CN114357681A - Hydrogen production and hydrogenation station distribution point optimization method considering comprehensive factor indexes - Google Patents

Abstract

The invention provides a hydrogen production and hydrogenation station distribution point optimization method considering comprehensive factor indexes. According to the method, a multi-node power distribution network model is introduced, and a node voltage deviation index, an active power margin level index, an active network loss index, a traffic flow index, a hydrogen production hydrogenation station service range index and a user satisfaction index are respectively constructed; respectively constructing active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station contact ratio constraint; and (3) constructing a hydrogen production hydrogenation station distribution optimization target, and combining a plurality of constraint conditions, and obtaining hydrogen production hydrogenation station optimized access nodes in the multi-node power distribution network model through optimization solution of the goblet sea squirt group optimization algorithm. The invention effectively reduces the voltage deviation of the power distribution network, improves the power margin of the line, reduces the loss of the power grid, and improves the convenience of the distribution of the hydrogen production and hydrogenation station traffic network.

Description

Hydrogen production and hydrogenation station distribution point optimization method considering comprehensive factor indexes

Technical Field

The invention belongs to the technical field of new energy optimization, and particularly relates to a hydrogen production and hydrogenation station distribution optimization method considering comprehensive factor indexes.

Background

The hydrogen fuel cell automobile is the most important ring in the field of hydrogen energy application, and in a certain sense, the smooth promotion of the new energy revolution is far from the rapid development of the new energy cell automobile. With the popularization of fuel cell automobiles, the hydrogen production and hydrogenation station will gradually replace part of the traditional automobile gas stations, but due to the uncertainty of the hydrogen production characteristics and the load of the hydrogen production and hydrogenation station, the distribution optimization strategy of the hydrogen production and hydrogenation station and the distribution mode of an electric automobile charging station cannot be generally known. Current electrical vehicle charging station distribution research is focused primarily on balancing the benefits between the charging station operator and the user and reducing the overall cost of the charging station arrangement. The hydrogen production and hydrogenation station represented by electrolytic hydrogen production can bring certain impact on the operation of a power grid after being connected into the power grid due to the particularity of a model, so that the influence of the hydrogen production and hydrogenation station on the power distribution grid needs to be considered when a point distribution optimization strategy is considered, the working modes of the hydrogen production and hydrogenation station and an electric vehicle charging station are greatly different due to the difference between the endurance mileage and demand characteristics of an electric vehicle and a fuel cell vehicle, and meanwhile, due to the chemical characteristic of hydrogen energy, the acceptance degree of surrounding residents needs to be additionally considered in the point distribution optimization strategy, so that the research on the point distribution optimization strategy of the hydrogen production and hydrogenation station is not slow.

In recent years, scholars at home and abroad make a great deal of research on the distribution optimization problem of hydrogen production and hydrogenation stations, and in order to combine a classical hydrogen supply chain network design model with a hydrogen station planning model, a new model formula is generated by intensively designing a mathematical model of the whole hydrogen supply network; aiming at a hydrogenation station site selection optimization model, the supply radius of a hydrogenation station, the hydrogen source capacity and geographic information factors are taken as constraint conditions to improve the applicability and the level of hydrogen energy; an optimal operation strategy is provided for realizing a hydrogen production and hydrogenation station, and the profit maximization is realized by selling electric power and hydrogen to users of pure electric vehicles and hydrogen fuel cell vehicles; by providing a long term location plan for the hydrogen refueling station, the hydrogen refueling station can meet the increasing demand of hydrogen.

However, the model and the size of the hydrogen production hydrogenation station are considered in most of the research, and most of the research is in an off-grid mode, so that the influence of the hydrogen production hydrogenation station on the power grid is difficult to describe. Based on the consideration, the operating cost of the hydrogen station is reduced by establishing a robust model of site selection and scale of the hydrogen station, considering the uncertainty of the hydrogen charging requirement of the fuel cell vehicle, bringing the additional loss of the fuel cell vehicle when the fuel cell vehicle is connected to a power grid into the model, and simultaneously planning hydrogen production and adjusting the capacity of each unit of the hydrogen station. However, the above research lacks consideration of road network factors, and has no determined model for automobile hydrogenation demand and service range. Therefore, at present, point placement optimization research on hydrogen production and hydrogenation stations mainly focuses on modeling of the hydrogen production and hydrogenation stations, optimization of operation cost and research on hydrogen production modes, and a large hole exists in research aiming at the aspect of influence of hydrogen production on a power grid, and point placement optimization of the hydrogen production and hydrogenation stations is not combined with a traffic network, and fluctuation of fuel cell automobile hydrogenation is not considered.

Meanwhile, due to the particularity of the hydrogen production and hydrogenation station model, the voltage of an access node is fluctuated in the hydrogen production time period, and the hydrogen production and hydrogenation station is used as a large load and can influence the tidal current distribution and network loss of a power distribution network during operation, and the traffic flow and the service range of the hydrogen production and hydrogenation station in a traffic network are different due to different distribution positions of the hydrogen production and hydrogenation station. Meanwhile, due to the chemical characteristics of hydrogen, the point distribution strategy is undoubtedly more favorable for the construction and the promotion of the hydrogen production and hydrogenation station in the actual life after considering the acceptance degree of surrounding residents on the hydrogen production and hydrogenation station from the aspects of reality and psychology. Therefore, the following two issues should be considered when considering the layout optimization problem of the hydrogen production and hydrogenation station:

the hydrogen production and hydrogenation station distribution optimization problem needs to be coupled with a power distribution network and a traffic network, and a distribution optimization model of the hydrogen production and hydrogenation station is constructed.

The model is solved through an improved goblet sea squirt group optimization algorithm, and the obtained strategy needs to solve the problems that the power distribution network is unstable in operation and the service range of the traffic network is not balanced with the traffic flow under the condition that the coupling influence of the power distribution network and a hydrogen fuel automobile is considered.

Disclosure of Invention

The invention provides a hydrogen production and hydrogenation station distribution optimization strategy comprehensively considering the coupling influence of a power distribution network and a hydrogen fuel automobile for the first time. Firstly, carrying out Monte Carlo simulation on a household hydrogen timing curve for a hydrogen fuel automobile to achieve deep analysis on the working mode of a hydrogen production and hydrogenation station; secondly, aiming at considering the safe operation of a traffic network and a power network, a hydrogen production hydrogenation station distribution optimization model considering the coupling influence of a power distribution network and a hydrogen fuel automobile under a traffic-power network framework is constructed, and the model is further solved by utilizing an improved goblet sea squirt group optimization algorithm to obtain an optimal hydrogen production hydrogenation station distribution optimization scheme.

The above problems of the present invention are mainly solved by the following technical solutions:

a hydrogen production and hydrogenation station distribution point optimization method considering comprehensive factor indexes is characterized by comprising the following steps:

step 1: introducing a multi-node power distribution network model, and respectively constructing a node voltage deviation index, an active power margin level index, an active network loss index, a traffic flow index, a hydrogen production hydrogenation station service range index and a user satisfaction index;

step 2: respectively constructing active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station contact ratio constraint;

and step 3: constructing a hydrogen production hydrogenation station distribution optimization target according to the node voltage deviation index, the active power margin level index, the active network loss index, the traffic flow index, the hydrogen production hydrogenation station service range index and the user satisfaction index in the step 1, and obtaining hydrogen production hydrogenation station optimization access nodes in a multi-node power distribution network model by optimizing and solving a zun sea squirt group optimization algorithm by using active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station overlap ratio constraint as constraint conditions of the hydrogen production hydrogenation station distribution optimization target;

preferably, the node voltage deviation indicator in step 1 is defined as:

wherein S is₁Is a node voltage deviation index; s_1-aIs an indicator of voltage fluctuation level; n is the number of nodes of the power distribution network; t belongs to T, T is the time T in the hydrogen production time, and T is the total hydrogen production time; u shape_bus-i,tIs a nodei node voltage deviation at time t; u shape_bus-i,t,minThe minimum value of the rated voltage deviation of the node i at the moment t; u shape_bus-i,t,maxThe maximum value of the rated voltage deviation of the node i at the moment t; u shape_NIs a rated voltage;

is the average of the voltage fluctuation level of node i over the calculation period.

