A method and system for site selection and capacity determination of fast charging stations based on a cut-off site selection model

Abstract

The application discloses a kind of based on the site selection method and system of fast charging station of intercept flow site selection model, the method includes S1: obtaining traffic network topology data and electric vehicle charging behavior data, according to the traffic network topology data and electric vehicle charging behavior data obtained determine traffic network topology structure;S2: the traffic network topology structure is analyzed, determines traffic key node as the candidate node of planning;S3: using SCFCLM two-stage optimization model to the candidate node of planning is optimized and handled, obtains the to-be-built position set of fast charging station and the capacity of corresponding each position fast charging station, the capacity of each position fast charging station as optimal fast charging station planning strategy;Guide traffic network in accordance with optimal strategy configuration charging station.The application is adjusted with the aid of SCFCLM model and can effectively charge load on each node, alleviate the capacity redundancy and capacity shortage problems that appear in traffic network charging station, also reduce the burden of power grid operation.

Description

Technical Field

[0001] This invention relates to the field of planning technology for electric vehicle supporting facilities in transportation networks, specifically to a method and system for site selection and capacity determination of fast charging stations based on a diversion site selection model. Background Technology

[0002] Current planning methods for electric vehicle fast charging stations primarily rely on capturing the distribution of traffic flow in the road network for site selection, and then optimizing the capacity of the fast charging station by combining the captured traffic flow or using queuing theory. Site selection based on road network traffic flow mainly includes the following methods: 1) Based on static road network topology information, dividing the charging service area, and using the Thiessen triangle method to determine the geometric center of each area as the location of the fast charging station; 2) Combining static road network topology information and dynamic traffic flow distribution, using the Current-Catching Location Model (FCLM) to find the set of nodes in the road network that can capture the maximum traffic flow, and then using queuing theory to plan the capacity of the fast charging station based on the traffic flow captured by the nodes in the set; 3) Considering the need for recharging during electric vehicle travel, using the Current-Catching Location Model (FRLM) that considers recharging, completing both site selection and capacity determination decisions within the model.

[0003] In practice, it has been proven that the fast charging station planning results based on the Thiessen triangle method ignore the dynamic characteristics of the road network and the actual charging needs of electric vehicles; while the traditional FCLM and FRLM ignore the selection order of nodes during optimization, resulting in fast charging stations with huge capacity or too small in the planning results, which will lead to low operating efficiency of the charging network, fail to meet the charging needs of a large number of electric vehicles in the future, and have poor practicality. Summary of the Invention

[0004] The technical problem to be solved by this invention is that traditional electric vehicle fast charging station planning methods based on the current-stop location model (FCLM) and the current-stop location model (FRLM) that considers recharging ignore the selection order of nodes during optimization, resulting in fast charging stations with huge capacity or too small in the planning results. This leads to low operating efficiency of the charging network, which does not meet the charging needs of a large number of electric vehicles in the future and has poor practicality.

[0005] The purpose of this invention is to provide a fast charging station location and capacity determination method and system based on the interception location model. This invention adopts an improved interception location model (i.e., a sequential capacity determination interception location model that considers the optimal capture order), which aims to solve the problem that the existing interception location model does not consider the capture order of road network traffic flow, and the planning results are that the fast charging station capacity is too large or too small, which affects the operation efficiency of the traffic network, cannot meet the growth of charging load, and has poor practicality.

[0006] This invention is achieved through the following technical solution:

[0007] In a first aspect, the present invention provides a method for site selection and capacity determination of fast charging stations based on a current-intercepting site selection model, the method comprising the following steps:

[0008] S1: Obtain traffic network topology data and electric vehicle charging behavior data, and determine the spatiotemporal distribution of traffic flow based on the obtained traffic network topology data and electric vehicle charging behavior data;

[0009] S2: Analyze the topology of the transportation network and the spatiotemporal distribution of traffic flow to identify key traffic nodes as candidate nodes for planning;

[0010] S3: Based on the candidate nodes of the plan, the SCFCLM two-stage optimization model is used to optimize and analyze the candidate nodes of the plan to obtain the set of locations to be built for fast charging stations and the capacity of fast charging stations at each location. The capacity of fast charging stations at each location is used as the optimal fast charging station planning strategy; thereby guiding the configuration of charging stations in the transportation network according to the optimal strategy.

[0011] Among them, the two-stage optimization model of SCFCLM is an improved flow-capturing location model, namely the sequential capacitated flow-capturing location model (SCFCLM) that takes into account the optimal capture order.

[0012] The working principle is as follows: Traditional electric vehicle fast charging station planning methods based on the current-capitalization flow-capturing location model (FCLM) and the current-capitalization flow-capturing location model (FRLM) considering recharging neglect the node selection order during optimization. This leads to the formation of fast charging stations with either excessively large or excessively small capacities in the planning results, resulting in low charging network operating efficiency and failing to meet the charging needs of a large number of future electric vehicles, thus having poor practicality. This invention, based on the existing current-capitalization flow-capturing location model (FCLM), combines charging load estimation and a sequential capacity-capturing model to obtain a sequentially capacitated flow-capturing location model (SCFCLM) that considers the optimal capture order. This model can effectively solve the problem of redundancy or shortage of capacity in charging stations in the charging network caused by the traditional FCLM model. The model uses maximizing captured vehicle flow and maximizing revenue and operating efficiency as optimization objectives in two stages to obtain the optimal candidate addresses and planned capacities, thereby improving the rationality of fast charging station planning strategies. Specifically, the model consists of a two-stage objective optimization: the first stage captures the maximum electric vehicle (EV) traffic flow in the transportation network to obtain the set of potential fast-charging station locations z, i.e., providing charging services to as many EV users as possible; the second stage finds and optimizes the optimal capture order of the potential fast-charging station set, thereby deriving the capacity of each potential fast-charging station to maximize the profit and service efficiency of the fast-charging station operator. Based on the EV behavior probability model obtained from actual data, it uses Monte Carlo Simulation (MCS) to estimate the spatiotemporal distribution of charging load. Thus, the capacity of each potential fast-charging station location is optimized using the designed model, serving as the optimal fast-charging station planning strategy; this guides the configuration of charging stations within the transportation network according to the optimal strategy.

