Coordinated operation method and system of hybrid energy storage system based on battery and hydrogen energy storage

Abstract

The present invention discloses a method and system for coordinated operation of a hybrid energy storage system based on batteries and hydrogen energy storage, and relates to the technical field of coordinated control of hybrid energy storage systems. The energy storage control instruction generation module of the present invention adopts a power distribution model to control energy storage power distribution data of a battery power supply unit and a hydrogen energy storage unit, and adds a demand side daily dynamic adjustment factor to the power distribution model. The demand side daily dynamic adjustment factor is generated based on the total power demand data of the next power supply cycle of the demand side and the current temperature data, thereby realizing dynamic energy storage adjustment based on the demand side and the ambient temperature, realizing more efficient energy storage, and making the energy storage system's power supply to the demand side more stable; based on the energy storage power distribution data, an energy storage power distribution dynamic control instruction is generated and transmitted to an energy storage controller; the energy storage controller can control the battery power supply unit and the hydrogen energy storage unit to store energy according to the energy storage power distribution dynamic control instruction by controlling the energy storage charging and discharging module.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of hybrid energy storage systems, and in particular to a coordinated operation method and system for a hybrid energy storage system based on battery and hydrogen energy storage. Background Art

[0002] A hybrid energy storage system (HESS) refers to the combination of two or more different types of energy storage technologies to achieve complementary advantages of various energy storage technologies and provide more efficient, stable and flexible energy storage solutions.

[0003] In hybrid energy storage systems, the coordinated operation and control of batteries and hydrogen energy storage is crucial to improving system performance and reducing power losses. The coordinated operation and control of hybrid energy storage systems of batteries and hydrogen energy storage is the key to achieving an efficient, reliable, economical and environmentally friendly power system, and is of great significance to promoting the transformation of energy structure and sustainable development.

[0004] However, the hybrid energy storage system in the prior art has the following defects:

[0005] Lack of effective coordinated control strategies: Existing methods often focus on the optimization of a single energy storage system and fail to fully consider the interaction and complementary advantages between batteries and hydrogen energy storage.

[0006] Ignoring the impact of power fluctuations on the system: During the power allocation process, the impact of power fluctuations on the life and efficiency of the energy storage system is not fully considered.

[0007] The dynamic characteristics of hydrogen energy storage and the characteristics of demand-side power supply demand are rarely considered: Hydrogen energy storage systems have slow dynamic response characteristics, and existing methods fail to fully consider this, resulting in poor control effects. Summary of the invention

[0008] In order to solve the above technical problems, the present invention provides a method and system for coordinated operation of a hybrid energy storage system based on batteries and hydrogen energy storage. The following technical solutions are adopted:

[0009] A coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage includes the following steps:

[0010] Step 1: The data acquisition module collects energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit and the hydrogen energy storage unit respectively and transmits them to the energy storage data analysis module respectively;

[0011] Step 2: The energy storage data analysis module analyzes the energy storage data at a time interval of the data analysis cycle, and the energy storage data includes the total power to be stored, the current energy storage data of the battery power supply unit and the hydrogen energy storage unit, the total energy storage power data and the real-time energy storage efficiency data; analyzes the power request data of the next power supply cycle on the demand side to obtain the total power demand data of the next power supply cycle on the demand side; analyzes the environmental data to obtain the current temperature data; the energy storage data analysis module transmits the analyzed data to the energy storage control instruction generation module;

[0012] Step 3: The energy storage control instruction generation module takes minimizing the total cost as the optimization goal, and uses the power distribution model to control the energy storage power distribution data of the battery power supply unit and the hydrogen energy storage unit. The power distribution model adds the demand side daily dynamic adjustment factor, which is generated based on the total power demand data of the next power supply cycle on the demand side and the current temperature data; based on the energy storage power distribution data, the energy storage power distribution dynamic control instruction is generated and transmitted to the energy storage controller;

[0013] Step 4: The energy storage controller controls the energy storage charging and discharging module to control the battery power supply unit and the hydrogen energy storage unit to store energy according to the energy storage power distribution dynamic control instruction.

