A method and device for site selection and capacity determination of an integrated power-to-hydrogen filling station considering power distribution network constraints

Abstract

The application provides a method and device for site selection and capacity determination of an electricity-hydrogen filling integrated station considering power distribution network constraints, which comprises the following steps: predicting traffic flow in a future urban planning area by using SSA-ANN, and then calculating charging and hydrogen filling demands of new energy vehicles; considering charging equipment, hydrogen filling equipment and power distribution network constraints, constructing a site selection and capacity determination model of an electricity-hydrogen filling integrated station with comprehensive cost; combining electricity-hydrogen demands at traffic intersections, setting the traffic intersections as Voronoi growth points, dividing service areas of the electricity-hydrogen filling integrated station by a Voronoi diagram, and solving the site selection and capacity determination scheme of the electricity-hydrogen filling integrated station with the minimum comprehensive cost by using a particle swarm algorithm. The method provided by the application considers two energy charging plans of charging and hydrogen filling in the filling station, and solves the optimal site selection and capacity determination scheme of the electricity-hydrogen filling integrated station under the constraints of charging equipment, hydrogen filling equipment and power grid by using the particle swarm algorithm.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of traffic energy planning, and particularly relates to a method and device for site selection and capacity determination of an electric-hydrogen filling integrated station considering power distribution network constraints. BACKGROUND

[0002] The electric-hydrogen filling integrated station is one of the basic supporting facilities for electric vehicles and hydrogen fuel cell vehicles, and affects the application level of new energy vehicles. Scientific and reasonable planning of the electric-hydrogen filling integrated station can not only reduce the investment and construction cost, reduce the land occupation area, and relieve the impact of a large number of new energy vehicles on the power distribution network, but also enable new energy vehicle users to enjoy convenient filling services, and has a positive promoting effect on the development of the new energy vehicle industry, and has important practical significance.

[0003] However, the current research on the layout planning of the electric-hydrogen filling integrated station often does not reasonably divide the service area of the filling station, and mostly uses the traditional particle swarm optimization algorithm to solve the model, which may face problems such as falling into a local optimal point and slow convergence speed in the later iteration. The above problems will lead to unreasonable layout planning of the electric-hydrogen filling integrated station, so that the charging and hydrogen filling needs of part of the new energy vehicle users cannot be met. SUMMARY

[0004] In order to solve the above technical problems, the application provides a method and device for site selection and capacity determination of an electric-hydrogen filling integrated station considering power distribution network constraints, which solves the optimal value of the objective function under the constraints of charging, hydrogen filling and power grid by using a particle swarm algorithm, and obtains a site selection and capacity determination scheme of the electric-hydrogen filling integrated station.

[0005] To achieve the above purpose, the technical scheme adopted by the application is as follows:

[0006] A method for site selection and capacity determination of an electric-hydrogen filling integrated station considering power distribution network constraints, comprising the following steps:

[0007] Step S1: using a singular spectrum analysis method SSA-ANN of an artificial neural network to predict the traffic flow in a future urban planning area, taking historical urban traffic intersection vehicle flow data as input data, and then calculating the electric vehicle charging demand and the hydrogen demand of the hydrogen fuel cell vehicle;

[0008] Step S2: according to the charging pile equipment, hydrogen filling equipment and power distribution network constraints, establishing an electric-hydrogen filling integrated station site selection model with the minimum sum of the construction investment cost, operation and maintenance cost, station network loss and hydrogen loss cost, auxiliary road construction cost and user average annual loss cost in the road to the filling station;

[0009] Step S3: Combining the electric vehicle charging demand and hydrogen demand of hydrogen fuel cell vehicles obtained in Step S1, the traffic intersections with demand are designated as Voronoi growth points. The service area of ​​the integrated electric-hydrogen charging and refueling station is divided using the Voronoi diagram. The particle swarm optimization algorithm is used to solve for the site selection and capacity determination scheme of the integrated electric-hydrogen charging and refueling station with the minimum overall cost.

[0010] In one implementation, step S1 includes:

[0011] Step S11: Arrange the historical vehicle data at urban traffic intersections into a one-dimensional time series with a time frequency of 1 minute and a daily vehicle count length of 1440. The daily time series is x1, x2, ..., x 1440 Set a suitable window length L, and construct the trajectory matrix of the vehicle sequence {X} at the traffic intersection based on the length:

[0012]

[0013] Where: K = N - L + 1, that is, X is an L × K trajectory matrix and

[0014] Step S12: Perform singular value decomposition on matrix X, decomposing matrix X into X = UΣV T Let S = XX T Eigenvalue decomposition of S yields eigenvalues ​​λ1>λ2…>λ L ≥0 and the corresponding eigenvectors U1, U2, ..., U L They are all singular spectra of the original sequence and satisfy the formula:

[0015]

[0016] In the formula: U and V become the left and right matrices, respectively, both of which are identity matrices, satisfying UU T =1, VV T =1;

[0017] Step S13: Divide the L components into c disjoint groups according to formula (3), representing different trend components respectively:

[0018] X = X L1 +…+X Lc (3)

[0019] In the formula: U m The eigenvectors of matrix S in step S12 are decomposed.

[0020] Step S14: Calculate sequence X i in U m Projection on:

[0021]

[0022]

[0023] In the formula: x i The i-th column of the trajectory matrix X is represented by the projection. Point X i The expressed temporal evolution in the original sequence x i+1 x i+2 ... x i+L The weights of the time intervals; the sum of the reconstructed sequence should be equal to that of the original sequence.

[0024] Step S15: Construct a three-layer ANN neural network: The first layer is defined as the input layer, with N nodes and neurons to receive the reconstructed matrix trajectory, using the traffic flow time series of traffic intersection nodes for the corresponding number of days in the historical year as input data; the second layer is defined as the hidden layer, with 24 nodes; the third layer is the output layer, used to output the traffic flow at traffic intersection node t time.

[0025] Step S16: Describe the charging and hydrogen refueling requirements of electric vehicles and hydrogen fuel cell vehicles using the following formula:

[0026] The charging demand of intersection node j during time period T:

[0027]

[0028] Hydrogen refueling demand at intersection node j within time period T:

[0029]

[0030] In the formula: q′ represents the traffic flow at intersection node j at time t; j This represents the charging demand of intersection node j within time period T; q′ j ′ represents the hydrogen refueling demand at intersection node j within time period T; k ev The proportion of electric vehicles in the total number of vehicles; k hev The proportion of hydrogen fuel cell vehicles in the total number of vehicles; k cd The proportion of all electric vehicles that require charging, i.e., the charging rate of electric vehicles; k hcd P represents the proportion of all hydrogen fuel cell vehicles that require hydrogen refueling, i.e., the hydrogen refueling rate of hydrogen fuel cell vehicles; ev P represents the average capacity of electric vehicle batteries. hev This represents the average capacity of a hydrogen fuel cell.

[0031] In one implementation, step S2 includes:

[0032] Step S21: An electric-hydrogen filling integrated station site selection and capacity determination model is constructed to minimize the sum of investment cost, operation and maintenance cost, station network loss cost, auxiliary road construction cost, and user average annual loss cost in the journey to the electric-hydrogen filling integrated station, represented as:

[0033]

[0034] In the formula: C represents the total cost of the electric-hydrogen filling integrated station from construction to the target year; C 1i represents the target year electric-hydrogen filling integrated station fixed construction cost converted to the planning period, including transformer, charging pile, electrolyzer, compressor, hydrogen storage tank, hydrogen dispenser and other construction costs; C 2i represents the annual operation and maintenance cost of the electric-hydrogen filling integrated station; C 3i represents the sum of network loss cost and loss cost of compressed hydrogen and filling hydrogen in the charging process of the electric-hydrogen filling integrated station; C 4i represents the auxiliary road construction cost of the electric-hydrogen filling integrated station; C 5i represents the road loss cost of electric vehicles and hydrogen fuel cell vehicles to the electric-hydrogen filling integrated station;

[0035] C 1i = (e i a1+u i b1+f i a2+v i b2+h i c2+w i d2+c i )λ

[0036] Wherein:

[0037]

[0038] In the formula: e i is the number of transformers configured in the filling station i; a1 is the unit price of the transformer; u i is the number of charging piles configured in the filling station i; b1 is the unit price of the charging pile in the filling station; f i is the rated power of the hydrogen compressor configured in the filling station i; a2 is the unit power price of the hydrogen compressor; v i is the number of hydrogen dispensers configured in the filling station i; b2 is the unit price of the hydrogen dispenser in the filling station; h i is the rated hydrogen storage capacity of the hydrogen storage tank in the filling station i; c2 is the unit capacity price of the hydrogen storage tank in the filling station; w i is the rated power of the electrolyzer configured in the filling station i; d2 is the unit power price of the electrolyzer; c iThe capital cost of filling station i; r0 is the discount rate; z is the operating life of filling station i;

