Multi-stage charging station planning method and system considering increase of ownership of electric vehicles

Abstract

The invention discloses a multi-stage charging station planning method and system considering electric vehicle ownership increase, and the method comprises the steps: building an electric vehicle charging station multi-target planning model based on the electric vehicle ownership in the initial year, and obtaining a current planning scheme through solving the model; charging station planning of the next year is carried out, the electric vehicle ownership of the next year is predicted, whether the user side cost exceeds a preset cost threshold value or not is judged under the current charging station planning scheme, if yes, the next stage is planned, and if not, the current planning scheme is used; constructing a multi-stage planning model, selecting a construction mode of the charging station in the next stage, and constructing a multi-target planning model based on the inventory of the next year to obtain a planning scheme of the year; and circularly repeating the charging station planning of the next year and the planning of the next stage until a charging station planning scheme of each year is obtained. The method comprehensively considers the dynamics of the increase of the inventory of the electric vehicles and the cost factors of the users and the charging stations, and improves the adaptability and flexibility of the planning of the charging stations of the electric vehicles.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging station planning, and particularly relates to a multi-stage charging station planning method and system considering the growth of electric vehicle ownership. Background Art

[0002] With the increasing global attention to environmental protection and sustainable development, electric vehicles, as a clean energy transportation means, are gradually widely used globally. In recent years, the market ownership of electric vehicles has shown a rapid growth trend, which not only brings a huge impact to the traditional fuel vehicle industry, but also poses new challenges to the urban infrastructure construction, especially the planning and layout of charging facilities.

[0003] As an important facility for electric vehicle energy replenishment, the planning and layout of charging stations are directly related to the practicability and convenience of electric vehicles. In the initial stage of the development of electric vehicles, charging stations were mostly built in a scattered manner, mainly distributed in the city center or specific commercial areas, and the quantity was difficult to meet the growing charging needs of electric vehicles. Moreover, most traditional charging station planning methods rely on static traffic flow and vehicle ownership data, and fail to fully consider the dynamic change trend of electric vehicle ownership and the actual usage habits and needs of users, resulting in the planning results of charging infrastructure being difficult to effectively adapt to the actual needs and lacking systematicness and foresight. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-stage charging station planning method and system considering the growth of electric vehicle ownership in view of the above problems existing in the prior art.

[0005] To achieve the above purpose, the technical solution of the present invention is as follows:

[0006] In the first aspect, the present invention proposes a multi-stage charging station planning method considering the growth of electric vehicle ownership, including:

[0007] S1. Based on the electric vehicle ownership in the starting year of the planning period, with the goal of minimizing the planning cost of electric vehicle charging stations, construct a multi-objective planning model for electric vehicle charging stations, and obtain the current charging station planning scheme by solving the multi-objective planning model for electric vehicle charging stations;

[0008] S2. Conduct the charging station planning for the next year. Based on the predicted electric vehicle ownership in the next year, judge whether the user-side cost in the next year exceeds the preset cost threshold under the current charging station planning scheme. If it exceeds, enter S3 to plan the next stage; if not, the charging station planning scheme for the next year is the same as the current charging station planning scheme, and output the charging station planning scheme for that year;

[0009] S3. Select the construction method of the next-stage charging station based on the constructed multi-stage planning model. Based on the number of electric vehicles in the next year, with the goal of minimizing the planning cost of electric vehicle charging stations, construct a multi-objective planning model for electric vehicle charging stations to obtain the charging station planning scheme for that year. Among them, the construction method of the charging station is to expand the existing charging station or build a new charging station on the basis of the existing charging station;

[0010] S4. Repeat S2 - S3 cyclically until the charging station planning schemes for each year within the planning period are obtained.

[0011] In the above-mentioned S1, the multi-objective optimization model for electric vehicle charging stations is:

[0012] min F = C cs +C user ;

[0013] In the above formula, F is the total planning cost of the electric vehicle charging station, C cs is the cost on the charging station side, and C uSer is the cost on the user side;

[0014] Among them, the cost C cs on the charging station side includes the construction investment and operation and maintenance costs of the charging station, and is calculated using the following formula:

[0015]

[0016]

[0017] C M，k = ηC B，k ;

[0018] In the above formula, C B，k is the construction investment cost of the kth charging station, C M，k is the operation and maintenance cost of the kth charging station, N is the total number of charging stations to be built, r 0 is the discount rate, n 0 is the operation life of the charging station, N ch，k is the number of charging piles of the kth charging station, c ch is the unit price of the charging pile, A k is the floor area of the kth charging station, c f，k is the unit area rent cost of the kth charging station, C o，k is other costs, and η is the conversion coefficient;

[0019] The cost C user on the user side includes the queuing time cost of user charging and the mileage anxiety cost of users reaching the charging station, and is calculated using the following formula:

[0020] Cuser = C wait + C anx ;

[0021]

[0022]

[0023] In the above formula, C wait is the queuing time for user charging, C anx is the mileage anxiety cost for the user to reach the charging station, f w is the conversion coefficient of the time cost for electric vehicle users, t ∈ [t 0 , t e is the time scale, representing the driving time of the electric vehicle, t 0 is the start time of the electric vehicle driving, t e is the end time of the electric vehicle driving, is the average queuing waiting time of the kth charging station within time t, n k，t is the number of electric vehicles starting to charge at the kth charging station within time t, f a is the conversion coefficient of the mileage anxiety cost for electric vehicle users, is the mileage anxiety value of the nth electric vehicle at the kth charging station within time t.

