A method and device for configuring the capacity of a photovoltaic hydrogen storage integrated charging station

Abstract

The present invention discloses a method and device for configuring the equipment capacity of a photo-hydrogen storage integrated charging station, the method comprising: establishing a comprehensive charging station system model including photovoltaic, energy storage battery and hydrogen energy storage system that can provide services for electric vehicles and hydrogen fuel electric vehicles at the same time; according to the comprehensive charging station system model, determining the energy flow strategy of the comprehensive charging station based on the energy flow coordination relationship between various devices of the comprehensive charging station; according to the energy flow strategy, based on energy balance constraints, component constraints, and power supply constraints of the power grid, establishing an equipment capacity configuration optimization model with the annual operating cost and autonomous operation capability of the comprehensive charging station as the target; using the NSGA‑II algorithm and fuzzy comprehensive evaluation to solve the equipment capacity configuration optimization model, and determine the capacity configuration scheme of the comprehensive charging station equipment. The present invention improves the autonomous operation capability of the comprehensive charging station and can provide a reference for the optimization of charging station equipment.

Description

Technical Field

[0001] The present invention relates to charging station equipment configuration optimization, and in particular to a method and device for configuring the equipment capacity of a photovoltaic hydrogen storage integrated charging station. Background Art

[0002] With the rapid development of new energy vehicles, the charging facilities that provide services have also received attention. Since the purchase of electricity from the power grid at charging stations also indirectly causes high carbon emissions, charging stations that consider the combination of distributed renewable energy have prospects. Although renewable energy provides us with convenience, they also have some disadvantages. Due to the randomness of output, they are difficult to be fully utilized. How to combine them with charging stations to achieve local consumption, reduce the impact on the power grid, and alleviate the pressure of power grid upgrades is a problem that needs to be solved. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method and device for configuring the equipment capacity of a light-hydrogen storage integrated charging station, which can serve electric vehicles and hydrogen fuel cell electric vehicles at the same time and improve the autonomous operation capability of the integrated charging station.

[0004] Technical solution: In order to achieve the above-mentioned invention object, the present invention proposes a method for configuring the capacity of a photovoltaic hydrogen storage integrated charging station, comprising the following steps:

[0005] Establish a comprehensive charging station system model that can serve both electric vehicles and hydrogen fuel cell electric vehicles, including photovoltaics, energy storage batteries and hydrogen energy storage systems;

[0006] According to the integrated charging station system model, the energy flow strategy of the integrated charging station is determined based on the energy flow coordination relationship between various devices in the integrated charging station;

[0007] According to the energy flow strategy, based on energy balance constraints, component constraints, and grid power constraints, an equipment capacity configuration optimization model is established with the annual operating cost and autonomous operation capability of the comprehensive charging station as the target;

[0008] The NSGA-II algorithm and fuzzy comprehensive evaluation are used to solve the equipment capacity configuration optimization model and determine the capacity configuration plan of the integrated charging station equipment.

[0009] Furthermore, the integrated charging station system model includes:

[0010] Photovoltaic output model:

[0011]

[0012] Among them G T is the sunlight intensity, t represents time t, T C is the ambient temperature, G STC and TSTC Light intensity and temperature under standard conditions, P pv-STC is the rated power of the PV;

[0013] Energy storage battery state of charge model:

[0014]

[0015] Where E B is the rated capacity of the energy storage, P B is the charging and discharging power of the energy storage, η is the operating electrical efficiency of the energy storage, and Δt is the time step;

[0016] The hydrogen energy storage system consists of an electrolyzer, a hydrogen storage tank, and a fuel cell. The electrolyzer model is as follows:

[0017] H we =P we η we / HHV

[0018] Where P we The output power of photovoltaic power generation for hydrogen production, η we is the working efficiency of the electrolyzer, HHV is the electric-hydrogen energy conversion coefficient;

[0019] The hydrogen storage capacity model of the hydrogen storage tank in each time period is as follows:

[0020] H(t)=H(t-1)+H we (t-1)-H l (t-1)-H fc (t-1)

[0021] Where H is the hydrogen content of the hydrogen storage tank, H we , H fc are the amounts of hydrogen supplied to hydrogen fuel cell electric vehicles and fuel cells, respectively;

[0022] The fuel cell model is as follows:

[0023] P fc =H fc η fc HV

[0024] Where P fc is the output power generated by burning hydrogen, η fc is the efficiency of the fuel cell.

[0025] Furthermore, the energy flow strategy of the integrated charging station includes: obtaining the photovoltaic output power P pv , determine the photovoltaic output power P pv Is it greater than the electric load demand P of electric vehicle users in each period? l , let DP = Ppv -P l If DP>0, then enter the first strategy branch, if DP<0, then enter the second strategy branch; in the first strategy branch and the second strategy branch, determine whether the hydrogen content H of the hydrogen storage tank is greater than the hydrogen volume H required by the hydrogen fuel cell electric vehicle user l , when DP>0 and HH l >0, implement strategy A. When DP>0 and HH l <0, implement strategy B; when DP<0 and HH l >0, implement strategy C. When DP<0 and HH l When <0, implement strategy D.

