Photovoltaic power plant operation mode determination method and device, equipment and storage medium

Abstract

The invention discloses a photovoltaic power plant operation mode determination method and device, equipment and a storage medium, and the method comprises the steps: converting a minimum cost function of a photovoltaic power plant into a quantum objective function according to a preset conversion matrix, and enabling the minimum cost objective function to be determined by the power supply cost and the power supply amount of the photovoltaic power plant; converting constraint conditions corresponding to the photovoltaic power plant into quantum constraint conditions according to the conversion matrix, wherein the constraint conditions comprise constraint conditions of demand electric quantity and power supply quantity and constraint conditions of photovoltaic power supply quantity; determining a target photovoltaic power supply amount and a target energy storage power supply amount of each pricing time period according to the quantum constraint condition, the quantum target function, the target time period and the cost parameter and the demand power amount corresponding to each pricing time period in the target time period; and determining an operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply amount and the target energy storage power supply amount in each pricing period. According to the technical scheme, the accuracy of the determined operation mode of the photovoltaic power plant is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of photovoltaic power generation technology, and in particular to a method, device, equipment and storage medium for determining an operating mode of a photovoltaic power plant. Background Art

[0002] Photovoltaic power plants are important facilities for generating electricity using solar energy. The determination of their efficient operation and reasonable operation mode is of great significance for meeting energy demand, reducing environmental pollution and promoting sustainable development. Moreover, the operation mode of photovoltaic power plants has a direct impact on their economic benefits, so it is particularly important to determine the operation mode that can minimize their operating costs.

[0003] However, it is not easy to determine the operation mode of a photovoltaic power plant under the minimum operation cost. This requires comprehensive consideration of multiple complex factors, such as the uncertainty of photovoltaic power generation, the dynamic characteristics of power demand, the cost of energy storage equipment, and the fluctuation of the price mechanism. At present, although the operation mode of a photovoltaic power plant can be determined by manual decision-making, this method cannot flexibly adjust the operation mode according to actual conditions to meet market demand, resulting in a mismatch between supply and demand and energy waste. Therefore, it is urgent to propose a new method to solve the above problems. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for determining an operation mode of a photovoltaic power plant, which can improve the accuracy of the determined operation mode of the photovoltaic power plant.

[0005] In a first aspect, an embodiment of the present invention provides a method for determining an operation mode of a photovoltaic power plant, comprising:

[0006] Converting the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix, wherein the minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant;

[0007] Converting the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix, wherein the constraints include constraints on the power demand and power supply and constraints on the photovoltaic power supply;

[0008] The target photovoltaic power supply and the target energy storage power supply for each pricing period are determined according to the quantum constraint conditions, the quantum objective function, the target period, and the cost parameters and the required electricity quantity corresponding to each pricing period within the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply for each pricing period.

[0009] The technical solution of the present invention first converts the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix, wherein the minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant; then converts the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix, wherein the constraints include the constraints of the demanded power and the power supply and the constraints of the photovoltaic power supply; finally, the target photovoltaic power supply and target energy storage power supply of each pricing period are determined according to the quantum constraints, the quantum objective function, the target period, and the cost parameters and the demanded power corresponding to each pricing period within the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply of each pricing period. The above technical solution first converts the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix. Then, the constraints corresponding to the photovoltaic power plant are converted into quantum constraints according to the conversion matrix. Finally, the target photovoltaic power supply and target energy storage power supply of each pricing period are determined according to the quantum constraint conditions, quantum objective function, target period, and the cost parameters and required electricity corresponding to each pricing period in the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and target energy storage power supply of each pricing period, which not only saves time, but also improves the accuracy of the determined operation mode of the photovoltaic power plant in each pricing period, which helps the photovoltaic power plant to reduce operating costs and improve economic benefits. At the same time, it can more reasonably allocate photovoltaic and energy storage resources, reduce resource waste, and improve resource utilization efficiency. Therefore, the present invention can solve the problem that the prior art cannot flexibly adjust the operation mode according to actual conditions to meet market demand, thereby causing supply and demand mismatch and energy waste.

[0010] In a second aspect, an embodiment of the present invention further provides a device for determining an operation mode of a photovoltaic power plant, the device comprising:

[0011] A target conversion module, used for converting the minimum cost function of the photovoltaic power plant into a quantum target function according to a preset conversion matrix, wherein the minimum cost target function is determined by the power supply cost and power supply of the photovoltaic power plant;

[0012] A constraint conversion module, used to convert the constraint conditions corresponding to the photovoltaic power plant into quantum constraint conditions according to the conversion matrix, wherein the constraint conditions include the constraint conditions of the required power and the power supply and the constraint conditions of the photovoltaic power supply;

[0013] A mode determination module is used to determine the target photovoltaic power supply and the target energy storage power supply of each pricing period according to the quantum constraint condition, the quantum objective function, the target period, and the cost parameters and required electricity corresponding to each pricing period within the target period, and determine the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply of each pricing period.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0015] at least one processor; and a memory communicatively coupled to the at least one processor;

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can implement any method for determining the operating mode of a photovoltaic power plant as described in the first aspect.

