Energy coordination method for wind and light hydrogen storage system

Abstract

The invention provides an energy coordination method for a wind-light hydrogen storage system. The system comprises an energy management center, a power generation unit, a power grid, a load, a power type energy storage unit and an energy type energy storage unit. The method comprises the four steps of S1 to S4, and by making a day-ahead start-stop plan of an energy type energy storage unit, the start-stop loss and the operation cost of equipment are reduced; dynamic balance between power generation and power utilization is ensured based on switching of a weight adaptive model prediction control strategy of a dynamic optimization period and multiple operation conditions, and the reliability and stability of the system are enhanced; reasonable distribution of net power of the system is realized, and the performance and service life of the hybrid energy storage system are optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power engineering, and in particular to a wind-solar-hydrogen storage system energy coordination method. BACKGROUND

[0002] With the acceleration of global energy structure transformation, the consumption of fossil energy and carbon emission problems are becoming increasingly serious, which promotes the widespread application of clean, low-carbon and renewable energy. In recent years, the proportion of new energy power generation in China has been increasing, and the power generation capacity of renewable energy such as wind and solar energy has also been steadily growing. However, due to the volatility and intermittency of renewable energy, how to efficiently utilize and store such energy is still one of the core challenges in realizing energy transformation. In this context, hydrogen energy, as a clean and sustainable energy carrier, is gradually becoming an important part of achieving the goal of "carbon peak and carbon neutral", and is considered as a key link in energy transformation. Green electricity hydrogen storage is becoming an important way to solve the volatility and intermittency of new energy power generation.

[0003] Existing wind-solar-hydrogen storage systems often cannot respond to changes in the demand of the power grid in a timely manner, leading to difficulties in power consumption or excessive system load. In the wind-solar-hydrogen storage system, how to realize the coordinated scheduling between wind energy, photovoltaic, energy storage, hydrogen production and hydrogen utilization, etc. between the subsystems becomes the key to improving the economic and stability of the system. Most existing systems use traditional independent control mode, lack of unified optimization scheduling, resulting in low energy utilization efficiency and poor economy. SUMMARY

[0004] The purpose of the present application is to overcome the shortcomings of the prior art and provide a wind-solar-hydrogen storage system energy coordination method that can solve the problem of low energy utilization efficiency and poor economy caused by the lack of unified optimization scheduling in the existing wind-solar-hydrogen storage system using traditional independent control mode.

[0005] To this end, the present application adopts the following technical solutions: A wind-solar-hydrogen storage system energy coordination method, the system comprising an energy management center, a power generation unit, a power grid, a load, a power-type energy storage unit and an energy-type energy storage unit; the method comprising the following steps performed by the energy management center: S1, establishing a wind-solar-hydrogen coupling system model, based on the output prediction of the power generation unit and the demand prediction of the load, formulating the day-ahead reference power of the energy-type energy storage unit and the day-ahead start-stop plan; S2, based on the real-time running state of the system, the real-time output prediction information of the power generation unit and the real-time demand prediction information of the load, using model predictive control for intraday rolling optimization to generate system net power instructions in real time; S3, decompose the system net power instruction into high frequency component and low frequency component by using signal decomposition algorithm; distribute the high frequency component as first power instruction to the power type energy storage unit for processing, and distribute the low frequency component as second power instruction to the energy type energy storage unit for processing; S4, according to system net power, equipment power constraint and energy storage state constraint, predefine multiple operation conditions, select target condition for execution from the pre-defined multiple operation conditions, and under the target condition, correct the second power instruction distributed to the energy type energy storage unit based on real-time state of the power type energy storage unit and the energy type energy storage unit, generate final power instruction and execute, so that the power difference between the power generation unit and the load is absorbed or compensated, and system power dynamic balance is achieved.

[0006] Based on the above technical scheme, the application can further adopt the following technical scheme, or use the following technical schemes in combination: The power generation unit comprises a photovoltaic power generation unit and a wind power generation unit, and the power type energy storage unit is a lithium battery. The energy type energy storage unit is a hydrogen energy storage system composed of an electrolytic cell, a hydrogen storage tank and a fuel cell.

[0007] In step S2, the model predictive control is a weight adaptive model predictive control based on dynamic optimization period, in step S3, the signal decomposition algorithm is a successive variational modal decomposition algorithm, and in step S4, the second power instruction distributed to the energy type energy storage unit is corrected by using double-layer fuzzy control.

[0008] In step S4, when the final power instruction is executed, a hydrogen energy storage short-time forced operation mechanism is introduced: when the energy type energy storage unit is started, it is controlled to run for a preset minimum time, so as to reduce the start-stop times.

