Rural distribution network-oriented multi-scene electric vehicle charging station planning method and device

Abstract

The invention provides a rural distribution network-oriented multi-scene electric vehicle charging station planning method and device, and belongs to the field of electric vehicles. The method comprises the steps of distinguishing types of electric vehicles, predicting EV charging demands of a rural scene, and determining a charging demand point set; wherein the type of the electric vehicle comprises a local vehicle and a foreign vehicle; according to the charging demand point set, considering the charging station construction operation and maintenance cost, the EV user charging satisfaction cost and the rural distribution network acceptance EV cost, performing multi-stage planning and solving on the charging station construction, and obtaining a planning scheme of each stage; wherein the planning scheme comprises the position and the capacity of the charging station; and the charging station position is a point position with concentrated charging demand points. According to the method, the charging demand point is determined by distinguishing the types of the electric vehicles and considering the rural scene, and then multi-stage planning is performed, so that charging station planning is more reasonable and more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a multi-scenario electric vehicle charging station planning method and device for rural distribution networks. Background Art

[0002] In the context of the global green and low-carbon transformation, rural areas are an important growth point in the future electric vehicle (EV) market, and the planning of their charging infrastructure is particularly important. In order to ensure the rationality of the planning of charging station infrastructure, it is necessary to predict the charging demand of electric vehicles to guide the construction of charging stations.

[0003] In the existing technology, the prediction of electric vehicle charging demand is mainly aimed at urban areas. Due to the vast territory, uneven population distribution and backward transportation and communication systems in rural areas, it is difficult to form economies of scale. The existing prediction methods have large deviations and cannot accurately predict the charging demand of electric vehicles in rural areas, resulting in unreasonable planning of charging stations. Summary of the invention

[0004] The embodiments of the present invention provide a multi-scenario electric vehicle charging station planning method and device for rural distribution networks to solve the problem of unreasonable planning of charging stations in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a multi-scenario electric vehicle charging station planning method for rural distribution networks, comprising:

[0006] Differentiate the types of electric vehicles, predict the EV charging demand in rural scenarios, and determine the charging demand point set; the types of electric vehicles include: local vehicles and external vehicles;

[0007] According to the charging demand point set, the construction and operation costs of charging stations, the charging satisfaction costs of EV users, and the cost of rural distribution networks accepting EVs are considered. Multi-stage planning and solution are carried out for the construction of charging stations to obtain planning schemes for each stage.

[0008] Among them, the planning scheme includes: the location and capacity of the charging station; the location of the charging station is the point where the charging demand points are concentrated.

[0009] In a second aspect, an embodiment of the present invention provides a multi-scenario electric vehicle charging station planning device for rural distribution networks, including:

[0010] The demand point determination module is used to distinguish the types of electric vehicles, predict the EV charging demand in rural scenarios, and determine the charging demand point set; the types of electric vehicles include: local vehicles and external vehicles;

[0011] The multi-stage planning module is used to plan and solve the charging station construction in multiple stages according to the charging demand point set, taking into account the charging station construction and operation and maintenance costs, EV user charging satisfaction costs, and the rural distribution network acceptance costs of EVs, and obtain the planning schemes for each stage;

[0012] The planning scheme includes: the location and capacity of charging stations; the location of charging stations is the point where charging demand points are concentrated.

[0013] The embodiment of the present invention provides a multi-scenario electric vehicle charging station planning method and device for rural distribution networks. The above-mentioned multi-scenario electric vehicle charging station planning method for rural distribution networks includes: distinguishing the types of electric vehicles, predicting the EV charging demand in rural scenarios, and determining the charging demand point set; wherein, the types of electric vehicles include: local vehicles and external vehicles; according to the charging demand point set, considering the construction and operation costs of charging stations, EV user charging satisfaction costs and rural distribution network acceptance EV costs, multi-stage planning and solving for charging station construction, and obtaining planning schemes for each stage; wherein, the planning scheme includes: charging station location and capacity; the charging station location is the point where the charging demand points are concentrated. In the embodiment of the present invention, the travel differences between local electric vehicles and external electric vehicles are considered, the types of electric vehicles are distinguished, and the charging demand points are determined considering rural scenarios, and then multi-stage planning is performed considering rural EV development, and the charging station planning is more reasonable and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a flowchart of a multi-scenario electric vehicle charging station planning method for rural distribution networks provided by an embodiment of the present invention;

[0016] Figure 2 is a road structure diagram of a rural area system provided by an embodiment of the present invention;

[0017] Figure 3 is a structural diagram of a distribution network in a rural area system provided by an embodiment of the present invention;

[0018] Figure 4 This is a result diagram of the distribution of charging demand points for electric vehicles provided by an embodiment of the present invention;

[0019] Figure 5 This is a result diagram of the distribution of functional areas of electric vehicle charging demand provided by an embodiment of the present invention;

[0020] Figure 6 It is the site selection result diagram corresponding to method 1 in the prior art;

[0021] Figure 7 This is a site selection result diagram corresponding to the multi-scenario electric vehicle charging station planning method for rural distribution networks provided by an embodiment of the present invention;

[0022] Figure 8 It is a structural diagram of a multi-scenario electric vehicle charging station planning device for rural distribution networks provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0024] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0025] Figure 1 A multi-scenario electric vehicle charging station planning method for rural distribution networks is provided in an embodiment of the present invention. The multi-scenario electric vehicle charging station planning method for rural distribution networks includes:

[0026] S101: Differentiate the types of electric vehicles, predict the EV charging demand in rural scenarios, and determine a charging demand point set; wherein the types of electric vehicles include: local vehicles and external vehicles;

[0027] Local electric vehicles in rural areas usually refer to electric vehicles that are registered locally, used and driven locally for a long time. Their owners are generally local residents, local enterprises and institutions, etc. Users have a strong purpose for travel and rarely travel from non-residential areas. The usage patterns of these vehicles are relatively stable.