[0014] By adopting the above technical solution, the data acquisition module is first used to respectively collect the energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit and the hydrogen energy storage unit. The data acquisition module can read the energy storage data of the battery power supply unit and the hydrogen energy storage unit control unit, including the previous energy storage data, the total energy storage power data and the real-time energy storage efficiency data. The environmental data can be realized based on the temperature sensor, etc. The power request data of the next power supply cycle on the demand side can be collected based on communication with the demand side, or a power supply demand active application module can be set on the demand side, and the power demand for the next power supply cycle can be manually entered in the power supply demand active application module;

[0015] After the above data are collected according to the data analysis cycle, they are transmitted to the energy storage data analysis module. The data analysis cycle can be daily or weekly. The energy storage data analysis module parses the above data and transmits them to the energy storage control instruction generation module. The energy storage control instruction generation module can be an intelligent body such as a computer or a tablet computer that can be equipped with a power distribution model. The energy storage control instruction generation module uses the power distribution model to control the energy storage power distribution data of the battery power supply unit and the hydrogen energy storage unit, and adds a demand side daily dynamic adjustment factor to the power distribution model. The demand side daily dynamic adjustment factor is generated based on the total power demand data of the next power supply cycle on the demand side and the current temperature data, thereby realizing dynamic energy storage adjustment based on the demand side and ambient temperature, achieving more efficient energy storage, and making the energy storage system's power supply to the demand side more stable; based on the energy storage power distribution data, a dynamic control instruction for energy storage power distribution is generated and transmitted to the energy storage controller;

[0016] The energy storage controller can control the battery power supply unit and the hydrogen energy storage unit to store energy by controlling the energy storage charging and discharging module according to the energy storage power distribution dynamic control instructions.

[0017] Achieve more efficient and stable coordinated control of hybrid energy storage systems based on batteries and hydrogen storage.

[0018] Optionally, in step 2, the total power to be stored is analyzed as P_total, the current energy storage data of the battery power supply unit is BAT_a, the total energy storage data is BAT_F, and the real-time energy storage efficiency data is BAT_Et; the current energy storage data of the hydrogen energy storage unit is H_a, the total energy storage data is H_F, and the real-time energy storage efficiency is H_Et;

[0019] The total power demand data of the next power supply cycle on the demand side is PO_total, and the current temperature data is T t .

[0020] Optionally, in step 4, the power allocation model allocates power based on the following power:

[0021] f(t)=min∑{δ_t×(C_Loss_BAT×L_BAT_t)+C_Loss_H×L_H_t};

[0022] P_total=P_total_t+P_H_t;

[0023] Where δ_t is the daily dynamic adjustment factor; L_BAT_t is the power loss rate of the battery power supply unit at time t, and L_H_t is the power loss rate of the hydrogen energy storage unit at time t;

[0024] C_Loss_BAT is the power loss cost coefficient of the battery power supply unit at time t, and C_Loss_H is the power loss cost coefficient of the hydrogen energy storage unit at time t;

[0025] P_total_t is the energy storage allocation power of the battery power supply unit at time t, and P_H_t is the energy storage allocation power of the hydrogen energy storage unit at time t.

[0026] Optionally, the daily dynamic adjustment factor is calculated as:

[0027] δ_t=f(PO_total,T t );

[0028] PO_total is the total power demand data of the next power supply cycle on the demand side, T t is the current temperature data;

[0029] f is the daily dynamic adjustment factor generation function, which determines the daily dynamic adjustment factor based on the total power demand data of the next power supply cycle on the demand side and the current temperature data.

[0030] Optionally, the daily dynamic adjustment factor generation function is a linear, nonlinear, or machine learning model based on historical data.

[0031] By adopting the above technical solution, the daily dynamic adjustment factor δ_t is defined, and the daily dynamic adjustment factor δ_t takes into account the following two main factors:

[0032] Demand side daily power demand PO_total: represents the power demand at time t, which can be predicted or actual data; current temperature T t : represents the ambient temperature at time t, which may affect the efficiency of the energy storage system.

[0033] Where f is a daily dynamic adjustment factor generation function, which determines the adjustment factor based on power demand and temperature. This function can be linear, nonlinear, or a machine learning model based on historical data.

[0034] Optionally, a demand-side total power threshold POmax is set. When PO_total<POmax, δ_t takes the value 1.