[0026] A. To meet the needs of electric vehicles and hydrogen fuel cell electric vehicles, the surplus power of photovoltaic power generation is used to charge the battery, electrolyze and store hydrogen in the electrolyzer, and finally the surplus power is connected to the grid;

[0027] B. To meet the demand for electric vehicles, there is a shortage of hydrogen fuel cell electric vehicles. The surplus power of photovoltaic power generation is first used for electrolysis and storage of hydrogen in electrolyzers, and then for charging batteries. There is still a shortage of hydrogen fuel cell electric vehicles, which is supplemented by the purchased spare hydrogen;

[0028] C. Meet the demand for hydrogen fuel cell electric vehicles. There is a shortage of demand for electric vehicles. Batteries and fuel cells are discharged once to meet the demand of electric vehicles, and finally the power grid will supplement it.

[0029] D. There is a shortage in demand for both electric vehicles and hydrogen fuel cell electric vehicles. The demand for electric vehicles is first discharged by the battery, and then the power grid supplements the power shortage. The demand for hydrogen fuel cell electric vehicles is supplemented by purchased spare hydrogen.

[0030] Furthermore, the energy flow strategy of the integrated charging station is determined to include:

[0031] Furthermore, the equipment capacity configuration optimization model is:

[0032] The annual operating cost of the comprehensive charging station is the lowest:

[0033] minF1=(C1+C2)CRF+C3-C4

[0034] C1=P pv e pv +P ba e ba +P we e we +P fc e fc +He H

[0035] C2=C1K

[0036]

[0037]

[0038]

[0039] Among them, C1, C2, C3, and C4 are respectively the equipment purchase cost, equipment maintenance cost, comprehensive charging station operation cost, and Internet access income; CRF is the annualized coefficient of each equipment; P pv , P ba , P we , P fc are the installed capacities of photovoltaic, battery, electrolyzer and fuel cell respectively; H is the capacity of hydrogen storage tank; e pv 、e ba 、e we 、e fc 、e H1 are the unit prices of the corresponding equipment respectively; K is the maintenance cost of the equipment; r is the actual annual interest rate, y is the operating life of the equipment; e H 、e Gi 、e Go They are the reserve hydrogen purchase, grid electricity purchase and surplus electricity grid connection price; H G , P Gi , P Go They are the standby hydrogen purchase amount, power purchased from the power grid and surplus power connected to the grid in each period;

[0040] The autonomous operation capability of the comprehensive charging station, and the proportion of purchased electricity and standby hydrogen:

[0041]

[0042]

[0043] The exchange of electric power and hydrogen volume in the integrated charging station system should always be balanced:

[0044] P l =P pv +P we +P ba +P fc +P G

[0045] H l =H+H G +H we -H fc

[0046] The transformer has capacity limitations, and the power grid has power constraints for the integrated charging station:

[0047] P G ≤P Gmax

[0048] The battery's state of charge (SOC) is within reasonable constraints:

[0049] SOC min ≤SOC(t)≤SOC max

[0050] Furthermore, solving the equipment capacity configuration optimization model specifically includes:

[0051] (a) Input the basic data of the integrated charging station and set the initial parameters and upper and lower limits of the decision variables of NSGA-II;

[0052] (b) Randomly initialize the population size to N;

[0053] (c) Calculate the fitness value of the objective function and then perform fast non-dominated sorting on the results;

[0054] (d) Calculate the individual crowding distance, use the elite strategy to select and retain excellent individuals, form a new population, and return to step (c);

[0055] (e) Determine the number of iterations. If the number of iterations meets the requirement, the iteration is terminated and the Pareto frontier is output. Otherwise, the iteration cycle continues.

[0056] (f) The fuzzy membership function of fuzzy comprehensive evaluation is used to calculate the membership of each Pareto solution, and the optimization solution with the highest membership is the final solution.

[0057] Furthermore, the membership function of fuzzy comprehensive evaluation is:

[0058]

[0059] in is the function value of the mth non-inferior solution of the nth objective function, The membership degree of the mth non-inferior solution of the nth objective function, are the maximum and minimum solutions of the Pareto solution respectively.

[0060] The present invention also provides a device for configuring the capacity of a photovoltaic hydrogen storage integrated charging station, comprising:

[0061] A comprehensive charging station system model building module is used to build a comprehensive charging station system model that can provide services for both electric vehicles and hydrogen fuel cell electric vehicles, including photovoltaic, energy storage batteries and hydrogen energy storage systems;

[0062] An energy flow strategy determination module determines the energy flow strategy of the integrated charging station based on the energy flow coordination relationship between various devices in the integrated charging station according to the integrated charging station system model;

[0063] The capacity configuration optimization model building module establishes an equipment capacity configuration optimization model based on the energy flow strategy, energy balance constraints, component constraints, and grid power constraints, with the annual operating cost and autonomous operation capability of the comprehensive charging station as the goal;

[0064] The capacity configuration solution module uses the NSGA-II algorithm and fuzzy comprehensive evaluation to solve the equipment capacity configuration optimization model and determine the capacity configuration solution for the integrated charging station equipment.

