CN115759568B - Site selection planning method for hydrogen energy highway hydrogen station - Google Patents
Site selection planning method for hydrogen energy highway hydrogen station Download PDFInfo
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- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 424
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- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 386
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- 239000000446 fuel Substances 0.000 claims abstract description 63
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- 229910052799 carbon Inorganic materials 0.000 claims description 22
- 125000004435 hydrogen atom Chemical group [H]* 0.000 claims description 20
- 150000002431 hydrogen Chemical class 0.000 claims description 18
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Abstract
The invention provides a hydrogen energy highway hydrogenation station site selection planning method, which specifically comprises the following steps: s1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of the highway to be optimized per kilometer, fuel economy data of a hydrogen fuel cell automobile and market share data of the hydrogen fuel cell automobile, and calculating hydrogen demand of the highway section to be optimized; s2, establishing a hydrogenation station site selection optimization mathematical model by taking the total cost of hydrogen for a hydrogen supply chain network as a target; s3, constructing constraint conditions according to the hydrogen demand, hydrogen source capacity, hydrogen adding station capacity and hydrogen adding station spacing of the highway section to be optimized; s4, solving the target model to obtain a hydrogen energy highway hydrogen station layout scheme; s5, determining the final hydrogen energy highway hydrogenation station site selection position by integrating the position coordinates of the hydrogenation station and the position information of the existing service area. The technical scheme of the invention adopts a scientific, reasonable, economical and efficient method to plan and layout the hydrogen adding station.
Description
Technical Field
The invention relates to the technical field of site selection planning of hydrogenation infrastructure in the hydrogen energy industry, in particular to a site selection planning method of a hydrogen energy highway hydrogenation station.
Background
The hydrogen energy is taken as clean carbon-free storable secondary energy, is one of important development directions in the technical field of new energy, and is regarded as an important way for realizing carbon emission reduction. Hydrogen fuel cell automobiles are the most typical field of application for hydrogen energy, and hydrogen stations are the supporting infrastructure for ensuring fuel supply to hydrogen fuel cell automobiles.
In the early stages of the permeation market of hydrogen fuel cell automobiles, the construction of infrastructure such as hydrogen stations is very important, however, the sporadic hydrogen stations obviously cannot guarantee stable energy supply during the running process of hydrogen fuel cell automobiles. Hydrogen fuel cell automobiles are accepted by the public, on one hand, the follow-up of supporting infrastructures such as hydrogen stations and the like is required, on the other hand, the improvement of a hydrogen energy road network is required, and the construction of the hydrogen energy road network is required to start from a hydrogen energy expressway. Therefore, how to arrange the hydrogen energy highway hydrogenation station by a scientific and reasonable method becomes a problem to be solved urgently.
Current hydrogen addition site selection methods are primarily directed from urban areas, and consider minimizing the hydrogen addition distance or maximizing the hydrogen addition service volume for the hydrogen supply from the hydrogen addition site to the consumer. In the stage from the hydrogen source to the hydrogen adding station, many factors such as hydrogen production, storage and transportation, and filling mode of the hydrogen adding station need to be fully considered, and these factors have an influence on the economy of the layout of the hydrogen adding station. Thus, to increase the economics of the hydrogen plant planning, the stages from the hydrogen source to the hydrogen plant in the hydrogen supply should also be included in the optimization.
In summary, how to plan and layout the hydrogen station by adopting a scientific, reasonable, economical and efficient method aiming at the hydrogen energy expressway is a technical problem which needs to be solved in the field, and has positive effects and practical significance for promoting the construction of a hydrogen energy road network and the popularization of hydrogen fuel cell automobiles.
Disclosure of Invention
Aiming at the technical problem of how to plan and layout the hydrogen station by adopting a scientific, reasonable, economical and efficient method, the site selection planning method for the hydrogen energy highway hydrogen station is provided.
The invention adopts the following technical means:
a hydrogen energy expressway hydrogen station site selection planning method specifically comprises the following steps:
s1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of the highway to be optimized per kilometer, fuel economy data of a hydrogen fuel cell automobile and market share data of the hydrogen fuel cell automobile, and calculating hydrogen demand of the highway section to be optimized; the fuel economy data of the hydrogen fuel cell automobile comprise hydrogen consumption per kilometer of the hydrogen fuel cell automobile and endurance mileage data of the hydrogen fuel cell automobile;
s2, establishing a two-dimensional coordinate model of a hydrogen energy highway, optimizing the stage of hydrogen energy supply from a hydrogen source to a hydrogenation station, dividing the hydrogen assembly cost for the hydrogen supply chain network into three parts of hydrogen production cost, hydrogen storage and transportation cost and hydrogenation station cost according to the cost composition of the hydrogen supply chain network, and establishing a site selection optimization mathematical model of the hydrogenation station according to the hydrogen production technology, the hydrogen storage and transportation mode and the hydrogen filling technology and taking the total cost minimization of the hydrogen for the hydrogen supply chain network as a target;
s3, constructing constraint conditions according to the hydrogen demand, hydrogen source capacity, hydrogen adding station capacity and hydrogen adding station spacing of the highway section to be optimized;
s4, solving the target model to obtain a hydrogen energy highway hydrogen station layout scheme, namely the position coordinates of a plurality of hydrogen stations;
s5, comprehensively solving the obtained position coordinates of the hydrogen adding station and the position information of the existing service area of the expressway to be optimized, and determining the final site selection position of the hydrogen adding station of the expressway;
s6, comprehensively evaluating the site selection position of the final hydrogen energy highway hydrogenation station, and carrying out carbon emission reduction measurement and calculation on a hydrogen supply chain network by combining a carbon emission factor method, and comprehensively evaluating the technical effect of the obtained site selection scheme.
