CN114357681B - Hydrogen production hydrogenation station distribution optimization method considering comprehensive factor indexes - Google Patents
Hydrogen production hydrogenation station distribution optimization method considering comprehensive factor indexes Download PDFInfo
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Abstract
The invention provides a hydrogen production hydrogenation station distribution optimization method taking comprehensive factor indexes into consideration. According to the method, a multi-node power distribution network model is introduced, and node voltage deviation indexes, active power margin level indexes, active network loss indexes, traffic flow indexes, hydrogen production and hydrogenation station service range indexes and user satisfaction indexes are respectively constructed; respectively constructing an active power upper limit constraint, a unit climbing rate constraint, a node voltage constraint, a power balance equation constraint, a fuel cell automobile hydrogen charging quantity and total demand constraint, a hydrogen production and hydrogen adding station quantity constraint, a hydrogen production and hydrogen adding station service range constraint and a hydrogen production and hydrogen adding station overlap ratio constraint; and constructing a hydrogen production hydrogenation station distribution optimization target, and combining a plurality of constraint conditions, and obtaining the hydrogen production hydrogenation station optimization access node in the multi-node power distribution network model through optimization solution of the sea squirt group optimization algorithm. The invention effectively reduces the voltage deviation of the power distribution network, improves the line power margin, reduces the network loss of the power grid, and improves the convenience of traffic road network distribution of the hydrogen production and hydrogen production station.
Description
Technical Field
The invention belongs to the technical field of new energy optimization, and particularly relates to a hydrogen production hydrogenation station distribution optimization method considering comprehensive factor indexes.
Background
The hydrogen fuel cell automobile is the most important one in the field of hydrogen energy application, and in a certain sense, the smooth promotion of new energy revolution can not leave the rapid development of the new energy cell automobile. With the popularization of fuel cell automobiles, the hydrogen production and hydrogen adding station gradually replaces a part of traditional automobile gas stations, but due to the hydrogen production characteristics and the load uncertainty of the hydrogen production and hydrogen adding station, the point distribution optimization strategy of the hydrogen production and hydrogen adding station and the point distribution mode of an electric automobile charging station cannot be roughly considered. The present distribution research on electric vehicle charging stations has focused mainly on balancing the interests between the charging station operators and users and reducing the overall cost of the charging station arrangement. The hydrogen production and hydrogenation station represented by electrolytic hydrogen production can bring a certain degree of impact to the operation of the power grid after being connected to the power grid due to the specificity of the model, so that the influence of the electric vehicle on the power distribution network needs to be considered in consideration of the distribution optimization strategy, and secondly, the working modes of the hydrogen production and hydrogenation station and the electric vehicle charging station are greatly different due to the difference of the endurance mileage and the demand characteristic of the electric vehicle and the fuel cell vehicle, meanwhile, the acceptance degree of surrounding households needs to be additionally considered in the distribution optimization strategy due to the chemical characteristic of hydrogen energy, so that the research on the distribution optimization strategy of the hydrogen production and hydrogenation station is not slow.
In recent years, students at home and abroad have made a great deal of researches on the problem of point distribution optimization of hydrogen production and hydrogen addition stations, and in order to combine a classical hydrogen supply chain network design model with a hydrogen addition station planning model, a mathematical model of the whole hydrogen supply network is designed through emphasis so as to generate a new model formula; aiming at the hydrogenation station site selection optimization model, the supply radius of the hydrogenation station, the hydrogen source capacity and the geographic information factors are taken as constraint conditions so as to improve the applicability and the level of the hydrogen energy; the optimal operation strategy is provided for realizing the 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 plant, the growing hydrogen demand is met.
However, most of the above researches consider the model and the size of the hydrogen production and hydrogen adding station, and most of the above researches are off-grid, so that it is difficult to describe the influence of the hydrogen production and hydrogen adding station on the power grid. Based on the above consideration, the uncertainty of the hydrogen charging requirement of the fuel cell automobile is considered by establishing a robust model of the site selection and the scale of the hydrogen production hydrogenation station, the additional loss of the fuel cell automobile when the fuel cell automobile is connected to a power grid is taken into the model, and simultaneously, hydrogen production planning and capacity adjustment are carried out on each unit of the hydrogen production hydrogenation station, so that the operation cost of the hydrogen production hydrogenation station is reduced. However, the above research lacks consideration of road network factors, and has no definite model for the hydrogenation requirement and service range of the automobile. Therefore, the research on the distribution optimization of the hydrogen production and hydrogen adding station is mainly focused on the modeling of the hydrogen production and hydrogen adding station, the optimization of the operation cost and the research on the hydrogen production mode, and the research on the aspect of the influence of hydrogen production on a power grid has a large hole, and the distribution optimization of the hydrogen production and hydrogen adding station is not combined with a traffic network, and the fluctuation of the hydrogen adding of a fuel cell automobile is not considered.
Meanwhile, due to the particularity of the hydrogen production and hydrogen adding station model, the voltage of an access node can be fluctuated in the hydrogen production time period, then the hydrogen production and hydrogen adding station is used as a large load, the power flow distribution and the network loss of the power distribution network can be influenced during operation, and the traffic flow and the service range of the hydrogen production and hydrogen adding station in the traffic network can be different due to the difference of the distribution positions of the hydrogen production and hydrogen adding stations. Meanwhile, due to the chemical characteristics of hydrogen, the distribution strategy after the acceptance degree of surrounding householders to the hydrogen production and hydrogenation station is considered is certainly more beneficial to the construction and promotion of the hydrogen production and hydrogenation station in the actual life in terms of reality and psychology. Thus, two issues should be considered when considering the problem of setpoint optimization for hydrogen production and hydrogen stations:
The problem of hydrogen production and hydrogen adding station distribution optimization is required to be coupled with a power distribution network and a traffic network, and a hydrogen production and hydrogen adding station distribution optimization model is constructed.
The model is solved through an improved optimization algorithm of the sea squirt group, and the obtained strategy is required to solve the problem that the operation of the distribution network is unstable under the condition that the coupling influence of the distribution network and the hydrogen fuel automobile is considered, and the service range and the traffic flow of the traffic network are unbalanced.
Disclosure of Invention
The invention provides a hydrogen production hydrogenation station distribution optimizing strategy comprehensively considering the coupling influence of a power distribution network and a hydrogen fuel automobile for the first time. Firstly, performing Monte Carlo simulation on a hydrogen time sequence curve for a hydrogen fuel automobile user to achieve deep analysis on a hydrogen production and hydrogenation station working mode; secondly, with the aim of considering the safe operation of the traffic network and the power network, a hydrogen production hydrogenation station distribution optimizing model taking the coupling influence of the power distribution network and the hydrogen fuel automobile into consideration under the traffic-power network frame is constructed, and the model is further solved by utilizing an improved sea-goblet sea squirt group optimizing algorithm to obtain an optimal hydrogen production hydrogenation station distribution optimizing scheme.