[0065] The present invention also provides a computer device, comprising:

[0066] one or more processors;

[0067] Memory; and

[0068] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the method for configuring the capacity of the light-hydrogen storage integrated charging station equipment are implemented as described above.

[0069] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for configuring the capacity of the integrated photovoltaic hydrogen storage charging station equipment are implemented.

[0070] Beneficial effects: The present invention proposes a method for configuring the equipment capacity of a comprehensive charging station with photovoltaic and hydrogen storage. By establishing a comprehensive charging station model including photovoltaic, energy storage batteries, and hydrogen energy storage systems, the energy flow strategy of the comprehensive charging station is designed, with the goal of minimizing the annual cost of the comprehensive charging station and minimizing the ratio of energy purchase to demand of the charging station, taking into account the operational benefits of the comprehensive charging station and its autonomous operation capability, an objective function is established, and the NSGA-II algorithm and fuzzy comprehensive evaluation are used to solve the optimal solution. The present invention can serve both electric vehicles and hydrogen fuel cell electric vehicles at the same time. Based on the NSGA-II algorithm and fuzzy comprehensive evaluation, the equipment configuration scheme given improves the autonomous operation capability of the comprehensive charging station and can provide a reference for the optimization of charging station equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flow chart of the capacity configuration method of the present invention;

[0072] Figure 2 It is the overall flow chart of energy flow of the present invention;

[0073] Figure 3 It is a specific flow chart of energy flow of the present invention;

[0074] Figure 4 It is the optimization flow chart established by the present invention;

[0075] Figure 5(a)-5(c) They are the photovoltaic, EV, and HV data diagrams required by the present invention respectively;

[0076] Figure 6 is the optimized Pareto set diagram of the present invention;

[0077] Figure 7(a)-7(d) They are respectively the power, hydrogen amount, SOC and SOHC capacity balance diagrams corresponding to the optimization results of the present invention. DETAILED DESCRIPTION

[0078] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0079] The present invention proposes a method for configuring the capacity of a photovoltaic hydrogen storage integrated charging station, so that the integrated charging station has a certain degree of autonomous energy supply capability and can serve both electric vehicles and hydrogen fuel cell electric vehicles at the same time. Figure 1 , the method comprises the following steps:

[0080] Step 1: Build a solar hydrogen storage integrated charging station system and model the various devices that need to be configured in the integrated charging station.

[0081] (1-1) Construct a comprehensive solar hydrogen storage charging station system.

[0082] In the implementation mode of the present invention, the integrated charging station system is mainly composed of photovoltaic arrays, battery energy storage, electrolyzers, hydrogen storage tanks, fuel cells, converters and other equipment and devices, wherein the electrolyzers, hydrogen storage tanks and fuel cells constitute the hydrogen energy storage system. The converter includes a DC-DC converter (for photovoltaic power generation supply and energy storage) and a DC-AC converter (for photovoltaic power generation to the grid). The charging station is connected to the power grid, and electricity and hydrogen energy flow in the station. This station can provide services for electric vehicles and hydrogen fuel cell electric vehicles at the same time. Electric vehicles are provided by the power grid, photovoltaics, fuel cells and batteries. The demand of hydrogen fuel cell electric vehicles is provided by hydrogen storage tanks and spare hydrogen.

[0083] (1-2) Establish models of various equipment in a comprehensive charging station.

[0084] Photovoltaic power generation is a process that uses the photovoltaic effect to convert solar radiation into electrical energy. It has the advantages of being clean, pollution-free, and renewable. Photovoltaic output power is related to many factors, but the main considerations are light intensity and ambient temperature. In the embodiment of the present invention, the photovoltaic output model is as follows:

[0085]

[0086] Among them is G T Solar intensity, T C is the ambient temperature. G STC and TSTC Light intensity and temperature under standard conditions. pv-STC It is the rated power of the photovoltaic power given by the supplier.

[0087] The state of charge (SOC) of the energy storage battery is related to the charge in the period (t-1), the charge and discharge of the energy storage battery in the period (t-1, t), and the amount of power decay per hour. The state of charge model is as follows:

[0088]

[0089] Where E B is the rated capacity of the energy storage, P B is the charging and discharging power of the energy storage, η is the operating electrical efficiency of the energy storage, and the Δt time step is 1 hour.

[0090] The hydrogen energy storage system consists of an electrolyzer, a hydrogen storage tank, and a fuel cell. Photovoltaic power generation generates hydrogen through electrolysis in the electrolyzer, and the hydrogen is stored in the hydrogen storage tank. The hydrogen in the hydrogen storage tank can provide hydrogen to hydrogen fuel cell electric vehicle users, and can also generate electricity through fuel cells to provide electric vehicle users with electricity. The hydrogen energy storage system has two functions. On the one hand, it provides hydrogen demand for hydrogen fuel cell electric vehicles. On the other hand, the electrolyzer-hydrogen storage tank-fuel cell model built by the battery has the same function as battery energy storage.