[0035] Optionally, the calculation method of POmax is: when the battery power supply unit and H_a are both in a storage state greater than 90%, POmax is calculated using the following formula;

[0036] POmax=θ a (BAT_F+H_F);

[0037] When the battery power supply unit and H_a are hydrogen energy storage units in other energy storage states, the following formula is used to calculate POmax;

[0038] POmax=θ b (BAT_a+H_a);

[0039] where θ a is the dynamic adjustment intervention ratio factor of the full-state day, which is adjustable between 0.4-0.5. BAT_F and H_F are the total energy storage data of the battery power supply unit and the hydrogen energy storage unit respectively;

[0040] θ b It is the daily dynamic adjustment intervention proportional factor of other energy storage states, which is adjustable between 0.5-0.6. BAT_a is the current energy storage data of the battery power supply unit, and H_a is the current energy storage data of the hydrogen energy storage unit.

[0041] By adopting the above technical solution, the total power threshold POmax on the demand side is to reduce the complexity of system control under low demand conditions and increase the stability of energy storage system operation. The total power threshold POmax on the demand side is mainly considered based on two states:

[0042] First, in the full energy storage state (generally, more than 90% energy storage is considered to be full energy storage state), when the energy storage state is full, if the daily power supply demand on the demand side is lower than 0.4-0.5 times the total energy storage data, then δ_t is set to 1, which reduces the complexity of system control under low demand conditions and increases the stability of energy storage system operation;

[0043] Second, when the energy storage is not full, it is necessary to dynamically adjust POmax based on the current energy storage data to avoid the daily dynamic adjustment factor not intervening in the low energy storage state, thereby reducing the system operation efficiency.

[0044] A coordinated operation system of a hybrid energy storage system based on battery and hydrogen energy storage is used to implement a coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage. The coordinated operation system of the hybrid energy storage system includes a data acquisition module, an energy storage data analysis module based on a data analysis chip, an energy storage control instruction generation module based on an instruction chip, an energy storage controller and an energy storage charging and discharging module. The data acquisition module includes an energy storage power supply data collector and an ambient temperature collector. The energy storage power supply data collector is respectively communicated with the battery power supply unit, the hydrogen energy storage unit and the demand side to respectively collect energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit and the hydrogen energy storage unit. The energy storage data analysis module is communicated with the data acquisition module, the energy storage control instruction generation module is communicated with the energy storage data analysis module, the energy storage controller is communicated with the energy storage control instruction generation module, the energy storage charging and discharging module controls the energy storage actions of the battery power supply unit and the hydrogen energy storage unit respectively, and the energy storage controller controls the execution actions of the energy storage charging and discharging module.

[0045] Optionally, a central control server is also included, which is respectively communicated with the data acquisition module, the energy storage data analysis module based on the data analysis chip, the energy storage control instruction generation module based on the instruction chip, the energy storage controller and the energy storage charging and discharging module to collect data and display it.

[0046] In summary, the present invention includes at least one of the following beneficial technical effects:

[0047] The present invention can provide a method and system for coordinated operation of a hybrid energy storage system based on batteries and hydrogen energy storage. The data acquisition module is first used to respectively collect energy storage data, environmental data and power request data of the next power supply cycle on the demand side of the battery power supply unit and the hydrogen energy storage unit. The energy storage data analysis module parses the above-mentioned various data and transmits them to the energy storage control instruction generation module. The energy storage control instruction generation module adopts a power distribution model to control the energy storage power distribution data of the battery power supply unit and the hydrogen energy storage unit, and adds a demand side daily dynamic adjustment factor to the power distribution model. The demand side daily dynamic adjustment factor is generated based on the total power demand data of the next power supply cycle on the demand side and the current temperature data, thereby realizing dynamic energy storage adjustment based on the demand side and the ambient temperature, realizing more efficient energy storage, and making the energy storage system's power supply to the demand side more stable; based on the energy storage power distribution data, a dynamic control instruction for energy storage power distribution is generated and transmitted to the energy storage controller;

[0048] The energy storage controller can control the battery power supply unit and the hydrogen energy storage unit to store energy by controlling the energy storage charging and discharging module according to the energy storage power distribution dynamic control instructions.

[0049] Achieve more efficient and stable coordinated control of hybrid energy storage systems based on batteries and hydrogen storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of a coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage according to the present invention;

[0051] Figure 2 It is a schematic diagram of the component connection principle of the coordinated operation system of the hybrid energy storage system based on battery and hydrogen energy storage of the present invention.