[0017] In a fourth aspect, an embodiment of the present invention further provides a storage medium including computer executable instructions,

[0018] The computer executable instructions, when executed by a computer processor, implement any method for determining an operating mode of a photovoltaic power plant as described in the first aspect.

[0019] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the photovoltaic power plant operation mode determination device, or may be packaged separately from the processor of the photovoltaic power plant operation mode determination device, and this application does not limit this.

[0020] The description of the second, third and fourth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third and fourth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0021] In this application, the name of the photovoltaic power plant operation mode determination device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalent technologies.

[0022] These and other aspects of the present application will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flow chart of a method for determining an operating mode of a photovoltaic power plant provided by an embodiment of the present invention;

[0025] Figure 2 A flowchart of another method for determining an operating mode of a photovoltaic power plant provided by an embodiment of the present invention;

[0026] Figure 3 A schematic diagram of the structure of a device for determining an operating mode of a photovoltaic power plant provided by an embodiment of the present invention;

[0027] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0029] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0030] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0031] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0032] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the embodiments in the present invention and the features in the embodiments can be combined with each other without conflict.

[0033] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0034] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0035] Figure 1 A flowchart of a method for determining an operation mode of a photovoltaic power plant provided by an embodiment of the present invention. This embodiment is applicable to the case of determining an operation mode of a photovoltaic power plant. The method can be performed by a photovoltaic power plant operation mode determination device, and specifically includes the following steps:

[0036] Step 110: Convert the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix.

[0037] Among them, the conversion matrix refers to the matrix that converts the minimum cost function and constraints of the photovoltaic power plant into the objective function and constraints in quantum form, with the purpose of making the objective function and constraints processable within the quantum computing framework. The minimum cost function refers to the mathematical expression that measures the operating cost of a photovoltaic power plant. The minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant. The power supply cost refers to the total cost incurred by the photovoltaic power plant when supplying power, and the power supply cost includes but is not limited to the total cost of photovoltaic power supply and the total cost of energy storage equipment power supply. The power supply refers to the actual amount of electricity generated by the photovoltaic power plant within a certain period of time (such as one day, one month or one year). The quantum objective function refers to the objective function obtained by converting the minimum cost function through the conversion matrix, which is used to describe the goal of minimizing the cost of photovoltaic power plants within the framework of quantum computing.

[0038] Specifically, before converting the minimum cost function of the photovoltaic power plant into the quantum objective function, the required quantum gate (such as Hadamard gate) is first determined in the correspondence table between the actual demand and the quantum gate based on the actual demand (such as finding the optimal operation mode), and then the conversion matrix is ​​determined from the correspondence table between the quantum gate and the conversion matrix according to the determined quantum gate, and then the minimum cost function of the photovoltaic power plant is converted into the quantum objective function according to the preset conversion matrix. Specifically, the minimum cost function can be rewritten into the form of Hamiltonian using the conversion matrix to obtain the quantum objective function. Among them, the quantum gate is the basic operation unit in quantum computing.

[0039] It should be noted that the correspondence table between actual demand and quantum gates is obtained based on big data training. During the big data training, a large amount of data from photovoltaic power plants under different operating conditions (such as equipment parameters, environmental data, cost data, and the effects of different operating modes, etc.) will be collected. Through in-depth analysis and training of these massive data, quantum gates that are suitable for different actual demand scenarios can be discovered. The correspondence table between quantum gates and conversion matrices is established based on the theoretical foundation of quantum computing and the basic principles of quantum mechanics.

[0040] In addition, after the required quantum gate is determined from the corresponding relationship table between the actual demand and the quantum gate based on the actual demand, the operation mode of the photovoltaic power plant can be quantum-encoded according to the determined quantum gate. For example, if the quantum gate is a Hadamard gate, the quantum state encoding of the operation mode of the photovoltaic power plant is as follows:

[0041]

[0042] Among them, a i Denotes the quantum bit q i The base state of , its value is |1>, |0>, |1> means the surplus power is connected to the grid, |0> means it is charged into the energy storage.

[0043] In this embodiment, by converting the minimum cost function of the photovoltaic power plant into a quantum objective function, a data basis is provided for subsequently determining the operation mode of the photovoltaic power plant in each pricing period.

