New energy vehicle charging pile construction planning method, system and device and storage medium

Abstract

The invention relates to the technical field of charging facility construction planning, and particularly discloses a new energy vehicle charging pile construction planning method, system and device and a storage medium, and the method comprises the steps: constructing a double-layer stochastic planning model for representing the new energy vehicle charging pile construction planning; wherein the double-layer stochastic programming model comprises an upper-layer programming model for representing power distribution network capacity expansion programming and a lower-layer programming model for representing fast charging station locating and sizing programming; and solving the double-layer stochastic programming model by using a target genetic algorithm in combination with charging pile construction planning data of a to-be-planned area to obtain a new energy vehicle charging pile construction planning scheme of the to-be-planned area. According to the method, the economy and rationality of new energy automobile charging pile construction planning can be improved, and the operation benefits of the power distribution network and the quick charging station are improved.

Description

Background Art

[0002] As new energy vehicles become more popular, the problem of insufficient charging facilities has also emerged. The lack of charging facilities is mainly reflected in the following aspects:

[0003] 1) In real life, it is difficult to coordinate the construction of power transformation in some existing areas, and it is impossible to build charging piles in fixed parking spaces; 2) The development of public charging facilities is uneven, and urban public charging stations are hot and cold; 3) Multi-platform start and stop and multi-channel payment have not yet been fully covered, and the charging route planning and station navigation functions are not perfect. It is difficult and slow for users to find charging piles, and the convenience of charging still needs to be improved; 4) Some charging piles are not operated and maintained in a timely manner, and there is a lack of charging guarantee plans. The problem of long charging queues on highways during holidays is becoming increasingly prominent; 5) The supervision system is not yet sound, and the safety management responsibilities and management mechanisms need to be improved urgently.

[0004] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a new energy vehicle charging pile construction planning method, system, device and storage medium.

[0006] In a first aspect, the present invention provides a new energy vehicle charging pile construction planning method, the technical solution of the method is as follows:

[0007] Construct a two-layer stochastic programming model for characterizing the construction planning of new energy vehicle charging piles; wherein the two-layer stochastic programming model includes: an upper-layer planning model for characterizing the capacity expansion planning of the distribution network and a lower-layer planning model for characterizing the site selection and capacity planning of fast charging stations;

[0008] The double-layer stochastic programming model is solved by using a target genetic algorithm and combining the charging pile construction planning data of the area to be planned to obtain a new energy vehicle charging pile construction planning scheme for the area to be planned.

[0009] The beneficial effects of the new energy vehicle charging pile construction planning method of the present invention are as follows:

[0010] The method of the present invention can improve the economy and rationality of the construction planning of new energy vehicle charging piles, and improve the operating efficiency of both the distribution network and the fast charging station.

[0011] On the basis of the above scheme, the new energy vehicle charging pile construction planning method of the present invention can also be improved as follows.

[0012] In an optional manner, the upper-level planning model includes: a first objective function, a distribution network radial constraint, and a relaxed power flow constraint; wherein the first objective function aims to minimize the annual investment cost.

[0013] In an optional manner, the lower-level planning model includes: a second objective function, traffic capture constraints, FCS planning constraints, available charging pile constraints within the station, charging station capacity planning constraints, charging station operating power constraints and electric vehicle power constraints; wherein, the second objective function aims to achieve the optimal combination of planning economy and operating efficiency.

[0014] In an optional manner, the charging pile construction planning data includes: urban traffic data, driving behavior data and power grid load data.

[0015] In a second aspect, the present invention provides a new energy vehicle charging pile construction planning system, the technical solution of the system is as follows:

[0016] Includes: building module and planning module;

[0017] The construction module is used to: construct a two-layer stochastic programming model for characterizing the construction planning of new energy vehicle charging piles; the two-layer stochastic programming model includes: an upper-layer planning model for characterizing the capacity expansion planning of the distribution network and a lower-layer planning model for characterizing the site selection and capacity planning of the fast charging station;

[0018] The planning module is used to solve the double-layer stochastic planning model by using a target genetic algorithm and combining the charging pile construction planning data of the area to be planned to obtain a new energy vehicle charging pile construction planning scheme for the area to be planned.

[0019] The beneficial effects of the new energy vehicle charging pile construction planning system of the present invention are as follows:

[0020] The system of the present invention can improve the economy and rationality of the construction planning of new energy vehicle charging piles, and improve the operating efficiency of both the distribution network and the fast charging station.

[0021] On the basis of the above scheme, the new energy vehicle charging pile construction planning system of the present invention can also be improved as follows.

[0022] In an optional manner, the upper-level planning model includes: a first objective function, a distribution network radial constraint, and a relaxed power flow constraint; wherein the first objective function aims to minimize the annual investment cost.

[0023] In an optional manner, the lower-level planning model includes: a second objective function, traffic capture constraints, FCS planning constraints, available charging pile constraints within the station, charging station capacity planning constraints, charging station operating power constraints and electric vehicle power constraints; wherein, the second objective function aims to achieve the optimal combination of planning economy and operating efficiency.

[0024] In an optional manner, the charging pile construction planning data includes: urban traffic data, driving behavior data and power grid load data.

[0025] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0026] It includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the new energy vehicle charging pile construction planning method of the present invention are implemented.

[0027] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0028] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the new energy vehicle charging pile construction planning method of the present invention.

