Economical optimization operation method and equipment for hydrogen-containing energy storage photovoltaic power station, medium and product

Abstract

The invention discloses a hydrogen-containing energy storage photovoltaic power station economic optimization operation method and device, a medium and a product, and relates to the technical field of power station operation optimization, and the method comprises the steps: obtaining a hydrogen energy storage photovoltaic power station system parameter, system internal electrical load tracking performance and system operation loss; constructing a mixed integer logic nonlinear model according to the system parameters of the hydrogen energy storage photovoltaic power station; according to the system internal electrical load tracking performance and the system operation loss, constructing an optimization objective function; performing operation optimization on the hydrogen energy storage photovoltaic power station system by adopting a cascade economic model prediction control scheme based on the mixed integer logic nonlinear model and the optimization objective function; the cascade economic model predictive control scheme comprises long-term economic model predictive control and short-term economic model predictive control. According to the invention, efficient electric power-hydrogen energy conversion can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of power station operation optimization, and in particular to a method, equipment, medium and product for economic optimization operation of a hydrogen energy storage photovoltaic power station. Background Art

[0002] With the rapid development of modern society, electricity demand has become one of the most basic needs. Although the traditional fossil energy system has long provided efficient and stable energy supply for industrial and economic development, the depletion of fossil fuels and environmental pollution have forced people to pay more attention to the use of clean and renewable energy. Renewable energy systems, such as photovoltaic power stations, can provide green and clean energy, reduce energy costs, and effectively reduce carbon footprints.

[0003] However, the inherent intermittent and random nature of solar energy poses a huge challenge to the stability and reliability of photovoltaic power plant power systems. To cope with the uncertainty of photovoltaic output, energy storage technologies, such as hydrogen energy storage technology, are introduced into the system to provide a balance between energy supply and demand, thereby improving energy efficiency. In a photovoltaic power plant power system with hydrogen energy storage, when there is an excess of solar energy, the excess energy is converted into hydrogen through chemical reactions and stored in hydrogen storage tanks. Conversely, when sudden changes in weather lead to insufficient solar energy, hydrogen fuel cells can generate electricity by consuming stored hydrogen.

[0004] In actual operation, hydrogen energy storage photovoltaic power stations face challenges brought by factors such as uncertainty in the external environment and complex energy coupling characteristics within the system. In order to improve energy utilization efficiency and enhance the resilience and reliability of the power system, a more efficient and flexible energy management strategy is needed to optimize energy utilization and dynamically adjust energy production and use strategies to adapt to changes in external conditions. At the same time, it is also necessary to comprehensively consider multiple factors, such as demand forecasting and equipment efficiency. Therefore, it is particularly necessary to construct a comprehensive objective function and propose a cascade economic model predictive control scheme based on this objective function to optimize the operation of hydrogen energy storage photovoltaic power stations. Summary of the invention

[0005] The purpose of this application is to provide an economically optimized operation method, equipment, medium and product for a hydrogen-containing energy storage photovoltaic power station, which can achieve efficient electricity-hydrogen energy conversion.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for economic optimization operation of a hydrogen energy storage photovoltaic power station, comprising:

[0008] Obtain the system parameters of the hydrogen energy storage photovoltaic power station, the system's internal electrical load tracking performance, and the system's operating losses.

[0009] According to the system parameters of the hydrogen energy storage photovoltaic power station, a mixed integer logic nonlinear model is constructed.

[0010] An optimization objective function is constructed based on the system's internal electrical load tracking performance and system operation losses.

[0011] Based on the mixed integer logic nonlinear model and the optimization objective function, a cascade economic model predictive control scheme is adopted to optimize the operation of the hydrogen energy storage photovoltaic power station system; the cascade economic model predictive control scheme includes long-term economic model predictive control and short-term economic model predictive control; the long-term economic model predictive control is used to solve the optimal variable sequence of the global optimization objective function based on the long-term prediction time domain; the short-term economic model predictive control is used to calculate the optimal variable sequence of the corresponding optimization objective function at the current sampling time based on the discrete control quantity in the optimal variable sequence predicted by the long-term economic model predictive control at the current moment and the battery state of charge of the hydrogen energy storage photovoltaic power station system at the current moment.

[0012] In a second aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned methods for economic optimization operation of a hydrogen energy storage photovoltaic power station.