[0024] The average queuing waiting time of the kth charging station within time t is calculated using the following formula:

[0025]

[0026]

[0027]

[0028] In the above formula, is the average queuing waiting time of the kth charging station within time t, c is the number of charging piles in the charging station, ρ is the service intensity of the charging piles, λ k，t is the number of charging demands per hour going to the kth charging station for service within time t, P 0 is the probability that there are no customers in the system, μ is the number of electric vehicles that each charging pile can service and complete within unit time;

[0029] The mileage anxiety value of the nth electric vehicle at the kth charging station within time t is calculated using the following formula:

[0030]

[0031] In the above formula, is the range anxiety value of the nth electric vehicle at the kth charging station at time t, is the anxiety mileage of the nth electric vehicle at the kth charging station at time t, R max is the maximum range anxiety, and DR is the remaining range of the electric vehicle when the user begins to experience range anxiety.

[0032] In S2, the number of electric vehicles in the next year is predicted using the following formula:

[0033] N(t+1)=N(t)+mf(t+1);

[0034] f(t+1)=p[1-F(t)]+qF(t)[1-F(t)];

[0035] In the above formula, t+1 is the next year, t is the current year, N(t+1) is the number of electric vehicles in the next year, m is the total number of potential consumers, f(t+1) is the ratio of the number of newly added electric vehicle consumers to the number of potential electric vehicle consumers, p is the external influence coefficient, which is used to measure the proportion of consumers who decide to buy electric vehicles due to external factors, F(t) is the ratio of the cumulative number of electric vehicle consumers in the current year to the number of potential electric vehicle consumers, and q is the internal influence coefficient, which is used to measure the proportion of consumers who buy electric vehicles due to the influence of other people's purchasing behavior and network effects.

[0036] In S3, the multi-stage planning model is:

[0037]

[0038] In the above formula, s is the current planning stage, s-1 is the previous planning stage, and w s，k is the transformation method of the kth charging station in the current planning stage. When it is 0, it means to expand the kth charging station. When it is 1, it means to build a new charging station based on the kth charging station. s-1，k is the number of charging piles available at the kth charging station, max k is the maximum capacity of the kth charging station;

[0039] When s，k =0, the location of the existing charging facilities remains unchanged, and the capacity of the existing charging stations is expanded;

[0040] When s，k =1, the location and capacity of the existing charging facilities remain unchanged, and new charging stations are built at other road network nodes.

[0041] Second aspect, the present invention proposes a multi-stage charging station planning system considering the growth of the electric vehicle ownership, including a multi-objective optimization model construction module, a next-year charging station planning module, a next-stage planning module, and a loop repetition module;

[0042] The multi-objective optimization model construction module is used to construct a multi-objective planning model for electric vehicle charging stations based on the electric vehicle ownership in the starting year of the planning period, with the goal of minimizing the planning cost of electric vehicle charging stations, and obtain the current charging station planning scheme by solving the multi-objective planning model for electric vehicle charging stations;

[0043] The next-year charging station planning module is used to plan the charging stations for the next year. Based on the predicted electric vehicle ownership in the next year, it judges whether the user-side cost in the next year exceeds the preset cost threshold under the current charging station planning scheme. If it exceeds, it enters the next-stage planning module to plan the next stage; if it does not exceed, the charging station planning scheme for the next year is the same as the current charging station planning scheme, and the charging station planning scheme for that year is output;

[0044] The next-stage planning module is used to select the construction method of the charging stations in the next stage based on the constructed multi-stage planning model, and construct a multi-objective planning model for electric vehicle charging stations with the goal of minimizing the planning cost of electric vehicle charging stations based on the electric vehicle ownership in the next year, and obtain the charging station planning scheme for that year, where the construction method of the charging stations is to expand the existing charging stations or build new charging stations on the basis of the existing charging stations;

[0045] The loop repetition module is used to loop and repeat the next-year charging station planning module and the next-stage planning module until the charging station planning schemes for each year within the planning period are obtained.

[0046] In the multi-objective optimization model construction module, the multi-objective optimization model for electric vehicle charging stations is:

[0047] min F=C cs +C user ;

[0048] In the above formula, F is the total planning cost of the electric vehicle charging stations, C cs is the charging station side cost, and C user is the user side cost;

[0049] Among them, the charging station side cost C cs includes the charging station construction investment and operation and maintenance costs, and is calculated using the following formula:

[0050]

[0051]

[0052] C M，k = ηC B，k ;

[0053] In the above formula, C B，k is the construction investment cost of the kth charging station, C M，k is the operation and maintenance cost of the kth charging station, N is the total number of charging stations to be built, r 0 is the discount rate, n 0 is the operation life of the charging station, N ch，k is the number of charging piles of the kth charging station, c ch is the unit price of the charging pile, A k is the floor area of the kth charging station, c f，k is the rent cost per unit area of the kth charging station, C o，k is other costs, and η is the conversion coefficient;

[0054] The user-side cost C user includes the queuing time cost of user charging and the range anxiety cost of users reaching the charging station, and is calculated by the following formula:

[0055] C user = C wait + C anx ;

[0056]

[0057]

[0058] In the above formula, C wait is the queuing time of user charging, C anx is the range anxiety cost of users reaching the charging station, f w is the conversion coefficient of the time cost of electric vehicle users, t ∈ [t 0 , t e is the time scale, representing the driving time of the electric vehicle, t 0 is the starting time of the electric vehicle driving, t e is the ending time of the electric vehicle driving, is the average queuing waiting time of the kth charging station at time t, n k，t is the number of electric vehicles starting to charge at the kth charging station at time t, f a is the conversion coefficient of the range anxiety cost of electric vehicle users, is the range anxiety value of the nth electric vehicle at the kth charging station at time t.