[0009] In step S1, the wind-solar-hydrogen coupling system model is: , In the formula: is the output voltage of a single electrolytic cell; is the working current of the electrolytic cell; is the number of electrolytic cell series modules; , , are reversible voltage, polarization voltage and ohmic voltage of a single electrolytic cell respectively; is the working temperature of the electrolytic cell; is the electrode area of the electrolytic cell; , is the ohmic parameter of the electrolyte; , , , , , These are the overvoltage parameters of the electrodes; Hydrogen production rate of electrolyzer and Faraday efficiency The expression is: , In the formula: It is Faraday's constant; A proton exchange membrane fuel cell model, its fuel cell output power The expression is: , , In the formula: This refers to the output voltage of the fuel cell. This is the operating current of the fuel cell; But the number of batteries connected in series; The thermodynamic electromotive force of a single cell; For activation polarization overvoltage; For ohmic polarization overvoltage of fuel cells; This represents the enrichment polarization overvoltage; a and b are both constant voltages; This refers to the operating temperature of the fuel cell stack. to The activation coefficient; and These are the effective partial pressures of hydrogen and oxygen at the catalyst-gas interface, respectively. This represents the dissolved oxygen concentration at the gas-liquid interface. This is the equivalent resistance of the electron flow channel; The thickness of the membrane; The activated area of ​​the membrane; , Concentration-based overpotential coefficient; Current density; The resistivity of the proton exchange membrane; Hydrogen storage tank model: Pressure of the hydrogen storage tank and hydrogen storage state The expression is: , In the formula: This is the initial pressure of the hydrogen storage tank; is the molar mass of hydrogen gas; It is the gas constant; Temperature of the hydrogen storage tank; This refers to the volume of the hydrogen storage tank. The hydrogen compressibility constant; The maximum value of the hydrogen gas pressure that can be stored in the hydrogen storage tank.

[0010] The target optimization function expression of the day-ahead reference power of the energy-type energy storage unit and the day-ahead start-stop plan is: , In the formula: t represents the t time period; is the total number of day-ahead time periods; , respectively represent the unit charging and discharging cost of the lithium battery; , respectively represent the day-ahead charging and discharging power of the lithium battery; , respectively represent the unit hydrogen production cost of the electrolytic tank and the unit power generation cost of the fuel cell; , respectively represent the day-ahead power of the electrolytic tank and the fuel cell; , respectively represent the unit start-stop cost of the electrolytic tank and the fuel cell; , respectively represent the start-stop times of the electrolytic tank and the fuel cell; is the day-ahead optimization period; Constraint conditions: System active power balance constraint: , In the formula , , , respectively represent the day-ahead power of the wind power, the photovoltaic, the lithium battery, and the load; is the super capacitor power; Energy-type energy storage power upper and lower limit constraint: , , In the formula , respectively represent the day-ahead charging and discharging state of the lithium battery; , respectively represent the start-stop state of the electrolytic tank and the fuel cell; the subscripts "min" and "max" respectively represent the minimum value and the maximum value of the corresponding variable, and the same below; Energy-type energy storage state constraint: , In the formula is the day-ahead state of charge of the lithium battery; is the charging and discharging efficiency of the lithium battery; is the maximum capacity of the lithium battery; hydrogen storage state at the present time; Energy storage power ramping constraint: , wherein: , respectively represent lithium battery, electrolyzer and fuel cell; Upper limit of energy storage ramping rate.

[0011] The multi-scenario operation scenario in step S4 includes the following nine operation scenarios: Scenario one: wind and light power is equal to load demand, the system does not need to store hydrogen or fuel cell operation; Scenario two: wind and light power is higher than load requirement, the remaining electricity is preferentially supplied to the electrolyzer to produce hydrogen; Scenario three: wind and light power is higher than load demand and the remaining electricity exceeds the rated power of the electrolyzer, the remaining electricity is sold to the grid; Scenario four: wind and light power is lower than load demand, the fuel cell starts to supplement power supply; Scenario five: wind and light power is lower than load demand and the fuel cell is insufficient, the grid purchases electricity to supplement; Scenario six: wind and light power is zero, the fuel cell and the grid supply power together; Scenario seven: load demand is zero, wind and light power is used for hydrogen production; Scenario eight: load demand is zero and wind and light power exceeds the capacity of the electrolyzer, the remaining electricity is connected to the grid; Scenario nine: the system is on standby, wind and light power and load demand are both zero.

[0012] In the model predictive control of step S2, the state and control weight factors in the intra-day rolling optimization are adaptively adjusted to coordinate short-term power smoothing and long-term state optimization, and the rolling optimization period is dynamically adjusted based on the dynamic decision value to improve the solving efficiency and reduce redundant adjustment; The model predictive control is realized by the following way: The model predictive control objective function is constructed to minimize the system net power tracking error, the energy storage state change and the energy storage power change As follows: , wherein , wherein: is the control time domain; is the prediction time domain; in this paper, wherein is the total number of intra-day time periods; , are the state variable and the control variable, respectively; represents the prediction of the time point to the time point is the state weight matrix; is the tracking error weight matrix; is the tracking error weight factor; is set to a constant value; is the controller reference output quantity,​ This is an energy storage power command.

