A dispatching method and system for an integrated electric-hydrogen energy charging station of new energy vehicles

Abstract

The present invention discloses a scheduling method and system for an integrated electric-hydrogen energy charging station of new energy vehicles, belonging to the technical field of optimal scheduling. The scheduling method includes: obtaining the battery data information, battery swapping requirements of electric vehicles, and hydrogen charging requirements of fuel cell vehicles; determining the battery swapping costs of electric vehicle users; optimizing the allocation of batteries according to the battery optimization allocation strategy; determining the hydrogen production, storage, hydrogen charging, and fuel cell models to optimize the production, storage, hydrogen charging, and power generation of hydrogen; determining the reference value of the hydrogen demand to optimize the production scheduling of hydrogen; determining the objective function of the integrated electric-hydrogen energy charging station of new energy vehicles; and optimizing the scheduling of the battery charging and discharging power of the battery swapping station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the objective function and constraint conditions. The present invention can effectively enhance the ability of the energy charging station to cut peaks and fill valleys in the power system while improving the revenue of the integrated electric-hydrogen energy charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal scheduling, and in particular to a scheduling method and system for an integrated electric-hydrogen charging station for new energy vehicles. Background Art

[0002] With the increasingly serious energy shortage and environmental pollution, countries around the world have begun to vigorously develop new energy vehicles. New energy vehicles mainly include electric vehicles driven by electricity and fuel cell vehicles driven by hydrogen energy. The green, low-carbon and environmentally friendly characteristics of new energy vehicles have enabled them to develop rapidly. Therefore, as intermediaries between new energy vehicles and the power grid, the optimal scheduling of swap stations and hydrogen stations is particularly important for the stable and economic operation of the power system.

[0003] Swap stations and hydrogen stations respectively provide battery swapping and hydrogen refueling services for electric vehicles and fuel cell vehicles to meet the travel needs of new energy vehicle users. Due to the disordered charging of new energy vehicle users, the periods when swap stations and hydrogen stations provide charging services often coincide with the peak load periods of the power system, resulting in the situation of "peak on peak" in the power grid, and the peak-valley difference further increases, which is not conducive to the safe operation of the entire power system. However, both swap stations and hydrogen stations have certain energy storage characteristics. They can charge and produce hydrogen during the low load period of the power grid, and feed back the stored energy to the power grid during the peak load period to help the power grid shave peaks and fill valleys, enhancing the stability, flexibility and economy of the power grid.

[0004] The invention patent of the present inventor (application number: 202010326244.4) discloses a scheduling method and system for an electric vehicle swap station, but does not consider fuel cell vehicles. In order to adapt to the current situation of the rapid growth in the number of fuel cell vehicles used, solve the huge impact on the power grid caused by the disordered charging of large-scale new energy vehicles, and reduce the burden on the construction and safe operation of the power grid, how to optimize the scheduling of the integrated charging station for new energy vehicles to achieve the stable operation of the power system is an urgent problem to be solved at present. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a scheduling method and system for an integrated electric-hydrogen charging station for new energy vehicles, which is applicable to electric vehicles and fuel cell vehicles, and can realize and reasonably optimize the process of purchasing electricity from the power grid and feeding back electric energy to the power grid by swap stations and hydrogen stations, as well as the energy exchange process between swap stations and hydrogen stations, improve the economy of the system, and help the power system shave peaks and fill valleys.

[0006] To solve the above technical problem, the technical solution adopted by the present invention is:

[0007] A scheduling method for an integrated electric-hydrogen charging station of new energy vehicles. The integrated electric-hydrogen charging station of new energy vehicles distributes the electric energy provided by the power grid to the battery swapping station and the hydrogen station; the battery swapping station charges the battery with electric energy, manages and distributes the battery. Part of the batteries are used for the battery swapping service of electric vehicles, and the other part of the batteries is regarded as a battery energy storage station, and the electric energy is sent back to the power grid or the hydrogen station; the hydrogen station uses the electric energy for hydrogen production, hydrogen storage and hydrogen charging. Part of the hydrogen in the hydrogen storage tank is used to meet the hydrogen charging needs of fuel cell vehicles, and the other part generates electricity for the fuel cell and sends the electric energy back to the power grid;

[0008] The scheduling method specifically includes the following steps:

[0009] Obtain the battery data information, battery swapping requirements of electric vehicles and hydrogen charging requirements of fuel cell vehicles;

[0010] Determine the battery swapping cost of electric vehicle users according to the battery data information of the battery swapping station and the user adaptive response model;

[0011] Optimize the distribution of the battery according to the battery optimization distribution strategy;

[0012] According to the prediction of the hydrogen demand scale of the hydrogen station, determine the hydrogen production, hydrogen storage, hydrogen charging and fuel cell models to optimize the hydrogen production, storage, hydrogen charging and power generation;

[0013] According to the daily hydrogen demand of fuel cell vehicles, determine the benchmark value of the hydrogen demand and optimize the hydrogen production scheduling;

[0014] Determine the objective function of the integrated electric-hydrogen charging station of new energy vehicles according to the charging and energy requirements of new energy vehicle users;

[0015] Optimize the charging and discharging power of the battery in the battery swapping station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the objective function and constraint conditions.

[0016] A further improvement of the technical solution of the present invention is that: the battery data information includes the state of charge of the battery, the state of health of the battery and the rated capacity of the battery.

[0017] A further improvement of the technical solution of the present invention is that: the user adaptive response model is:

[0018] r i =w 1 ·SOC s (i)+w 2 ·SOH s (i)+w 3 ·R i

[0019] w 1 +w 2+w 3 = 1

[0020] The battery swapping cost for electric vehicle users is:

[0021] R i = a·(SOC s (i) - SOC c (i)) + b·(SOH s (i) - SOH c (i))

[0022] Wherein, R i represents the battery swapping cost of the i-th electric vehicle user, a and b respectively represent the cost coefficients of SOC and SOH before and after battery swapping, SOC c (i) and SOC s (i) respectively represent the state of charge of the battery before and after battery swapping of the i-th electric vehicle user, SOH c (i) and SOH s (i) respectively represent the state of health of the battery before and after battery swapping of the i-th electric vehicle user, r i represents the response value of the i-th electric vehicle, w 1 , w 2 , w 3 respectively represent the weight coefficients of SOC, SOH and swapping cost after battery swapping.