The problems of the invention are mainly solved by the following technical proposal:
The hydrogen production hydrogenation station distribution optimizing method taking comprehensive factor indexes into consideration is characterized by comprising the following steps of:
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 an active power upper limit constraint, a unit climbing rate constraint, a node voltage constraint, a power balance equation constraint, a fuel cell automobile hydrogen charging quantity and total demand constraint, a hydrogen production and hydrogen adding station quantity constraint, a hydrogen production and hydrogen adding station service range constraint and a hydrogen production and hydrogen adding station overlap ratio constraint;
Step 3: constructing a hydrogen production and hydrogen addition 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 and hydrogen addition station service range index and the user satisfaction index, which are described in the step 1, and optimizing and solving the constraint conditions of the hydrogen production and hydrogen addition station distribution optimization target by using the active power upper limit constraint, the unit climbing rate constraint, the node voltage constraint, the power balance equation constraint, the fuel cell automobile hydrogen addition quantity and total demand constraint, the hydrogen production and hydrogen addition station quantity constraint, the hydrogen production and hydrogen addition station service range constraint and the hydrogen production and hydrogen addition station overlap ratio constraint through a goblet sea squirt group optimization algorithm to obtain a hydrogen production and hydrogen addition station optimization access node in the multi-node power distribution network model;
preferably, the node voltage deviation index in step 1 is defined as:
S 1 is a node voltage deviation index; s 1-a is a voltage fluctuation level index; n is the number of nodes of the power distribution network; t is T, T is the time T in the hydrogen production time, and T is the total hydrogen production time; u bus-i,t is the node voltage deviation of the node i at the time t; u bus-i,t,min is the minimum value of rated voltage deviation of the node i at the time t; u bus-i,t,max is the maximum value of rated voltage deviation of the node i at the time t; u N is the rated voltage; the average value of the voltage fluctuation level in the calculation period is taken as the node i.
The active power margin level index in step 1 is defined as:
Wherein S 2 is an active power margin level indicator; s 2-a is an influence coefficient of power fluctuation and maximum power deviation on an objective function; k is 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,t is the average power of the ac line connected to node i at time t; p k,t is 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-max is the upper limit of the transmission power of line k connected to node i.
The active network loss index in step 1 is defined as:
Wherein S 3 is an active network loss index; g ij is the conductance of the branch between node i and node j; u i is the voltage amplitude of node i; u j is the voltage amplitude of node j; n l∈NL,nL is the N L th transmission line in the multi-node distribution network model, and N L is the number of transmission lines in the multi-node distribution network model; θ i is the phase angle of the voltage at node i, and θ j is the phase angle of the voltage at node j.
The traffic flow index in step 1 is defined as:
Wherein S 4 is a traffic flow index; f u is a vehicle weight coefficient of the line starting point u; f v is a vehicle weight coefficient of the line end point v; l is L, i is the first traffic path in the multi-node traffic network model, and L is the number of traffic paths in the multi-node traffic network model; d uv_l is the path length of the starting point u and the ending point v of the road line in the traffic road network; n _JT is the total node of the road network.
The service range index of the hydrogen production and hydrogen adding station in the step 1 is defined as:
S 5 is a hydrogen production hydrogenation station service range index, M is M, M is the M-th hydrogen production hydrogenation station, and M is the total number of hydrogen production hydrogenation stations; s m_CS represents the attraction of the mth hydrogen production and hydrogen addition station to a user, P HPRS_m represents the power of the mth hydrogen production and hydrogen addition station, lambda q represents the influence weight of other factors of a road network node q, d HPRS_l represents the length of a fuel cell automobile reaching a hydrogen production and hydrogen addition station path l, E FCV represents the hydrogen consumption amount per unit distance, and P FCV represents the hydrogen price of the hydrogen production and hydrogen addition station;
the user satisfaction index in step1 is defined as:
Wherein S 6 is a user satisfaction index; s _num is the influence of the number of hydrogen production hydrogen stations on the satisfaction degree of users; The weight of the number of factors in user satisfaction; s _ljd is the influence of the proximity on the user satisfaction; n m_o∈Nm_o,nm_o is the o node contained in the service range of the mth hydrogen production hydrogenation station, and N m_o is the total node contained in the service range of the mth hydrogen production hydrogenation station; k b_m∈Kb_m,kb_m is the b-th path connected with the m-th hydrogen production hydrogenation station, and K j_m is the total number of paths connected with the m-th hydrogen production hydrogenation station; l b_m is the length of path d of the b-th path to the mth hydrogen production and hydrogenation station; l N_JT denotes the total path length in the road network; n EV_u represents the number of vehicles of the road network node u; n EV represents the total number of vehicles in the traffic network.
Preferably, the upper limit constraint of the active power in the step 2 is defined as:
Wherein P ij,t is the active power of the line l ij at the time t; p ij max is the upper active power limit of line l ij at time t.
Step 2, the unit climbing rate constraint is defined as:
Wherein P x gr is the upper limit of the unit time change of the active output of the unit x; -P x gr is the lower limit of the unit time variation of the unit x active output; The active power of the unit x at the time t; /(I) The active power of the unit x at the time t-1.
The node voltage constraint in step 2 is defined as:
Ubus-i,t,min≤Ubus-i,t≤Ubus-i,t,max
Wherein U bus-i,t is the node voltage deviation of the node i at the time t; u bus-i,t,min is the minimum value of rated voltage deviation of the node i at the time t; u bus-i,t,max is the maximum value of the nominal voltage deviation of node i at time t.
The constraint of the power balance equation in the step2 is defined as:
Wherein P i is the active power input at node i; q i is the reactive power input at node i; p Li is the active power of the load at node i; q Li is the reactive power of the load at node i; g ij is the conductance of the branch; b ij is susceptance of the branch; node voltage of U i node i; u j is the node voltage of node j; p DGi is the active power injected into node i; q DGi injects reactive power into node i; θ ij is the phase angle difference of the voltage.
And 2, the constraint of the hydrogen charging quantity and the total demand of the fuel cell automobile is defined as:
Wherein N FCV_m is the hydrogen charging quantity of the fuel cell car in the mth hydrogen production and hydrogen charging station; n HPRS_m is the number of hydrogen filling permitted of the mth hydrogen production and hydrogen filling station; v is the number of hydrogen storage tanks in the hydrogen production and hydrogenation station; s CQG is the capacity of the hydrogen storage tank; s FCV is the capacity of the fuel cell car.
The hydrogen production and hydrogen station number constraint in the step 2 is defined as:
nq_HPRS＝1
Wherein n q_HPRS is the number of hydrogen production hydrogen adding stations at the road network node q, and each road network node can only build one hydrogen production hydrogen adding station in the planning process.
And 2, the service range constraint of the hydrogen production and hydrogen adding station is defined as:
2≤NHPRS_m≤10
where N HPRS_m is the range of influence of the mth hydrogen production hydrogen station including the number of nodes.
And 2, the hydrogen production hydrogenation station overlap ratio constraint is defined as:
wherein: n HPRS_m is the influence range of the mth hydrogen production hydrogenation station and comprises the node number; n HPRS_s is the influence range of the s-th hydrogen production hydrogenation station, and comprises the node number, namely the service range of the hydrogen production hydrogenation station; and xi represents the same node number, namely the contact ratio, in the service range of the two hydrogen production and hydrogen adding stations.