[0039] C 2i = (e i a1+u i b1+f i a2+v i b2+h i c2+w i d2+c i )η

[0040] In the formula: the cost term represents the daily maintenance, equipment wear and tear, staff maintenance input and wages of the electric-hydrogen filling integrated station; the operation and maintenance cost is converted to initial investment consideration using the conversion coefficient; η represents the above conversion coefficient;

[0041]

[0042] In the formula: C Fe and C Cu are the iron and copper losses of the transformer respectively; C L is the line loss of the filling station charging pile converted to the loss value of each filling station; C D is the charging pile filling loss of a single filling station; k1 is the simultaneous rate of multiple charging piles in the filling station; T v is the average effective filling time of the filling station per day; p0 is the electricity purchase price paid by the filling station to the power company.f i is the number of compressors; n i is the number of hydrogen filling machines in the filling station; γ comp is the hydrogen loss of the compressor; are the average daily hydrogen filling quantity of the compressor and a single hydrogen filling machine respectively; γ tank is the hydrogen loss of the hydrogen filling machine; k2 is the simultaneous rate of multiple hydrogen filling machines in the filling station; p1 is the hydrogen purchase price of the filling station from the hydrogen production plant;

[0043]

[0044] In the formula: ξ g is the road investment cost per unit length; l i is the length of the auxiliary road newly built for the filling station i to enter and exit the station from the traffic intersection.

[0045] C 5i = h1+h2

[0046] Wherein:

[0047]

[0048]

[0049] ∑L i,ev and ∑L i,hev are the total distances from all traffic intersections within the service range of the filling station i to the electric vehicles and fuel cell vehicles of the filling station i, respectively; g ev and g hev are the driving ranges of the electric vehicles and fuel cell vehicles per unit of electricity and hydrogen, respectively; k is the value of the user's travel time, which can be estimated from the average income of the residents in the planning area; v ev and v hev are the average driving speeds of the electric vehicles and fuel cell vehicles, respectively.

[0050] Step S22: Determine the constraints that the electric-hydrogen filling integrated station site selection model needs to satisfy:

[0051] (1) Electric-hydrogen filling integrated station quantity constraint:

[0052] N min ≤ N ≤ N max

[0053] In the formula: N min and N max are the minimum and maximum values of the number of filling stations allowed to be built in the planning area, respectively.

[0054] (2) Electric-hydrogen filling integrated station charging pile quantity constraint:

[0055] u min ≤ u i ≤ u max i = 1, 2, …, N

[0056] In the formula: u min and u max are the minimum and maximum values of the number of charging piles allowed to be configured in the filling station, respectively.

[0057] (3) Electric-hydrogen filling integrated station hydrogen dispenser quantity constraint:

[0058] v min ≤ v i ≤ v max i = 1, 2, …, N

[0059] In the formula: v min and v max are the minimum and maximum values of the number of hydrogen dispensers allowed to be configured in the filling station, respectively.

[0060] (4) Distance constraint between electric-hydrogen filling integrated stations:

[0061] D min ≤ D ij ≤ D maxi,j = 1,2,…,N,i≠j

[0062] where D ij is the straight-line distance between filling station i and filling station j; D min and D max are the minimum distance and the maximum distance between filling station i and filling station j, respectively.

[0063] (5) Distance constraint from traffic intersection to electric-hydrogen filling integrated station:

[0064] d ij ≤ d max i = 1,2,…,N,j = 1,2,…,N cross

[0065] where d max is the maximum distance from traffic intersection to filling station; N cross is the number of traffic intersections.

[0066] (6) Electrolyzer hydrogen production constraint:

[0067]

[0068]

[0069]

[0070]

[0071] where: is the input power of electrolyzer of filling station i at time t in a year; H H is the high heat value of hydrogen (3.509 KW·h / m 3 ); is the hydrogen production rate of electrolyzer of filling station i at time t; η elec is the energy conversion efficiency of electrolyzer; Γ i,elec is the hydrogen production capacity of electrolyzer of filling station i (kg / h); Γ i,elec , respectively represent the lower limit and the upper limit of the hydrogen production rate of electrolyzer of filling station i;

[0072] (7) Compressor device constraint:

[0073] The hydrogen produced by electrolyzer needs to be stored in hydrogen storage tank, and there is a pressure difference between electrolyzer and hydrogen storage tank, so the hydrogen produced by electrolyzer needs to be pressurized by compressor. The power consumed by hydrogen compressor of filling station i at time t is:

[0074] ​

[0075]

[0076]

[0077] where: is the amount of hydrogen flowing into the compressor at time t from the hydrogen filling station i; is the power of the compressor at the reference operating pressure; β comp is the normal operating pressure of the compressor; β0is the standard atmospheric pressure; is the reference operating pressure of the compressor; γ comp is the hydrogen dissipation rate from the electrolyzer to the compressor; is the hydrogen production rate of the electrolyzer at time t from the hydrogen filling station i.

[0078] (8) Hydrogen storage tank equipment constraints:

[0079] The hydrogen produced by the electrolyzer is not immediately consumed by the hydrogen fuel cell vehicle, but is stored in the hydrogen storage tank:

[0080]

[0081]

[0082]

[0083]

[0084] where: and are the hydrogen storage amounts of the hydrogen storage tank at time t and at time t+1, respectively, from the electric-hydrogen filling integrated station i; is the hydrogen demand amount at time t from the electric-hydrogen filling integrated station i; is the amount of hydrogen flowing into the hydrogen storage tank at time t from the hydrogen filling station i in the electric-hydrogen filling integrated station i; γ tank is the hydrogen dissipation rate from the compressor to the hydrogen storage tank; h i is the hydrogen storage tank capacity; h i is the lower limit of the rated hydrogen storage capacity of the hydrogen storage tank from the filling station i; is the upper limit of the rated hydrogen storage capacity of the hydrogen storage tank from the filling station i;

[0085] (9) Maximum electric power of the electric vehicle allowed to be connected to the power distribution network constraint:

[0086]

[0087] P Ci = Q i + M i · H H

[0088] P Ci is the electric power of the electric-hydrogen filling integrated station i; is the maximum electric power of the electric vehicles and hydrogen fuel cell vehicles allowed to access the distribution network.

[0089] (10) Upper and lower limit constraints of node voltage amplitude:

[0090] V i min ≤V i ≤V i max , i = 1, 2,..., M

[0091] V i is the voltage amplitude of the distribution network node i; V i min and V i max are the upper and lower limits of the node voltage amplitude, respectively; M is the number of nodes of the distribution network under study.

[0092] (11) Maximum current constraint of feeder:

[0093] |I ij |≤I ijmax , i, j = 1, 2,..., M

[0094] I ij and I ijmax are the current and the maximum current allowed to flow through the feeder ij in the distribution network, respectively.

[0095] Step S23: Sum the traffic flow density of the number of road segments connected to the intersection node to obtain the traffic flow density of the node, and use the traffic flow density of the city traffic intersection node to obtain the charging demand of electric vehicles in new energy vehicles and the hydrogen demand of hydrogen fuel cell vehicles in new energy vehicles to construct a constant volume model:

[0096]

[0097]

[0098] Q i represents the charging demand met by the electric-hydrogen filling integrated station i in the T time period; ρ1 is the charging and energy filling margin of the filling station; P1 is the rated charging power of a single charging pile; k 1,x is the charging efficiency of the charging pile; k1 is the simultaneous charging rate of the charging pile in the filling station; T v is the average effective filling time of the filling station per day; M i represents the hydrogen demand met by the electric-hydrogen filling integrated station i in the T time period; The average hydrogenation amount of a single hydrogenation machine in one day; [] is the upward rounding symbol.

[0099] In an embodiment, step S3 comprises:

[0100] Step S31: initialize the particle swarm, set the population size as N, the iteration number as Y, the particle dimension as D, the learning factors as c1 and c2, modify the inertia weight ω using the linear decreasing weight method, and ω can be expressed as:

[0101]

[0102] wherein ω max and ω min are the maximum value and the minimum value of ω respectively; Y is the current iteration number; Y max is the maximum iteration number;

[0103] Step S32: according to the electric quantity of electric vehicles in the urban planning area and the geographic location of the electric quantity required for hydrogen production of hydrogen fuel cell vehicles, and in combination with the urban traffic intersection, the electric-hydrogen filling integrated station is established at the nearby traffic intersection with electric energy demand, and the traffic intersection is taken as the initial station address of the electric-hydrogen filling integrated station.