[0028] Foreign vehicles refer to electric vehicles from outside the local area, including self-driving vehicles of non-local residents, official vehicles or business vehicles from other places coming to the local area for business, and long-distance travel vehicles passing through the local area. The travel of foreign vehicles is somewhat random and temporary, and their stay time and charging needs in local rural areas are relatively unstable.

[0029] Based on this, in the embodiment of the present invention, the types of electric vehicles are distinguished, and the EV charging demand in rural scenarios is predicted, and the prediction result is more accurate.

[0030] In a possible implementation, S101 may include:

[0031] S1011: For any electric vehicle in the rural scene, according to the type of the electric vehicle, a user charging decision model is established based on a regret algorithm to obtain the charging demand point corresponding to the electric vehicle;

[0032] In a possible implementation, S1011 may include:

[0033] 1. Determine the departure time, initial SOC and destination stop time of the electric vehicle according to the type of the electric vehicle;

[0034] The first trip time of local EVs every day conforms to the normal distribution law, but the morning and evening peaks of EV travel in rural areas are delayed and advanced by 1 hour respectively compared with urban areas. At the same time, the probability density of EV users' stay time in residential areas and other areas is fitted by Weibull distribution and generalized extreme value distribution respectively. The driving time of the vehicle can be calculated by the driving road distance and driving speed. The departure time of EV users and the stay time in residential areas and other areas can be calculated using the probability density formula:

[0035]

[0036]

[0037] in, is the travel time probability density function; are the probability density functions of the length of time that EV stays in residential areas and other areas after traveling on road ij; μ 0 , σ 0 is the function mean and variance, with values ​​of 8.82 and 1; H ,θ O is the scale parameter; k and ξ are the shape parameters.

[0038] The local EV initial SOC follows the parameters (0.5, 0.15 2 ) is a normal distribution.

[0039] For external EVs, the mean random distribution simulation algorithm is used to determine the departure time and the target stop time. At the same time, after a long period of driving, when the external EV is connected to the rural road network through an external connection point, the initial SOC of the external EV is usually at a low state, and the obedience parameters are (0.4, 0.1 2 ) is a normal distribution.

[0040] 2. Determine the travel itinerary of the electric vehicle according to the type of the electric vehicle;

[0041] In a possible implementation, determining the travel itinerary of the electric vehicle according to the type of the electric vehicle may include:

[0042] (1) If the type of the electric vehicle is a local vehicle, the travel chain model is used to determine the travel itinerary of the electric vehicle;

[0043] (2) If the type of the electric vehicle is a foreign vehicle, the OD probability model is used to determine the travel itinerary of the electric vehicle.

[0044] For external EVs, rural road networks are connected to intercity highways through external nodes. External EVs may access rural road networks at any time of the day. Car owners usually go to rural areas to visit relatives and friends or travel to scenic spots. Travel behavior has strong uncertainty in time and space. Therefore, the OD probability matrix modeling is considered to predict the travel itinerary of external EVs through the traffic occurrence volume at the starting point of each row of the matrix and the traffic attraction volume at the end point of each column.

[0045] For local EVs, since their travel purpose is strong, the travel chain model can be used to predict the travel itinerary of local EVs.

[0046] 3. According to the travel itinerary of the electric vehicle and based on the energy consumption model, determine whether the electric vehicle needs to be charged during driving;

[0047] In order to more accurately predict the EV charging demand, the embodiment of the present invention constructs an energy consumption model to determine whether there is a charging demand. Assuming that the grades of rural roads and urban secondary roads are consistent, considering the characteristics of different types of EVs, in one possible implementation, the energy consumption model may include:

[0048]

[0049] Among them, v t is the speed of the electric car at time t; v ref N is the reference speed of electric vehicles on road ij when there is no traffic; ij,t is the total number of cars on road ij at time t; L ij is the length of road ij; P ij,t is the energy consumption per unit mileage of the electric vehicle on road ij at time t; ΔSOC ij is the reduction in charge capacity of the electric vehicle when it travels on road ij; η is the battery efficiency of the electric vehicle; S B is the battery capacity of electric vehicles; SOC 0 SOC is the initial state of charge of the electric vehicle; T is the charge state of the electric vehicle at the end point.

[0050] Based on the energy consumption model, the charge state of the electric vehicle at the end point can be calculated. If it is large, it means that the power is sufficient and there is no need to charge; otherwise, it means that the power is insufficient and charging is required.