[0091] The electrolyzer model is as follows:

[0092] H we =P we η we / HHV (3)

[0093] Where P we is the output power of photovoltaic power generation for hydrogen production, η we is the working efficiency of the electrolyzer, and HHV is the electricity-to-hydrogen energy conversion coefficient.

[0094] The electrolyzer and fuel cell work independently of each other. Unlike batteries, they can charge and discharge hydrogen at the same time. Combined with the needs of hydrogen fuel cell electric vehicles, a comprehensive model is built. The hydrogen storage capacity model of the hydrogen storage tank in each time period is as follows:

[0095] H(t)=H(t-1)+H we (t-1)-H l (t-1)-H fc (t-1) (4)

[0096] Where H is the hydrogen content of the hydrogen storage tank, H we , H fc are the amount of hydrogen provided to hydrogen fuel cell electric vehicles and fuel cells respectively. The capacity of the hydrogen storage tank is not zero at the initial moment.l It is the amount of hydrogen required by hydrogen fuel cell electric vehicle users.

[0097] The fuel cell model is as follows:

[0098] P fc =H fc η fc HHV (5)

[0099] Where P fc is the output power of electricity generated by burning hydrogen, η fc is the efficiency of the fuel cell.

[0100] Step 2: Based on the energy flow coordination relationship between various devices in the integrated charging station, design the energy flow strategy of the integrated charging station.

[0101] The energy flow strategy of the integrated charging station affects the output of each device. In the embodiment of the present invention, four operation strategies are formulated according to the difference between the demand of electric vehicles and hydrogen fuel cell electric vehicles, and the working conditions of the battery and hydrogen energy storage system are determined in each time period t. Figure 2 Four mobility strategies for integrated charging stations are shown, as follows:

[0102] A. To meet the needs of electric vehicles and hydrogen fuel cell electric vehicles, the surplus power of photovoltaic power generation is used to charge the battery, electrolyze and store hydrogen in the electrolyzer, and finally the surplus power is connected to the grid.

[0103] B. To meet the demand for electric vehicles, there is a shortage of hydrogen fuel cell electric vehicles. The surplus power of photovoltaic power generation is first used for electrolysis and storage of hydrogen in electrolyzers, and then for charging batteries. There is still a shortage of hydrogen fuel cell electric vehicles, which is supplemented by purchased spare hydrogen.

[0104] C. Meet the demand for hydrogen fuel cell electric vehicles. There is a shortage in the demand for electric vehicles. Batteries and fuel cells are discharged once to meet the demand for electric vehicles, and finally supplemented by the power grid.

[0105] D. There is a shortage in demand for both electric vehicles and hydrogen fuel cell electric vehicles. The demand for electric vehicles is first discharged by the battery, and then the power grid supplements the power shortage. The demand for hydrogen fuel cell electric vehicles is supplemented by purchased backup hydrogen.

[0106] Figure 3 A detailed flow chart of energy flow is shown, see Figure 2 and Figure 3 , the strategy of energy flow is as follows: read meteorological data, obtain photovoltaic output power P pv , determine the photovoltaic output power P pv Is it greater than the electric load demand P of electric vehicle users in each period? l , let DP = P pv -P lIf DP>0, then enter the A or B strategy branch, if DP<0, then enter the C or D strategy branch; then determine whether the hydrogen content H in the hydrogen storage tank is greater than the hydrogen volume H required by the hydrogen fuel cell electric vehicle user l , when DP>0 and HH l >0, implement strategy A. When DP>0 and HH l <0, implement strategy B; when DP<0 and HH l >0, implement strategy C. When DP<0 and HH l When <0, implement strategy D.

[0107] Furthermore, the specific method of strategy A is as follows: First, determine whether DP is greater than the battery capacity P ba If it is greater than, it is energy storage charging, otherwise it is determined whether DP is less than the battery capacity P ba and electrolytic cell capacity P we If P ba <DP<P ba +P we , then it is energy storage charging, and the electrolyzer produces hydrogen at the same time; if DP<P ba +P we , the energy storage is charged, while the electrolyzer produces hydrogen, and the surplus electricity is connected to the grid.

[0108] The specific method of strategy B is as follows: First, determine whether DP is less than the electrolytic cell capacity P we If it is less than, the electrolyzer produces hydrogen, otherwise it is determined whether DP is less than the battery capacity P ba and electrolytic cell capacity P we If P we <DP<P ba +P we , the electrolyzer produces hydrogen and charges the energy storage at the same time; if DP>P ba +P we , the electrolyzer produces hydrogen, charges the energy storage at the same time, and the surplus electricity is connected to the grid. If the hydrogen produced by the electrolyzer does not meet the difference in hydrogen demand, hydrogen is purchased.