Preferably, the hydrogen production hydrogenation station distribution optimization target in the step 3 is defined as:
Wherein: s is an optimized distribution comprehensive index of a hydrogen production hydrogenation station in a hydrogen production time period; k 1 is a proportionality coefficient of the voltage deviation of the bus node in the comprehensive index; k 2 is a proportionality coefficient of the active power margin level of the alternating current line in the comprehensive index; k 3 is the proportionality coefficient of the network loss level of the whole network in the comprehensive index; k 4 is the proportionality coefficient of the traffic flow in the comprehensive index; k 5 is the proportionality coefficient of the hydrogen production hydrogenation station service range in the comprehensive index; k 6 is the proportionality coefficient of the user satisfaction in the comprehensive index; s 1 is the voltage deviation of the bus node; s 2 is the active power margin level of the alternating current line; s 3 is the network loss level of the whole network; s 4 is traffic flow; s 5 is the hydrogen production and hydrogen station service range; s 6 is user satisfaction.
Step 3, obtaining a hydrogen production hydrogenation station optimization access node in a multi-node power distribution network model through optimization solution of a goblet sea squirt group optimization algorithm, wherein the optimization access node specifically comprises the following steps:
step 3.1, inputting the optimized number of hydrogen production and hydrogen adding stations and the number of optimizing strategies
Inputting H optimizing strategies at the initial stage, namely searching individuals; simultaneously inputting the corresponding hydrogen production and hydrogenation station number R in a single optimizing strategy, and generating the European space of H multiplied by R as follows:
X is European space; h is the space dimension, namely the number of the optimizing strategies; r is population number, namely hydrogen production and hydrogen addition station number in the optimizing strategy;
Step 3.2, initializing the hydrogen production hydrogenation station point distribution position
Initializing hydrogen production and hydrogen adding station distribution positions in an optimizing strategy by a sea squirt group optimizing algorithm, wherein the ith hydrogen production and hydrogen adding station distribution optimizing strategy in a space is represented by X i:
And (3) locating the point for the (r) hydrogen production hydrogen station in the (i) th strategy.
Step 3.3, optimizing the hydrogen production hydrogenation station distribution strategy
The leader is used as the first vector of the X matrix and represents the optimal strategy of the current optimizing process, and before the ending condition is not met, the optimizing strategy of the hydrogen production and hydrogen station behind is guided to approach to the optimal distribution optimizing strategy, and the position updating formula of the leader is as follows:
Wherein, F j is the position of the optimal point distribution strategy of the leader and the hydrogen production and hydrogen addition station in the j-dimensional space; max j and min j are respectively the upper and lower boundaries of the j-dimensional space value; c 2 determines the movement length, c 3 determines the movement direction; c 1 is a convergence factor, c 2 and c 3 are random numbers generated within the interval [0,1 ];
Wherein T, T max are the current iteration number and the maximum iteration number, respectively.
The location update formula for the follower is as follows:
Wherein, Representing the coordinates of the ith hydrogen production hydrogenation station distribution optimizing strategy in the j-dimensional space of t iterations;
step 3.4, outputting hydrogen production hydrogenation station distribution optimizing strategy
When the termination condition is met, outputting an optimal point distribution strategy of the hydrogen production and hydrogenation station, namely the current European space first phasor X i_min, wherein the specific formula is as follows:
Wherein, And (5) setting the position of the r hydrogen production and hydrogen adding station in the output hydrogen production and hydrogen adding station optimal setting strategy.
For the purpose of In the optimizing process, the problem of local searching is easily involved, weights are set for H searching individuals, global information is reasonably applied, and searching is prevented from being finished in advance due to the fact that optimal values limited in a local range are avoided. The weight formula is as follows:
Wherein K W is the overall weight; f b is an optimal distribution objective function of the hydrogen production and hydrogen addition station at the tail of the current sequencing; f F,fxi is the optimal value of the current hydrogen production hydrogenation station optimizing strategy objective function and the x i optimizing strategy objective function value; f is the optimal distribution strategy position of the hydrogen production and hydrogenation station; (f b -f)/α is the weight of the individual. The improved leader formula is:
xi＝ωxi+rand×(KW-xi)
wherein, l is the current iteration number, the improved formula ensures that omega can control individual optimizing positions in the whole exploration process, and avoids sinking into local positions to obtain an optimal value;
Aiming at the problem of unbalance between global search and local search, self-adaptive inertial weights are added, the search range is enlarged in the initial stage of exploration, the local search capacity is enhanced in the later stage, and the self-adaptive inertial weights are as follows:
The position formula of the improved follower is as follows:
The invention has the following advantages: the hydrogen production and hydrogenation station distribution optimization strategy comprehensively considering the coupling influence of the power distribution network and the hydrogen fuel automobile is provided, the safety operation of a traffic network and a power network is taken into consideration, a hydrogen production and hydrogenation station distribution optimization model considering the coupling influence of the power distribution network and the hydrogen fuel automobile under a traffic-power network frame is constructed, and the model is further solved by utilizing an improved goblet-sea squirt swarm algorithm; the obtained hydrogen production and hydrogen adding station distribution optimizing strategy not only can effectively reduce the voltage deviation of the power distribution network, improve the line power margin and reduce the network loss of the power grid, but also can improve the convenience of hydrogen production and hydrogen adding station traffic road network distribution.
Drawings
Fig. 1 is a graph of hydrogen charge demand profile.
FIG. 2 is a diagram of a hydrogen plant operating model.
FIG. 3 is a block diagram of an optimized setpoint model for hydrogen production and hydrogen addition stations.
Fig. 4 is a diagram of a circuit-to-electrical coupling framework.
Fig. 5 is a night load demand profile.
FIG. 6 is a graph of quantitative indicators for hydrogen-producing periods of an initial strategy.
Fig. 7 is a graph of voltage deviations at various nodes at the initial strategic charging time.
Fig. 8 is an initial policy service scope diagram.
Fig. 9 is a flowchart of optimizing.
FIG. 10 is a graph of quantitative indicators for hydrogen production periods of an optimization strategy.
FIG. 11 is a graph of voltage deviations at various nodes of an optimization strategy charging time.
Fig. 12 is an optimization strategy service scope diagram.
FIG. 13 is a graph showing a comparison of the improved optimization algorithm of the sea squirt group with the optimization algorithm of the sea squirt group.
Fig. 14 is a flow chart of the steps of the present invention.
Detailed Description
In order to facilitate the understanding and practice of the invention, those of ordinary skill in the art will now make further details with reference to the drawings and examples, it being understood that the examples described herein are for the purpose of illustration and explanation only and are not intended to limit the invention thereto.
Embodiments of the present invention are described below with reference to fig. 1-14.
The hydrogen production hydrogenation station distribution optimizing method taking comprehensive factor indexes into consideration is characterized by comprising the following steps of:
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 following analysis is performed on the running condition of a large-sized fuel cell car according to the data of an electric car service and management center (SMC-EV) of the national engineering laboratory of Beijing university:
1) The daily driving mileage of the fuel cell automobile is mainly concentrated in 70-230 km, more than 80% of the daily driving mileage of the bus is within 210 km, and the daily driving mileage is 147 km. The daily driving mileage of the logistics vehicles is concentrated at 90-170 km. Approximately 90% of logistic vehicles have a daily driving distance of less than 310 km, and an average driving distance of 178 km.
2) The bus has two peaks of between 210 km and 250 km and between 130 km and 160 km respectively. The average driving mileage is 203 km; and the next hydrogen fuel supply is started when the driving mileage reaches 40-50% of the 65% of the vehicles, and the supply is started when the driving mileage reaches 50-60% of the 9% of the vehicles. The hydrogen fuel endurance mileage of all vehicles does not exceed 60% of the mileage; for logistic vehicles, the distance travelled by the refueling interval is mostly concentrated at 190-230 km, 57% of the vehicles begin the next hydrogen refueling when the mileage reaches 60-70%, and 17% of the vehicles begin the next refueling when the mileage reaches 70-80%.
