Fast calculation method of comprehensive prediction energy management for offshore wind power diversification consumption

Abstract

The present application relates to a kind of offshore wind power diversification consumption comprehensive prediction energy management fast calculation method, belong to new energy field.The method specifically includes the following steps: establish the nonlinear optimization control model and optimization control problem of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery coupling system, the active power of wind turbine is carried out day-ahead, short-time and ultra-short-time load prediction within day, global optimization is carried out using dynamic programming and convex programming combination iteration method, respectively obtain the energy storage state of battery and hydrogen storage tank under day-ahead scheduling, the switch sequence of hydrogen production electrolytic cell under short-time prediction control within day, the real-time output of battery, electrolytic cell and hydrogen fuel cell under ultra-short-time prediction control, the method is aimed at the diversification consumption problem of offshore wind power-hydrogen production-hydrogen fuel cell-battery coupling system, constructs multi-time scale prediction energy management and realizes the solution of different time scale prediction control problem.

Description

Technical Field

[0001] This invention relates to the technical field of offshore wind power curtailment and consumption, and in particular to a rapid calculation method for comprehensive predictive energy management of diversified offshore wind power consumption. Background Technology

[0002] The development of offshore wind power is booming, and how to achieve large-scale utilization of offshore wind power is a problem that needs to be solved in my country's offshore wind power development. Coupled with hydrogen production and storage, battery energy storage and hydrogen fuel cells, it is possible to achieve flexible grid connection and diversified utilization of offshore wind power and hydrogen production and supply, thereby improving the operational economics of offshore wind farms.

[0003] Predictive control can improve the operating efficiency of a coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, but it involves complex calculations and increases control costs. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a rapid calculation method for comprehensive predictive energy management of diversified offshore wind power consumption. By combining dynamic programming and convex optimization, it achieves multi-timescale hierarchical predictive control of the coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, thereby improving control accuracy and reducing computational complexity.

[0005] To achieve the above objectives, the present invention can adopt the following technical solutions:

[0006] A rapid calculation method for energy management is used for comprehensive prediction of diversified consumption in a coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery. The system includes: a power grid, which is electrically connected to a wind farm and electrically connected to a battery, an electrolyzer, and a hydrogen fuel cell through an AC / DC converter. A compressor and a hydrogen storage tank are also coupled between the electrolyzer and the hydrogen fuel cell.

[0007] The method includes the following steps:

[0008] Step 1: Establish the nonlinear optimization control model and optimization control problem of the system;

[0009] Step 2: Predict the active power of each wind turbine for the day-ahead, intraday short-term, and ultra-short-term periods;

[0010] Step 3: Use a combination of dynamic programming and convex programming iterative method to perform global optimization solutions for day-ahead scheduling and intraday short-term predictive control, and obtain the state trajectories of the battery and hydrogen storage tank under day-ahead scheduling and the switching sequence of the hydrogen electrolyzer under intraday short-term predictive control, respectively.

[0011] Step 4: Use dynamic programming and convex programming methods to solve the ultra-short time predictive control to obtain the real-time output of the battery, electrolyzer, and hydrogen fuel cell, and issue the current output command of each unit.

[0012] Furthermore, based on the fast energy management calculation method described above, the nonlinear optimization control model of the system is as follows:

[0013] (1-1)

[0014]

[0015] , (1-2)

[0016] , (1-3)

[0017] (1-4)

[0018] (1-5)

[0019] (1-6)

[0020] (1-7)

[0021] (1-8)

[0022] (1-9)

[0023] (1-10)

[0024] (1-11)

[0025] (1-12)

[0026] (1-13)

[0027] …

[0028] (1-14)

[0029] (1-15)

[0030] (1-16)

[0031] (1-17)

[0032] (1-18)

[0033] (1-19)

[0034]

