Charging station capacity optimal planning method considering space-time charging scheduling of electric vehicle

Abstract

The invention provides a charging station capacity optimal planning method considering space-time charging scheduling of an electric vehicle. The method comprises the following steps: carrying out functional region division on a city region with a traffic-power coupling system; counting the number of nodes, node loads and branch impedance in the power distribution network, and road types, road lengths and the number of roads in the traffic network; the method comprises the following steps: acquiring data such as a typical daily road travel time index, a power load and electric vehicle behaviors, and performing ordered charging intention evaluation on electric vehicle users in a region to obtain a charging mode of each electric vehicle; and taking the charging facility capacity of the electric vehicle charging station as an optimization variable, taking the minimum comprehensive cost as a target, establishing a target function, combining the operation constraint and the time-space constraint, establishing an optimization model, and solving to obtain a planning scheme. According to the method, the influence of the charging behavior of the electric vehicle on the running state of the traffic and power system can be deeply excavated, meanwhile, the benefit requirements of multiple related subjects are considered, and remarkable economic benefits are achieved.

Description

Technical Field

[0001] The present invention belongs to the field of power system planning and operation, and specifically relates to a charging station capacity optimal planning method considering the spatiotemporal charging scheduling of electric vehicles. Background Art

[0002] The World Meteorological Organization (WMO) released the "Global Climate Status in 2022" report, pointing out that due to record levels of heat-trapping greenhouse gases, land, oceans and the atmosphere have changed globally. In terms of global temperature, the global average temperature in 2022 was about 1.15 degrees Celsius higher than the pre-industrial level. The problem of excessive greenhouse gas emissions has become a common concern of people all over the world, and the widespread use of traditional fuel vehicles is one of the main causes of this problem. In order to mitigate the greenhouse effect, people began to switch from fuel vehicles to clean and low-carbon electric vehicles, which led to a rapid increase in electric vehicles. Therefore, more charging facilities need to be built to cope with the sudden increase in charging demand, and it is crucial to build electric vehicle charging stations with a reasonable layout and sufficient capacity.

[0003] Based on the above background, the present invention provides a method for optimal planning of charging station capacity taking into account the spatiotemporal charging scheduling of electric vehicles. The planning problem involves both the selection of charging locations in the spatial dimension and the generation of charging curves in the temporal dimension, and accurately simulates the impact of electric vehicle charging behavior on the operation of the power-transportation coupling system; the annualized social cost of the interests of multiple stakeholders, including electric vehicle users, electric vehicle charging station operators, power systems, and transportation networks, is comprehensively considered to make the planning of electric vehicle charging stations more reasonable; linearization methods such as variable substitution, polyhedron approximation based on "ε relaxation", and equivalent substitution are used to transform the optimization model into a MILP problem, so that the proposed model can be effectively solved. The optimal planning method for charging station capacity proposed in the present invention can deeply explore the impact of electric vehicle charging behavior on the operating status of transportation and power systems, while taking into account the interests of multiple relevant entities, and has significant economic benefits. Summary of the invention

[0004] The purpose of the present invention is to provide a charging station capacity optimal planning method taking into account the spatiotemporal charging scheduling of electric vehicles, which can effectively and feasibly plan the capacity of electric vehicle charging stations.

[0005] In order to achieve the above technical objectives, the present invention adopts the following scheme:

[0006] A charging station capacity optimal planning method considering the spatiotemporal charging scheduling of electric vehicles, comprising:

[0007] The area to be planned is divided into several functional areas, and the number of nodes, node loads, and branch impedance in the power distribution network of each area are counted; based on the historical traffic data of each road in the area to be planned, all roads are divided into four types of roads: congestion during both morning and evening peaks, congestion during the morning peak, congestion during the evening peak, and no congestion throughout the day, and the length and number of each type of road are obtained;

[0008] Obtain the typical daily travel time index (TTI), power load data, and electric vehicle behavior data for each road type, and evaluate the orderly charging willingness of electric vehicle users in the area to obtain the charging mode of each electric vehicle;

[0009] The capacity of charging facilities to be built at each node of the electric vehicle charging station to be built is used as the optimization variable; the relevant stakeholders, including the transportation department, power company, charging station operator, and electric vehicle users, are considered, and the objective function is established with the minimum comprehensive cost as the goal. The optimal planning model of the electric vehicle charging station capacity is established and solved in combination with the operation constraints to obtain the capacity planning scheme of each electric vehicle charging station to be built in the region;

[0010] Among them, the comprehensive cost calculation includes the annualized additional traffic costs caused by spatial scheduling, and the annualized traffic congestion costs considering the traffic flow conditions on the vehicle's station search path; the constraints are determined based on the operating status of the distribution network in the region, the spatiotemporal charging scheduling behavior of electric vehicles, and the operating status of the charging stations.

