An energy-saving and carbon-reducing electric bus charging station site selection planning method, system, device and medium

Abstract

The application belongs to the technical field of urban planning, and discloses an energy-saving and carbon-reducing electric bus charging station site selection planning method, system, device and medium, which comprises the following steps: determining a candidate charging station site, analyzing the energy consumption state of electric buses between different sites, and constructing an electric bus running state model; according to the economic and carbon emission dimensions, a multi-objective optimization model is established and constraint conditions are introduced; the weight coefficient of the multi-objective optimization model is determined by the analytic hierarchy process, and the multi-objective optimization model is converted into a single-objective optimization model; and the electric bus charging station layout scheme is obtained by solving the single-objective optimization model combined with the electric bus running state model. The application realizes the optimization of the charging station site selection scheme under the premise of meeting the vehicle endurance requirement, and provides data support for the development of the electric bus system.

Description

Technical Field

[0001] This invention relates to the field of urban planning technology, and in particular to a method, system, equipment and medium for site selection and planning of electric bus charging stations for energy conservation and carbon reduction. Background Technology

[0002] With the increasing severity of the global energy crisis and environmental pollution, energy conservation and emission reduction in the transportation sector have become a focus of attention for countries worldwide. Electric buses, with their advantages of zero emissions, low noise, and high energy efficiency, are gradually replacing traditional fuel-powered buses and becoming an important part of urban public transportation. However, the widespread application of electric buses cannot be separated from the support of a sound charging infrastructure. The site selection and planning of charging stations directly affect the operational efficiency, economic costs, and energy conservation and emission reduction effects of the public transportation system.

[0003] However, existing methods for planning the location of electric bus charging stations often have limitations. For example, while multi-objective joint configuration models for charging stations have been constructed by analyzing urban functional zoning and user travel data, they primarily focus on the economic benefits of fast charging stations and user time costs, thus lacking a comprehensive consideration of energy consumption and carbon emissions during the charging process. Furthermore, existing methods also emphasize pure electric bus charging plans considering time-of-use pricing and joint optimization of charging resource allocation. Although they take into account the impact of pricing mechanisms on charging plans, they fail to fully consider the impact of charging station location selection on the sustainable development of the entire electric bus system, especially the carbon emissions during the construction and operation of charging stations.

[0004] Therefore, there is a need for a site selection and planning methodology for electric bus charging stations that comprehensively considers multiple objectives such as construction and operation costs, charging process costs, and minimizing carbon emissions, in order to support the sustainable development of electric bus systems. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a method, system, equipment, and medium for site selection and planning of electric bus charging stations for energy conservation and carbon reduction, which solves the problems that existing site selection methods fail to balance economic benefits and carbon emission benefits, and lack accurate analysis of the real-time operating status of electric buses.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction, comprising: Determine candidate charging station locations, analyze the energy consumption status of electric buses at different stations, and construct an electric bus operation status model. Based on economic and carbon emission dimensions, a multi-objective optimization model is established and constraints are introduced. The weight coefficients of the multi-objective optimization model are determined by the analytic hierarchy process (AHP), and the multi-objective optimization model is transformed into a single-objective optimization model. The layout scheme of electric bus charging stations is obtained by solving the single-objective optimization model in combination with the electric bus operation status model.

[0008] As a preferred embodiment of the energy-saving and carbon-reduction-oriented electric bus charging station site selection and planning method described in this invention, the method includes: analyzing the energy consumption status of electric buses at different stations and constructing an electric bus operation status model, including: Based on the initial battery level of the electric bus, the number of round trips during the operating period, the travel distance between stations, and the power consumption per kilometer, an operating status model of the electric bus is constructed to calculate the real-time battery level of the electric bus.

[0009] As a preferred embodiment of the energy-saving and carbon-reduction-oriented electric bus charging station site selection and planning method described in this invention, a multi-objective optimization model is established based on economic and carbon emission dimensions, including: Construct objective functions for construction and operation costs and charging costs based on economic dimensions; Construct carbon emission target functions for construction and charging based on carbon emission dimensions.