[0109] The specific method of strategy C is as follows: Since DP<0, it is expressed as an absolute value |DP|. First, determine whether |DP| is less than the battery capacity P ba If it is less than, the energy storage is discharged, otherwise it is determined whether |DP| is less than the battery capacity P ba and fuel cell capacity P fc If P ba <|DP|<P ba +P fc , the energy storage discharges and the fuel cell discharges at the same time; if |DP|>P ba +P fc, the energy storage discharges, the fuel cell discharges, and electricity is purchased from the grid at the same time.

[0110] The specific method of D strategy is as follows: First, determine whether |DP| is less than the battery capacity P ba If it is less than, the energy storage discharge is carried out to merge hydrogen, otherwise it is determined whether |DP| is greater than the battery capacity P ba , if P ba <|DP|, the energy storage discharges and acquires hydrogen, while purchasing electricity from the grid.

[0111] Step 3: Under the constraints of energy balance, component constraints, and grid constraints, an optimal equipment configuration model is established with the annual operating cost and autonomous operation capability of the comprehensive charging station as the target.

[0112] Goal 1: Minimize the annual operating cost of the comprehensive charging station:

[0113] minF1=(C1+C2)CRF+C3-C4 (6)

[0114] C1=P pv e pv +P ba e ba +P we e we +P fc e fc +He H (7)

[0115] C2=C1K (8)

[0116]

[0117]

[0118]

[0119] Among them, C1, C2, C3, and C4 are respectively the equipment purchase cost, equipment maintenance cost, comprehensive charging station operation cost, and Internet access income. CRF is the annualized coefficient of each equipment. pv , P ba , P we , P fc are the installed capacities of photovoltaic, battery, electrolyzer and fuel cell respectively. H is the capacity of hydrogen storage tank. pv 、e ba 、e we 、e fc 、e H1 are the unit prices of the corresponding equipment. K is the maintenance cost of the equipment. r is the actual annual interest rate, and y is the operating life of the equipment. H 、e Gi 、e GoThey are the reserve hydrogen purchase, grid electricity purchase and surplus electricity grid connection price. G , P Gi , P Go They are the standby hydrogen purchase amount, power purchased from the power grid and surplus power connected to the grid in each period.

[0120] Objective 2: The autonomous operation capability of comprehensive charging stations, and the proportion of purchased electricity and standby hydrogen.

[0121]

[0122]

[0123] minF2 and minF3 are both ratios. The smaller the ratio, the less electricity is purchased from the power grid, the less backup hydrogen is purchased from outside, and the stronger the ability to operate on its own. T is the total period of a year, 8760. P l (t) is the electric load demand of electric vehicle users during period t.

[0124] Constraint 1: The exchange of electric power and hydrogen volume in the integrated charging station system should be balanced at all times.

[0125] P l =P pv +P we +P ba +P fc +P G (14)

[0126] H l =H+H G +H we -H fc (15)

[0127] Constraint 2: The transformer has capacity limitations, and the power grid has power constraints on the integrated charging station.

[0128] P G ≤P Gmax (16)

[0129] P Gmax It is the maximum power that the power grid supplies to the charging station, that is, the power limit of the transformer connecting the power grid to the charging station.

[0130] Constraint 3: Overcharging or over-discharging of the battery will affect its service life, so its state of charge (SOC) should be within a reasonable constraint range.

[0131] SOC min ≤SOC(t)≤SOC max (17)

[0132] SOC max and SOCmin They respectively represent the upper and lower limits of the battery energy storage power.

[0133] Step 4: Based on MATLAB programming, the NSGA-II algorithm and fuzzy comprehensive evaluation are used to optimize the optimal equipment capacity.

[0134] Figure 4 The optimization process of NSGA-II and four embedded operation strategies used in the embodiment of the present invention is shown, and the specific steps are as follows:

[0135] Step 401: Input the basic data of the integrated charging station, set the initial parameters of NSGA-II and the upper and lower limits of decision variables.

[0136] Step 402: Randomly initialize the size of the population to N.

[0137] Step 403: Calculate the fitness value of the objective function.

[0138] Step 404: Perform non-dominated sorting on the individuals in the population and stratify the individuals according to the objective function.

[0139] Step 405: Calculate the virtual fitness value of the individual, that is, the crowding value.

[0140] Step 406: Select individuals from each layer in a round-robin system, and perform crossover and mutation on the selected individuals, using SBX crossover and normal mutation operators to perform crossover and mutation operations to form a new offspring population of the same size.

[0141] Step 407: merge the parent population and the newly formed offspring population, perform non-dominated sorting on the merged population, and select the first N individuals to form a new population.

[0142] Step 408: Determine the number of iterations. If it meets the requirement, the iteration is terminated and the Pareto frontier is output. Otherwise, step 403 is repeated until the number of evolutions meets the designed number and the result is output. C represents the number of iterations and Cmax is the maximum number of iterations.

[0143] Step 409: Calculate the membership of each Pareto solution using the fuzzy membership function. The optimization solution with the highest membership is the final solution.

[0144] Among them, for the selection of membership function of fuzzy comprehensive evaluation, the smaller the optimization target, the better, and a relatively small trapezoidal distribution is selected.