[0044] Step 120: Convert the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix.

[0045] Among them, the constraint conditions refer to the conditions related to the operating mode when the photovoltaic power plant is operating normally. The constraint conditions include the constraint conditions of the demand power and the power supply and the constraint conditions of the photovoltaic power supply. The constraint conditions of the demand power and the power supply mean that the power provided by the photovoltaic power plant must meet the needs of the user or the power grid. The constraint conditions of the photovoltaic power supply refer to the different photovoltaic power supplies corresponding to different operating modes. The quantum constraint conditions refer to the constraints obtained by converting the constraints corresponding to the photovoltaic power plant through the conversion matrix, which are used to represent the constraints in the framework of quantum computing.

[0046] Specifically, the constraints corresponding to the photovoltaic power plant can be rewritten into the form of an auxiliary Hamiltonian using a conversion matrix to obtain quantum constraints.

[0047] In this embodiment, by converting the constraints corresponding to the photovoltaic power plant into quantum constraints, a data basis is provided for subsequently determining the operation mode of the photovoltaic power plant in each pricing period.

[0048] Step 130: determine the target photovoltaic power supply and target energy storage power supply for each pricing period according to the quantum constraint conditions, quantum objective function, target period, and cost parameters and power demand corresponding to each pricing period within the target period, and determine the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and target energy storage power supply for each pricing period.

[0049] Among them, the target period refers to the time period determined according to the actual situation or demand. For example, the target period can be 8 hours, 12 hours, one day or one month. The pricing period refers to the time period divided according to different cost parameters within the target period. The target photovoltaic power supply refers to the photovoltaic power supply when the power supply demand is met and the operating cost is the lowest. The target energy storage power supply refers to the energy storage power supply when the power supply demand is met and the operating cost is the lowest. The operating mode refers to the working mode of the photovoltaic power plant, for example, the operating mode includes surplus power grid access, charging into energy storage and abnormal mode. The cost parameter refers to the parameters of the various costs required by the photovoltaic power plant when supplying power. For example, the cost parameter includes the price of using photovoltaic equipment to generate one kilowatt-hour of power and the price of using energy storage equipment to generate one kilowatt-hour of power. The demand power refers to the power demand of the power consumption area or user corresponding to the photovoltaic power plant in each pricing period within the target period.

[0050] Specifically, after obtaining the quantum constraint conditions and the quantum objective function, a quantum solution function can be constructed according to the quantum constraint conditions and the quantum objective function. Then, the target period is determined according to the actual situation or demand, and then based on the target period, the cost parameters, demand power and candidate photovoltaic power supplies corresponding to each pricing period in the target period are obtained from the database storing the relevant parameters of each pricing period of the photovoltaic power plant (including cost parameters, candidate photovoltaic power supply and demand power). Then, the cost parameters, demand power and candidate photovoltaic power supplies corresponding to each pricing period in the target period are substituted into the quantum solution function, and the quantum solution function is solved using the quantum computing platform to obtain the evaluation value of the operating cost of the photovoltaic power plant in each pricing period, and then the minimum evaluation value is selected from each evaluation, and the photovoltaic power supply of each pricing period corresponding to the minimum evaluation value is determined as the target photovoltaic power supply of each pricing period. At the same time, the target energy storage power supply of each pricing period can be determined according to the difference between the demand power and the target photovoltaic power supply of each pricing period. Then, the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply of each pricing period. Specifically, the operation mode of the photovoltaic power plant in each pricing period can be determined from the correspondence table between the photovoltaic power supply, the energy storage power supply and the operation mode according to the target photovoltaic power supply and the target energy storage power supply in each pricing period. Among them, the correspondence table between the photovoltaic power supply, the energy storage power supply and the operation mode is set in advance according to the actual situation or demand.

[0051] In this embodiment, through the above steps, the target photovoltaic power supply and target energy storage power supply for each pricing period can be accurately determined according to actual conditions, which not only saves determination time, but also improves the accuracy of the determined operating mode of the photovoltaic power plant in each pricing period, which helps the photovoltaic power plant to reduce operating costs and improve economic benefits. At the same time, it can more reasonably allocate photovoltaic and energy storage resources, reduce resource waste, and improve resource utilization efficiency.

[0052] In an embodiment of the present invention, the minimum cost function of the photovoltaic power plant is first converted into a quantum objective function according to a pre-set conversion matrix. Then, the constraints corresponding to the photovoltaic power plant are converted into quantum constraints according to the conversion matrix. Finally, the target photovoltaic power supply and target energy storage power supply of each pricing period are determined according to the quantum constraints, quantum objective function, target period, and the cost parameters and required electricity corresponding to each pricing period in the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and target energy storage power supply of each pricing period, which not only saves time, but also improves the accuracy of the determined operation mode of the photovoltaic power plant in each pricing period, which helps the photovoltaic power plant to reduce operating costs and improve economic benefits. At the same time, it can more reasonably allocate photovoltaic and energy storage resources, reduce resource waste, and improve resource utilization efficiency. Therefore, the present invention can solve the problem that the prior art cannot flexibly adjust the operation mode according to actual conditions to meet market demand, thereby causing supply and demand mismatch and energy waste.