[0035] in, It is the optimal economic indicator. and These are the grid-connected electricity price and the hydrogen price. For grid-connected active power, This represents the remaining capacity of the hydrogen storage tank. It is the cost of starting and stopping the hydrogen electrolyzer. This indicates the on / off status of the electrolytic cell. This refers to the input power of the electrolytic cell. , and It is the fitting coefficient for the quality of hydrogen produced by the electrolyzer. The hydrogen production rate of the electrolyzer. For fuel cell output power, , and These are the fitting coefficients for fuel cells. This refers to the hydrogen consumption rate of the electrolyzer. The loss is caused by the internal resistance of the battery. and It refers to the battery's internal resistance and open-circuit voltage. The number of batteries. For the output power of the storage battery, For the energy of the storage battery, and These are the upper and lower limits of the battery's output power. and These are the upper and lower limits of the energy that a battery can store. and These are the upper and lower limits of the hydrogen storage tank's capacity to store hydrogen. , , ..., This refers to the active power of each wind turbine, and the individual wind turbines at... The maximum active power at time is expressed as , …, , The number of wind turbines in the wind farm is represented by the grid-connected load. , To constrain grid-connected active power fluctuations, This refers to the charging and discharging state of the battery. Efficiency of bidirectional converters for storage batteries.

[0036] The energy management rapid calculation method described above further includes, in step 3:

[0037] S31: Preset the switching state sequence of the hydrogen production electrolyzer and the charging and discharging state sequence of the battery, construct the convex optimization problem of the coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, and convexly relax the constraint (1-20).

[0038] After convex relaxation of equation (1-20), it becomes equation (2). The convex optimization problems of day-ahead scheduling and intraday short-term predictive control are shown in equations (3) and (4).

[0039]

[0040] (3-1)

[0041]

[0042] (3-2)

[0043] (4-1)

[0044]

[0045] (4-2)

[0046] (4-3)

[0047] (4-4)

[0048] In equation (2), and These represent the preset electrolytic cell switching state and battery charging / discharging state, respectively. In equations (4-3) and (4-4), It is the end time of short-term predictive control. The dispatch date corresponds to the first The optimal state of the battery at any given time. The dispatch date corresponds to the first The optimal state of the hydrogen storage tank at any given time;

[0049] S32: The optimal power allocation for the hydrogen electrolyzer, battery, and each blower is obtained by solving the convex optimization problem in S31 using the convex optimization method. , , , and the dual variables of energy storage state and hydrogen storage state. and , now the standard for This indicates the optimization results of the current scheduling, indicated by the subscript. for This represents the optimization result of short-term predictive control;

[0050] in, It is the optimal output for grid connection. This is the optimal output of the battery. It is the optimal output of the hydrogen electrolyzer. This is the optimal output of the fuel cell;

[0051] S33: Based on the optimal output result in S32, construct a dynamic programming problem for the switching state of the hydrogen electrolyzer array and the charging and discharging state of the battery in the offshore wind power-hydrogen-storage coupling system, that is, rewrite the objective function (1) as shown in equation (5) and equation (6) respectively;

[0052]

[0053]

[0054] in, and These correspond to the hydrogen production and consumption under optimal output conditions. This is the hydrogen storage state under optimal output;

[0055] S34: Dynamic programming is used to solve the optimal switching state of the hydrogen electrolyzer array and the optimal charge / discharge sequence of the battery using equations (5) and (6), and this switching state is used as the preset switching state. Repeat S31 to S34 until the final value condition of equation (4) is satisfied:

[0056] (7)

[0057] in, It is an economic convergence error. It is the economic index value of the i-th iteration.

[0058] The energy management fast calculation method described above is further expressed as follows: the ultrashort-time predictive control problem in step 4 is:

[0059] (8-1)

[0060]

[0061] (8-2)

[0062] (8-3)

[0063] (8-4)

[0064] In the formula, It is the end time of ultra-short time predictive control;

[0065] Solving ultra-short-time predictive control using convex programming and dynamic programming includes:

[0066] S41: Substitute the start-up and shutdown state of the hydrogen electrolyzer obtained from S3 into the optimization problem of equation (8), and preset the charging and discharging state of the battery to obtain the convex optimization problem;

[0067] S42: The optimization results of ultra-short time predictive control are obtained by solving the convex optimization problem in S41 using the convex optimization method. , , , , ;