[0029] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0031] Figure 1 A schematic diagram of a flow chart of an embodiment of a new energy vehicle charging pile construction planning method of the present invention;

[0032] Figure 2 Schematic diagram of the transfer relationship of the two-level stochastic programming model

[0033] Figure 3 Schematic diagram of the process of solving the genetic algorithm for the lower-level planning model;

[0034] Figure 4 Schematic diagram of the process of solving the two-level stochastic programming model;

[0035] Figure 5 It is the transportation network topology diagram;

[0036] Figure 6 A schematic diagram of the structure of an embodiment of a new energy vehicle charging pile construction planning system of the present invention;

[0037] Figure 7 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0038] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0039] Figure 1 A flow chart of an embodiment of a new energy vehicle charging pile construction planning method provided by the present invention is shown, and the new energy vehicle charging pile construction planning method can be executed by an electronic device such as a terminal device or a server. Among them, the terminal device can be any fixed or mobile terminal such as a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The server can be a single server or a server cluster composed of multiple servers. Any electronic device can implement the new energy vehicle charging pile construction planning method by calling computer-readable instructions stored in a memory through a processor. As Figure 1 As shown, the following steps are included:

[0040] S1. Construct a two-level stochastic programming model to characterize the construction planning of new energy vehicle charging piles.

[0041] Among them, the two-layer stochastic planning model includes: an upper-level planning model that represents the distribution network expansion planning and a lower-level planning model that represents the fast charging station site selection and capacity planning.

[0042] S2. Using the target genetic algorithm and combining it with the charging pile construction planning data of the area to be planned, the double-layer stochastic programming model is solved to obtain the new energy vehicle charging pile construction planning scheme for the area to be planned.

[0043] Among them, the charging pile construction planning data includes but is not limited to: urban traffic data, driving behavior data and power grid load data.

[0044] Preferably, the upper-level planning model includes: a first objective function, a distribution network radial constraint and a relaxed power flow constraint.

[0045] Among them, the first objective function aims to minimize the annual investment cost.

[0046] Preferably, the lower-level planning model includes: a second objective function, a flow capture constraint, an FCS planning constraint, an available charging pile constraint within the station, a charging station capacity planning constraint, a charging station operating power constraint, and an electric vehicle power constraint.

[0047] Among them, the second objective function aims to optimize the combination of planning economy and operation efficiency.

[0048] Specifically, the present invention is described using the following embodiments:

[0049] S10. Construct a two-layer stochastic programming model for characterizing the construction planning of new energy vehicle charging piles. The two-layer stochastic programming model refers to: a two-layer stochastic programming model for the coordination of fast charging stations and distribution networks. The two-layer stochastic programming model includes: an upper-level planning model that characterizes the capacity expansion planning of the distribution network and a lower-level planning model that characterizes the site selection and capacity planning of fast charging stations. First, in this two-layer coordinated planning model, considering that the capacity of the distribution network is a key factor restricting the planning of charging stations, the distribution network planning is used as the upper layer and the charging station planning is used as the lower layer. Secondly, in order to protect the privacy of both parties in the planning, only the charging load curve of each station and the upper limit of the planned capacity of the charging station are used as inter-layer interactive information. Next, considering the problem of the simultaneous rate of the charging load peak and the traditional load peak, the opportunity constraint is introduced to relax the power flow constraint, so as to increase the economy and rationality of the construction planning of new energy vehicle charging piles and improve the operating efficiency of both parties (fast charging stations and distribution networks). Specifically:

[0050] S11. Construct an upper-level planning model, which includes: a first objective function, a distribution network radial constraint, and a relaxed power flow constraint; wherein the first objective function aims to minimize the annual investment cost.

[0051] (1) First objective function.

[0052] The expansion planning of the distribution network considers minimizing the annual investment cost over the entire life cycle of the equipment. The components of the optimization goal of the distribution network (minimizing the annual investment cost) include: substation expansion cost, line investment and construction cost, distribution network operation and maintenance cost, distribution network equipment scrapping cost, network loss cost and power outage cost. The first objective function is expressed as:

[0053] minC DSO =C sub +C line +C DN,O&M +C D,DN +C loss +C ENS #(1)

[0054] Among them, C sub is the substation expansion cost, C line is the line investment and construction cost, C DN,O&M is the operation and maintenance cost of the distribution network, C D,DN is the scrapping cost of distribution network equipment, C loss is the network loss cost, C ENS Cost of power outage.

[0055] Substation expansion cost C sub for:

[0056]

[0057] Among them, T sub is the life cycle of substation equipment, Ω sub is the substation set, x i is a binary decision variable, x i =1 means that the i-th substation (node) needs to be expanded, otherwise the i-th substation (node) does not need to be expanded; c sub The cost of expanding the capacity of a single substation.

[0058] Line investment and construction cost C line for:

[0059]

[0060] Among them, T line is the line operation life cycle, Ω line,cons is the set of newly created lines, Ω line,up is the set of lines that need to be updated; ij is a binary decision variable, y ij =1 means that the line where the i-th node and the j-th node are located needs to be expanded, otherwise it does not need to be expanded; c line is the line investment cost per unit length, l ij is the length of the line between the i-th node and the j-th node.

[0061] Distribution network operation and maintenance cost C DN,O&M for:

[0062] C DN,O&M =λ DNO (C sub +C line )#(4)

[0063] Among them, λ DNO is the charging station operation and maintenance cost coefficient, which is λ by default DNO =0.1.