[0053] Figure 2 A flowchart of another method for determining an operation mode of a photovoltaic power plant provided by an embodiment of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, the method may also include:

[0054] Step 210: Convert the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix.

[0055] Among them, the minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant.

[0056] Further, step 210 may specifically include: determining the variables related to the operating mode of the photovoltaic power plant in the minimum cost objective function as influencing variables, and determining the variables other than the influencing variables in the minimum cost objective function as non-influencing variables; determining the quantum influencing variables according to the product of the influencing variables and the influencing conversion matrix; determining the quantum non-influencing variables according to the product of the non-influencing variables and the non-influencing conversion matrix; and converting the minimum cost function into a quantum objective function according to the quantum influencing variables and the quantum non-influencing variables.

[0057] Among them, the conversion matrix includes an influence conversion matrix and a non-influence conversion matrix. Influence variables refer to variables that are directly related to the operating mode of the photovoltaic power plant in the minimum cost objective function. Non-influence variables refer to other variables in the minimum cost objective function except the variables (influence variables) related to the operating mode. The influence conversion matrix refers to a matrix that converts the influence variables in the minimum cost objective function into quantum influence variables in the quantum objective function. The non-influence conversion matrix refers to a matrix that converts the non-influence variables in the minimum cost objective function into quantum non-influence variables in the quantum objective function. Quantum influence variables refer to quantum variables that are directly related to the operating mode of the photovoltaic power plant in the quantum objective function. Quantum non-influence variables refer to other quantum variables in the quantum objective function except quantum influence variables.

[0058] Specifically, the variables related to the operation mode of the photovoltaic power plant in the minimum cost objective function can be first determined as influencing variables, and the variables other than the influencing variables in the minimum cost objective function can be determined as non-influencing variables, and then the product of the influencing variables and the influencing conversion matrix can be determined as quantum influencing variables, and the product of the non-influencing variables and the non-influencing conversion matrix can be determined as quantum non-influencing variables, and finally the minimum cost function can be converted into a quantum objective function according to the quantum influencing variables and the quantum non-influencing variables. Specifically, the minimum cost function can be rewritten into the form of Hamiltonian using quantum influencing variables and quantum non-influencing variables to obtain a quantum objective function.

[0059] Exemplarily, the minimum cost objective function is as follows:

[0060]

[0061] Where C is the operating cost, c i is the power supply cost of photovoltaic equipment for one kilowatt-hour of electricity in the i-th pricing period, y i is the photovoltaic power supply in the i-th pricing period, p i is the power supply cost of the energy storage device for one kilowatt-hour of electricity in the i-th pricing period, s i is the energy storage power supply in the i-th pricing period, d i is the penalty cost for unsatisfied electricity demand in the i-th pricing period, D i is the electricity demand in the ith pricing period, and n is the total number of pricing periods.

[0062] The basic constraints are:

[0063] Among them, a=1 means that the operation mode of the photovoltaic power plant is to connect the surplus power to the grid, a=0 means that the operation mode of the photovoltaic power plant is to charge the energy storage, y max,i The maximum value of photovoltaic power supply in the i-th pricing period, s max,irepresents the maximum capacity of the energy storage device in the i-th pricing period, η c Indicates the charging efficiency, P c Represents the charging power, η d Indicates the efficiency of discharge, P d Indicates the discharge power.

[0064] From the basic constraints, it can be found that the only variable related to the operation mode a of the photovoltaic power plant is y i Therefore, the variable related to the operation mode of the photovoltaic power plant in the minimum cost objective function can be determined as y i , will be i is determined as an influencing variable. i The other variables can be obtained from the database in advance, which is equivalent to constants, so s i Determined as a non-influencing variable.

[0065] Finally, the product of the influencing variables and the influencing conversion matrix is ​​determined as the quantum influencing variables, and the product of the non-influencing variables and the non-influencing conversion matrix is ​​determined as the quantum non-influencing variables. Then, the minimum cost function is rewritten into the form of Hamiltonian using the quantum influencing variables and quantum non-influencing variables to obtain the quantum objective function. The quantum objective function is shown below:

[0066]

[0067] Among them, H c is the quantum objective function, is the impact transformation matrix, is the non-affecting transformation matrix.