[0013] Compared with the traditional FCLM model fast charging station planning method, the fast charging station site selection and capacity determination method of the present invention (1) optimizes the capture order of each node, so that the charging station can plan its own capacity more reasonably and maximize its own economic benefits and operating efficiency; (2) the SCFCLM model of the present invention can effectively adjust the charging load on each node, alleviate the problem of capacity redundancy and insufficient capacity of charging stations in the transportation network, and also reduce the burden of power grid operation.

[0014] Furthermore, the traffic network topology data includes traffic network and traffic flow data, and the traffic network and traffic flow data comes from the classic 25-node road network topology; the electric vehicle charging behavior data is data formed by the charging behavior of electric vehicle drivers, and comes from the Shenzhen Electric Vehicle Driving and Charging Behavior Survey.

[0015] Furthermore, in step S3, the SCFCLM two-stage optimization model is used to optimize and analyze the candidate nodes of the plan. The SCFCLM two-stage optimization model includes a first-stage current-cutting and site selection model and a second-stage charging load estimation and sequential capacity determination model.

[0016] The traffic flow of the traffic network is captured using the interception and site selection model of the first stage. The maximum electric vehicle (EV) flow is captured to select the set of locations z to be built for fast charging stations as a preliminary fast charging station planning strategy.

[0017] Using the charging load estimation and sequential capacity model of the second stage, the capacity of the selected fast charging stations is solved to improve the fast charging station planning strategy, and the capacity of the fast charging station based on each location to be built is obtained as the optimal fast charging station planning strategy.

[0018] Furthermore, the method of using the traffic flow interception and site selection model of the first stage to capture traffic network flow, capturing the largest electric vehicle (EV) flow, and selecting the set of potential fast charging station locations z as the initial fast charging station planning strategy; specifically includes:

[0019] A1: Initialize the initial solution for throttling and address selection, and generate the initial solution z. ι(ι＝0) For each critical traffic node, calculate the fitness value f of the initial solution. ι(ι＝0) This refers to the total amount of traffic captured across the entire network.

[0020] A2: Determine whether the iteration condition for output |f is met. t -f t-1 If |≤0, then the selection, crossover, and mutation processes of the genetic algorithm are performed to proceed to the next round of calculation, and ι=ι+1, where ι is the iteration number; if the condition is met, then the set of selected node positions is output to obtain the set of node positions to be built z, and the second stage of charging load estimation and sequential capacity model processing is entered.

[0021] Furthermore, the interception and location model in the first stage utilizes graph G. T (N T E T Let N represent a city's transportation network. T It is a set of traffic nodes, E T These are roads connecting various nodes, which generally represent key road interactions, major areas, or locations of traffic congestion; assuming electric vehicle users travel according to the origin-destination pair OD(Origin-Destination(OD)pair)q represented by matrix Q, where... The starting point of the connection is o (o∈N) T ) and endpoint d (d∈N) TThe path of ), i.e., OD to q; the objective function of the first-stage interception and location model can be expressed as:

[0022]

[0023] The constraints are given by formulas (2) and (3):

[0024]

[0025]

[0026]

[0027] in, Let OD be the traffic flow on road segment e at time t, as shown in formula (4); This is a binary variable; it represents the traffic flow belonging to path e of OD pair q when it is captured. Conversely, it is 0; z u For binary variables, when node u builds a new fast charging station, then z u =1; otherwise 0; K is the total number of newly built fast charging stations in the area; and These are the weights for nodes as start and end points, respectively. These weights are derived from traffic surveys (considering factors such as road capacity, average speed, and traffic congestion), and represent the degree to which a node attracts EVs within the road network; D q Let be the shortest path distance between OD and q.

[0028] Formula (1) maximizes the total number of traffic flows captured in a day. Formula (2) indicates that only when a fast charging station exists on q, and the traffic flow is on segment e... q Only traffic flow on the road network will be captured. Constraint formula (3) limits the total number of planned fast charging stations in the road network to K.

[0029] Furthermore, the second-stage charging load estimation and sequential capacity model are used to solve for the capacity of selected fast charging stations, thus refining the fast charging station planning strategy and obtaining the capacity of fast charging stations at each proposed location, which serves as the optimal fast charging station planning strategy. Specifically, this includes:

[0030] B1: Based on the set of node locations z obtained using the interception and site selection model of the first stage, generate an initial solution n. ι(ι＝0) The Monte Carlo method (MCS) is used to simulate the driving and charging behavior of electric vehicles, and then the electric vehicle load is accumulated to obtain the daily charging load of the entire network.

[0031] B2: Based on the charging load analysis obtained in step B1 and the preliminary fast charging station planning strategy obtained from the current interception and site selection model in the first stage, calculate the fitness value of each fast charging station to be built.

[0032] B3: Determine whether the individual fast charging stations to be built meet the output iteration conditions. If the conditions are not met, a genetic algorithm is used for selection, crossover, and mutation, and then the next round of calculation is performed, with ι = ι + 1. If the conditions are met, the capacity of each fast charging station at the location to be built is obtained as the optimal fast charging station planning strategy, and the optimal fast charging station planning strategy for the traffic network is output.