[0013] The particle swarm optimization algorithm can calculate the optimal capacity configuration of each unit of the wind-solar-hydrogen storage system under different scenarios, and further determine the optimal operating condition selection based on different net wind and solar power. The particle swarm optimization (PSO) algorithm is used to solve the intraday energy management model and optimize the power generation of photovoltaic and wind power. The PSO algorithm can be expressed by the following formula: , In the formula —Inertia constant; —Individual learning factors; As a group learning factor, it is generally taken as ; —— Random numbers; —— Let the d-dimensional variable of the i-th particle be the variable; —— Let the d-dimensional variable of the i-th particle be the variable; —The d-dimensional variable of the i-th particle in the (t+1)-th generation; — t represents the d-dimensional variable that represents the i-th extreme value; —t represents the d-dimensional variable of the globally optimal particle.

[0014] In particle swarm optimization (PSO), these particles adjust their flight trajectories based on their historical experience with other particles, thus treating them all as a single solution. The optimal region traversed by each particle during its flight is called the individual extreme value, and the optimal solution within that region is the current optimal solution for the entire population. Furthermore, the intraday energy management model is as follows: the prediction model within the energy management center predicts the load and photovoltaic power generation for the next 15-minute time interval based on real-time collected information, establishing an intraday grid-connected energy management model with the following objective function: , In the formula —The power value of the i-th microsource in the system during the k-th 15-minute interval of time t; —The power value of the i-th micro-source in the system during time period t; a — weighting coefficient; The objective function for optimizing intraday network offloading is: , In the formula — the kth 15min power value of the ith micro-source in the system within the t time period; — the power value of the ith micro-source in the system within the t time period; — the maximum output power value of wind power obtained by intraday prediction — the kth 15min power value of wind power in the system within the t time period; — the maximum output power value of photovoltaic obtained by intraday prediction — the kth 15min power value of photovoltaic in the system within the t time period; a — weight coefficient; In the formula, the first term is that the average value of the intraday optimization result of the t time period is close to the day-ahead optimization result, the second term is that the optimization result of each 15min within the t time period is close to the day-ahead optimization result. The third term and the fourth term are the minimum abandoned wind power and abandoned photovoltaic.

[0015] Compared with the prior art, the present application has the following advantages and beneficial effects: by formulating a day-ahead start-stop plan of the energy type energy storage unit, the equipment start-stop loss and operation cost are reduced; based on the weight adaptive model predictive control strategy of the dynamic optimization period and the switching of multiple operating conditions, the dynamic balance between power generation and power consumption is ensured, and the system reliability and stability are enhanced; the reasonable distribution of the system net power is realized, and the performance and service life of the hybrid energy storage system are optimized. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a structural schematic diagram of the present application.

[0017] Figure 2 is a flowchart of the present application.

[0018] Figure 3 is a power distribution result schematic diagram of the present application.

[0019] Figure 4 is a performance index schematic diagram of the present application. DETAILED DESCRIPTION

[0020] In order for those skilled in the art to better understand the technical solutions of the present application, the preferred embodiments of the present application are described below in combination with specific examples, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar functional elements throughout the drawings, but it should be understood that the drawings are only for illustrative purposes and cannot be understood as limiting the present application; in order to better illustrate the present embodiment, some components in the drawings may be omitted, enlarged or reduced, and do not represent the actual size of the product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted, and the positional relationship described in the drawings is only for illustrative purposes and cannot be understood as limiting the present application.

[0021] The present application is further described below in combination with the drawings and examples, but is not as the basis for limiting the present application.

[0022] The present application provides a wind-solar-hydrogen energy coordination method, the system comprises an energy management center, a power generation unit, a power grid, a load, a power-type energy storage unit and an energy-type energy storage unit; the method comprises the following steps executed by the energy management center: S1, a wind-solar-hydrogen coupling system model is established, based on the output prediction of the power generation unit and the demand prediction of the load, the day-ahead reference power of the energy-type energy storage unit and the day-ahead start-stop plan are formulated; S2, based on the real-time running state of the system, the real-time output prediction information of the power generation unit and the real-time demand prediction information of the load, model predictive control is used for intraday rolling optimization to generate system net power instructions in real time; S3, the signal decomposition algorithm is used to decompose the system net power instructions into high-frequency components and low-frequency components; the high-frequency components are distributed to the power-type energy storage unit as first power instructions for processing, and the low-frequency components are distributed to the energy-type energy storage unit as second power instructions for processing; S4, a plurality of operating conditions are predefined according to the system net power, the equipment power constraints and the energy storage state constraints, a target operating condition is selected from the predefined plurality of operating conditions for execution, and in the target operating condition, the second power instructions distributed to the energy-type energy storage unit are corrected based on the real-time state of the power-type energy storage unit and the energy-type energy storage unit, the final power instructions are generated and executed, so that the power difference between the power generation unit and the load is absorbed or compensated, and the system power dynamic balance is realized.

[0023] The power generation unit comprises a photovoltaic power generation unit and a wind power generation unit, and the power-type energy storage unit is a lithium battery. The energy-type energy storage unit is a hydrogen energy storage system composed of an electrolytic tank, a hydrogen storage tank and a fuel cell.