Step 1, the active power margin level index is defined as:

wherein S is₂The active power margin level index is obtained; s_2-aThe influence coefficient of the power fluctuation and the maximum power deviation on the objective function is considered; k belongs to K, K is the kth alternating current line connected with the node i, and K is the total number of the alternating current lines connected with the node i; p_FCV，tIs the average power of the ac line connected to node i at time t; p_k，tIs the active power level of line k connected to node i at time t; a is the weight of the power fluctuation in the influence coefficient; b is the weight of the maximum power deviation in the influence coefficient; p_k-maxIs the upper limit of the transmission power of the line k connected to node i.

Step 1, the active network loss index is defined as:

wherein S is₃The index is an active network loss index; g_ijConductance of the branch between node i and node j; u shape_iIs the voltage amplitude of node i; u shape_jIs the voltage amplitude of node j; n is_l∈N_L，n_LFor the nth node in the multi-node power distribution network model_LStrip transmission line, N_LThe number of the transmission lines in the multi-node power distribution network model is determined; theta_iIs the phase angle, θ, of the voltage at node i_jThe phase angle of the voltage at node j.

The traffic flow index in the step 1 is defined as:

wherein S is₄Is a traffic flow index; f_uA vehicle weight coefficient for a route start point u; f_vA vehicle weight coefficient being a line end point v; l belongs to L, wherein L is the L-th traffic path in the multi-node traffic network model, and L is the number of the traffic paths in the multi-node traffic network model; d_{uv_l}The path lengths of a starting point u and an end point v of a line in a traffic network; n is a radical of_{_JT}Is a road network main node.

Step 1, the service range index of the hydrogen production and hydrogenation station is defined as:

wherein S is₅For the service range index of the hydrogen production hydrogenation station, M belongs to M, M is the mth hydrogen production hydrogenation station, and M is the total number of the hydrogen production hydrogenation stations; s_{m_CS}Represents the attraction of the mth hydrogen production and hydrogenation station to the user, P_{HPRS_m}Denotes the power, lambda, of the mth hydrogen production and hydrogenation station_qOther factors representing the road network node q influence the weight, d_{HPRS_l}Represents the length of a path l from a fuel cell vehicle to a hydrogen production and hydrogenation station, E_FCVRepresents the hydrogen consumption per unit distance, P_FCVRepresenting the hydrogen price of the hydrogen production and hydrogenation station;

step 1, the user satisfaction index is defined as:

wherein S is₆Is a user satisfaction index; s_{_num}The influence of the number of hydrogen stations for hydrogen production on the satisfaction degree of users;

weight in user satisfaction for the number factor; s_{_ljd}Is the influence of proximity on user satisfaction; n is_{m_o}∈N_{m_o}，n_{m_o}The o node, N, contained in the service scope of the m hydrogen production and hydrogenation station_{m_o}The service range of the mth hydrogen production and hydrogenation station comprises a total node; k is a radical of_{b_m}∈K_{b_m}，k_{b_m}Is the b-th path connected with the m-th hydrogen production and hydrogenation station, K_{j_m}The total number of paths connected with the mth hydrogen production hydrogenation station; l_{b_m}Is the length of the b-th path d connected to the m-th hydrogen production and hydrogenation station; l is_{N_JT}Representing the total path length in the road network; n is_{EV_u}Representing the number of vehicles of a road network node u; n is a radical of_EVThe total number of vehicles in the traffic network is shown.

Preferably, the active power upper limit constraint in step 2 is defined as:

in the formula, P_ij，tIs a line l_ijActive power at time t; p_ij ^maxIs a line l_ijThe upper active power limit at time t.

Step 2, the unit climbing rate is restricted, and the restriction is defined as:

in the formula, P_x ^grThe unit x active output unit time change upper limit is set; -P_x ^grThe lower limit of the unit x active output unit time change is set;

the active power of the unit x at the moment t is obtained;

the active power of the unit x at the moment t-1.

Step 2, the node voltage constraint is defined as:

U_bus-i,t,min≤U_bus-i,t≤U_bus-i,t,max

in the formula of U_bus-i,tThe node voltage deviation of the node i at the time t is shown; u shape_bus-i,t,minThe minimum value of the rated voltage deviation of the node i at the moment t; u shape_bus-i,t,maxIs the maximum value of the rated voltage deviation of the node i at the moment t.

Step 2, the power balance equation constraint is defined as:

in the formula, P_iIs the active power input at node i; q_iIs the reactive power input at node i; p_LiIs the active power of the load at node i; q_LiIs the reactive power of the load at node i; g_ijIs the conductance of the branch; b is_ijIs the susceptance of the branch; u shape_iA node voltage of node i; u shape_jA node voltage at node j; p_DGiInjecting active power of the node i; q_DGiInjecting reactive power into the node i; theta_ijIs the phase angle difference of the voltage.

Step 2, the hydrogen charging quantity and the total demand constraint of the fuel cell automobile are defined as follows:

in the formula, N_{FCV_m}The number of the hydrogen charges of the fuel cell vehicle in the mth hydrogen production and hydrogenation station; n is a radical of_{HPRS_m}The allowable hydrogen charging number of the mth hydrogen production and hydrogenation station; v is the number of hydrogen storage tanks in the hydrogen production and hydrogenation station; s_CQGThe capacity of the hydrogen storage tank; s_FCVThe capacity of a fuel cell vehicle.

And 2, restricting the number of hydrogen production hydrogenation stations, and defining as follows:

n_{q_HPRS}＝1

in the formula, n_{q_HPRS}And in the planning process, only one hydrogen production and hydrogenation station can be built for each road network node.

Step 2, restricting the service range of the hydrogen production hydrogenation station, which is defined as:

2≤N_{HPRS_m}≤10

in the formula, N_{HPRS_m}The influence range of the mth hydrogen production and hydrogenation station contains the number of nodes.

Step 2, the contact ratio constraint of the hydrogen production hydrogenation station is defined as:

in the formula: n is a radical of_{HPRS_m}The influence range of the mth hydrogen production and hydrogenation station comprises the node number; n is a radical of_{HPRS_s}The influence range of the s-th hydrogen production and hydrogenation station comprises the number of nodes, namely the service range of the hydrogen production and hydrogenation station; ξ represents the same number of nodes, i.e., the degree of coincidence, within the service range of two hydrogen-producing and hydrogen-adding stations.

Preferably, the hydrogen production hydrogenation station layout optimization target in the step 3 is defined as:

in the formula: s is an optimized distribution comprehensive index of the hydrogen production hydrogenation station in the hydrogen production time period; k is a radical of₁The bus node voltage deviation is a proportionality coefficient in the comprehensive index; k is a radical of₂The ratio coefficient of the active power margin level of the alternating current line in the comprehensive index is obtained; k is a radical of₃The ratio coefficient of the network loss level of the whole network in the comprehensive index is shown; k is a radical of₄The proportional coefficient of the traffic flow in the comprehensive index; k is a radical of₅The proportionality coefficient of the service range of the hydrogen production hydrogenation station in the comprehensive index is obtained; k is a radical of₆A proportionality coefficient of the user satisfaction in the comprehensive index; s₁Is the bus node voltage deviation; s₂The active power margin level of the alternating current line is set; s₃The network loss level of the whole network; s₄Is the traffic flow; s₅To makeThe service range of the hydrogen hydrogenation station; s₆Is the satisfaction degree of the user.