[0011] As a preferred implementation, numerical calculations are performed in units of typical days, including: dividing a year into eight typical days, namely, working days in spring, summer, autumn, and winter, and weekends, obtaining calculation parameters and establishing a calculation model in units of typical days; during calculation, each typical day is discretized into n time periods, and all states are the same within each time period.

[0012] As a preferred implementation, the method of evaluating the orderly charging willingness of electric vehicle users in the region is:

[0013] According to whether electric vehicle users have time anxiety or range anxiety, four scenarios are constructed through permutations and combinations, and the probability of users participating in orderly charging when they have time anxiety or range anxiety is preset, so as to obtain the probability of users participating in orderly charging of electric vehicles in different scenarios; the orderly charging willingness of electric vehicle users in the region is evaluated through a probability-based random number generation method;

[0014] Among them, the time anxiety is determined based on the expected parking time of the electric vehicle. When the expected parking time of the electric vehicle is less than the required charging time, it is considered that the user has time anxiety about this charging; the mileage anxiety is determined based on the state of charge of the electric vehicle. When the state of charge of the electric vehicle is not sufficient to support the user's next expected journey, it is determined that the user has mileage anxiety about this charging.

[0015] As a preferred implementation, the method for determining whether the electric vehicle user has time anxiety or mileage anxiety is as follows:

[0016] The following formula is used to determine whether electric vehicle users have time anxiety:

[0017]

[0018] In the formula, B ta Indicates the time anxiety of electric vehicle users, B ta =1 means the user has time anxiety, B ta =0 means the user has no time anxiety, T park is the parking time, T rech is the charging time, Δdur is the preset parking-charging time margin;

[0019] Determine whether electric vehicle users have range anxiety based on the following formula:

[0020]

[0021] In the formula, B ra represents the range anxiety state of electric vehicle users, B ra =1 indicates that the user has mileage anxiety, B ra =0 means that the user has no mileage anxiety, and Δsoc is the preset economic operation margin of the electric vehicle.

[0022] As a preferred implementation, the method for obtaining the parameters required for determining time anxiety or mileage anxiety is as follows:

[0023] Based on the electric vehicle user behavior dataset and vehicle status dataset in the planned area, the average daily driving distance dis, the average monthly charging times ct, and the state of charge SOC at the start of charging of electric vehicles are obtained. ar , time to reach the charging station T ar , time to leave the charging station T le 、Rated capacity of vehicle battery S cap and the rated range of electric vehicles D cap ;

[0024] The state of charge (SOC) that the electric vehicle needs to achieve during this charging is calculated based on the following formula: le、Parking time T park And charging time T rech :

[0025]

[0026]

[0027] T park =T le -T ar

[0028]

[0029] In the formula, SOC rele is the actual required state of charge, SOC bd is the preset minimum power. is the charging power.

[0030] As a preferred implementation, the annualized additional transportation cost is calculated based on the following formula:

[0031]

[0032] Among them, C ET is the annualized additional transportation cost; EVCS A set of candidate addresses for electric vehicle charging stations; are the number of electric vehicles with node i as destination on weekdays and weekends in season s, respectively; c et is the unit additional transportation cost; and is a 0-1 variable, representing the spatial dispatch state of the kth electric vehicle with node i as the destination on weekdays / weekends in season s. Assume or Then the electric car goes to the electric car charging station at node j to charge, otherwise it does not go to the electric car charging station at node j to charge.

[0033] As a preferred implementation, the annualized traffic congestion cost is calculated based on the following formula:

[0034]

[0035] Among them, C TJ is the annualized traffic congestion cost; c tj is the unit congestion cost; are the TTI from node i to node j in time period t on weekdays and weekends, respectively. V ev is the average driving speed of electric vehicles in the area to be planned; Ω EVCS A set of candidate addresses for electric vehicle charging stations; is a 0-1 variable, representing the spatial dispatching state of the kth electric vehicle with node i as the destination on weekdays and weekends in season s. Assume or Then the electric car goes to the electric car charging station at node j to charge, otherwise it does not go to the electric car charging station at node j to charge; are the number of electric vehicles with node i as the destination on weekdays and weekends in season s; N S 、N P Represent the season and the total number of distribution network nodes respectively.