[0023] A further improvement of the technical solution of the present invention lies in: optimizing the allocation of batteries according to the battery optimization allocation strategy, specifically:

[0024] l 2 ≤ SOH ≤ l 1

[0025] l 3 ≤ SOH < l 2

[0026] SOH < l 3

[0027] Wherein, l 1 , l 2 , l 3 respectively represent the boundaries of SOH classification.

[0028] A further improvement of the technical solution of the present invention lies in: the hydrogen production, hydrogen storage, hydrogen filling, fuel cell model is:

[0029]

[0030] SOE min ≤ SOE t ≤ SOE max

[0031] SOE 0 = SOE T

[0032]

[0033] In the formula, P t ele represents the input power of the electrolyzer, represents the hydrogen outflow of the electrolyzer, γ PtH represents the electro-hydrogen conversion factor, η ele represents the electrolyzer efficiency, represents the lower heating value of hydrogen, represents the hydrogen density, V t ele represents the amount of hydrogen produced by the electrolyzer within the t time slot, represents the total amount of hydrogen produced by the electrolyzer within a scheduling period T, SOE t 、SOE t-1 respectively represent the hydrogen storage states in the hydrogen storage tank within the time slots t and t - 1, V t H2 represents the amount of hydrogen stored in the hydrogen storage tank within the time slot t, V tank represents the total amount of hydrogen that can be stored in the hydrogen storage tank under a certain pressure, V t ele 、V t FCEV 、V t FC respectively represent the amount of hydrogen produced by the electrolyzer, the hydrogen demand of the fuel cell vehicle, and the amount of hydrogen consumed by the fuel cell within the time slot t, SOE min 、SOE max are respectively the upper and lower limits of the hydrogen storage state, SOE 0 、SOE T respectively represent the amount of hydrogen in the hydrogen storage tank at the beginning and end of a scheduling period, represents the hydrogen demand of the jth fuel cell vehicle within the time slot t, V t FCEV represents the total hydrogen demand of all fuel cell vehicles within the time slot t, represents the total hydrogen demand of fuel cell vehicles within a scheduling period T, E(t, j) represents the binary variable for whether the fuel cell vehicle user chooses to refuel or not, F t fc represents the amount of hydrogen consumed by the fuel cell within the time slot t, k' represents the conversion coefficient for converting the hydrogen flow rate from moles per hour to cubic meters per hour, P fc represents the power generated by the hydrogen consumed by the fuel cell within the time slot t, ηfc represents the efficiency of the fuel cell, and F is the Faraday constant.

[0034] A further improvement of the technical solution of the present invention lies in that: the reference value of the hydrogen demand is:

[0035]

[0036]

[0037] wherein, represents the reference value of the hydrogen demand of the fuel cell vehicle, T represents a scheduling period, and V t FCEV represents the hydrogen demand of the fuel cell vehicle within the time slot t, and k 1 、k 2 、k 3 are respectively the scheduling coefficients of hydrogen production under different electricity prices.

[0038] A further improvement of the technical solution of the present invention lies in that: the objective function is:

[0039]

[0040] wherein,

[0041]

[0042] wherein, c 1 represents the battery swapping income of the swapping station, c 2 represents the charging cost of the swapping station, c 3 represents the discharging income of the swapping station, c 4 represents the charging and discharging loss cost of the swapping station, c 5 represents the battery processing cost of the swapping station, c 6 represents the hydrogen selling income of the hydrogen station, c 7 represents the hydrogen production cost of the hydrogen station, c 8 represents the fuel cell power generation income of the hydrogen station, T is the total optimization period, respectively represent the charging and discharging powers of the i-th battery within the time slot t, P t B2H represents the power provided by the swapping station to the hydrogen station, f t represents the electricity price, represents the hydrogen price sold to the fuel cell vehicle, d is the battery loss cost coefficient, e is the battery processing cost coefficient, and S(t,i) represents the binary variable of whether the electric vehicle user chooses to swap the battery or not.

[0043] A further improvement of the technical solution of the present invention lies in that: the constraint conditions include the constraint conditions of the swapping station model, the constraint conditions of the hydrogen station model, and the state constraint conditions;

[0044] The constraint conditions of the battery swapping station model are as follows:

[0045] 0 ≤ SOC s ≤ 100%

[0046] 0 ≤ SOC c ≤ 100%

[0047] 0 ≤ SOH s ≤ 100%

[0048] 0 ≤ SOH c ≤ 100%

[0049] 0 ≤ SOC s +P ch ·η·t / Cap ≤ 100%

[0050] 0 ≤ SOC s -P dis / η·t / Cap ≤ 100%

[0051]

[0052] N = b 1 +b 2 +b 3

[0053]

[0054] Wherein, P ch , P dis respectively represent the charging and discharging power of the battery, η represents the charging and discharging efficiency, Cap represents the battery capacity, b 1 , b 2 , b 3 respectively represent the number of batteries in battery storage 1, 2, and 3, N represents the total number of batteries, respectively represent the maximum charging and discharging power of the battery, P t B2H,max represents the upper limit of the power provided by the battery swapping station to the hydrogen station, B 1 (t, i) represents the binary variable of the charging state of the battery in the battery swapping station, B 2 (t, i) represents the binary variable of the discharging state of the battery in the battery swapping station, represents the binary variable of the state of the battery swapping station providing electrical energy to the hydrogen station;

[0055] The constraint conditions of the hydrogen station model are as follows:

[0056]

[0057] V t ele ≥ 0

[0058] V t FC ≥ 0

[0059] V t FCEV ≥ 0

[0060] V t ele + SOE t-1 ·V tank ≥ V t FCEV

[0061] V t ele + SOE t-1 ·V tank ≤ V tank

[0062] Wherein, P ele,min 、P ele,max respectively represent the upper and lower limits of the input power of the electrolyzer, and P FC,min 、P FC,max respectively represent the upper and lower limits of the output power of the fuel cell, respectively represent the binary variables of the working states of the fuel cell and the electrolyzer;

[0063] The state constraint conditions include:

[0064] For the constraint formula that the same battery in the battery swapping station can only be in one of the charging, discharging or idle states is as follows:

[0065] B 1 (t,i) + B 2 (t,i) ≤ 1

[0066] For the constraint formula that the electrolyzer and the fuel cell in the hydrogen station cannot operate simultaneously is as follows:

[0067]

[0068] For the constraint formula that the battery swapping station can only supply power to the hydrogen station when the electrolyzer is working is as follows:

[0069]

[0070] A system for a dispatching method of a new energy vehicle electric-hydrogen integrated charging and energy storage station, comprising:

[0071] A data acquisition module, configured to acquire battery data information, battery swapping requirements of electric vehicles, and hydrogen refueling requirements of fuel cell vehicles;

[0072] A battery swapping cost calculation module, which is used to determine the battery swapping cost according to the battery data information of the swapping station and the user adaptive response model;

[0073] A battery allocation module, which is used to optimize the allocation of batteries according to the battery optimization allocation strategy;

[0074] A hydrogen station construction module, which is used to predict the hydrogen demand scale of the hydrogen station, determine the hydrogen production, storage, hydrogen filling, and fuel cell models, so as to optimize the hydrogen production, storage, hydrogen filling, and power generation;

[0075] A hydrogen demand benchmark value calculation module, which is used to determine the benchmark value of the hydrogen demand according to the daily hydrogen demand of fuel cell vehicles, and optimize the production scheduling of hydrogen;

[0076] A target function determination module, which is used to determine the target function of the electric-hydrogen integrated charging station for new energy vehicles according to the charging energy demand of new energy vehicle users;

[0077] An optimization module, which is used to optimize the scheduling of the battery charge and discharge power of the swapping station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the target function and constraint conditions.

[0078] Due to the adoption of the above technical solutions, the technical progress achieved by the present invention is:

[0079] The present invention provides a scheduling method and system for an electric-hydrogen integrated charging station for new energy vehicles, which obtains the battery data information, battery swapping demand of electric vehicles, and hydrogen filling demand of fuel cell vehicles of electric vehicles, determines the battery swapping cost of electric vehicle users according to the battery data information of the swapping station and the user adaptive response model, optimizes the allocation of batteries according to the battery optimization allocation strategy, determines the hydrogen production, storage, hydrogen filling, and fuel cell models according to the prediction of the hydrogen demand scale of the hydrogen station, so as to optimize the hydrogen production, storage, hydrogen filling, and power generation, determines the benchmark value of the hydrogen demand according to the daily hydrogen demand of fuel cell vehicles, optimizes the production scheduling of hydrogen, determines the target function of the electric-hydrogen integrated charging station for new energy vehicles according to the charging energy demand of new energy vehicle users, and optimizes the scheduling of the battery charge and discharge power of the swapping station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the target function and constraint conditions, which can effectively enhance the peak shaving and valley filling ability of the charging station for the power system while improving the revenue of the electric-hydrogen integrated charging station. Description of the Drawings

[0080] Figure 1 It is a schematic diagram of the optimization scheduling framework of the electric-hydrogen integrated charging station for new energy vehicles in the present invention;

[0081] Figure 2 It is a flowchart of the scheduling method of the electric-hydrogen integrated charging station for new energy vehicles in the present invention;

[0082] Figure 3 It is the structural diagram of the scheduling system of the electric-hydrogen integrated charging station for new energy vehicles in the present invention;

[0083] Among them, 101 is the data acquisition module, 102 is the battery swapping cost calculation module, 103 is the battery allocation module, 104 is the hydrogen station construction module, 105 is the hydrogen demand baseline value calculation module, 106 is the objective function determination module, and 107 is the optimization module. Specific implementation manners

[0084] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0085] As Figure 1 shown, an electric-hydrogen integrated charging station for new energy vehicles includes a battery swapping station and a hydrogen station, which provides convenient and efficient charging services for new energy vehicles. New energy vehicles include electric vehicles and fuel cell vehicles. To meet the charging needs of new energy vehicles, the electric-hydrogen integrated charging station for new energy vehicles needs to purchase electric energy from the power grid. The power grid transports the electric energy to the electric-hydrogen integrated charging station for new energy vehicles through transmission lines, and the electric-hydrogen integrated charging station for new energy vehicles reasonably distributes the electric energy to the battery swapping station and the hydrogen station. In the battery swapping station inside the electric-hydrogen integrated charging station for new energy vehicles, the electric energy distributed by the electric-hydrogen integrated charging station for new energy vehicles is reasonably charged to the battery, and the battery is managed and distributed. A part of the batteries is used for the battery swapping service of electric vehicles, and the battery swapping process considers the adaptive response of users; another part of the spare batteries is regarded as a battery energy storage station. Through the "battery-to-grid" technology, the electric energy in the batteries can be sent back to the power grid or the hydrogen station, enhancing the flexibility and stability of the charging station and the power system. Inside the hydrogen station, first, the electric energy is transported to the electrolyzer equipment to produce hydrogen by electrolyzing water, and then a suitable hydrogen storage method is selected. Using compressors and cooling equipment, etc., the hydrogen is pressurized and stored in the hydrogen storage tank. Finally, a part of the hydrogen in the hydrogen storage tank is used to meet the hydrogen charging needs of fuel cell vehicles, and another part is input to the fuel cell for power generation, and the electric energy is sent back to the power grid through the "fuel cell-to-grid" technology, further enhancing the flexibility and stability of the charging station system and the power system.

[0086] As Figure 2 shown, a scheduling method for an electric-hydrogen integrated charging station for new energy vehicles includes the following steps:

[0087] S1. Obtain the battery data information, battery swapping requirements of electric vehicles, and hydrogen charging requirements of fuel cell vehicles;

[0088] The battery data information includes the state of charge of the battery, the health state of the battery, and the rated capacity of the battery.

[0089] S2. Determine the battery swapping cost of electric vehicle users based on the battery data information of the swapping station and the user adaptive response model, specifically including:

[0090] Different electric vehicle users have different sensitivities to the state of charge of the battery, the state of health of the battery, and the swapping cost. They will make choices based on the information provided. If the response value of the user is greater than the response threshold, the user will choose to swap the battery; otherwise, the user may abandon this swapping service.