[0059] The average queuing waiting time of the kth charging station at time t is calculated by the following formula:

[0060]

[0061]

[0062]

[0063] In the above formula, is the average queuing waiting time of the k-th charging station within time t, c is the number of charging piles in the charging station, ρ is the service intensity of the charging piles, and λ k，t is the number of charging demands per hour for the k-th charging station to receive service within time t, and P 0 is the probability that there is no customer in the system, and μ is the number of electric vehicles that each charging pile can service and complete within a unit time;

[0064] The mileage anxiety value of the n-th electric vehicle at the k-th charging station within time t is calculated using the following formula:

[0065]

[0066] In the above formula, is the mileage anxiety value of the n-th electric vehicle at the k-th charging station within time t, is the anxiety mileage traveled by the n-th electric vehicle at the k-th charging station within time t, and R max is the maximum mileage anxiety, and DR is the remaining cruising range of the electric vehicle when the user starts to enter mileage anxiety.

[0067] In the charging station planning module for the next year, the electric vehicle ownership for the next year is predicted using the following formula:

[0068] N(t + 1) = N(t) + mf(t + 1);

[0069] f(t + 1) = p[1 - F(t)] + qF(t)[1 - F(t)];

[0070] In the above formula, t + 1 is the next year, t is the current year, N(t + 1) is the electric vehicle ownership for the next year, m is the total number of potential consumers, f(t + 1) is the ratio of the number of newly added electric vehicle consumers to the number of potential electric vehicle consumers, p is the external influence coefficient, which is used to measure the proportion of consumers who decide to purchase electric vehicles due to external factors, F(t) is the ratio of the cumulative number of electric vehicle consumers in the current year to the number of potential electric vehicle consumers, and q is the internal influence coefficient, which is used to measure the proportion of consumers who purchase electric vehicles due to the influence of others' purchase behavior and network effects.

[0071] In the next-stage planning module, the multi-stage planning model is as follows:

[0072]

[0073] In the above formula, s is the current planning stage, s - 1 is the previous planning stage, w s，k is the transformation measure for the k-th charging station in the current planning stage. When it takes 0, it means expanding the k-th charging station. When it takes 1, it means building a new charging station on the basis of the k-th charging station. n s-1，k is the existing number of charging piles at the k-th charging station, and max k is the maximum capacity of the k-th charging station;

[0074] When w s，k = 0, the positions of the existing charging facilities remain unchanged, and the capacity of the existing charging station is expanded;

[0075] When w s，k = 1, the positions and capacities of the existing charging facilities remain unchanged, and a new charging station is built at other road network nodes.

[0076] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0077] 1. The present invention proposes a multi-stage charging station planning method and system considering the growth of electric vehicle ownership. This method first constructs a multi-objective planning model for electric vehicle charging stations with the goal of minimizing the planning cost of electric vehicle charging stations based on the electric vehicle ownership in the starting year of the planning period, and obtains the current charging station planning scheme by solving the multi-objective planning model for electric vehicle charging stations. Then, it conducts the charging station planning for the next year. Based on the predicted electric vehicle ownership in the next year, it determines whether the user-side cost in the next year exceeds the preset cost threshold under the current charging station planning scheme. If it exceeds, it enters the next planning stage; if not, the charging station planning scheme for the next year is the same as the current one, and the charging station planning scheme for that year is output. It selects the construction method of the charging station in the next stage based on the constructed multi-stage planning model, and constructs a multi-objective planning model for electric vehicle charging stations with the goal of minimizing the planning cost of electric vehicle charging stations based on the electric vehicle ownership in the next year to obtain the charging station planning scheme for that year, where the construction method of the charging station is to expand the existing charging station or build a new charging station on the basis of the existing charging station. Finally, it repeatedly conducts the charging station planning for the next year and selects the construction method of the charging station in the next stage until the charging station planning schemes for each year within the planning period are obtained. On the one hand, this method uses the user-side cost as the decision basis for entering the next stage, which can more accurately reflect the actual charging experience and charging needs of users. When the user-side cost increases, it means that the current charging station layout and service capacity cannot meet the expectations of users, providing a basis for adjusting the charging station planning scheme, making the charging station planning decision more in line with user needs, avoiding premature expansion or blind construction of charging stations, and reducing the occupation and waste of funds. On the other hand, this method selects the construction method of the charging station by constructing a multi-stage planning model, which can flexibly expand charging piles to adapt to the dynamic growth of the electric vehicle market and the changes in user behavior, not only optimizing cost-effectiveness but also meeting the charging needs of electric vehicle users in different stages, and having long-term adaptability.