[0036] As a preferred implementation, the electric vehicle spatiotemporal charging scheduling constraints are as follows:

[0037]

[0038]

[0039]

[0040] in, This means that each electric car can only choose to go to one charging station for charging; is a 0-1 variable, indicating the time scheduling state of the kth electric vehicle with node i as the destination in the t period of season s. Assume It means that the electric vehicle is charged in time period t, otherwise it is not charged in time period t; represents the charging scheduling state of the kth electric vehicle with node i as the destination in period t of season s, assuming It means that the electric car goes to the electric car charging station at node j and charges within time period t; Indicates the user's willingness to participate in orderly charging, =1 means that the user is willing to charge in an orderly manner. Expressed unwillingness to conduct orderly charging; denote the time when the kth electric vehicle at node i arrives at / leaves the charging station in season s, respectively.

[0041] As a preferred implementation, the electric vehicle charging facility is of a multi-charger multi-port type.

[0042] As a preferred implementation, when solving the model, the nonlinear part of the model is linearized by using variable substitution, polyhedron approximation based on ε relaxation, or an equivalent substitution method.

[0043] The present invention has the following beneficial effects:

[0044] (1) The planning problem involves both the selection of charging locations in the spatial dimension and the generation of charging curves in the temporal dimension, and accurately simulates the impact of electric vehicle charging behavior on the operation of the power-transportation coupling system;

[0045] (2) It comprehensively considers the annual social costs of multiple stakeholders, including electric vehicle users, electric vehicle charging station operators, power systems, and transportation networks. It can balance the interests of multiple stakeholders, making the planning of electric vehicle charging stations more reasonable and having significant economic benefits.

[0046] (3) Through qualitative analysis of the user's scenario and willingness prediction, a user orderly charging willingness evaluation system was established. The orderly charging willingness of regional electric vehicle users was included in the factors that need to be considered in the charging station planning stage, which can improve the charging satisfaction of electric vehicle users and assist in setting up a more reasonable charging station capacity.

[0047] (4) By adopting linearization methods such as variable substitution, polyhedron approximation based on “ε relaxation” and equivalent substitution, the optimization model is transformed into a MILP problem, so that the proposed model can be effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is an urban area topology diagram including a traffic-electricity coupling system used in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of TTI data used in Example 1.

[0050] Figure 3 This is a schematic diagram of the power load data used in Example 1.

[0051] Figure 4 It is the annual total social cost and sub-item cost composition under the three cases in Example 2.

[0052] Figure 5 It is the voltage deviation of the distribution network under the three cases of Example 2. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Figure 1It is a topological diagram of the traffic-power coupling test system used in the embodiment of the present invention, and the diagram includes topological information of the distribution network and the traffic network after transformation based on IEEE-14 nodes.

[0055] Example 1

[0056] A charging station capacity optimal planning method considering the spatiotemporal charging scheduling of electric vehicles comprises the following steps:

[0057] (1) For the urban areas to be planned that have transportation networks and power distribution systems, they are divided into four functional areas based on land use: residential areas, commercial areas, office areas, and green areas. They are numbered as follows (Table 1), where each functional area corresponds to a load type.

[0058] Table 1

[0059] No.(f) Functional area 1 Residential area 2 Commercial District 3 Office Area 4 Green Area

[0060] (2) Traverse the traffic network of the urban area, count the road types, road lengths and number of roads in the network, and number each road.

[0061] In this embodiment, the traffic network of the urban area is considered, and all roads are divided into four types of roads based on the historical traffic data of each road: congestion during both morning and evening peaks, congestion during the morning peak, congestion during the evening peak, and no congestion throughout the day. The length and number of roads are counted, and a total of 31 roads are numbered using natural numbers l=1, 2, ..., 31.

[0062] (3) Traverse the power distribution network in the urban area, count the node load types, node numbers, and branch impedance values ​​in the network, and number each node from 1 to N and each branch from 1 to B.