[0024] In this embodiment, the energy management center receives real-time information of the power grid, load, photovoltaic power generation, wind power generation, power storage unit, energy storage unit (electrolyzer, hydrogen storage tank, fuel cell) to perform minute-level scheduling of internal power of the system, and hydrogen production, hydrogen storage, and hydrogen consumption equipment are coupled to the AC bus. The energy management center issues commands according to wind and light output, load scheduling, electrolyzer and fuel cell power level, and hydrogen storage tank pressure level, and each unit layer controller acts on each unit in the wind and light hydrogen storage system after receiving the commands, so as to ensure safe and stable operation of the system.

[0025] In step S2, the model predictive control is a weight adaptive model predictive control based on a dynamic optimization period, in step S3, the signal decomposition algorithm is a successive variational modal decomposition algorithm, and in step S4, a double-layer fuzzy control is used to correct the second power instruction allocated to the energy storage unit.

[0026] In this embodiment, the system net power is allocated to the energy storage unit by using the successive variational modal decomposition, and the double-layer fuzzy control is used to correct the energy storage power instruction. Real-time correction is performed on the wind power, photovoltaic power, and load power prediction, and the electric / hydrogen HESS power is redistributed.

[0027] In step S4, when the final power instruction is executed, a hydrogen storage energy short-time forced operation mechanism is introduced: when the energy storage unit is started, it is controlled to run continuously for a preset minimum time, so as to reduce the start-stop times.

[0028] In this embodiment, if the safety constraint is reached within the preset minimum time, the energy storage unit is immediately stopped; wherein the safety constraint includes at least one of the following: the operating power of the energy storage unit exceeds the maximum or minimum allowable power thereof; the temperature of the energy storage unit exceeds the safe operation threshold thereof; the state of charge of the power storage unit exceeds the safe operation range thereof; the hydrogen storage state of the energy storage unit exceeds the safe operation range thereof.

[0029] The wind and light hydrogen storage coupling system model in step S1 is: , In the formula: is the output voltage of the single electrolyzer cell; is the working current of the electrolyzer cell; is the number of series modules of the electrolyzer cell; , , are the reversible voltage, polarization voltage, and ohmic voltage of the single electrolyzer cell, respectively; is the working temperature of the electrolyzer cell; is the electrode area of the electrolyzer cell; , is the ohmic parameter of the electrolyte; , , , , , These are the overvoltage parameters of the electrodes; Hydrogen production rate of electrolyzer and Faraday efficiency The expression is: , In the formula: It is Faraday's constant; A proton exchange membrane fuel cell model, its fuel cell output power The expression is: , , In the formula: This refers to the output voltage of the fuel cell. This is the operating current of the fuel cell; But the number of batteries connected in series; The thermodynamic electromotive force of a single cell; For activation polarization overvoltage; For ohmic polarization overvoltage of fuel cells; This represents the enrichment polarization overvoltage; a and b are both constant voltages; This refers to the operating temperature of the fuel cell stack. to The activation coefficient; and These are the effective partial pressures of hydrogen and oxygen at the catalyst-gas interface, respectively. This represents the dissolved oxygen concentration at the gas-liquid interface. This is the equivalent resistance of the electron flow channel; The thickness of the membrane; The activated area of ​​the membrane; , Concentration-based overpotential coefficient; Current density; The resistivity of the proton exchange membrane; Hydrogen storage tank model: Pressure of the hydrogen storage tank and hydrogen storage state The expression is: , In the formula: This is the initial pressure of the hydrogen storage tank; is the molar mass of hydrogen gas; It is the gas constant; Temperature of the hydrogen storage tank; This refers to the volume of the hydrogen storage tank; is the hydrogen compression constant; is the maximum value of hydrogen storage pressure of hydrogen storage tank.

[0030] The target optimization function expression of day-ahead reference power and day-ahead start-stop plan of energy type energy storage unit is: , In the formula: represents the t time period; is the total number of day-ahead time periods; , respectively are the unit charging and discharging cost of lithium battery; , respectively are the day-ahead charging and discharging power of lithium battery; , respectively are the unit hydrogen production cost of electrolytic tank and the unit power generation cost of fuel cell; , respectively are the day-ahead power of electrolytic tank and fuel cell; , respectively are the unit start-stop cost of electrolytic tank and fuel cell; , respectively are the start-stop times of electrolytic tank and fuel cell; is the day-ahead optimization period; Constraint condition: System active power balance constraint: , In the formula , , , respectively are the day-ahead power of wind power, photovoltaic, lithium battery and load; is the super capacitor power; Energy type energy storage power upper and lower limit constraint: , , In the formula , respectively represent the day-ahead charging and discharging state of lithium battery; , respectively represent the start-stop state of electrolytic tank and fuel cell; Subscript "min" and "max" respectively represent the minimum value and maximum value of the corresponding variable, same below; Energy type energy storage state constraint: , In the formula is the day-ahead state of charge of lithium battery; is the charging and discharging efficiency of lithium battery; The maximum capacity of the lithium battery; The hydrogen storage state in the day before; The energy type energy storage power ramping constraint: , In the formula: , respectively, represent the lithium battery, electrolytic tank and fuel cell; The upper limit of the ramping rate of the energy type energy storage.