[0078] 2. The present invention proposes a multi-stage charging station planning method and system considering the growth of electric vehicle ownership. When constructing the multi-objective planning model for electric vehicle charging stations, it comprehensively considers various factors such as the user queuing time cost, the user psychological anxiety cost, and the construction and operation costs of charging stations, realizing the comprehensive optimization of charging stations, effectively coping with the growth of electric vehicle numbers, optimizing the charging station layout, reducing the planning cost of electric vehicle charging stations, improving the operation efficiency of charging stations, and providing a scientific basis for the orderly control of electric vehicle charging behaviors, the reasonable planning of charging station layouts, and the safe and stable operation of distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is the overall flowchart of the method described in Embodiment 1.

[0080] Figure 2 It is the structural diagram of the system described in Embodiment 2. Specific implementation manners

[0081] The present invention will be further described in detail below in combination with specific implementation manners and the accompanying drawings.

[0082] The present invention proposes a multi-stage charging station planning method and system considering the growth of the electric vehicle ownership, comprehensively considering various factors such as the user queuing time cost, the user psychological anxiety cost, and the construction and operation costs of the charging station, constructing a multi-objective planning model for the electric vehicle charging station, and obtaining a charging station planning scheme by solving the multi-objective planning model for the electric vehicle charging station; deeply analyzing the electric vehicle market trend based on the Bass model, predicting the electric vehicle ownership, and using the user-side cost as the decision basis for whether to enter the next stage, accurately reflecting the actual charging experience and charging demand of the user; constructing a multi-stage planning model in the next stage to select the construction method of the charging station, comprehensively considering the dynamics of the growth of the electric vehicle ownership and the multi-faceted cost factors of the user and the charging station, improving the adaptability and flexibility of the electric vehicle charging station planning, and meeting the development needs of the future electric vehicle market.

[0083] Embodiment 1:

[0084] As Figure 1 shown, the multi-stage charging station planning method considering the growth of the electric vehicle ownership is carried out in sequence according to the following steps:

[0085] 1. Based on the electric vehicle ownership in the starting year of the planning period, with the goal of minimizing the planning cost of the electric vehicle charging station, construct a multi-objective planning model for the electric vehicle charging station, and obtain the current charging station planning scheme by solving the multi-objective planning model for the electric vehicle charging station;

[0086] The multi-objective optimization model for the electric vehicle charging station aims to minimize the charging station-side cost and the user-side cost in the electric vehicle charging station planning:

[0087] min F = C cs + C user ;

[0088] In the above formula, F is the total planning cost of the electric vehicle charging station, C cs is the charging station-side cost, and C user is the user-side cost;

[0089] The charging station-side cost C cs includes the charging station construction investment and the operation and maintenance cost, and is calculated by using the following formula:

[0090]

[0091]

[0092] C M，k = ηC B，k ;

[0093] In the above formula, C B，k is the construction investment cost of the k-th charging station, C M，k is the operation and maintenance cost of the k-th charging station, N is the total number of charging stations to be built, r 0 is the discount rate, n o is the operation life of the charging station, N ch，k is the number of charging piles of the k-th charging station, c ch is the unit price of the charging pile, A k is the floor area of the k-th charging station, c f，k is the rent cost per unit area of the k-th charging station, C o，k is, η is the conversion coefficient;

[0094] The user-side cost C user includes the queuing time cost of user charging and the range anxiety cost of users reaching the charging station, and is calculated by the following formula:

[0095] C user = C wait + C anx ;

[0096]

[0097]

[0098] In the above formula, C wait is the queuing time of user charging, C anx is the range anxiety cost of users reaching the charging station, f w is the conversion coefficient of the time cost of electric vehicle users, t ∈ [t 0 , t e is the time scale, representing the driving time of the electric vehicle. Set t = 1 hour, t 0 is the starting time of the electric vehicle driving, t e is the ending time of the electric vehicle driving, is the average queuing waiting time of the k-th charging station at time t, n k，t is the number of electric vehicles starting to charge at the k-th charging station at time t, f a is the conversion coefficient of the range anxiety cost of electric vehicle users, is the range anxiety value of the n-th electric vehicle at the k-th charging station at time t;

[0099] Among them, it is assumed that the process of vehicles arriving at each charging station follows a Poisson distribution, and the average queuing waiting time of the k-th charging station within time t is calculated through the queuing theory M / M / c model:

[0100]

[0101]

[0102]

[0103] In the above formula, is the average queuing waiting time of the k-th charging station within time t, c is the number of charging piles in the charging station, ρ is the service intensity of the charging piles, ρ < 1, and λ k，t is the number of charging demands per hour going to the k-th charging station for service within time t, and P 0 is the probability that there are no customers in the system, μ is the number of electric vehicles that each charging pile can service and complete within a unit time, which is the reciprocal of the charging duration of the electric vehicle;

[0104] The range anxiety value of the n-th electric vehicle at the k-th charging station within time t is related to the distance traveled by the user in the state of range anxiety before arriving at the charging station. A power function is used to represent the change of the user's range anxiety in a driving cycle. This model can more accurately describe the change trend of the user's anxiety degree with the increase of the driving distance and depict the psychological anxiety of the user during the driving journey;

[0105] During the driving process, when the remaining driving range of the user is lower than a certain value, the user enters the state of range anxiety. As the driving distance increases, the user's range anxiety degree becomes greater. The range anxiety value is expressed as:

[0106]

[0107] In the above formula, is the range anxiety value of the n-th electric vehicle at the k-th charging station within time t, is the anxiety driving mileage of the n-th electric vehicle at the k-th charging station within time t, and R max is the maximum range anxiety, and DR is the remaining driving range of the electric vehicle when the user starts to enter the range anxiety.