[0063] In this embodiment, the power distribution system in the urban area is considered, and the load type (functional area type) of each node in the network is counted and displayed in Figure 1 In the figure, the total number of distribution network nodes is 14, the total number of branches is 13, the resistance value Rij and the reactance value Xij of each branch, and the 14 distribution network nodes are numbered with natural numbers i = 1, 2, ..., 14, and the 13 distribution network branches are numbered with natural numbers b = 1, 2, ..., 13. The parameters of the distribution network nodes and branches are shown in Table 2 below, with a base capacity of 100MVA and a rated voltage of 23kV.

[0064] Table 2

[0065]

[0066] (4) Cluster analysis is performed on the temporal distribution of load in the urban area. The year is divided into weekdays and weekends in spring, summer, autumn, and winter, with a total of 8 typical day scenarios. Each typical day is discretized into 96 time periods, each of which is 15 minutes. It is assumed that the status in each time period (traffic flow status, power load status, electric vehicle behavior status, etc.) does not change.

[0067] Based on massive electric vehicle behavior and power load historical data, the TTI (Travel Time Index), power load and electric vehicle behavior data for each time period in each typical day scenario are generated. The power load data is the active power and reactive power of the conventional load (the load other than the electric vehicle load in this invention) of each distribution network node. The electric vehicle behavior data includes the functional area i where the destination is located (indicated by the distribution network node number), the time T of arriving at the charging station, and the time T of reaching the charging station. ar , time to leave the charging station T le 、Parking time T park (T le -T ar ), State of charge SOC at the start of charging ar This embodiment conducts simulation experiments on 258 electric vehicles from weekdays and 233 electric vehicles from weekends, using TTI data such as Figure 2 As shown, the power load data used is as follows Figure 3 shown.

[0068] (5) Based on the orderly charging willingness evaluation system, the orderly charging willingness of electric vehicle users in the city area is evaluated, and the charging mode of each electric vehicle is obtained, as follows:

[0069] The time anxiety and range anxiety that electric vehicle users have during charging are defined as follows:

[0070] 1) When the estimated parking time of an electric vehicle is less than the required charging time, it is considered that the electric vehicle user has time anxiety during this charging.

[0071] 2) When the SOC charged by the electric vehicle is not enough to support the user's next planned journey, the user will have range anxiety.

[0072] Based on the above definition, whether electric vehicle users have time anxiety can be judged by the following formula:

[0073]

[0074] B ta Indicates the time anxiety of electric vehicle users, B ta =1 means the user has time anxiety, B ta= 0 means that the user has no time anxiety. Considering that electric vehicle users often cannot obtain accurate charging time during actual charging and can only obtain estimated charging time in most cases, this application assumes that there is a deviation Δdur between the estimated charging time and the actual charging time, which is called the parking-charging time margin. When T rech +Δdur>T park Electric car users have time anxiety.

[0075] T rech is the charging time, calculated based on the following formula:

[0076]

[0077]

[0078]

[0079] In the formula, SOC le SOC is the state of charge that the electric vehicle needs to achieve during this charging. ar S is the state of charge at the start of charging, cap为 On-board battery rated capacity, SOC rele is the actual required state of charge, SOC bd is the preset minimum power. is the charging power, dis is the average daily driving distance of the electric vehicle, ct is the average monthly charging times of the electric vehicle, and D cap Rated range for electric vehicles.

[0080] The user's mileage anxiety can be expressed as follows:

[0081]

[0082] B ra represents the range anxiety state of electric vehicle users, B ra =1 indicates that the user has mileage anxiety, B ra =0 indicates that the user does not have range anxiety. Most electric vehicle users will not make the SOC of the electric vehicle reach 1, because this will damage the service life of the battery, so the present invention sets the economic operation margin Δsoc of the electric vehicle.

[0083] According to whether the user has time anxiety and mileage anxiety, four possible scenarios are obtained by permuting and combining them, as shown in Table 3 below:

[0084] Table 3

[0085]

[0086] In order to characterize the probability of electric vehicle users participating in the orderly charging of electric vehicles in the above four scenarios, the present invention makes the following assumptions: Assume that the probability of users participating in the orderly charging in the absence of time anxiety is p 1a , the probability of participating in orderly charging in the presence of time anxiety is p 1b , the probability of participating in orderly charging in the absence of range anxiety is p 2a , the probability of participating in orderly charging in the presence of range anxiety is p 2b Since the above four situations are independent events, the probability of electric vehicle users participating in the orderly charging of electric vehicles in the above four scenarios is represented as shown in Table 4:

[0087] Table 4

[0088]

[0089] Among them, P 1a represents the probability of users participating in orderly charging without time anxiety, P 1b represents the probability of participating in orderly charging in the presence of time anxiety, P 2a represents the probability of participating in orderly charging in the absence of range anxiety, P 2b represents the probability of participating in orderly charging in the presence of range anxiety. 1a =0.8, P 1b =0.2, P 2a =0.8, P 2b =0.2.