[0104] Step S33: randomly generate n c electric-hydrogen filling integrated station site coordinates, and compile them as the initial position of the particle swarm;

[0105] Step S34: take the electric-hydrogen filling integrated station site as the growth point to draw a Voronoi diagram to demarcate the service area of each electric-hydrogen filling integrated station, and then according to the new energy vehicle charging and hydrogen filling demand in the service area, a constant-volume model is used to determine the number of charging piles and hydrogen filling machines required to be configured in the electric-hydrogen filling integrated station;

[0106] Step S35: the construction investment annual cost C 1i of the electric-hydrogen filling integrated station, the operation and maintenance annual cost C 2i , the annual cost C 3i of the station internal network loss and hydrogen loss, the auxiliary road construction annual cost C 4i , and the average annual loss cost C 5i of the user in the journey to the filling station are calculated, then the total annual cost C of the charging station is calculated, which is taken as the function value of the particle, finally the individual extreme value P best and the global extreme value G best are found; for the particles that do not meet the constraint conditions, the penalty function method is used to process them;

[0107] Step S36: update the speed and position of the particle, if the maximum iteration number is not reached, go to step S34 for cyclic iteration until the maximum iteration number is reached.

[0108] Step S37: output the optimal site selection and capacity determination scheme of the electric-hydrogen filling integrated station.

[0109] The application also provides a device for implementing the method for site selection and capacity determination of the electric-hydrogen filling integrated station considering power distribution network constraints, comprising:

[0110] The automobile electric-hydrogen demand calculation module: adopts the SSA-ANN model to predict the traffic flow in the future urban planning area, and then calculates the charging and hydrogen filling demand of new energy vehicles;

[0111] The filling integrated station site selection and capacity determination module: on the basis of considering the charging pile equipment, hydrogen filling equipment and power distribution network constraints, an electric-hydrogen filling integrated station site selection model is established, which aims to minimize the sum of the construction investment cost, operation and maintenance cost, in-station network loss and hydrogen loss cost, auxiliary road construction cost and average annual loss cost of users in the route to the filling station.

[0112] The filling integrated station planning solution module: in combination with the electric-hydrogen demand at the traffic intersection, the city traffic intersection is set as a Voronoi growth point, the electric-hydrogen filling station integrated station service area is divided through the Voronoi diagram, and then the improved particle swarm algorithm is used for global optimization to obtain the optimal site selection and capacity determination scheme of the electric-hydrogen filling integrated station in the planning area.

[0113] The planning scheme output module: outputs and saves the optimal planning scheme of the electric-hydrogen filling integrated station site selection and capacity determination.

[0114] The application also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for site selection and capacity determination of the electric-hydrogen filling integrated station considering power distribution network constraints when executing the program.

[0115] The application also provides a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps of the method for site selection and capacity determination of the electric-hydrogen filling integrated station considering power distribution network constraints.

[0116] The above one or more technical solutions of the application have at least one or more of the following technical effects:

[0117] The application overcomes the defects of the conventional particle swarm optimization algorithm in solving the model, solves the problems of falling into a local optimal point and slow convergence speed in the later iteration, makes the layout planning of the electric-hydrogen filling integrated station more reasonable, and meets the charging and hydrogen filling demand of new energy vehicle users. BRIEF DESCRIPTION OF DRAWINGS

[0118] Figure 1 It is a whole flowchart of a method for site selection and capacity determination of an electric-hydrogen filling integrated station considering power distribution network constraints. Detailed Implementation

[0119] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0120] This invention proposes a site selection and capacity determination method for integrated electric-hydrogen refueling stations considering power distribution network constraints. First, it presents a method for calculating the energy demand distribution of new energy vehicles within urban areas, representing traffic flow in the road network using the traffic flow at each intersection node. The traffic flow density of each intersection node is obtained by summing the traffic flow densities of the road segments connected to it. Using the traffic flow density at urban intersection nodes, the charging demand of electric vehicles and the hydrogen demand of hydrogen fuel cell vehicles are obtained. Based on these two figures, a site selection and capacity determination model for integrated electric-hydrogen refueling stations is constructed, using construction investment costs, operation and maintenance costs, network losses and hydrogen losses within the station, auxiliary road construction costs, and the average annual loss cost per user on the journey to the refueling station as the overall objective function. Constraints include electric vehicle charging demand, hydrogen fuel cell vehicle refueling demand, the number of charging piles, the number and capacity of hydrogen refueling machines, and the planned area power. Based on the demand for hydrogen and electricity at traffic intersections, urban traffic intersections are designated as Voronoi growth points. The service area of ​​the integrated hydrogen and electricity refueling station is divided using a Voronoi diagram. Then, an improved particle swarm optimization algorithm is used for global optimization to obtain the optimal site selection and capacity setting scheme for the integrated hydrogen and electricity refueling station within the planning area.

[0121] like Figure 1 As shown, this invention proposes a site selection and capacity determination method for integrated hydrogen charging and refueling stations considering distribution network constraints. Based on Voronoi diagrams and an improved particle swarm optimization algorithm, it includes the following steps:

[0122] Step S1: Use the SSA-ANN model to predict traffic flow in the future urban planning area, and then calculate the charging and hydrogen refueling demand of new energy vehicles.

[0123] Step S2: Considering the constraints of charging pile equipment, hydrogen refueling equipment, and power distribution network, construct a site selection and capacity determination model for integrated electric hydrogen refueling stations, including comprehensive costs such as construction investment, operation and maintenance, and hydrogen loss.

[0124] Step S3: in combination with the traffic intersection electric hydrogen demand, the city traffic intersection is set as a Voronoi growth point, the service area of the electric hydrogen filling station integrated station is divided by a Voronoi diagram, and then the improved particle swarm algorithm is used for global optimization to obtain the optimal scheme of site selection and capacity of the electric hydrogen filling integrated station in the planning area;

[0125] Specifically, the Voronoi diagram, also known as the Thiessen polygon, is composed of a group of continuous polygons composed of vertical bisectors connecting two adjacent points, and the particle swarm algorithm is a random search algorithm based on group cooperation by simulating the foraging behavior of a bird swarm. In the application, the improved particle swarm algorithm mainly improves the calculation method of the inertia weight. In the conventional method, the inertia weight is a constant value, and in the application, the inertia weight is linearly decreased in the iteration process.

[0126] The electric hydrogen filling integrated station site selection model is to minimize the sum of the construction investment cost, the operation and maintenance cost, the station internal network loss and hydrogen loss fee, the auxiliary road construction cost and the average annual loss cost of users in the journey to the filling station, and to set the site and service area of the electric-hydrogen filling integrated station under the corresponding constraint conditions. The function of the electric-hydrogen filling integrated station service area electric-hydrogen filling integrated station capacity model is to determine the number of charging piles and hydrogen dispensers arranged in the filling station to match the charging and hydrogen filling demand in the service area of the electric-hydrogen filling integrated station, which is used as the capacity scheme in the final site selection and capacity scheme.

[0127] In one embodiment, step S1 comprises:

[0128] Step S11: arranging the historical vehicle data at the city traffic intersection according to a one-dimensional time sequence, the time frequency is 1 minute per day, the length of the vehicle number sequence is 1440, and the time sequence of one day is x1, x2, …, x 1440 , a suitable window length L is set, and the trajectory matrix of the vehicle sequence {X} at the traffic intersection is constructed according to the length:

[0129]

[0130] Wherein: K=N-L+1, that is, X is a trajectory matrix of LxK and

[0131] Step S12: singular value decomposition is performed on the matrix X, and the matrix X is decomposed into X=UΣV T . Let S=XX T , the eigenvalues λ1>λ2…>λ L ≥0 and the corresponding eigenvectors U1, U2, …, U L are obtained by performing eigenvalue decomposition on S, which are all singular spectra of the original sequence and satisfy the formula:

[0132]

[0133] In the formula: U, V become left matrix and right matrix respectively, both are unit matrix, satisfying UU T =1, VV T =1;

[0134] Step S13: According to formula (3), L components are divided into c disjoint groups, which represent different trend components respectively:

[0135] X=X L1 +…+X Lc (3)

[0136] In the formula: U m is the eigenvector of matrix S decomposition in step S12;

[0137] Step S14: Calculate the projection of sequence X i on U m :

[0138]

[0139]

[0140] In the formula: x i represents the i-th column of trajectory matrix X, and the projection indicates the weight of time evolution expressed by X i in the original sequence at x i+1 , x i+2 , …, x i+L period; the sum of the reconstructed sequence should be equal to the original sequence,

[0141] Step S15: Build a three-layer ANN neural network: the first layer is defined as the input layer, and N node neurons are established to receive the reconstructed matrix trajectory. The traffic flow time series of the corresponding day in the historical year is used as input data; the second layer is defined as the hidden layer, and 24 nodes are established; the third layer is the output layer, which is used to output the traffic flow at time t;

[0142] Step S16: The charging demand and hydrogen demand of electric vehicles and hydrogen fuel cell vehicles are described by the following formula:

[0143] The charging demand of intersection node j in T time period:

[0144]

[0145] The hydrogen demand of intersection node j in T time period:

[0146]

[0147] wherein: denotes the traffic flow of intersection node j at time t; q' j denotes the charging demand of intersection node j in T time period; q" j denotes the hydrogen refueling demand of intersection node j in T time period; k ev is the proportion of electric vehicles in the total amount of vehicles; k hev is the proportion of hydrogen fuel cell vehicles in the total amount of vehicles; k cd is the proportion of electric vehicles that need to be charged among all electric vehicles, i.e., the charging rate of electric vehicles; k hcd is the proportion of hydrogen fuel cell vehicles that need to be refueled among all hydrogen fuel cell vehicles, i.e., the refueling rate of hydrogen fuel cell vehicles; P ev is the average capacity of electric vehicle batteries; P hev is the average capacity of hydrogen fuel cell batteries.