3) The one-way time length of the logistics vehicles is concentrated in 30-60 minutes, rarely exceeds 70 minutes, and the one-way time length of the buses is more than 70 minutes, and is concentrated in 70-90 minutes.
4) Both vehicle types have obvious clusters at the beginning of the first trip and at the end of the last trip. Bus trips are typically from 6 a.m. to 7 a.m.. There are few starts with the first trip before 6 pm or ends at 7 pm. In addition, 16% of buses end the last journey at 10 pm. For logistics vehicles, 54% of the first travel starting time of the vehicles is concentrated at 6 to 8 points in the morning and 57% of the travel ending time of the vehicles is concentrated at 6 to 8 points in the evening. From the comparison result, the bus travel starting time distribution is relatively centralized, and almost no vehicles start traveling before 6 am, but more vehicles start running before 6 am, so that the characteristics of the logistics industry are reflected. For the end time of the last trip of the day, the proportion of the logistics vehicles to finish the last trip of the bus before 6 pm is higher. Generally, the start-stop time of a logistics vehicle is early, the start-stop time of a bus is late, and the travel rule is as follows:
Table 1 fuel cell vehicle travel law
And analyzing the data, and simulating the number of daily hydrogenation vehicles and the hydrogenation amount of the single hydrogen production and hydrogenation station through Monte Carlo. The hydrogen charging requirements and the hydrogen charging time distribution of the hydrogen production and hydrogen charging stations per day are obtained after considering the hydrogen charging characteristics of each vehicle in different driving distance intervals, the daily working time and the influence factors of single driving distance and hydrogen consumption, and are shown in the figure 1.
According to the working intervals of two types of fuel cell automobiles, the hydrogenation period is divided into 11 sections at 1.5h as an interval, the highest hydrogenation requirement in the noon period is known, and the data in one month are subjected to induction and sorting to obtain the average value, so that the one-day hydrogenation requirement of a single hydrogen production and hydrogenation station is about 934.74kg.
According to the travel rule of the fuel cell vehicle described in the previous section, the hydrogenation requirement of the fuel cell vehicle is drastically reduced after 10 pm, and according to the hydrogen requirement distribution diagram of fig. 1, the hydrogenation requirement of the fuel cell vehicle in the morning and in the evening is smaller. The hydrogen adding time of a fuel cell automobile is about 3 minutes, the hydrogen adding station can basically complete the hydrogen adding task before 12 points at night, and the hydrogen adding station begins to produce hydrogen after 12 points, and the working model of the hydrogen adding station is shown in figure 2.
Based on the running characteristics of the fuel cell automobile and the working mode of the hydrogen production and hydrogen adding station, the multi-aspect targets such as the power network influence index, the hydrogen production and hydrogen adding station traffic network index, the user psychological influence index and the like are comprehensively considered, and the hydrogen production and hydrogen adding station optimizing distribution model is constructed, and a specific optimizing model frame is shown in figure 3.
At the power network level: setting S 1 as voltage deviation, reducing S 1;S2 as active power margin of an alternating current line through point distribution optimization after the hydrogen production and hydrogenation station is connected to a power distribution network, and measuring an important index of stable operation of a power system; s 3 is an overall grid loss, and an excessive S 3 can result in electric energy loss and energy waste, and reducing S 3 is an important economic goal.
At the traffic network level: s 4 is set as the traffic flow captured by the hydrogen production and hydrogenation station, and the traffic flow is captured as much as possible through point distribution optimization to meet the hydrogen charging requirement of a user; s 5 is a hydrogen production and hydrogen station service range, and a large-scale service network is a precondition that a hydrogen supply chain and a hydrogen energy transaction mode are successfully operated;
At the psychological level of the user: the S 6 is set as the user satisfaction, a plurality of cases exist at home and abroad that the scheme cannot be normally implemented due to the fact that the user acceptance degree is not considered, and the literature about the distribution optimization of the hydrogen production and hydrogenation stations at home and abroad has few consideration on the user satisfaction. S 6 is a basic condition for normal execution of the scenario.
The node voltage deviation index in step1 is defined as:
S 1 is a node voltage deviation index; s 1-a is a voltage fluctuation level index; n=30 is the number of nodes of the distribution network; t is T, T is the time T in the hydrogen production time, and T=6h is the total hydrogen production time; u bus-i,t is the node voltage deviation of the node i at the time t; u bus-i,t,min is the minimum value of rated voltage deviation of the node i at the time t; u bus-i,t,max is the maximum value of rated voltage deviation of the node i at the time t; u N is the rated voltage; the average value of the voltage fluctuation level in the calculation period is taken as the node i.
The active power margin level index in step 1 is defined as:
Wherein S 2 is an active power margin level indicator; s 2-a is an influence coefficient of power fluctuation and maximum power deviation on an objective function; k is 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,t is the average power of the ac line connected to node i at time t; p k,t is the active power level of line k connected to node i at time t; a=0.5 is the weight of the power fluctuation in the influence coefficient; b=0.5 is the weight of the maximum power deviation in the influence coefficient; p k-max is the upper limit of the transmission power of line k connected to node i.
The active network loss index in step 1 is defined as:
Wherein S 3 is an active network loss index; g ij is the conductance of the branch between node i and node j; u i is the voltage amplitude of node i; u j is the voltage amplitude of node j; n l∈NL,nL is the N L th transmission line in the multi-node distribution network model, and N L is the number of transmission lines in the multi-node distribution network model; θ i is the phase angle of the voltage at node i, and θ j is the phase angle of the voltage at node j.
The traffic flow index in step 1 is defined as:
Wherein S 4 is a traffic flow index; f u is a vehicle weight coefficient of the line starting point u; f v is a vehicle weight coefficient of the line end point v; l is L, i is the first traffic path in the multi-node traffic network model, and L is the number of traffic paths in the multi-node traffic network model; d uv_l is the path length of the starting point u and the ending point v of the road line in the traffic road network; n _JT is the total node of the road network.
The service range index of the hydrogen production and hydrogen adding station in the step 1 is defined as:
Wherein S 5 is a hydrogen production hydrogen adding station service range index, M e M, M is the M-th hydrogen production hydrogen adding station, and m=3 is the total number of hydrogen production hydrogen adding stations; s m_CS represents the attraction of the mth hydrogen production and hydrogen addition station to the user, P HPRS_m represents the power of the mth hydrogen production and hydrogen addition station, lambda q represents the influence weight of other factors of the road network node q, d HPRS_l represents the length of the fuel cell automobile reaching the hydrogen production and hydrogen addition station path l, E FCV =1 kg/100km represents the hydrogen consumption amount, and P FCV =70 yuan/kg represents the hydrogen price of the hydrogen production and hydrogen addition station;
the user satisfaction index in step1 is defined as:
Wherein S 6 is a user satisfaction index; s _num is the influence of the number of hydrogen production hydrogen stations on the satisfaction degree of users; The weight of the number of factors in user satisfaction; s _ljd is the influence of the proximity on the user satisfaction; n m_o∈Nm_o,nm_o is the o node contained in the service range of the mth hydrogen production hydrogenation station, and N m_o is the total node contained in the service range of the mth hydrogen production hydrogenation station; k b_m∈Kb_m,kb_m is the b-th path connected with the m-th hydrogen production hydrogenation station, and K j_m is the total number of paths connected with the m-th hydrogen production hydrogenation station; l b_m is the length of path d of the b-th path to the mth hydrogen production and hydrogenation station; l N_JT denotes the total path length in the road network; n EV_u represents the number of vehicles of the road network node u; n EV represents the total number of vehicles in the traffic network.