Further, the step S1 specifically includes the following steps:
s1.1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of a highway to be optimized per unit kilometer, unit kilometer hydrogen consumption of a hydrogen fuel cell automobile and market share data of the hydrogen fuel cell automobile;
s1.2, calculating the hydrogen demand of the highway section to be optimized according to the data acquired in the step S1.1 by adopting the following formula:
D＝L×M S ×c veh ×l veh (1)
wherein D is the total daily hydrogen demand of the expressway section to be optimized, L is the total mileage of the expressway section to be optimized, and M S Is hydrogen fuel cell automobile market share, c veh Hydrogen consumption per kilometer of hydrogen fuel cell car, l veh The average daily vehicle driving mileage of the expressway to be optimized is unit kilometers;
s1.3, outputting the hydrogen demand of the highway section to be optimized.
Further, the hydrogenation station site selection optimization mathematical model established in the step S2 is as follows:
Minimize C＝Σ i∈N Σ j∈M w ij ×[p j +(A×d ij +B)+c hi ] (2)
wherein C is the total hydrogen cost of the hydrogen supply chain network, i and j are indexes of the hydrogen adding station and the hydrogen source respectively, N and M are the set of the hydrogen adding station and the hydrogen source respectively, and w ij The hydrogen source j is the mass of hydrogen supplied to the hydrogen station i, p j The unit hydrogen production cost of the hydrogen source j, A is the cost index of the transportation means, d ij B is hydrogen storage and transportation cost index, c is the distance between a hydrogen source j and a hydrogen adding station i hi Is the cost per unit of hydrogen addition station.
Further, in the mathematical model of site selection optimization of the hydrogen station:
unit hydrogen production cost p of hydrogen source j j The hydrogen production analysis model is calculated;
hydrogen storage and transportation cost index B and unit hydrogen station cost c hi By hydrogen gasCarrying out evaluation by using a conveying scene analysis model;
cost index A of the vehicle and distance d between hydrogen source j and hydrogen addition station i ij Calculated by the following formula respectively:
d ij ＝|x i -x j |+d (4)
wherein f p Unit price for fuel for hydrogen transportation vehicle, length is hydrogen transportation path length, mpg is fuel economy for hydrogen transportation vehicle, l h For hydrogen transport vehicle load, x i For the hydrogen station i coordinate (x i ,y i )，x j For the coordinates (x) corresponding to hydrogen source j j ,y j ) D is the path distance between the hydrogen source j and the transportation node perpendicular to the direction of the highway.
Further, the step S2 specifically includes the following steps:
s2.1, acquiring corresponding technical economy parameters according to a hydrogen production technology, a hydrogen storage and transportation mode and a hydrogen filling technology adopted by a hydrogen supply chain network;
s2.2, inputting the technical economy parameter acquired in the step 2.1 into a hydrogen production analysis model, a hydrogen transportation scene analysis model and formulas (3) - (4) for calculation;
and S2.3, the calculation result obtained in the step S2.2 is used as calculation input of the hydrogenation station site selection optimization mathematical model.
Further, the constraint conditions constructed in step S3 are:
Σ i∈N w ij ≤L j (5)
∑ j∈M w ij ≤w i (6)
∑ i∈N w i ≥D (7)
∑ j∈M L j ≥∑ i∈N w i (8)
d i,i+1 ≤200 (9)
wherein L is j For the daily maximum capacity, w, of hydrogen source j i For daily hydrogenation capacity of hydrogenation station i, d i,i+1 Is the distance between adjacent hydrogen stations.
Further, the step S3 specifically includes the following steps:
s3.1, integrating the optimized highway section hydrogen demand, the hydrogen fuel cell automobile endurance mileage data, the hydrogen source capacity and the hydrogen station capacity in the step S1 and the step S2;
s3.2, inputting the data of the S3.1 as constraint condition parameters, and constructing constraint conditions of formulas (5) - (9).