[0148] In an embodiment, step S2 comprises:

[0149] Step S21: An electric-hydrogen filling integrated station site selection and capacity determination model is constructed with the objective of minimizing the sum of the construction investment cost, the operation and maintenance cost, the station internal network loss cost, the auxiliary road construction cost, and the user average annual loss cost in the journey to the electric-hydrogen filling integrated station, which is represented as:

[0150]

[0151] wherein: C denotes the total cost of the electric-hydrogen filling integrated station from the construction to the target year; C 1i denotes the fixed construction cost of the electric-hydrogen filling integrated station in the target year converted to the planning period, which includes the transformer, the charger, the electrolyzer, the compressor, the hydrogen storage tank, the hydrogen refueling machine, and other construction costs; C 2i denotes the annual operation and maintenance cost of the electric-hydrogen filling integrated station; C 3i denotes the sum of the network loss cost in the charging process and the loss cost of compressed hydrogen and filling hydrogen in the electric-hydrogen filling integrated station; C 4i denotes the auxiliary road construction cost of the electric-hydrogen filling integrated station; C 5i denotes the journey loss cost of electric vehicles and hydrogen fuel cell vehicles to the electric-hydrogen filling integrated station;

[0152] C 1i = (e i a1+u i b1+f i a2+v i b2+h i c2+w i d2+c i )λ

[0153] wherein:

[0154]

[0155] wherein: e i the number of transformers configured for the filling station i; a1 is the unit price of the transformer; u i the number of charging piles configured for the filling station i; b1 is the unit price of the charging pile in the filling station; f i the rated power of the hydrogen compressor configured for the filling station i; a2 is the unit power price of the hydrogen compressor; v i the number of hydrogen dispensers configured for the filling station i; b2 is the unit price of the hydrogen dispenser in the filling station; h i the rated hydrogen storage capacity of the hydrogen storage tank for the filling station i; c2 is the unit capacity price of the hydrogen storage tank in the filling station; w i the rated power of the electrolyzer configured for the filling station i; d2 is the unit power price of the electrolyzer; c i the capital cost of the filling station i; r0 is the discount rate; z is the operating life of the filling station i;

[0156] C 2i = (e i a1+u i b1+f i a2+v i b2+h i c2+w i d2+c i )η

[0157] wherein: this cost represents the daily maintenance, equipment wear and tear, employee input maintenance and wages and other costs of the electricity-hydrogen filling integrated station; the operation and maintenance cost is converted to the initial investment consideration by using the conversion coefficient; η represents the above conversion coefficient;

[0158]

[0159] wherein: C Fe and C Cu are the iron loss and copper loss of the transformer, respectively; C L is the line loss in the charging pile of the filling station, which is converted to the loss value of each filling station; C D is the charging pile filling loss of a single filling station; k1 is the simultaneous rate of multiple charging piles in the filling station; T v is the average effective filling time of the filling station per day; p0 is the electricity purchase price paid by the filling station to the power company; f i is the number of compressors; n i is the number of hydrogen dispensers in the filling station; γ comp is the hydrogen loss of the compressor; are the average number of hydrogen filling per day of the compressor and a single hydrogen dispenser, respectively; γtank k2 is the hydrogen consumption of the hydrogen refueling machine; p1 is the simulcasting rate of multiple hydrogen refueling machines in the filling station; p1 is the price at which the filling station purchases hydrogen from the hydrogen production plant.

[0160]

[0161] In the formula: ξ g Cost per unit length of road investment; i The length of the newly constructed auxiliary road for entering and exiting the station between filling station i and the traffic intersection.

[0162] C 5i =h1+h2

[0163] in:

[0164]

[0165]

[0166] Where: ∑L i,ev and ∑L i,hev These represent the combined distances from all traffic intersections within the service area of ​​refueling station i to both electric vehicles and fuel cell vehicles at refueling station i; g ev and g hev These represent the driving range per unit of electricity and hydrogen for electric vehicles and fuel cell vehicles, respectively; k represents the user's travel time value, which can be estimated from the average income of residents within the planning area; v ev and v hev These represent the average driving speeds of electric vehicles and fuel cell vehicles, respectively.

[0167] Step S22: Determine the constraints that the site selection model for the integrated electric-hydrogen refueling station must satisfy:

[0168] (1) Constraints on the number of integrated electric-hydrogen refueling stations:

[0169] N min ≤N≤N max

[0170] Where: N min and N max These represent the minimum and maximum number of filling stations permitted to be built within the planning area, respectively.

[0171] (2) Constraints on the number of charging piles in integrated electric-hydrogen refueling stations:

[0172] u min ≤u i ≤u max i = 1, 2, ..., N

[0173] In the formula: u min and u maxrespectively, are the minimum and maximum values of the number of charging piles allowed to be configured by the filling station.

[0174] (3) The number of hydrogen filling machines in the electric-hydrogen filling integrated station constraint:

[0175] v min ≤v i ≤v max i=1,2,…,N

[0176] In the formula: v min and v max are the minimum and maximum values of the number of hydrogen filling machines allowed to be configured by the filling station.

[0177] (4) The distance constraint between electric-hydrogen filling integrated stations:

[0178] D min ≤D ij ≤D max i,j=1,2,…,N,i≠j

[0179] In the formula: D ij is the straight-line distance between filling station i and filling station j; D min and D max are the minimum and maximum distances between filling station i and filling station j.

[0180] (5) The distance constraint from the traffic intersection to the electric-hydrogen filling integrated station:

[0181] d ij ≤d max i=1,2,…,N,j=1,2,…,N cross

[0182] In the formula: d max is the maximum distance from the traffic intersection to the filling station; N cross is the number of traffic intersections.

[0183] (6) Electrolytic cell hydrogen production constraint:

[0184]

[0185]

[0186]

[0187]

[0188] In the formula: is the input power of the electrolytic cell of filling station i at time t in a year; H H is the high heat value of hydrogen (3.509 KW·h / m 3 ). η represents the hydrogen production rate of the electrolyzer at filling station i at time t; elec Energy conversion efficiency of the electrolyzer; Γ i,elec The hydrogen production capacity (kg / h) of the electrolyzer in filling station i; Γ i,elec ,Γ i,elec These represent the lower and upper limits of the hydrogen production rate of the electrolyzer in filling station i, respectively.

[0189] (7) Compressor equipment constraints:

[0190] The gas produced during the operation of the electrolyzer needs to be stored in a hydrogen storage tank. A pressure difference exists between the electrolyzer and the storage tank, therefore a compressor is required to pressurize the hydrogen produced in the electrolyzer. The power consumed by the hydrogen compressor at filling station i at time t is... for:

[0191]

[0192]

[0193]

[0194] In the formula: The amount of hydrogen flowing into the compressor at time t for the electric-hydrogen refueling station i; β represents the compressor's power at the reference operating pressure. comp β0 is the normal operating pressure of the compressor; β0 is the standard atmospheric pressure. The reference operating pressure of the compressor; γ comp The hydrogen dissipation rate from the electrolyzer to the compressor; Let t be the hydrogen production rate of the electrolyzer at the hydrogen filling station i at time t.