[0068] in, It is the optimal output for grid connection. This is the optimal output of the battery. It is the optimal output of the hydrogen electrolyzer. This is the optimal output of the fuel cell. It is a dual variable;

[0069] S43: Based on the optimization results in S42, construct a dynamic programming problem for the charging and discharging state of the battery in the offshore wind power-hydrogen-storage coupling system, that is, rewrite the objective function (8-1) as follows:

[0070] (9)

[0071] S44: Use dynamic programming to solve for the optimal charge-discharge sequence of the battery in equation (9), and use this charge-discharge state as a preset value. Repeat S41 to S44 until the final value condition of equation (10) is met.

[0072] (10)

[0073] in, It is the convergence error of the ultra-short-time predictive control economy. It is the economic index value of the i-th iteration.

[0074] The energy management rapid calculation method described above further uses the first moment output value of the ultra-short time predictive control optimization result as an instruction to the battery, hydrogen electrolyzer, wind turbine and fuel cell to realize control.

[0075] The energy management rapid calculation method described above further includes, but is not limited to, hours and minutes, the time scale.

[0076] Compared with existing technologies, the advantages of this invention are as follows: This invention establishes a nonlinear optimization control model and optimization control problem for a coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cells-battery. It performs day-ahead, intraday short-time, and ultra-short-time load predictions on the active power of the wind turbine, and uses a combined iterative method of dynamic programming and convex programming for global optimization. This yields the energy storage status of the battery and hydrogen storage tank under day-ahead scheduling, the switching sequence of the hydrogen electrolyzer under intraday short-time predictive control, and the real-time output of the battery, electrolyzer, and hydrogen fuel cell under ultra-short-time predictive control. This method addresses the diversified energy consumption problem of the coupled system of offshore wind power-hydrogen production-hydrogen fuel cells-battery, constructs multi-timescale predictive energy management, and realizes the solution of predictive control problems at different time scales, thereby improving control accuracy and reducing computational complexity. Attached Figure Description

[0077] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a flowchart of the algorithm for diversified offshore wind power consumption and integrated predictive energy management proposed in this invention.

[0079] Figure 2 This is a flowchart of the algorithm for the combined iterative optimization method of dynamic programming and convex programming proposed in this invention.

[0080] Figure 3 This is a schematic diagram of the offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery coupling system used in this invention. Detailed Implementation

[0081] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0082] Example:

[0083] It should be noted that the terms "comprising" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0084] See Figures 1 to 3 , Figure 1 This is a flowchart of the algorithm for diversified offshore wind power consumption and integrated predictive energy management proposed in this invention. Figure 2 This invention presents a combined iterative optimization method using dynamic programming and convex programming. Figure 3 This is the offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery coupling system used in this invention.

[0085] The aforementioned rapid calculation method for diversified offshore wind power consumption and integrated predictive energy management includes the following steps:

[0086] S1: Establish a nonlinear optimization control model and optimization control problem for a coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery;

[0087] S2: Predict the active power of each wind turbine during the day-ahead, intraday short-term, and ultra-short-term periods. The time scale for this period includes, but is not limited to, hours and minutes.

[0088] Each wind turbine The maximum active power at time t can be expressed as , …, ,in, The number of wind turbines in the wind farm; the grid-connected load can be expressed as... ;

[0089] S3: A combined iterative method of dynamic programming and convex programming is used to perform global optimization solutions for day-ahead scheduling and intraday short-term predictive control, respectively obtaining the state trajectories of batteries and hydrogen storage tanks under day-ahead scheduling and the switching sequence of hydrogen electrolyzers under intraday short-term predictive control;

[0090] S4: The dynamic programming and convex programming methods are used to solve the ultra-short time predictive control to obtain the real-time output of the storage battery, electrolyzer and hydrogen fuel cell, and to issue the output command of each unit.