Step 2: respectively constructing an active power upper limit constraint, a unit climbing rate constraint, a node voltage constraint, a power balance equation constraint, a fuel cell automobile hydrogen charging quantity and total demand constraint, a hydrogen production and hydrogen adding station quantity constraint, a hydrogen production and hydrogen adding station service range constraint and a hydrogen production and hydrogen adding station overlap ratio constraint;
the upper limit constraint of the active power in the step2 is defined as:
Wherein P ij,t is the active power of the line l ij at the time t; p ij max is the upper active power limit of line l ij at time t.
Step 2, the unit climbing rate constraint is defined as:
Wherein P x gr is the upper limit of the unit time change of the active output of the unit x; -P x gr is the lower limit of the unit time variation of the unit x active output; The active power of the unit x at the time t; /(I) The active power of the unit x at the time t-1.
The node voltage constraint in step 2 is defined as:
Ubus-i,t,min≤Ubus-i,t≤Ubus-i,t,max
Wherein U bus-i,t is the node voltage deviation of the node i at the time t; u bus-i,t,min is the minimum value of rated voltage deviation of the node i at the time t; u bus-i,t,max is the maximum value of the nominal voltage deviation of node i at time t.
The constraint of the power balance equation in the step2 is defined as:
Wherein P i is the active power input at node i; q i is the reactive power input at node i; p Li is the active power of the load at node i; q Li is the reactive power of the load at node i; g ij is the conductance of the branch; b ij is susceptance of the branch; node voltage of U i node i; u j is the node voltage of node j; p DGi is the active power injected into node i; q DGi injects reactive power into node i; θ ij is the phase angle difference of the voltage.
And 2, the constraint of the hydrogen charging quantity and the total demand of the fuel cell automobile is defined as:
Wherein N FCV_m is the hydrogen charging quantity of the fuel cell car in the mth hydrogen production and hydrogen charging station; n HPRS_m is the number of hydrogen filling permitted of the mth hydrogen production and hydrogen filling station; v is the number of hydrogen storage tanks in the hydrogen production and hydrogenation station; s CQG is the capacity of the hydrogen storage tank; s FCV is the capacity of the fuel cell car.
The hydrogen production and hydrogen station number constraint in the step 2 is defined as:
nq_HPRS＝1
Wherein n q_HPRS is the number of hydrogen production hydrogen adding stations at the road network node q, and each road network node can only build one hydrogen production hydrogen adding station in the planning process.
And 2, the service range constraint of the hydrogen production and hydrogen adding station is defined as:
2≤NHPRS_m≤10
where N HPRS_m is the range of influence of the mth hydrogen production hydrogen station including the number of nodes.
And 2, the hydrogen production hydrogenation station overlap ratio constraint is defined as:
wherein: n HPRS_m is the influence range of the mth hydrogen production hydrogenation station and comprises the node number; n HPRS_s is the influence range of the s-th hydrogen production hydrogenation station, and comprises the node number, namely the service range of the hydrogen production hydrogenation station; ζ=5 is the same number of nodes, i.e., overlap, in the service area of the two hydrogen stations.
According to the scheme, an IEEE30 standard calculation example and a coupling frame of 30 path network nodes are adopted for simulation analysis, a corresponding traffic route-power topology coupling frame is shown in a figure 4, the unit distance in the figure is 1KM, the positions of corresponding hydrogen production and hydrogen adding stations are represented by yellow nodes, the number of the hydrogen production and hydrogen adding stations is 3 as an example, and a dotted line connected in the figure is a path-electric coupling node.
The initial hydrogen production hydrogenation station is the original road network gas station position and is positioned at 9, 12 and 21 nodes. For the three-stage station, the hydrogen production and hydrogenation station adopts a hydrogen cylinder model with the rated pressure of 35MPa-35000L (for vehicles, a III-type hydrogen cylinder with the rated pressure of 35 MPa-140L). According to the ideal gas state equation:
PV＝nRT
p is pressure; v is the gas volume; n is the amount of the substance; t is the temperature; r is molar gas constant. The hydrogen adding capacity of one hydrogen adding station is about 1079kg per day under the standard condition.
Night load demand is shown in fig. 5:
The specific parameters of the hydrogen production and hydrogenation station are shown in the following table:
Table 2 hydrogen production and hydrogen station parameters
The impact factor weights of the nodes are as follows:
TABLE 3 influence factor weight table
| Node | Influence factor | Node | Influence factor | Node | Influence 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 |
First, the original scheme is evaluated, and the obtained traffic network index, the network line influence index, the psychological index and other results are shown in fig. 6.
In the power grid line influence index, the power margin is too high, which indicates that the line faces the risk of overload, and the active power of the corresponding line is as follows:
Table 4 active power of each line
The power transfer sizes of the 6-9 branch and the 9-10 branch differ by 2.5 times, the power transfer sizes of the 12-14 branch, the 12-16 branch and the 4-12 branch differ by approximately 3.3 times, and the power transfer sizes of the 10-21 branch and the 21-22 branch differ by approximately 14 times. The problem of unbalanced power transmission exists, which is unfavorable for the stable operation of the power grid. At night with fifteen minutes as an interval, the voltage fluctuates as shown in fig. 7. In the traffic road 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 range information table
| Strategy | Coverage of range | Number of uncovered nodes | Superposition ratio | Percentage of no coverage |
| Original policy | 90.00％ | 3 | 10.00％ | 10.00％ |
It can be seen by combining tables 5,6 and fig. 8 that when the hydrogen production and hydrogen adding station is located at the original gas station position, the original traffic advantage is inherited, the traffic flow and the service range are both dominant in the traffic road network index, the service coverage rate reaches 90%, and the specific service range node diagram is shown in fig. 8.
The user satisfaction degree of the initial strategy is 2.93, and the influence of the number, distance, quantity and vehicle flow of branches of the hydrogen production and hydrogen adding station on the user satisfaction degree is as follows:
TABLE 7 user satisfaction impact Table
| Quantitative index | Quantity of | Number of branches | Distance of | Traffic flow |
| Satisfaction impact value | 0.2 | 0.25 | 0.14 | 0.09 |
According to the evaluation result of each index, when the hydrogen production and hydrogen adding station is located to replace the original gas station, the original advantages are inherited in the traffic road network index, but the load characteristic of the hydrogen production and hydrogen adding station has great influence on the power grid, and the stable operation of the power grid is not facilitated.