Step 3, obtaining the optimized access node of the hydrogen production and hydrogen refueling station in the multi-node power distribution network model through optimization solution of the goblet sea squirt group optimization algorithm, which specifically comprises the following steps:

step 3.1, inputting the optimized number and the optimized strategy number of hydrogen production and hydrogenation stations

Inputting H optimizing strategies in the initial stage, namely searching individuals; simultaneously inputting the number R of corresponding hydrogen production and hydrogenation stations in a single optimization strategy, and generating an H multiplied by R Euclidean space as follows:

x is European space; h is a space dimension, namely the number of the optimizing strategies; r is the population number, namely the number of hydrogen production and hydrogenation stations in the optimization strategy;

step 3.2, initializing layout positions of hydrogen production hydrogenation stations

Initializing the layout position of the hydrogen production hydrogenation station in the optimization strategy through the goblet sea squirt group optimization algorithm, and using X for the layout optimization strategy of the ith hydrogen production hydrogenation station in the space_iRepresents:

the distribution position of the r hydrogen-making hydrogenation station in the ith strategy is shown.

Step 3.3, optimizing the distribution strategy of hydrogen production hydrogenation stations

The leader is used as a first vector of the X matrix and represents the optimal strategy of the current optimizing process, the optimizing strategy of the hydrogen production hydrogen refueling station is guided to approach the optimal point distribution optimizing strategy before the ending condition is not met, and the position updating formula of the leader is as follows:

wherein the content of the first and second substances,

F_jthe optimal point distribution strategy for the leader and the hydrogen production hydrogenation station is in the position of the j-dimensional space; max_jAnd min_jUpper and lower boundaries of j-dimensional space values, respectively; c. C₂Determining the length of movement, c₃Determining a moving direction; c. C₁As a convergence factor, c₂And c₃Is the interval [0, 1]Internally generated random numbers;

wherein, T, T_maxRespectively the current iteration number and the maximum iteration number.

The follower's location update formula is as follows:

wherein the content of the first and second substances,

representing the coordinates of the ith hydrogen production and hydrogenation station distribution optimization strategy in a j-dimensional space in t iterations;

step 3.4, outputting a hydrogen production hydrogenation station distribution optimization strategy

Outputting an optimal point distribution strategy of the hydrogen production hydrogenation station when the termination condition is met, namely the current European space initial phasor X_{i_min}The concrete formula is as follows:

wherein the content of the first and second substances,

is outputtedAnd the distribution position of the r-th hydrogen production hydrogenation station in the optimal distribution strategy of the hydrogen production hydrogenation station.

To is directed at

In the optimizing process, the problem of local search is easily caused, weights are set for H search individuals, global information is reasonably applied, and the condition that the search is ended in advance due to limitation of an optimal value in a local range is avoided. The weight formula is as follows:

wherein, K_WIs the overall weight; f. of_bOptimizing a point distribution objective function for the hydrogen production and hydrogenation station at the tail of the current sequencing; f. of_F，f_xiOptimizing strategy objective function optimal value and xth for all hydrogen production hydrogenation stations_iObjective function values of the optimization strategies; f is the optimal point distribution strategy position of the hydrogen production hydrogenation station; (f)_b-f)/α is the individual weight. The improved leader formula is:

xⁱ＝ωxⁱ+rand×(K_W-xⁱ)

wherein l is the current iteration number, and the improved formula ensures that omega can control the individual optimum position in the whole exploration process, so as to avoid falling into a local position and obtain an optimal value;

aiming at the problem of unbalance of global search and local search, the self-adaptive inertia weight is added, the search range is expanded at the initial stage of exploration, the local search capability is enhanced at the later stage, and the self-adaptive inertia weight formula is as follows:

the improved follower position formula is as follows:

the invention has the following advantages: the hydrogen production hydrogenation station distribution optimization strategy comprehensively considering the coupling influence of the power distribution network and the hydrogen fuel automobile is provided, a hydrogen production hydrogenation station distribution optimization model considering the coupling influence of the power distribution network and the hydrogen fuel automobile under a traffic-power network framework is constructed by taking the safe operation of a traffic network and a power network as a target, and the model is further solved by using an improved goblet sea squirt group algorithm; the obtained hydrogen production hydrogenation station distribution optimization strategy can effectively reduce the voltage deviation of a power distribution network, improve the power margin of a line, reduce the network loss of the power grid and improve the convenience of distribution of a hydrogen production hydrogenation station traffic network.

Drawings

FIG. 1: is the hydrogen filling demand profile.

FIG. 2: is a working model diagram of a hydrogen production and hydrogenation station.

FIG. 3: is a block diagram of an optimized distribution model of a hydrogen production hydrogenation station.

FIG. 4: is a circuit-electrical coupling block diagram.

FIG. 5: is the nighttime load demand profile.

FIG. 6: is a quantitative index chart of each hydrogen production period of the initial strategy.

FIG. 7: is a voltage deviation graph of each node at the initial strategy hydrogen charging moment.

FIG. 8: is an initial policy service scope graph.

FIG. 9: is an optimization flow chart.

FIG. 10: is a quantitative index map for optimizing strategy hydrogen production time interval.

FIG. 11: the method is a voltage deviation graph of each node at the time of optimizing strategy hydrogen filling.

FIG. 12: is an optimized policy service scope graph.

FIG. 13: is a comparison graph of the optimization process of the improved goblet sea squirt group optimization algorithm and the goblet sea squirt group optimization algorithm.

FIG. 14: is a flow chart of patent steps

Detailed Description

In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.

Embodiments of the present invention will now be described with reference to fig. 1-14.

A hydrogen production and hydrogenation station distribution point optimization method considering comprehensive factor indexes is characterized by comprising the following steps:

step 1: introducing a multi-node power distribution network model, and respectively constructing a node voltage deviation index, an active power margin level index, an active network loss index, a traffic flow index, a hydrogen production hydrogenation station service range index and a user satisfaction index;

the operating conditions of large fuel cell vehicles were analyzed according to the data of the electric vehicle service and management center (SMC-EV) by the national engineering laboratory of electric vehicles, university of beijing chessman, as follows:

1) the daily driving mileage of the fuel cell automobile is mainly concentrated on 70-230 kilometers, more than 80% of the daily average driving mileage of the bus is within 210 kilometers, and the daily average driving mileage is 147 kilometers. The daily mileage of the logistic vehicle is concentrated on 90-170 kilometers. Nearly 90% of logistics vehicles travel within 310 km per day, averaging 178 km.

2) The bus refueling mileage has two peaks, which are respectively between 250 kilometers at 210 plus and 160 kilometers at 130 plus. The average driving mileage is 203 kilometers; and 65% of vehicles start the next hydrogen refueling when the driving mileage reaches 40-50%, and 9% of vehicles start the refueling when the driving mileage reaches 50-60%. The hydrogen fuel endurance mileage of all vehicles does not exceed 60% of the driving mileage; for logistics vehicles, the distance between refueling is mostly concentrated at 190-.

3) The logistics vehicle one-way time is concentrated on 30-60 minutes and rarely exceeds 70 minutes, while the bus one-way time is mostly more than 70 minutes and concentrated on 70-90 minutes.