[0051] Specifically, in a possible implementation, according to the travel itinerary of the electric vehicle and based on the energy consumption model, determining whether the electric vehicle has a charging demand during travel may include:

[0052] (1) determining the state of charge of the electric vehicle at the destination according to the travel itinerary of the electric vehicle and based on an energy consumption model;

[0053] (2) If the state of charge of the electric vehicle at the end point is greater than a preset value, there is no need to charge;

[0054] (3) If the state of charge of the electric vehicle at the end point is not greater than a preset value, there is a need for charging.

[0055] It should be noted that the preset value can be determined according to actual application requirements and is not specifically limited here.

[0056] 4. If there is a charging demand, the charging demand point corresponding to the electric vehicle is obtained based on the charging decision model;

[0057] There are multiple optional charging stations around electric vehicles, so you need to consider many factors to choose the most suitable charging station.

[0058] Specifically, the present invention uses a charging decision model based on regret theory to determine the charging demand point. Specifically, in one possible implementation, the charging decision model may include:

[0059] R a,b =λ x α x (x a ,x b )+λ y α y (y a ,y b )+λ z α z (z a ,z b )

[0060]

[0061] Among them, R a,b is the comprehensive regret value of charging decision at charging stations a and b; α x (x a ,x b ), α y (y a,y b ), α z (z a ,z b ) are the regret values ​​caused by charging cost, detour time and attraction cost of tourist attractions; λ x , y , z All are coefficients; N ch is the total number of charging station locations to be selected; R k The charging points corresponding to electric vehicles.

[0062] When EVs have charging needs, the incomplete rationality of user decisions is described from three perspectives: charging cost, idling cost, and rural tourism and surrounding resource costs. The decision-making plan is determined based on the regret theory to ensure the rationality of the charging demand forecast. The regret theory believes that decision makers will not rationally choose the best decision-making plan, but will choose the plan with the smallest regret value from many plans.

[0063] Specifically, for charging station a, after comparing with charging station b, it should continue to compare with all other candidate charging stations. Finally, the regret value R of charging station a is a is the maximum regret value between charging station a and other charging stations, and the charging station k that the user finally decides to go to is the minimum regret value of all charging stations.

[0064] 5. If there is no need to charge, determine whether to end the trip;

[0065] 6. If the trip is over, the charging demand point corresponding to the electric vehicle will be empty;

[0066] If there is no charging demand and the trip is over, it means that the electric vehicle does not need to be charged, and the charging demand point is not output and is empty.

[0067] 7. If the trip is not completed, jump to the step of determining the travel itinerary of the electric vehicle according to the type of the electric vehicle and continue to execute.

[0068] Based on the above, the embodiment of the present invention is based on the energy consumption model and the regret algorithm to fit the uncertainty of the user's charging behavior, and establishes a charging decision model to capture the charging demand points of each electric vehicle.

[0069] S1012: Clustering charging demand points corresponding to each electric vehicle to obtain multiple cluster centers, and the points corresponding to each cluster center form a charging demand point set.

[0070] For each electric vehicle, its charging demand point is determined, and then the collected charging demand points are clustered. Each cluster center represents a concentrated area of ​​a group of charging demand points. The points corresponding to these cluster centers form a charging demand point set, which can meet the charging needs of each electric vehicle.

[0071] Specifically, the K-means clustering algorithm can be used to cluster based on the Euclidean distance between discrete points to generate a charging demand point set. The objective function of clustering is as follows:

[0072]

[0073] Where N is the number of discrete points for total demand forecast; d i is the i-th demand point sample; μ j is the cluster center of the jth traffic node; ω ij It is a 0-1 judgment function, 0 means sample d i Not belonging to the center point μ j , 1 means it belongs to .

[0074] S102: Based on the charging demand point set, considering the construction and operation cost of the charging station, the EV user charging satisfaction cost, and the rural distribution network acceptance cost of EV, the charging station construction is planned and solved in multiple stages to obtain the planning scheme for each stage;

[0075] Among them, the planning scheme includes: the location and capacity of the charging station; the location of the charging station is the point where the charging demand points are concentrated.

[0076] In a possible implementation, S102 may include:

[0077] S1021: Determine the first phase planning scheme based on the charging demand point set and the current EV ownership scenario;

[0078] S1022: Extract n based on Monte Carlo method k EV ownership scenarios for the next planning phase;

[0079] S1023: Based on the bat algorithm, n k The scenario is used as the initial scenario, the target planning model is input and solved, and the planning location of the charging station in the current stage is obtained;

[0080] S1024: Determine the capacity corresponding to each planned location of the charging station based on the EV ownership scenario determined by the expected growth rate;

[0081] S1025: Determine whether all stage planning is completed; if so, end; if not, jump to extracting n based on the Monte Carlo method k Continue with the steps for the next planning phase EV ownership scenario.

[0082] When planning, considering the promotion of the policy of new energy vehicles going to the countryside, the number of EVs in rural areas will increase significantly in the future. In order to ensure the rationality of the charging station planning, multi-stage planning is carried out. According to the data of the National Energy Administration, the EV growth rate in rural areas is normally fitted, and the annual growth rate of EVs complies with the obedience parameters (4.45, 0.984 2 ) is normally distributed, and the annual growth of the total number of cars is in line with (4,0.984 2 ), based on which several rural EV ownership scenarios can be generated according to the Monte Carlo method. The uncertainty of the growth rate of EV ownership can be planned in multiple stages, which is in line with the actual development of rural EVs, delays the investment in charging station construction, and improves the charging experience of EV users.