[0013] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the above-mentioned methods for economic optimization operation of a hydrogen energy storage photovoltaic power station.

[0014] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any one of the above-mentioned methods for economic optimization operation of a hydrogen energy storage photovoltaic power station.

[0015] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0016] The present application provides a method, device, medium and product for economic optimization operation of a hydrogen energy storage photovoltaic power station. The method involves the steps of obtaining system parameters of a hydrogen energy storage photovoltaic power station, the system internal electric load tracking performance and the system operation loss. Based on the system parameters of the hydrogen energy storage photovoltaic power station, the method constructs a mixed integer logic nonlinear model. At the same time, according to the system internal electric load tracking performance and the system operation loss, the method constructs an optimization objective function. By applying the mixed integer logic nonlinear model and the optimization objective function, the method adopts a cascade economic model predictive control strategy to optimize the operation of the hydrogen energy storage photovoltaic power station system. The cascade economic model predictive control strategy includes two parts: long-term economic model predictive control and short-term economic model predictive control. The long-term economic model predictive control aims to predict the optimal variable sequence of the global optimization objective function based on the long-term prediction time domain. The short-term economic model predictive control predicts the optimal variable sequence of the corresponding optimization objective function at the current sampling time based on the discrete control quantity in the optimal variable sequence predicted by the long-term economic model predictive control at the current moment and the battery state of charge of the hydrogen energy storage photovoltaic power station system at the current moment. This application proposes to use a cascade economic model predictive control strategy to optimize the operation of a hydrogen energy storage photovoltaic power station, which can achieve efficient conversion between electricity and hydrogen energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is an application environment diagram of an economic optimization operation method for a hydrogen energy storage photovoltaic power station in one embodiment of the present application.

[0019] Figure 2 A schematic flow chart of a method for economically optimizing operation of a hydrogen-containing energy storage photovoltaic power station provided in one embodiment of the present application.

[0020] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The embodiment of the present application provides a method for economic optimization operation of a hydrogen energy storage photovoltaic power station, which can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 through a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the obtained hydrogen energy storage photovoltaic power station system parameters, the system internal electric load tracking performance and the system operation loss to the server 104. The server 104 receives the obtained hydrogen energy storage photovoltaic power station system parameters, the system internal electric load tracking performance and the system operation loss. The server 104 constructs a mixed integer logic nonlinear model according to the hydrogen energy storage photovoltaic power station system parameters; constructs an optimization objective function according to the system internal electric load tracking performance and the system operation loss; based on the mixed integer logic nonlinear model and the optimization objective function, a cascade economic model predictive control scheme is adopted to optimize the operation of the hydrogen energy storage photovoltaic power station system; the cascade economic model predictive control scheme includes long-term economic model predictive control and short-term economic model predictive control; the long-term economic model predictive control is used to solve the optimal variable sequence of the global optimization objective function based on the long-term prediction time domain; the short-term economic model predictive control is used to calculate the optimal variable sequence corresponding to the optimization objective function at the current sampling time based on the discrete control quantity in the optimal variable sequence predicted by the long-term economic model predictive control at the current moment and the battery charge state quantity of the hydrogen energy storage photovoltaic power station system at the current moment. The server 104 can feedback the obtained optimal variable sequence to the terminal 102. In addition, in some embodiments, a method for economic optimization operation of a hydrogen energy storage photovoltaic power station can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the system parameters of the hydrogen energy storage photovoltaic power station to be processed, the system's internal electrical load tracking performance, and the system's operating losses, or the server 104 can obtain the system parameters of the hydrogen energy storage photovoltaic power station to be processed, the system's internal electrical load tracking performance, and the system's operating losses from the data storage system, and process the system parameters of the hydrogen energy storage photovoltaic power station to be processed, the system's internal electrical load tracking performance, and the system's operating losses.

[0024] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0025] In an exemplary embodiment, Figure 2 As shown, a method for economic optimization operation of a hydrogen energy storage photovoltaic power station is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, and the steps include the following steps 201 to 204.

[0026] in:

[0027] Step 201, obtaining the system parameters of the hydrogen energy storage photovoltaic power station, the system internal electric load tracking performance and the system operation loss.

[0028] Step 202: construct a mixed integer logic nonlinear model according to the hydrogen energy storage photovoltaic power station system parameters.

[0029] Step 203: construct an optimization objective function according to the internal electrical load tracking performance of the system and the system operation loss.