[0090] The orderly charging willingness evaluation system is used to evaluate the orderly charging willingness of electric vehicle users in the city, and the charging mode of each electric vehicle is obtained. Some electric vehicle behavior data are shown in Table 5 below.

[0091] Table 5

[0092]

[0093] (6) Determine the node number of the power distribution system to which the electric vehicle charging stations to be planned in the urban area are connected, and set the capacity to be built of each charging station as the optimization variable.

[0094] In this embodiment, it is planned to build four charging stations in the urban area, the node number set of the power distribution system connected to the charging stations is {2, 3, 5, 14}, and the capacity to be built of each charging station is set as the optimization variable.

[0095] (7) Taking into account the interests of multiple relevant parties, including the transportation department, power companies, electric vehicle charging station operators, and electric vehicle users, the objective function of the optimal planning model for electric vehicle charging stations is established with the goal of minimizing the annual comprehensive cost. The specific objective function is as follows:

[0096] min Cost = C NL +C CI +C OM +C ET +C TJ

[0097] Among them, C NL , C CI , C OM , C ET , C TJ They represent the annualized network loss cost, the annualized electric vehicle charging station construction investment cost, the annualized electric vehicle charging station operation and maintenance cost, the annualized additional traffic cost due to spatial scheduling, and the annualized traffic congestion cost. The annualized additional traffic cost refers to the traffic cost caused by charging behavior, including the traffic cost (spatial cost) from the destination to the corresponding node of the charging station; the annualized traffic congestion cost refers to the traffic cost (time cost) caused by the road congestion during charging. The detailed expressions of the above sub-items are as follows:

[0098] 1) Annualized network loss cost

[0099]

[0100] Where s, t, i / j represent season, time period, and distribution network node index respectively; N S 、N T 、N P Respectively represent season, time period, and the total number of distribution network nodes; u(i) represents the set of downstream nodes connected to node i; c nl Represents the unit network loss cost. In this embodiment, c nl =0.08$ / kWh; They represent the current values ​​flowing through branch ij during the working day and weekend period t in season s respectively; R ij represents the resistance of branch ij; Δt represents the duration of each time period.

[0101] 2) Annualized investment cost of electric vehicle charging station construction

[0102]

[0103]

[0104] Among them, α c is an auxiliary variable; cci represents the unit construction investment cost of the charging facility. In this embodiment, c ci =3250$ / unit; ξ represents the discount rate, which is 0.03 in this embodiment; represents the number of chargers inside the charging station at node i; y c Indicates the economic life of the charging facility. In this embodiment, y c = 10. It should be noted that the present invention uses a multiple-charger multiple-port charging facility (MCMP). Since the number of charging ports of MCMP is consistent with the peak parking number of electric vehicles in the charging station, the planning of the number of charging ports does not belong to the optimization problem. The present invention focuses on the optimization of the number of chargers.

[0105] 3) Annualized operation and maintenance costs of electric vehicle charging stations

[0106]

[0107] Among them, c om Represents the operation and maintenance cost of unit charging facilities.

[0108] 4) Annualized additional transportation costs

[0109]

[0110] Among them, Ω EVCS represents a set of candidate addresses for electric vehicle charging stations; are the number of electric vehicles with node i as destination on weekdays / weekends in season s; c et represents the unit additional transportation cost, which is 0.5$ / km in this embodiment; and is a 0-1 variable, indicating the spatial scheduling status of the kth electric vehicle with node i as the destination on weekdays / weekends in season s. or Then the electric car goes to the electric car charging station at node j to charge, otherwise it does not go to the electric car charging station at node j to charge, d ij Represents the distance from node i to node j.

[0111] 5) Annualized traffic congestion costs

[0112]

[0113] Among them, c tj represents the unit traffic congestion cost, which is 0.20$ / min in this embodiment; Respectively represent the TTI from node i to node j in time period t on weekdays and weekends (in the formula );V ev represents the average speed of electric vehicles in the city area, and in this embodiment, the value is 33.75 km / h. Electric vehicle users often start looking for charging stations when they are about to reach their destination, and the time to find a charging station is short. Therefore, the present invention ignores the time to find a charging station and uses the time before the electric vehicle arrives at the destination. Indicates the traffic flow conditions on the vehicle's station-seeking path.