4) There are distinct clusters for both models at the beginning of the first trip and at the end of the last trip. Bus trips typically range from 6 a.m. to 7 a.m. Very rarely starts the first trip before 6 pm or ends at 7 pm. In addition, 16% of the buses end the last trip at 10 pm. For the logistics vehicle, 54% of the first trip start time of the vehicle is concentrated at 6 to 8 points in the morning and 57% of the trip end time of the vehicle is concentrated at 6 to 8 points in the evening. From the comparison result, the starting time distribution of bus travel is relatively centralized, almost no vehicles start traveling before 6 am, and relatively more vehicles start to run before 6 am, which also reflects the characteristics of the logistics industry. For the end time of the last trip in one day, the logistics vehicles are higher in proportion before 6 pm than the buses finish the last trip. Generally, the start-stop time of the logistics vehicle is earlier, the start-stop time of the bus is later, and the travel rule is as follows:

TABLE 1 travel law of fuel cell vehicle

The data are analyzed, and the daily hydrogenation vehicle number and the hydrogenation amount of a single hydrogen production hydrogenation station are simulated by Monte Carlo. After considering the hydrogenation characteristics of each vehicle in different driving mileage intervals, the daily working time, the single driving mileage and the influence factors of hydrogen consumption, the hydrogen charging demand and the hydrogen charging time distribution of the daily hydrogen production and hydrogenation station are obtained as shown in the attached drawing 1.

According to the working interval of two types of fuel cell automobiles, 1.5h is taken as an interval, the hydrogenation time interval is divided into 11 sections, the hydrogenation demand is the highest in the noon time interval, the data in one month are summarized, sorted and averaged, and the one-day hydrogenation demand of a single hydrogen production hydrogenation station is about 934.74 kg.

According to the trip rule of the fuel cell vehicle described in the previous paragraph, after 10 pm, the hydrogenation demand of the fuel cell vehicle will be sharply reduced, and as can be seen from the hydrogen demand profile of fig. 3, the hydrogenation demand of the fuel cell vehicle in the morning and at night is small. The hydrogenation time of a fuel cell automobile is about 3 minutes, the hydrogen production hydrogenation station can basically complete the hydrogenation task before 12 o 'clock at night, the hydrogen production work is started after 12 o' clock, and the working model of the hydrogen production hydrogenation station is shown in figure 2.

Based on the operation characteristics of a fuel cell automobile and the working mode of the hydrogen production and hydrogenation station, the hydrogen production and hydrogenation station optimized distribution model is constructed by comprehensively considering various targets such as power network influence indexes, hydrogen production and hydrogenation station traffic network indexes, user psychological influence indexes and the like, and a specific optimization model framework is shown as an attached drawing 3.

At the level of the power network: let S₁For voltage deviation, the hydrogen production hydrogenation station is connected to a power distribution network and then reduces S through point distribution optimization₁；S₂The active power margin of the alternating current line is an important index for measuring the stable operation of the power system; s₃As a whole loss of network, S₃The too large can cause the loss of electric energy and the waste of energy, and reduce S₃Is an important economic objective.

At the level of a traffic network: let S₄In order to meet the hydrogen filling requirement of a user, the traffic flow captured by the hydrogen production and hydrogenation station is captured as much as possible through stationing optimization; s₅For the service range of the hydrogen production and hydrogenation station, a large-scale service network is the premise that a hydrogen supply chain and a hydrogen energy trading mode successfully operate;

at the user psychological level: let S₆For the satisfaction degree of users, many cases exist at home and abroad that the scheme can not be normally implemented due to strong interference without considering the acceptance degree of users, and few documents about hydrogen production and hydrogenation station distribution optimization at home and abroad are satisfactory to usersConsideration of the degree. S₆Is the basic condition for normal execution of the scheme.

The node voltage deviation index in step 1 is defined as:

wherein S is₁Is a node voltage deviation index; s_1-aIs an indicator of voltage fluctuation level; n is 30 points of the power distribution network; t belongs to T, T is the time T in the hydrogen production time, and T is 6h which is the total hydrogen production time; u shape_bus-i,tThe node voltage deviation of the node i at the time t is shown; u shape_bus-i,t,minThe minimum value of the rated voltage deviation of the node i at the moment t; u shape_bus-i,t,maxThe maximum value of the rated voltage deviation of the node i at the moment t; u shape_NIs a rated voltage;

is the average of the voltage fluctuation level of node i over the calculation period.

Step 1, the active power margin level index is defined as:

wherein S is₂The active power margin level index is obtained; s_2-aThe influence coefficient of the power fluctuation and the maximum power deviation on the objective function is considered; k belongs to K, K is the kth alternating current line connected with the node i, and K is the total number of the alternating current lines connected with the node i; p_FCV，tIs the average power of the ac line connected to node i at time t; p_k，tIs the active power level of line k connected to node i at time t; a is 0.5, and the weight of the power fluctuation in the influence coefficient is obtained; b is the weight of the maximum power deviation in the influence coefficient when being equal to 0.5; p_k-maxIs the upper limit of the transmission power of the line k connected to node i.

Step 1, the active network loss index is defined as:

wherein S is₃The index is an active network loss index; g_ijConductance of the branch between node i and node j; u shape_iIs the voltage amplitude of node i; u shape_jIs the voltage amplitude of node j; n is_l∈N_L，n_LFor the nth node in the multi-node power distribution network model_LStrip transmission line, N_LThe number of the transmission lines in the multi-node power distribution network model is determined; theta_iIs the phase angle, θ, of the voltage at node i_jThe phase angle of the voltage at node j.

The traffic flow index in the step 1 is defined as:

wherein S is₄Is a traffic flow index; f_uA vehicle weight coefficient for a route start point u; f_vA vehicle weight coefficient being a line end point v; l belongs to L, wherein L is the L-th traffic path in the multi-node traffic network model, and L is the number of the traffic paths in the multi-node traffic network model; d_{uv_l}The path lengths of a starting point u and an end point v of a line in a traffic network; n is a radical of_{_JT}Is a road network main node.

Step 1, the service range index of the hydrogen production and hydrogenation station is defined as:

wherein S is₅For the service range index of the hydrogen production and hydrogenation station, M belongs to M, M is the mth hydrogen production and hydrogenation station, and M is 3, which is the total number of the hydrogen production and hydrogenation stations; s_{m_CS}Represents the attraction of the mth hydrogen production and hydrogenation station to the user, P_{HPRS_m}Denotes the power, lambda, of the mth hydrogen production and hydrogenation station_qOther factors representing the road network node q influence the weight, d_{HPRS_l}Represents the length of a path l from a fuel cell vehicle to a hydrogen production and hydrogenation station, E_FCV1kg/100km tableIndicating the amount of hydrogen consumed, P_FCV70 yuan/kg represents the hydrogen price of the hydrogen production and hydrogenation station;

step 1, the user satisfaction index is defined as:

wherein S is₆Is a user satisfaction index; s_{_num}The influence of the number of hydrogen stations for hydrogen production on the satisfaction degree of users;

weight in user satisfaction for the number factor; s_{_ljd}Is the influence of proximity on user satisfaction; n is_{m_o}∈N_{m_o}，n_{m_o}The o node, N, contained in the service scope of the m hydrogen production and hydrogenation station_{m_o}The service range of the mth hydrogen production and hydrogenation station comprises a total node; k is a radical of_{b_m}∈K_{b_m}，k_{b_m}Is the b-th path connected with the m-th hydrogen production and hydrogenation station, K_{j_m}The total number of paths connected with the mth hydrogen production hydrogenation station; l_{b_m}Is the length of the b-th path d connected to the m-th hydrogen production and hydrogenation station; l is_{N_JT}Representing the total path length in the road network; n is_{EV_u}Representing the number of vehicles of a road network node u; n is a radical of_EVThe total number of vehicles in the traffic network is shown.

Step 2: respectively constructing active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station contact ratio constraint;

step 2, the active power upper limit constraint is defined as:

in the formula, P_ij，tIs a line l_ijActive power at time t; p_ij ^maxIs a line l_ijThe upper active power limit at time t.