[0030] Step 204, based on the mixed integer logic nonlinear model and the optimization objective function, a cascade economic model predictive control scheme is used to optimize the operation of the hydrogen energy storage photovoltaic power station system; the cascade economic model predictive control scheme includes long-term economic model predictive control and short-term economic model predictive control; the long-term economic model predictive control is used to predict the optimal variable sequence of the global optimization objective function based on the long-term prediction time domain; the short-term economic model predictive control is used to predict the optimal variable sequence of the corresponding optimization objective function at the current sampling moment based on the discrete control quantity in the optimal variable sequence predicted by the long-term economic model predictive control at the current moment and the battery state of charge of the hydrogen energy storage photovoltaic power station system at the current moment.

[0031] In some embodiments, when executing step 202, the specific steps may be as follows:

[0032] A mixed integer logic nonlinear model of a hydrogen energy storage photovoltaic power station system is established. The overall dynamic mathematical model of the system is expressed as follows:

[0033]

[0034] y(t)=h(x(t),u(t),z(t),d(t)).

[0036] in: is the derivative, x, u, z, d and y are the state quantity, continuous input quantity, discrete input quantity, interference quantity and output quantity of the model respectively, and f and h are the mapping functions respectively.

[0037] In some embodiments, when executing step 203, the specific steps may be as follows:

[0038] According to the system's internal electrical load tracking performance and system operation losses, an optimization objective function is constructed, including:

[0039] According to the internal electrical load tracking performance of the system, the first optimization objective function is constructed.

[0040] According to the system operation loss, the second optimization objective function is constructed.

[0041] The optimization objective function is determined according to the first optimization objective function and the second optimization objective function.

[0042] Specifically, the formula expression of the first optimization objective function is:

[0043]

[0044] Among them, T is the prediction time domain, P sl and P d are the total system output power and internal electrical load respectively, and α is the corresponding weight coefficient.

[0045] Specifically, considering the operating losses of the system components, the electrolytic cell, the hydrogen fuel cell, the hydrogen storage tank, and the battery, the second optimization objective function is constructed as follows:

[0046]

[0047] Where: h ez 、h fc 、h t and h b are the operating losses of the electrolytic cell, hydrogen fuel cell, hydrogen storage tank and battery; θ ez,on ,θ fc,on ,θ ez,s and θ fc,s They are the loss of the electrolytic cell remaining in the on state, the loss of the hydrogen fuel cell remaining in the on state, the loss of the electrolytic cell start-stop state switching, and the loss of the hydrogen fuel cell start-stop state switching; P ez and P fcis the power consumed by the electrolytic cell and the power generated by the hydrogen fuel cell; zez and zfc are integer logical variables representing the start and stop of the electrolytic cell and the hydrogen fuel cell, "1" means normally open, and "0" means normally closed; V H2 、N TC and MHL are the volume of the hydrogen storage tank, the total number of hydrogen filling and discharging times, and the hydrogen content of the metal hydride; C iba , CN and V dc N are the total fixed capacity, standard capacity and battery voltage of the battery respectively; cycles , η ba and P ba It is the battery equivalent cycle number, battery efficiency and battery charge / discharge power.

[0048] Specifically, the optimization objective function is determined according to the first optimization objective function and the second optimization objective function, as follows:

[0049] The overall optimization objective function is composed of: H(u, x) = h b +h t +h ez +h fc +h d .

[0050] In some embodiments, when executing step 204, the specific steps may be as follows:

[0051] In this embodiment, a hierarchical economic model predictive control (HEMPC) scheme is proposed, which is based on the mixed integer logic nonlinear model constructed in step 202 and the optimization objective function defined in step 203. The HEMPC scheme consists of two layers: long-term economic model predictive control (EMPC) and short-term economic model predictive control (EMPC). According to the design theory of HEMPC, this embodiment selects different prediction time domains and sampling periods for long-term EMPC and short-term EMPC, respectively.