[0083] In order to improve the reliability of planning, the interests of charging station operators, EV users, and distribution networks should be considered. At the same time, the attractiveness of regional tourism resources to EV users should be taken into account, and multiple scenarios should be divided according to the seasonal characteristics of rural farming, so as to improve the accuracy of the target planning model.

[0084] In a possible implementation, the goal planning model may include:

[0085] The objective function is:

[0086] minF=F 1 +F 2 +F 3

[0087]

[0088]

[0089] F 3 =υ 1 f 1 +υ 2 f 2

[0090]

[0091] Among them, F 1 is the total construction and operation and maintenance cost of the charging station operator; F 2 The sum of user charging costs, idle driving costs, rural tourism and surrounding resource costs; F 3 The cost of power quality degradation caused by accepting EVs in rural distribution networks; C l is the land purchase cost; C s is the construction cost of the charging station; C o Operation and maintenance costs for charging stations; x i is the charging cost at charging station i; c 1is the time cost coefficient; c ch The charging cost per unit of electricity for electric vehicles; i The detour cost to recharge at charging station i; c 2 is the unit extra detour distance cost; i The additional cost of surrounding resources after the user arrives at charging station i; c 3 is the consumption coefficient of the point of interest; F 3 Reduce the cost of power quality on the grid side;υ 1 ,υ 2 f is the conversion coefficient of power quality degradation cost of the power grid; 1 is the voltage offset rate;

[0092] Among them, the additional cost of surrounding resources after the user arrives at charging station i is calculated based on the data of points of interest (POI) of the road network nodes. A functional area may have multiple functions. When EV owners arrive at charging station a for charging, especially in the peak tourist season, foreign EVs are often attracted by nearby catering services, scenic spots and other resources, which incurs additional costs such as food and tickets. The POI function weights refer to Table 1.

[0093] Table 1 POI function weight distribution table

[0094]

[0095]

[0096] The target planning model also needs to meet relevant constraints, such as the location and capacity constraints of charging station construction, node voltage and feeder current constraints, power flow constraints, and coupling constraints between the transportation network and the distribution system. The specific constraints are as follows:

[0097] The location and capacity constraints of charging station construction are:

[0098] δ i E i,min ≤E i ≤δ i E i,max

[0099] Among them, E i E is the capacity of the charging station built at traffic node i; i,max 、E i,min are the upper and lower limits of the capacity of the charging station built at traffic node i; δ i It is a function to judge whether the node i is a candidate address for charging pile;

[0100] The node voltage and feeder current constraints are:

[0101] U m,min ≤Um ≤U m,max

[0102] |I xy |≤I xy,max

[0103] Among them, U m,max , U m,min I is the upper and lower limits of the voltage amplitude allowed at the distribution network node m; xy,max The upper limit of the current allowed to pass through the feeder between the distribution network node x and node y;

[0104] The power flow constraint is:

[0105]

[0106] Among them, P grid,m , Q grid,m are the active power and reactive power at the distribution network node m respectively; G mn , B mn are the real and imaginary parts of the (m, n)th element of the admittance matrix of the distribution network node; θ mn is the voltage phase angle difference between distribution network nodes m and n;

[0107] There is a one-to-one coupling relationship between the distribution network nodes and the road network traffic nodes in rural areas. The distribution network needs to supply the original load and the additional EV charging load connected through the charging stations at the traffic nodes. Therefore, the coupling constraint between the traffic network and the distribution system is:

[0108]

[0109] Among them, P grid,m,t , Q grid,m,t are the total active power and reactive power absorbed at the distribution network node m at time t; the active power absorbed by the charging station of type at time t; are respectively the active load and reactive load absorbed by the original load at the distribution network node m at time t; i is the judgment function of whether to build a charging station at the traffic node i. 1 means to build a charging station at this location, and 0 means not to build it; P road,i,t , P road,i,t are the active power and reactive power consumed by the charging station at traffic node i at time t, respectively.

[0110] The above method is described in detail below with reference to specific embodiments.

[0111] Figure 2 , Figure 3 The improved IEEE33-node system with 33-node transportation network coupling is shown. Figure 2 , Figure 2The planned road network shown has 33 road network nodes and 39 traffic routes, of which 4 traffic routes are connected to the external urban area through nodes 1, 16, 22, and 25. Considering the sparseness of roads in rural areas, the topological structure is adjusted, and the average length of traffic routes is now 10km. In this rural area, the area where nodes 5-7 and 26-29 are located is the market area, the area where nodes 9-15 are located is the farming area, the area where nodes 3-4, 23-24, 17-18, and 33 are located is the tourist area, and the remaining nodes are all residential areas. The design speed of each road is 40km / h, and the design capacity is 1200 vehicles / h. Figure 3 The reference bus voltage of the rural distribution network system shown is 10.5kV and the benchmark capacity is 10MVA. Table 2 shows the road network-grid coupling relationship, that is, the correspondence between the traffic network nodes and the grid nodes. The initial penetration rate of EV is 7%. In the example, 12 nodes are selected in the road network as candidate locations for charging stations. The total planning period is 20 years, and each 5 years is a planning stage.