Further, the step S4 uses the position coordinates of the to-be-constructed hydrogen station as unknowns, and combines the site selection optimization mathematical model of the hydrogen station established in the step S2 and the constraint conditions constructed in the step S3, so as to satisfy the hydrogen demand of the highway section to be optimized in the step S1, and solves the site selection optimization mathematical model of the hydrogen station by using MATLAB and a particle swarm algorithm, and specifically includes the following steps:
s4.1, taking the position of the hydrogen station to be built as a decision variable, recording the position as an n-dimensional vector and taking the n-dimensional vector as a particle, and setting the algorithm population size as 500;
s4.2, randomly initializing decision variables, calculating objective function values, comparing initial fitness of all particles, and finding out initial global optimal fitness and global optimal positions of the position particle swarm;
s4.3, comparing with the global optimum, updating the speeds of all particles by the following formula, updating the positions of all particles, and the speed and position of each particle are checked for out-of-limit, the maximum number of iterations is set to 5,000:
where k is the number of iterations, i is the index of particles in the algorithm population, and w isInertia factor, c 1 、c 2 The learning rate of the particles on the individual and global optimal positions, r 1 、r 2 The random numbers are between 0 and 1, v and x are the speed and the position of the particles in iteration, pbest is the individual optimal position of the particles in the iteration process, and Gbest is the global optimal position;
and S4.4, stopping iteration and outputting a related result, namely the position coordinate information of a plurality of hydrogenation stations, when the designated iteration times reach 5,000.
Further, step S5 integrates the position coordinates of the hydrogen adding station obtained by solving in step S4 and the position information of the existing service area of the expressway to be optimized, and in combination with practical situations, the hydrogen adding station close to the position of the existing service area in the solving scheme is considered to be built in the service area, so that land resources are saved, investment cost is reduced, and the final site selection position of the hydrogen adding station of the expressway with hydrogen energy is determined.
Further, step S6 carries out comprehensive evaluation on the location of the final hydrogen energy highway hydrogenation station determined in the step S5, and carries out carbon emission reduction measurement and calculation on a hydrogen energy supply chain network by combining a carbon emission factor method, wherein the calculation method of the carbon emission factor method comprises the following steps:
wherein,is CO 2 Is discharged by M f For fuel consumption, +.>CO as consumed fuel 2 An emission factor.
Compared with the prior art, the invention has the following advantages:
according to traffic flow data, the market penetration condition and the hydrogen consumption condition of a hydrogen fuel cell automobile, the hydrogen energy supply is optimized from a hydrogen source to a hydrogen station stage on the premise that the total hydrogen demand of the expressway is met, the hydrogen cost of each part of a hydrogen supply chain network is considered, the hydrogen production technology, the hydrogen storage and transportation mode and the hydrogen filling technology are fully considered, the constraints of hydrogen source productivity, hydrogen station capacity and hydrogen station spacing are added, and the site selection layout is scientifically and economically carried out on the hydrogen energy expressway along the hydrogen station by combining the actual conditions; meanwhile, comprehensively evaluating a final site selection scheme, and measuring and calculating carbon emission reduction of a highway hydrogen supply chain network; the method can provide reliable reference and effective guidance for the subsequent optimization planning of the automobile service site of the hydrogen fuel cell, thereby promoting the positive forward development of the hydrogen energy expressway network and the hydrogen energy automobile industry.
For the reasons, the method can be widely popularized in the field of site selection planning of hydrogenation infrastructures in the hydrogen energy industry.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to the drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart of a hydrogen energy highway hydrogenation station site selection planning method.
Fig. 2 is a diagram showing the simulation convergence result of example 1.
FIG. 3 is a schematic diagram of a site selection location of a hydrogen station determined by the site selection planning method of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the invention provides a hydrogen energy highway hydrogenation station site selection planning method, which specifically comprises the following steps:
s1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of the highway to be optimized per unit kilometer, fuel oil economical efficiency data (including hydrogen consumption per unit kilometer of a hydrogen fuel cell vehicle, continuous mileage data of the hydrogen fuel cell vehicle and the like) of the hydrogen fuel cell vehicle, and calculating hydrogen demand of the highway section to be optimized;
in step S1, each item of data may be collected by a transportation department (data report of transportation department) and a big data platform of a hydrogen fuel cell car (provided by a hydrogen fuel cell car enterprise);
s2, establishing a two-dimensional coordinate model of a hydrogen energy highway, optimizing the stage of hydrogen energy supply from a hydrogen source to a hydrogenation station, dividing the hydrogen assembly cost for the hydrogen supply chain network into three parts of hydrogen production cost, hydrogen storage and transportation cost and hydrogenation station cost according to the cost composition of the hydrogen supply chain network, and establishing a site selection optimization mathematical model of the hydrogenation station according to the hydrogen production technology, the hydrogen storage and transportation mode and the hydrogen filling technology and taking the total cost minimization of the hydrogen for the hydrogen supply chain network as a target;
s3, constructing constraint conditions according to the hydrogen demand, hydrogen source capacity, hydrogen adding station capacity and hydrogen adding station spacing of the highway section to be optimized;
s4, solving the target model by utilizing MATLAB and an optimization algorithm to obtain a hydrogen energy highway hydrogen station layout scheme, namely the position coordinates of a plurality of hydrogen stations;
s5, comprehensively solving the obtained position coordinates of the hydrogen adding station and the position information of the existing service area of the expressway to be optimized, and determining the final site selection position of the hydrogen adding station of the expressway;
s6, comprehensively evaluating the site selection position of the final hydrogen energy highway hydrogenation station, and performing carbon emission reduction measurement and calculation on a hydrogen supply chain network by combining a carbon emission factor method, so as to comprehensively evaluate the technical effect of the obtained site selection scheme in terms of carbon emission reduction.