[0091] The nonlinear optimization control model and optimization control problem of the offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery coupled system in S1 can be expressed as follows:

[0092] (1-1)

[0093]

[0094] , (1-2)

[0095] , (1-3)

[0096] (1-4)

[0097] (1-5)

[0098] (1-6)

[0099] (1-7)

[0100] (1-8)

[0101] (1-9)

[0102] (1-10)

[0103] (1-11)

[0104] (1-12)

[0105] (1-13)

[0106] …

[0107] (1-14)

[0108] (1-15)

[0109] (1-16)

[0110] (1-17)

[0111] (1-18)

[0112] (1-19)

[0113]

[0114] in, It is the optimal economic indicator. and These are the grid-connected electricity price and the hydrogen price. It is the cost of starting and stopping the hydrogen electrolyzer. For the output power of the storage battery, and It refers to the battery's internal resistance and open-circuit voltage. The number of batteries. For the energy of the storage battery, The loss is caused by the internal resistance of the battery. This represents the remaining capacity of the hydrogen storage tank. and These are the upper and lower limits of the hydrogen storage tank's capacity to store hydrogen. and These are the upper and lower limits of the energy that a battery can store. and These are the upper and lower limits of the battery's output power. The hydrogen production rate of the electrolyzer. The input power of the electrolytic cell, , , ..., This refers to the active power of each wind turbine. It refers to the number of wind turbines. For grid-connected active power, To constrain grid-connected active power fluctuations, , and It is the fitting coefficient for the quality of hydrogen produced by the electrolyzer. This indicates the on / off status of the electrolytic cell. This refers to the charging and discharging state of the battery. , and These are the fitting coefficients for fuel cells. and Fuel cell output power and hydrogen consumption rate, Efficiency of bidirectional converter for storage batteries;

[0115] The steps for solving the economic problem by combining dynamic programming and convex optimization methods in S3 are as follows:

[0116] S31: Preset the switching state sequence of the hydrogen production electrolyzer and the charging and discharging state sequence of the battery, construct the convex optimization problem of the coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, and convexly relax the constraint (1-20).

[0117] After convex relaxation of equation (1-20), it becomes equation (2). The convex optimization problems of day-ahead scheduling and intraday short-term predictive control are shown in equations (3) and (4):

[0118]

[0119] (3-1)

[0120]

[0121] (3-2)

[0122] (4-1)

[0123]

[0124] (4-2)

[0125] (4-3)

[0126] (4-4)

[0127] In equation (2), and These represent the preset electrolytic cell switching state and battery charging / discharging state, respectively. In equations (4-3) and (4-4), It is the end time of short-term predictive control. The dispatch date corresponds to the first The optimal state of the battery at any given time. The dispatch date corresponds to the first The optimal state of the hydrogen storage tank at any given time;

[0128] S32: The optimal power allocation for the hydrogen electrolyzer, battery, and each blower is obtained by solving the convex optimization problem in S31 using the convex optimization method. , , , and the dual variables of energy storage state and hydrogen storage state. and , now the standard for This indicates the optimization results of the current scheduling, indicated by the subscript. for This represents the optimization result of short-term predictive control;

[0129] in, It is the optimal output for grid connection. This is the optimal output of the battery. It is the optimal output of the hydrogen electrolyzer. This is the optimal output of the fuel cell;

[0130] S33: Based on the optimal output result in S32, construct a dynamic programming problem for the switching state of the hydrogen electrolyzer array and the charging and discharging state of the battery in the offshore wind power-hydrogen-storage coupling system, that is, rewrite the objective function (1) as shown in equation (5) and equation (6) respectively;

[0131]

[0132]

[0133] in, and These correspond to the hydrogen production and consumption under optimal output conditions. This is the hydrogen storage state under optimal output;

[0134] S34: Dynamic programming is used to solve the optimal switching state of the hydrogen electrolyzer array and the optimal charge / discharge sequence of the battery using equations (5) and (6), and this switching state is used as the preset switching state. Repeat S31 to S34 until the final value condition of equation (4) is satisfied:

[0135] (7)

[0136] in, It is an economic convergence error. It is the economic index value of the i-th iteration;