[0064] Distribution network equipment scrapping cost C D,DN for:

[0065]

[0066] Among them, d sub is the depreciation rate of the substation, d line is the depreciation rate of the line.

[0067] Network loss cost C loss for:

[0068]

[0069] Among them, c E The price of electricity sold by the power grid, is the network loss power of the line between the i-th node and the j-th node, R ij is the resistance of the line between the ith node and the jth node.

[0070] Power outage cost C ENS for:

[0071]

[0072] Among them, ω ij is the power outage probability of the line where the i-th node and the j-th node are located, Ω bus is the set of grid nodes, is the maximum traditional load value of the jth node, c ENS is the power outage loss coefficient, is the maximum power value of the charging station at the jth node, T fault is the average power outage time.

[0073] (2)Constraints.

[0074] The constraints of the upper-level planning model include: distribution network radial constraints and relaxed power flow constraints.

[0075] ① The power flow constraints specifically include: node power balance constraints, power flow limit constraints, node voltage constraints and line planning variable constraints.

[0076] The node power balance constraint is:

[0077]

[0078] in, is the injected active power of the ith node at the tth time, is the injected reactive power of the ith node at the tth time, is the traditional active load of the ith node at time t, is the reactive load value of the ith node at the tth time, The fast charging load of the charging station at the i-th node at time t, U i,t is the node voltage of the ith node at time t, R ij The resistance of the line between the ith node and the jth node, Q ij is the reactance of the line between the ith node and the jth node.

[0079] The power flow limit constraint is:

[0080]

[0081] in, is the apparent power of the ith (distribution network) node at the tth time. is the power upper limit constraint of the ith (distribution network) node, s ij,t is the apparent power of the line between the i-th node and the j-th node at the t-th moment, s ij is the upper limit constraint of the power flow on the line where the i-th node and the j-th node are located.

[0082] The node voltage constraints are:

[0083]

[0084] in, U i is the voltage lower limit of the ith node, U i is the voltage upper limit constraint of the ith node.

[0085] The route planning variable constraints are:

[0086]

[0087] ② Radial constraints of distribution network.

[0088] The extended tree method is used to represent the radial constraints of the distribution network. First, two binary variables δ ij and where δ ij represents the connection status between the i-th node and the j-th node, δ ij =1 means the i-th node is connected to the j-th node, otherwise they are not connected; Represents the parent-child relationship between the i-th node and the j-th node. If the i-th node is the parent node of the j-th node, then Otherwise, it is 0. In the expansion tree formed by the radial distribution network, all nodes except the root node (substation node) have one and only one parent node. The radial constraints of the distribution network are expressed as equations (15) to (20). Equation (15) constrains the number of lines in the entire radial power grid; Equation (16) indicates that in the expansion tree, only when one of the i-th node or the j-th node is the parent node of the other, the line where the i-th node and the j-th node are located is connected; Equation (17) indicates that a node has one and only one parent node, and Equation (18) indicates that the root node has no parent node.

[0089] Formula (19) constrains variable δ ij is a binary variable, and the constraint variable in formula (20) is a binary variable.

[0090]

[0091] δ ij ∈{0,1},ij∈Ω line #(20)

[0092] Among them, n bus is the number of grid load nodes, n sub is the number of substations in the distribution network, Ω i is the set of nodes connected to the i-th node.

[0093] S12. Construct a lower-level planning model, which includes: a second objective function, traffic capture constraints, FCS planning constraints, available charging pile constraints in the station, charging station capacity planning constraints, charging station operating power constraints and electric vehicle power constraints; among which, the second objective function aims to achieve the best combination of planning economy and operating efficiency.

[0094] The optimization process of fast charging station site selection and capacity planning can be divided into two stages: traffic capture and redistribution: 1) Select the node set that captures the most traffic in the entire transportation network and use it as the candidate planning node of FCS, aiming to meet the charging needs of vehicles to the greatest extent and complete the site selection process of FCS; 2) Redistribute the captured traffic according to planning economy or operation efficiency, determine the capacity of each FCS, and complete the FCS capacity process. At the same time, the charging load of the entire network can be generated through Monte Carlo simulation (MCS) charging behavior.

[0095] (1) OD matrix analysis method.

[0096] First, we briefly introduce the Origin-Destination Matrix Analysis method. Consider a transportation network G(N T ,E T ), N T is the set of key nodes in the transportation network, E T is the set of key sections connecting nodes. Assume that every car starts from any starting point u 1 ∈N T Departure to the destination 2 ∈N T (u 1 ≠u 2 ),u 1 and u 2 The traffic flow of the origin-destination pair (ODpair) increases by one unit flow. Then, u 1 and u 2 The traffic flow can be expressed as Furthermore, a day is divided into 24 time periods, and the traffic distribution of the entire network at time t can be obtained by the OD matrix ODt Expressed as formula (21).

[0097]

[0098] OD t Each element in the matrix represents the traffic flow of each OD pair at time t, which is calculated by the classical gravity model (22).

[0099]

[0100] Among them, w t,o is the weight of the starting point at time t, w t,d is the weight of the end point at time t. These weights come from traffic survey data and represent the attractiveness of a traffic node to PEVs. They take into account multiple factors including road capacity, average vehicle speed, traffic congestion, etc.; D od is the distance between the OD pairs.