[0068] In this embodiment, by determining the influencing variables and the non-influencing variables, the minimum cost function can be more accurately converted into a quantum objective function.

[0069] Step 211: Convert the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix.

[0070] Among them, the constraints include the constraints on the demand power and power supply and the constraints on the photovoltaic power supply.

[0071] Furthermore, before the constraint conditions corresponding to the photovoltaic power plant are converted into quantum constraint conditions according to the conversion matrix, the method further includes: determining the constraint conditions related to the influencing variables in the basic constraint conditions as constraint conditions.

[0072] Among them, the basic constraints are the conditions that must be met for the normal operation of a photovoltaic power plant.

[0073] Specifically, constraints related to influencing variables can be screened out from basic constraints and determined as constraints corresponding to the photovoltaic power plant.

[0074] In this embodiment, by determining the constraint conditions related to the influencing variables in the basic constraint conditions as the constraint conditions corresponding to the photovoltaic power plant, the accuracy of the subsequently determined quantum constraint conditions is improved.

[0075] For example, according to the above content, the basic constraints are: The influencing variable is y i .

[0076] Therefore, the constraints corresponding to the photovoltaic power plant are:

[0077] The constraint is then rewritten into the form of an auxiliary Hamiltonian using the transformation matrix, resulting in the quantum constraint as follows:

[0078]

[0079] Among them, H a represents the quantum constraint condition.

[0080] Step 212: construct a quantum solution function according to the quantum constraint conditions and the quantum objective function.

[0081] Among them, the quantum solution function refers to a set of functions used to find the optimal value (such as the minimum value) of the quantum objective function in a quantum computing environment.

[0082] Specifically, a suitable quantum computing model (such as a quantum circuit model, a quantum annealing model or a quantum approximate optimization algorithm, etc.) can be selected according to actual conditions or needs, and then the selected quantum computing model can be applied to transform the quantum constraints and the quantum objective function into the form of the selected model, thereby obtaining a quantum solution function.

[0083] Exemplarily, if the selected quantum computing model is a quantum approximate optimization algorithm, the quantum solution function is as follows:

[0084]

[0085] Among them, U(θ) represents the quantum solution function, i represents an imaginary number, and θ is a parameter with a value of π / 2.

[0086] In this embodiment, by constructing a quantum solution function, the complexity of subsequent calculations is reduced and the overall efficiency is improved.

[0087] Step 213: Substitute the cost parameters, candidate photovoltaic power supplies and required power corresponding to each pricing period within the target period into the quantum solution function to obtain the cost evaluation value for each candidate photovoltaic power supply corresponding to each pricing period, and sort the evaluation values ​​to obtain the sorting result.

[0088] Among them, the candidate photovoltaic power supply refers to the value of photovoltaic power supply when the photovoltaic power plant is in operation, which is pre-set according to the actual situation or demand. The evaluation value refers to a quantitative result obtained by substituting the cost parameters, candidate photovoltaic power supply and required power corresponding to the pricing period into the quantum solution function. The sorting result refers to the result of sorting the cost evaluation values ​​obtained when each candidate photovoltaic power supply corresponds to each pricing period.

[0089] Specifically, after obtaining the quantum solution function, the cost parameters, candidate photovoltaic power supply and demand power corresponding to each pricing period within the target period can be passed to the quantum solution function as input parameters. Then, the quantum computing platform is used to solve the quantum solution function to obtain the cost evaluation value of each candidate photovoltaic power supply corresponding to each pricing period. Then, the evaluation values ​​can be sorted in ascending order to obtain the sorting result.

[0090] In this embodiment, the above steps provide a data basis for subsequently determining the target photovoltaic power supply and the target energy storage power supply.

[0091] Step 214: determine the target photovoltaic power supply for each pricing period according to the sorting results, and determine the target energy storage power supply according to the power demand and the target photovoltaic power supply for each pricing period.

[0092] Specifically, because the goal of this embodiment is to minimize costs, the photovoltaic power supply for each pricing period corresponding to the minimum evaluation value in the sorting results can be determined as the target photovoltaic power supply for each pricing period. Then, the difference between the demand power for each pricing period and the target photovoltaic power supply can be calculated to obtain the target energy storage power supply for each pricing period.

[0093] In this embodiment, by obtaining the target photovoltaic power supply and the target energy storage power supply, a data basis is provided for subsequently determining the operation mode of the photovoltaic power plant in each pricing period.

[0094] Furthermore, the target photovoltaic power supply for each pricing period is determined according to the sorting results, including: determining the photovoltaic power supply for each pricing period corresponding to the minimum evaluation value in the sorting results as the target photovoltaic power supply for each pricing period.