Step 3: constructing a hydrogen production and hydrogen addition 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 and hydrogen addition station service range index and the user satisfaction index, which are described in the step 1, and optimizing and solving the constraint conditions of the hydrogen production and hydrogen addition station distribution optimization target by using the active power upper limit constraint, the unit climbing rate constraint, the node voltage constraint, the power balance equation constraint, the fuel cell automobile hydrogen addition quantity and total demand constraint, the hydrogen production and hydrogen addition station quantity constraint, the hydrogen production and hydrogen addition station service range constraint and the hydrogen production and hydrogen addition station overlap ratio constraint through a goblet sea squirt group optimization algorithm to obtain a hydrogen production and hydrogen addition station optimization access node in the multi-node power distribution network model;
and 3, the hydrogen production and hydrogen adding station distribution optimization target is defined as:
Wherein: s is an optimized distribution comprehensive index of a hydrogen production hydrogenation station in a hydrogen production time period; k 1 =0.181 is the proportionality coefficient of the bus node voltage deviation in the comprehensive index; k 2 =0.181 is the proportionality coefficient of the ac line active power margin level in the comprehensive index; k 3 =0.181 is the proportionality coefficient of the network loss level of the whole network in the comprehensive index; k 4 =0.153 is the proportionality coefficient of traffic flow in the comprehensive index; k 5 =0.160 is a proportionality coefficient of hydrogen production and hydrogen station service range in the comprehensive index; k 6 =0.143 is the proportionality coefficient of the user satisfaction in the comprehensive index; s 1 is the voltage deviation of the bus node; s 2 is the active power margin level of the alternating current line; s 3 is the network loss level of the whole network; s 4 is traffic flow; s 5 is the hydrogen production and hydrogen station service range; s 6 is user satisfaction.
Step 3, obtaining the hydrogen production hydrogenation station optimized access node in the multi-node power distribution network model through optimization solution of the sea squirt group optimization algorithm, as shown in fig. 9, specifically comprising the following steps:
step 3.1, inputting the optimized number of hydrogen production and hydrogen adding stations and the number of optimizing strategies
Inputting H optimizing strategies at the initial stage, namely searching individuals; simultaneously inputting the corresponding hydrogen production and hydrogenation station number R in a single optimizing strategy, and generating the European space of H multiplied by R as follows:
X is European space; h is the space dimension, namely the number of the optimizing strategies; r is population number, namely hydrogen production and hydrogen addition station number in the optimizing strategy;
Step 3.2, initializing the hydrogen production hydrogenation station point distribution position
Initializing hydrogen production and hydrogen adding station distribution positions in an optimizing strategy by a sea squirt group optimizing algorithm, wherein the ith hydrogen production and hydrogen adding station distribution optimizing strategy in a space is represented by X i:
And (3) locating the point for the (r) hydrogen production hydrogen station in the (i) th strategy.
Step 3.3, optimizing the hydrogen production hydrogenation station distribution strategy
The leader is used as the first vector of the X matrix and represents the optimal strategy of the current optimizing process, and before the ending condition is not met, the optimizing strategy of the hydrogen production and hydrogen station behind is guided to approach to the optimal distribution optimizing strategy, and the position updating formula of the leader is as follows:
Wherein, F j is the position of the optimal point distribution strategy of the leader and the hydrogen production and hydrogen addition station in the j-dimensional space; max j and min j are respectively the upper and lower boundaries of the j-dimensional space value; c 2 determines the movement length, c 3 determines the movement direction; c 1 is a convergence factor, c 2 and c 3 are random numbers generated within the interval [0,1 ];
Wherein T, T max are the current iteration number and the maximum iteration number, respectively.
The location update formula for the follower is as follows:
Wherein, Representing the coordinates of the ith hydrogen production hydrogenation station distribution optimizing strategy in the j-dimensional space of t iterations;
step 3.4, outputting hydrogen production hydrogenation station distribution optimizing strategy
When the termination condition is met, outputting an optimal point distribution strategy of the hydrogen production and hydrogenation station, namely the current European space first phasor X i_min, wherein the specific formula is as follows:
Wherein, And (5) setting the position of the r hydrogen production and hydrogen adding station in the output hydrogen production and hydrogen adding station optimal setting strategy.
For the purpose of In the optimizing process, the problem of local searching is easily involved, weights are set for H searching individuals, global information is reasonably applied, and searching is prevented from being finished in advance due to the fact that optimal values limited in a local range are avoided. The weight formula is as follows:
Wherein K W is the overall weight; f b is an optimal distribution objective function of the hydrogen production and hydrogen addition station at the tail of the current sequencing; f F,fxi is the optimal value of the current hydrogen production hydrogenation station optimizing strategy objective function and the x i optimizing strategy objective function value; f is the optimal distribution strategy position of the hydrogen production and hydrogenation station; (f b -f)/α is the weight of the individual. The improved leader formula is:
xi＝ωxi+rand×(KW-xi)
wherein, l is the current iteration number, the improved formula ensures that omega can control individual optimizing positions in the whole exploration process, and avoids sinking into local positions to obtain an optimal value;
Aiming at the problem of unbalance between global search and local search, self-adaptive inertial weights are added, the search range is enlarged in the initial stage of exploration, the local search capacity is enhanced in the later stage, and the self-adaptive inertial weights are as follows:
The position formula of the improved follower is as follows:
the optimization point distribution model is solved through an improved optimization algorithm of the sea squirt group, and the optimization strategy is as follows:
Table 8 optimization scheme
| Hydrogen production and hydrogen station numbering | 1 | 2 | 3 |
| Node | 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, obviously reduces indexes such as power margin and the like, reduces the power loss by 1.52MW, ensures that the voltage deviation of each node is shown as figure 11, and specifically, 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 overload condition of the line does not occur, the optimized power margin index is 45.87, and 36.38 is reduced compared with the initial scheme, so that the stable operation of the power grid is facilitated. The voltage fluctuation of 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, so that the risk of boundary crossing is reduced. In the traffic road network index, the specific node traffic flow and service range are as follows:
TABLE 10 road network factor table
Table 11 service area information table
| Strategy | Coverage of range | Number of uncovered nodes | Superposition ratio | Percentage of no coverage |
| Original policy | 83.33％ | 5 | 13.33％ | 16.67％ |
As can be seen from tables 10,11 and fig. 12, compared with the original strategy, the optimized strategy obtained by the improved sea squirt group optimizing algorithm has the advantages that the service coverage area is reduced by 6.67% and the traffic flow index is increased by 7.55, but in the power network influence index, the voltage deviation, the power margin and the network loss are obviously reduced, and in general, the comprehensive index is reduced by 0.5466, and compared with the original strategy, the influence on the power grid operation index is reduced. A specific service scope node diagram is shown in fig. 12.
The user satisfaction is 3.85, and the user satisfaction is increased by 0.92, which shows that the user has higher acceptance degree to the optimization strategy, and the influence of the hydrogen production hydrogenation station branch number, distance, quantity and vehicle flow on the user satisfaction is as follows:
table 12 user satisfaction impact table
| Quantitative index | Quantity of | Number of branches | Distance of | Traffic flow |
| Satisfaction impact value | 0.2 | 0.13 | 0.07 | 0.12 |
The above table shows that the influence of the node branch number and the path length on the user satisfaction degree is obviously reduced, and the influence of the vehicle flow is slightly increased, but the influence is not great from the viewpoint of the user satisfaction degree.
In summary, if the hydrogen production hydrogenation station is distributed at the original gas station position, the advantages of the traffic road network are inherited, but the influence on the operation index of the power grid is larger, the improved sea-squirt group optimization algorithm is used for solving the optimal distribution model of the scheme, and the new strategy is slightly inferior to the original strategy in terms of traffic flow and service range, but obviously reduces the influence on the power grid, and is more beneficial to the stable operation of the power grid compared with the original scheme.