[0114] (8) Considering factors such as the operating status of the distribution network in the urban area, the spatiotemporal charging scheduling behavior of electric vehicles, and the operating status of charging stations, the constraints of the optimal planning model for electric vehicle charging stations are established.

[0115] In step (8), the constraints of the optimization model comprehensively consider the safety and economic constraints of the distribution network, the spatiotemporal charging scheduling constraints of electric vehicles, the operation constraints of charging facilities, etc., as shown below. It is worth noting that since the constraints in the typical day scenarios of weekdays and weekends are the same, they are no longer distinguished.

[0116] 1) Distribution network power flow constraints

[0117]

[0118]

[0119]

[0120]

[0121] Where v(j) represents the set of upstream nodes connected to the distribution network node j; P s,t,ij / Q s,t,ij represents the active / reactive power on branch ij in time period t of season s; and They represent the conventional active / reactive load at node j in time period t of season s respectively; represents the power demand of the electric vehicle charging station located at node j; Ω N / Ω L Respectively represent the distribution network node / branch set; X ij Indicates the reactance value of the distribution network branch ij; U s,t,i represents the voltage amplitude at node i in time period t of season s.

[0122] 2) Distribution network security constraints

[0123]

[0124]

[0125] Among them, U min Indicates the lower limit of the voltage amplitude of the distribution network node, which is 0.95pu in this embodiment; U max I represents the upper limit of the voltage amplitude of the distribution network node, which is 1.05pu in this embodiment; max It represents the maximum current allowed to flow through the distribution network branch. In this embodiment, the value is 450A.

[0126] 3) Electric vehicle spatiotemporal charging scheduling constraints

[0127]

[0128]

[0129]

[0130] in, is a 0-1 variable, indicating the time scheduling state of the kth electric vehicle with node i as the destination in the t period of season s. Assume It means that the electric vehicle is charged in time period t, otherwise it is not charged in time period t; represents the charging scheduling state of the kth electric vehicle with node i as the destination in period t of season s, assuming It means that the electric car goes to the electric car charging station at node j and charges within time period t; Indicates the user's willingness to participate in orderly charging, Indicates that the user is willing to charge in an orderly manner. Indicates that the user is unwilling to perform orderly charging; They represent the time when the kth electric vehicle at node i arrives at / leaves the charging station in season s. In the above three formulas, the first formula indicates that each electric vehicle can only choose to go to one charging station for charging, and the third formula indicates that the charging scheduling state is a combination of the time scheduling state and the space scheduling state.

[0131] 4) Operation constraints of electric vehicle charging stations

[0132]

[0133]

[0134]

[0135]

[0136] in, is a 0-1 variable, indicating the driving and stopping status of the kth electric vehicle with node i as the destination during the t period of season s. Assume It means that the electric vehicle is in the charging station during time period t, otherwise it means that it is driving; g(j) represents the set of electric vehicles going to the electric vehicle charging station at node j; Indicates the charging power of the kth electric vehicle with node i as the destination. Among the above four formulas, the first and second formulas indicate that electric vehicles can only be charged during the time period when the vehicle is located in the charging station, and the number of chargers and power requirements of the electric vehicle charging station are determined by the third and fourth formulas.

[0137] (9) For the nonlinear parts in (7) and (8), linearization is performed using methods such as variable substitution, polyhedral approximation based on "ε relaxation", and equivalent substitution, so that the optimization model is transformed into a mixed integer linear programming problem that is easy to solve;

[0138] In the step (9), the nonlinear expressions in the steps (7) and (8) are linearized using methods such as variable substitution, polyhedral approximation based on "ε relaxation", and equivalent substitution, so that the optimization model is transformed into a mixed integer linear programming problem that is easy to solve. The specific method is as follows.

[0139] 1) Variable substitution

[0140]

[0141]

[0142] Through the above variable substitution, some quadratic expressions in the optimization model can be converted into linear expressions.

[0143] 2) Polyhedral approximation based on “ε relaxation”

[0144]

[0145] Among them, S s,t,ij , and represents an intermediate variable; X=11 represents the number of iterations, and x=1, 2, 3, ..., 11. Through the above-mentioned polyhedron approximation method based on "ε relaxation", the quadratic constraints in the power flow constraints can be converted into multiple linear constraints.