[0145]

[0146] is the function value of the mth non-inferior solution of the nth objective function, The membership degree of the mth non-inferior solution to the nth objective function. are the maximum and minimum solutions of the Pareto solution respectively.

[0147] The validity of the method of the present invention is verified by an example below. The data of this embodiment are as follows:

[0148] Capacity configuration is performed on the charging stations that have been built and operated in a certain place, and a comprehensive charging station with solar hydrogen storage is established. The light intensity data of the area and the daily demand of two types of new energy vehicles are selected as data. The comprehensive charging station is operated for one year as a cycle, and the equipment investment, operating cost and energy purchase ratio of the comprehensive charging station are calculated. The specific light intensity and EV and HV demand parameters are as follows: Figure 5(a)-5(c) shown.

[0149] The distributed photovoltaic grid-connected price refers to the "Notice on the 2020 Photovoltaic Power Grid-Connected Electricity Price Policy" issued by relevant departments. The real-time electricity price for distribution network purchase is 0.7962 yuan / kWh, and the hydrogen market price is 35 yuan / kg. The parameters of each device in the comprehensive charging station are as follows.

[0150] Table 1. Equipment parameters

[0151]

[0152] In the MATLAB environment, the integrated charging station equipment optimization configuration model is simulated and optimized according to the method of the present invention. The verification results of this embodiment show that the method of the present invention can reduce the operating cost of the integrated charging station and improve the independent supply capacity. The Pareto solution set obtained by simulation is as follows Figure 6 The fuzzy comprehensive evaluation is used to obtain the optimal configuration of the balance of electricity and hydrogen, as shown in Figure 7(a)-7(d) shown.

[0153] Depend on Figure 6 It can be seen that the optimal solution of the optimization configuration of the integrated charging station is distributed on the Pareto frontier, reflecting the diversity and uniformity of the solution. Multiple optimization solutions can be provided for the optimization configuration. At the same time, there is a non-dominant relationship between the various objectives. If the operating cost of the integrated charging station is reduced, the proportion of the integrated charging station purchasing electricity from the power grid and the purchase of standby hydrogen will increase, reducing the autonomous operation capability of the integrated charging station. Therefore, it is necessary to comprehensively weigh the factors considered for each optimization goal and make an objective decision.

[0154] From the operation result diagram of each device, it can be concluded that the result of the optimized configuration can enable the comprehensive charging station to operate effectively. As shown in Figure 7(a), in the 1st to 6th period, electric vehicles are mainly powered by battery energy storage, hydrogen energy storage system and grid supplementary power supply. In the 7th to 17th period, there is sufficient sunlight. The photovoltaic output is used to supply electricity and electrolyze water to produce hydrogen on the premise of meeting the needs of electric vehicles. In the 18th to 24th period, electric vehicles are mainly powered by batteries and the grid. As shown in Figure 7(b), in the early morning and at night, the remaining hydrogen in the hydrogen storage tank and the purchased spare hydrogen mainly meet the needs of hydrogen fuel vehicles. During the daytime, photovoltaic output is used to electrolyze hydrogen. After removing the hydrogen consumed by the fuel cell, the net hydrogen can meet the demand, and the remaining hydrogen in the hydrogen storage tank can also meet the demand during the night. Figures 7(c) and 7(d) reflect the working status of the battery and hydrogen storage tank in a day.

[0155] The present invention also provides a device for configuring the capacity of a photovoltaic hydrogen storage integrated charging station, comprising:

[0156] A comprehensive charging station system model building module is used to build a comprehensive charging station system model that can provide services for both electric vehicles and hydrogen fuel cell electric vehicles, including photovoltaic, energy storage batteries and hydrogen energy storage systems;

[0157] An energy flow strategy determination module determines the energy flow strategy of the integrated charging station based on the energy flow coordination relationship between various devices in the integrated charging station according to the integrated charging station system model;

[0158] The capacity configuration optimization model building module establishes an equipment capacity configuration optimization model based on the energy flow strategy, energy balance constraints, component constraints, and grid power constraints, with the annual operating cost and autonomous operation capability of the comprehensive charging station as the goal;

[0159] The capacity configuration solution module uses the NSGA-II algorithm and fuzzy comprehensive evaluation to solve the equipment capacity configuration optimization model and determine the capacity configuration solution for the integrated charging station equipment.

[0160] It should be understood that the capacity configuration device of the integrated photohydrogen storage charging station equipment in the embodiment of the present invention can implement all the technical solutions in the above method embodiment, and the functions of its various functional modules can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can refer to the relevant description in the above embodiment, which will not be repeated here.

[0161] The present invention also provides a computer device, comprising:

[0162] one or more processors;

[0163] Memory; and

[0164] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the method for configuring the capacity of the light-hydrogen storage integrated charging station equipment are implemented as described above.

[0165] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for configuring the capacity of the integrated photovoltaic hydrogen storage charging station equipment are implemented.