[0010] As a preferred embodiment of the energy-saving and carbon-reduction-oriented electric bus charging station site selection and planning method described in this invention, wherein: the objective functions include: The objective function for construction and operation costs is obtained by introducing a discount rate and a charging station service life factor into the engineering and equipment installation costs and operation and maintenance costs of the site. The charging cost objective function is obtained by summing the time-of-use electricity price for each time period of the day with the electricity obtained by electric buses on each route during the corresponding time period. The construction carbon emission objective function is obtained by incorporating the total carbon emissions over the entire life cycle of the site into the charging station's service life factor. The target function for carbon emissions from charging is obtained by combining the electricity obtained by an electric bus during a single charging session at a station with the life-cycle carbon emission coefficient and power generation for different types of electricity sources in different regions.

[0011] As a preferred embodiment of the energy-saving and carbon-reduction-oriented electric bus charging station site selection and planning method described in this invention, the following constraints are introduced: Set charging capacity constraints to limit the amount of electricity that an electric bus can obtain each time it charges at a charging station to no more than the maximum amount of electricity that the charging pile can provide in a single charge. Set power and operational reliability constraints to maintain the power level of electric buses within the range of the remaining power warning value and the maximum value during operating hours; Set charging status constraints to restrict electric buses to charging only at stations with charging stations, and to the actual charging amount ranging from zero to the maximum amount of electricity that can be obtained.

[0012] As a preferred embodiment of the energy-saving and carbon-reduction-oriented electric bus charging station site selection and planning method of the present invention, the method includes: determining the weight coefficients of the multi-objective optimization model through the analytic hierarchy process (AHP) and transforming the multi-objective optimization model into a single-objective optimization model, including: Using the Saaty scaling method, a judgment matrix is ​​constructed based on objective functions of construction and operation costs, charging costs, carbon emissions, and charging carbon emissions. Calculate the eigenvectors of the judgment matrix and perform a consistency check to determine the weight coefficients, thus transforming the multi-objective optimization model into a single-objective optimization model.

[0013] As a preferred embodiment of the energy-saving and carbon-reducing electric bus charging station site selection and planning method described in this invention, the electric bus charging station layout scheme, which includes the bus stops for establishing charging stations and the economic costs and carbon emissions of each objective function, is obtained by solving the single-objective optimization model in combination with the electric bus operation status model.

[0014] Secondly, the present invention provides a site selection and planning system for electric bus charging stations aimed at energy conservation and carbon reduction, comprising: The operation status module is used to determine the locations of candidate charging stations, analyze the energy consumption status of electric buses at different stations, and build an operation status model for electric buses. The multi-objective optimization module is used to establish a multi-objective optimization model and introduce constraints based on economic and carbon emission dimensions. The single-objective optimization module is used to determine the weight coefficients of the multi-objective optimization model through the analytic hierarchy process, and to transform the multi-objective optimization model into a single-objective optimization model. The layout scheme module is used to solve the electric bus charging station layout scheme by combining the single-objective optimization model with the electric bus operation status model.

[0015] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the site selection and planning method for electric bus charging stations oriented towards energy conservation and carbon reduction.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention breaks through the limitations of traditional site selection that only considers construction and operation costs, and innovatively introduces evaluation indicators for "construction carbon emissions" and "charging carbon emissions." By comprehensively considering the carbon footprint throughout the entire life cycle, the planned charging station layout is not only economically reasonable but also in line with the green and low-carbon development trend. By constructing an electric bus operation status model, it is possible to simulate and analyze the real-time energy consumption of buses at different stops. This dynamic analysis method is more consistent with actual operating scenarios than traditional static range estimation, ensuring the reliability of the site selection scheme in actual operation. The analytic hierarchy process (AHP) combined with Saaty scaling and consistency checks is used to assign weights to multiple conflicting objective functions. Furthermore, multiple constraints are introduced into the model to ensure the feasibility of the site selection scheme in terms of physical infrastructure capacity, vehicle battery safety range, and operational scheduling logic. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall process of the site selection and planning method for electric bus charging stations for energy conservation and carbon reduction according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of an electric bus route in Yangjiang City, illustrating the site selection and planning method for electric bus charging stations aimed at energy conservation and carbon reduction according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram illustrating the optimization results of the objective functions of the site selection and planning method for electric bus charging stations aimed at energy conservation and carbon reduction, as described in an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram illustrating the selection of a fast charging station for Route 1, as described in an embodiment of the energy-saving and carbon-reducing electric bus charging station site selection and planning method of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As one embodiment of the present invention, a method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction is provided, comprising: S100: Determine candidate charging station locations, analyze the energy consumption status of electric buses at different stations, and construct an electric bus operation status model. S200: Based on economic and carbon emission dimensions, a multi-objective optimization model is established and constraints are introduced; S300: The weight coefficients of the multi-objective optimization model are determined by the analytic hierarchy process (AHP), and the multi-objective optimization model is transformed into a single-objective optimization model. S400: The layout scheme of electric bus charging stations is obtained by solving the problem through a single-objective optimization model combined with the electric bus operation status model.