Step 2, the unit climbing rate is restricted, and the restriction is defined as:

in the formula, P_x ^grThe unit x active output unit time change upper limit is set; -P_x ^grThe lower limit of the unit x active output unit time change is set;

the active power of the unit x at the moment t is obtained;

the active power of the unit x at the moment t-1.

Step 2, the node voltage constraint is defined as:

U_bus-i,t,min≤U_bus-i,t≤U_bus-i,t,max

in the formula of U_bus-i,tThe node voltage deviation of the node i at the time t is shown; u shape_bus-i,t,minThe minimum value of the rated voltage deviation of the node i at the moment t; u shape_bus-i,t,maxIs the maximum value of the rated voltage deviation of the node i at the moment t.

Step 2, the power balance equation constraint is defined as:

in the formula, P_iIs the active power input at node i; q_iIs the reactive power input at node i; p_LiIs the active power of the load at node i; q_LiIs the reactive power of the load at node i; g_ijIs the conductance of the branch; b is_ijIs the susceptance of the branch; u shape_iA node voltage of node i; u shape_jA node voltage at node j; p_DGiInjecting active power of the node i;Q_DGiinjecting reactive power into the node i; theta_ijIs the phase angle difference of the voltage.

Step 2, the hydrogen charging quantity and the total demand constraint of the fuel cell automobile are defined as follows:

in the formula, N_{FCV_m}The number of the hydrogen charges of the fuel cell vehicle in the mth hydrogen production and hydrogenation station; n is a radical of_{HPRS_m}The allowable hydrogen charging number of the mth hydrogen production and hydrogenation station; v is the number of hydrogen storage tanks in the hydrogen production and hydrogenation station; s_CQGThe capacity of the hydrogen storage tank; s_FCVThe capacity of a fuel cell vehicle.

And 2, restricting the number of hydrogen production hydrogenation stations, and defining as follows:

n_{q_HPRS}＝1

in the formula, n_{q_HPRS}And in the planning process, only one hydrogen production and hydrogenation station can be built for each road network node.

Step 2, restricting the service range of the hydrogen production hydrogenation station, which is defined as:

2≤N_{HPRS_m}≤10

in the formula, N_{HPRS_m}The influence range of the mth hydrogen production and hydrogenation station contains the number of nodes.

Step 2, the contact ratio constraint of the hydrogen production hydrogenation station is defined as:

in the formula: n is a radical of_{HPRS_m}The influence range of the mth hydrogen production and hydrogenation station comprises the node number; n is a radical of_{HPRS_s}The influence range of the s-th hydrogen production and hydrogenation station comprises the number of nodes, namely the service range of the hydrogen production and hydrogenation station; and xi is 5, which is the same node number in the service range of the two hydrogen production and hydrogenation stations, namely the contact ratio.

The scheme adopts an IEEE30 standard calculation example and a coupling frame of 30 paths of network nodes for simulation analysis, the corresponding traffic route-power topology coupling frame is shown in figure 4, the unit distance in the figure is 1KM, the positions of corresponding hydrogen production and hydrogen addition stations are represented by yellow nodes, the number of the hydrogen production and hydrogen addition stations is taken as 3 by taking M as an example, and the dotted line connected in the figure is a path-electric coupling node.

The initial hydrogen production hydrogenation station arrangement positions are the positions of the original road network gas stations and are positioned at nodes 9, 12 and 21. For the third-stage station, the hydrogen production hydrogenation station selects a hydrogen storage tank model as a hydrogen cylinder (for vehicles, a III type hydrogen cylinder with the rated pressure of 35 MPa-140L) with the rated pressure of 35 MPa-35000L. According to an ideal gas state equation:

PV＝nRT

p is the pressure intensity; v is the gas volume; n is the amount of substance; t is the temperature; r is a molar gas constant. The hydrogenation capacity of one hydrogen production hydrogenation station under standard conditions was about 1079kg per day.

The nighttime load demand is shown in figure 5:

the specific parameters of the hydrogen production hydrogenation station are shown in the following table:

TABLE 2 Hydrogen production hydrogenation station parameters

The influence factor weight of each node is as follows:

TABLE 3 influence factor weight Table

Node point	Influencing factor	Node point	Influencing factor	Node point	Influencing factor
							1	1.3	11	0.8	21	0.9
2	1.4	12	1.0	22	0.7
						3	1.5	13	1.2	23	1.4
4	1.3	14	1.3	24	1.2
						5	0.7	15	1.3	25	1.1
6	0.9	16	1.1	26	1.0
						7	0.7	17	0.9	27	0.9
8	0.6	18	0.7	28	0.7
						9	1.2	19	1.2	29	0.6
10	1.1	20	1.2	30	0.2

Firstly, the original scheme is evaluated, and the obtained results of traffic network indexes, power grid line influence indexes, psychological indexes and the like are shown in the attached figure 6.

In the power grid line influence indexes, the power margin is too high, which indicates that the line faces the overload risk, and the active power of the corresponding line is as follows:

TABLE 4 active power of each line

The power transmission size of the 6-9 branch and the 9-10 branch is different by 2.5 times, the power transmission size of the 12-14 branch, the 12-16 branch and the 4-12 branch is different by nearly 3.3 times, and the power transmission size of the 10-21 branch and the 21-22 branch is different by nearly 14 times. The problem of unbalanced power transmission exists, and the stable operation of the power grid is not facilitated. At fifteen minute intervals during the night, the voltage fluctuations are shown in figure 7. In the traffic network index, the specific node traffic flow and service range are shown in the following table:

table 5 road network factor table

Table 6 service scope information table

Policy	Coverage rate of range	Number of non-covered nodes	Ratio of overlap	Percentage of non-coverage
					Primitive policy	90.00％	3	10.00％	10.00％

It can be seen from tables 6 and 7 and fig. 8 that when hydrogen production and hydrogenation stations are located at the original gas station, the original traffic advantages are inherited, the traffic flow and the service range in the traffic network index are dominant, the service coverage rate reaches 90%, and the specific service range node diagram is shown in fig. 8.

The initial strategy user satisfaction is 2.93, and the influences of the specific hydrogen production hydrogenation station branch number, distance, number and traffic flow on the user satisfaction are as follows:

TABLE 7 influence of user satisfaction

Quantitative index	Number of	Number of branches	Distance between two adjacent plates	Flow rate of vehicle
					Satisfaction impact value	0.2	0.25	0.14	0.09

According to the evaluation result of each index, when the distribution point position of the hydrogen production hydrogenation station replaces the position of the original gas station, although the original advantages are inherited in the indexes of the traffic network, the hydrogen production hydrogenation station has larger influence on the power grid due to the load characteristic of the hydrogen production hydrogenation station, and is not beneficial to the stable operation of the power grid.

And step 3: constructing a hydrogen production hydrogenation station distribution optimization target according to the node voltage deviation index, the active power margin level index, the active network loss index, the traffic flow index, the hydrogen production hydrogenation station service range index and the user satisfaction index in the step 1, and obtaining hydrogen production hydrogenation station optimization access nodes in a multi-node power distribution network model by optimizing and solving a zun sea squirt group optimization algorithm by using active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station overlap ratio constraint as constraint conditions of the hydrogen production hydrogenation station distribution optimization target;

and 3, the hydrogen production hydrogenation station distribution optimization target is defined as:

in the formula: s is an optimized distribution comprehensive index of the hydrogen production hydrogenation station in the hydrogen production time period; k is a radical of₁The proportional coefficient of the voltage deviation amount of the bus node in the comprehensive index is 0.181; k is a radical of₂The proportionality coefficient of the active power margin level of the alternating current line in the comprehensive index is 0.181; k is a radical of₃The proportionality coefficient of the network loss level of the whole network in the comprehensive index is 0.181; k is a radical of₄The proportion coefficient of the traffic flow in the comprehensive index is 0.153; k is a radical of₅The proportionality coefficient of the hydrogen production hydrogenation station service range in the comprehensive index is 0.160; k is a radical of₆The proportion coefficient of the user satisfaction in the comprehensive index is 0.143; s₁Is the bus node voltage deviation; s₂The active power margin level of the alternating current line is set; s₃The network loss level of the whole network; s₄Is the traffic flow; s₅To makeThe service range of the hydrogen hydrogenation station; s₆Is the satisfaction degree of the user.