[0108] 2. Conduct the charging station planning for the next year, deeply analyze the electric vehicle market trend based on the Bass model, and predict the electric vehicle ownership for the next year according to the electric vehicle ownership in the current year;

[0109] The electric vehicle ownership for the next year is predicted using the Bass model, and its discrete-time version can be expressed as:

[0110] N(t+1)=N(t)+mf(t+1);

[0111] f(t+1)=p[1-F(t)]+qF(t)[1-F(t)];

[0112] In the above formula, t+1 is the next year, t is the current year, N(t+1) is the number of electric vehicles in the next year, m is the total number of potential consumers, f(t+1) is the ratio of the number of newly added electric vehicle consumers to the number of potential electric vehicle consumers, p is the external influence coefficient, which is used to measure the proportion of consumers who decide to buy electric vehicles due to external factors, F(t) is the ratio of the cumulative number of electric vehicle consumers in the current year to the number of potential electric vehicle consumers, and q is the internal influence coefficient, which is used to measure the proportion of consumers who buy electric vehicles due to the influence of other people's purchasing behavior and network effects.

[0113] 3. Determine whether the user-side cost for the next year exceeds the preset cost threshold under the current charging station planning scheme. If so, proceed to step 4 to plan the next stage. If not, the charging station planning scheme for the next year is consistent with the current charging station planning scheme, and the charging station planning scheme for that year is output;

[0114] Evaluate whether the current charging station planning scheme meets the charging needs of electric vehicle users, that is, C user ≤ε holds true, where C user is the user-side cost, ε is the preset cost threshold. If it is established, it means that the user-side cost is within the control range, the current charging station planning scheme can meet the charging needs of electric vehicle users, and the charging station planning scheme for next year is consistent with the current charging station planning scheme; if it is not established, it means that the current charging station planning scheme cannot meet the charging needs of electric vehicle users, and it is necessary to enter the next stage to expand the existing charging stations or build new charging stations.

[0115] 4. Based on the constructed multi-stage planning model, the construction method of the charging station in the next stage is selected, and based on the number of electric vehicles in the next year, the multi-objective planning model of the electric vehicle charging station is constructed with the goal of minimizing the planning cost of the electric vehicle charging station, and the charging station planning scheme for that year is obtained;

[0116] As the number of electric vehicles increases, when charging stations cannot meet the charging needs of users, the user's charging costs will increase, that is, the user-side cost of the charging station planning scheme exceeds the preset cost threshold, and the next stage of planning needs to be started. In the planning, priority is given to expanding charging stations to reduce construction costs. At this time, the cost of the charging station side is determined by the number of expanded charging piles or newly built charging stations;

[0117] Construct a multi-stage planning model, based on the decision variables w s，k Determine whether to expand an existing charging station or build a new charging station based on an existing charging station:

[0118]

[0119] In the above formula, s is the current planning stage, s-1 is the previous planning stage, and w s，k is the transformation method of the kth charging station in the current planning stage. When it is 0, it means to expand the kth charging station. When it is 1, it means to build a new charging station based on the kth charging station. s-1，k is the number of charging piles available at the kth charging station, max k is the maximum capacity of the kth charging station;

[0120] When s，k =0, it indicates that the plan to expand the existing charging station is feasible, that is, the location of the existing charging facilities remains unchanged, the capacity of the existing charging station is expanded, and it is evaluated whether the planning plan of the expanded charging station can meet the charging needs of users. If not, it is necessary to adjust the expansion plan or consider building a new charging station;

[0121] When s，k =1, it indicates that the plan to expand the existing charging station is not feasible, and a new charging station needs to be built, that is, the location and capacity of the existing charging facilities remain unchanged, and new charging stations are built at other road network nodes to meet the charging needs of users;

[0122] After the construction method of the charging stations in the next stage is selected, based on the number of electric vehicles in the next year, with the goal of minimizing the planning cost of electric vehicle charging stations, a multi-objective planning model for electric vehicle charging stations is constructed to obtain the charging station planning plan for that year.

[0123] 5. Repeat steps 2 to 4 in a loop. According to the implementation results of new and expanded charging stations, continuously iterate and optimize the charging station planning scheme, regularly evaluate the effectiveness of the scheme, and adjust it according to actual needs until the charging station planning scheme for each year within the planning cycle is obtained.

[0124] Embodiment 2:

[0125] like Figure 2 As shown, a multi-stage charging station planning system considering the growth of electric vehicle ownership includes a multi-objective optimization model building module, a next year charging station planning module, a next stage planning module, and a cyclic repetition module;

[0126] The multi-objective optimization model building module is used to build a multi-objective planning model for electric vehicle charging stations based on the number of electric vehicles in the starting year of the planning cycle, with the goal of minimizing the planning cost of electric vehicle charging stations, and obtain the current charging station planning scheme by solving the multi-objective planning model for electric vehicle charging stations;

[0127] The next year's charging station planning module is used to plan the charging stations for the next year. Based on the predicted number of electric vehicles in the next year, it is determined whether the user-side cost for the next year exceeds the preset cost threshold under the current charging station planning scheme. If it exceeds, the next stage planning module is entered to plan the next stage; if it does not exceed, the next year's charging station planning scheme is consistent with the current charging station planning scheme, and the charging station planning scheme for that year is output;

[0128] The next stage planning module is used to select the construction method of the charging station in the next stage based on the constructed multi-stage planning model, and based on the number of electric vehicles in the next year, with the goal of minimizing the planning cost of the electric vehicle charging station, construct a multi-objective planning model for the electric vehicle charging station, and obtain the charging station planning plan for that year, wherein the construction method of the charging station is to expand the existing charging station or to build a new charging station on the basis of the existing charging station;

[0129] The cyclic repetition module is used to cyclically repeat the next year's charging station planning module and the next stage planning module until the charging station planning schemes for each year in the planning cycle are obtained.