[0166] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A method for configuring the capacity of a photovoltaic hydrogen storage integrated charging station, characterized in that: The following steps are involved: Establish a comprehensive charging station system model that can serve both electric vehicles and hydrogen fuel cell electric vehicles, including photovoltaics, energy storage batteries and hydrogen energy storage systems; According to the integrated charging station system model, the energy flow strategy of the integrated charging station is determined based on the energy flow coordination relationship between various devices in the integrated charging station, including: obtaining the photovoltaic output power P pv , determine the photovoltaic output power P pv Is it greater than the electric load demand P of electric vehicle users in each period? l , let DP = P pv -P l , if DP>0, then enter the first strategy branch, if DP<0, then enter the second strategy branch; In the first strategy branch and the second strategy branch, it is determined whether the hydrogen content H of the hydrogen storage tank is greater than the hydrogen volume H required by the user of the hydrogen fuel cell electric vehicle. l , when DP>0 and HH l >0, implement strategy A. When DP>0 and HH l <0, implement strategy B; when DP<0 and HH l >0, implement strategy C. When DP<0 and HH l When the power consumption of electric vehicles is less than 0, the D strategy is implemented; the specific strategies are as follows: A. To meet the needs of electric vehicles and hydrogen fuel cell electric vehicles, the surplus power of photovoltaic power generation is used to charge the energy storage battery in turn, the electrolyzer is used to electrolyze and store hydrogen, and finally the surplus power is connected to the grid; B. To meet the needs of electric vehicles, there is a shortage in the demand for hydrogen fuel cell electric vehicles. The surplus power of photovoltaic power generation is first used for electrolysis and storage of hydrogen in the electrolyzer, and then for charging the energy storage battery. There is still a shortage of hydrogen fuel cell electric vehicles, which is supplemented by the purchased spare hydrogen; C. To meet the needs of hydrogen fuel cell electric vehicles, there is a shortage in the demand for electric vehicles, and the energy storage battery and fuel cell are discharged once to provide the demand for electric vehicles, which is finally supplemented by the power grid; D. There is a shortage in the demand for both electric vehicles and hydrogen fuel cell electric vehicles. The demand for electric vehicles is first discharged by the energy storage battery, and then the power grid supplements the power shortage. The demand for hydrogen fuel cell electric vehicles is supplemented by the purchased spare hydrogen; According to the energy flow strategy, based on energy balance constraints, component constraints, and grid power constraints, an equipment capacity configuration optimization model is established with the annual operating cost and autonomous operation capability of the comprehensive charging station as the target; The NSGA-II algorithm and fuzzy comprehensive evaluation are used to solve the equipment capacity configuration optimization model and determine the capacity configuration plan of the integrated charging station equipment.

2. The method for configuring the capacity of the light hydrogen storage integrated charging station equipment according to claim 1 is characterized in that: The comprehensive charging station system model includes: Photovoltaic output model: Among them G T is the sunlight intensity, t represents time t, T C is the ambient temperature, G STC and T STC is the light intensity and temperature under standard conditions, P pv-STC is the rated power of the PV; Energy storage battery state of charge model: Where SOC is the state of charge of the energy storage battery, E B is the rated capacity of the energy storage battery, P B is the charging and discharging power of the energy storage battery, η is the operating electrical efficiency of the energy storage battery, and Δt is the time step; The hydrogen energy storage system consists of an electrolyzer, a hydrogen storage tank, and a fuel cell. The electrolyzer model is as follows: H we =P we η we / HHV Where P we is the output power of photovoltaic power generation for hydrogen production, η we is the working efficiency of the electrolyzer, HHV is the electric-hydrogen energy conversion coefficient; The hydrogen storage capacity model of the hydrogen storage tank in each time period is as follows: H(t)=H(t-1)+H we (t-1)-H l (t-1)-H fc (t-1) Where H is the hydrogen content of the hydrogen storage tank, H we , H fc are the amounts of hydrogen supplied to hydrogen fuel cell electric vehicles and fuel cells, respectively; The fuel cell model is as follows: P fc =H fc η fc HHV Where P fc is the output power generated by burning hydrogen, η fc is the efficiency of the fuel cell.