[0052] Explanation of the accompanying drawings: 1. Battery power supply unit; 2. Hydrogen energy storage unit; 3. Data acquisition module; 31. Energy storage power supply data collector; 32. Ambient temperature collector; 4. Energy storage data analysis module; 5. Energy storage control instruction generation module; 6. Energy storage controller; 7. Energy storage charging and discharging module; 100. Demand side. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0054] The embodiment of the present invention discloses a method and system for coordinated operation of a hybrid energy storage system based on battery and hydrogen energy storage.

[0055] Reference Figure 1 and Figure 2 , a coordinated operation method of a hybrid energy storage system based on batteries and hydrogen energy storage, comprising the following steps:

[0056] Step 1, the data acquisition module 3 collects the energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit 1 and the hydrogen energy storage unit 2 respectively, and transmits them to the energy storage data analysis module 4;

[0057] Step 2, the energy storage data analysis module 4 analyzes the energy storage data at a time interval of the data analysis cycle, the energy storage data including the total power to be stored, the current energy storage data of the battery power supply unit 1 and the hydrogen energy storage unit 2, the total energy storage power data and the real-time energy storage efficiency data; analyzes the power request data of the next power supply cycle on the demand side to obtain the total power demand data of the next power supply cycle on the demand side; analyzes the environmental data to obtain the current temperature data; the energy storage data analysis module 4 transmits the analyzed data to the energy storage control instruction generation module 5;

[0058] Step 3, the energy storage control instruction generation module 5 takes minimizing the total cost as the optimization goal, adopts the power distribution model to control the energy storage power distribution data of the battery power supply unit 1 and the hydrogen energy storage unit 2, and adds the demand side daily dynamic adjustment factor to the power distribution model. The demand side daily dynamic adjustment factor is generated based on the total power demand data of the next power supply cycle on the demand side and the current temperature data; based on the energy storage power distribution data, an energy storage power distribution dynamic control instruction is generated and transmitted to the energy storage controller 6;

[0059] Step 4, the energy storage controller 6 controls the energy storage charging and discharging module 7 to control the battery power supply unit 1 and the hydrogen energy storage unit 2 to store energy according to the energy storage power distribution dynamic control instruction.

[0060] First, use the data acquisition module 3 to respectively collect the energy storage data, environmental data and power request data of the next power supply cycle on the demand side of the battery power supply unit 1 and the hydrogen energy storage unit 2. The data acquisition module 3 can read the energy storage data of the control unit of the battery power supply unit 1 and the hydrogen energy storage unit 2, including the previous energy storage data, the total energy storage power data and the real-time energy storage efficiency data. The environmental data can be realized based on the temperature sensor, etc. The power request data of the next power supply cycle on the demand side can be collected based on communication with the demand side 100, or a power supply demand active application module can be set on the demand side 100, and the power demand for the next power supply cycle can be manually entered in the power supply demand active application module;

[0061] After the above data are collected according to the data analysis cycle, they are transmitted to the energy storage data analysis module 4. The data analysis cycle can be daily or weekly. The energy storage data analysis module 4 parses the above data and transmits them to the energy storage control instruction generation module 5. The energy storage control instruction generation module 5 can be an intelligent body such as a computer or a tablet computer that can be equipped with a power distribution model. The energy storage control instruction generation module 5 uses the power distribution model to control the energy storage power distribution data of the battery power supply unit 1 and the hydrogen energy storage unit 2, and adds a demand side daily dynamic adjustment factor to the power distribution model. The demand side daily dynamic adjustment factor is generated based on the total power demand data of the next power supply cycle on the demand side and the current temperature data, thereby realizing dynamic energy storage adjustment based on the demand side and the ambient temperature, achieving more efficient energy storage, and making the energy storage system's power supply to the demand side more stable; based on the energy storage power distribution data, a dynamic control instruction for energy storage power distribution is generated and transmitted to the energy storage controller 6;

[0062] The energy storage controller 6 can control the energy storage charging and discharging module 7 to control the battery power supply unit 1 and the hydrogen energy storage unit 2 to store energy according to the energy storage power distribution dynamic control instruction.