[0101] (2) Interception site selection model.

[0102] Similar to FCLM, the first stage of SCFCLM aims to capture the maximum traffic flow of the entire network, as shown below:

[0103]

[0104] Among them, z is the candidate node set and optimization variable; flow t,od represents the traffic flow of path od at time t; od is a binary variable, 1 means the traffic flow of path od is captured, otherwise it is not captured; Q is the set of od pairs of the traffic network; N D2T is the set of nodes in the transportation network where FCS can be constructed; K is the total number of FCSs planned to be constructed.

[0105] Formula (23)-(25) is the interception site selection model. Formula (23) is the objective function, which means capturing the maximum traffic flow of the traffic network in a typical day; Formula (24) is a constraint, which means the flow capture constraint of the candidate node and the related path. Formula (25) is also a constraint, which ensures that there are only K FCSs planned and built in the planning area.

[0106] (3) Charging load estimation, traffic redistribution and charging station capacity planning.

[0107] ①Charging behavior simulation and load estimation

[0108] The spatiotemporal distribution estimation of charging load needs to be calculated based on the traffic flow, and there is a proportional relationship between the calculated traffic flow and the actual traffic flow. Therefore, the number of PEVs captured by node u in period t can be obtained by formula (26).

[0109]

[0110] In this embodiment, only PEVs with fast charging requirements are considered. Since such PEVs may choose to leave before being fully charged, only the access time and charging duration of electric vehicles are considered. A day is divided into four time periods with different parameters, and for electric vehicles with fast charging requirements in each time period, their access time follows the Gaussian distribution as shown in formula (27), and their charging duration follows the chi-square distribution (28).

[0111]

[0112] The Monte Carlo method is used to simulate the driving and charging behavior of charging vehicles. Since the current fast charging duration is between 15 minutes and 120 minutes, in order to reduce the computational burden, the time interval is set to 15 minutes, that is, there are 96 time periods in a day, recorded as τ = 1, 2, ..., 96. Then, the number of PEVs being charged at the u-th node in the τ-th time period is calculated by formula (29), and the charging load is calculated by formula (30). In addition, formula (29) should also ensure the continuity of the FCS and PEV states, so for the initial and end time periods of a day, we get:

[0113]

[0114] in, is the number of PEVs being charged in the u-th fast charging station during the τ-th period; is the number of PEVs entering the u-th fast charging station for charging in the τ-th period, The number of PEVs that finish charging and leave the u-th fast charging station in the τ-th period; is the charging load power value of the u-th fast charging station in the τ-th period, p rated Indicates the charging power of the charging pile.

[0115] ②Charging station capacity planning.

[0116] The traditional interception site selection model only captures the overall maximum traffic flow of the entire network, but does not allocate the captured traffic flow to each station. As a result, the capacity of some planned charging stations is too large or too small, which leads to the following two problems: First, large-capacity charging stations have capacity redundancy, that is, the service efficiency of large-capacity charging stations is too low during non-peak hours, and the utilization rate of charging piles is reduced; second, small-capacity charging stations have insufficient capacity and tight charging resources, resulting in the loss of potential customers for such charging stations and causing economic losses.

[0117] To solve the above two problems, in the second stage, the vehicle flow is redistributed for the candidate nodes in the candidate set z, aiming to formulate a more reasonable charging station capacity planning strategy.

[0118] 1. Objective function.

[0119] In the planning and operation of charging stations, charging station operators not only pay attention to the economic issues of planning, but also pay attention to factors such as the utilization efficiency and idleness of charging piles. Therefore, the planning economy and operation efficiency are combined as the objective function of optimization.

[0120] maxF 1 =R E -C Cons -C FCS,O&M -C D,FCS #(31)

[0121]

[0122] Among them, F 1 is the net income of the charging station in the transportation network, indicating the economic efficiency of the planning and operation of the charging station; R E Profit from charging station operation; C Cons Investment and construction cost; C O&M is the operation and maintenance cost of the charging station; C FCS,D F is the equipment scrapping cost; 2 It is the inverse of the load variance of each charging station, which is used to indicate the impact of capacity redundancy or insufficiency on operating efficiency; is the maximum power of the u-th charging station on a typical day, is the average power of the u-th charging station on a typical day.

[0123] In formula (31), the charging station operation income R E for:

[0124]

[0125] Among them, u s ∈S is the optimization variable, s represents the capture order of the u-th node, which determines its priority in capturing traffic; is the final optimization vector, where each element represents the selected planning node, and the order of the elements represents the order of optimization; η is the efficiency of the charging pile, which defaults to 0.9; c FCS Charging service fee, c DS The cost of purchasing electricity from the grid.

[0126] Charging station investment and construction cost C Cons for:

[0127]

[0128] Among them, r is the residual value rate, T FCS is the operating year of the charging station, c pile is the unit price of the charging pile, A is the area occupied by a single charging pile, is the unit land price, is the capacity of the u-th charging station, which is the optimization variable; C other Other costs required to build charging stations.

[0129] Charging station operating cost C O&M for:

[0130] C O&M =λ CSO C Cons #(35)

[0131] Among them, λ CSO is the charging station operation and maintenance cost coefficient, which is λ by default CSO =0.1.

[0132] Equipment scrapping cost of charging station C FCS,D for:

[0133]

[0134] Among them, C CSO,dc is the scrapping cost; FCS is the depreciation coefficient, obtained from formula (37); K FCS,d is the residual value of the charging station at the end of year T.