Further, the step S1 specifically includes the following steps:
s1.1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of a highway to be optimized per unit kilometer, unit kilometer hydrogen consumption of a hydrogen fuel cell automobile and market share data of the hydrogen fuel cell automobile;
s1.2, calculating the hydrogen demand of the highway section to be optimized according to the data acquired in the step S1.1 by adopting the following formula:
D＝L×M S ×c veh ×l veh (1)
wherein D is the total daily hydrogen demand of the expressway section to be optimized, L is the total mileage of the expressway section to be optimized, and M S Is hydrogen fuel cell automobile market share, c veh Hydrogen consumption per kilometer of hydrogen fuel cell car, l veh The average daily vehicle driving mileage of the expressway to be optimized is unit kilometers;
s1.3, outputting the hydrogen demand of the highway section to be optimized.
Further, the hydrogenation station site selection optimization mathematical model established in the step S2 is as follows:
Minimize C＝Σ i∈N Σ j∈M w ij ×[p j +(A×d ij +B)+c hi ] (2)
wherein C is the total hydrogen cost of the hydrogen supply chain network, i and j are indexes of the hydrogen adding station and the hydrogen source respectively, N and M are the set of the hydrogen adding station and the hydrogen source respectively, and w ij The hydrogen source j is the mass of hydrogen supplied to the hydrogen station i, p j The unit hydrogen production cost of the hydrogen source j, A is the cost index of the transportation means, d ij B is hydrogen storage and transportation cost index, c is the distance between a hydrogen source j and a hydrogen adding station i hi Is the cost per unit of hydrogen addition station.
Further, in the mathematical model of site selection optimization of the hydrogen station:
unit hydrogen production cost of hydrogen source jp j Calculated by a hydrogen production analysis model (H2A);
hydrogen storage and transportation cost index B and unit hydrogen station cost c hi Evaluation was performed by a Hydrogen Delivery Scene Analysis Model (HDSAM);
cost index A of the vehicle and distance d between hydrogen source j and hydrogen addition station i ij Calculated by the following formula respectively:
d ij ＝|x i -x j |+d (4)
wherein f p Unit price for fuel for hydrogen transportation vehicle, length is hydrogen transportation path length, mpg is fuel economy for hydrogen transportation vehicle, l h For hydrogen transport vehicle load, x i For the hydrogen station i coordinate (x i ,y i )，x j For the coordinates (x) corresponding to hydrogen source j j ,y j ) D is the path distance between the hydrogen source j and the transportation node perpendicular to the direction of the highway.
Further, the step S2 specifically includes the following steps:
s2.1, acquiring corresponding technical economy parameters according to a hydrogen production technology, a hydrogen storage and transportation mode and a hydrogen filling technology adopted by a hydrogen supply chain network;
the technical economy parameter specifically refers to the technical economy parameter corresponding to different technologies adopted in the hydrogen production, storage and transportation and filling processes: if PEM water electrolysis technology is adopted in the hydrogen production process, data such as hydrogen production scale, pile equipment parameters, raw material consumption, electricity price and the like need to be collected; if a gas storage and transportation filling mode is adopted in the hydrogen storage and transportation and filling process, parameters such as hydrogen storage and transportation pressure, transportation distance, loading of transportation means, fuel economy, filling pressure, efficiency and the like need to be collected. The parameters are mainly used as the input of an H2A and HDSAM two-open-source analysis model, and are easy to obtain;
s2.2, inputting the technical economy parameter acquired in the step 2.1 into a hydrogen production analysis model, a hydrogen transportation scene analysis model and formulas (3) - (4) for calculation;
and S2.3, the calculation result obtained in the step S2.2 is used as calculation input of the hydrogenation station site selection optimization mathematical model.
Further, the constraint conditions constructed in step S3 are:
∑ i∈N w ij ≤L j (5)
∑ j∈M w ij ≤w i (6)
∑ i∈N w i ≥D (7)
∑ j∈M L j ≥∑ i∈N w i (8)
d i,i+1 ≤200 (9)
wherein L is j For the daily maximum capacity, w, of hydrogen source j i For daily hydrogenation capacity of hydrogenation station i, d i,i+1 Is the distance between adjacent hydrogen stations.