[0195] (8) Constraints on hydrogen storage tank equipment:

[0196] The hydrogen produced by the electrolyzer is not immediately consumed by the fuel cell vehicle, but is stored in a hydrogen storage tank:

[0197]

[0198]

[0199]

[0200]

[0201] In the formula: and These represent the hydrogen storage capacity of the hydrogen storage tank at time t and time t+1 of the integrated electric-hydrogen refueling station i. hydrogen demand of the electric-hydrogen refueling integrated station i at time t; hydrogen flow into the hydrogen storage tank of the hydrogen dispenser in the electric-hydrogen refueling integrated station i at time t; γ tank hydrogen dissipation rate from the compressor to the hydrogen storage tank; h i hydrogen storage tank capacity; h i lower limit of the rated hydrogen storage capacity of the hydrogen storage tank of the refueling station i; upper limit of the rated hydrogen storage capacity of the hydrogen storage tank of the refueling station i;

[0202] (9) Maximum electric power constraint of the electric vehicles allowed to access the distribution network:

[0203]

[0204] P Ci = Q i + M i · H H

[0205] wherein: P Ci is the electric power of the electric-hydrogen refueling integrated station i; is the maximum electric power of the electric vehicles and hydrogen fuel cell vehicles allowed to access the distribution network.

[0206] (10) Upper and lower limit constraints of the voltage amplitude of the nodes:

[0207] V i min ≤ V i ≤ V i max , i = 1, 2,..., M

[0208] wherein: V i is the voltage amplitude of the node i of the distribution network; V i min and V i max are the upper and lower limits of the voltage amplitude of the node, respectively; M is the number of nodes of the distribution network under study.

[0209] (11) Maximum current constraint of the feeders:

[0210] |I ij |≤ I ijmax , i, j = 1, 2,..., M

[0211] wherein: I ij and I ijmax are the current and the maximum current allowed to flow through the feeder ij in the distribution network, respectively.

[0212] Step S23: According to the future traffic flow in urban traffic, the charging demand of electric vehicles in the planning area and the hydrogen demand of hydrogen fuel cell vehicles in new energy vehicles are calculated, so as to build a constant volume model:

[0213]

[0214]

[0215] In the formula: Q i represents the charging demand met by the electric-hydrogen filling integrated station i in the T time period; ρ1 is the charging and energy filling margin of the filling station; P1 is the rated charging power of a single charging pile; k 1,x is the charging efficiency of the charging pile; k1 is the simultaneous charging rate of the charging pile in the filling station; T v is the average effective filling time of the filling station per day; M i represents the hydrogen demand met by the electric-hydrogen filling integrated station i in the T time period; is the average hydrogen filling amount of a single hydrogen filling machine per day; [] is the upward rounding symbol.

[0216] In an embodiment, step S3 comprises:

[0217] Step S31: Initialize the particle swarm, set the population size as N, the iteration number as Y, the particle dimension as D, the learning factor as c1 and c2, modify the inertia weight ω using the linear decreasing weight method, and ω can be represented as:

[0218]

[0219] wherein ω max and ω min are the maximum and minimum values of ω respectively; Y is the current iteration number; Y max is the maximum iteration number;

[0220] Step S32: According to the geographic location of the electric vehicle power and the electric power required to meet the hydrogen production of hydrogen fuel cell vehicles in the urban planning area, combined with the city traffic intersection, the electric-hydrogen filling integrated station is established at the nearby traffic intersection with electric energy demand, and the traffic intersection is taken as the initial station address of the electric-hydrogen filling integrated station.

[0221] Step S33: Randomly generate n c electric-hydrogen filling integrated station site coordinates, and compile them as the initial position of the particle swarm;

[0222] Step S34: Draw a Voronoi diagram with the electric-hydrogen filling integrated station site as the growth point to delimit the service area of each electric-hydrogen filling integrated station, and then according to the new energy vehicle charging and hydrogen filling demand in the service area, the constant volume model is used to determine the number of charging piles and hydrogen filling machines required to be configured in the electric-hydrogen filling integrated station.

[0223] Step S35: respectively calculate the target year electric-hydrogen filling integrated station fixed construction cost C converted to the planning period 1i , the annual operation and maintenance cost C of the electric-hydrogen filling integrated station 2i , the sum of the network loss cost and the loss cost of compressed hydrogen and filling hydrogen in the charging process in the electric-hydrogen filling integrated station C 3i , the auxiliary road construction cost C of the electric-hydrogen filling integrated station 4i , the travel loss cost C of electric vehicles and hydrogen fuel cell vehicles to the electric-hydrogen filling integrated station 5i , then calculate the total cost C of the electric-hydrogen filling integrated station from the construction to the operation to the target year, take it as the function value of the particle, and finally find the individual extreme value P best and the global extreme value G best ; for the particles that do not meet the constraint conditions, use the penalty function method to process them;

[0224] Step S36: update the speed and position of the particle, and if the maximum number of iterations is not reached, go to step S34 for cyclic iteration until the maximum number of iterations is reached.

[0225] Step S37: output the optimal scheme of electric-hydrogen filling integrated station site selection and capacity determination.

[0226] The above embodiments are provided only for the purpose of describing the present application, and are not intended to limit the scope of the present application. The scope of the present application is defined by the appended claims. Various equivalent replacements and modifications made without departing from the spirit and principles of the present application should be encompassed within the scope of the present application.

[0227] The present application also provides a device for realizing the electric-hydrogen filling integrated station site selection and capacity determination method considering the distribution network constraint, specifically comprising:

[0228] The automobile electric-hydrogen demand calculation module: uses the SSA-ANN model to predict the traffic flow in the future urban planning area, and then calculates the charging and hydrogen filling demand of new energy vehicles;

[0229] The filling integrated station site selection and capacity determination module: on the basis of considering the charging pile equipment, hydrogen filling equipment and distribution network constraint, an electric-hydrogen filling integrated station site selection model is established, which minimizes the sum of the construction investment cost, operation and maintenance cost, in-station network loss and hydrogen loss cost, auxiliary road construction cost and average annual loss cost of users in the travel to the filling station of the electric-hydrogen filling integrated station;

[0230] The filling integrated station planning solution module: in combination with the electric hydrogen demand of the traffic intersection, the city traffic intersection is set as a Voronoi growth point, the service area of the electric hydrogen filling station integrated station is divided through the Voronoi diagram, and then the improved particle swarm algorithm is utilized for global optimization, so that the optimal scheme of the electric hydrogen filling integrated station site selection and capacity determination in the planning area is obtained;

[0231] The planning scheme output module: the optimal planning scheme of the electric hydrogen filling integrated station site selection and capacity determination is output and saved.

[0232] The application further provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the program.

[0233] The application further provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above method.

[0234] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system or a computer program product. Therefore, the application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the application can adopt a computer program product in the form of one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes. The solutions in the embodiments of the application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0235] The application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems) and computer program products of the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and the combination of the flows and / or blocks can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The devices for implementing the functions specified in one flow or multiple flows and / or blocks

[0236] These computer program instructions can also be stored in a computer readable storage medium capable of guiding the computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams.Figure 1 one or more processes and / or functions specified in a block Figure 1 one or more blocks or multiple blocks.

[0237] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide steps for implementing the processes specified in the flow Figure 1 one or more processes and / or functions specified in a block Figure 1 one or more blocks or multiple blocks.

[0238] Although preferred embodiments of the application have been described, those skilled in the art will be able to make additional modifications and variations to these embodiments without departing from the spirit and scope of the application. Accordingly, the appended claims are intended to encompass all such modifications and variations as falling within the scope of the application.

[0239] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, it is intended that the present application encompass all such modifications and changes as fall within the scope of the claims and their equivalents.