[0025] It should be noted that the site selection and planning of electric bus charging stations involves multiple factors such as urban traffic flow, power grid load, and carbon emissions, making it a typical multi-objective combined optimization problem. Traditional static planning methods are difficult to adapt to the energy replenishment needs of electric buses during dynamic operation, leading to a disconnect between the planned scheme and actual operation. Therefore, accurate site selection planning based on vehicle operating status is of great significance.

[0026] Therefore, to address the aforementioned problems, after determining candidate bus stops through steps S100-S400, an electric bus operation status model was constructed to analyze the energy consumption status of electric buses at different stops. A multi-objective optimization model was established with construction and maintenance costs, charging costs, charging carbon emissions, and construction carbon emissions as objective functions, and charging capacity constraints, power consumption and operational reliability constraints, and charging status constraints were set to construct a comprehensive evaluation system. The model was then converted into a single-objective optimization model using the scalar method, and the weights were determined through the analytic hierarchy process. Finally, the optimal layout was obtained by solving the model jointly.

[0027] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a site selection and planning method for electric bus charging stations oriented towards energy conservation and carbon reduction is provided.

[0028] In this embodiment of the application, step S100 involves determining candidate charging station locations, analyzing the energy consumption status of electric buses at different stations, and constructing an electric bus operation status model, including: Specifically, the route data of a given bus route is obtained, the one-way travel route is extracted, and the one-way travel route is divided into multiple stops, with each stop serving as a candidate charging station.

[0029] Furthermore, the parameters for each candidate charging station are determined, including but not limited to: charging and discharging capacity, electricity rate, construction cost, maintenance cost, service life, electricity consumption cost per unit mileage, electricity price per unit kilowatt-hour, and carbon emissions per unit mileage.

[0030] In one optional implementation, the candidate charging station locations for each bus stop in step S100 can be screened in conjunction with the load status of the urban power distribution network. For example, the remaining capacity data of the distribution transformers around each bus stop can be obtained; a minimum access capacity threshold can be set, and stations with remaining power grid capacity below the threshold and difficult to expand can be removed from the candidate list, retaining only stations with power access conditions as candidate charging station locations, so as to reduce the hidden costs of subsequent power grid transformation.

[0031] In another optional implementation, the process of obtaining candidate charging station locations for each bus stop in step S100 can also be pre-screened in conjunction with the topology of the urban power distribution network. For example, data on the remaining capacity and expansion feasibility of the distribution transformers around each bus stop can be obtained. A power grid capacity threshold is set, and bus stops with remaining capacity of the surrounding power distribution network that is lower than the threshold and cannot be expanded are removed from the candidate charging station location set, leaving only the stations that meet the power access conditions as the final candidate charging station locations.

[0032] Furthermore, during operation, as the electric bus travels a longer distance, its battery capacity gradually decreases. Therefore, in-depth analysis and modeling of the real-time operating status of the electric bus are necessary to accurately determine when it needs charging, thereby constructing an operating status model for the electric bus and calculating its real-time battery level. Specifically, these include: In the formula, The initial charge at the starting point; For electric bus stops; This is the collection of round-trip times during the operating period; For the first Second time at the site Electrical energy obtained from the source; Electricity consumption per kilometer; For the first The train passed through the station. , The distance between them; For the first Second time at the site The electrical energy obtained from the source.