[0146] 3) Equivalent substitution

[0147]

[0148] Through the above equivalent substitution method, the nonlinear equality constraints in the spatiotemporal charging scheduling constraints of electric vehicles can be converted into multiple linear inequality constraints.

[0149] Through the above three transformation methods, the original optimization model can be transformed from a mixed integer quadratic programming problem with non-convex constraints to a mixed integer linear programming problem.

[0150] (10) Solve the optimization model established in (7)-(9) to obtain the capacity planning plan for each electric vehicle charging station to be built in the urban area.

[0151] In step (10), the optimization model is solved using the solver gurobi to obtain the number of chargers to be installed at each node to be built in the urban area and the required capacity of each charging station as shown in Table 6 below:

[0152] Table 6

[0153]

[0154] Example 2

[0155] In order to verify the effectiveness of the electric vehicle charging station capacity planning method, this embodiment sets three cases for effect comparison, and the three cases are as follows:

[0156] Case 1: Optimal planning method for charging station capacity considering spatiotemporal charging scheduling of electric vehicles (planning method proposed in this invention);

[0157] Case 2: Optimal planning method for charging station capacity considering electric vehicle time scheduling;

[0158] Case 3: Optimal planning method for charging station capacity considering spatial scheduling of electric vehicles.

[0159] Among them, Case 2 and Case 3 are both single-dimensional charging scheduling methods (based on Case 1, the time / space scheduling content is removed). The annual total social cost and sub-item cost composition under the three cases are as follows: Figure 4 The voltage deviation of the distribution network under the three cases is shown in Figure 5 shown.

[0160] According to the simulation results, it can be seen that the charging station capacity optimal planning method considering the spatiotemporal charging scheduling of electric vehicles proposed in the present invention can reduce traffic congestion costs, distribution network line loss costs and voltage deviations, electric vehicle charging station construction and operation and maintenance costs, and additional traffic costs compared to the method that only considers single-dimensional charging scheduling. It is beneficial in alleviating traffic congestion, improving the safe and economical operation level of the distribution system, saving expenses of electric vehicle charging station operators, and improving user charging satisfaction, and has significant economic benefits.

[0161] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0162] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for optimal planning of charging station capacity considering the spatiotemporal charging scheduling of electric vehicles, characterized in that: include: Divide the area to be planned into several functional areas, and count the number of nodes, node loads, and branch impedance in the power distribution network of each area; Based on the historical traffic data of each road in the planned area, all roads are divided into four types: congestion during both morning and evening peaks, congestion during the morning peak, congestion during the evening peak, and no congestion throughout the day, and the length and number of each type of road are obtained; Obtain the typical daily travel time index (TTI), power load data, and electric vehicle behavior data for each road type, and evaluate the orderly charging willingness of electric vehicle users in the area to obtain the charging mode of each electric vehicle; The capacity of charging facilities to be built at each node of the electric vehicle charging station to be built is used as the optimization variable; the relevant stakeholders, including the transportation department, power company, charging station operator, and electric vehicle users, are considered, and the objective function is established with the minimum comprehensive cost as the goal. The optimal planning model of the electric vehicle charging station capacity is established and solved in combination with the operation constraints to obtain the capacity planning scheme of each electric vehicle charging station to be built in the region; Among them, the comprehensive cost calculation includes the annualized additional traffic costs caused by spatial scheduling, and the annualized traffic congestion costs considering the traffic flow conditions on the vehicle's station search path; the constraints are determined based on the operating status of the distribution network in the region, the spatiotemporal charging scheduling behavior of electric vehicles, and the operating status of the charging stations.

2. The method according to claim 1, characterized in that The numerical calculation is based on typical days, including: dividing a year into eight typical days: spring, summer, autumn, and winter, working days and weekends, obtaining calculation parameters and establishing a calculation model based on typical days; during calculation, each typical day is discretized into n time periods, and all states are the same in each time period.