Claims

1. A method for site selection and capacity determination of an integrated power-to-hydrogen station considering power distribution network constraints, characterized in that, Comprising the following steps: Step S1: using singular spectrum analysis method SSA-ANN of artificial neural network to predict the traffic flow in the future urban planning area, taking the historical urban traffic intersection automobile flow data as input data, and then calculating the electric demand of electric vehicles and the hydrogen demand of hydrogen fuel cell vehicles; Step S2: according to the charging equipment, hydrogen filling equipment, power distribution network constraint, an electric hydrogen filling integrated station site selection model is established, which minimizes the sum of the construction investment cost, operation and maintenance cost, station network loss and hydrogen loss cost, auxiliary road construction cost, and user average annual loss cost in the route to the filling station; Step S21: an electric hydrogen filling integrated station site selection and capacity determination model is constructed, which minimizes the sum of the construction investment cost, operation and maintenance cost, station network loss cost, auxiliary road construction cost in the electric hydrogen filling integrated station, and user average annual loss cost in the route to the electric hydrogen filling integrated station, and is expressed as: ； In the formula: represents the total cost of the electric-hydrogen filling integrated station from the construction operation to the target year; represents the fixed construction cost of the electric-hydrogen filling integrated station converted to the planning period, including the transformer, charging pile, electrolytic cell, compressor, hydrogen storage tank, hydrogen filling machine and other construction costs; represents the annual operation and maintenance cost of the electric-hydrogen filling integrated station; represents the sum of the network loss cost and the loss cost of compressed hydrogen and filling hydrogen in the charging process in the electric-hydrogen filling integrated station; represents the auxiliary road construction cost of the electric-hydrogen filling integrated station; represents the travel loss cost of electric vehicles and hydrogen fuel cell vehicles to the electric-hydrogen filling integrated station; Step S22: determining the constraint conditions that the electric hydrogen filling integrated station site selection model needs to meet, including the electric hydrogen filling integrated station quantity constraint, the electric hydrogen filling integrated station charging pile quantity constraint, the electric hydrogen filling integrated station hydrogen dispenser quantity constraint, the distance constraint between electric hydrogen filling integrated stations, the distance constraint from the traffic intersection to the electric hydrogen filling integrated station, the electrolytic cell hydrogen production constraint, the compressor equipment constraint, the hydrogen storage tank equipment constraint, the maximum electric power constraint of the new energy vehicle allowed to access the power distribution network, the upper and lower limit constraint of the node voltage amplitude, and the maximum current constraint of the feeder; Step S23: the electric vehicle charging demand in the new energy vehicles and the hydrogen demand of the hydrogen fuel cell vehicles in the new energy vehicles in the planning area are calculated according to the future traffic flow in the city, so as to construct the capacity determination model: ； ； In the formula: represents an electricity-hydrogen refueling integrated station In the charging demand met within a time period; is the charging energy margin of the refueling station; is the rated charging power of a single charging pile; is the charging efficiency of the charging pile; is the simultaneous charging rate of the charging piles in the refueling station; is the average effective refueling time per day of the refueling station; represents an electricity-hydrogen refueling integrated station In the hydrogen demand met within a time period; is the average hydrogen refueling amount of a single hydrogen refueling machine per day; [] is the ceiling symbol; Step S3: combining the electric vehicle charging demand and the hydrogen demand of the hydrogen fuel cell vehicles obtained in step S1, setting the traffic intersections with demand as Voronoi growth points, dividing the electric hydrogen filling station service area through Voronoi diagram, and using particle swarm algorithm to obtain the electric hydrogen filling integrated station site selection and capacity determination scheme with the minimum comprehensive cost.

2. The method of claim 1, wherein, The step S1 comprises: Step S11: The historical vehicle data at the urban traffic intersection is arranged in one-dimensional time sequence, the time frequency is 1 minute, the length of the vehicle number sequence is 1440, and the time sequence of one day is , the window length is set to , and the trajectory matrix of the vehicle sequence at the traffic intersection is constructed according to the length. （1） wherein: , i.e. is a trajectory matrix of ; Step S12: Singular Value Decomposition is performed on matrix to decompose matrix into ; let , eigenvalue decomposition is performed on to obtain eigenvalues and corresponding eigenvectors , which are singular spectrum of the original sequence, and satisfy the formula: ， （2） In the formula: , respectively become left and right matrices, both of which are unit matrices, satisfying , ; Step S13: according to formula (3), the L components are divided into c disjoint groups, representing different trend components respectively: （3） In the formula: , is the matrix of step S12 decomposes the eigenvector; Step S14: Calculate sequence In Projection on （4） （5） wherein: denotes the i-th column of the trajectory matrix , the projection refers to the time evolution expressed in the original sequence , , the weight of the time period; the sum of the reconstructed sequence should be equal to the original sequence, Step S15: a three-layer ANN neural network is constructed: the first layer is defined as the input layer, N node neurons are established to receive the reorganized matrix trajectory, and the traffic intersection node traffic flow time series of the corresponding days in the historical years is taken as the input data; the second layer is defined as the hidden layer, 24 nodes are established; and the third layer is the output layer, which is used to output the traffic intersection node traffic flow at time t; Step S16: the charging demand of electric vehicles and the hydrogen demand of hydrogen fuel cell vehicles are described by the following formula: Intersection nodes within the time period Charging requirements: ； Time period within which the intersection node hydrogen demand: ； In the formula: denotes the intersection node At the time of traffic flow; denotes the charging demand of the intersection node in the time period; denotes the hydrogen refueling demand of the intersection node in the time period; is the proportion of electric vehicles in the total amount of vehicles; is the proportion of hydrogen fuel cell vehicles in the total amount of vehicles; is the proportion of electric vehicles that need to be charged among all electric vehicles, that is, the charging rate of electric vehicles; is the proportion of hydrogen fuel cell vehicles that need to be refueled among all hydrogen fuel cell vehicles, that is, the refueling rate of hydrogen fuel cell vehicles; is the average capacity of the electric vehicle battery; is the average capacity of the hydrogen fuel cell.

3. The method of claim 2, wherein the method further comprises: In step S2, ; wherein: ; In the formula: The number of filling stations The number of transformers configured; The unit price of the transformer; The number of filling stations The number of chargers configured; The unit price of the charging pile in the filling station; The number of filling stations The rated power of the hydrogen compressor configured; The unit price of the hydrogen compressor; The number of filling stations The number of hydrogen dispensers configured; The unit price of the hydrogen dispenser in the filling station; The number of filling stations The rated hydrogen storage capacity of the hydrogen storage tank; The unit capacity price of the hydrogen storage tank in the filling station; The number of filling stations The rated power of the electrolytic cell configured; The unit power price of the electrolytic cell; The number of filling stations The capital cost; The discount rate; The number of filling stations The operating life; ； In the formula, the cost item represents the daily maintenance, equipment wear and tear, staff maintenance input, and wages of the electric-hydrogen filling integrated station; a conversion coefficient is used to convert the operation and maintenance cost to initial investment consideration; represents the above conversion coefficient; ； In the formula: and are the iron loss and copper loss of the transformer, respectively; is the line loss in the filling station charger converted to the loss value of each filling station; is the filling loss of a single filling station charging pile; is the simultaneous rate of multiple charging piles in the filling station; is the average daily effective filling time of the filling station; is the electricity purchase price paid by the filling station to the power company; is the number of compressors; is the number of hydrogen filling machines in the filling station; is the hydrogen loss of the compressor; , are the average daily hydrogen filling quantity of the compressor and a single hydrogen filling machine, respectively; is the hydrogen loss of the hydrogen filling machine; is the simultaneous rate of multiple hydrogen filling machines in the filling station; is the hydrogen purchase price of the filling station from the hydrogen production plant; ； In the formula: is the unit length of road investment cost; is the length of the newly built auxiliary road for the filling station is the length of the newly built auxiliary road for the traffic intersection ； Wherein: ； ； In the formula: and respectively are the comprehensive distance of electric vehicles and fuel cell vehicles from all traffic intersections within the service range of the filling station to the filling station; respectively are the comprehensive distance of electric vehicles and fuel cell vehicles from all traffic intersections within the service range of the filling station to the filling station; and respectively are the driving mileage per unit of electric quantity and hydrogen of electric vehicles and hydrogen fuel cell vehicles; is the travel time value of the user, which can be estimated by the average income of residents in the planning area; and respectively are the average driving speed of electric vehicles and hydrogen fuel cell vehicles;​ Step S22: determining the constraint conditions that the electric hydrogen filling integrated station site selection model needs to meet: (1) electric hydrogen filling integrated station quantity constraint: ； wherein: and Min and Max are the minimum and maximum number of injection stations allowed in the planning zone, respectively. (2) electric hydrogen filling integrated station charging pile quantity constraint: ； wherein: and are respectively a minimum value and a maximum value of the number of charging piles allowed to be configured by the filling station; (3) electric hydrogen filling integrated station hydrogen dispenser quantity constraint: ； wherein: and are respectively a minimum value and a maximum value of the number of hydrogen dispensers allowed to be configured by the refilling station; (4) distance constraint between electric hydrogen filling integrated stations: wherein: is the filling station is the straight-line distance between the filling station and the vehicle; and are the minimum distance and the maximum distance, respectively, between the filling station and the vehicle; and the vehicle. (5) Distance constraint from traffic intersection to integrated station of electric hydrogen filling: ； wherein: is the maximum distance from the traffic intersection to the refilling station; is the number of traffic intersections; (6) Electrolytic cell hydrogen production constraint: ； ； ； ； In the formula: For filling station Throughout the year The input power of the electrolytic cell at any given time; The high calorific value of hydrogen ; For filling station exist Hydrogen production rate of the electrolyzer at any given time; Energy conversion efficiency of the electrolyzer; For filling station Hydrogen production capacity of the electrolyzer ( ); , These represent the filling stations. Lower and upper limits of hydrogen production rate in electrolyzers; (7) Compressor equipment constraint: At Moment of filling station Power consumed by the hydrogen compressor Is: ； ； ； wherein: is the electric-hydrogen filling station at the instant hydrogen quantity flowing into the compressor; is the power of the compressor at the reference working pressure; is the normal working pressure of the compressor; is the standard atmospheric pressure; is the reference working pressure of the compressor; is the hydrogen dissipation rate from the electrolyser to the compressor; is the filling station hydrogen filling machine at the instant hydrogen production rate of the electrolyser; (8) Hydrogen storage tank equipment constraint: ； ； ； ； In the formula: and These are integrated electric-hydrogen refueling stations. exist Time and The amount of hydrogen stored in the hydrogen storage tank at any given time; Electricity-hydrogen refueling station exist The amount of hydrogen required at any given time; Electricity-hydrogen refueling station China hydrogen refueling machine The amount of hydrogen flowing into the hydrogen storage tank at any given time; The hydrogen dissipation rate from the compressor to the hydrogen storage tank; This refers to the capacity of the hydrogen storage tank. For filling station Lower limit of rated hydrogen storage capacity of hydrogen storage tank; For filling station Maximum rated hydrogen storage capacity of hydrogen storage tanks; (9) Maximum electric power constraint of new energy vehicles allowed to access power distribution network: ； ； In the formula: electro-hydrogen filling integrated station electrical power; maximum electrical power of electric vehicles and hydrogen fuel cell vehicles allowed to access the distribution network; (10) Upper and lower limit constraint of node voltage amplitude: ； wherein is the voltage amplitude of a node of the power distribution network; is the voltage amplitude of a node of the power distribution network; and are the upper and lower limits of the voltage amplitude of the node, respectively; is the number of nodes of the power distribution network under study; (11) Maximum current constraint of feeder: ； wherein: and are the current and the maximum current allowed to flow through the feeder in the power distribution grid, respectively.