[0112] Table 2 Node coupling relationship table of electrical-alternating network

[0113] Traffic network nodes 2 5 9 11 13 14 18 20 23 31 Distribution network nodes 19 5 10 15 32 30 23 21 1 27

[0114] The activities of rural residents have seasonal characteristics that are significantly different from those of urban residents. Based on whether a typical day is a tourist season or a busy farming season, a year is divided into four typical scenarios, see Table 3, and a comprehensive planning scenario 5 is constructed based on the weighted proportion of days in each scenario. During the peak tourist season in rural areas, the proportion of external EVs will increase significantly; during the busy farming season, the probability of local EVs going to farming areas and the length of stay will increase significantly.

[0115] Table 3 Scene parameter setting table

[0116]

[0117] The charging demand points obtained by 3500 predictions for each scenario are clustered by time to obtain the spatiotemporal characteristics of the average charging demand for the corresponding scenario. Taking scenario 5 as an example, the prediction results of the charging demand points on a typical day are as follows: Figure 4 and Figure 5 As shown. Figure 4 and Figure 5 It can be seen that in this scenario, the charging demand points are mainly concentrated in residential areas, while there are also certain charging demand points in agricultural areas and market areas. The charging demand points in residential areas are mainly concentrated in the afternoon and evening, when EV owners have just completed one or more trips, while the charging demand points in other functional areas are evenly distributed during the day.

[0118] Depend on Figure 4 and Figure 5It can be seen that within the time range of 0:00-8:00, only a small number of external EVs have charging needs, and they are concentrated in the external nodes of the rural road network. Starting from 8:00, some local EVs start their first trip with a high power state, and then make a second trip without recharging, so there will be a peak period of electricity consumption in the evening from 17:00 to 19:00.

[0119] For comprehensive scenario 5, two methods are used to compare planning results:

[0120] Method 1: Keeping the number and penetration rate of EVs unchanged, a one-time planning is conducted over a 20-year period;

[0121] Method 2: Using the method provided in the embodiment of the present invention, four-stage planning is carried out with a planning period of 5 years.

[0122] The planning results of the two methods are as follows Figure 6 and Figure 7 The planning cost comparison is shown in Table 4.

[0123] Table 4 Comparison of planning results

[0124]

[0125] The total cost of the multi-stage planning scheme based on method 2 increased by 67.94% compared with method 1. This is because method 2 takes into account the growth of EV scale. The number of EVs in the 20th year is about 2.4 times that of the first year, which will significantly increase the construction and operation and maintenance costs of charging stations and the network loss costs on the distribution network side. At the same time, the increase in the number of planned charging piles and the number of newly built charging stations increased by 4, which narrowed the service scope of each charging station, improved the service capacity of the charging station, reduced the average idle driving cost and queuing time of EV users, and was able to meet the charging needs of more users more timely.

[0126] Depend on Figure 6 , Figure 7 It can be seen that compared with the static one-time planning, the cost on the EVCS side, the cost on the EV user side, and the cost on the distribution network side increased by 68.75%, 65.73%, and 66.29%, respectively. Compared with the increased number of EVs in the region, the average cost per EV has decreased significantly.

[0127] In multi-stage planning, in order to further verify the necessity of dividing multiple scenarios into tourism season and busy farming season for planning in this paper, a one-time planning method is adopted. The corresponding planning results of each scenario are shown in Table 5.

[0128] Table 5 Cost of EV charging station planning schemes under different scenarios

[0129]

[0130]

[0131] In multi-stage planning, the total cost of scenario 1 and scenario 3 is greater than that of scenario 5, while the total cost of scenario 2 and scenario 4 is less than that of scenario 5. This is because a large number of foreign EVs enter rural areas during the peak tourist season corresponding to scenarios 1 and 3, and the demand for charging increases dramatically. The number of charging piles required for planning increases accordingly. Through weighted calculation, the number of vehicles corresponding to scenario 5 is 4027, and the average planning cost per EV is the smallest. During the busy farming season corresponding to scenarios 1 and 2, the demand for local EV users to go to farming areas for work increases, and the length of stay in farming areas increases, which significantly increases the cost on the EV user side. Although scenario 4 has the lowest comprehensive cost, it only occupies 120 days in a year, and the planning scheme cannot meet the EV charging needs in other scenarios. The planning scheme obtained by scenario 5 can better meet the travel needs of EV users in rural areas that change throughout the year.

[0132] In summary, the multi-scenario electric vehicle charging station planning for rural distribution networks provided by the embodiments of the present invention is effective and reasonable.

