of adolescent smoking on physical training vital capacity in eastern coastal areas Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G ) on the set of maximal subgroups ofp -groupsResearch on loyalty prediction of e-commerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A study of local smoothness-informed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network