[0135] 2. Constraints.

[0136] The constraints include equation (38) for the available charging piles in the station; equation (39) for the charging station capacity planning constraint; equation (40) for the charging station operating power constraint; and equation (41) for the electric vehicle power constraint.

[0137]

[0138] SoC min ≤So C τ,ev ≤SoC max #(41)

[0139] in, is the number of available (idle) charging piles at the u-th charging station in the τ-th period; is the maximum number of charging piles that can be installed at the u-th charging station; SoC is the maximum power value that the power grid can bear; min / max Minimum / maximum power for electric vehicles; SoC τ,ev is the amount of electricity of the electric vehicle ev in the τth period.

[0140] It should be noted that in some cases (especially at the end of the capture order), there may be some traffic that can only be captured by one node. At this time, the node captures the traffic and its order is independent, which will lead to different optimal orders at the same time. However, this does not affect the location and capacity planning strategy of the charging station, so it will not be expanded.

[0141] 3. Normalization of objective function.

[0142] The different dimensions of F 1 and F 2 Normalization is performed to obtain the final optimization objective function of the second stage of SCFCLM, as shown in formula (43).

[0143]

[0144] in, is the normalized value of the objective function i; is the minimum / maximum value of the objective function i.

[0145] In summary, the second objective function of the lower-level planning model is expressed by equations (24) and (43), the traffic capture constraint is expressed by equation (24), the FCS planning constraint is expressed by equation (25), the available charging piles constraint in the station is expressed by equation (38), the charging station capacity planning constraint is expressed by equation (39), the charging station operating power constraint is expressed by equation (40), and the electric vehicle power constraint is expressed by equation (41).

[0146] The site selection and interception planning of charging stations is divided into two stages. The first stage captures the maximum traffic flow of the entire network and selects the node that captures the maximum traffic flow; the second stage plans the capacity of charging stations based on the nodes selected and the captured traffic flow in the first stage according to the two goals of economy and operation efficiency.

[0147] S13. Relax the power flow constraints in the upper-level planning model to obtain the relaxed power flow constraints.

[0148] When planning a power grid, the operation of the maximum load of the power grid is usually considered. However, due to the problem of load simultaneity, the peak load of the power grid cannot be obtained by simply adding up the maximum values ​​of various loads. Too strict power flow constraints are often unnecessary from the perspective of power grid safety and economy. Therefore, in view of the peak load simultaneity problem of fast charging loads and traditional loads, the method of introducing chance constraints is used to relax power flow constraints. Chance constraints refer to the introduction of random variables in the constraints, allowing the decisions made to not meet the constraints to a certain extent, but the probability that the constraints are satisfied is not less than a sufficiently small confidence level. Introducing confidence levels ε1 and ε2, as shown in equations (44) and (45), introducing a suitable slack variable can improve the economy of power grid planning while ensuring the safe operation of the power grid.

[0149]

[0150] To conveniently express the charging load (P FCS ) and normal load (P LD ) relationship, the variable s in the power flow calculation ij,t and the subnode voltage U j,t Using conventional active load P i,t and reactive load Q i,t , fast charging station charging load and node voltage U i,t To express, as shown in equation (46) and equation (47):

[0151]

[0152] For the two-layer stochastic programming model, it should be noted that the upper distribution network party first calculates the normal load growth situation. And reserve charging load access capacity for each node To plan the expansion of the distribution network, including the expansion of substations (x i ), the addition and expansion of lines (y ij );The lower-level charging station investors shall allocate the reserved capacity of the power grid according to the Used as a reference for site selection and capacity planning of charging stations Charging load To ensure the privacy of both parties, the information transmission between the two layers will only involve the necessary parameters and variables. After the initial planning of the upper distribution network layer, the set of charging station nodes that can be constructed (ND2T) and the maximum capacity of charging stations that can be accepted will be The lower-level charging station party will find the optimal charging station planning strategy based on the upper-level information, taking the capture of the maximum vehicle flow in the entire network and the maximum operating benefit as the optimization goals of the two stages, and then calculate the typical daily charging load. The upper layer will receive the charging load Replaces previous generation charging loads Then carry out the distribution network planning, and then start the next round of cycle optimization. The data and variable transmission relationship between the two layers is as follows: Figure 2 shown.

[0153] S20, using the target genetic algorithm, combined with the charging pile construction planning data of the area to be planned, solve the double-layer stochastic planning model to obtain the new energy vehicle charging pile construction planning scheme for the area to be planned. Specifically:

[0154] S21. Determine the genetic algorithm of the upper-level planning model.

[0155] The two-level stochastic programming model is a large-scale random mixed integer nonlinear optimization model. This problem is actually a combinatorial optimization problem and an NP-hard problem in optimization theory. Because its solution space is discontinuous and the solution neighborhood cannot be expressed, it is difficult to solve using traditional numerical algorithms. Heuristic algorithms (such as genetic algorithms) have a natural advantage in dealing with such random and nonlinear optimization problems, that is, their solution time does not increase with the increase of problem scale and complexity. However, the limitations of genetic algorithms are obvious. Traditional genetic algorithms have the disadvantages of being easy to mature prematurely, falling into local optimality, and being destroyed by mutation. Although this problem can be alleviated by increasing the number of populations, increasing the number of iterations, and increasing the probability of crossover and mutation, it requires a huge amount of computational time. In order to efficiently solve the constructed model, an improved genetic algorithm (IGA) is introduced to solve the stochastic programming problem of the upper distribution network. The improved genetic algorithm has made improvements in the two operations of crossover and mutation:

[0156] (1) Crossover operation.