[0130] In the multi-objective optimization model construction module, the multi-objective optimization model of the electric vehicle charging station is:

[0131] min F=C cs +C user ;

[0132] In the above formula, F is the total planning cost of the electric vehicle charging station, C cs is the cost of the charging station, C user Cost on the user side;

[0133] Among them, the cost of charging station side C cs Including the construction investment and operation and maintenance costs of the charging station, it is calculated using the following formula:

[0134]

[0135]

[0136] C M，k =ηC B，k ;

[0137] In the above formula, C B，kis the construction investment cost of the k-th charging station, C M，k is the operation and maintenance cost of the k-th charging station, N is the total number of charging stations to be built, r 0 is the discount rate, n 0 is the operation life of the charging station, N ch，k is the number of charging piles of the k-th charging station, c ch is the unit price of the charging pile, A k is the floor area of the k-th charging station, c f，k is the rent cost per unit area of the k-th charging station, C o，k is other costs, η is the conversion coefficient;

[0138] The user-side cost C user includes the queuing time cost of user charging and the range anxiety cost of users reaching the charging station, and is calculated by the following formula:

[0139] C user = C wait + C anx ;

[0140]

[0141]

[0142] In the above formula, C wait is the queuing time of user charging, C anx is the range anxiety cost of users reaching the charging station, f w is the conversion coefficient of the time cost of electric vehicle users, t ∈ [t 0 , t e is the time scale, representing the driving time of the electric vehicle, t 0 is the starting time of the electric vehicle driving, t e is the ending time of the electric vehicle driving, is the average queuing waiting time of the k-th charging station within the time t, n k，t is the number of electric vehicles starting to charge at the k-th charging station within the time t, f a is the conversion coefficient of the range anxiety cost of electric vehicle users, is the range anxiety value of the n-th electric vehicle at the k-th charging station within the time t.

[0143] The average queuing waiting time of the k-th charging station within the time t is calculated by the following formula:

[0144]

[0145]

[0146]

[0147] In the above formula, is the average queuing waiting time of the kth charging station within time t, c is the number of charging piles in the charging station, ρ is the service intensity of the charging piles, and λ k，t is the number of charging demands per hour going to the kth charging station for service within time t, and P 0 is the probability that there are no customers in the system, and μ is the number of electric vehicles that each charging pile can service and complete within unit time;

[0148] For the kth charging station within time t, the mileage anxiety value of the nth electric vehicle is calculated using the following formula:

[0149]

[0150] In the above formula, is the mileage anxiety value of the nth electric vehicle at the kth charging station within time t, is the anxiety mileage traveled by the nth electric vehicle at the kth charging station within time t, and R max is the maximum mileage anxiety, and DR is the remaining cruising range of the electric vehicle when the user starts to enter the mileage anxiety.

[0151] In the charging station planning module for the next year, the electric vehicle ownership for the next year is predicted using the following formula:

[0152] N(t + 1) = N(t) + mf(t + 1);

[0153] f(t + 1) = p[1 - F(t)] + qF(t)[1 - F(t)];

[0154] In the above formula, t + 1 is the next year, t is the current year, N(t + 1) is the electric vehicle ownership for the next year, m is the total number of potential consumers, f(t + 1) is the ratio of the number of newly added electric vehicle consumers to the number of potential electric vehicle consumers, p is the external influence coefficient, used to measure the proportion of consumers who decide to purchase electric vehicles due to external factors, F(t) is the ratio of the cumulative number of electric vehicle consumers in the current year to the number of potential electric vehicle consumers, and q is the internal influence coefficient, used to measure the proportion of consumers who purchase electric vehicles due to the influence of others' purchase behaviors and network effects.

[0155] In the next stage planning module, the multi-stage planning model is:

[0156]

[0157] In the above formula, s is the current planning stage, s-1 is the previous planning stage, and w s，k is the transformation measure for the k-th charging station in the current planning stage. When it takes 0, it means expanding the k-th charging station. When it takes 1, it means building a new charging station on the basis of the k-th charging station. n s-1，k is the existing number of charging piles at the k-th charging station, and max k is the maximum capacity of the k-th charging station;

[0158] When w s，k = 0, the location of the existing charging facilities remains unchanged, and the capacity of the existing charging station is expanded;

[0159] When w s，k = 1, the location and capacity of the existing charging facilities remain unchanged, and a new charging station is built at other road network nodes.