[0033] Furthermore, the expression for the electric vehicle load in step B1 is:

[0034]

[0035]

[0036] In the formula, The charging load of the fast charging station u at time τ; p rated Power of a single charging station; These represent the number of electric vehicles (EVs) that are charging, connected to charging, and have completed charging and left the fast charging station at time τ.

[0037] Furthermore, the capacity expression for each fast charging station at the location to be built in step B3 is as follows:

[0038]

[0039] In the formula, Let i, F be the normalized objective function for charging station capacity planning. CSO This is the normalized overall objective function.

[0040] Secondly, the present invention also provides a fast charging station site selection and capacity determination system based on a current-cutting site selection model. This system supports the aforementioned fast charging station site selection and capacity determination method based on a current-cutting site selection model. The system includes:

[0041] The acquisition unit is used to acquire traffic network topology data and electric vehicle charging behavior data, and to determine the spatiotemporal distribution of traffic flow based on the acquired traffic network topology data and electric vehicle charging behavior data.

[0042] The traffic critical node identification unit is used to analyze the traffic network topology and the spatiotemporal distribution of traffic flow to identify traffic critical nodes as candidate nodes for planning.

[0043] The SCFCLM two-stage optimization unit is used to optimize and analyze the candidate nodes of the plan using the SCFCLM two-stage optimization model to obtain the set of locations to be built for fast charging stations and the capacity of fast charging stations at each location. The capacity of fast charging stations at each location is used as the optimal fast charging station planning strategy.

[0044] The output unit is used to output the optimal fast charging station planning strategy to guide the configuration of charging stations in the transportation network according to the optimal strategy.

[0045] Furthermore, the SCFCLM two-stage optimization unit uses the SCFCLM two-stage optimization model to optimize and analyze the planned candidate nodes. The SCFCLM two-stage optimization model includes a first-stage current-cutting and site selection model and a second-stage charging load estimation and sequential capacity determination model.

[0046] The traffic flow of the traffic network is captured using the interception and site selection model of the first stage. The maximum electric vehicle (EV) flow is captured to select the set of locations z to be built for fast charging stations as a preliminary fast charging station planning strategy.

[0047] Using the charging load estimation and sequential capacity model of the second stage, the capacity of the selected fast charging stations is solved to improve the fast charging station planning strategy, and the capacity of the fast charging station based on each location to be built is obtained as the optimal fast charging station planning strategy.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] 1. Based on the existing current-capacity interception and location model (FCLM), this invention combines charging load estimation and sequential capacity quantification model to obtain a sequential capacity quantification and location model (SCFCLM) that considers the optimal capture order. It can effectively solve the problem of redundancy or shortage of capacity of each charging station in the charging network caused by the traditional FCLM model. The model takes maximizing the capture of traffic flow and maximizing revenue and operating efficiency as the optimization objectives in two stages to obtain the optimal candidate address and planned capacity, thereby improving the rationality of fast charging station planning strategy.

[0050] 2. This invention optimizes the capture order of each node, enabling charging stations to plan their capacity more rationally and maximize their economic benefits and operating efficiency.

[0051] 3. The two-stage optimization of SCFCLM in this invention can effectively adjust the charging load on each node, alleviate the problem of capacity redundancy and insufficient capacity of charging stations in the transportation network, and also reduce the burden on power grid operation. Attached Figure Description

[0052] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0053] Figure 1 This is a flowchart of a fast charging station site selection and capacity determination method based on a current interception site selection model according to the present invention.

[0054] Figure 2 This is a comparison chart of traffic flow under different capture sequences according to the present invention.

[0055] Figure 3 This is a diagram of a 25-node traffic network system in an embodiment of the present invention.

[0056] Figure 4 This is a diagram of the OD matrix at time t (t=7) in an embodiment of the present invention.

[0057] Figure 5 The diagram shows the planning results of Examples 1-4 of this invention.

[0058] Figure 6 The daily charging load curves are shown in Examples 1-4 of this invention.

[0059] Figure 7 This is the target result diagram for all cases of this invention.

[0060] Figure 8 This is a detailed flowchart of a fast charging station site selection and capacity determination method based on a current interception site selection model according to the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0062] Example 1

[0063] like Figures 1 to 8 As shown, this invention provides a method for site selection and capacity determination of fast charging stations based on a current-limiting site selection model. Figure 1 and Figure 8 As shown, the method includes the following steps:

[0064] S1: Obtain traffic network topology data and electric vehicle charging behavior data, and determine the spatiotemporal distribution of traffic flow based on the obtained traffic network topology data and electric vehicle charging behavior data;

[0065] S2: Analyze the topology of the transportation network and the spatiotemporal distribution of traffic flow to identify key traffic nodes as candidate nodes for planning;

[0066] S3: Based on the candidate nodes of the plan, the SCFCLM two-stage optimization model is used to optimize and analyze the candidate nodes of the plan to obtain the set of locations to be built for fast charging stations and the capacity of fast charging stations at each location. The capacity of fast charging stations at each location is used as the optimal fast charging station planning strategy; thereby guiding the configuration of charging stations in the transportation network according to the optimal strategy.

[0067] Among them, the two-stage optimization model of SCFCLM is an improved flow-capturing location model, namely the sequential capacitated flow-capturing location model (SCFCLM) that takes into account the optimal capture order.

[0068] In this embodiment, the traffic network topology data includes traffic network and traffic flow data, and the traffic network and traffic flow data comes from the classic 25-node road network topology; the electric vehicle charging behavior data is data formed by the charging behavior of electric vehicle drivers, which comes from the Shenzhen Electric Vehicle Driving and Charging Behavior Survey.