[0033] In this embodiment of the application, step S200 establishes a multi-objective optimization model based on economic and carbon emission dimensions and introduces constraints, including the following steps A1-A5: It should be noted that to determine the site selection for energy-saving and carbon-reducing electric bus charging stations, two types of factors must be considered: economic factors and carbon emission factors. Therefore, this method establishes four objectives to achieve the final charging station site selection plan. Considering the major initial construction costs and subsequent maintenance of charging stations, this method first sets the construction and operation costs of electric bus charging stations as the objective function. Furthermore, during subsequent operation, electric buses need to purchase electricity from the grid for charging, so the charging cost of electric buses must be considered as the second objective function. In addition, carbon emission costs must be considered; therefore, the carbon emissions from the construction of electric vehicle charging stations and the carbon emissions from the operation of electric buses must also be considered as the third and fourth objective functions.

[0034] A1: Objective function to minimize the construction and operation costs of electric bus charging stations : In the formula, For the site The engineering and equipment installation costs; For the site The operating and maintenance costs; These are decision variables.

[0035] Among these, engineering and equipment installation costs refer to investments in infrastructure and equipment, including land acquisition fees, equipment purchase fees, installation and construction fees, and power line construction fees. Due to the substantial amount of engineering, equipment, and installation costs, it is necessary to quantify the time factor and distribute the relevant costs evenly over each year of the service life. In the formula, , , , Sites Land acquisition fees, equipment purchase fees, installation and construction fees, and power distribution line construction fees; The discount rate; This is a factor representing the service life of the charging station.

[0036] Operation and maintenance costs mainly include labor costs, equipment maintenance and repair costs, and electricity costs. Because this cost is complex to calculate, it is calculated as a percentage of the engineering and equipment installation costs. In the formula, This is the proportionality coefficient.

[0037] A2: Objective function to minimize the charging cost of electric bus charging stations : Specifically, the charging cost of electric buses mainly refers to electricity costs, which have certain time characteristics. The research on charging costs is based on time-of-use electricity pricing, with an annual calculation period. The charging cost expression is as follows: In the formula, K represents dividing the 24 hours of a day into K time periods, with an interval of 1 hour; For the collection of operating routes; For vehicle assembly; For time period Time-of-use electricity pricing; For the line No. bus Always at the site The electrical energy obtained.

[0038] A3: Minimize the carbon emission objective function for the construction of electric bus charging stations. : Specifically, carbon emissions from the construction of electric bus charging stations include carbon emissions from the production, transportation, construction and installation of charging piles, and the recycling phase: In the formula, For the site Carbon emissions from construction; Representing the sites Carbon emissions from the production, transportation, construction and installation, and recycling of charging piles.

[0039] A4: Minimize the carbon emission objective function for electric bus charging stations. : Specifically, although electric buses significantly reduce carbon emissions compared to traditional energy sources, they still inevitably generate a certain amount of carbon emissions. Therefore, the expression is: In the formula, A set of regional power source types; for The life-cycle carbon emission coefficient of each unit of electricity generated by a particular power generation method; for The amount of electricity generated by each power generation method; The electrical energy obtained by an electric bus during a single charging session at a station; Improving the charging efficiency of electric buses; This refers to the power transmission line loss rate.

[0040] A5: Set constraints, including: ① Charging capacity constraints; Specifically, the electrical energy that an electric bus can obtain each time it charges at a charging station should not exceed the maximum electrical energy that the charging pile can provide on a single charge. In the formula, This indicates the maximum electrical energy provided to an electric bus in a single trip. This indicates the maximum charging power of the electric bus; Charging time for electric buses.

[0041] ② Constraints on power consumption and operational reliability; Specifically, the battery level of electric buses should be maintained between the warning value and the maximum value during operating hours: In the formula, This is the warning value for the remaining battery power of the electric bus; This is the maximum battery capacity for electric buses. ③ Charging state constraints; Specifically, if electric buses want to stop at stations Charging station Charging stations must be built at the locations; and electric buses must be available at the stations. The electrical energy obtained at the point is constrained by the charging state decision variable. If the charging variable is 1, then at... The electrical energy obtained at a point is between 0 and the maximum electrical energy that can be obtained. If the charging state variable is 0, then... No charging is performed at this location: In the formula, For charging state decision variables, if it is 1, then at the station If the value is 0, then it is not charging. Charging station; If it is a decision variable, then... If a charging station is established at a certain location, the value is 1; otherwise, it is 0.