[0052] Considering the internal load tracking performance of the hydrogen energy storage photovoltaic power station system and the system operation loss, the proposed long-term EMPC is as follows:

[0053]

[0054] Wherein, min means minimizing the objective function, H(u, x) is the optimized objective function, the subscript l is the long-term EMPC, ulmin, xlmin and Δulmin are the minimum input, minimum state and minimum input change rate of the long-term economic model predictive control, ulmax, xlmax and Δulmax are the maximum input, maximum state and maximum input change rate of the long-term economic model predictive control, is an integer variable; l represents long-term EMPC; f l is the state update equation for predictive control of the long-term economic model, f s is the state update equation of the short-term economic model predictive control, h l is the output equation of the long-term economic model predictive control, h s Output equation for predictive control of short-term economic model.

[0055] Specifically, at each sampling time k, the long-term EMPC solves the optimization problem and obtains the corresponding optimal variable sequence: {z * l (k), z * l (k+1),…,z * l (k+T l -1)} corresponds to the discrete control quantity, {u * l (k),u * l (k+1),…,u * l (k+T l -1)} corresponds to the continuous control quantity, {x * l (k), x * l (k+1),…,x * l (k+T l -1)} corresponds to the optimal state quantity.

[0056] Then, the optimal value z * l (k) is transmitted to the microgrid system, and z * l (k) and sent to the short-term EMPC as a reference signal, where is the battery state of charge.

[0057] At the next sampling time point k+1, the above process is repeated to calculate a new optimal variable sequence. In addition, a reference signal for short-term EMPC is defined.

[0058] Considering the reference signal from the long-term EMPC, the online rolling optimization method is used to solve the short-term EMPC optimization expression as follows:

[0059]

[0060] Among them, usmin, xsmin and Δusmin are the minimum input quantity, minimum state quantity and minimum input quantity change rate of the short-term economic model predictive control, usmax, xsmax, Δusmax are the maximum input quantity, maximum state quantity and maximum input quantity change rate of the short-term economic model predictive control; is the reference signal from the long-term EMPC, x s,6 (k+i+1) corresponds to the battery state of charge variable, and s represents the short-term EMPC; specifically, at each sampling time k, the short-term EMPC solves the optimization problem to obtain the corresponding optimal variable sequence: {u*s(k), u*s(k+1), …, u*s(k+Ts-1)}, which is used to control the system; at the next sampling time point k+1, the above process is repeated to calculate a new optimal variable sequence.

[0061] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store hydrogen energy storage photovoltaic power station system parameters, system internal electrical load tracking performance and system operation loss label processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for economic optimization operation of a hydrogen energy storage photovoltaic power station is implemented.

[0062] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0063] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0064] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0065] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0066] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0067] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0068] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0069] In summary, this application has the following technical effects:

[0070] 1) This application proposes a cascade economic model predictive control (HEMPC) framework for optimizing the operation of a photovoltaic power plant system with hydrogen energy storage. This framework takes into account the reference signal of the long-term EMPC, and combines an accurate nonlinear mathematical model with a comprehensive objective function, aiming to achieve stable and safe operation of the system and coordinate the dynamic stability of each subcomponent within the system, while achieving efficient electricity-hydrogen energy conversion.

[0071] 2) This application can better overcome the uncertainty of photovoltaic output, especially can effectively deal with the influence of internal load and external conditions of the system, and has obvious advantages for planning and control.

[0072] 3) This application points out a new direction of using the cascade EMPC strategy to achieve safe, stable, flexible operation and dynamic coordination of hydrogen energy storage photovoltaic power stations.

[0073] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0074] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for economic optimization operation of a hydrogen energy storage photovoltaic power station, characterized in that: The method for economic optimization operation of a hydrogen-containing energy storage photovoltaic power station comprises: Obtain the system parameters of the hydrogen energy storage photovoltaic power station, the system's internal electrical load tracking performance, and the system's operating losses; According to the hydrogen energy storage photovoltaic power station system parameters, a mixed integer logic nonlinear model is constructed; Construct an optimization objective function based on the system's internal electrical load tracking performance and system operation losses; Based on the mixed integer logic nonlinear model and the optimization objective function, a cascade economic model predictive control scheme is adopted to optimize the operation of the hydrogen energy storage photovoltaic power station system; the cascade economic model predictive control scheme includes long-term economic model predictive control and short-term economic model predictive control; the long-term economic model predictive control is used to solve the optimal variable sequence of the global optimization objective function based on the long-term prediction time domain; the short-term economic model predictive control is used to calculate the optimal variable sequence of the corresponding optimization objective function at the current sampling time based on the discrete control quantity in the optimal variable sequence predicted by the long-term economic model predictive control at the current moment and the battery state of charge of the hydrogen energy storage photovoltaic power station system at the current moment.