Further, the step S3 specifically includes the following steps:
s3.1, integrating the optimized highway section hydrogen demand, the hydrogen fuel cell automobile endurance mileage data, the hydrogen source capacity and the hydrogen station capacity in the step S1 and the step S2;
s3.2, inputting the data of the S3.1 as constraint condition parameters, and constructing constraint conditions of formulas (5) - (9):
optimizing the hydrogen demand of the highway section as the minimum value of the total capacity of the full section hydrogen adding station to restrict the hydrogen demand of the hydrogen supply chain network; the cruising mileage of the hydrogen fuel cell automobile is used as the maximum distance between adjacent stations of the hydrogen station to restrict the station building distance; the hydrogen source capacity and the capacity of the hydrogen addition station are constrained by the maximum hydrogen source capacity and the maximum hydrogen addition station capacity.
The construction of constraint conditions ensures that the hydrogen demand of each station from the same hydrogen source does not exceed the hydrogen source capacity; ensuring that the hydrogen purchasing amount of each station from different hydrogen sources does not exceed the self capacity; ensuring that the capacity of all the hydrogenation stations meets the total hydrogen requirement of the road section; ensuring that the capacity of all hydrogen stations does not exceed the capacity of all hydrogen sources; limiting the maximum distance between two adjacent stations. The demand constraints determine the capacity and number of the docking stations and the distance limitations determine the docking locations of the docking stations on the highway.
Further, the step S4 uses the position coordinates of the to-be-constructed hydrogen station as unknowns, and combines the site selection optimization mathematical model of the hydrogen station established in the step S2 and the constraint conditions constructed in the step S3, so as to satisfy the hydrogen demand of the highway section to be optimized in the step S1, and solves the site selection optimization mathematical model of the hydrogen station by using MATLAB and a particle swarm algorithm, and specifically includes the following steps:
s4.1, taking the position of the hydrogen station to be built as a decision variable, recording the position as an n-dimensional vector and taking the n-dimensional vector as a particle, and setting the algorithm population size as 500;
s4.2, randomly initializing decision variables, calculating objective function values, comparing initial fitness of all particles, and finding out initial global optimal fitness and global optimal positions of the position particle swarm;
s4.3, comparing with the global optimum, updating the speeds of all particles by the following formula, updating the positions of all particles, and the speed and position of each particle are checked for out-of-limit, the maximum number of iterations is set to 5,000:
where k is the number of iterations, i is the index of particles in the algorithm population, w is the inertia factor, c 1 、c 2 The learning rate of the particles on the individual and global optimal positions, r 1 、r 2 The random numbers are between 0 and 1, v and x are the speed and the position of the particles in iteration, pbest is the individual optimal position of the particles in the iteration process, and Gbest is the global optimal position;
global optimization is an optimal solution found when the current iteration times of the whole particle swarm are carried out, each particle is assigned an initial position, an initial speed and an initial fitness when the particle swarm starts to iterate, the minimum value of the initial position and the initial fitness of the particle in the step S4.2 is taken as an initial individual optimal position and a global optimal position, the individual optimal fitness and the global optimal fitness are calculated by substituting each particle into an objective function in each iteration, a new individual optimal position and a new global optimal position are obtained through comparison, then the respective speed and position are updated by a formula in the step S4.3, and then the next iteration is carried out until the iteration termination condition is met;
and S4.4, stopping iteration and outputting a related result, namely the position coordinate information of a plurality of hydrogenation stations, when the designated iteration times reach 5,000.
Further, step S5 integrates the position coordinates of the hydrogen adding station obtained by solving in step S4 and the position information of the existing service area of the expressway to be optimized, and in combination with practical situations, the hydrogen adding station close to the position of the existing service area in the solving scheme is considered to be built in the service area, so that land resources are saved, investment cost is reduced, and the final site selection position of the hydrogen adding station of the expressway with hydrogen energy is determined.
Further, step S6 carries out comprehensive evaluation on the location of the final hydrogen energy highway hydrogenation station determined in the step S5, and carries out carbon emission reduction measurement and calculation on a hydrogen energy supply chain network by combining a carbon emission factor method, wherein the calculation method of the carbon emission factor method comprises the following steps:
wherein,is CO 2 Is discharged by M f For fuel consumption, +.>CO as consumed fuel 2 An emission factor.