[0157] First, the parent individuals are paired according to the principle of "equal status", that is, the parent population individuals are sorted by fitness value, and two adjacent individuals are paired (individuals with large fitness are paired with adjacent individuals with large fitness, and individuals with small fitness are paired with individuals with small fitness). Then the chaotic sequence is used to determine the location of the intersection, and finally the determined intersection items are crossed. For example, individual A 1 and individual A 2 , where A 1 =(α 1 ,α 2 ,α 3 ,…,α n ),A 2 =(β 1 ,β 2 ,β 3 ,…,βn ), use the Logistic chaotic sequence x(i+1)=4x(i)·(1-x(i)) to generate a positive integer between 2 and n. The specific steps are as follows:

[0158] 1) Take a random initial value of (0,1):

[0159] 2) Use the formula x(i+1)=4x(i)·(1-x(i)) to generate a chaotic value on (0,1) for each individual iteration:

[0160] 3) Then multiply the chaotic value x(i) of the current generation by n and round it to the integer. The resulting value is the crossover position of the gene on the selected chromosome.

[0161] 4) Save the currently obtained chaotic value as the initial value of the chaotic iteration to generate the next generation of cross terms;

[0162] 5) Return to execute 2) for the next iteration.

[0163] This pairing method ensures that individuals with high fitness in the current population cross over with each other, and that the genes of individuals with excellent fitness can be exchanged with each other. In addition to protecting individuals with high fitness, it also increases the genetic diversity of the population.

[0164] In addition, considering the late-stage collection problem, in order to increase the iterative collection speed, an adaptive function is introduced to dynamically change the crossover probability. c The calculation formula is as follows:

[0165]

[0166] Among them, P c is the crossover probability, is the lower limit of the crossover probability, is the upper limit of the crossover probability, a is the base parameter in the crossover; f is the fitness value of the individual that may be switched at present; f min is the minimum fitness value of the current population; f max is the maximum fitness value of the current population; f avg is the average fitness value of the current population; N iter is the current iteration number; k c is a constant parameter.

[0167] It can be seen from formula (48) that as the number of iterations increases, the crossover probability of the population individuals will gradually decrease, which ensures that the algorithm has a larger crossover probability P in the early stage of evolution. cSearch to avoid premature maturation. At the same time, for individuals with fitness values ​​higher than the average fitness value of the group, a lower crossover probability corresponds to the individual being protected and entering the next generation; while for individuals with fitness values ​​lower than the average fitness value, a higher crossover probability corresponds to the individual being modified and entering the next generation. Therefore, the crossover permutation improvement of the adaptive genetic algorithm can provide the optimal crossover probability of an individual under a certain iteration.

[0168] (2) Mutation operation.

[0169] Similarly, the mutation operation also includes two parts: mutation point location and probability operation. The mutation point location also adopts the method of Logistic chaotic sequence to randomly generate an integer between [1, n]. The specific location steps are similar to the calculation and crossover operation, which will not be repeated here. In addition, considering the problem of premature convergence of genetic algorithms, an adaptive function is also introduced in the mutation operation to dynamically adjust the probability of chromosome mutation. Introducing an adaptive function that changes with the number of iterations, the mutation probability P in the adaptive genetic algorithm m The calculation formula is as follows:

[0170]

[0171] Among them, P m is the probability of chromosome mutation, b is the base parameter in the mutation; f is the fitness value of the individual that may be mutated at present; f min is the minimum fitness value of the current population; f max is the maximum fitness value of the current population; f avg is the average fitness value of the current population; N iter is the current iteration number; k m is a constant. Similarly, as shown in formula (50), with the increase of the number of iterations, the mutation probability of the population individuals will gradually increase, thus ensuring that the algorithm has a smaller mutation probability P in the early stage of evolution. m In the later stages of evolution, with a larger P m Perform global search, which can speed up the iteration. In addition, individuals with large fitness values ​​are given a smaller probability of mutation, which can also protect the excellent objects of the contemporary population to a certain extent.

[0172] S22. Determine the genetic algorithm of the lower-level planning model.

[0173] Since the optimization of the lower-level planning model requires simulation and estimation of random charging behavior and load, it has a certain degree of randomness. Therefore, a genetic algorithm is used to solve the lower-level planning model. The genetic algorithm calculation process for solving the lower-level planning model is as follows:

[0174] Start calculation and input the original data of transportation network planning;

[0175] 1. Set the number of iterations;

[0176] 2. Generate the first stage optimization individuals and select K nodes as the charging station construction addresses. Initialize the population and generate |N D2 T |! / (|N D2 T |-K-1) individuals are used to capture traffic, each of which is 1×N D2 T A 0-1 vector, where 1 means that the candidate node is selected as the charging station construction site, and 0 means that the node is not selected;

[0177] 3. Calculate the traffic flow value of the entire network captured by each individual;

[0178] 4. Select the individual z that captures the most traffic in the entire network * ;

[0179] 5. Based on individual z * Generate the second stage optimization individuals, that is, optimize the traffic capture order s for the K selected nodes. Initialize the population and generate n CSO individual;