[0069] In this embodiment, step S3 uses the SCFCLM two-stage optimization model to optimize and analyze the planned candidate nodes. The SCFCLM two-stage optimization model includes a first-stage current-cutting and site selection model and a second-stage charging load estimation and sequential capacity determination model.

[0070] (1) The traffic flow of the traffic network is captured by the interception and site selection model of the first stage, and the maximum electric vehicle (EV) flow is captured in order to select the set of locations to be built for fast charging stations z as the initial fast charging station planning strategy.

[0071] (2) Using the charging load estimation and sequential capacity model of the second stage, the capacity of the selected fast charging stations is solved to improve the fast charging station planning strategy, and the capacity of the fast charging station based on each location to be built is obtained as the optimal fast charging station planning strategy. Based on the electric vehicle (EV) behavior probability model obtained from actual data, Monte Carlo Simulation (MCS) is used to estimate the spatiotemporal distribution of charging load.

[0072] Specifically, the interception and location selection model for the first stage is as follows:

[0073] A. Interception Site Selection Model

[0074] Using graph G T (N T E T Let N represent a city's transportation network. T It is a set of traffic nodes, E T These are the roads connecting the nodes, which generally represent key road interactions, major areas, or locations of traffic congestion. Assume that electric vehicle users travel according to the origin-destination (OD) pair q represented by matrix Q, where... The starting point of the connection is o (o∈N) T ) and endpoint d (d∈N) TThe path is ), i.e., OD pair q. Therefore, the optimization objective in this stage is to capture the maximum electric vehicle (EV) traffic flow, with the optimization variable z being the candidate set of fast charging station locations. The objective function can be expressed as:

[0075]

[0076] The constraints are given by formulas (2) and (3):

[0077]

[0078]

[0079]

[0080] in, Let OD be the traffic flow on road segment e at time t, as shown in formula (4); This is a binary variable; it represents the traffic flow belonging to path e of OD pair q when it is captured. Conversely, it is 0; z u For binary variables, when node u builds a new fast charging station, then z u =1; otherwise 0; K is the total number of newly built fast charging stations in the area. and These are the weights for nodes as start and end points, respectively. These weights are derived from traffic surveys (considering factors such as road capacity, average speed, and traffic congestion), and represent the attractiveness of a node to electric vehicles (EVs) within the road network; D q Let be the shortest path distance between OD and q.

[0081] Formula (1) maximizes the total number of traffic flows captured in a day. Formula (2) indicates that only when a fast charging station exists on q, and the traffic flow is on segment e... q Only traffic flow on the road network will be captured. Constraint formula (3) limits the total number of planned fast charging stations in the road network to K.

[0082] Specifically, the charging load estimation and sequential capacity stabilization model in the second stage includes a charging load estimation model and a sequential capacity stabilization model, as follows:

[0083] First, B. Charging load estimation model

[0084] Existing technology has proven that the number of vehicles is directly related to traffic flow. Therefore, the number of vehicles captured by node u at time t is:

[0085]

[0086] Where, N PEV The total number of EVs within the road network; flow t,u This represents the traffic flow captured at node u at time t.

[0087] This invention only considers electric vehicles (EVs) that require fast charging. For charging stations, the charging information of EVs includes access time and charging duration (some EVs will not be fully charged before setting off). Dividing the day into four time periods—1-8 AM, 9-3 PM, 4-7 PM, and 8-12 AM—the combination of EV access times following a normal distribution is shown in formula (6), and the combination of their charging durations following a chi-square distribution is shown in formula (7). Using this information, Monte Carlo Simulation (MCS) can be used to simulate the charging behavior of each EV.

[0088]

[0089]

[0090] Since fast charging typically lasts between 15 and 120 minutes, this invention sets the timescale to 15 minutes, denoted by τ, to improve computational efficiency without sacrificing accuracy. Therefore, the number of EVs charging at fast charging station u at time τ is shown in formula (8), and the charging load of fast charging station u at time τ is shown in formula (9).

[0091]

[0092]

[0093] in, Let N represent the number of EVs charging at fast charging station u at time τ, respectively, the number of EVs currently charging, the number of EVs connected to charging, and the number of EVs that have completed charging and left. Furthermore, formula (8) ensures that the State of Charge (SOC) and charging state of each EV are continuous at the end of the day and the beginning of the next day. Therefore, for a total of N... PEV The charging load simulation of the vehicle at 96 time points can be obtained using the above method.

[0094] The spatiotemporal distribution of the charging load of the entire transportation network is obtained from formula (9) of the B. charging load estimation model.

[0095] Second, C. Sequential saturation model

[0096] (1) Capture order analysis

[0097] We found that for a given set of candidate nodes z, the traffic flow captured is fixed, but the traffic flow captured by each individual node changes depending on the order in which it is captured in the model. A simple example is... Figure 2As shown, let the candidate set z = {A, B, C} be set. The straight lines of different gray colors in the same road segment between nodes represent the traffic flow belonging to different OD pairs. Then the thickness of the entire road segment can represent the size of the traffic flow passing through that road segment.