[0095] Specifically, after obtaining the sorting results, they can be traversed to find the minimum evaluation value, and then the photovoltaic power supply in each pricing period corresponding to the minimum evaluation value can be determined as the target photovoltaic power supply in each pricing period.

[0096] In this embodiment, by selecting the photovoltaic power supply corresponding to the minimum evaluation value, it can be ensured that in each pricing period, while meeting the electricity demand, the power generation cost is minimized.

[0097] Step 215: Determine the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply in each pricing period.

[0098] Further, step 215 may specifically include: for the current pricing period, if the target photovoltaic power supply for the current pricing period is not zero, then determining that the operating mode of the photovoltaic power plant in the current pricing period is to access the grid with surplus power; if the target photovoltaic power supply for the current pricing period is zero and the target energy storage power supply is not zero, then determining that the operating mode of the photovoltaic power plant in the current pricing period is to charge the energy storage.

[0099] Among them, surplus power grid access refers to the photovoltaic power plant mainly using the power generated by photovoltaic equipment to meet the power supply demand, and energy storage equipment plays a supporting role. Charging into energy storage means that the photovoltaic power plant only relies on energy storage equipment to supply power.

[0100] Specifically, after obtaining the target photovoltaic power supply and target energy storage power supply for each pricing period, for the current pricing period, the operating mode of the current pricing period can be determined according to the values ​​of the target photovoltaic power supply and target energy storage power supply for the current pricing period. Specifically, if the target photovoltaic power supply for the current pricing period is not zero, then the operating mode of the photovoltaic power plant in the current pricing period is determined to be surplus power access to the grid; if the target photovoltaic power supply for the current pricing period is zero and the target energy storage power supply is not zero, then the operating mode of the photovoltaic power plant in the current pricing period is determined to be charging energy storage. If the target photovoltaic power supply and target energy storage power supply for the current pricing period are both zero, then the operating mode of the photovoltaic power plant in the current pricing period is determined to be an abnormal mode.

[0101] In this embodiment, through the above steps, the photovoltaic power plant can flexibly adjust the operation mode according to actual conditions, thereby enhancing the adaptability of the photovoltaic power plant to changes in market and user demand.

[0102] Step 216: Send the operating mode of each pricing period to the terminal held by the staff.

[0103] The terminal refers to the device used by staff to receive, process and display information. For example, the terminal can be a smartphone, tablet computer or laptop computer.

[0104] Specifically, after determining the operating mode of the photovoltaic power plant in each pricing period within the target period, it can be sent to the terminal held by the staff to help the staff adjust the operating strategy of the photovoltaic power plant in real time according to the operating mode of each pricing period, thereby improving operational efficiency, optimizing energy costs, and enhancing grid stability, thereby avoiding energy waste and supply and demand mismatch, and enabling photovoltaic power plants to operate in a more efficient, economical and stable manner.

[0105] Therefore, the technical solution of the present invention first converts the minimum cost function of the photovoltaic power plant into a quantum objective function according to a pre-set conversion matrix. Then, the constraints corresponding to the photovoltaic power plant are converted into quantum constraints according to the conversion matrix. Then, a quantum solution function is constructed according to the quantum constraints and the quantum objective function, which reduces the complexity of subsequent calculations and improves the overall efficiency. Then, the cost parameters, candidate photovoltaic power supply and demand power corresponding to each pricing period in the target period are substituted into the quantum solution function to obtain the evaluation value of the cost when each pricing period corresponds to each candidate photovoltaic power supply, and the evaluation values ​​are sorted to obtain the sorting result. Then, the target photovoltaic power supply for each pricing period is determined according to the sorting result, and the target energy storage power supply is determined according to the demand power and the target photovoltaic power supply for each pricing period. Then, the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply for each pricing period, which not only saves the determination time, but also improves the accuracy of the determined operation mode of the photovoltaic power plant in each pricing period. Finally, the operation mode of each pricing period is sent to the terminal held by the staff, helping the staff to adjust the operation strategy of the photovoltaic power plant in real time according to the operation mode of each pricing period, thereby improving the operation efficiency, optimizing the energy cost, and enhancing the stability of the power grid, thereby avoiding energy waste and mismatch between supply and demand, so that the photovoltaic power plant can operate in a more efficient, economical and stable manner. Therefore, the present invention can solve the problem that the prior art cannot flexibly adjust the operation mode according to the actual situation to meet the market demand, thus causing mismatch between supply and demand and energy waste.