[0180] 6. Through equations (26) to (28), use Monte Carlo to simulate the charging behavior of each electric vehicle in a typical day;

[0181] 7. Based on the Monte Carlo simulation results, the typical daily charging load curve is calculated by equations (29) and (30);

[0182] 8. Calculate the capacity of the charging station based on the charging load;

[0183] 9. Calculate the individual fitness value according to formula (31), formula (32), formula (42) and formula (43);

[0184] 10. Perform genetic operations on each individual (including roulette wheel selection with elite retention, crossover, and mutation rewriting);

[0185] 11. Determine whether the solution object satisfies the constraints: Equation (38)-Equation (41). If not, add a penalty term;

[0186] 12. Determine whether the iteration termination condition is met. If it is met, output the optimal address and capacity solution and end the calculation. If it is not met, increase the number of iterations and perform the next iterative calculation.

[0187] In summary, the genetic algorithm solution process of the lower-level planning model is as follows: Figure 3 shown.

[0188] S23. Target genetic algorithm process of the two-level stochastic programming model.

[0189] The solution flow chart of the two-level stochastic programming model is as follows: Figure 4 As shown, the specific steps are as follows:

[0190] 1. Start the calculation, input the charging pile construction planning data, and set the upper and lower genetic algorithm parameters;

[0191] 2. Set the genetic algorithm parameters and iteration times of the upper and lower layers, and set the double-layer iteration convergence conditions;

[0192] 3. Generate the optimized individuals for the distribution network expansion plan. As the estimated value of charging load in distribution network planning, the fitness value of each individual is then calculated;

[0193] 4. Perform selection, crossover, mutation and rewriting operations on the adaptive improved genetic algorithm, and then generate a new generation for fitness calculation;

[0194] 5. Perform power flow calculation on the current distribution network topology. If the topology constraints are met, proceed to the next step. If the topology constraints are not met, continue to repair the grid until the constraints are met.

[0195] 6. Determine whether the power flow calculation satisfies the power flow constraints under the opportunity constraints. If so, proceed to the next step; if not, impose a sufficiently large penalty cost to proceed to the next step;

[0196] 7. Calculate various costs, including investment and construction costs, operating costs, line loss costs, power outage costs and scrapping costs.

[0197] 8. Determine whether all individuals have been calculated. If so, proceed to the next step; if not, return to step 5 to calculate the next individual.

[0198] 9. Determine whether the maximum number of iterations has been reached. If not, return to step 4 to perform the next generation of calculations. If reached, select the optimal strategy, integrate and calculate the set of nodes that can be constructed and the capacity that can be accommodated by each node under the strategy, and pass it to the lower layer for charging station planning.

[0199] 10. After selecting the optimal planning strategy for the current charging station, determine whether the double-layer collaborative optimization output conditions are met. If so, output the current distribution network planning results and charging station planning results; if not, pass the typical daily load curve under the strategy to the upper layer for a new round of collaborative planning.

[0200] S23. Example analysis of the solution process.

[0201] Traffic network topology diagram Figure 5The node information is shown in Table 1, and the road data is shown in Table 2. Figure 5 In the above example, it is assumed that all nodes can build charging stations.

[0202] Table 1:

[0203] node Land price (yuan / square meter) 1 7950 2 13500 3 7950 4 13500 5 13500 6 7950 7 20100 8 28350 9 13500 10 20100

[0204] Table 2:

[0205] First Node End Node Length / km 1 2 4 1 5 5 2 3 3 2 4 4 3 4 4 3 9 4 4 5 3 4 7 5 4 8 5 4 9 6 5 6 5 5 7 5 6 7 3 7 8 3 8 9 6 8 10 6 9 10 6

[0206] Other planning simulation data are: the price of electricity purchased from the power grid is 0.55 yuan / kWh, the price of charging service provided to electric vehicles is 2 yuan, the equipment discount rate is 0.08, the average service life of the charging station is 10 years, the operation and maintenance cost is 0.1 of the initial investment, the other construction cost of the charging station is 40,000 yuan, the average area of ​​each charging pile is 20 square meters, the charging power of the charging pile is, the charging pile operation efficiency is 0.9, the charging pile unit price is 88,000 yuan, assuming that the number of electric vehicles with fast charging demand in the road network is 2,000, and the values ​​of the parameters used in the electric vehicle behavior simulation in four periods of a typical day are shown in Table 3. The algorithm parameters are set as follows: 100 individuals are generated by the GA initial solution object, the crossover probability is 0.85, the mutation probability is 0.85, and the number of iterations is 100.

[0207] Table 3:

[0208]

[0209] The technical solution of this embodiment can improve the economy and rationality of the construction planning of new energy vehicle charging piles, and improve the operating efficiency of both the distribution network and the fast charging station.

[0210] Figure 6 FIG. 2 shows a schematic diagram of a structure of an embodiment of a new energy vehicle charging pile construction planning system 200 provided by the present invention. Figure 6 As shown, the system 200 includes: a construction module 210 and a planning module 220;

[0211] The construction module 210 is used to: construct a two-layer stochastic programming model for characterizing the construction planning of new energy vehicle charging piles; the two-layer stochastic programming model includes: an upper-layer planning model for characterizing the capacity expansion planning of the distribution network and a lower-layer planning model for characterizing the site selection and capacity planning of the fast charging station;

[0212] The planning module 220 is used to solve the double-layer stochastic planning model by using the target genetic algorithm and combining the charging pile construction planning data of the area to be planned to obtain the new energy vehicle charging pile construction planning scheme of the area to be planned.