[0063] Achieve more efficient and stable coordinated control of hybrid energy storage systems based on batteries and hydrogen storage.

[0064] In step 2, the total power to be stored is analyzed to be P_total, the current energy storage data of the battery power supply unit 1 is BAT_a, the total energy storage data is BAT_F, and the real-time energy storage efficiency data is BAT_Et; the current energy storage data of the hydrogen energy storage unit 2 is H_a, the total energy storage data is H_F, and the real-time energy storage efficiency is H_Et;

[0065] The total power demand data of the next power supply cycle on the demand side is PO_total, and the current temperature data is T t .

[0066] In step 4, the power allocation model allocates power based on the following:

[0067] f(t)=minΣ{δ_t×(C_Loss_BAT×L_BAT_t)+C_Loss_H×L_H_t};

[0068] P_total=P_total_t+P_H_t;

[0069] Where δ_t is the daily dynamic adjustment factor; L_BAT_t is the power loss rate of the battery power supply unit 1 at time t, and L_H_t is the power loss rate of the hydrogen energy storage unit 2 at time t;

[0070] C_Loss_BAT is the power loss cost coefficient of the battery power supply unit 1 at time t, and C_Loss_H is the power loss cost coefficient of the hydrogen energy storage unit 2 at time t;

[0071] P_total_t is the energy storage allocation power of the battery power supply unit 1 at time t, and P_H_t is the energy storage allocation power of the hydrogen energy storage unit 2 at time t.

[0072] The calculation formula for the daily dynamic adjustment factor is:

[0073] δ_t=f(PO_total,T t );

[0074] PO_total is the total power demand data of the next power supply cycle on the demand side, T t is the current temperature data;

[0075] f is the daily dynamic adjustment factor generation function, which determines the daily dynamic adjustment factor based on the total power demand data of the next power supply cycle on the demand side and the current temperature data.

[0076] The daily dynamic adjustment factor generation function is a linear, nonlinear or machine learning model based on historical data.

[0077] Define the daily dynamic adjustment factor δ_t, which takes into account the following two main factors:

[0078] Demand side daily power demand PO_total: represents the power demand at time t, which can be predicted or actual data; current temperature T t : represents the ambient temperature at time t, which may affect the efficiency of the energy storage system.

[0079] Where f is a daily dynamic adjustment factor generation function, which determines the adjustment factor based on power demand and temperature. This function can be linear, nonlinear, or a machine learning model based on historical data.

[0080] Set the total power threshold POmax on the demand side. When PO_total<POmax, δ_t takes the value 1.

[0081] The calculation method of POmax is: when the battery power supply unit 1 and H_a is the hydrogen energy storage unit 2 are both in a storage state greater than 90%, POmax is calculated using the following formula;

[0082] POmax=θ a (BAT_F+H_F);

[0083] When the battery power supply unit 1 and H_a are hydrogen energy storage units 2 in other energy storage states, the following formula is used to calculate POmax;

[0084] POmax=θ b (BAT_a+H_a);

[0085] where θ a is the dynamic adjustment intervention proportional factor of the full-state day, which is adjustable between 0.4 and 0.5. BAT_F and H_F are the total energy storage data of the battery power supply unit 1 and the hydrogen energy storage unit 2 respectively;

[0086] θ b It is the daily dynamic adjustment intervention proportional factor of other energy storage states, which is adjustable between 0.5-0.6. BAT_a is the current energy storage data of battery power supply unit 1, and H_a is the current energy storage data of hydrogen energy storage unit 2.

[0087] The total power threshold POmax on the demand side is to reduce the complexity of system control under low demand conditions and increase the stability of energy storage system operation. The total power threshold POmax on the demand side is mainly considered based on two states:

[0088] First, in the full energy storage state (generally, more than 90% energy storage is considered to be full energy storage state), when the energy storage state is full, if the daily power supply demand on the demand side is lower than 0.4-0.5 times the total energy storage data, then δ_t is set to 1, which reduces the complexity of system control under low demand conditions and increases the stability of energy storage system operation;

[0089] Second, when the energy storage is not full, it is necessary to dynamically adjust POmax based on the current energy storage data to avoid the daily dynamic adjustment factor not intervening in the low energy storage state, thereby reducing the system operation efficiency.