[0106] In addition, the minimum cost function can be more accurately converted into a quantum objective function based on quantum influencing variables and quantum non-influencing variables. The constraints related to the influencing variables in the basic constraints are determined as the constraints corresponding to the photovoltaic power plant, which improves the accuracy of the subsequently determined quantum constraints. Then, the minimum evaluation value is found from the sorting results, and the corresponding photovoltaic power supply in each pricing period is determined as the target photovoltaic power supply in each pricing period. This can ensure that in each pricing period, the photovoltaic power plant can meet the electricity demand while minimizing the power generation cost. Finally, the operating mode of the current pricing period is determined based on the values ​​of the target photovoltaic power supply and target energy storage power supply in the current pricing period, which can enable the photovoltaic power plant to flexibly adjust the operating mode according to the actual situation, and enhance the adaptability of the photovoltaic power plant to changes in market and user demand.

[0107] Figure 3 A schematic diagram of the structure of a photovoltaic power plant operating mode determination device provided in an embodiment of the present invention. The device and the photovoltaic power plant operating mode determination method of the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the photovoltaic power plant operating mode determination device, reference can be made to the embodiment of the above-mentioned photovoltaic power plant operating mode determination method.

[0108] like Figure 3 As shown, the device comprises:

[0109] A target conversion module 310, configured to convert the minimum cost function of the photovoltaic power plant into a quantum target function according to a preset conversion matrix, wherein the minimum cost target function is determined by the power supply cost and power supply of the photovoltaic power plant;

[0110] A constraint conversion module 320, configured to convert the constraint conditions corresponding to the photovoltaic power plant into quantum constraint conditions according to the conversion matrix, wherein the constraint conditions include the constraint conditions of the required power and the power supply and the constraint conditions of the photovoltaic power supply;

[0111] The mode determination module 330 is used to determine the target photovoltaic power supply and the target energy storage power supply of each pricing period according to the quantum constraint condition, the quantum objective function, the target period, and the cost parameters and required electricity corresponding to each pricing period within the target period, and determine the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply of each pricing period.

[0112] Based on the above embodiment, the conversion matrix includes an impact conversion matrix and a non-impact conversion matrix. The target conversion module 310 is specifically used for:

[0113] Determine the variables related to the operation mode of the photovoltaic power plant in the minimum cost objective function as influencing variables, and determine the variables other than the influencing variables in the minimum cost objective function as non-influencing variables;

[0114] Determining a quantum influence variable according to the product of the influence variable and the influence conversion matrix;

[0115] Determining quantum non-influence variables according to the product of the non-influence variables and the non-influence transformation matrix;

[0116] The minimum cost function is converted into the quantum objective function according to the quantum influencing variables and the quantum non-influencing variables.

[0117] Based on the above embodiment, the device further includes:

[0118] The screening module is used to determine the constraints related to the influencing variables in the basic constraints as constraints before converting the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix.

[0119] On the basis of the above embodiment, the target photovoltaic power supply and the target energy storage power supply for each pricing period are determined according to the quantum constraint condition, the quantum objective function, the target period, and the cost parameters and the required power corresponding to each pricing period within the target period, including:

[0120] Constructing a quantum solution function according to the quantum constraint condition and the quantum objective function;

[0121] Substituting the cost parameter, candidate photovoltaic power supply and required power corresponding to each pricing period within the target period into the quantum solution function, obtaining an evaluation value of the cost when each pricing period corresponds to each candidate photovoltaic power supply, and sorting the evaluation values ​​to obtain a sorting result;

[0122] The target photovoltaic power supply amount for each pricing period is determined according to the sorting result, and the target energy storage power supply amount is determined according to the required power amount and the target photovoltaic power supply amount for each pricing period.

[0123] On the basis of the above embodiment, determining the target photovoltaic power supply amount for each pricing period according to the sorting result includes:

[0124] The photovoltaic power supply amount in each pricing period corresponding to the minimum evaluation value in the sorting result is determined as the target photovoltaic power supply amount in each pricing period.

[0125] On the basis of the above embodiment, determining the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply in each pricing period includes:

[0126] For the current pricing period, if the target photovoltaic power supply amount for the current pricing period is not zero, determining that the operation mode of the photovoltaic power plant for the current pricing period is surplus power access to the grid;

[0127] If the target photovoltaic power supply in the current pricing period is zero and the target energy storage power supply is not zero, it is determined that the operation mode of the photovoltaic power plant in the current pricing period is charging energy storage.

[0128] Based on the above embodiment, the device further includes:

[0129] The display module is used to send the operating mode of each pricing period to the terminal held by the staff after determining the operating mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply in each pricing period.