And 3, obtaining hydrogen production hydrogenation station optimized access nodes in the multi-node power distribution network model through optimization solution of the goblet sea squirt group optimization algorithm, wherein the hydrogen production hydrogenation station optimized access nodes are shown in the attached figure 9 and specifically comprise the following steps:

step 3.1, inputting the optimized number and the optimized strategy number of hydrogen production and hydrogenation stations

Inputting H optimizing strategies in the initial stage, namely searching individuals; simultaneously inputting the number R of corresponding hydrogen production and hydrogenation stations in a single optimization strategy, and generating an H multiplied by R Euclidean space as follows:

x is European space; h is a space dimension, namely the number of the optimizing strategies; r is the population number, namely the number of hydrogen production and hydrogenation stations in the optimization strategy;

step 3.2, initializing layout positions of hydrogen production hydrogenation stations

Initializing the layout position of the hydrogen production hydrogenation station in the optimization strategy through the goblet sea squirt group optimization algorithm, and using X for the layout optimization strategy of the ith hydrogen production hydrogenation station in the space_iRepresents:

the distribution position of the r hydrogen-making hydrogenation station in the ith strategy is shown.

Step 3.3, optimizing the distribution strategy of hydrogen production hydrogenation stations

The leader is used as a first vector of the X matrix and represents the optimal strategy of the current optimizing process, the optimizing strategy of the hydrogen production hydrogen refueling station is guided to approach the optimal point distribution optimizing strategy before the ending condition is not met, and the position updating formula of the leader is as follows:

wherein the content of the first and second substances,

F_jthe optimal point distribution strategy for the leader and the hydrogen production hydrogenation station is in the position of the j-dimensional space; max_jAnd min_jUpper and lower boundaries of j-dimensional space values, respectively; c. C₂Determining the length of movement, c₃Determining a moving direction; c. C₁As a convergence factor, c₂And c₃Is the interval [0, 1]Internally generated random numbers;

wherein, T, T_maxRespectively the current iteration number and the maximum iteration number.

The follower's location update formula is as follows:

wherein the content of the first and second substances,

representing the coordinates of the ith hydrogen production and hydrogenation station distribution optimization strategy in a j-dimensional space in t iterations;

step 3.4, outputting a hydrogen production hydrogenation station distribution optimization strategy

Outputting an optimal point distribution strategy of the hydrogen production hydrogenation station when the termination condition is met, namely the current European space initial phasor X_{i_min}The concrete formula is as follows:

wherein the content of the first and second substances,

and arranging the position of the ith hydrogen production and hydrogenation station in the output optimal point arrangement strategy of the hydrogen production and hydrogenation station.

To is directed at

In the optimizing process, the problem of local search is easily caused, weights are set for H search individuals, global information is reasonably applied, and the condition that the search is ended in advance due to limitation of an optimal value in a local range is avoided. The weight formula is as follows:

wherein, K_WIs the overall weight; f. of_bOptimizing a point distribution objective function for the hydrogen production and hydrogenation station at the tail of the current sequencing; f. of_F，f_xiOptimizing strategy objective function optimal value and xth for all hydrogen production hydrogenation stations_iObjective function values of the optimization strategies; f is the optimal point distribution strategy position of the hydrogen production hydrogenation station; (f)_b-f)/α is the individual weight. The improved leader formula is:

xⁱ＝ωxⁱ+rand×(K_W-xⁱ)

wherein l is the current iteration number, and the improved formula ensures that omega can control the individual optimum position in the whole exploration process, so as to avoid falling into a local position and obtain an optimal value;

aiming at the problem of unbalance of global search and local search, the self-adaptive inertia weight is added, the search range is expanded at the initial stage of exploration, the local search capability is enhanced at the later stage, and the self-adaptive inertia weight formula is as follows:

the improved follower position formula is as follows:

the optimized point distribution model is solved through an improved goblet sea squirt group optimization algorithm, and the optimization strategy is as follows:

TABLE 8 optimization scheme

Hydrogen production and hydrogenation station numbering	1	2	3
				Node point	30	23	17

The results of the indexes after optimization are shown in figure 10.

Compared with the initial point distribution strategy, the optimized strategy reduces the influence on the power grid, indexes such as power margin and the like are obviously reduced, the grid loss is reduced by 1.52MW, the voltage deviation of each node is shown in the attached figure 11, and the active power of each line is as follows:

TABLE 9 active power of each line

The power transmission of each line is within the constraint condition, the line overload condition does not occur, the optimized power margin index is 45.87, 36.38 is reduced compared with the initial scheme, and the stable operation of the power grid is facilitated. The voltage fluctuation at each node is shown in fig. 11.

Compared with the original scheme, the voltage deviation amplitude of each node is smaller and tends to the rated voltage, and the risk of boundary crossing is reduced. In the traffic network indexes, the specific node traffic flow and service range are as follows:

table 10 road network factor table

Table 11 service scope information table

Policy	Coverage rate of range	Number of non-covered nodes	Ratio of overlap	Percentage of non-coverage
					Primitive policy	83.33％	5	13.33％	16.67％

As can be seen from tables 11 and 12 and fig. 12, compared with the original strategy, the optimization strategy obtained by the improved ascidian group optimization algorithm has the advantages of losing advantages slightly on the traffic network side, decreasing the service coverage by 6.67%, increasing the traffic flow index by 7.55, but significantly decreasing the voltage deviation, the power margin and the network loss in the power network influence index, and as a whole, decreasing the comprehensive index by 0.5466, and reducing the influence on the power network operation index compared with the original strategy. A specific service area node diagram is shown in figure 12.

The satisfaction degree of the user is 3.85, the improvement is 0.92, the acceptance degree of the user on the optimization strategy is higher, and the influences of the branch number, the distance, the number and the traffic flow of the hydrogen production hydrogenation station on the satisfaction degree of the user are as follows:

TABLE 12 influence of user satisfaction

Quantitative index	Number of	Number of branches	Distance between two adjacent plates	Flow rate of vehicle
					Satisfaction impact value	0.2	0.13	0.07	0.12

It can be known from the above table that the influence of the node branch number and the path length on the user satisfaction is obviously reduced, and although the influence index of the traffic flow is slightly increased, the influence is not large from the user satisfaction index.

In conclusion, if the hydrogen production and hydrogenation station is distributed at the original position of the gas station, although the advantages of a traffic network are inherited, the influence on the operation indexes of a power grid is large, the optimized distribution model of the scheme is solved through the improved goblet sea squirt group optimization algorithm, the new strategy is slightly inferior to the original strategy in terms of traffic flow and service range, the influence on the power grid is obviously reduced, and the stable operation of the power grid is more facilitated compared with the initial scheme.