[0042] In one alternative implementation, setting multiple constraints in step S200 can also increase battery health constraints. For example, considering the impact of fast charging on battery life, a daily limit on the number of fast charges can be set (e.g., no more than 4 fast charges per day), or a maximum current rate constraint can be set during charging to ensure that the planned charging scheme will not cause the electric bus battery to be prematurely scrapped during its entire life cycle.

[0043] In another optional implementation, the multiple constraints set in step S200 can also include queuing time constraints. For example, a queuing theory model (M / M / c model) can be introduced to calculate the expected waiting time after the electric bus arrives at the charging station during peak hours. A maximum allowable waiting time threshold (e.g., not exceeding 10 minutes) can be set. If a certain location scheme causes the waiting time to exceed the limit, the scheme is determined to not meet the constraints.

[0044] It should be noted that by innovatively introducing the carbon emissions from construction and charging throughout the entire life cycle into the model, a multi-objective optimization model with both economic and environmental dimensions was established. Combined with multiple physical and logical constraints such as charging capacity, operational reliability, and charging status, the planning results not only have economic feasibility for investment, but also reduce the carbon footprint of the urban public transportation system from the source, respond to the green and low-carbon urban development strategy, and achieve deep synergy between economic and environmental benefits.

[0045] In this embodiment of the application, step S300, which determines the weight coefficients of the multi-objective optimization model using the analytic hierarchy process (AHP) and transforms the multi-objective optimization model into a single-objective optimization model, includes: Specifically, when constructing a multi-objective optimization model for charging stations, it is necessary to comprehensively consider multiple conflicting or mutually restrictive objectives in order to seek the overall optimal solution. In the process of constructing the multi-objective optimization model, decision variables include two types: 0-1 integer variables and continuous variables. To facilitate the solution, a scalarization method is adopted to transform the multi-objective model into a single-objective model. By assigning different weights to each objective, the importance of each objective is quantified.

[0046] During the scalarization process, each objective function is assigned a certain weight, and these weights... This represents the importance of each objective function in the overall optimization process. The mathematical model for multi-objective optimization is expressed as: When assigning weights to objective functions, the importance of each objective function to the overall objective is different, and the weight of each objective function needs to be determined based on its contribution to the corresponding objective.

[0047] It should be noted that by flexibly adjusting the weighting coefficients, the policy orientations of different cities can be adjusted, such as prioritizing environmental protection or cost, thus avoiding the subjective blindness of human experience-based decision-making and ensuring the mathematical rigor and logical consistency of the site selection decision-making process.

[0048] Therefore, the weights are determined using the analytic hierarchy process, which includes sub-steps B1-B2: B1: Using the Saaty scaling method, construct a judgment matrix based on the objective functions of construction and operation costs, charging costs, carbon emissions, and charging carbon emissions; Specifically, using Saaty's 1-9 scale, pairwise comparisons are made based on the importance of each indicator's contribution to the higher-level objective. In the analytic hierarchy process (AHP), to quantitatively display the importance of each element in the matrix, 1 indicates that two elements are equally important, and 9 indicates that one element is extremely important compared to the other. By comparing the influence of n elements on a certain factor in the higher level, a pairwise judgment matrix is ​​constructed. , Representative elements Relative to element The importance of , The constructed judgment matrix is ​​shown in Table 1.

[0049] Table 1: Judgment Matrix

[0050] B2: Calculate the eigenvectors of the judgment matrix and perform a consistency check to determine the weight coefficients of each objective function; Specifically, the eigenvalue method is used to normalize the judgment matrix, and the arithmetic mean of each row is calculated as the approximate value of the eigenvector of each indicator, i.e., the weight coefficient, as shown in Table 2.

[0051] Table 2: Weighting Coefficients

[0052] To verify the logical consistency of the judgment matrix, the following checks are performed: Multiplying the judgment matrix by the eigenvector yields the largest eigenvalue. Calculate the consistency index : in, A CI value indicates that the judgment matrices are completely consistent, and the larger the CI value, the higher the degree of inconsistency in the judgment matrices.