[0098] This invention selects the node selection order s = (A1→B2→C3) of FCLM and an arbitrary selection order s = (B1→C2→A3), as shown below. Figure 2 (a) and Figure 2 As shown in (b), after the final capture, the remaining uncaptured traffic flow in the entire traffic network is exactly the same in both sequences. However, in the existing interception and location model FCLM ( Figure 2 In the capture order of (a)), each selection chooses the node with the most traffic currently captured. This can lead to nodes that capture traffic first having a large amount of traffic, such as... Figure 2 (a) Node A; while nodes at the end of the capture sequence will only capture a very small amount of traffic, such as Figure 2 (b) Node C. Calculating the charging station capacity based on this capture order would result in charging station A having a huge capacity, while charging station C would only have a very small number of charging piles. Therefore, the capture order based on the existing current interception and location model FCLM has two drawbacks:

[0099] a. When it is not during peak charging periods, those charging stations with large capacities will have capacity redundancy, resulting in low efficiency of the charging stations.

[0100] b. Due to a shortage of charging stations, some small-capacity charging stations will lose access to vehicles that can be charged. Underestimating charging capacity will negatively impact the revenue of charging stations.

[0101] (2) Charging station capacity optimization model

[0102] To overcome the above shortcomings, the second stage of the SCFCLM two-stage optimization model of this invention will optimize the capacity of each fast charging station based on the candidate set z obtained in the first stage. The optimization objectives of the second stage are to maximize the annual net present value (NPV) and utilization efficiency of the fast charging station, as shown in formulas (10) and (11), respectively.

[0103] maxF1 CSO =R E -C Cons -C FCS,O&M -C D,FCS (10)

[0104]

[0105] Formula (11) is the reciprocal of the peak charging variance of each station, representing the impact of charging station capacity redundancy or insufficiency on operating efficiency. Revenue R from providing charging services E Investment cost C Cons Operation and maintenance costs C FCS,O&M and depreciation cost C FCS,D The costs are calculated using formulas (12)-(15) respectively. To simplify the problem, this invention makes the operation and maintenance costs proportional to the investment costs.

[0106]

[0107]

[0108] C FCS,O&M =λ CSO C Cons (14)

[0109]

[0110]

[0111] In the formula, u s Let u represent the node u that captures EV traffic at the s-th order, u∈z; η is the charging efficiency of the charging pile; c FCS c DS These represent the charging price at the charging station and the electricity purchase price from the grid, respectively; r is the depreciation rate; T FCS c. The operating life of the charging station; pile A represents the unit price of a charging station; A represents the floor area occupied by each charging station. The land price at node u is expressed in yuan / m2. The optimization variable represents the number of charging piles at charging station u; C other Other costs required for installing charging stations; λ CSO To represent the constant coefficient of operation, this invention takes 0.1; C CSO,dc For the depreciation cost of charging stations; d FCS The depreciation rate for charging stations is given by formula (16); K FCS,d For T FCS The residual value of charging stations at the end of the year.

[0112] In addition to the formulas (2)-(3) in the first stage, the constraints on the construction of charging stations also include:

[0113]

[0114]

[0115]

[0116] SoC min≤SoC τ,ev ≤SoC max (20)

[0117] Among them, Formula (17) is the constraint on the number of available charging piles at charging station u at time t; Formula (18) ensures that the number of charging piles installed at charging station u can meet the charging demand at any time of day, while not exceeding its upper limit; Formula (19) indicates that the charging load is simultaneously limited by the charging station capacity and the maximum power that the power grid can accept; Formula (20) indicates the range anxiety of electric vehicle EV owners, limiting the SOC within a certain range.

[0118] (3) Standardization of planning objectives

[0119] Formulas (10) and (11) are measurements in two different units, therefore they need to be standardized:

[0120]

[0121] in, Let these be the minimum and maximum values ​​of the i-th objective function in the second stage. Therefore, the objective function... The value lies in the interval [0, 1]. The final objective function for the second stage is:

[0122]

[0123] Based on the first-stage interception and site selection model, the set of locations z to be built for fast charging stations is obtained. Then, combined with the second-stage charging load estimation and sequential capacity determination model, the capacity of fast charging stations at each location is obtained, which guides the subsequent layout of charging stations.

[0124] The innovation of this invention lies in combining the charging load estimation model and the sequential capacity stabilization model to form an improved current stabilization location model based on the current stabilization location model.

[0125] The present invention conducts simulation experiments based on the above method steps as follows:

[0126] A. Simulation parameter settings

[0127] This invention uses a 25-node urban traffic network system as a case study to test the proposed improved interception and location model. According to formula (4), the traffic flow distribution (t=7) represented by the OD matrix is ​​as follows: Figure 4As shown, it can be seen that during the morning rush hour, there is a large flow of traffic on the road segments associated with node 6 or node 15. This invention assumes that the number of electric vehicles in the planning area is in a dynamic equilibrium state. The planning area has 2,000 electric vehicles (NPEV = 2,000), and 6 fast charging stations (K = 6) are planned. The parameters μ, σ, and n in formulas (6)-(7) are probability distribution parameters obtained from real data in Shenzhen, used to describe the charging behavior of electric vehicle drivers. Their values ​​for the four time periods are shown in Table 3. Other parameters are shown in Table 4.

[0128] Table 3 Charging behavior parameters

[0129] Time (h) <![CDATA[μ t ]]> <![CDATA[σ t ]]> <![CDATA[n t ]]> (0，8] 3.93 1.86 0.35 (8，15] 11.94 1.36 0.3 (15，19] 16.98 0.90 0.33 (19，24] 21.56 0.98 0.27

[0130] Table 4. Parameters for Two-Level Programming

[0131] parameter value parameter value <![CDATA[p rated ]]> <![CDATA[44kW [6] ]]> η 90％ <![CDATA[c FCS ]]> ¥2 / kWh r 0.08 <![CDATA[c pile ]]> 88000￥ <![CDATA[T FCS ]]> 10 years A <![CDATA[20m 2 ]]> <![CDATA[c DS ]]> 0.5 RMB / kWh <![CDATA[C other ]]> 40000￥

[0132] B. Deterministic Programming Results and Analysis

[0133] The superiority of the proposed two-stage optimization model SCFCLM (hereinafter referred to as SCFCLM) is illustrated through the following four case studies.