[0090] A hybrid energy storage system coordinated operation system based on battery and hydrogen energy storage is used to implement a hybrid energy storage system coordinated operation method based on battery and hydrogen energy storage. The hybrid energy storage system coordinated operation system includes a data acquisition module 3, an energy storage data analysis module 4 based on a data analysis chip, an energy storage control instruction generation module 5 based on an instruction chip, an energy storage controller 6 and an energy storage charging and discharging module 7. The data acquisition module 3 includes an energy storage power supply data collector 31 and an ambient temperature collector 32. The energy storage power supply data collector 31 is respectively connected to the battery power supply unit 1, the hydrogen energy storage unit 2 and The demand side 100 is connected in communication, and the energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit 1 and the hydrogen energy storage unit 2 are respectively collected. The energy storage data analysis module 4 is connected in communication with the data collection module 3, the energy storage control instruction generation module 5 is connected in communication with the energy storage data analysis module 4, the energy storage controller 6 is connected in communication with the energy storage control instruction generation module 5, the energy storage charging and discharging module 7 controls the energy storage actions of the battery power supply unit 1 and the hydrogen energy storage unit 2 respectively, and the energy storage controller 6 controls the execution actions of the energy storage charging and discharging module 7.

[0091] It also includes a central control server 8, which is respectively connected to the data acquisition module 3, the energy storage data analysis module 4 based on the data analysis chip, the energy storage control instruction generation module 5 based on the instruction chip, the energy storage controller 6 and the energy storage charging and discharging module 7 to communicate and collect data and display it.

[0092] The following is a specific case study to illustrate the implementation principle of generating daily dynamic adjustment factors based on the coordinated operation method of a hybrid energy storage system with batteries and hydrogen energy storage:

[0093] The daily dynamic adjustment factor generation function uses the following sample code to generate the daily dynamic adjustment factor δ_t:

[0094] Import numpy as np

[0095] #Assume that we have a predicted demand-side daily power demand and a predicted ambient temperature

[0096] #These data need to be obtained from actual data or prediction models

[0097] demand_data = np.array([...]) # Daily power demand data on the demand side

[0098] temperature_data = np.array([...]) #environmental temperature data

[0099] #Define the generation function of the daily dynamic adjustment factor

[0100] Def generate_daily_dynamic_adjustment_factor(demand_data,temperature_data):

[0101] delta_data = []

[0102] For demand,temp in zip(demand_data,temperature_data):

[0103] #Or consider historical data and seasonal changes

[0104] delta=demand*0.01+temp*0.005#This is a sample formula

[0105] delta_data.append(delta)

[0106] returnnp.array(delta_data)

[0107] #Sample data

[0108] demand_data = np.array([1000,1200,900,1500,1100,1300,1400,1000,1200,900]) #Unit: kWh

[0109] temperature_data = np.array([25,26,24,28,27,29,30,25,26,24]) #Unit: Celsius

[0110] #Generate daily dynamic adjustment factor

[0111] δ_t=generate_daily_dynamic_adjustment_factor(demand_data,temperature_data)

[0112] # Output adjustment factor data

[0113] print(δ_t).

[0114] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for coordinated operation of a hybrid energy storage system based on batteries and hydrogen energy storage, characterized in that: The following steps are involved: Step 1, the data acquisition module (3) respectively collects energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit (1) and the hydrogen energy storage unit (2), and transmits them to the energy storage data analysis module (4); Step 2, the energy storage data analysis module (4) analyzes the energy storage data at a data analysis period as a time interval, the energy storage data including the total power to be stored, the current energy storage data of the battery power supply unit (1) and the hydrogen energy storage unit (2), the total energy storage power data and the real-time energy storage efficiency data; analyzes the power request data of the next power supply cycle on the demand side to obtain the total power demand data of the next power supply cycle on the demand side; analyzes the environmental data to obtain the current temperature data; the energy storage data analysis module (4) transmits the analyzed data to the energy storage control instruction generation module (5); Step 3, the energy storage control instruction generation module (5) takes minimizing the total cost as the optimization goal, uses the power distribution model to control the energy storage power distribution data of the battery power supply unit (1) and the hydrogen energy storage unit (2), and adds the demand side daily dynamic adjustment factor to the power distribution model. The demand side daily dynamic adjustment factor is generated based on the total power demand data of the next power supply cycle of the demand side and the current temperature data; based on the energy storage power distribution data, an energy storage power distribution dynamic control instruction is generated and transmitted to the energy storage controller (6); Step 4, the energy storage controller (6) controls the energy storage charging and discharging module (7) to control the battery power supply unit (1) and the hydrogen energy storage unit (2) to store energy according to the energy storage power distribution dynamic control instruction; In step 4, the power allocation model allocates power based on the following formula: ; ; in This is the daily dynamic adjustment factor; is the power loss rate of the battery power supply unit (1) at time t, is the power loss rate of the hydrogen energy storage unit (2) at time t; is the power loss cost coefficient of the battery power supply unit (1) at time t, is the power loss cost coefficient of the hydrogen energy storage unit (2) at time t; is the energy storage distribution power of the battery power supply unit (1) at time t, is the energy storage allocation power of the hydrogen energy storage unit (2) at time t; is the total power to be stored.