4. The method of claim 3, wherein, Step S3 comprises: Step S31: initialize the particle swarm, set the population size to , the number of iterations to , the particle dimension to , the learning factor to , , and modify the inertia weight using the linearly decreasing weight method , may be expressed as: ； wherein and are the maximum and minimum values, respectively of is the current iteration number; is the maximum iteration number; Step S32: According to the electric power demand of electric vehicles and the geographic location of the electric power required to meet the hydrogen production of hydrogen fuel cell vehicles in the urban planning area, the integrated station of electric hydrogen filling is established at the nearby traffic intersection with electric power demand, and the traffic intersection is taken as the initial station address of the integrated station of electric hydrogen filling; Step S33: Randomly generate coordinates of the hydrogen refueling station site and compile them as initial positions of the particle swarm; Step S34: Taking the station address of the integrated station of electric hydrogen filling as the growth point, a Voronoi diagram is drawn to divide the service area of each integrated station of electric hydrogen filling, and then according to the charging and hydrogen filling demand of new energy vehicles in the service area, a constant volume model is used to determine the number of charging piles and hydrogen filling machines required in the integrated station of electric hydrogen filling; Step S35: Calculate the target year electric-hydrogen filling integrated station fixed construction cost converted to the planning year respectively , Electric-hydrogen filling integrated station annual operation and maintenance cost , The sum of network loss cost and loss cost of compressed hydrogen and filling hydrogen in the charging process in the electric-hydrogen filling integrated station , Auxiliary road construction cost of electric-hydrogen filling integrated station , The travel loss cost of electric vehicles and hydrogen fuel cell vehicles to the electric-hydrogen filling integrated station , Then calculate the total cost of electric-hydrogen filling integrated station construction and operation to the target year , Take it as the function value of the particle, and finally find out the individual extreme value And the global extreme value ; For the particles that do not meet the constraint conditions, use the penalty function method to process them; Step S36: Update the speed and position of the particle, and if the maximum number of iterations is not reached, go to step S34 for cyclic iteration until the maximum number of iterations is reached; Step S37: Output the optimal site selection and capacity determination scheme of the integrated station of electric hydrogen filling.

5. A device for implementing the method for site selection and capacity determination of an integrated power-to-hydrogen station considering power distribution network constraints according to any one of claims 1-4, characterized in that, Comprise: Automobile electric hydrogen demand calculation module: The singular spectrum analysis method SSA-ANN of artificial neural network is used to predict the future traffic flow in the urban planning area, and the historical urban traffic intersection vehicle flow data is used as input data, and then the charging and hydrogen filling demand of new energy vehicles is calculated; Charging and filling integrated station site selection and capacity determination module: According to the charging pile equipment, hydrogen filling equipment and power distribution network constraint, an integrated station of electric hydrogen filling site selection model is established to minimize the sum of the construction investment cost, operation and maintenance cost, station network loss and hydrogen loss cost, auxiliary road construction cost and user average annual loss cost in the road to the filling station; Charging and filling integrated station planning solution module: The charging demand of electric vehicles and the hydrogen demand of hydrogen fuel cell vehicles at the traffic intersection are obtained by combining the automobile electric hydrogen demand calculation module, the traffic intersection with demand is taken as the Voronoi growth point, the service area of the integrated station of electric hydrogen filling is divided by Voronoi diagram, and the integrated station of electric hydrogen filling site selection and capacity determination scheme with minimum comprehensive cost is obtained by using particle swarm algorithm; Planning scheme output module: The optimal planning scheme of the integrated station of electric hydrogen filling site selection and capacity determination is output and saved.

6. The apparatus of claim 5, wherein, The historical vehicle data at the urban traffic intersection is arranged according to a one-dimensional time sequence, the time frequency is 1 minute, the vehicle number sequence length is 1440, and the time sequence of one day is , the window length is set to , and the trajectory matrix of the vehicle sequence at the traffic intersection is constructed according to the length. （1） wherein: , i.e. is the trajectory matrix of ; On the matrix singular value decomposition, the matrix is decomposed into ; let , the eigenvalue decomposition of is performed to obtain eigenvalues and corresponding eigenvectors , which are both singular spectra of the original sequence, and satisfy the formula: ， （2） In the formula: , respectively become left and right matrices, both of which are unit matrices, satisfying , ; According to formula (3), L components are divided into c disjoint groups, representing different trend components: （3） In the formula: , is the matrix of step S12 decomposes the eigenvector; Computing sequence On Projection: （4） （5） wherein: denotes the i-th column of the trajectory matrix , the projection denotes the i-th column of the trajectory matrix the time evolution expressed in the original sequence , , the weight of the time period; the sum of the reconstructed sequence should be equal to the original sequence, A three-layer ANN neural network is constructed: the first layer is defined as the input layer, N node neurons are established to receive the reorganized matrix trajectory, and the traffic intersection node vehicle flow time series of the corresponding day in the historical year is taken as the input data; the second layer is defined as the hidden layer, 24 nodes are established; the third layer is the output layer, which is used to output the traffic intersection node vehicle flow at time t; The charging demand of electric vehicles and the hydrogen demand of hydrogen fuel cell vehicles are described by the following formula: Intersection nodes within the time period Charging requirements: ； Intersection nodes within the time period Hydrogenation demand: ； In the formula: denotes the intersection node at the time t; denotes the charging demand of the intersection node in the time period t; denotes the hydrogen demand of the intersection node in the time period t; is the proportion of electric vehicles in the total amount of vehicles; is the proportion of hydrogen fuel cell vehicles in the total amount of vehicles; is the proportion of electric vehicles that need to be charged among all electric vehicles, i.e. the charging rate of electric vehicles; is the proportion of hydrogen fuel cell vehicles that need to be hydrogenated among all hydrogen fuel cell vehicles, i.e. the hydrogenation rate of hydrogen fuel cell vehicles; is the average capacity of the electric vehicle battery; is the average capacity of the hydrogen fuel cell.