[0134] Case 1: Using the SCFCLM two-stage optimization model to capture electric vehicle (EV) traffic; the objectives of CSO include NPV and operational efficiency.

[0135] Case 2: Capturing electric vehicle (EV) traffic using the existing interception location model FCLM (hereinafter referred to as FCLM); CSO objectives include NPV and operational efficiency.

[0136] Case 3: The SCFCLM two-stage optimization model based on the present invention captures the EV traffic flow; the target of CSO is only the NPV of fast charging stations.

[0137] Case 4: The SCFCLM two-stage optimization model based on the present invention captures the EV traffic flow; the CSO objective is only the operating efficiency of fast charging stations.

[0138] The planning topology results for the four cases are as follows: Figure 2 As shown. The planning results for each case are marked near the node to be planned, such as... Figure 5 As shown. The charging demand curve for each case is as follows. Figure 6 As shown in Table 5. The planning results for each case are presented in Table 5 and... Figure 7 As shown.

[0139] Table 5. Planning Results for All Cases

[0140]

[0141] like Figure 5 As shown in Table 5, all four cases selected the same fast-charging station construction nodes, verifying the consistency and effectiveness of the SCFCLM two-stage optimization model of this invention with the existing interception site selection model FCLM (Case 2). These nodes captured 76.37% of the average traffic flow of the road network throughout the day, and the different capture order resulted in different traffic flows captured by each node, further affecting the number of charging piles planned for each fast-charging station and its operating efficiency. Since node 14 plays an important role as a transportation hub in the transportation network, this node is used as an example to compare the results of the SCFCLM two-stage optimization model of this invention and the existing interception site selection model FCLM in Case 1 and Case 2. In Case 2, as shown in Table 5 and... Figure 6 Node 14 in (b) Figure 6 As shown by the unmarked curve on the center line, Node 14 was the first node to capture traffic flow, thus capturing a large volume of traffic. Therefore, its charging station capacity was the largest among all cases (52 charging piles), and its peak charging load was also the highest (2.488MW). In Case 2, Node 14's Service Capacity (SA) (the ratio of EVs charging during off-peak hours to the total number of charging piles) was only 4.52%, while the average SA of all charging stations in Case 2 was 5.42%. This indicates that the planning strategy based on the existing FCLM (Cost-Free Location Model) resulted in significant charging station capacity redundancy during off-peak charging periods. Furthermore, Node 5 in Case 2 only had 8 charging piles planned, making it unable to meet the future charging demands of a large number of electric vehicles. Compared to Case 2, the Node 14 charging station in Case 1, planned based on SCFCLM, had better performance, with a peak charging load of 1.584MW and an SA of 11.11%. In addition, because some of the charging load from the high-capture nodes in Case 1 was transferred to other nodes, this helped the charging station cope with future increases in charging demand.

[0142] Unlike Case 1, Cases 3 and 4 focus only on the operational efficiency of NPV or CSO, respectively. Node 14 captures EV traffic at the last position of the sequence in all three cases applying the SCFCLM two-stage optimization model of this invention. Its load peaks in Cases 3 and 4 are as follows: Figure 6 As shown in (c) and (d), the capacities are 1.628MW and 1540MW, respectively. Similarly, in these three cases, node 13 is set as the node that first captures EV traffic, indicating that node 13 performs well in terms of both economy and operational efficiency. In case 3, the peak load and charging station capacity of node 2 are both higher than in case 4, indicating that building a larger charging station at node 2 would bring better economic benefits. (Compare Table 5 and...) Figure 7 Among the three case studies with different objectives, Case 3 showed the best economic performance, with an NPV of ¥16.726; Case 4 demonstrated the highest operational efficiency, at ¥7.49 × 10⁻⁶.-4 Case 1 showed the best overall performance, with an NPV of 16.308M¥ and an operating efficiency of 7.36×10⁻⁶. -4 The average SA is 9.91%. Considering the profitability and operation of charging stations, Case 1 is clearly the best. Furthermore, in the case using the invented SCFCLM two-stage optimization model, the captured flow of nodes with high land prices is less than that of nodes in the same FCLM. Therefore, charging stations located at high land price nodes will reduce their capacity, making the invented SCFCLM two-stage optimization model method more practical for fast charging station planning.

[0143] Therefore, the fast charging station location and capacity determination method based on the interception location model of this invention considers a two-stage interception location model with optimal capture order. This model takes maximizing the captured traffic flow and maximizing revenue and operating efficiency as the optimization objectives of the two stages, respectively, to obtain the optimal candidate address and planned capacity, thereby improving the rationality of the fast charging station planning strategy. According to the probability distribution function, the Monte Carlo method is used to simulate the charging behavior of electric vehicles to obtain the daily charging load curve of the target area. Simulation results show that: 1) Compared with the traditional FCLM model, the fast charging station location and capacity determination method based on SCFCLM proposed in this invention can optimize the capture order of each node, enabling the charging station to plan its own capacity more rationally, thereby maximizing its own economic benefits and operating efficiency; 2) The fast charging station location and capacity determination method based on SCFCLM proposed in this invention can effectively adjust the charging load on each node, alleviate the problem of capacity redundancy and insufficient capacity of charging stations in the transportation network, and also reduce the burden on the power grid operation.