The method of the invention is described in detail below by taking Liaoning Heda high-speed Dalian-Dandong as an example:
the whole length of the Crane high-speed DALIAN-DANDONG section is about 300km, 3 service areas are shared in the whole process, the DALIANCity is taken as a coordinate origin, the direction from DALIANYU to DANDONG is taken as the positive direction of the x-axis after being straightened, and the x-axis is rotated clockwise by 90 degrees to be taken as the positive direction of the y-axis to establish a coordinate system;
according to step S1, the collected traffic flow data and the domestic hydrogen fuel cell automobile Rongwei 950 are used as the standard to obtain 194kg/km/d of hydrogen consumed by the hydrogen fuel cell automobile per kilometer, and the total hydrogen demand of the Heda high-speed DAN-DAN east segment is calculated to be 2,930kgH 2 And/d. The method is characterized in that the offshore wind power-port proton exchange membrane water electrolysis mode is used for producing hydrogen as a hydrogen source, high-pressure hydrogen storage and transportation is used as a main hydrogen storage and transportation mode, filling pressure of a hydrogen station is set to be 35MPa, cost parameters of each part of a hydrogen supply chain network are calculated through a hydrogen production analysis model and a hydrogen transportation scene analysis model, and relevant calculation results are summarized as shown in tables 1-2.
TABLE 1 Hydrogen source location information
TABLE 2 summary of cost results for each part of the hydrogen supply chain network
The relevant result parameters are used as the input of the model and the constraint in the steps S2 and S3, and the MATLAB and the particle swarm algorithm are applied in the step S4 to iteratively solve the model, and the simulation convergence result is shown in the figure 2 under the hydrogen supply mode of the double hydrogen source.
According to step S5, the site selection scheme of the hydrogen station is fitted with the actual expressway service area position, and the site selection position of the hydrogen station of the final hydrogen energy expressway is determined by considering that the hydrogen station close to the existing service area position in the solving scheme is established in the service area, as shown in FIG. 3.
According to step S6, comprehensively evaluating the site selection position of the final hydrogen energy highway hydrogenation station obtained in step S5, and calculating the carbon emission reduction of the hydrogen energy supply chain network by combining the carbon emission factor method, wherein the carbon emission reduction results are summarized in Table 3, and the unit hydrogen production and the hydrogen demand CO are shown in the specification 2 Emission reduction and unit highway mileage CO 2 The emission reduction is obtained by comprehensively calculating the emission reduction results of the hydrogen production end and the demand end, the hydrogen source yield, the road section length and the total hydrogen demand, and different fuels in the formula (12) correspond to different M f Andcan be inquired through the IPCC national greenhouse gas inventory guide in 2006 and the Chinese energy statistics annual book 2019 issued by the Chinese national statistics office.
Table 3 results of the crane high speed and large continuous-dan east hydrogen supply chain network carbon emission reduction measurement.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the technical solutions according to the embodiments of the present invention.
Claims (3)
1. The site selection planning method for the hydrogen energy highway hydrogenation station is characterized by comprising the following steps of:
s1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of the highway to be optimized per kilometer, fuel economy data of a hydrogen fuel cell automobile and market share data of the hydrogen fuel cell automobile, and calculating hydrogen demand of the highway section to be optimized; the fuel economy data of the hydrogen fuel cell automobile comprise hydrogen consumption per kilometer of the hydrogen fuel cell automobile and endurance mileage data of the hydrogen fuel cell automobile;
s2, establishing a two-dimensional coordinate model of a hydrogen energy highway, optimizing the stage of hydrogen energy supply from a hydrogen source to a hydrogenation station, dividing the hydrogen assembly cost for the hydrogen supply chain network into three parts of hydrogen production cost, hydrogen storage and transportation cost and hydrogenation station cost according to the cost composition of the hydrogen supply chain network, and establishing a site selection optimization mathematical model of the hydrogenation station according to the hydrogen production technology, the hydrogen storage and transportation mode and the hydrogen filling technology and taking the total cost minimization of the hydrogen for the hydrogen supply chain network as a target;
s3, constructing constraint conditions according to the hydrogen demand, hydrogen source capacity, hydrogen adding station capacity and hydrogen adding station spacing of the highway section to be optimized;
s4, solving the target model to obtain a hydrogen energy highway hydrogen station layout scheme, namely the position coordinates of a plurality of hydrogen stations;
s5, comprehensively solving the obtained position coordinates of the hydrogen adding station and the position information of the existing service area of the expressway to be optimized, and determining the final site selection position of the hydrogen adding station of the expressway;