7. The apparatus of claim 5, wherein, The filling integrated station site selection and capacity determination module constructs an electric-hydrogen filling integrated station site selection and capacity determination model with the minimum sum of construction investment cost, operation and maintenance cost, station network loss cost, auxiliary road construction cost of the filling integrated station, and average annual loss cost of users in the journey to the electric-hydrogen filling integrated station as the target, and is expressed as: ； In the formula: represents the total cost of the operation of the electricity-hydrogen filling integrated station from the construction to the target year; represents the fixed construction cost of the electricity-hydrogen filling integrated station converted to the planning period, including the transformer, charging pile, electrolytic cell, compressor, hydrogen storage tank, hydrogen filling machine and other construction costs; represents the annual operation and maintenance cost of the electricity-hydrogen filling integrated station; represents the sum of the network loss cost and the loss cost of compressed hydrogen and filling hydrogen in the charging process in the electricity-hydrogen filling integrated station; represents the auxiliary road construction cost of the electricity-hydrogen filling integrated station; represents the travel loss cost of the electric vehicle and the hydrogen fuel cell vehicle to the electricity-hydrogen filling integrated station; ； Wherein: ； In the formula: the number of filling stations the number of transformers configured; the unit price of the transformer; the number of filling stations the number of charging piles configured; the unit price of the charging pile in the filling station; the number of filling stations the rated power of the hydrogen compressor configured; the unit price of the hydrogen compressor; the number of filling stations the number of hydrogen dispensers configured; the unit price of the hydrogen dispenser in the filling station; the number of filling stations the rated hydrogen storage capacity of the hydrogen storage tank; the unit capacity price of the hydrogen storage tank in the filling station; the number of filling stations the rated power of the electrolytic cell configured; the unit power price of the electrolytic cell; the capital cost of the filling station ; the discount rate; the operating life of the filling station ; ； In the formula, the cost item represents the daily maintenance, equipment wear and tear, staff maintenance input, and wages of the electric-hydrogen filling integrated station; a conversion coefficient is used to convert the operation and maintenance cost to initial investment consideration; represents the above conversion coefficient; ； In the formula: and are the iron loss and copper loss of the transformer, respectively; is the line loss of the charging pile in the filling station converted to the loss value of each filling station; is the charging pile filling loss of a single filling station; is the simultaneous rate of multiple charging piles in the filling station; is the average daily effective filling time of the filling station; is the electricity purchase price paid by the filling station to the power company; is the number of compressors; is the number of hydrogen dispensers in the filling station; is the hydrogen loss of the compressor; , are the average daily hydrogen injection quantity of the compressor and a single hydrogen dispenser, respectively; is the hydrogen loss of the hydrogen dispenser; is the simultaneous rate of multiple hydrogen dispensers in the filling station; is the hydrogen purchase price of the filling station from the hydrogen production plant;​ ； In the formula: is the unit length of road investment cost; is the length of the new auxiliary road for the filling station is the length of the new auxiliary road for the traffic intersection ； Wherein: ； ； In the formula: and are the comprehensive distance of electric vehicles and fuel cell vehicles from all traffic intersections within the service range of the filling station to the filling station are the comprehensive distance of electric vehicles and fuel cell vehicles from all traffic intersections within the service range of the filling station to the filling station and are the driving range per unit of electric quantity and hydrogen of electric vehicles and fuel cell vehicles, respectively; is the user travel time value, which can be estimated from the average income of residents in the planning area; and are the average driving speeds of electric vehicles and fuel cell vehicles, respectively.​ 8. The apparatus of claim 5, wherein, The filling integrated station site selection and capacity determination module determines the constraint conditions that need to be met by the electric-hydrogen filling integrated station site selection model, including: (1) Electric-hydrogen filling integrated station quantity constraint: ； wherein: and are, respectively, a minimum and a maximum value of the number of filling stations allowed in the planning zone; (2) Electric-hydrogen filling integrated station charging pile quantity constraint: ； wherein: and are respectively a minimum value and a maximum value of the number of charging piles allowed to be configured by the filling station; (3) Electric-hydrogen filling integrated station hydrogen dispenser quantity constraint: ； wherein: and are respectively a minimum value and a maximum value of the number of hydrogen dispensers allowed to be configured by the refilling station; (4) Distance constraint between electric-hydrogen filling integrated stations: ； wherein: is the filling station is the straight-line distance between the filling station and the filling station and are the minimum distance and the maximum distance, respectively, between the filling station and the filling station and the filling station (5) Distance constraint from traffic intersection to electric-hydrogen filling integrated station: ； wherein: is the maximum distance from the traffic intersection to the refilling station; is the number of traffic intersections; (6) Electrolytic cell hydrogen production constraint: ； ； ； ； In the formula: For filling station Throughout the year The input power of the electrolytic cell at any given time; The high calorific value of hydrogen ; For filling station exist Hydrogen production rate of the electrolyzer at any given time; Energy conversion efficiency of the electrolyzer; For filling station Hydrogen production capacity of the electrolyzer ( ); , These represent the filling stations. Lower and upper limits of hydrogen production rate in electrolyzers; (7) Compressor equipment constraint: At Moment of filling station Power consumed by the hydrogen compressor Is: ； ； ； wherein: electro-hydrogen filling station at the instant; P is the power of the compressor at the reference working pressure; P is the normal working pressure of the compressor; P is the standard atmospheric pressure; P is the reference working pressure of the compressor; P is the hydrogen dissipation rate from the electrolyser to the compressor; filling station hydrogen dispenser at the instant; (8) Hydrogen storage tank equipment constraint: ； ； ； ； wherein: and are an electric-hydrogen filling integrated station at time and time the hydrogen storage amount of the hydrogen storage tank; is the hydrogen demand amount of the electric-hydrogen filling integrated station at time; is the hydrogen amount flowed into the hydrogen storage tank from the hydrogen dispenser in the electric-hydrogen filling integrated station at time; is the hydrogen dissipation rate from the compressor to the hydrogen storage tank; is the hydrogen storage tank capacity; is the lower limit of the rated hydrogen storage capacity of the hydrogen storage tank in the filling station ; and is the upper limit of the rated hydrogen storage capacity of the hydrogen storage tank in the filling station . (9) Maximum electric power constraint of new energy vehicles allowed to access the power distribution network: ； ； wherein: electro-hydrogen filling integrated station electrical power; maximum electrical power of electric vehicles and hydrogen fuel cell vehicles allowed to access the distribution grid; (10) Upper and lower limit constraint of node voltage amplitude: ； wherein: is the voltage magnitude of a node of the power distribution network; is the voltage magnitude of a node of the power distribution network; and are the upper and lower limits of the voltage magnitude of the node, respectively; is the number of nodes of the power distribution network under study; (11) Maximum current constraint of feeder: ； wherein: and are the current of the feeder and the maximum current allowed to flow through the feeder in the power distribution grid, respectively.

9. The apparatus of claim 5, wherein, The electric-hydrogen filling integrated station site selection and capacity determination module calculates the electric vehicle charging demand and hydrogen demand of hydrogen fuel cell vehicles in new energy vehicles in the planning area according to the future traffic flow in urban traffic, thereby constructing the capacity determination model: ； ； In the formula: represents the electricity-hydrogen filling integrated station In the charging demand met in the time period; is the charging energy margin of the filling station; is the rated charging power of a single charging pile; is the charging efficiency of the charging pile; is the simultaneous charging rate of the charging piles in the filling station; is the average daily effective filling time of the filling station; represents the electricity-hydrogen filling integrated station In the hydrogen demand met in the time period; is the average hydrogen filling amount of a single hydrogen filling machine per day; [] is the ceiling symbol.

10. The apparatus of claim 5, wherein, The filling integrated station planning solution module includes: Step S31: initialize the particle swarm, set the population size to , the number of iterations to , the particle dimension to , the learning factor to , , and modify the inertia weight using the linearly decreasing weight method , may be expressed as: ； wherein and are the maximum and minimum values, respectively of is the current iteration number; is the maximum iteration number; 2) According to the geographic location of the electric vehicle electric quantity and the electric quantity required to meet the hydrogen production of hydrogen fuel cell vehicles in the urban planning area, and combining the city traffic intersection, the electric-hydrogen filling integrated station is established at the nearby traffic intersection with electric energy demand, and the traffic intersection is taken as the initial station site of the electric-hydrogen filling integrated station; 3) randomly generated a coordinate of the site of the integrated station of electric hydrogen filling and compiled as the initial position of the particle group; 4) The service area of each electric-hydrogen filling integrated station is determined by taking the electric-hydrogen filling integrated station site as the growth point to make a Voronoi diagram, and then the number of charging piles and hydrogen dispensers required to be configured in the electric-hydrogen filling integrated station is determined according to the new energy vehicle charging and hydrogen filling demand in the service area; 5) Calculate the target year electric-hydrogen filling integrated station fixed construction cost converted to the planning year respectively , Electric-hydrogen filling integrated station annual operation and maintenance cost , The sum of the network loss cost in the charging process and the loss cost of compressed hydrogen and filling hydrogen in the electric-hydrogen filling integrated station , Auxiliary road construction cost of electric-hydrogen filling integrated station , The travel loss cost of electric vehicles and hydrogen fuel cell vehicles to electric-hydrogen filling integrated station , Then calculate the total cost of electric-hydrogen filling integrated station construction and operation to the target year , Take it as the function value of the particle, and finally find out the individual extreme value And the global extreme value ; For the particles that do not meet the constraint conditions, use the penalty function method to process them; 6) The speed and position of the particle are updated, and if the maximum iteration number is not reached, the step S34 is turned to for cyclic iteration until the maximum iteration number is reached.

11. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the electric-hydrogen filling integrated station site selection and capacity determination method considering power distribution network constraints according to any one of claims 1-4 when executing the program.

12. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the electric-hydrogen filling integrated station site selection and capacity determination method considering power distribution network constraints according to any one of claims 1-4 when executed by a processor.

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