[0091] The user adaptive response model is:

[0092] r i = w 1 ·SOC s (i) + w 2 ·SOH s (i) + w 3 ·R i

[0093] w 1 + w 2 + w 3 = 1

[0094] The battery swapping cost of electric vehicle users is:

[0095] R i = a·(SOC s (i) - SOC c (i)) + b·(SOH s (i) - SOH c (i))

[0096] In the formula, R i represents the battery swapping cost of the i-th electric vehicle user, a and b respectively represent the cost coefficients of SOC and SOH before and after battery swapping, SOC c (i) and SOC s (i) respectively represent the state of charge of the battery of the i-th electric vehicle user before and after battery swapping, SOH c (i) and SOH s (i) respectively represent the state of health of the battery of the i-th electric vehicle user before and after battery swapping, r i represents the response value of the i-th electric vehicle user, w 1 , w 2 , w 3 respectively represent the weight coefficients of SOC, SOH and swapping cost after battery swapping.

[0097] S3. Optimize the allocation of batteries according to the battery optimization allocation strategy, specifically including:

[0098] Optimize the allocation of the batteries according to the battery optimization allocation strategy:

[0099] l 2 ≤SOH≤l 1

[0100] l 3 ≤SOH<l 2

[0101] SOH<l 3

[0102] In the formula, l 1 、l 2 、l 3 respectively represent the boundaries of SOH classification.

[0103] The battery optimization allocation strategy realizes the allocation and scheduling of the batteries. The battery optimization allocation strategy divides the batteries in the station into Battery Bank 1, Battery Bank 2, and Battery Bank 3, corresponding to l 2 ≤SOH≤l 1 、l 3 ≤SOH<l 2 、SOH<l 3 . From Battery Bank 1 to Battery Bank 3, the battery health status decreases accordingly. When the battery health status is lower than a certain value, its performance in aspects such as endurance can no longer meet the needs of electric vehicles. Therefore, the uses of the batteries in different battery banks are also different. Battery Bank 1 is optimized to serve electric vehicles, the power system, and the hydrogen station. Battery Bank 2 is optimized to serve the power system and the hydrogen station. Since the battery health status of Battery Bank 3 can no longer meet the usage requirements, it needs to be repaired and recycled for secondary use.

[0104] S4. According to the prediction of the hydrogen demand scale of the hydrogen station, determine the hydrogen production, storage, hydrogen filling, and fuel cell models to optimize the production, storage, hydrogen filling, and power generation of hydrogen, specifically including:

[0105] The hydrogen station mainly provides hydrogen filling services for fuel cell vehicles and auxiliary services for the power system. The present invention adopts the method of on-site hydrogen production to carry out the production scheduling of hydrogen. Different types of hydrogen production, storage, and fuel cell technologies have different characteristics and application conditions. The present invention comprehensively considers the requirements of production scheduling, economy, and flexibility, and finally selects alkaline electrolysis technology to produce hydrogen, high-pressure gaseous storage technology to store hydrogen, and proton exchange membrane fuel cell technology to convert hydrogen energy into electrical energy.

[0106] The hydrogen production, storage, hydrogen filling, and fuel cell models are:

[0107]

[0108] SOE min ≤SOEt ≤SOE max

[0109] SOE 0 =SOE T

[0110]

[0111] In the formula, P t ele represents the input power of the electrolyzer, represents the hydrogen outflow of the electrolyzer, γ PtH represents the electro-hydrogen conversion factor, η ele represents the electrolyzer efficiency, represents the lower heating value of hydrogen, represents the hydrogen density, V t ele represents the amount of hydrogen produced by the electrolyzer within the t time slot, represents the total amount of hydrogen produced by the electrolyzer in a scheduling period T, SOE t 、SOE t-1 respectively represent the hydrogen storage states in the hydrogen storage tank within the time slots t and t - 1, represents the amount of hydrogen stored in the hydrogen storage tank within the time slot t, V tank represents the total amount of hydrogen that can be stored in the hydrogen storage tank under a certain pressure, V t ele 、V t FCEV 、V t FC respectively represent the amount of hydrogen produced by the electrolyzer, the hydrogen demand of the fuel cell vehicle, and the amount of hydrogen consumed by the fuel cell within the time slot t, SOE min 、SOE max are respectively the upper and lower limits of the hydrogen storage state, SOE 0 、SOE T respectively represent the amount of hydrogen in the hydrogen storage tank at the beginning and end of a scheduling period, represents the hydrogen demand of the j-th fuel cell vehicle within the time slot t, V t FCEV represents the hydrogen demand of all fuel cell vehicles within the time slot t, represents the total hydrogen demand of fuel cell vehicles in a scheduling period T, E(t, j) represents the binary variable for whether the fuel cell vehicle user chooses to refuel or not, F t fc represents the amount of hydrogen consumed by the fuel cell within the time slot t, k′ represents the conversion coefficient for converting the hydrogen flow rate from moles per hour to cubic meters per hour, P fcrepresents the power generated by the fuel cell consuming hydrogen within time slot t, η fc represents the efficiency of the fuel cell, and F is the Faraday constant.

[0112] S5. Determine the reference value of the hydrogen demand according to the daily hydrogen demand of fuel cell vehicles, and optimize the hydrogen production scheduling, specifically including:

[0113] The reference value of the hydrogen demand is:

[0114]

[0115] In the formula, represents the reference value of the hydrogen demand of fuel cell vehicles, T represents a scheduling period, and V t FCEV represents the hydrogen demand of fuel cell vehicles within time slot t, k 1 、k 2 、k 3 are the scheduling coefficients of hydrogen production under different electricity prices respectively.

[0116] Conduct hydrogen production scheduling according to the time-of-use electricity price. During the low electricity price period, produce hydrogen at no more than k 1 times the hydrogen demand reference value to store a large amount of hydrogen and achieve the goal of "filling the valley" for the power grid. At this time, the hydrogen is mainly used for fuel cell vehicles; during the flat electricity price period, produce hydrogen at no more than k 2 times the hydrogen demand reference value to store a small amount of hydrogen. At this time, the hydrogen is mainly used for fuel cell vehicles; during the high electricity price period, produce hydrogen at no more than k 3 times the hydrogen demand reference value and do not store hydrogen; or when the hydrogen is sufficient, use the FC2G technology to generate electricity to achieve the goal of "peak shaving" for the power grid. At this time, the hydrogen is mainly used for fuel cell vehicles and the power grid.