2. The economic optimization operation method of a hydrogen-containing energy storage photovoltaic power station according to claim 1, characterized in that: The formula for constructing the mixed integer logic nonlinear model is: y(t)=h(x(t),u(t),z(t),d(t)); in, is the derivative, x, u, z, d and y are the state quantity, continuous input quantity, discrete input quantity, interference quantity and output quantity of the mixed integer logic nonlinear model respectively, and f and h are the mapping functions respectively.

3. The economic optimization operation method of a hydrogen-containing energy storage photovoltaic power station according to claim 1, characterized in that: According to the system's internal electrical load tracking performance and system operation losses, an optimization objective function is constructed, including: According to the internal electrical load tracking performance of the system, a first optimization objective function is constructed; According to the system operation loss, a second optimization objective function is constructed; The optimization objective function is determined according to the first optimization objective function and the second optimization objective function.

4. The economic optimization operation method of a hydrogen-containing energy storage photovoltaic power station according to claim 3 is characterized in that: The formula expression of the first optimization objective function is: Among them, T is the prediction time domain, P sl and P d are the total system output power and internal electrical load respectively, and α is the corresponding weight coefficient.

5. The economic optimization operation method of a hydrogen-containing energy storage photovoltaic power station according to claim 4, characterized in that: The formula expression of the second optimization objective function is: Among them, h ez 、h fc 、h t and h b are the operating losses of the electrolytic cell, hydrogen fuel cell, hydrogen storage tank and battery; θ ez,on ,θ fc,on ,θ ez,s and θ fc,s They are the loss of the electrolytic cell remaining in the on state, the loss of the hydrogen fuel cell remaining in the on state, the loss of the electrolytic cell start-stop state switching, and the loss of the hydrogen fuel cell start-stop state switching; P ez and P fc is the power consumed by the electrolytic cell and the power generated by the hydrogen fuel cell; ez and z fc is an integer logical variable for starting and stopping the electrolytic cell and hydrogen fuel cell; N TC and MHL are the volume of the hydrogen storage tank, the total number of hydrogen filling and discharging times, and the hydrogen content of the metal hydride; C iba , CN and V dc N are the total fixed capacity, standard capacity and battery voltage of the battery respectively; cycles , η ba and P ba It is the battery equivalent cycle number, battery efficiency and battery charge / discharge power.

6. The economic optimization operation method of a hydrogen-containing energy storage photovoltaic power station according to claim 5, characterized in that: Determining the optimization objective function according to the first optimization objective function and the second optimization objective function specifically includes: According to the formula H(u,x)=h b +h t +h ez +h fc +h d , determine the optimization objective function.

7. The method for economic optimization operation of a hydrogen-containing energy storage photovoltaic power station according to claim 1, characterized in that: The formula expression of the long-term economic model predictive control in the cascade economic model predictive control scheme is specifically: The formula expression of the short-term economic model predictive control in the cascade economic model predictive control scheme is specifically: Among them, min is to minimize the objective function, H(u, x) is the optimization objective function, the subscript l is the long-term economic model predictive control, u l min 、x l min and Δu l min is the minimum input quantity, minimum state quantity and minimum input quantity change rate of the long-term economic model prediction control, u l max 、x l max and Δu l max The maximum input quantity, maximum state quantity and maximum input quantity change rate for long-term economic model prediction control, is the integer variable of the long-term economic model prediction control; s is the short-term economic model prediction control, k is the k-th sampling moment, k+i is the k+i-th sampling moment, u s min 、x s min and Δu s min is the minimum input quantity, minimum state quantity and minimum input quantity change rate of the short-term economic model prediction control, u S max , x S max , Δu s max is the maximum input, maximum state and maximum input change rate of the short-term economic model prediction control, f l is the state update equation for predictive control of the long-term economic model, f s is the state update equation of the short-term economic model predictive control, h l is the output equation of the long-term economic model predictive control, h s Output equation for predictive control of short-term economic model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for economically optimizing operation of a hydrogen energy storage photovoltaic power station as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a method for economic optimization operation of a hydrogen energy storage photovoltaic power station described in any one of claims 1-7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, a method for economic optimization operation of a hydrogen energy storage photovoltaic power station described in any one of claims 1-7 is implemented.

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