[0031] The multi-condition operation scene in step S4 includes the following nine operation conditions: Condition one: the wind and light power is equal to the load demand, and the system does not need to store hydrogen or operate the fuel cell; Condition two: the wind and light power is higher than the load requirement, and the excess electricity is preferentially supplied to the electrolytic tank to produce hydrogen; Condition three: the wind and light power is higher than the load demand and the excess electricity exceeds the rated power of the electrolytic tank, and the excess electricity is sold to the grid; Condition four: the wind and light power is lower than the load demand, and the fuel cell starts to supplement power supply; Condition five: the wind and light power is lower than the load demand and the fuel cell is insufficient, and the grid purchases electricity to supplement; Condition six: the wind and light power is zero, and the fuel cell and the grid supply power together; Condition seven: the load demand is zero, and the wind and light power is all used to produce hydrogen; Condition eight: the load demand is zero and the wind and light power exceeds the electrolytic tank capacity, and the excess electricity is connected to the grid; Condition nine: the system is on standby, and the wind and light power and the load demand are both zero.

[0032] Specifically as follows: 1. When the physical condition , i.e. the net power of the wind and light power generation system is less than the minimum starting power of the electrolytic tank, the electrolytic tank does not start, and the system stores all the electrical energy generated by the wind and light power generation in the energy storage system.

[0033] 2. When the physical condition , , i.e. the net power of the wind and light power generation system is greater than the minimum starting power of the electrolytic tank and less than the maximum operating power of the electrolytic tank, and the hydrogen pressure in the hydrogen storage tank is less than the upper limit of the hydrogen pressure in the hydrogen storage tank at this time, the electrolytic tank starts to operate, and the fuel cell is in a stopped working state.

[0034] 3. When the physical condition , , i.e. the net power of the wind and light power generation system is greater than the minimum starting power of the electrolytic tank and less than the maximum operating power of the electrolytic tank, and the hydrogen pressure in the hydrogen storage tank is greater than the maximum value of the hydrogen pressure in the hydrogen storage tank at this time, the electrolytic tank stops operating, the fuel cell consumes part of the hydrogen in the hydrogen storage tank, and the wind and light power generation system generates rich electrical energy which is stored in the energy storage system.

[0035] 4. When the physical condition , ​, i.e. the net power of the wind-solar power generation system is greater than the maximum operating power of the electrolyzer, and at this time the pressure in the hydrogen storage tank is less than the maximum pressure of the hydrogen storage tank. The electrolyzer operates at a greater power, and the fuel cell is in a stopped operating state.

[0036] 5. When the physical condition , , i.e. the net power of the wind-solar power generation system is greater than the maximum operating power of the electrolyzer, and at this time the pressure in the hydrogen storage tank is greater than the maximum pressure of the hydrogen storage tank. At this time, the electrolyzer no longer produces hydrogen, and the rich electrical energy generated by the wind-solar power generation system is stored in the energy storage system.

[0037] 6. When the physical condition , , i.e. the pressure in the hydrogen storage tank has reached the lower limit value, the fuel cell cannot operate, the electrolyzer is in a stopped operating state, and the system will absorb the net power compensation from the energy storage system or the power grid.

[0038] 7. When the physical condition , , i.e. the pressure in the hydrogen storage tank is at a normal value, the fuel cell starts to consume hydrogen at a certain power to compensate for the power difference of the system.

[0039] 8. When the physical condition , , i.e. the pressure in the hydrogen storage tank has reached the lower limit value, the fuel cell consumes hydrogen, the electrolyzer water electrolysis system stops operating, and the system needs to absorb all the power difference from the energy storage or the power grid.

[0040] 9. When the physical condition , , i.e. the pressure in the hydrogen storage tank has reached the lower limit value, the fuel cell operates at a greater power, and the electrolyzer is in a stopped operating state.

[0041] In the model predictive control of step S2, the state and control weight factors in the intra-day rolling optimization are adaptively adjusted to coordinate short-term power smoothing and long-term state optimization, and the rolling optimization period is dynamically adjusted based on the dynamic decision value to improve the solving efficiency and reduce redundant adjustment; The model predictive control is realized by the following way: The model predictive control objective function is constructed to minimize the system net power tracking error, the energy storage state change, and the energy storage power change as follows: , wherein , In the formula: is the control time domain; is the prediction time domain; in this paper, wherein This represents the total number of time periods within a day. , These are the changes in the state variables and control variables, respectively. express Always Predicting the timing; This is the state weight matrix; The tracking error weight matrix; Set as a tracking error weighting factor; set to a constant value; For the controller reference output, This is an energy storage power command.