s6, comprehensively evaluating the site selection position of the final hydrogen energy highway hydrogenation station, calculating the carbon emission reduction of a hydrogen supply chain network by combining a carbon emission factor method, and comprehensively evaluating the technical effect of the obtained site selection scheme;
the constraint conditions constructed in step S3 are:
∑ i∈N w ij ≤L j (5)
∑ j∈M w ij ≤w i (6)
∑ i∈N w i ≥D (7)
∑ j∈M L j ≥∑ i∈N w i (8)
d i,i+1 ≤200 (9)
wherein L is j For the daily maximum capacity, w, of hydrogen source j i For daily hydrogenation capacity of hydrogenation station i, d i,i+1 Is the distance between adjacent hydrogen stations;
the step S3 specifically comprises the following steps:
s3.1, integrating the optimized highway section hydrogen demand, the hydrogen fuel cell automobile endurance mileage data, the hydrogen source capacity and the hydrogen station capacity in the step S1 and the step S2;
s3.2, inputting the data of the S3.1 as constraint condition parameters, and constructing constraint conditions of formulas (5) - (9);
the construction of constraint conditions ensures that the hydrogen demand of each station from the same hydrogen source does not exceed the hydrogen source capacity; ensuring that the hydrogen purchasing amount of each station from different hydrogen sources does not exceed the self capacity; ensuring that the capacity of all the hydrogenation stations meets the total hydrogen requirement of the road section; ensuring that the capacity of all hydrogen stations does not exceed the capacity of all hydrogen sources; limiting the maximum distance between two adjacent stations; the demand constraint determines the capacity and the number of the hydrogen adding stations, and the distance constraint determines the site building position of the hydrogen adding stations on the expressway;
the step S1 specifically comprises the following steps:
s1.1, acquiring total mileage of a highway section to be optimized, average vehicle driving mileage of a highway to be optimized per unit kilometer, unit kilometer hydrogen consumption of a hydrogen fuel cell automobile and market share data of the hydrogen fuel cell automobile;
s1.2, calculating the hydrogen demand of the highway section to be optimized according to the data acquired in the step S1.1 by adopting the following formula:
D＝L×M S ×c veh ×l veh (1)
wherein D is the total daily hydrogen demand of the expressway section to be optimized, L is the total mileage of the expressway section to be optimized, and M S Is hydrogen fuel cell automobile market share, c veh Hydrogen consumption per kilometer of hydrogen fuel cell car, l veh The average daily vehicle driving mileage of the expressway to be optimized is unit kilometers;
s1.3, outputting the hydrogen demand of the highway section to be optimized;
the hydrogenation station site selection optimization mathematical model established in the step S2 is as follows:
Minimize C＝∑ i∈N ∑ j∈M w ij ×[p j +(A×d ij +B)+c hi ] (2)
wherein C is the total hydrogen cost of the hydrogen supply chain network, i and j are indexes of the hydrogen adding station and the hydrogen source respectively, N and M are the set of the hydrogen adding station and the hydrogen source respectively, and w ij The hydrogen source j is the mass of hydrogen supplied to the hydrogen station i, p j The unit hydrogen production cost of the hydrogen source j, A is the cost index of the transportation means, d ij B is hydrogen storage and transportation cost index, c is the distance between a hydrogen source j and a hydrogen adding station i hi Cost per unit of hydrogen addition station;
in the mathematical model of site selection optimization of the hydrogen station:
unit hydrogen production cost p of hydrogen source j j The hydrogen production analysis model is calculated;
hydrogen storage and transportation cost index B and unit hydrogen station cost c hi Evaluating through a hydrogen transportation scene analysis model;
cost index A of the vehicle and distance d between hydrogen source j and hydrogen addition station i ij Calculated by the following formula respectively:
d ij ＝|x i -x j |+d (4)
wherein f p Unit price for fuel for hydrogen transportation vehicle, length is hydrogen transportation path length, mpg is fuel economy for hydrogen transportation vehicle, l h For hydrogen transport vehicle load, x i For the hydrogen station i coordinate (x i ,y i )，x j For the coordinates (x) corresponding to hydrogen source j j ,y j ) D is the path distance between the hydrogen source j and the transportation node perpendicular to the highway direction;
the step S2 specifically comprises the following steps:
s2.1, acquiring corresponding technical economy parameters according to a hydrogen production technology, a hydrogen storage and transportation mode and a hydrogen filling technology adopted by a hydrogen supply chain network;
s2.2, inputting the technical economy parameter acquired in the step 2.1 into a hydrogen production analysis model, a hydrogen transportation scene analysis model and formulas (3) - (4) for calculation;
s2.3, the calculation result obtained in the step S2.2 is used as calculation input of the hydrogenation station site selection optimization mathematical model;
and S5, combining the position coordinates of the hydrogen adding station obtained by solving in the step S4 with the position information of the existing service area of the expressway to be optimized, and combining the actual situation, taking the hydrogen adding station close to the position of the existing service area in the solving scheme into consideration to be built in the service area so as to save land resources and reduce investment cost, and determining the final site selection position of the hydrogen adding station of the expressway.