[0117] S6. Determine the objective function of the new energy vehicle integrated charging and hydrogen refueling station according to the charging and hydrogen refueling demands of new energy vehicle users, specifically including:

[0118] As the energy supply station for new energy vehicles, the integrated charging and hydrogen refueling station can, on the one hand, provide battery swapping services for electric vehicle users, and on the other hand, provide hydrogen refueling services for fuel cell vehicle users, and charge a certain fee to ensure its profitability. In addition, the battery swapping station and hydrogen station in the integrated charging and hydrogen refueling station can both feed back electric energy to the power grid, provide auxiliary services for the power system, and increase their own revenues at the same time.

[0119] To maximize the revenue of the new energy vehicle integrated charging and hydrogen refueling station, establish the objective function of the new energy vehicle integrated charging and hydrogen refueling station:

[0120]

[0121] Among them,

[0122]

[0123] In the formula, c 1 represents the battery swapping income of the swapping station, c 2 represents the charging cost of the swapping station, c 3 represents the discharging income of the swapping station, c 4 represents the charging and discharging loss cost of the swapping station, c 5 represents the battery processing cost of the swapping station, c 6 represents the hydrogen selling income of the hydrogen station, c 7 represents the hydrogen production cost of the hydrogen station, c 8 represents the fuel cell power generation income of the hydrogen station, T is the total optimization period, respectively represent the charging and discharging powers of the i-th battery within the time slot t, P t B2H represents the power provided by the swapping station to the hydrogen station, f t represents the electricity price, represents the hydrogen price sold to fuel cell vehicles, d is the battery loss cost coefficient, e is the battery processing cost coefficient, and S(t, i) represents the binary variable of whether the electric vehicle user chooses to swap batteries or not.

[0124] S7. Optimize and schedule the battery charging and discharging powers of the swapping station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the objective function and constraint conditions, specifically including:

[0125] Optimize the integrated charging station using CPLEX in the MATLAB environment according to the objective function and constraint conditions; the constraint conditions include the constraint conditions of the swapping station model, the constraint conditions of the hydrogen station model, and the state constraint conditions.

[0126] The constraint conditions of the swapping station model include the state of charge constraint of the battery, the state of health constraint of the battery, the battery charging power constraint, the battery discharging power constraint, the battery quantity constraint, the constraint of the swapping station providing electric energy to the hydrogen station, and the battery state constraint;

[0127] The constraint conditions of the swapping station model are:

[0128] 0 ≤ SOC s ≤ 100%

[0129] 0 ≤ SOC c ≤ 100%

[0130] 0 ≤ SOH s ≤ 100%

[0131] 0 ≤ SOHc ≤100%

[0132] 0 ≤ SOC s +P ch ·η·t / Cap ≤ 100%

[0133] 0 ≤ SOC s -P dis / η·t / Cap ≤ 100%

[0134]

[0135] N = b 1 +b 2 +b 3

[0136]

[0137] Wherein, P ch 、P dis respectively represent the charge and discharge power of the battery, η represents the charge and discharge efficiency, Cap represents the battery capacity, b 1 、b 2 、b 3 respectively represent the number of batteries in battery banks 1, 2, and 3, N represents the total number of batteries, respectively represent the maximum charge and discharge power of the battery, P t B2H,max represents the upper limit of the power provided by the battery swapping station to the hydrogen station, B 1 (t, i) represents the binary variable of the battery charging state in the battery swapping station, B 2 (t, i) represents the binary variable of the battery discharging state in the battery swapping station, represents the binary variable of the state of the battery swapping station providing electrical energy to the hydrogen station.

[0138] The constraint conditions of the hydrogen station model include the electrolyzer input power constraint, the fuel cell output power constraint, the electrolyzer hydrogen production amount constraint, the hydrogen consumption amount constraints of the fuel cell and the fuel cell vehicle, and the hydrogen storage tank capacity constraint;

[0139] The constraint conditions of the hydrogen station model are:

[0140]

[0141] V t ele ≥ 0

[0142] V t FC ≥ 0

[0143] V t FCEV ≥ 0

[0144] V t ele +SOE t-1 ·V tank ≥V t FCEV

[0145] V t ele +SOE t-1 ·V tank ≤V tank

[0146] Wherein, P ele,min , P ele,max respectively represent the upper and lower limits of the input power of the electrolyzer, and P FC,min , P FC,max respectively represent the upper and lower limits of the output power of the fuel cell, respectively represent the binary variables of the working states of the fuel cell and the electrolyzer.

[0147] The state constraint conditions include:

[0148] For the constraint formula that the same battery in the battery swapping station can only be in one of the states of charging, discharging or idle is as follows:

[0149] B 1 (t,i)+B 2 (t,i)≤1

[0150] For the constraint formula that the electrolyzer and the fuel cell in the hydrogen station cannot operate simultaneously is as follows:

[0151]

[0152] For the constraint formula that the battery swapping station can only supply electric energy to the hydrogen station when the electrolyzer is working is as follows:

[0153]

[0154] As Figure 3 shown, a scheduling system for an integrated electric-hydrogen energy charging station for new energy vehicles includes:

[0155] A data acquisition module 101, configured to acquire battery data information, battery swapping requirements of electric vehicles, and hydrogen charging requirements of fuel cell vehicles; the battery information includes the state of charge of the battery, the state of health of the battery, and the rated capacity of the battery.

[0156] A battery swapping cost calculation module 102, configured to determine the battery swapping cost according to the battery data information of the battery swapping station and the user adaptive response model.

[0157] The battery swapping cost calculation module 102 specifically includes:

[0158] The user adaptive response model is:

[0159] r i = w 1 ·SOC s (i) + w 2 ·SOH s (i) + w 3 ·R i

[0160] w 1 + w 2 + w 3 = 1

[0161] The battery swapping cost for an electric vehicle user is:

[0162] R i = a·(SOC s (i) - SOC c (i)) + b·(SOH s (i) - SOH c (i))

[0163] In the formula, R i represents the battery swapping cost of the i-th electric vehicle user, a and b respectively represent the cost coefficients of SOC and SOH before and after battery swapping, SOC c and SOC s respectively represent the state of charge of the battery before and after battery swapping, SOH c and SOH s respectively represent the state of health of the battery before and after battery swapping, r i represents the response value of the i-th electric vehicle, w 1 、w 2 、w 3 respectively represent the weight coefficients of SOC, SOH and swapping cost after battery swapping.