[0042] The particle swarm optimization algorithm can calculate the optimal capacity configuration of each unit of the wind-solar-hydrogen storage system under different scenarios, and further determine the optimal operating condition selection based on different net wind and solar power. The particle swarm optimization (PSO) algorithm is used to solve the intraday energy management model and optimize the power generation of photovoltaic and wind power. The PSO algorithm can be expressed by the following formula: , In the formula —Inertia constant; —Individual learning factors; As a group learning factor, it is generally taken as ; —— Random numbers; —— Let the d-dimensional variable of the i-th particle be the variable; —— Let the d-dimensional variable of the i-th particle be the variable; —The d-dimensional variable of the i-th particle in the (t+1)-th generation; — t represents the d-dimensional variable that represents the i-th extreme value; —t represents the d-dimensional variable of the globally optimal particle.

[0043] In particle swarm optimization (PSO), these particles adjust their flight trajectories based on their historical experience with other particles, thus treating them all as a single solution. The optimal region traversed by each particle during its flight is called the individual extreme value, and the optimal solution within that region is the current optimal solution for the entire population. A further intraday energy management model is established: the prediction model within the energy management center predicts the load and photovoltaic power generation for the next 15-minute time interval based on real-time collected information, establishing an intraday grid-connected energy management model with the objective function: , wherein — the kth 15min power value of the ith micro-source in the system in the t period; — the power value of the ith micro-source in the system in the t period; a — weight coefficient; The off-grid optimization objective function of the system in a day is: , wherein — the kth 15min power value of the ith micro-source in the system in the t period; — the power value of the ith micro-source in the system in the t period; — the maximum output power value of wind power obtained by day-ahead prediction — the kth 15min power value of wind power in the system in the t period; — the maximum output power value of photovoltaic obtained by day-ahead prediction — the kth 15min power value of photovoltaic in the system in the t period; a — weight coefficient; wherein the first term is that the average value of the t period day-ahead optimization result is close to the day-ahead optimization result, the second term is that the optimization result of each 15min in the t period is close to the day-ahead optimization result, and the third and fourth terms are the minimum abandoned wind and light of wind power and photovoltaic.

[0044] The particle swarm algorithm provides an efficient solution for energy management optimization of the wind-solar-hydrogen system, and combines wind-solar power prediction, optimization operation cost, realizes reliability and stability of wind-solar-hydrogen system grid connection.

[0045] The traditional energy management strategy has poor regulation flexibility, fails to fully utilize the operation characteristics of the electric / hydrogen HESS, and has poor coordination between short-term power smoothing and long-term state optimization period, and the improved energy management strategy based on the dynamic optimization period can reduce the system operation cost by 36.2% through day-ahead optimization and short-time forced operation of hydrogen energy storage, effectively improves the system operation economy, reasonably coordinates the short-term power smoothing and long-term state optimization of hydrogen energy storage through adaptive adjustment of the weight factor, fully utilizes the large-scale regulation advantage of hydrogen energy storage through short-time low-power and high-power operation of hydrogen energy storage, and reasonably corrects the power of the electric / hydrogen HESS through double-layer fuzzy control, and effectively improves the safety, reliability and robustness of system operation.

[0046] Figure 3 The middle reflects the power distribution result in the system when the strategy is applied, and the parameters in the performance evaluation system are as follows: The rated power of wind power is 1500KW, and the rated wind speed is 10 The radius of the wind blade is 46.5 The maximum power of the photovoltaic module is 186 The number of series modules is 100, the number of parallel groups is 60, the rated voltage of the energy storage battery is 400 The rated capacity is 6 The maximum charging and discharging power The unit charging cost is 0.45 yuan The unit discharging cost is 0.41 yuan The number of fuel cell series is 40, and the maximum power is 800 The maximum hydrogen-electric conversion efficiency of the fuel cell is .

[0047] As Figure 4 shown in the comparison, the hydrogen energy storage state coefficient is lower than that of the lithium battery state coefficient , which indicates that the standby capacity of hydrogen energy storage is larger, and has the advantages of long storage time and strong long-term regulation capacity The strategy reduces the hydrogen energy storage start-stop cost through the hydrogen energy storage day-ahead start-stop plan and hydrogen energy storage short-time forced operation, improves the system economy through day-ahead optimization, and the system operation cost is reduced by up to 36.2%.

[0048] The strategy improves the power supply reliability of the system, prevents the state of the energy storage from exceeding the limit, and improves the service life of the electric / hydrogen HESS, and the energy imbalance rate is reduced by up to 63.1%, is reduced by up to 41.6%. is reduced by up to 35.7%.

[0049] According to the description and drawings of the present application, those skilled in the art can easily manufacture or use the wind-solar-hydrogen energy storage system energy coordination method, and can produce the positive effects described in the present application.

[0050] It should be noted that the terms "first", "second" are used for simplicity in description, and do not indicate or imply relative importance.

[0051] Furthermore, those skilled in the art will recognize that boundaries between the functionality of the above described operations merely illustrative. The functionality of each of the operations can be combined with functionality of others or distributed to numerous other computational devices or processors to realize increased efficiency, mobility or cost-reduction advantages. Similarly, functionality of some or all of the various operations described herein can be implemented by hardware or software stored on a non-transitory computer readable medium and executed by a processing element to accomplish the operations described herein.