[0137] The S4 problem is solved using convex programming and dynamic programming. The ultra-short time predictive control problem is shown in equation (8):

[0138] (8-1)

[0139]

[0140] (8-2)

[0141] (8-3)

[0142] (8-4)

[0143] In the formula, It is the end time of ultra-short time predictive control;

[0144] When using convex programming and dynamic programming to solve equation (8), the following steps can be taken:

[0145] S41: Substitute the start-up and shutdown state of the hydrogen electrolyzer obtained from S3 into the optimization problem of equation (8), and preset the charging and discharging state of the battery to obtain the convex optimization problem;

[0146] S42: The optimization results of ultra-short time predictive control are obtained by solving the convex optimization problem in S41 using the convex optimization method. , , , , ;

[0147] in, It is the optimal output for grid connection. This is the optimal output of the battery. It is the optimal output of the hydrogen electrolyzer. This is the optimal output of the fuel cell. It is a dual variable;

[0148] S43: Based on the optimization results in S42, construct a dynamic programming problem for the charging and discharging state of the battery in the offshore wind power-hydrogen-storage coupling system, that is, rewrite the objective function (8-1) as follows:

[0149] (9)

[0150] S44: Use dynamic programming to solve for the optimal charge-discharge sequence of the battery in equation (9), and use this charge-discharge state as a preset value. Repeat S41 to S44 until the final value condition of equation (10) is met.

[0151] (10)

[0152] in, It is the convergence error of the ultra-short-time predictive control economy. It is the economic index value of the i-th iteration;

[0153] The output value at the first moment of the ultra-short time predictive control optimization result is used as an instruction to the battery, hydrogen electrolyzer, wind turbine and fuel cell to achieve control.

[0154] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0155] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A rapid energy management calculation method for comprehensive prediction of diversified energy consumption in a coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, the system comprising: A power grid, electrically connected to a wind farm, and electrically connected to a battery, an electrolyzer, and a hydrogen fuel cell via an AC / DC converter, wherein a compressor and a hydrogen storage tank are coupled between the electrolyzer and the hydrogen fuel cell, characterized in that... The method includes the following steps: Step 1: Establish the nonlinear optimization control model and optimization control problem of the system; The nonlinear optimal control model of the system is as follows: (1-1) ， (1-2) ， (1-3) (1-4) (1-5) (1-6) (1-7) (1-8) (1-9) (1-10) (1-11) (1-12) (1-13) … (1-14) (1-15) (1-16) (1-17) (1-18) (1-19) (1-20) in, It is the optimal economic indicator. and These are the grid-connected electricity price and the hydrogen price. This represents the remaining capacity of the hydrogen storage tank. It is the cost of starting and stopping the hydrogen electrolyzer. This indicates the on / off status of the electrolytic cell. This refers to the input power of the electrolytic cell. , and It is the fitting coefficient for the quality of hydrogen produced by the electrolyzer. The hydrogen production rate of the electrolyzer. For fuel cell output power, , and These are the fitting coefficients for fuel cells. This refers to the hydrogen consumption rate of the electrolyzer. The loss is caused by the internal resistance of the battery. and It refers to the battery's internal resistance and open-circuit voltage. The number of batteries. For the output power of the storage battery, For the energy of the storage battery, and These are the upper and lower limits of the battery's output power. and These are the upper and lower limits of the energy that a battery can store. and These are the upper and lower limits of the hydrogen storage tank's capacity to store hydrogen. , , ..., This refers to the active power of each wind turbine, and the individual wind turbines at... The maximum active power at time is expressed as , …, , The number of wind turbines in the wind farm is represented by the grid-connected load. , To constrain grid-connected active power fluctuations, It refers to the charging and discharging state of the battery. Efficiency of bidirectional converter for storage batteries; Step 2: Predict the active power of each wind turbine for the day-ahead, intraday short-term, and ultra-short-term periods; Step 3: Use a combination of dynamic programming and convex programming iterative method to perform global optimization solutions for day-ahead scheduling and intraday short-term predictive control, and obtain the state trajectories of the battery and hydrogen storage tank under day-ahead scheduling and the switching sequence of the hydrogen electrolyzer under intraday short-term predictive control, respectively. Step 4: Use dynamic programming and convex programming methods to solve the ultra-short time predictive control to obtain the real-time output of the battery, electrolyzer, and hydrogen fuel cell, and issue the current output command of each unit.