3. The method according to claim 1, characterized in that The method for evaluating the orderly charging willingness of electric vehicle users in the region is as follows: According to whether electric vehicle users have time anxiety or range anxiety, four scenarios are constructed through permutations and combinations, and the probability of users participating in orderly charging when they have time anxiety or range anxiety is preset, so as to obtain the probability of users participating in orderly charging of electric vehicles in different scenarios; the orderly charging willingness of electric vehicle users in the region is evaluated through a probability-based random number generation method; Among them, the time anxiety is determined based on the expected parking time of the electric vehicle. When the expected parking time of the electric vehicle is less than the required charging time, it is considered that the user has time anxiety about this charging; the mileage anxiety is determined based on the state of charge of the electric vehicle. When the state of charge of the electric vehicle is not sufficient to support the user's next expected journey, it is determined that the user has mileage anxiety about this charging.

4. The method according to claim 1 or 3, characterized in that: The method for judging whether the electric vehicle user has time anxiety or range anxiety is as follows: The following formula is used to determine whether electric vehicle users have time anxiety: In the formula, B ta Indicates the time anxiety of electric vehicle users, B ta =1 means the user has time anxiety, B ta =0 means the user has no time anxiety, T park is the parking time, T rech is the charging time, Δdur is the preset parking-charging time margin; Determine whether electric vehicle users have range anxiety based on the following formula: In the formula, B ra represents the range anxiety state of electric vehicle users, B ra =1 indicates that the user has mileage anxiety, B ra =0 means that the user has no mileage anxiety, and Δsoc is the preset economic operation margin of the electric vehicle.

5. The method according to claim 4, characterized in that The parameters required to determine time anxiety or range anxiety are obtained as follows: Based on the electric vehicle user behavior dataset and vehicle status dataset in the planned area, the average daily driving distance dis, the average monthly charging times ct, and the state of charge SOC at the start of charging of electric vehicles are obtained. ar , time to reach the charging station T ar , time to leave the charging station T le 、Rated capacity of vehicle battery S cap and the rated range of electric vehicles D cap ; The state of charge (SOC) that the electric vehicle needs to achieve during this charging is calculated based on the following formula: le 、Parking time T park And charging time T rech : T park =T le -T ar In the formula, SOC rele is the actual required state of charge, SOC bd is the preset minimum power. is the charging power.

6. The method according to claim 1, characterized in that The annualized additional transportation costs are calculated based on the following formula: Among them, C ET is the annualized additional transportation cost; EVCS A set of candidate addresses for electric vehicle charging stations; are the number of electric vehicles with node i as the destination on weekdays and weekends in season s, respectively; c et is the unit additional transportation cost; and is a 0-1 variable, representing the spatial dispatch state of the kth electric vehicle with node i as the destination on weekdays / weekends in season s. Assume or Then the electric car goes to the electric car charging station at node j to charge, otherwise it does not go to the electric car charging station at node j to charge.

7. The method according to claim 1, characterized in that The annualized traffic congestion cost is calculated based on the following formula: Among them, C TJ is the annual traffic congestion cost; c tj is the unit congestion cost; are the TTI from node i to node j in time period t on weekdays and weekends, respectively. V ev is the average driving speed of electric vehicles in the area to be planned; Ω EVCS A set of candidate addresses for electric vehicle charging stations; is a 0-1 variable, representing the spatial dispatching state of the kth electric vehicle with node i as the destination on weekdays and weekends in season s. Assume or Then the electric car goes to the electric car charging station at node j to charge, otherwise it does not go to the electric car charging station at node j to charge; are the number of electric vehicles with node i as the destination on weekdays and weekends in season s; N S 、N P Represent the season and the total number of distribution network nodes respectively.

8. The method according to claim 1, characterized in that: The electric vehicle spatiotemporal charging scheduling constraints are as follows: in, This means that each electric car can only choose to go to one charging station for charging; is a 0-1 variable, indicating the time scheduling state of the kth electric vehicle with node i as the destination in the t period of season s. Assume It means that the electric vehicle is charged in time period t, otherwise it is not charged in time period t; represents the charging scheduling state of the kth electric vehicle with node i as the destination in period t of season s, assuming It means that the electric car goes to the electric car charging station at node j and charges within time period t; Indicates the user's willingness to participate in orderly charging, =1 means that the user is willing to charge in an orderly manner. Expressed unwillingness to conduct orderly charging; denote the time when the kth electric vehicle at node i arrives at / leaves the charging station in season s, respectively.

9. The method according to claim 1, characterized in that: The electric vehicle charging facility is of a multi-charger multi-port type.

10. The method according to claim 1, characterized in that When solving the model, the nonlinear part of the model is linearized by using variable substitution, polyhedral approximation based on ε relaxation or equivalent substitution method.

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