[0052] The foregoing is considered as illustrative only of the principles of the application. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the application to the exact construction and practice described. Accordingly, all suitable modifications and equivalents can be resorted to falling within the scope of the application as defined by the claims that follow.

Claims

1. A method for coordinating the energy of a wind-solar-hydrogen storage system, characterized in that, The system includes an energy management center, power generation units, a power grid, loads, power-type energy storage units, and energy-type energy storage units; the method includes the following steps performed by the energy management center: S1. Establish a wind-solar-hydrogen storage coupled system model. Based on the output prediction of the power generation unit and the demand prediction of the load, formulate the day-ahead reference power and day-ahead start-up and shutdown plan of the energy storage unit. S2. Based on the real-time operating status of the system, the real-time output prediction information of the power generation unit, and the real-time demand prediction information of the load, model predictive control is used for intraday rolling optimization to generate the system net power command in real time. S3. The net power command of the system is decomposed into high-frequency components and low-frequency components using a signal decomposition algorithm; the high-frequency components are allocated as the first power command to the power-type energy storage unit for processing, and the low-frequency components are allocated as the second power command to the energy-type energy storage unit for processing. S4. Based on the system net power, equipment power constraints, and energy storage status constraints, predefine multiple operating conditions, select a target operating condition from the predefined multiple operating conditions, and under the target operating condition, based on the real-time status of the power-type energy storage unit and the energy-type energy storage unit, correct the second power command allocated to the energy-type energy storage unit, generate a final power command and execute it, so that the power difference between the power generation unit and the load is absorbed or compensated, and the system power dynamic balance is achieved.

2. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The power generation unit includes a photovoltaic power generation unit and a wind power generation unit, and the power-type energy storage unit is a lithium battery. The energy-type energy storage unit is a hydrogen energy storage system consisting of an electrolyzer, a hydrogen storage tank, and a fuel cell.

3. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 1, characterized in that, In step S2, the model predictive control is a weighted adaptive model predictive control based on a dynamic optimization cycle. In step S3, the signal decomposition algorithm is a successive variational mode decomposition algorithm. In step S4, a two-layer fuzzy control is used to correct the second power command allocated to the energy storage unit.

4. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 1, characterized in that, In step S4, when executing the final power command, a short-term forced operation mechanism for hydrogen energy storage is introduced: after the energy storage unit is started, it is controlled to run continuously for a preset minimum time, thereby reducing the number of times it starts and stops.

5. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 2, characterized in that, The wind-solar-hydrogen storage coupling system model mentioned in step S1 is as follows: ， In the formula: This refers to the output voltage of a single electrolytic cell; This is the operating current of the electrolytic cell; This refers to the number of electrolytic cell series modules; , , These are the reversible voltage, polarization voltage, and ohmic voltage of a single electrolytic cell, respectively. This refers to the operating temperature of the electrolytic cell. The electrode area of ​​the electrolytic cell; , The ohmic parameter of the electrolyte; , , , , , These are the overvoltage parameters of the electrodes; Hydrogen production rate of electrolyzer and Faraday efficiency The expression is: ， In the formula: It is Faraday's constant; A proton exchange membrane fuel cell model, its fuel cell output power The expression is: ， ， In the formula: This refers to the output voltage of the fuel cell. This is the operating current of the fuel cell; But the number of batteries connected in series; The thermodynamic electromotive force of a single cell; For activation polarization overvoltage; For ohmic polarization overvoltage of fuel cells; This represents the enrichment polarization overvoltage; a and b are both constant voltages; This refers to the operating temperature of the fuel cell stack. to The activation coefficient; and These are the effective partial pressures of hydrogen and oxygen at the catalyst-gas interface, respectively. This represents the dissolved oxygen concentration at the gas-liquid interface. This is the equivalent resistance of the electron flow channel; The thickness of the membrane; The activated area of ​​the membrane; , Concentration-based overpotential coefficient; Current density; The resistivity of the proton exchange membrane; Hydrogen storage tank model: Pressure of the hydrogen storage tank and hydrogen storage state The expression is: ， In the formula: This is the initial pressure of the hydrogen storage tank; is the molar mass of hydrogen gas; It is the gas constant; Temperature of the hydrogen storage tank; This refers to the volume of the hydrogen storage tank; The hydrogen compressibility constant; This represents the maximum pressure at which the hydrogen storage tank can store hydrogen.

6. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 2, characterized in that, The objective optimization function expressions for the day-ahead reference power and day-ahead start-up / shutdown plan of the energy storage unit are as follows: ， In the formula: Represents the time period t; This represents the total number of time periods in the preceding days; , These are the unit charging and discharging costs of lithium batteries, respectively. , These are the daily charging and discharging power of the lithium battery, respectively. , These are the unit hydrogen production cost of an electrolyzer and the unit power generation cost of a fuel cell, respectively. , These are the day-ahead power of the electrolyzer and the fuel cell, respectively. , These are the unit start-up and shutdown costs for electrolyzers and fuel cells, respectively. , These represent the number of start-ups and shutdowns for the electrolyzer and fuel cell, respectively. Optimize the cycle for the current day; Constraints: System active power balance constraints: ， In the formula , , , These are the day-ahead power of wind power, photovoltaic power, lithium batteries, and load, respectively. This refers to the power of the supercapacitor. Upper and lower limits of energy storage power constraints: ， ， In the formula , These represent the current charge and discharge states of the lithium battery, respectively. , These represent the start-up and shutdown states of the electrolyzer and fuel cell, respectively; the subscripts "min" and "max" represent the minimum and maximum values ​​of the corresponding variables, respectively, and so on. Energy storage state constraints: ， In the formula This refers to the current state of charge (SOC) of the lithium battery. The charging and discharging efficiency of lithium batteries; This is the maximum capacity of the lithium battery; This is the current hydrogen storage status; Energy storage power ramping constraints: ， In the formula: These represent lithium batteries, electrolyzers, and fuel cells, respectively. for The upper limit of the ramp rate for energy storage of the energy type.

7. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The multi-condition operation scenarios described in step S4 include the following nine operating conditions: Condition 1: Wind and solar power equals load demand, and the system does not require hydrogen storage or fuel cell operation; Condition 2: Wind and solar power exceeds load demand, and surplus electricity is prioritized for hydrogen production in the electrolyzer; Condition 3: Wind and solar power exceeds load demand and surplus electricity exceeds the rated power of the electrolyzer, and surplus electricity is sold to the grid; Condition 4: Wind and solar power is lower than load demand, and the fuel cell starts to supplement power supply; Condition 5: Wind and solar power is lower than load demand and the fuel cell is insufficient, and electricity is purchased from the grid to supplement power supply; Condition 6: Wind and solar power is zero, and the fuel cell and the grid share power supply; Condition 7: Load demand is zero, and all wind and solar power is used for hydrogen production; Condition 8: Load demand is zero, and wind and solar power exceeds the capacity of the electrolyzer, and surplus electricity is sold to the grid; Condition 9: The system is in standby mode, and both wind and solar power and load demand are zero.

8. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 1, characterized in that, In the model predictive control described in step S2, the state and control weight factors in the intraday rolling optimization are adaptively adjusted to coordinate short-term power smoothing and long-term state optimization, and the rolling optimization cycle is dynamically adjusted based on the dynamic decision value to improve solution efficiency and reduce redundant adjustments. The model predictive control is achieved in the following ways: To minimize the system's net power point tracking error, energy storage state variation, and energy storage power variation, a model predictive control objective function is constructed. as follows: ， in , In the formula: To control the time domain; To predict the time domain; this paper sets in This represents the total number of time periods within a day. , These are the changes in the state variables and control variables, respectively. express Always Predicting the timing; This is the state weight matrix; The tracking error weight matrix; Set as a tracking error weighting factor; set to a constant value; For the controller reference output, This is an energy storage power command.

9. The energy coordination method for a wind-solar-hydrogen storage system as described in claim 7, characterized in that, The particle swarm optimization algorithm can calculate the optimal capacity configuration of each unit of the wind-solar-hydrogen storage system under different scenarios, and further determine the optimal operating condition selection based on different net wind and solar power. The particle swarm optimization (PSO) algorithm is used to solve the intraday energy management model and optimize the power generation of photovoltaic and wind power. The PSO algorithm can be expressed by the following formula: ， In the formula —Inertia constant; —Individual learning factors; As a group learning factor, it is generally taken as ; —— Random numbers; —— Let the d-dimensional variable of the i-th particle be the variable; —— Let the d-dimensional variable of the i-th particle be the variable; —The d-dimensional variable of the i-th particle in the (t+1)-th generation; — t represents the d-dimensional variable that represents the i-th extreme value; — t represents the d-dimensional variables of the globally optimal particle; In particle swarm optimization (PSO), these particles adjust their flight trajectories based on their historical experience with other particles, thus treating them all as a single solution. The optimal region traversed by each particle during its flight is called the individual extreme value, and the optimal solution within that region is the current optimal solution for the entire population. Furthermore, the intraday energy management model is as follows: the prediction model within the energy management center predicts the load and photovoltaic power generation for the next 15-minute time interval based on real-time collected information, establishing an intraday grid-connected energy management model with the following objective function: ， In the formula —The power value of the i-th microsource in the system during the k-th 15-minute interval of time t; —The power value of the i-th micro-source in the system during time period t; a — weighting coefficient; The objective function for optimizing intraday network offloading is: ， In the formula —The power value of the i-th microsource in the system during the k-th 15-minute interval of time t; —The power value of the i-th micro-source in the system during time period t; —Maximum wind power output value predicted during the day —The power value of wind power in the system during the kth 15-minute interval of time period t; —The maximum photovoltaic output power value predicted within the day; —The power value of the photovoltaic system in the kth 15-minute interval within time period t; a — weighting coefficient; The first term in the formula represents the average value of the optimization results within the t-period of the day, which is similar to the optimization results of the previous day. The second term represents the optimization results of each 15-minute interval within the t-period, which are similar to the optimization results of the previous day. The third and fourth terms represent the minimum wind and solar curtailment.

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