2. The rapid calculation method for energy management according to claim 1, characterized in that, Step 3 includes: S31: Preset the switching state sequence of the hydrogen production electrolyzer and the charging and discharging state sequence of the battery, construct the convex optimization problem of the coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, and convexly relax the constraint (1-20). After convex relaxation, equation (1-20) becomes equation (2). The convex optimization problems of day-ahead scheduling and intraday short-term forecast control are shown in equations (3-1) and (4-1): (2) (3-1) (3-2) (4-1) (4-2) (4-3) (4-4) In equation (2), and These represent the preset electrolytic cell switching state and battery charging / discharging state, respectively. In equations (4-3) and (4-4), It is the end time of short-term predictive control. The dispatch date corresponds to the first The optimal state of the battery at any given time. The dispatch date corresponds to the first The optimal state of the hydrogen storage tank at any given time; S32: The optimal power allocation for the hydrogen electrolyzer, battery, and each blower is obtained by solving the convex optimization problem in S31 using the convex optimization method. , , , and the dual variables of energy storage state and hydrogen storage state. and , now the standard for This indicates the optimization results of the current scheduling, indicated by the subscript. for This represents the optimization result of short-term predictive control; in, It is the optimal output for grid connection. This is the optimal output of the battery. It is the optimal output of the hydrogen electrolyzer. This is the optimal output of the fuel cell; S33: Based on the optimal output results in S32, construct a dynamic programming problem for the switching state of the hydrogen electrolyzer array and the charging and discharging state of the battery in the coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery. That is, rewrite the objective function (1-1) as shown in equation (5) and equation (6) respectively. (5) (6) in, and These correspond to the hydrogen production and consumption under optimal output conditions. This is the hydrogen storage state under optimal output; S34: Dynamic programming is used to solve the optimal switching state of the hydrogen electrolyzer array and the optimal charge / discharge sequence of the battery using equations (5) and (6), and this switching state is used as the preset switching state. Repeat steps S31 to S34 until the termination condition of equation (7) is met: (7) in, It is an economic convergence error. It is the economic index value of the i-th iteration.

3. The rapid calculation method for energy management according to claim 2, characterized in that, The ultrashort-time predictive control problem in step 4 is expressed as: (8-1) (8-2) (8-3) (8-4) In the formula, It is the end time of ultra-short time predictive control; Solving ultra-short-time predictive control using convex programming and dynamic programming includes: S41: Substitute the start-up and shutdown state of the hydrogen electrolyzer obtained in step 3 into the optimization problem of equation (8-1), and preset the charging and discharging state of the battery to obtain the convex optimization problem. S42: The optimization results of ultra-short time predictive control are obtained by solving the convex optimization problem in S41 using the convex optimization method. , , , , ; in, It is the optimal output for grid connection. This is the optimal output of the battery. It is the optimal output of the hydrogen electrolyzer. This is the optimal output of the fuel cell. It is a dual variable; S43: Based on the optimization results in S42, construct a dynamic programming problem for the charging and discharging state of the battery in the coupled system of offshore wind power-hydrogen production and storage-hydrogen fuel cell-battery, that is, rewrite the objective function (8-1) as follows: (9) S44: Use dynamic programming to solve the optimal charge-discharge sequence of the battery in equation (9), and use this charge-discharge state as a preset value. Repeat S41 to S44 until the termination condition of equation (10) is met: (10) in, It is the convergence error of the ultra-short-time predictive control economy. It is the economic index value of the i-th iteration.

4. The rapid calculation method for energy management according to claim 1, characterized in that, The output value at the first moment of the ultra-short time predictive control optimization result is used as an instruction to the battery, hydrogen electrolyzer, wind turbine and fuel cell to achieve control.

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