[0133] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0134] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0135] Figure 8 The schematic diagram of the structure of the multi-scenario electric vehicle charging station planning device for rural distribution network provided by an embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0136] like Figure 8 As shown, the multi-scenario electric vehicle charging station planning device for rural distribution network includes:

[0137] The demand point determination module 21 is used to distinguish the types of electric vehicles, predict the EV charging demand in rural scenarios, and determine the charging demand point set; wherein the types of electric vehicles include: local vehicles and external vehicles;

[0138] The multi-stage planning module 22 is used to plan and solve the construction of charging stations in multiple stages according to the charging demand point set, taking into account the construction and operation costs of charging stations, the charging satisfaction costs of EV users, and the costs of rural distribution networks accepting EVs, and obtain planning schemes for each stage;

[0139] Among them, the planning scheme includes: the location and capacity of the charging station; the location of the charging station is the point where the charging demand points are concentrated.

[0140] In a possible implementation, the demand point determination module 21 may include:

[0141] The point output unit is used to establish a user charging decision model based on the regret algorithm for any electric vehicle in the rural scene according to the type of the electric vehicle, and obtain the charging demand point corresponding to the electric vehicle;

[0142] The clustering unit is used to cluster the charging demand points corresponding to each electric vehicle to obtain multiple cluster centers, and the points corresponding to each cluster center form a charging demand point set.

[0143] In a possible implementation, the point output unit may include:

[0144] A travel data initialization subunit, used to determine the departure time, initial SOC and destination stop time of the electric vehicle according to the type of the electric vehicle;

[0145] A trip determination subunit, used to determine the travel trip of the electric vehicle according to the type of the electric vehicle;

[0146] A charging demand determination subunit is used to determine whether the electric vehicle needs to be charged during travel according to the travel itinerary of the electric vehicle and based on an energy consumption model;

[0147] The first judgment subunit is used to obtain the charging demand point corresponding to the electric vehicle based on the charging decision model if there is a charging demand;

[0148] A second judgment subunit is used to determine whether to end the trip if there is no charging demand;

[0149] The third judgment subunit is used to determine that if the trip is finished, the charging demand point corresponding to the electric vehicle is empty;

[0150] The fourth judgment subunit is used to jump to the step of determining the travel itinerary of the electric vehicle according to the type of the electric vehicle if the travel is not completed.

[0151] In a possible implementation, the charging decision model may include:

[0152] R a,b =λ x α x (x a ,x b )+λ y α y (y a ,y b )+λ z α z (za ,z b )

[0153]

[0154] Among them, R a,b is the comprehensive regret value of charging decision at charging stations a and b; α x (x a ,x b ), α y (y a ,y b ), α z (z a ,z b ) are the regret values ​​caused by charging cost, detour time and attraction cost of tourist attractions; λ x , y , z All are coefficients; N ch is the total number of charging station locations to be selected; R k The charging points corresponding to electric vehicles.

[0155] In a possible implementation manner, the charging requirement determination subunit may be specifically configured to:

[0156] 1. According to the travel itinerary of the electric vehicle and based on the energy consumption model, determine the charge state of the electric vehicle at the destination;

[0157] 2. If the state of charge of the electric vehicle at the end point is greater than the preset value, there is no need to charge;

[0158] 3. If the state of charge of the electric vehicle at the end point is not greater than the preset value, there is a need for charging.

[0159] In a possible implementation, the energy consumption model may include:

[0160]

[0161] Among them, v t is the speed of the electric car at time t; v ref N is the reference speed of electric vehicles on road ij when there is no traffic; ij,t is the total number of cars on road ij at time t; L ij is the length of road ij; P ij,t is the energy consumption per unit mileage of the electric vehicle on road ij at time t; ΔSOC ij is the reduction in charge capacity of the electric vehicle when it travels on road ij; η is the battery efficiency of the electric vehicle; S B is the battery capacity of electric vehicles; SOC 0SOC is the initial state of charge of the electric vehicle; T is the charge state of the electric vehicle at the end point.

[0162] In a possible implementation manner, the itinerary determination subunit may be specifically used for:

[0163] 1. If the type of the electric vehicle is a local vehicle, the travel chain model is used to determine the travel itinerary of the electric vehicle;

[0164] 2. If the type of the electric vehicle is a foreign vehicle, the OD probability model is used to determine the travel itinerary of the electric vehicle.

[0165] In a possible implementation, the multi-stage planning module 22 may include:

[0166] The initial planning unit is used to determine the first-stage planning scheme based on the charging demand point set and the current EV ownership scenario;

[0167] The scene extraction unit is used to extract n based on the Monte Carlo method. k EV ownership scenarios for the next planning phase;

[0168] The charging station position output unit is used to convert n k The scenario is used as the initial scenario, the target planning model is input and solved, and the planning location of the charging station in the current stage is obtained;

[0169] A capacity output unit, for determining the capacity corresponding to each planned location of a charging station based on an EV ownership scenario determined by an expected growth rate;

[0170] The repeated execution unit is used to determine whether all stage planning is completed; if so, it ends; if not, it jumps to extracting n based on the Monte Carlo method k Continue with the steps for the next planning phase EV ownership scenario.