Aiming at the point distribution optimization problem of the hydrogen production hydrogenation station, the improved optimization process of the goblet sea squirt group and the optimization process of the goblet sea squirt group are compared, and the comparison result is shown in the attached figure 13. Within the range of 500 iterations, the improved goblet sea squirt group optimization algorithm finds the optimal value before the goblet sea squirt group optimization algorithm, the convergence condition is reached more quickly, and the found optimal value is smaller. The model is solved by two algorithms, and the obtained result is as follows:

TABLE 13 algorithm comparison information Table

Compared with the goblet sea squirt group optimization algorithm, when the improved goblet sea squirt group optimization algorithm is used for solving the model, the power grid index and the road network index are slightly superior to the result obtained by the goblet sea squirt group optimization algorithm, the convergence speed and the solving precision are superior to those of the goblet sea squirt group optimization algorithm, the improved goblet sea squirt group optimization algorithm is higher in solving speed and smaller in occupied memory while meeting the solving precision, and the improved goblet sea squirt group optimization algorithm is more suitable for solving the hydrogen production hydrogenation station optimized distribution model.

The scheme establishes an optimized distribution model of the hydrogen production and hydrogenation station as shown in the attached figure 14. And obtaining the optimal distribution strategy of the hydrogen production hydrogenation station under the consideration of the coupling influence of the power distribution network and the hydrogen fuel automobile. Simulation results show that:

1) the working mode of the hydrogen production hydrogenation station can be established on the basis of the trip rule of the fuel cell vehicle, the hydrogen production rule of the fuel cell vehicle is analyzed, and the daily hydrogen charging demand distribution of the fuel cell vehicle is generated by means of Monte Carlo, so that the method is a prerequisite factor for solving the optimized distribution point model of the hydrogen production hydrogenation station.

2) Aiming at the uncertainty of the hydrogen production characteristics and the hydrogen charging requirements of the hydrogen production and hydrogenation station, the hydrogen production and hydrogenation station distribution optimization problem is coupled with a power distribution network and a traffic network by the hydrogen production and hydrogenation station distribution optimization model, so that the network loss is reduced, the node voltage deviation is reduced, the transmission of line power is stabilized, the service range network is expanded, the psychological burden of a user is reduced, and the hydrogenation requirements of a fuel cell automobile are met.

3) The optimized distribution model of the hydrogen production and hydrogen refueling station is solved through an improved goblet sea squirt group optimization algorithm, and the obtained strategy solves the problems that the power distribution network is unstable in operation and the service range of the traffic network is not balanced with the traffic flow under the condition that the coupling influence of the power distribution network and a hydrogen fuel automobile is considered.

The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (4)

1. A hydrogen production and hydrogenation station distribution point optimization method considering comprehensive factor indexes is characterized by comprising the following steps:

step 1: introducing a multi-node power distribution network model, and respectively constructing a node voltage deviation index, an active power margin level index, an active network loss index, a traffic flow index, a hydrogen production hydrogenation station service range index and a user satisfaction index;

step 2: respectively constructing active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station contact ratio constraint;

and step 3: and (2) constructing a hydrogen production hydrogenation station distribution optimization target according to the node voltage deviation index, the active power margin level index, the active network loss index, the traffic flow index, the hydrogen production hydrogenation station service range index and the user satisfaction index in the step (1), and obtaining hydrogen production hydrogenation station optimization access nodes in the multi-node power distribution network model through optimization solution of a zun sea squirt group optimization algorithm by using active power upper limit constraint, unit climbing rate constraint, node voltage constraint, power balance equation constraint, fuel cell vehicle hydrogen charging quantity and total demand constraint, hydrogen production hydrogenation station quantity constraint, hydrogen production hydrogenation station service range constraint and hydrogen production hydrogenation station overlap ratio constraint as constraint conditions of the hydrogen production hydrogenation station distribution optimization target.

2. The hydrogen production and hydrogenation station distribution optimization method considering the comprehensive factor indexes in claim 1, wherein the node voltage deviation index in step 1 is defined as:

wherein S is₁Is a node voltage deviation index; s_1-aIs an indicator of voltage fluctuation level; n is the number of nodes of the power distribution network; t belongs to T, T is the time T in the hydrogen production time, and T is the total hydrogen production time; u shape_bus-i,tThe node voltage deviation of the node i at the time t is shown; u shape_bus-i,t,minThe minimum value of the rated voltage deviation of the node i at the moment t; u shape_bus-i,t,maxThe maximum value of the rated voltage deviation of the node i at the moment t; u shape_NIs a rated voltage;

the average value of the voltage fluctuation level of the node i in the calculation period is obtained;

step 1, the active power margin level index is defined as:

wherein S is₂The active power margin level index is obtained; s_2-aThe influence coefficient of the power fluctuation and the maximum power deviation on the objective function is considered; k belongs to K, K is the kth alternating current line connected with the node i, and K is the total number of the alternating current lines connected with the node i; p_FCV，tIs the average power of the ac line connected to node i at time t; p_k，tIs the active power level of line k connected to node i at time t; a is the weight of the power fluctuation in the influence coefficient; b is the weight of the maximum power deviation in the influence coefficient; p_k-maxThe upper limit of the transmission power of a line k connected with the node i;

step 1, the active network loss index is defined as:

wherein S is₃The index is an active network loss index; g_ijConductance of the branch between node i and node j; u shape_iIs the voltage amplitude of node i; u shape_jIs the voltage amplitude of node j; n is_l∈N_L，n_LFor the nth node in the multi-node power distribution network model_LStrip transmission line, N_LThe number of the transmission lines in the multi-node power distribution network model is determined; theta_iIs the phase angle, θ, of the voltage at node i_jIs the phase angle of the voltage at node j;

the traffic flow index in the step 1 is defined as:

wherein S is₄Is a traffic flow index; f_uA vehicle weight coefficient for a route start point u; f_vA vehicle weight coefficient being a line end point v; l belongs to L, wherein L is the L-th traffic path in the multi-node traffic network model, and L is the number of the traffic paths in the multi-node traffic network model; d_{uv_l}The path lengths of a starting point u and an end point v of a line in a traffic network; n is a radical of_{_JT}Is a road network main node;

step 1, the service range index of the hydrogen production and hydrogenation station is defined as:

wherein S is₅For the service range index of the hydrogen production hydrogenation station, M belongs to M, M is the mth hydrogen production hydrogenation station, and M is the total number of the hydrogen production hydrogenation stations; s_{m_CS}Represents the attraction of the mth hydrogen production and hydrogenation station to the user, P_{HPRS_m}Denotes the power, lambda, of the mth hydrogen production and hydrogenation station_qOther factors representing the road network node q influence the weight, d_{HPRS_l}Represents the length of a path l from a fuel cell vehicle to a hydrogen production and hydrogenation station, E_FCVRepresents the hydrogen consumption per unit distance, P_FCVRepresenting the hydrogen price of the hydrogen production and hydrogenation station;

step 1, the user satisfaction index is defined as:

wherein S is₆Is a user satisfaction index; s_{_num}The influence of the number of hydrogen stations for hydrogen production on the satisfaction degree of users;

weight in user satisfaction for the number factor; s_{_ljd}Is the influence of proximity on user satisfaction; n is_{m_o}∈N_{m_o}，n_{m_o}The o node, N, contained in the service scope of the m hydrogen production and hydrogenation station_{m_o}The service range of the mth hydrogen production and hydrogenation station comprises a total node; k is a radical of_{b_m}∈K_{b_m}，k_{b_m}Is the m-thThe b-th path, K, connected to the hydrogen-producing and hydrogenating station_{j_m}The total number of paths connected with the mth hydrogen production hydrogenation station; l_{b_m}Is the length of the b-th path d connected to the m-th hydrogen production and hydrogenation station; l is_{N_JT}Representing the total path length in the road network; n is_{EV_u}Representing the number of vehicles of a road network node u; n is a radical of_EVThe total number of vehicles in the traffic network is shown.