3. The method for configuring the capacity of the light hydrogen storage integrated charging station equipment according to claim 2 is characterized in that: The equipment capacity configuration optimization model is: The annual operating cost of the comprehensive charging station is the lowest: minF1=(C1+C2)CRF+C3-C4 C1=P PV e pv +P BA e ba +P WE e we +P FC e fc +H C e H C2=C1K Among them, C1, C2, C3, and C4 are respectively the equipment purchase cost, equipment maintenance cost, comprehensive charging station operation cost, and Internet access income; F1 is the annual operation cost of the comprehensive charging station; CRF is the annualized coefficient of each device; P PV , P BA , P WE , P FC are the installed capacity of photovoltaic, energy storage battery, electrolyzer and fuel cell respectively; H C is the capacity of the hydrogen storage tank; e pv 、e ba 、e we 、e fc 、e H are the unit prices of the corresponding equipment respectively; K is the maintenance cost of the equipment; r is the actual annual interest rate, y is the operating life of the equipment; e Hi 、e Gi 、e Go They are the reserve hydrogen purchase, grid electricity purchase and surplus electricity grid connection price; H G , P Gi , P Go They are the standby hydrogen purchase amount, power purchased from the power grid and surplus power connected to the grid in each period; The autonomous operation capacity of the comprehensive charging station, that is, the proportion of purchased electricity and standby hydrogen is: Among them, F2 is the proportion of electricity purchase, and F3 is the proportion of standby hydrogen purchase; The exchange of electric power and hydrogen volume in the integrated charging station system should always be balanced: P l =P pv +P we +P B +P fc +P Gi H l =H+H G +H we -H fc The transformer has capacity limitations, and the power grid has power constraints for the integrated charging station: P Gi ≤P Gmax The state of charge (SOC) of the energy storage battery is within reasonable constraints: SOC min ≤SOC(t)≤SOC max 。 4. The method for configuring the capacity of the light hydrogen storage integrated charging station equipment according to claim 1 is characterized in that: Solving the equipment capacity configuration optimization model specifically includes: (a) Input the basic data of the integrated charging station and set the initial parameters and upper and lower limits of the decision variables of NSGA-II; (b) Randomly initialize the population size to N; (c) Calculate the fitness value of the objective function and then perform fast non-dominated sorting on the results; (d) Calculate the individual crowding distance, use the elite strategy to select and retain excellent individuals, form a new population, and return to step (c); (e) Determine the number of iterations. If the number of iterations meets the requirement, the iteration is terminated and the Pareto frontier is output. Otherwise, the iteration cycle continues. (f) The fuzzy membership function of fuzzy comprehensive evaluation is used to calculate the membership of each Pareto solution, and the optimization solution with the highest membership is the final solution.

5. The method for configuring the capacity of the light hydrogen storage integrated charging station equipment according to claim 4 is characterized in that: The membership function of fuzzy comprehensive evaluation is: in is the function value of the mth non-inferior solution of the nth objective function, is the membership degree of the mth non-inferior solution of the nth objective function, are the maximum and minimum solutions of the Pareto solution respectively.

6. A device for configuring the capacity of a photovoltaic hydrogen storage integrated charging station, characterized in that: include: A comprehensive charging station system model building module is used to build a comprehensive charging station system model that can provide services for both electric vehicles and hydrogen fuel cell electric vehicles, including photovoltaic, energy storage batteries and hydrogen energy storage systems; The energy flow strategy determination module determines the energy flow strategy of the integrated charging station based on the integrated charging station system model and the energy flow coordination relationship between the various devices in the integrated charging station, including: obtaining the photovoltaic output power P pv , determine the photovoltaic output power P pv Is it greater than the electric load demand P of electric vehicle users in each period? l , let DP = P pv -P l , if DP>0, then enter the first strategy branch, if DP<0, then enter the second strategy branch; In the first strategy branch and the second strategy branch, it is determined whether the hydrogen content H of the hydrogen storage tank is greater than the hydrogen volume H required by the user of the hydrogen fuel cell electric vehicle. l , when DP>0 and HH l >0, implement strategy A. When DP>0 and HH l <0, implement strategy B; when DP<0 and HH l >0, implement strategy C. When DP<0 and HH l When the power consumption of electric vehicles is less than 0, the D strategy is implemented; the specific strategies are as follows: A. To meet the needs of electric vehicles and hydrogen fuel cell electric vehicles, the surplus power of photovoltaic power generation is used to charge the energy storage battery in turn, the electrolyzer is used to electrolyze and store hydrogen, and finally the surplus power is connected to the grid; B. To meet the needs of electric vehicles, there is a shortage in the demand for hydrogen fuel cell electric vehicles. The surplus power of photovoltaic power generation is first used for electrolysis and storage of hydrogen in the electrolyzer, and then for charging the energy storage battery. There is still a shortage of hydrogen fuel cell electric vehicles, which is supplemented by the purchased spare hydrogen; C. To meet the needs of hydrogen fuel cell electric vehicles, there is a shortage in the demand for electric vehicles, and the energy storage battery and fuel cell are discharged once to provide the demand for electric vehicles, which is finally supplemented by the power grid; D. There is a shortage in the demand for both electric vehicles and hydrogen fuel cell electric vehicles. The demand for electric vehicles is first discharged by the energy storage battery, and then the power grid supplements the power shortage. The demand for hydrogen fuel cell electric vehicles is supplemented by the purchased spare hydrogen; The capacity configuration optimization model building module establishes an equipment capacity configuration optimization model based on the energy flow strategy, energy balance constraints, component constraints, and grid power constraints, with the annual operating cost and autonomous operation capability of the comprehensive charging station as the goal; The capacity configuration solution module uses the NSGA-II algorithm and fuzzy comprehensive evaluation to solve the equipment capacity configuration optimization model and determine the capacity configuration solution for the integrated charging station equipment.

7. A computer device, characterized in that: include: one or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the method for configuring the capacity of the light hydrogen storage integrated charging station equipment are implemented as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the method for configuring the capacity of the light-hydrogen storage integrated charging station equipment as described in any one of claims 1-5 are implemented.

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