[0171] In a possible implementation, the goal planning model may include:

[0172] The objective function is:

[0173] minF=F 1 +F 2 +F 3

[0174]

[0175]

[0176] F 3 =υ 1 f 1 +υ 2f 2

[0177]

[0178] Among them, F 1 is the total construction and operation and maintenance cost of the charging station operator; F 2 The sum of user charging costs, idle driving costs, rural tourism and surrounding resource costs; F 3 The cost of power quality degradation caused by accepting EVs in rural distribution networks; C l is the land purchase cost; C s is the construction cost of the charging station; C o Operation and maintenance costs for charging stations; x i is the charging cost at charging station i; c 1 is the time cost coefficient; c ch The charging cost per unit of electricity for electric vehicles; i The detour cost to recharge at charging station i; c 2 is the unit extra detour distance cost; i The additional cost of surrounding resources after the user arrives at charging station i; c 3 is the consumption coefficient of the point of interest; F 3 Reduce the cost of power quality on the grid side;υ 1 ,υ 2 f is the conversion coefficient of power quality degradation cost of the power grid; 1 is the voltage offset rate;

[0179] The constraints are:

[0180] The location and capacity constraints of charging station construction are:

[0181] δ i E i,min ≤E i ≤δ i E i,max

[0182] Among them, E i E is the capacity of the charging station built at traffic node i; i,max 、E i,min are the upper and lower limits of the capacity of the charging station built at traffic node i; δ i It is a function to judge whether the node i is a candidate address for charging pile;

[0183] The node voltage and feeder current constraints are:

[0184] U m,min ≤U m ≤U m,max

[0185] |I xy|≤I xy,max

[0186] Among them, U m,max , U m,min I is the upper and lower limits of the voltage amplitude allowed at the distribution network node m; xy,max The upper limit of the current allowed to pass through the feeder between the distribution network node x and node y;

[0187] The power flow constraint is:

[0188]

[0189] Among them, P grid,m , Q grid,m are the active power and reactive power at the distribution network node m respectively; G mn , B mn are the real and imaginary parts of the (m, n)th element of the admittance matrix of the distribution network node; θ mn is the voltage phase angle difference between distribution network nodes m and n;

[0190] The coupling constraints between the transportation network and the power distribution system are:

[0191]

[0192] Among them, P grid,m,t , Q grid,m,t are the total active power and reactive power absorbed at the distribution network node m at time t; the active power absorbed by the charging station of type at time t; are respectively the active load and reactive load absorbed by the original load at the distribution network node m at time t; i is the judgment function of whether to build a charging station at the traffic node i. 1 means to build a charging station at this location, and 0 means not to build it; P road,i,t , P road,i,t are the active power and reactive power consumed by the charging station at traffic node i at time t, respectively.

[0193] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0194] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0195] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned multi-scenario electric vehicle charging station planning method embodiment for rural distribution network. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0196] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A multi-scenario electric vehicle charging station planning method for rural distribution networks, characterized in that: include: Differentiate the types of electric vehicles, predict the EV charging demand in rural scenarios, and determine the charging demand point set; wherein the types of electric vehicles include: local vehicles and external vehicles; According to the charging demand point set, the construction and operation cost of the charging station, the EV user charging satisfaction cost and the rural distribution network acceptance cost of EV are considered, and the charging station construction is planned and solved in multiple stages to obtain the planning scheme for each stage; Among them, the planning scheme includes: the location and capacity of the charging station; the location of the charging station is the point where the charging demand points are concentrated.

2. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 1 is characterized in that: The method of predicting EV charging demand in rural scenarios and determining a charging demand point set includes: For any electric vehicle in the rural scenario, according to the type of the electric vehicle, a user charging decision model is established based on the regret algorithm to obtain the charging demand point corresponding to the electric vehicle; The charging demand points corresponding to each electric vehicle are clustered to obtain a plurality of cluster centers, and the points corresponding to each cluster center form the charging demand point set.

3. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 2 is characterized in that: The method of establishing a user charging decision model based on a regret algorithm according to the type of the electric vehicle to obtain a charging demand point corresponding to the electric vehicle includes: Determine the departure time, initial SOC and destination stop time of the electric vehicle according to the type of the electric vehicle; Determine the travel itinerary of the electric vehicle according to the type of the electric vehicle; According to the travel itinerary of the electric vehicle and based on the energy consumption model, determining whether the electric vehicle needs to be charged during driving; If there is a charging demand, the charging demand point corresponding to the electric vehicle is obtained based on the charging decision model; If there is no need to charge, determine whether to end the trip; If the trip is finished, the charging demand point corresponding to the electric vehicle is empty; If the trip is not finished, the process jumps to the step of determining the travel itinerary of the electric vehicle according to the type of the electric vehicle and continues to execute.

4. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 3 is characterized in that: The charging decision model includes: R a,b =λ x a x (x a ,x b )+λ y a y (y a ,y b )+λ z a z (z a ,z b ) Among them, R a,b is the comprehensive regret value of charging decision at charging stations a and b; α x (x a ,x b ), α y (y a ,y b ), α z (z a ,z b ) are the regret values ​​caused by charging cost, detour time and attraction cost of tourist attractions; λ x , y , z All are coefficients; N ch is the total number of charging station locations to be selected; R k It is the charging demand point corresponding to the electric vehicle.

5. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 3 is characterized in that: The step of determining whether the electric vehicle needs to be charged during travel based on the travel itinerary of the electric vehicle and the energy consumption model includes: Determining the state of charge of the electric vehicle at the destination based on the energy consumption model according to the travel itinerary of the electric vehicle; If the state of charge of the electric vehicle at the end point is greater than a preset value, there is no need to charge; If the state of charge of the electric vehicle at the end point is not greater than the preset value, there is a need for charging.

6. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 5 is characterized in that: The energy consumption model includes: Among them, v t is the speed of the electric car at time t; v ref N is the reference speed of electric vehicles on road ij when there is no traffic; ij,t is the total number of cars on road ij at time t; L ij is the length of road ij; P ij,t ΔSOC is the energy consumption per unit mileage of the electric vehicle on road ij at time t; ij is the reduction in charge capacity of the electric vehicle when it travels on road ij; η is the battery efficiency of the electric vehicle; S B is the battery capacity of the electric vehicle; SOC0 is the initial state of charge of the electric vehicle; SOC T is the charge state of the electric vehicle at the end point.

7. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 3 is characterized in that: Determining the travel itinerary of the electric vehicle according to the type of the electric vehicle includes: If the type of the electric vehicle is a local vehicle, the travel chain model is used to determine the travel itinerary of the electric vehicle; If the type of the electric vehicle is a foreign vehicle, the OD probability model is used to determine the travel itinerary of the electric vehicle.

8. The multi-scenario electric vehicle charging station planning method for rural distribution network according to any one of claims 1 to 7, characterized in that: The multi-stage planning and solving of the charging station construction are performed to obtain the planning schemes for each stage, including: Determine the first phase planning scheme based on the charging demand point set and the current EV ownership scenario; Based on the Monte Carlo method, n k EV ownership scenarios for the next planning phase; Based on the bat algorithm, n k The scenario is used as the initial scenario, the target planning model is input and solved, and the planning location of the charging station in the current stage is obtained; Determine the capacity of each charging station planning location based on the EV ownership scenario determined by the expected growth rate; Determine whether all stage planning is completed; if so, end; if not, jump to the Monte Carlo method to extract n k Continue with the steps for the next planning phase EV ownership scenario.

9. The multi-scenario electric vehicle charging station planning method for rural distribution network according to claim 8 is characterized in that: The target programming model includes: The objective function is: minF=F1+F2+F3 F3=υ1f1+υ2f2 Among them, F1 is the total construction and operation and maintenance cost of the charging station operator; F2 is the total cost of user charging, idle driving, rural tourism and surrounding resource costs; F3 is the cost of power quality degradation caused by the acceptance of EVs in the rural distribution network; C l is the land purchase cost; C s is the construction cost of the charging station; C o Operation and maintenance costs for charging stations; x i is the charging cost at charging station i; c1 is the time cost coefficient; c ch The charging cost per unit of electricity for electric vehicles; i is the detour cost to recharge at charging station i; c2 is the unit additional detour distance cost; z i is the additional cost of surrounding resources after the user arrives at charging station i; c3 is the consumption coefficient of the point of interest; F3 is the cost of power quality degradation on the grid side; υ1 and υ2 are the conversion coefficients of power quality degradation cost on the grid; f1 is the voltage deviation rate; The constraints are: The location and capacity constraints of charging station construction are: δ i AND i,min ≤E i ≤δ i AND i,max Among them, E i E is the capacity of the charging station built at traffic node i; i,max 、E i,min are the upper and lower limits of the capacity of the charging station built at traffic node i; δ i It is a function to judge whether the node i is a candidate address for charging pile; The node voltage and feeder current constraints are: IN m,min ≤U m ≤U m,max |I xy |≤I xy,max Among them, U m,max , U m,min I is the upper and lower limits of the voltage amplitude allowed at the distribution network node m; xy,max The upper limit of the current allowed to pass through the feeder between the distribution network node x and node y; The power flow constraint is: Among them, P grid,m , Q grid,m are the active power and reactive power at the distribution network node m respectively; G mn , B mn are the real and imaginary parts of the (m, n)th element of the admittance matrix of the distribution network node; θ mn is the voltage phase angle difference between distribution network nodes m and n; The coupling constraints between the transportation network and the power distribution system are: Among them, P grid,m,t , Q grid,m,t are the total active power and reactive power absorbed at the distribution network node m at time t; the active power absorbed by the charging station of type at time t; are respectively the active load and reactive load absorbed by the original load at the distribution network node m at time t; i is the judgment function of whether to build a charging station at the traffic node i. 1 means to build a charging station at this location, and 0 means not to build it; P road,i,t , P road,i,t are the active power and reactive power consumed by the charging station at traffic node i at time t, respectively.

10. A multi-scenario electric vehicle charging station planning device for rural distribution networks, characterized in that: include: A demand point determination module is used to distinguish the types of electric vehicles, predict the EV charging demand in rural scenarios, and determine the charging demand point set; wherein the types of electric vehicles include: local vehicles and external vehicles; A multi-stage planning module is used to plan and solve the construction of charging stations in multiple stages according to the charging demand point set, taking into account the construction and operation costs of charging stations, the charging satisfaction costs of EV users, and the cost of rural distribution networks accepting EVs, and obtain planning schemes for each stage; Among them, the planning scheme includes: the location and capacity of the charging station; the location of the charging station is the point where the charging demand points are concentrated.

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