3. The hydrogen production and hydrogenation station distribution optimization method considering the comprehensive factor indexes according to claim 1, wherein the active power upper limit constraint in the step 2 is defined as:

in the formula, P_ij，tIs a line l_ijActive power at time t; p_ij ^maxIs a line l_ijThe upper limit of active power at time t;

step 2, the unit climbing rate is restricted, and the restriction is defined as:

in the formula, P_x ^grThe unit x active output unit time change upper limit is set; -P_x ^grThe lower limit of the unit x active output unit time change is set;

the active power of the unit x at the moment t is obtained;

the active power of the unit x at the time t-1 is obtained;

step 2, the node voltage constraint is defined as:

U_bus-i,t,min≤U_bus-i,t≤U_bus-i,t,max

in the formula of U_bus-i,tThe node voltage deviation of the node i at the time t is shown; u shape_bus-i,t,minThe minimum value of the rated voltage deviation of the node i at the moment t; u shape_bus-i,t,maxThe maximum value of the rated voltage deviation of the node i at the moment t;

step 2, the power balance equation constraint is defined as:

in the formula, P_iIs the active power input at node i; q_iIs the reactive power input at node i; p_LiIs the active power of the load at node i; q_LiIs the reactive power of the load at node i; g_ijIs the conductance of the branch; b is_ijIs the susceptance of the branch; u shape_iA node voltage of node i; u shape_jA node voltage at node j; p_DGiInjecting active power of the node i; q_DGiInjecting reactive power into the node i; theta_ijIs the phase angle difference of the voltage;

step 2, the hydrogen charging quantity and the total demand constraint of the fuel cell automobile are defined as follows:

in the formula, N_{FCV_m}The number of the hydrogen charges of the fuel cell vehicle in the mth hydrogen production and hydrogenation station; n is a radical of_{HPRS_m}The allowable hydrogen charging number of the mth hydrogen production and hydrogenation station; v is the number of hydrogen storage tanks in the hydrogen production and hydrogenation station; s_CQGThe capacity of the hydrogen storage tank; s_FCVThe capacity of a fuel cell vehicle;

and 2, restricting the number of hydrogen production hydrogenation stations, and defining as follows:

n_{q_HPRS}＝1

in the formula, n_{q_HPRS}The number of hydrogen production and hydrogenation stations at the road network node q is determined, and only one hydrogen production and hydrogenation station can be built at each road network node in the planning process;

step 2, restricting the service range of the hydrogen production hydrogenation station, which is defined as:

2≤N_{HPRS_m}≤10

in the formula, N_{HPRS_m}The influence range of the mth hydrogen production and hydrogenation station comprises the node number;

step 2, the contact ratio constraint of the hydrogen production hydrogenation station is defined as:

m≠s,|N_{HPRS_m}|≠0

in the formula: n is a radical of_{HPRS_m}The influence range of the mth hydrogen production and hydrogenation station comprises the node number; n is a radical of_{HPRS_s}The influence range of the s-th hydrogen production and hydrogenation station comprises the number of nodes, namely the service range of the hydrogen production and hydrogenation station; ξ represents the same number of nodes, i.e., the degree of coincidence, within the service range of two hydrogen-producing and hydrogen-adding stations.

4. The hydrogen production and hydrogenation station stationing optimization method considering the comprehensive factor indexes as claimed in claim 1, wherein the hydrogen production and hydrogenation station stationing optimization objective of step 3 is defined as:

in the formula: s is an optimized distribution comprehensive index of the hydrogen production hydrogenation station in the hydrogen production time period; k is a radical of₁The bus node voltage deviation is a proportionality coefficient in the comprehensive index; k is a radical of₂The ratio coefficient of the active power margin level of the alternating current line in the comprehensive index is obtained; k is a radical of₃The ratio coefficient of the network loss level of the whole network in the comprehensive index is shown; k is a radical of₄The proportional coefficient of the traffic flow in the comprehensive index; k is a radical of₅The proportionality coefficient of the service range of the hydrogen production hydrogenation station in the comprehensive index is obtained; k is a radical of₆A proportionality coefficient of the user satisfaction in the comprehensive index; s₁Is the bus node voltage deviation; s₂The active power margin level of the alternating current line is set; s₃The network loss level of the whole network; s₄Is the traffic flow; s₅The service range of the hydrogen production and hydrogenation station is expanded; s₆The satisfaction degree of the user is obtained;

step 3, obtaining the optimized access node of the hydrogen production and hydrogen refueling station in the multi-node power distribution network model through optimization solution of the goblet sea squirt group optimization algorithm, which specifically comprises the following steps:

step 3.1, inputting the optimized number and the optimized strategy number of hydrogen production and hydrogenation stations

Inputting H optimizing strategies in the initial stage, namely searching individuals; simultaneously inputting the number R of corresponding hydrogen production and hydrogenation stations in a single optimization strategy, and generating an H multiplied by R Euclidean space as follows:

x is European space; h is a space dimension, namely the number of the optimizing strategies; r is the population number, namely the number of hydrogen production and hydrogenation stations in the optimization strategy;

step 3.2, initializing layout positions of hydrogen production hydrogenation stations

Initializing the layout position of the hydrogen production hydrogenation station in the optimization strategy through the goblet sea squirt group optimization algorithm, and using X for the layout optimization strategy of the ith hydrogen production hydrogenation station in the space_iRepresents:

the distribution position of the r hydrogen production and hydrogenation station in the ith strategy is determined;

step 3.3, optimizing the distribution strategy of hydrogen production hydrogenation stations

The leader is used as a first vector of the X matrix and represents the optimal strategy of the current optimizing process, the optimizing strategy of the hydrogen production hydrogen refueling station is guided to approach the optimal point distribution optimizing strategy before the ending condition is not met, and the position updating formula of the leader is as follows:

wherein the content of the first and second substances,

F_jthe optimal point distribution strategy for the leader and the hydrogen production hydrogenation station is in the position of the j-dimensional space; max_jAnd min_jUpper and lower boundaries of j-dimensional space values, respectively; c. C₂Determining the length of movement, c₃Determining a moving direction; c. C₁As a convergence factor, c₂And c₃Is the interval [0, 1]Internally generated random numbers;

wherein, T, T_maxRespectively representing the current iteration times and the maximum iteration times;

the follower's location update formula is as follows:

wherein the content of the first and second substances,

representing the coordinates of the ith hydrogen production and hydrogenation station distribution optimization strategy in a j-dimensional space in t iterations;

step 3.4, outputting a hydrogen production hydrogenation station distribution optimization strategy

Outputting an optimal point distribution strategy of the hydrogen production hydrogenation station when the termination condition is met, namely the current European space initial phasor X_{i_min}The concrete formula is as follows:

wherein，

The distribution position of the r-th hydrogen production hydrogenation station in the output hydrogen production hydrogenation station optimal distribution strategy is determined;

to is directed at

Setting weights for H searching individuals to reasonably apply global information so as to avoid ending the search in advance due to limitation of an optimal value in a local range; the weight formula is as follows:

wherein, K_WIs the overall weight; f. of_bOptimizing a point distribution objective function for the hydrogen production and hydrogenation station at the tail of the current sequencing; f. of_F，f_xiOptimizing strategy objective function optimal value and xth for all hydrogen production hydrogenation stations_iObjective function values of the optimization strategies; f is the optimal point distribution strategy position of the hydrogen production hydrogenation station; (f)_b-f)/α is the individual weight; the improved leader formula is:

xⁱ＝ωxⁱ+rand×(K_W-xⁱ)

wherein l is the current iteration number, and the improved formula ensures that omega can control the individual optimum position in the whole exploration process, so as to avoid falling into a local position and obtain an optimal value;

aiming at the problem of unbalance of global search and local search, the self-adaptive inertia weight is added, the search range is expanded at the initial stage of exploration, the local search capability is enhanced at the later stage, and the self-adaptive inertia weight formula is as follows:

the improved follower position formula is as follows:

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