Claims

1. A multi-stage charging station planning method considering the growth of electric vehicle ownership, characterized by: The method comprises: S1. Based on the number of electric vehicles in the starting year of the planning cycle, a multi-objective planning model for electric vehicle charging stations is constructed with the goal of minimizing the planning cost of electric vehicle charging stations, and the current charging station planning scheme is obtained by solving the multi-objective planning model for electric vehicle charging stations; S2. Carry out the charging station planning for the next year. Based on the predicted number of electric vehicles in the next year, determine whether the user-side cost for the next year exceeds the preset cost threshold under the current charging station planning scheme. If it exceeds, enter the next stage of planning in S3. If it does not exceed, the charging station planning scheme for the next year is consistent with the current charging station planning scheme, and the charging station planning scheme for that year is output; S3. Based on the constructed multi-stage planning model, the construction method of the charging station in the next stage is selected, and based on the number of electric vehicles in the next year, the multi-objective planning model of the electric vehicle charging station is constructed with the goal of minimizing the planning cost of the electric vehicle charging station, and the planning scheme of the charging station for that year is obtained, wherein the construction method of the charging station is to expand the existing charging station or to build a new charging station on the basis of the existing charging station; S4. Repeat S2-S3 in a loop until the charging station planning scheme for each year within the planning period is obtained.

2. The multi-stage charging station planning method considering the growth of electric vehicle ownership according to claim 1 is characterized in that: In S1, the multi-objective optimization model of the electric vehicle charging station is: min F=C cs +C user In the above formula, F is the total planning cost of the electric vehicle charging station, C cs is the cost of the charging station, C user Cost on the user side; Among them, the cost of charging station side C cs Including the construction investment and operation and maintenance costs of the charging station, it is calculated using the following formula: C M，k ηC B，k ; In the above formula, C B，k is the investment cost of the kth charging station, C M，k is the operation and maintenance cost of the kth charging station, N is the total number of charging stations to be built, r0 is the discount rate, n o is the operating life of the charging station, N ch，k is the number of charging piles at the kth charging station, c ch is the unit price of the charging pile, A k is the area occupied by the kth charging station, c f，k is the unit area rental cost of the kth charging station, C o，k is other costs, η is the conversion factor; User side cost C user Including the user's charging queue time cost and the user's mileage anxiety cost when reaching the charging station, it is calculated using the following formula: C user =C wait +C anx ; In the above formula, C wait The waiting time for charging for users, C anx The range anxiety cost for users to reach the charging station, f w is the time cost conversion coefficient of electric vehicle users, t∈[t0, t e ] is the time scale, which indicates the driving time of the electric vehicle, t0 is the starting time of the electric vehicle, t e is the end time of the electric vehicle’s driving, is the average waiting time in the queue at the kth charging station at time t, n k，t is the number of electric vehicles that start charging at the kth charging station at time t, f a is the cost conversion coefficient for mileage anxiety of electric vehicle users, is the range anxiety value of the nth electric car at the kth charging station at time t.

3. The multi-stage charging station planning method considering the growth of electric vehicle ownership according to claim 2 is characterized in that: The average waiting time in the queue at the kth charging station at time t It is calculated using the following formula: In the above formula, is the average waiting time in the queue at the kth charging station at time t, c is the number of charging piles in the charging station, ρ is the service intensity of the charging piles, and λ k，t is the number of charging demands that go to the kth charging station for service every hour at time t, P0 is the probability that there are no customers in the system, and μ is the number of electric vehicles that can be served by each charging pile in unit time; The range anxiety value of the nth electric vehicle at the kth charging station at time t is It is calculated using the following formula: In the above formula, is the range anxiety value of the nth electric vehicle at the kth charging station at time t, is the anxiety mileage of the nth electric vehicle at the kth charging station at time t, R max is the maximum range anxiety, and DR is the remaining range of the electric vehicle when the user begins to experience range anxiety.

4. The multi-stage charging station planning method considering the growth of electric vehicle ownership according to claim 1 is characterized in that: In S2, the number of electric vehicles in the next year is predicted using the following formula: N(t+1)=N(t)+mf(t+1); f(t+1)=p[1-F(t)]+qF(t)[1-F(t)]; In the above formula, t+1 is the next year, t is the current year, N(t+1) is the number of electric vehicles in the next year, m is the total number of potential consumers, f(t+1) is the ratio of the number of newly added electric vehicle consumers to the number of potential electric vehicle consumers, p is the external influence coefficient, which is used to measure the proportion of consumers who decide to buy electric vehicles due to external factors, F(t) is the ratio of the cumulative number of electric vehicle consumers in the current year to the number of potential electric vehicle consumers, and q is the internal influence coefficient, which is used to measure the proportion of consumers who buy electric vehicles due to the influence of other people's purchasing behavior and network effects.

5. The multi-stage charging station planning method considering the growth of electric vehicle ownership according to claim 1 is characterized in that: In S3, the multi-stage planning model is: In the above formula, s is the current planning stage, s-1 is the previous planning stage, and w s，k is the transformation method of the kth charging station in the current planning stage. When it is 0, it means to expand the kth charging station. When it is 1, it means to build a new charging station based on the kth charging station. s-1，k is the number of charging piles available at the kth charging station, max k is the maximum capacity of the kth charging station; When s，k =0, the location of the existing charging facilities remains unchanged, and the capacity of the existing charging stations is expanded; When s，k =10, the location and capacity of the existing charging facilities remain unchanged, and new charging stations are built at other road network nodes.