[0144] Example 2

[0145] like Figures 1 to 8 As shown, the difference between this embodiment and Embodiment 1 is that this embodiment provides a fast charging station site selection and capacity determination system based on a current-cutting site selection model. This system supports the fast charging station site selection and capacity determination method based on a current-cutting site selection model described in Embodiment 1. The system includes:

[0146] The acquisition unit is used to acquire traffic network topology data and electric vehicle charging behavior data, and to determine the spatiotemporal distribution of traffic flow based on the acquired traffic network topology data and electric vehicle charging behavior data.

[0147] The traffic critical node identification unit is used to analyze the traffic network topology and the spatiotemporal distribution of traffic flow to identify traffic critical nodes as candidate nodes for planning.

[0148] The SCFCLM two-stage optimization unit is used to optimize and analyze the candidate nodes of the plan using the SCFCLM two-stage optimization model to obtain the set of locations to be built for fast charging stations and the capacity of fast charging stations at each location. The capacity of fast charging stations at each location is used as the optimal fast charging station planning strategy.

[0149] The output unit is used to output the optimal fast charging station planning strategy to guide the configuration of charging stations in the transportation network according to the optimal strategy.

[0150] In specific implementation, the SCFCLM two-stage optimization unit uses the SCFCLM two-stage optimization model to optimize and analyze the planned candidate nodes. The SCFCLM two-stage optimization model includes a first-stage current-cutting and site selection model and a second-stage charging load estimation and sequential capacity determination model.

[0151] The traffic flow of the traffic network is captured using the interception and site selection model of the first stage. The maximum electric vehicle (EV) flow is captured to select the set of locations z to be built for fast charging stations as a preliminary fast charging station planning strategy.

[0152] Using the charging load estimation and sequential capacity model of the second stage, the capacity of the selected fast charging stations is solved to improve the fast charging station planning strategy, and the capacity of the fast charging station based on each location to be built is obtained as the optimal fast charging station planning strategy.

[0153] The execution process of each unit can be carried out according to the steps of the fast charging station location and capacity determination method based on the interception location model described in Example 1. In this example, they will not be described in detail.

[0154] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0158] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for site selection and capacity determination of fast charging stations based on a current interception site selection model, characterized in that, The method includes the following steps: S1: Obtain traffic network topology data and electric vehicle charging behavior data, and determine the spatiotemporal distribution of traffic flow based on the obtained traffic network topology data and electric vehicle charging behavior data; S2: Analyze the topology of the transportation network and the spatiotemporal distribution of traffic flow to identify key traffic nodes as candidate nodes for planning; S3: Based on the planned candidate nodes, the SCFCLM two-stage optimization model is used to optimize the planned candidate nodes to obtain the set of locations to be built for fast charging stations and the capacity of fast charging stations at each location. The capacity of fast charging stations at each location is used as the optimal fast charging station planning strategy; thereby guiding the configuration of charging stations in the transportation network according to the optimal strategy. In step S3, the SCFCLM two-stage optimization model is used to optimize and analyze the candidate nodes of the plan. The SCFCLM two-stage optimization model includes a first-stage current-cutting and site selection model and a second-stage charging load estimation and sequential capacity determination model. The traffic flow of the traffic network is captured using the interception and site selection model of the first stage. The maximum electric vehicle (EV) flow is captured to select the set of locations z to be built for fast charging stations as a preliminary fast charging station planning strategy. Using the charging load estimation and sequential capacity model of the second stage, the capacity of the selected fast charging stations is solved to improve the fast charging station planning strategy, and the capacity of the fast charging station based on each location to be built is obtained as the optimal fast charging station planning strategy. The method of using the traffic flow interception and site selection model of the first stage to capture traffic network flow, capturing the largest electric vehicle (EV) flow, and selecting the set of potential fast charging station locations z as the initial fast charging station planning strategy; specifically including: A1: Initialize the initial solution for throttling and address selection, and generate the initial solution z. ι(ι＝0) For each critical traffic node, calculate the fitness value f of the initial solution. ι(ι＝0) This refers to the total amount of traffic captured across the entire network. A2: Determine whether the iteration condition for output |f is met. ι -f ι-1 If |≤0, then the selection, crossover, and mutation processes of the genetic algorithm are performed to proceed to the next round of calculation, and ι=ι+1, where ι is the iteration number; if the condition is met, then the set of selected node positions is output to obtain the set of node positions to be built z, and the second stage of charging load estimation and sequential capacity stabilization model processing is entered. The second-stage charging load estimation and sequential capacity quantification model is used to solve for the capacity of selected fast charging stations, thus refining the fast charging station planning strategy and obtaining the capacity of fast charging stations at each proposed location as the optimal fast charging station planning strategy. Specifically, this includes: B1: Based on the set of node locations z obtained using the interception and site selection model of the first stage, generate an initial solution n. ι(ι＝0) The Monte Carlo method (MCS) is used to simulate the driving and charging behavior of electric vehicles, and then the electric vehicle load is accumulated to obtain the daily charging load of the entire network. B2: Based on the charging load analysis obtained in step B1 and the preliminary fast charging station planning strategy obtained from the current interception and site selection model in the first stage, calculate the fitness value of each fast charging station to be built. B3: Determine whether the individual fast charging stations to be built meet the output iteration conditions. If the conditions are not met, a genetic algorithm is used for selection, crossover, and mutation, and then the next round of calculation is performed, with ι = ι + 1. If the conditions are met, the capacity of each fast charging station at the location to be built is obtained as the optimal fast charging station planning strategy, and the optimal fast charging station planning strategy for the traffic network is output.