2. The method for planning hydrogen energy highway hydrogenation station site selection according to claim 1, wherein step S4 uses the position coordinates of the hydrogenation station to be built as unknowns, and combines the hydrogenation station site selection optimization mathematical model established in step S2 and the constraint conditions constructed in step S3 to solve the hydrogenation station site selection optimization mathematical model by using MATLAB and a particle swarm algorithm on the premise of meeting the hydrogen demand of the highway section to be optimized in step S1, and specifically comprises the following steps:
s4.1, taking the position of the hydrogen station to be built as a decision variable, recording the position as an n-dimensional vector and taking the n-dimensional vector as a particle, and setting the algorithm population size as 500;
s4.2, randomly initializing decision variables, calculating objective function values, comparing initial fitness of all particles, and finding out initial global optimal fitness and global optimal positions of the position particle swarm;
s4.3, comparing with the global optimum, updating the speeds of all particles by the following formula, updating the positions of all particles, and the speed and position of each particle are checked for out-of-limit, the maximum number of iterations is set to 5,000:
where k is the number of iterations, i is the index of particles in the algorithm population, w is the inertia factor, c 1 、c 2 The learning rate of the particles on the individual and global optimal positions, r 1 、r 2 The random numbers are between 0 and 1, v and x are the speed and the position of the particles in iteration, pbest is the individual optimal position of the particles in the iteration process, and Gbest is the global optimal position;
and S4.4, stopping iteration and outputting a related result, namely the position coordinate information of a plurality of hydrogenation stations, when the designated iteration times reach 5,000.
3. The method for planning site selection of a hydrogen energy highway hydrogenation station according to claim 1, wherein step S6 is to comprehensively evaluate the site selection position of the final hydrogen energy highway hydrogenation station obtained in step S5, and calculate carbon emission reduction of a hydrogen energy supply chain network by combining a carbon emission factor method, and the calculation method of the carbon emission factor method is as follows:
wherein,is CO 2 Is discharged by M f For fuel consumption, +.>CO as consumed fuel 2 An emission factor.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110287612A (en) * | 2019-06-28 | 2019-09-27 | 成都理工大学 | A kind of hydrogenation stations addressing intelligent algorithm |
| WO2020227429A1 (en) * | 2019-05-06 | 2020-11-12 | Strong Force Iot Portfolio 2016, Llc | Platform for facilitating development of intelligence in an industrial internet of things system |
| CN113222387A (en) * | 2021-04-30 | 2021-08-06 | 北京理工新源信息科技有限公司 | Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle |
| CN113420382A (en) * | 2021-07-06 | 2021-09-21 | 北理新源(佛山)信息科技有限公司 | Hydrogen production and transportation and hydrogenation scheduling system based on big data |
| CN113672857A (en) * | 2021-08-11 | 2021-11-19 | 北理新源(佛山)信息科技有限公司 | Method for carrying out hydrogenation station site selection based on hydrogen energy automobile and hydrogen production plant data |
| WO2022111563A1 (en) * | 2020-11-25 | 2022-06-02 | 中国第一汽车股份有限公司 | Driving range prediction method and apparatus, device, and storage medium |
| CN115204546A (en) * | 2021-07-27 | 2022-10-18 | 中国电建集团华东勘测设计研究院有限公司 | Electrolytic hydrogen production hydrogenation station hydrogen production and storage configuration method considering construction and operation costs |
-
2022
- 2022-10-21 CN CN202211295950.2A patent/CN115759568B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020227429A1 (en) * | 2019-05-06 | 2020-11-12 | Strong Force Iot Portfolio 2016, Llc | Platform for facilitating development of intelligence in an industrial internet of things system |
| CN110287612A (en) * | 2019-06-28 | 2019-09-27 | 成都理工大学 | A kind of hydrogenation stations addressing intelligent algorithm |
| WO2022111563A1 (en) * | 2020-11-25 | 2022-06-02 | 中国第一汽车股份有限公司 | Driving range prediction method and apparatus, device, and storage medium |
| CN113222387A (en) * | 2021-04-30 | 2021-08-06 | 北京理工新源信息科技有限公司 | Multi-objective scheduling and collaborative optimization method for hydrogen fuel vehicle |
| CN113420382A (en) * | 2021-07-06 | 2021-09-21 | 北理新源(佛山)信息科技有限公司 | Hydrogen production and transportation and hydrogenation scheduling system based on big data |
| CN115204546A (en) * | 2021-07-27 | 2022-10-18 | 中国电建集团华东勘测设计研究院有限公司 | Electrolytic hydrogen production hydrogenation station hydrogen production and storage configuration method considering construction and operation costs |
| CN113672857A (en) * | 2021-08-11 | 2021-11-19 | 北理新源(佛山)信息科技有限公司 | Method for carrying out hydrogenation station site selection based on hydrogen energy automobile and hydrogen production plant data |
Non-Patent Citations (4)
| Title |
|---|
| "Multi-objective optimization of cascade storage system in hydrogen refuelling station for minimum cooling energy and maximum state of charge";Hao Luo et.al.;《International Journal of Hydrogen Energy》;第47卷(第20期);第10963-10975页 * |
| "基于全生命周期的加氢站选址定容优化";孙浩然;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;第2018年卷(第09期);第C035-70页 * |
| 基于路段通行能力限制的机动车污染物总排放量最优化研究;朱志高;李铁柱;李文权;;交通运输系统工程与信息;第8卷(第02期);第84-88页 * |
| 张建国.《建筑行业温室气体减排机会指南》.中国经济出版社,2018,第27页. * |
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