[0164] The battery allocation module 103 is used to optimize the allocation of batteries according to the battery optimization allocation strategy.

[0165] The battery allocation module 103 specifically includes:

[0166] Optimize the allocation of batteries according to the battery optimization allocation strategy:

[0167] l 2 ≤ SOH ≤ l 1

[0168] l 3 ≤ SOH < l 2

[0169] SOH < l 3

[0170] where l 1 、l 2 、l 3 respectively represent the boundaries of SOH classification.

[0171] The hydrogen station construction module 104 is used to determine hydrogen production, storage, filling, and fuel cell models according to the predicted hydrogen demand scale of the hydrogen station to optimize hydrogen production, storage, filling, and power generation.

[0172] The hydrogen station construction module 104 specifically includes:

[0173] Determine hydrogen production, storage, filling, and fuel cell models according to the following formula:

[0174]

[0175] SOE min ≤ SOE t ≤ SOE max

[0176] SOE 0 = SOE T

[0177]

[0178] where P t ele represents the input power of the electrolyzer, represents the hydrogen outflow from the electrolyzer, γ PtH represents the electro-hydrogen conversion factor, η ele represents the electrolyzer efficiency, represents the lower calorific value of hydrogen, represents the hydrogen density, V t ele represents the amount of hydrogen produced by the electrolyzer in the t time slot, represents the total amount of hydrogen produced by the electrolyzer in a scheduling period T, SOE t 、SOE t-1 respectively represent the hydrogen storage states in the hydrogen storage tank in time slots t and t - 1, represents the amount of hydrogen stored in the hydrogen storage tank in time slot t, V tank represents the total amount of hydrogen that can be stored in the hydrogen storage tank at a certain pressure, V t ele 、V t FCEV 、V t FCThey are respectively the amount of hydrogen generated by the electrolyzer, the hydrogen demand of the fuel cell vehicle, and the amount of hydrogen consumed by the fuel cell within time slot t, SOE min 、SOE max They are respectively the upper and lower limits of the hydrogen storage state, SOE 0 、SOE T They respectively represent the amount of hydrogen in the hydrogen storage tank at the beginning and end of a scheduling period represents the hydrogen demand of the j-th fuel cell vehicle within time slot t, V t FCEV represents the hydrogen demand of all fuel cell vehicles within time slot t represents the total hydrogen demand of fuel cell vehicles within a scheduling period T. E(t, j) represents the binary variable for whether the fuel cell vehicle user chooses to refuel with hydrogen, F t fc represents the amount of hydrogen consumed by the fuel cell within time slot t. k′ represents the conversion coefficient for converting the hydrogen flow rate from moles per hour to cubic meters per hour, P fc represents the power generated by the hydrogen consumed by the fuel cell within time slot t, η fc represents the efficiency of the fuel cell. F is the Faraday constant

[0179] The hydrogen demand reference value calculation module 105 is used to determine the reference value of the hydrogen demand according to the daily hydrogen demand of the fuel cell vehicle, so as to optimize the hydrogen production scheduling

[0180] The hydrogen demand reference value calculation module 105 specifically includes

[0181] Determine the reference value of the hydrogen demand according to the following formula

[0182]

[0183] In the formula represents the reference value of the hydrogen demand of the fuel cell vehicle. T represents a scheduling period, V t FCEV represents the hydrogen demand of the fuel cell vehicle within time slot t, k 1 、k 2 、k 3 They are respectively the scheduling coefficients for hydrogen production under different electricity prices

[0184] The objective function determination module 106 is used to determine the objective function of the new energy vehicle electric-hydrogen integrated charging station according to the charging energy demand of the new energy vehicle users; the new energy vehicle users include electric vehicle users and fuel cell vehicle users

[0185] The objective function determination module 106 specifically includes

[0186] Determine the objective function of the integrated electric-hydrogen charging station according to the following formula:

[0187]

[0188] where

[0189]

[0190] In the formula, c 1 represents the battery swapping income of the swapping station, c 2 represents the charging cost of the swapping station, c 3 represents the discharging income of the swapping station, c 4 represents the charging and discharging loss cost of the swapping station, c 5 represents the battery disposal cost of the swapping station, c 6 represents the hydrogen selling income of the hydrogen station, c 7 represents the hydrogen production cost of the hydrogen station, c 8 represents the fuel cell power generation income of the hydrogen station, T is the total optimization period, respectively represent the charging and discharging power of the i-th battery in time slot t, P t B2H represents the power provided by the swapping station to the hydrogen station, f t represents the electricity price, represents the hydrogen price sold to fuel cell vehicles, d is the battery loss cost coefficient, e is the battery disposal cost coefficient, and S(t, i) represents the binary variable of whether the electric vehicle user chooses to swap batteries or not.

[0191] The optimization module 107 is used to optimize the charging and discharging power of the batteries in the swapping station and the electrolyzer hydrogen production power and fuel cell power generation power in the hydrogen station of the integrated charging station according to the objective function and the constraint conditions; the constraint conditions include the state of charge constraint of the battery, the state of health constraint of the battery, the battery quantity constraint, the battery charging power constraint, the battery discharging power constraint, the constraint of the swapping station providing electric energy to the hydrogen station, the battery state constraint, the electrolyzer input power constraint, the fuel cell output power constraint, the electrolyzer hydrogen production quantity constraint, the hydrogen consumption quantity constraint of the fuel cell and the fuel cell vehicle, and the hydrogen storage tank capacity constraint.