2. The coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage according to claim 1, characterized in that: The calculation formula for the daily dynamic adjustment factor is: ; is the total power demand data of the next power supply cycle on the demand side, is the current temperature data; It is a daily dynamic adjustment factor generation function, which determines the daily dynamic adjustment factor according to the total power demand data of the next power supply cycle on the demand side and the current temperature data.

3. The coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage according to claim 2 is characterized in that: The daily dynamic adjustment factor generation function is a linear, nonlinear or machine learning model based on historical data.

4. The coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage according to claim 3 is characterized in that: Set the total power threshold on the demand side ,when hour, The value is 1.

5. The coordinated operation method of a hybrid energy storage system based on battery and hydrogen energy storage according to claim 4 is characterized in that: The calculation method is: when the battery power supply unit (1) and the hydrogen energy storage unit (2) are both in a state of energy storage greater than 90%, the following formula is used to calculate : ; When the battery power supply unit (1) and the hydrogen energy storage unit (2) are both in other energy storage states, the following formula is used to calculate: : ; in It is the dynamic adjustment intervention ratio factor of the full state day, which is adjustable between 0.4-0.

5. and They are the total energy storage data of the battery power supply unit (1) and the hydrogen energy storage unit (2); It is the daily dynamic adjustment intervention proportional factor of other energy storage states, which can be adjusted between 0.5-0.

6. is the current energy storage data of the battery power supply unit (1), is the current energy storage data of the hydrogen energy storage unit (2).

6. A coordinated operation system of a hybrid energy storage system based on batteries and hydrogen energy storage, characterized by: The method for realizing the coordinated operation of a hybrid energy storage system based on battery and hydrogen energy storage as claimed in claim 5, wherein the coordinated operation system of the hybrid energy storage system comprises a data acquisition module (3), an energy storage data analysis module (4) based on a data analysis chip, an energy storage control instruction generation module (5) based on a computer, an energy storage controller (6) and an energy storage charging and discharging module (7), wherein the data acquisition module (3) comprises an energy storage power supply data collector (31) and an ambient temperature collector (32), wherein the energy storage power supply data collector (31) is respectively connected to the battery power supply unit (1), the hydrogen energy storage unit (2) and the demand side (100), respectively. Energy storage data, environmental data and power request data of the next power supply cycle of the battery power supply unit (1) and the hydrogen energy storage unit (2) are collected, the energy storage data analysis module (4) is connected in communication with the data collection module (3), the energy storage control instruction generation module (5) is connected in communication with the energy storage data analysis module (4), the energy storage controller (6) is connected in communication with the energy storage control instruction generation module (5), the energy storage charging and discharging module (7) controls the energy storage actions of the battery power supply unit (1) and the hydrogen energy storage unit (2) respectively, and the energy storage controller (6) controls the execution actions of the energy storage charging and discharging module (7).

7. The coordinated operation system of the hybrid energy storage system based on battery and hydrogen energy storage according to claim 6 is characterized in that: The system also includes a central control server (8), which is respectively connected to the data acquisition module (3), the energy storage data analysis module (4) based on the data analysis chip, the energy storage control instruction generation module (5) based on the instruction chip, the energy storage controller (6) and the energy storage charging and discharging module (7) for communication, and collects and displays data.

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