[0213] In an optional manner, the upper-level planning model includes: a first objective function, a distribution network radial constraint, and a relaxed power flow constraint; wherein the first objective function aims to minimize the annual investment cost.

[0214] In an optional manner, the lower-level planning model includes: a second objective function, traffic capture constraints, FCS planning constraints, available charging pile constraints within the station, charging station capacity planning constraints, charging station operating power constraints and electric vehicle power constraints; wherein the second objective function aims to achieve the optimal combination of planning economy and operating efficiency.

[0215] In an optional manner, the charging pile construction planning data includes: urban traffic data, driving behavior data and power grid load data.

[0216] It should be noted that the beneficial effects of the new energy vehicle charging pile construction planning system provided in the above embodiment are the same as the beneficial effects of the new energy vehicle charging pile construction planning method, which will not be repeated here. In addition, when the system provided in the above embodiment realizes its functions, it only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0217] Among them, the new energy vehicle charging pile construction planning system of the present invention can be a computer program (including program code) running in a computer device. For example, the new energy vehicle charging pile construction planning system of the present invention is an application software that can be used to execute the corresponding steps in the new energy vehicle charging pile construction planning method of the present invention.

[0218] In some embodiments, the new energy vehicle charging pile construction planning system of the present invention can be implemented in a combination of software and hardware. As an example, the new energy vehicle charging pile construction planning system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the new energy vehicle charging pile construction planning method of the present invention. For example, the processor in the form of a hardware decoding processor can adopt one or more application-specific integrated circuits (ASIC, Application Specific Integrated Circuit), DSP, programmable logic device (PLD, Programmable Logic Device), complex programmable logic device (CPLD, Complex Programmable Logic Device), field programmable gate array (FPGA, Field-Programmable Gate Array) or other electronic components.

[0219] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The name of a module does not limit the module itself in some cases.

[0220] like Figure 7 As shown, an electronic device 300 of an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above-mentioned new energy vehicle charging pile construction planning methods, specifically:

[0221] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements any of the new energy vehicle charging pile construction planning methods provided in the above embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for realizing device functions, which will not be repeated here.

[0222] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that the computer implements any of the above-mentioned new energy vehicle charging pile construction planning methods.

[0223] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0224] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes any of the above-mentioned new energy vehicle charging pile construction planning methods.

[0225] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0226] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0227] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0228] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A new energy vehicle charging pile construction planning method, characterized in that: include: Construct a two-layer stochastic programming model for characterizing the construction planning of new energy vehicle charging piles; wherein the two-layer stochastic programming model includes: an upper-layer planning model for characterizing the capacity expansion planning of the distribution network and a lower-layer planning model for characterizing the site selection and capacity planning of fast charging stations; The double-layer stochastic programming model is solved by using a target genetic algorithm and combining the charging pile construction planning data of the area to be planned to obtain a new energy vehicle charging pile construction planning scheme for the area to be planned.

2. The new energy vehicle charging pile construction planning method according to claim 1 is characterized in that: The upper-level planning model includes: a first objective function, a distribution network radial constraint and a relaxed power flow constraint; wherein the first objective function aims to minimize the annual investment cost.

3. The new energy vehicle charging pile construction planning method according to claim 1 is characterized in that: The lower-level planning model includes: a second objective function, traffic capture constraints, FCS planning constraints, available charging pile constraints within the station, charging station capacity planning constraints, charging station operating power constraints and electric vehicle power constraints; wherein, the second objective function aims to optimize the combination of planning economy and operating efficiency.

4. The new energy vehicle charging pile construction planning method according to any one of claims 1 to 3, characterized in that: The charging pile construction planning data includes: urban traffic data, driving behavior data and power grid load data.

5. A new energy vehicle charging pile construction planning system, characterized in that: include: Building modules and planning modules; The construction module is used to: construct a two-level stochastic programming model for characterizing the construction planning of new energy vehicle charging piles; The two-layer stochastic planning model includes: an upper-layer planning model representing the capacity expansion planning of the distribution network and a lower-layer planning model representing the location and capacity planning of the fast charging station; The planning module is used to solve the double-layer stochastic planning model by using a target genetic algorithm and combining the charging pile construction planning data of the area to be planned to obtain a new energy vehicle charging pile construction planning scheme for the area to be planned.

6. The new energy vehicle charging pile construction planning system according to claim 5 is characterized in that: The upper-level planning model includes: a first objective function, a distribution network radial constraint and a relaxed power flow constraint; wherein the first objective function aims to minimize the annual investment cost.

7. The new energy vehicle charging pile construction planning system according to claim 5 is characterized in that: The lower-level planning model includes: a second objective function, traffic capture constraints, FCS planning constraints, available charging pile constraints within the station, charging station capacity planning constraints, charging station operating power constraints and electric vehicle power constraints; wherein, the second objective function aims to optimize the combination of planning economy and operating efficiency.

8. The new energy vehicle charging pile construction planning system according to any one of claims 5 to 7, characterized in that: The charging pile construction planning data includes: urban traffic data, driving behavior data and power grid load data.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the new energy vehicle charging pile construction planning method as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: At least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the new energy vehicle charging pile construction planning method as described in any one of claims 1 to 4.

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