6. A multi-stage charging station planning system considering the growth of electric vehicle ownership, characterized by: The system includes a multi-objective optimization model building module, a next year charging station planning module, a next stage planning module, and a cyclic repetition module; The multi-objective optimization model building module is used to build a multi-objective planning model for electric vehicle charging stations based on the number of electric vehicles in the starting year of the planning cycle, with the goal of minimizing the planning cost of electric vehicle charging stations, and obtain the current charging station planning scheme by solving the multi-objective planning model for electric vehicle charging stations; The next year's charging station planning module is used to plan the charging stations for the next year. Based on the predicted number of electric vehicles in the next year, it is determined whether the user-side cost for the next year exceeds the preset cost threshold under the current charging station planning scheme. If it exceeds, the next stage planning module is entered to plan the next stage. If not, the charging station planning scheme for the next year is consistent with the current charging station planning scheme, and the charging station planning scheme for that year is output; The next stage planning module is used to select the construction method of the charging station in the next stage based on the constructed multi-stage planning model, and based on the number of electric vehicles in the next year, with the goal of minimizing the planning cost of the electric vehicle charging station, construct a multi-objective planning model for the electric vehicle charging station, and obtain the charging station planning plan for that year, wherein the construction method of the charging station is to expand the existing charging station or to build a new charging station on the basis of the existing charging station; The cyclic repetition module is used to cyclically repeat the next year's charging station planning module and the next stage planning module until the charging station planning schemes for each year in the planning cycle are obtained.

7. The multi-stage charging station planning system considering the growth of electric vehicle ownership according to claim 6 is characterized in that: In the multi-objective optimization model construction module, the multi-objective optimization model of the electric vehicle charging station is: min F=C cs +C user ; In the above formula, F is the total planning cost of the electric vehicle charging station, C cs is the cost of the charging station, C user Cost on the user side; Among them, the cost of charging station side C cs Including the construction investment and operation and maintenance costs of the charging station, it is calculated using the following formula: C M，k ηC B，k ; In the above formula, C B，k is the investment cost of the kth charging station, C M，k is the operation and maintenance cost of the kth charging station, N is the total number of charging stations to be built, r0 is the discount rate, n0 is the operating life of the charging station, and N ch，k is the number of charging piles at the kth charging station, c ch is the unit price of the charging pile, A k is the area occupied by the kth charging station, c f，k is the unit area rental cost of the t-th charging station, C o，k is other costs, η is the conversion factor; User side cost C user Including the user's charging queue time cost and the user's mileage anxiety cost when reaching the charging station, it is calculated using the following formula: C user =C wait +C anx ; In the above formula, C wait The waiting time for charging for users, C anx The range anxiety cost for users to reach the charging station, f w is the time cost conversion coefficient of electric vehicle users, t∈[t0, t e ] is the time scale, which indicates the driving time of the electric vehicle, t0 is the starting time of the electric vehicle, t e is the end time of the electric vehicle’s driving, is the average waiting time in the queue at the kth charging station at time t, n k，t is the number of electric vehicles that start charging at the kth charging station at time t, f a is the cost conversion coefficient for mileage anxiety of electric vehicle users, is the range anxiety value of the nth electric car at the kth charging station at time t.

8. The multi-stage charging station planning system considering the growth of electric vehicle ownership according to claim 7 is characterized in that: The average waiting time in the queue at the kth charging station at time t It is calculated using the following formula: In the above formula, is the average waiting time in the queue at the kth charging station at time t, c is the number of charging piles in the charging station, ρ is the service intensity of the charging piles, and λ k，t is the number of charging demands that go to the kth charging station for service every hour at time t, P0 is the probability that there are no customers in the system, and μ is the number of electric vehicles that can be served by each charging pile in unit time; The range anxiety value of the nth electric vehicle at the kth charging station at time t is It is calculated using the following formula: In the above formula, is the range anxiety value of the nth electric vehicle at the kth charging station at time t, is the anxiety mileage of the nth electric vehicle at the kth charging station at time t, R max is the maximum range anxiety, and DR is the remaining range of the electric vehicle when the user begins to experience range anxiety.

9. The multi-stage charging station planning system considering the growth of electric vehicle ownership according to claim 6 is characterized in that: In the next year's charging station planning module, the number of electric vehicles in the next year is predicted using the following formula: N(t+1)=N(t)+mf(t+1); f(t+1)=p[1-F(t)]+qF(t)[1-F(t)]; In the above formula, t+1 is the next year, t is the current year, N(t+1) is the number of electric vehicles in the next year, m is the total number of potential consumers, f(t+1) is the ratio of the number of newly added electric vehicle consumers to the number of potential electric vehicle consumers, p is the external influence coefficient, which is used to measure the proportion of consumers who decide to buy electric vehicles due to external factors, F(t) is the ratio of the cumulative number of electric vehicle consumers in the current year to the number of potential electric vehicle consumers, and q is the internal influence coefficient, which is used to measure the proportion of consumers who buy electric vehicles due to the influence of other people's purchasing behavior and network effects.

10. The multi-stage charging station planning system considering the growth of electric vehicle ownership according to claim 6, characterized in that: In the next stage planning module, the multi-stage planning model is: In the above formula, s is the current planning stage, s-1 is the previous planning stage, and w s，k is the transformation method of the kth charging station in the current planning stage. When it is 0, it means to expand the kth charging station. When it is 1, it means to build a new charging station based on the kth charging station. s-1，k is the number of charging piles available at the kth charging station, max k is the maximum capacity of the kth charging station; When s，k =0, the location of the existing charging facilities remains unchanged, and the capacity of the existing charging stations is expanded; When s，k =1, the location and capacity of the existing charging facilities remain unchanged, and new charging stations are built at other road network nodes.

Images