[0192] The optimization module 107 specifically includes:

[0193] Optimize the integrated charging station using CPLEX in the MATLAB environment according to the objective function and the constraint conditions;

[0194] The constraint conditions of the swapping station model are:

[0195] 0 ≤ SOC s ≤ 100%

[0196] 0 ≤ State of Charge (SOC) c ≤ 100%

[0197] 0 ≤ State of Health (SOH) s ≤ 100%

[0198] 0 ≤ State of Health (SOH) c ≤ 100%

[0199] 0 ≤ State of Charge (SOC) s + P ch · η · t / Cap ≤ 100%

[0200] 0 ≤ State of Charge (SOC) s - P dis / η · t / Cap ≤ 100%

[0201] N = b 1 + b 2 + b 3

[0202]

[0203] Wherein, P ch 、P dis respectively represent the charge and discharge power of the battery, η represents the charge and discharge efficiency, Cap represents the battery capacity, b 1 、b 2 、b 3 respectively represent the number of batteries in battery banks 1, 2, and 3, N represents the total number of batteries, respectively represent the maximum charge and discharge power of the battery, P t B2H,max represents the upper limit of the power provided by the swapping station to the hydrogen station, B 1 (t, i) represents the binary variable of the charging state of the battery in the swapping station, B 2 (t, i) represents the binary variable of the discharging state of the battery in the swapping station, represents the binary variable of the state of the swapping station providing electrical energy to the hydrogen station.

[0204] The constraint conditions of the hydrogen station model are:

[0205]

[0206] V t ele ≥ 0

[0207] V t FCEV ≥ 0

[0208] V t FC ≥ 0

[0209] V t ele +SOE t-1 ·V tank ≥V t FCEV

[0210] V t ele +SOE t-1 ·V tank ≤V tank

[0211] In the formula, P ele,min 、P ele,max respectively represent the upper and lower limits of the input power of the electrolyzer, and P FC,min 、P FC,max respectively represent the upper and lower limits of the output power of the fuel cell, respectively represent the binary variables of the working states of the fuel cell and the electrolyzer.

[0212] The constraint formula for the same battery in the battery swapping station can only be in one of the charging, discharging or idle states is as follows:

[0213] B 1 (t,i)+B 2 (t,i)≤1

[0214] The constraint formula for the electrolyzer and the fuel cell in the hydrogen station cannot operate simultaneously is as follows:

[0215]

[0216] The constraint formula for the battery swapping station can only supply electric energy to the hydrogen station when the electrolyzer is working is as follows:

[0217]

[0218] In summary, for the scheduling method and system of the electric-hydrogen integrated charging station of a new energy vehicle of the present invention, while improving the revenue of the electric-hydrogen integrated charging station, it can effectively enhance the ability of the charging station to cut peaks and fill valleys in the power system.

Claims

1. A scheduling method for an integrated electric-hydrogen charging station of new energy vehicles, characterized in that: The integrated electric-hydrogen charging station of new energy vehicles distributes the electric energy provided by the power grid to the battery swapping station and the hydrogen station; the battery swapping station charges the battery with electric energy, manages and distributes the battery, a part of the batteries are used for the battery swapping service of electric vehicles, and the other part of the batteries are regarded as a battery energy storage station, and the electric energy is sent back to the power grid or the hydrogen station; The hydrogen station uses electric energy for hydrogen production, hydrogen storage and hydrogen filling. A part of the hydrogen in the hydrogen storage tank is used to meet the hydrogen filling needs of fuel cell vehicles, and the other part is used for the fuel cell to generate electricity and send the electric energy back to the power grid; The scheduling method specifically includes the following steps: Obtain the battery data information, battery swapping requirements of electric vehicles and hydrogen filling requirements of fuel cell vehicles; Determine the battery swapping cost of electric vehicle users according to the battery data information of the battery swapping station and the user adaptive response model; Optimize the distribution of batteries according to the battery optimization distribution strategy; According to the prediction of the hydrogen demand scale of the hydrogen station, determine the hydrogen production, hydrogen storage, hydrogen filling, fuel cell models to optimize hydrogen production, storage, hydrogen filling and power generation; According to the daily hydrogen demand of fuel cell vehicles, determine the benchmark value of the hydrogen demand and optimize the production scheduling of hydrogen; Determine the objective function of the integrated electric-hydrogen charging station of new energy vehicles according to the charging and energy requirements of new energy vehicle users; Optimize the scheduling of the battery charge and discharge power of the battery swapping station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the objective function and constraint conditions; The hydrogen production, hydrogen storage, hydrogen filling, fuel cell models are: SOE min ≤ SOE t ≤ SOE max SOE 0 = SOE T Wherein, P t ele represents the input power of the electrolyzer, represents the hydrogen flow rate out of the electrolyzer, γ PtH represents the electro-hydrogen conversion factor, η ele represents the electrolyzer efficiency, represents the lower heating value of hydrogen, represents the hydrogen density, V t ele represents the amount of hydrogen produced by the electrolyzer within the t time slot, represents the total amount of hydrogen produced by the electrolyzer within a scheduling period T, SOE t 、SOE t-1 respectively represent the hydrogen storage states in the hydrogen storage tank within the time slots t and t - 1, represents the amount of hydrogen stored in the hydrogen storage tank within the time slot t, V tank represents the total amount of hydrogen that can be stored in the hydrogen storage tank under a certain pressure, V t ele 、V t FCEV 、V t FC respectively represent the amount of hydrogen produced by the electrolyzer, the hydrogen demand of the fuel cell vehicle, and the amount of hydrogen consumed by the fuel cell within the time slot t, SOE min 、SOE max respectively are the upper and lower limits of the hydrogen storage state, SOE 0 、SOE T respectively represent the amount of hydrogen in the hydrogen storage tank at the beginning and end of a scheduling period, represents the hydrogen demand of the jth fuel cell vehicle within the time slot t, V t FCEV represents the hydrogen demand of all fuel cell vehicles within the time slot t, represents the total hydrogen demand of fuel cell vehicles within a scheduling period T, E(t, j) represents the binary variable for whether the fuel cell vehicle user chooses to refuel or not, F t fc represents the amount of hydrogen consumed by the fuel cell within the time slot t, k′ represents the conversion coefficient for converting the hydrogen flow rate from moles per hour to cubic meters per hour, P fc represents the power generated by the hydrogen consumed by the fuel cell within the time slot t, η fc represents the efficiency of the fuel cell, and F is the Faraday constant.

2. The scheduling method for an integrated electric-hydrogen charging station of new energy vehicles according to claim 1, characterized in that: The battery data information includes the state of charge of the battery, the state of health of the battery and the rated capacity of the battery.