[0053] Query the average random consistency index See Table 3 for reference.

[0054] Table 3: Average Random Consistency Index

[0055] Due to the objective function Corresponding Therefore, the consistency ratio is calculated at this time. : The calculation results show that, The test was passed and the results are consistent.

[0056] Based on the above weight determination process, the multi-objective optimization model is transformed into a single-objective optimization model: In this embodiment of the application, step S400 uses a single-objective optimization model combined with an electric bus operation state model to obtain an electric bus charging station layout scheme, including: Specifically, since the operating status of electric buses affects the final solution of the optimization model, the operating status of electric buses is used as the input of the single-objective optimization model to obtain the layout scheme of electric bus charging stations. This includes the specific number and optimal location of electric bus charging stations that need to be built for different bus routes, and also provides data on the power required by electric buses at different times and different stations.

[0057] In one optional implementation, the layout scheme of electric bus charging stations output in step S400 can be used to generate a site selection heat map. For example, the calculated recommended construction priority values ​​of each site can be mapped to color gradients, and the charging demand heat map and carbon emission reduction benefit heat map can be overlaid on the city GIS map to intuitively show the potential benefits of building charging stations in different areas and assist planners in making decisions.

[0058] In another optional implementation, the layout plan for electric bus charging stations output in step S400 can also output phased construction suggestions. For example, based on the weight sensitivity analysis of each objective function, the finally selected charging stations can be divided into three batches: those urgently needed in the near term, those planned for construction in the medium term, and those reserved for construction in the long term. A phased charging station construction schedule can be output to alleviate the financial pressure in the early stage of construction.

[0059] Example 3, referring to Figures 2-4 As an embodiment of the present invention, based on the above embodiment, a simulation example analysis of the site selection planning of electric bus charging stations for energy conservation and carbon reduction is provided to verify its feasibility and effectiveness. Specifically, the simulation established in this example uses Yangjiang City, Guangdong Province as the scenario. The relevant parameters of the electric bus charging station site selection optimization model are solved by solving the above mathematical model, analyzing the influence trend of different factors on the optimization results, and solving for the optimal design scheme.

[0060] To simplify the analysis process, such as Figure 2 This is a schematic diagram of electric bus routes in Yangjiang City. This invention assumes that all 14 operating bus routes use the same model of electric buses, namely BYD K9 pure electric buses. According to data from BYD's official website, the relevant parameters of the K9 bus are shown in Table 4.

[0061] Table 4: Relevant Parameters of K9 Bus

[0062] Determine the relevant parameters of the multi-objective optimization model, including the following: Construction and operation costs: The engineering and equipment installation cost of the electric bus charging station is 81,372.70 yuan, and the operation and maintenance cost is 16,274.54 yuan. The ratio between the operation and maintenance cost and the engineering and equipment installation cost is... The discount rate is 0.2, the service life of the fast charging station is 10 years, and the discount rate is 0.1.

[0063] Charging costs: Yangjiang City currently implements a peak-valley time-of-use electricity pricing policy, as shown in Table 5.

[0064] Table 5: Reference for Time-of-Use Electricity Prices

[0065] Carbon emissions from construction: Refer to Table 6 for carbon emission parameters related to the Tritium pure electric vehicle charging station (Veefilpk model).

[0066] Table 6: Carbon Emission Related Parameters

[0067] Carbon emissions from charging: According to data from the National Bureau of Statistics, the power generation structure in Guangdong Province is shown in Table 7. Currently, thermal power generation still dominates in Guangdong Province, accounting for 72.77%, followed by nuclear power generation, accounting for 18.82%. Wind power generation, hydropower generation, and solar power generation account for a small proportion, less than 10%.

[0068] Table 7: Power Generation Structure

[0069] By combining the proportion of different power generation structures in Guangdong Province with the greenhouse gas emissions per unit of power generation, the calculated carbon emission value per unit in Yangjiang City was obtained. It is 601.73 The calculation process is shown in Table 8.