Aiming at the problem of point distribution optimization of the hydrogen production and hydrogenation station in the scheme, an improved optimization algorithm of the sea squirt group in the goblet is compared with an optimization process of the sea squirt group in the goblet, and a comparison result is shown in figure 13. In the 500 iteration range, the improved ascidian optimization algorithm is used for searching an optimal value before the ascidian optimization algorithm, the convergence condition is reached more quickly, and the searched optimal value is smaller. Solving the model by two algorithms, wherein the obtained result is as follows:
Table 13 algorithm comparison information table
Compared with the optimization algorithm of the sea squirt group, when the improved optimization algorithm of the sea squirt group solves a model, the power grid index and the road network index are slightly better than the result obtained by the optimization algorithm of the sea squirt group, the convergence speed and the solving precision are better than those of the optimization algorithm of the sea squirt group, the improved optimization algorithm of the sea squirt group meets the solving precision, meanwhile, the solving speed is faster, the occupied memory is smaller, and the improved optimization algorithm of the sea squirt group is more suitable for solving the optimization distribution model of the hydrogen production and hydrogenation station.
The scheme establishes a hydrogen production hydrogenation station optimizing distribution model as shown in fig. 14. The optimal hydrogen production and hydrogen adding station distribution strategy under the consideration of the coupling influence of the power distribution network and the hydrogen fuel automobile is obtained. Simulation results show that:
1) The working mode of the hydrogen production and hydrogen adding station can be established on the basis of the travel rule of the fuel cell automobile, the hydrogen adding rule of the fuel cell automobile is analyzed, the daily hydrogen adding demand distribution of the fuel cell automobile is generated by means of Monte Carlo, and the method is a prerequisite factor for solving an optimal distribution model of the hydrogen production and hydrogen adding station.
2) The hydrogen production hydrogenation station optimizing distribution model aims at the uncertainty of hydrogen production characteristics and hydrogen charging requirements of the hydrogen production hydrogenation station, couples the hydrogen production hydrogenation station distribution optimizing problem with a power distribution network and a traffic network, reduces network loss, reduces node voltage deviation, stabilizes transmission of line power, enlarges a service range network, reduces psychological burden of users, and meets the hydrogen charging requirements of fuel cell automobiles.
3) The hydrogen production hydrogenation station optimization distribution model is solved through an improved sea squirt group optimization algorithm, and the obtained strategy solves the problems that the operation of a power distribution network is unstable under the condition that the coupling influence of the power distribution network and a hydrogen fuel automobile is considered, and the service range and traffic flow of a traffic network are unbalanced.
The specific embodiments described herein are offered by way of illustration only. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (3)
1. The hydrogen production hydrogenation station distribution optimizing method taking comprehensive factor indexes into consideration is characterized by comprising the following steps of:
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 an active power upper limit constraint, a unit climbing rate constraint, a node voltage constraint, a power balance equation constraint, a fuel cell automobile hydrogen charging quantity and total demand constraint, a hydrogen production and hydrogen adding station quantity constraint, a hydrogen production and hydrogen adding station service range constraint and a hydrogen production and hydrogen adding station overlap ratio constraint;
Step 3: constructing a hydrogen production and hydrogen addition 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 and hydrogen addition station service range index and the user satisfaction index, which are described in the step 1, and optimizing and solving the constraint conditions of the hydrogen production and hydrogen addition station distribution optimization target by using the active power upper limit constraint, the unit climbing rate constraint, the node voltage constraint, the power balance equation constraint, the fuel cell automobile hydrogen addition quantity and total demand constraint, the hydrogen production and hydrogen addition station quantity constraint, the hydrogen production and hydrogen addition station service range constraint and the hydrogen production and hydrogen addition station overlap ratio constraint through a goblet sea squirt group optimization algorithm to obtain a hydrogen production and hydrogen addition station optimization access node in the multi-node power distribution network model;
and 3, the hydrogen production and hydrogen adding station distribution optimization target is defined as:
Wherein: s is an optimized distribution comprehensive index of a hydrogen production hydrogenation station in a hydrogen production time period; k 1 is a proportionality coefficient of the voltage deviation of the bus node in the comprehensive index; k 2 is a proportionality coefficient of the active power margin level of the alternating current line in the comprehensive index; k 3 is a proportionality coefficient of the network loss level of the active network in the comprehensive index; k 4 is the proportionality coefficient of the traffic flow in the comprehensive index; k 5 is the proportionality coefficient of the hydrogen production hydrogenation station service range in the comprehensive index; k 6 is the proportionality coefficient of the user satisfaction in the comprehensive index; s 1 is the voltage deviation of the bus node; s 2 is the active power margin level of the alternating current line; s 3 is the network loss level of the active network; s 4 is traffic flow; s 5 is the hydrogen production and hydrogen station service range; s 6 is user satisfaction;
step 3, obtaining a hydrogen production hydrogenation station optimization access node in a multi-node power distribution network model through optimization solution of a goblet sea squirt group optimization algorithm, wherein the optimization access node specifically comprises the following steps:
step 3.1, inputting the optimized number of hydrogen production and hydrogen adding stations and the number of optimizing strategies
Inputting H optimizing strategies at the initial stage, namely searching individuals; simultaneously inputting the corresponding hydrogen production and hydrogenation station number R in a single optimizing strategy, and generating the European space of H multiplied by R as follows:
X is European space; h is the space dimension, namely the number of the optimizing strategies; r is population number, namely hydrogen production and hydrogen addition station number in the optimizing strategy;
Step 3.2, initializing the hydrogen production hydrogenation station point distribution position
Initializing hydrogen production and hydrogen adding station distribution positions in an optimizing strategy by a sea squirt group optimizing algorithm, wherein the ith hydrogen production and hydrogen adding station distribution optimizing strategy in a space is represented by X i:
a point setting position for the (r) hydrogen production and hydrogen adding station in the (i) th strategy;
step 3.3, optimizing the hydrogen production hydrogenation station distribution strategy
The leader is used as the first vector of the X matrix and represents the optimal strategy of the current optimizing process, and before the ending condition is not met, the optimizing strategy of the hydrogen production and hydrogen station behind is guided to approach to the optimal distribution optimizing strategy, and the position updating formula of the leader is as follows:
Wherein, F j is the position of the optimal point distribution strategy of the leader and the hydrogen production and hydrogen addition station in the j-dimensional space; max j and min j are respectively the upper and lower boundaries of the j-dimensional space value; c 2 determines the movement length, c 3 determines the movement direction; c 1 is a convergence factor, c 2 and c 3 are random numbers generated within the interval [0,1 ];
wherein T, T max are the current iteration number and the maximum iteration number respectively;
The location update formula for the follower is as follows:
Wherein, Representing the coordinates of the ith hydrogen production hydrogenation station distribution optimizing strategy in the j-dimensional space of t iterations;
step 3.4, outputting hydrogen production hydrogenation station distribution optimizing strategy
When the termination condition is met, outputting an optimal point distribution strategy of the hydrogen production and hydrogenation station, namely the current European space first phasor X i_min, wherein the specific formula is as follows:
Wherein, Setting a point position of the r hydrogen production and hydrogen adding station in the output hydrogen production and hydrogen adding station optimal point setting strategy;
For the purpose of The problem of easy local searching in the optimizing process is solved, weights are set for H searching individuals, global information is reasonably applied, and searching is prevented from being finished in advance due to the fact that optimal values in local ranges are limited; the weight formula is as follows:
Wherein K W is the overall weight; f b is an optimal distribution objective function of the hydrogen production and hydrogen addition station at the tail of the current sequencing; f F,fxi is the optimal value of the current hydrogen production hydrogenation station optimizing strategy objective function and the x i optimizing strategy objective function value; f is the optimal distribution strategy position of the hydrogen production and hydrogenation station; (f b -f)/α is the weight of the individual; the improved leader formula is:
xi＝ωxi+rand×(KW-xi)
wherein, l is the current iteration number, the improved formula ensures that omega can control individual optimizing positions in the whole exploration process, and avoids sinking into local positions to obtain an optimal value;
Aiming at the problem of unbalance between global search and local search, self-adaptive inertial weights are added, the search range is enlarged in the initial stage of exploration, the local search capacity is enhanced in the later stage, and the self-adaptive inertial weights are as follows:
The position formula of the improved follower is as follows:
2. the hydrogen production hydrogen distribution optimization method considering the comprehensive factor index as claimed in claim 1, wherein the node voltage deviation index in step 1 is defined as:
S 1 is a node voltage deviation index; s 1-a is a voltage fluctuation level index; n is the number of nodes of the power distribution network; t is T, T is the time T in the hydrogen production time, and T is the total hydrogen production time; u bus-i,t is the node voltage deviation of the node i at the time t; u bus-i,t,min is the minimum value of rated voltage deviation of the node i at the time t; u bus-i,t,max is the maximum value of rated voltage deviation of the node i at the time t; u N is the rated voltage; u bus-i is the average value of the voltage fluctuation level of the node i in the calculation period;
the active power margin level index in step 1 is defined as:
wherein S 2 is an active power margin level indicator; s 2-a is an influence coefficient of power fluctuation and maximum power deviation on an objective function; k is 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,t is the average power of the ac line connected to node i at time t; p k,t is 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-max is the upper limit of the transmission power of line k connected to node i;
the active network loss index in step 1 is defined as:
Wherein S 3 is an active network loss index; g ij is the conductance of the branch between node i and node j; u i is the voltage amplitude of node i; u j is the voltage amplitude of node j; n l∈NL,nL is the N L th transmission line in the multi-node distribution network model, and N L is the number of transmission lines in the multi-node distribution network model; θ i is the phase angle of the voltage at node i, and θ j is the phase angle of the voltage at node j;
The traffic flow index in step 1 is defined as:
Wherein S 4 is a traffic flow index; f u is a vehicle weight coefficient of the line starting point u; f v is a vehicle weight coefficient of the line end point v; l is L, i is the first traffic path in the multi-node traffic network model, and L is the number of traffic paths in the multi-node traffic network model; d uv_l is the path length of the starting point u and the ending point v of the road line in the traffic road network; n _JT is the total node of the road network;
The service range index of the hydrogen production and hydrogen adding station in the step 1 is defined as:
S 5 is a hydrogen production hydrogenation station service range index, M is M, M is the M-th hydrogen production hydrogenation station, and M is the total number of hydrogen production hydrogenation stations; s m_CS represents the attraction of the mth hydrogen production and hydrogen addition station to a user, P HPRS_m represents the power of the mth hydrogen production and hydrogen addition station, lambda q represents the influence weight of other factors of a road network node q, d HPRS_l represents the length of a fuel cell automobile reaching a hydrogen production and hydrogen addition station path l, E FCV represents the hydrogen consumption amount per unit distance, and P FCV represents the hydrogen price of the hydrogen production and hydrogen addition station;
the user satisfaction index in step1 is defined as:
Wherein S 6 is a user satisfaction index; s _num is the influence of the number of hydrogen production hydrogen stations on the satisfaction degree of users; The weight of the number of factors in user satisfaction; s _ljd is the influence of the proximity on the user satisfaction; n m_o∈Nm_o,nm_o is the o node contained in the service range of the mth hydrogen production hydrogenation station, and N m_o is the total node contained in the service range of the mth hydrogen production hydrogenation station; k b_m∈Kb_m,kb_m is the b-th path connected with the m-th hydrogen production hydrogenation station, and K j_m is the total number of paths connected with the m-th hydrogen production hydrogenation station; l b_m is the length of path d of the b-th path to the mth hydrogen production and hydrogenation station; l N_JT denotes the total path length in the road network; n EV_u represents the number of vehicles of the road network node u; n EV represents the total number of vehicles in the traffic network.
3. The hydrogen production hydrogen distribution optimization method considering the comprehensive factor index according to claim 1, wherein the active power upper limit constraint in step 2 is defined as:
Wherein P ij,t is the active power of the line l ij at the time t; p ij max is the upper limit of the active power of the line l ij at the time t;
Step 2, the unit climbing rate constraint is defined as:
Wherein P x gr is the upper limit of the unit time change of the active output of the unit x; -P x gr is the lower limit of the unit time variation of the unit x active output; The active power of the unit x at the time t; /(I) The active power of the unit x at the time t-1;
the node voltage constraint in step 2 is defined as:
Ubus-i,t,min≤Ubus-i,t≤Ubus-i,t,max
Wherein U bus-i,t is the node voltage deviation of the node i at the time t; u bus-i,t,min is the minimum value of rated voltage deviation of the node i at the time t; u bus-i,t,max is the maximum value of rated voltage deviation of the node i at the time t;
The constraint of the power balance equation in the step2 is defined as:
Wherein P i is the active power input at node i; q i is the reactive power input at node i; p Li is the active power of the load at node i; q Li is the reactive power of the load at node i; g ij is the conductance of the branch; b ij is susceptance of the branch; u i is the node voltage of node i; u j is the node voltage of node j; p DGi is the active power injected into node i; q DGi injects reactive power into node i; θ ij is the phase angle difference of the voltage;
and 2, the constraint of the hydrogen charging quantity and the total demand of the fuel cell automobile is defined as:
Wherein N FCV_m is the hydrogen charging quantity of the fuel cell car in the mth hydrogen production and hydrogen charging station; n HPRS_m is the number of hydrogen filling permitted of the mth hydrogen production and hydrogen filling station; v is the number of hydrogen storage tanks in the hydrogen production and hydrogenation station; s CQG is the capacity of the hydrogen storage tank; s FCV is the capacity of the fuel cell automobile;
The hydrogen production and hydrogen station number constraint in the step 2 is defined as:
nq_HPRS＝1
Wherein n q_HPRS is the number of hydrogen production and hydrogen adding stations at the road network node q, and each road network node can only build one hydrogen production and hydrogen adding station in the planning process;
and 2, the service range constraint of the hydrogen production and hydrogen adding station is defined as:
2≤NHPRS_m≤10
wherein N HPRS_m is the influence range of the mth hydrogen production and hydrogenation station and comprises the node number;
And 2, the hydrogen production hydrogenation station overlap ratio constraint is defined as:
wherein: n HPRS_m is the influence range of the mth hydrogen production hydrogenation station and comprises the node number; n HPRS_s is the influence range of the s-th hydrogen production hydrogenation station, and comprises the node number, namely the service range of the hydrogen production hydrogenation station; and xi represents the same node number, namely the contact ratio, in the service range of the two hydrogen production and hydrogen adding stations.
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