2. The fast charging station site selection and capacity determination method based on the interception site selection model according to claim 1, characterized in that, The traffic network topology data includes traffic network and traffic flow data, and the traffic network and traffic flow data comes from the classic 25-node road network topology; the electric vehicle charging behavior data is data formed by the charging behavior of electric vehicle drivers.

3. The method for site selection and capacity determination of fast charging stations based on a current interception site selection model according to claim 1, characterized in that, The first-stage interception and location model utilizes graph G. T (N T E T Let N represent a city's transportation network. T It is a set of traffic nodes, E T It is the road connecting the nodes; assume that electric users travel according to the origin-end pair OD q represented by matrix Q, where The starting point of the connection is o (o∈N) T ) and endpoint d (d∈N) T The path of ), i.e., OD to q; the objective function of the first-stage interception and location model can be expressed as: The constraints are given by formulas (2) and (3): in, Let OD be the traffic flow on road segment e at time t, as shown in formula (4); This is a binary variable; it represents the traffic flow belonging to path e of OD pair q when it is captured. Conversely, it is 0; z u For binary variables, when node u builds a new fast charging station, then z u =1; otherwise 0; K is the total number of newly built fast charging stations in the area; and , respectively, represent the weights of nodes as start and end points, indicating the degree to which a node attracts EVs within the road network; D q Let be the shortest path distance between OD and q.

4. The method for site selection and capacity determination of fast charging stations based on the interception site selection model according to claim 1, characterized in that, The expression for the electric vehicle load in step B1 is: In the formula, The charging load of the fast charging station u at time τ; p rated Rated power of a single charging station; These represent the number of electric vehicles (EVs) that are charging, connected to charging, and have completed charging and left the fast charging station at time τ.

5. The method for site selection and capacity determination of fast charging stations based on the interception site selection model according to claim 1, characterized in that, The capacity expression for each fast charging station to be built at each location in step B3 is as follows: In the formula, F i CSO,norm Let i, F be the normalized objective function for charging station capacity planning. CSO This is the normalized overall objective function.

6. A fast charging station site selection and capacity determination system based on a current interception site selection model, characterized in that, The system supports a fast charging station location and capacity determination method based on a current-limiting location model as described in any one of claims 1 to 5, and the system includes: The acquisition unit is used to acquire traffic network topology data and electric vehicle charging behavior data, and to determine the spatiotemporal distribution of traffic flow based on the acquired traffic network topology data and electric vehicle charging behavior data. The traffic critical node identification unit is used to analyze the traffic network topology and the spatiotemporal distribution of traffic flow to identify traffic critical nodes as candidate nodes for planning. The SCFCLM two-stage optimization unit is used to optimize and analyze the candidate nodes of the plan using the SCFCLM two-stage optimization model to obtain the set of locations to be built for fast charging stations and the capacity of fast charging stations at each location. The capacity of fast charging stations at each location is used as the optimal fast charging station planning strategy. The output unit is used to output the optimal fast charging station planning strategy to guide the configuration of charging stations in the transportation network according to the optimal strategy. The SCFCLM two-stage optimization unit uses the SCFCLM two-stage optimization model to optimize and analyze the planned candidate nodes. The SCFCLM two-stage optimization model includes a first-stage current-cutting and site selection model and a second-stage charging load estimation and sequential capacity determination model. The traffic flow of the traffic network is captured using the interception and site selection model of the first stage. The maximum electric vehicle (EV) flow is captured to select the set of locations z to be built for fast charging stations as a preliminary fast charging station planning strategy. Using the charging load estimation and sequential capacity model of the second stage, the capacity of the selected fast charging stations is solved to improve the fast charging station planning strategy, and the capacity of the fast charging station based on each location to be built is obtained as the optimal fast charging station planning strategy. The method of using the traffic flow interception and site selection model of the first stage to capture traffic network flow, capturing the largest electric vehicle (EV) flow, and selecting the set of potential fast charging station locations z as the initial fast charging station planning strategy; specifically including: A1: Initialize the initial solution for throttling and address selection, and generate the initial solution z. ι(ι＝0) For each critical traffic node, calculate the fitness value f of the initial solution. ι(ι＝0) This refers to the total amount of traffic captured across the entire network. A2: Determine whether the iteration condition for output |f is met. ι -f ι-1 If |≤0, then the selection, crossover, and mutation processes of the genetic algorithm are performed to proceed to the next round of calculation, and ι=ι+1, where ι is the iteration number; if the condition is met, then the set of selected node positions is output to obtain the set of node positions to be built z, and the second stage of charging load estimation and sequential capacity stabilization model processing is entered. The second-stage charging load estimation and sequential capacity quantification model is used to solve for the capacity of selected fast charging stations, thus refining the fast charging station planning strategy and obtaining the capacity of fast charging stations at each proposed location as the optimal fast charging station planning strategy. Specifically, this includes: B1: Based on the set of node locations z obtained using the interception and site selection model of the first stage, generate an initial solution n. ι(ι＝0) The Monte Carlo method (MCS) is used to simulate the driving and charging behavior of electric vehicles, and then the electric vehicle load is accumulated to obtain the daily charging load of the entire network. B2: Based on the charging load analysis obtained in step B1 and the preliminary fast charging station planning strategy obtained from the current interception and site selection model in the first stage, calculate the fitness value of each fast charging station to be built. B3: Determine whether the individual fast charging stations to be built meet the output iteration conditions. If the conditions are not met, a genetic algorithm is used for selection, crossover, and mutation, and then the next round of calculation is performed, with ι = ι + 1. If the conditions are met, the capacity of each fast charging station at the location to be built is obtained as the optimal fast charging station planning strategy, and the optimal fast charging station planning strategy for the traffic network is output.

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