[0070] Table 8: Calculation of Carbon Emissions Per Unit

[0071] Based on data from some bus routes in Yangjiang City, Guangdong Province, this study verifies that the model and algorithm can determine the most suitable locations for deploying electric bus charging stations. Specific data and operational plans for bus operations in Yangjiang City are shown in Table 9.

[0072] Table 9: Public Transportation Operation Status

[0073] The model uses the BYD K9 electric bus as the standard vehicle, with a total battery capacity of 355 kWh and a maximum charging power of 200 kW. The land acquisition cost for the charging station is 100,000 yuan, equipment purchase cost is 300,000 yuan, installation and construction cost is 40,000 yuan, and power line construction cost is 60,000 yuan. Furthermore, the operation and maintenance cost of the charging station accounts for 20% of the total equipment and installation cost. The operating life of the charging station is set at 10 years, and the discount rate used is 0.1. The fast charging station site selection scheme derived using the proposed optimization model is shown in Table 10.

[0074] Table 10: Site Selection Scheme

[0075] Based on the proposed electric bus charging station site selection optimization model, the charging demand of 14 operating bus routes in Yangjiang city was analyzed. The results show that to meet the operational needs of these routes, a total of 43 fast charging stations are required. These stations are mostly located at transportation hubs where different routes overlap, which is consistent with the actual situation and conducive to increasing the frequency and efficiency of charging station use. The optimization results of each objective function obtained using the multi-objective optimization method are as follows: Figure 3 The figures show: Construction and maintenance cost of charging stations: 4,198,831.335 yuan; charging cost of electric buses: 2,240,865.388 yuan; carbon emissions from electric bus charging: 1,264,216.308 yuan. The carbon emissions from the construction of charging stations amounted to 35,284.08 kJ. .

[0076] Among them, such as Figure 4 The image shows the selection of fast charging stations for Route 1 as an example. The first and last bus stops of Route 1 are marked in red. Blue marks indicate bus stops selected for fast charging stations on the outbound route: Yingge Electric and Yangjiang Customs. Yellow marks indicate bus stops selected for fast charging stations on the inbound route: Guangdong Ocean University and Yinglei Building. The remaining bus stops not selected for fast charging stations are marked in gray.

[0077] In summary, by constructing a multi-objective optimization model that includes construction and operation costs, charging costs, construction carbon emissions, and charging carbon emissions, and combining it with a real-time operation status model of electric buses, the model achieves a comprehensive optimization of economic costs and carbon emission costs while satisfying constraints on bus charging capacity, power supply and operational reliability, and charging status. It also optimizes the feasible solutions for charging station site selection, providing data support for the development of electric bus systems.

[0078] Example 4 illustrates a schematic scheme for a site selection and planning method for electric bus charging stations aimed at energy conservation and carbon reduction. It should be noted that the technical solution of this system for site selection and planning of electric bus charging stations aimed at energy conservation and carbon reduction is based on the same concept as the technical solution of the aforementioned method. Details not described in detail in the technical solution of the system for site selection and planning of electric bus charging stations aimed at energy conservation and carbon reduction in this embodiment can be found in the description of the technical solution of the aforementioned method.

[0079] This embodiment also provides a site selection and planning system for electric bus charging stations aimed at energy conservation and carbon reduction, including: The operation status module is used to determine the locations of candidate charging stations, analyze the energy consumption status of electric buses at different stations, and build an operation status model for electric buses. The multi-objective optimization module is used to establish a multi-objective optimization model and introduce constraints based on economic and carbon emission dimensions. The single-objective optimization module is used to determine the weight coefficients of the multi-objective optimization model through the analytic hierarchy process, and to transform the multi-objective optimization model into a single-objective optimization model. The layout scheme module is used to solve the electric bus charging station layout scheme by combining the single-objective optimization model with the electric bus operation status model.

[0080] This embodiment also provides an electronic device applicable to the site selection planning of electric bus charging stations for energy conservation and carbon reduction, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the site selection planning method for electric bus charging stations for energy conservation and carbon reduction proposed in the above embodiment.

[0081] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as proposed in the above embodiments.