3. The scheduling method for an integrated electric-hydrogen charging station of new energy vehicles according to claim 1, characterized in that: The user adaptive response model is: r i = w 1 ·SOC s (i) + w 2 ·SOH s (i) + w 3 ·R i w 1 +w 2 +w 3 = 1 The battery swapping cost of electric vehicle users is: R i = a·(SOC s (i) - SOC c (i)) + b·(SOH s (i) - SOH c (i)) where, R i represents the battery swapping cost of the i-th electric vehicle user, a and b respectively represent the cost coefficients of SOC and SOH before and after battery swapping, SOC c (i) and SOC s (i) respectively represent the state of charge of the battery before and after battery swapping of the i-th electric vehicle user, SOH c (i) and SOH s (i) respectively represent the state of health of the battery before and after battery swapping of the i-th electric vehicle user, r i represents the response value of the i-th electric vehicle, w 1 , w 2 , w 3 respectively represent the weight coefficients of SOC, SOH and swapping cost after battery swapping.

4. The scheduling method for an integrated electric-hydrogen charging station of new energy vehicles according to claim 1, characterized in that: Optimally distribute the batteries according to the battery optimization distribution strategy, specifically: l 2 ≤SOH≤l 1 l 3 ≤SOH<l 2 SOH < l 3 where l 1 , l 2 , and l 3 respectively represent the boundaries of the SOH classification.

5. The scheduling method for an integrated electric-hydrogen charging station of new energy vehicles according to claim 1, characterized in that: The benchmark value of the hydrogen demand is: In the formula, represents the reference value of the hydrogen demand of the fuel cell vehicle, T represents a scheduling period, and V t FCEV represents the hydrogen demand of the fuel cell vehicle in time slot t, and k 1 , k 2 , k 3 are the scheduling coefficients of hydrogen production under different electricity prices respectively.

6. The scheduling method for an integrated electric-hydrogen charging station of new energy vehicles according to claim 1, characterized in that: The objective function is: Wherein, Wherein, c 1 represents the battery swapping revenue of the battery swapping station, c 2 represents the charging cost of the battery swapping station, c 3 represents the discharging revenue of the battery swapping station, c 4 represents the charging and discharging loss cost of the battery swapping station, c 5 represents the battery processing cost of the battery swapping station, c 6 represents the hydrogen selling revenue of the hydrogen station, c 7 represents the hydrogen production cost of the hydrogen station, c 8 represents the fuel cell power generation revenue of the hydrogen station, T is the total optimization period, respectively represent the charging and discharging powers of the i-th battery within the time slot t, P t B2H represents the power provided by the battery swapping station to the hydrogen station, f t represents the electricity price, represents the hydrogen price sold to fuel cell vehicles, d is the battery loss cost coefficient, e is the battery processing cost coefficient, and S(t, i) represents the binary variable of whether the electric vehicle user chooses to swap the battery or not.

7. The scheduling method for an integrated electric-hydrogen charging station of new energy vehicles according to claim 1, characterized in that: The constraint conditions include the constraint conditions of the battery swapping station model, the constraint conditions of the hydrogen station model and the state constraint conditions; The constraint conditions of the battery swapping station model are: 0 ≤ State of Charge (SOC) s ≤ 100% 0 ≤ State of Charge c ≤ 100% 0 ≤ SOH s ≤ 100% 0 ≤ SOH c ≤ 100% 0 ≤ State of Charge (SOC) s +P ch · η · t / Cap ≤ 100% 0 ≤ State of Charge (SOC) s -P dis / η · t / Cap ≤ 100% N = b 1 + b 2 + b 3 Wherein, P ch , P dis respectively represent the charging and discharging power of the battery, η represents the charging and discharging efficiency, Cap represents the battery capacity, b 1 , b 2 , b 3 respectively represent the number of batteries in battery banks 1, 2, and 3, N represents the total number of batteries, respectively represent the maximum charging and discharging power of the battery, P t B2H,max represents the upper limit of the power provided by the swapping station to the hydrogen station, B 1 (t, i) represents the binary variable of the charging state of the battery in the swapping station, B 2 (t, i) represents the binary variable of the discharging state of the battery in the swapping station, represents the binary variable of the state of the swapping station providing electrical energy to the hydrogen station; The constraint conditions of the hydrogen station model are: V t ele ≥0 V t FC ≥0 V t FCEV ≥0 V t ele +SOE t-1 ·V tank ≥V t FCEV V t ele +SOE t-1 ·V tank ≤V tank Wherein, P ele,min and P ele,max respectively represent the upper and lower limits of the input power of the electrolyzer, P FC,min and P FC,max respectively represent the upper and lower limits of the output power of the fuel cell, respectively represent the binary variables of the working states of the fuel cell and the electrolyzer; The state constraint conditions include: For the constraint formula that the same battery in the battery swapping station can only be in one of the states of charging, discharging or idle is as follows: B 1 (t,i)+B 2 (t,i) ≤ 1 For the constraint formula that the electrolyzer and the fuel cell in the hydrogen station cannot operate simultaneously is as follows: The constraint formula for the power exchange station to supply electrical energy to the hydrogen station only when the electrolyzer is working is as follows:

8. A system for scheduling a new energy vehicle electric-hydrogen integrated charging station using the scheduling method according to any one of claims 1 to 7, characterized in that: It includes: A data acquisition module (101) for acquiring battery data information, battery replacement requirements of electric vehicles, and hydrogen charging requirements of fuel cell vehicles; A battery replacement cost calculation module (102) for determining the battery replacement cost according to the battery data information of the power exchange station and the user adaptive response model; A battery allocation module (103) for optimizing the allocation of batteries according to the battery optimization allocation strategy; A hydrogen station construction module (104) for predicting the hydrogen demand scale of the hydrogen station, determining the hydrogen production, storage, hydrogen charging, and fuel cell models to optimize hydrogen production, storage, hydrogen charging, and power generation; A hydrogen demand benchmark value calculation module (105) for determining the benchmark value of hydrogen demand according to the daily hydrogen demand of fuel cell vehicles and optimizing the production scheduling of hydrogen; An objective function determination module (106) for determining the objective function of the new energy vehicle electric-hydrogen integrated charging station according to the charging energy requirements of new energy vehicle users; An optimization module (107) for optimizing the scheduling of the battery charging and discharging power of the power exchange station and the electrolyzer hydrogen production power and fuel cell power generation power of the hydrogen station according to the objective function and constraint conditions.

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