[0082] The storage medium proposed in this embodiment and the method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0083] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0084] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A site selection and planning method for electric bus charging stations aimed at energy conservation and carbon reduction, characterized in that, include: Determine candidate charging station locations, analyze the energy consumption status of electric buses at different stations, and construct an electric bus operation status model. Based on economic and carbon emission dimensions, a multi-objective optimization model is established and constraints are introduced. The weight coefficients of the multi-objective optimization model are determined by the analytic hierarchy process (AHP), and the multi-objective optimization model is transformed into a single-objective optimization model. The layout scheme of electric bus charging stations is obtained by solving the single-objective optimization model in combination with the electric bus operation status model.

2. The method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as described in claim 1, characterized in that, Analyze the energy consumption status of electric buses at different stops and construct an operational status model for electric buses, including: Based on the initial battery level of the electric bus, the number of round trips during the operating period, the travel distance between stations, and the power consumption per kilometer, an operating status model of the electric bus is constructed to calculate the real-time battery level of the electric bus.

3. The method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as described in claim 2, characterized in that, Based on economic and carbon emission dimensions, a multi-objective optimization model is established, including: Construct objective functions for construction and operation costs and charging costs based on economic dimensions; Construct carbon emission target functions for construction and charging based on carbon emission dimensions.

4. The method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as described in claim 3, characterized in that, The objective functions include: The objective function for construction and operation costs is obtained by introducing a discount rate and a charging station service life factor into the engineering and equipment installation costs and operation and maintenance costs of the site. The charging cost objective function is obtained by summing the time-of-use electricity price for each time period of the day and the electricity obtained by electric buses on each route during the corresponding time period. The construction carbon emission objective function is obtained by incorporating the total carbon emissions over the entire life cycle of the charging station into the charging station's service life factor. The target function for carbon emissions from charging is obtained by combining the electricity obtained by an electric bus during a single charging session at a station with the life-cycle carbon emission coefficient and power generation for different types of electricity sources in different regions.

5. The method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as described in claim 1 or 3, characterized in that, Introduce constraints, including: Set charging capacity constraints to limit the amount of electricity that an electric bus can obtain each time it charges at a charging station to no more than the maximum amount of electricity that the charging pile can provide in a single charge. Set power and operational reliability constraints to maintain the power level of electric buses within the range of the remaining power warning value and the maximum value during operating hours; Set charging status constraints to restrict electric buses to charging only at stations with charging stations, and to the actual charging amount ranging from zero to the maximum amount of electricity that can be obtained.

6. The method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as described in claim 5, characterized in that, The weight coefficients of the multi-objective optimization model are determined using the analytic hierarchy process (AHP), transforming the multi-objective optimization model into a single-objective optimization model, including: Using the Saaty scaling method, a judgment matrix is ​​constructed based on objective functions of construction and operation costs, charging costs, construction carbon emissions, and charging carbon emissions. Calculate the eigenvectors of the judgment matrix and perform a consistency check to determine the weight coefficients, thus transforming the multi-objective optimization model into a single-objective optimization model.

7. The method for site selection and planning of electric bus charging stations for energy conservation and carbon reduction as described in claim 6, characterized in that, By combining the single-objective optimization model with the electric bus operation status model, a layout scheme for electric bus charging stations is obtained, including the bus stops for establishing charging stations and the economic costs and carbon emissions of each objective function.

8. A site selection and planning system for electric bus charging stations aimed at energy conservation and carbon reduction, employing the method as described in any one of claims 1-7, characterized in that, include: The operation status module is used to determine the locations of candidate charging stations, analyze the energy consumption status of electric buses at different stations, and build an operation status model for electric buses. The multi-objective optimization module is used to establish a multi-objective optimization model and introduce constraints based on economic and carbon emission dimensions. The single-objective optimization module is used to determine the weight coefficients of the multi-objective optimization model through the analytic hierarchy process, and to transform the multi-objective optimization model into a single-objective optimization model. The layout scheme module is used to solve the layout scheme of electric bus charging stations by combining a single-objective optimization model with an electric bus operation status model.

9. An electronic device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the site selection and planning method for electric bus charging stations according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It includes storing computer-executable instructions that, when executed by a processor, implement the steps of the site selection and planning method for electric bus charging stations according to any one of claims 1 to 7.

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