[0130] The photovoltaic power plant operation mode determination device provided in the embodiment of the present invention can execute the photovoltaic power plant operation mode determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0131] It is worth noting that in the above-mentioned embodiment of the photovoltaic power plant operation mode determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0132] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0133] like Figure 4 As shown, the electronic device 4 is in the form of a general purpose computing electronic device. The components of the electronic device 4 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0134] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0135] The electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0136] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0137] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0138] The electronic device 4 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the electronic device 4, and / or any device that enables the electronic device 4 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the electronic device 4 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0139] The processing unit 16 executes various functional applications and page displays by running the program stored in the system memory 28, for example, implementing the photovoltaic power plant operation mode determination method provided in the embodiment of the present invention, the method comprising:

[0140] Converting the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix, wherein the minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant;

[0141] Converting the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix, wherein the constraints include constraints on the power demand and power supply and constraints on photovoltaic power supply;

[0142] The target photovoltaic power supply and the target energy storage power supply for each pricing period are determined according to the quantum constraint conditions, the quantum objective function, the target period, and the cost parameters and the required electricity quantity corresponding to each pricing period within the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply for each pricing period.

[0143] Of course, those skilled in the art can understand that the processor can also implement the technical solution of the method for determining the operation mode of a photovoltaic power plant provided by any embodiment of the present invention.

[0144] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, for example, a method for determining an operation mode of a photovoltaic power plant provided in an embodiment of the present invention is implemented. The method includes:

[0145] Converting the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix, wherein the minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant;

[0146] Converting the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix, wherein the constraints include constraints on the power demand and power supply and constraints on photovoltaic power supply;

[0147] The target photovoltaic power supply and the target energy storage power supply for each pricing period are determined according to the quantum constraint conditions, the quantum objective function, the target period, and the cost parameters and the required electricity quantity corresponding to each pricing period within the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply for each pricing period.

[0148] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0149] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0150] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0151] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0152] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0153] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations.

[0154] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining an operation mode of a photovoltaic power plant, characterized in that: The method comprises: Converting the minimum cost function of the photovoltaic power plant into a quantum objective function according to a preset conversion matrix, wherein the minimum cost objective function is determined by the power supply cost and power supply of the photovoltaic power plant; Converting the constraints corresponding to the photovoltaic power plant into quantum constraints according to the conversion matrix, wherein the constraints include constraints on the power demand and power supply and constraints on the photovoltaic power supply; The target photovoltaic power supply and the target energy storage power supply for each pricing period are determined according to the quantum constraint conditions, the quantum objective function, the target period, and the cost parameters and the required electricity quantity corresponding to each pricing period within the target period, and the operation mode of the photovoltaic power plant in each pricing period is determined according to the target photovoltaic power supply and the target energy storage power supply for each pricing period.

2. The method for determining the operation mode of a photovoltaic power plant according to claim 1, characterized in that: The conversion matrix includes an impact conversion matrix and a non-impact conversion matrix. The minimum cost function of the photovoltaic power plant is converted into a quantum objective function according to the preset conversion matrix, including: Determine the variables related to the operation mode of the photovoltaic power plant in the minimum cost objective function as influencing variables, and determine the variables other than the influencing variables in the minimum cost objective function as non-influencing variables; Determining a quantum influence variable according to the product of the influence variable and the influence conversion matrix; Determining quantum non-influence variables according to the product of the non-influence variables and the non-influence transformation matrix; The minimum cost function is converted into the quantum objective function according to the quantum influencing variables and the quantum non-influencing variables.

3. The method for determining the operation mode of a photovoltaic power plant according to claim 2, characterized in that: Before converting the constraint conditions corresponding to the photovoltaic power plant into quantum constraint conditions according to the conversion matrix, the method further includes: The constraint condition related to the influencing variable in the basic constraint conditions is determined as the constraint condition.

4. The method for determining the operation mode of a photovoltaic power plant according to claim 2, characterized in that: Determining the target photovoltaic power supply and the target energy storage power supply for each pricing period according to the quantum constraint condition, the quantum objective function, the target period, and the cost parameters and power demand corresponding to each pricing period within the target period includes: Constructing a quantum solution function according to the quantum constraint condition and the quantum objective function; Substituting the cost parameter, candidate photovoltaic power supply and required power corresponding to each pricing period within the target period into the quantum solution function, obtaining an evaluation value of the cost when each pricing period corresponds to each candidate photovoltaic power supply, and sorting the evaluation values ​​to obtain a sorting result; The target photovoltaic power supply amount for each pricing period is determined according to the sorting result, and the target energy storage power supply amount is determined according to the required power amount and the target photovoltaic power supply amount for each pricing period.

5. The method for determining the operation mode of a photovoltaic power plant according to claim 4, characterized in that: Determining the target photovoltaic power supply amount for each pricing period according to the sorting result includes: The photovoltaic power supply amount in each pricing period corresponding to the minimum evaluation value in the sorting result is determined as the target photovoltaic power supply amount in each pricing period.

6. The method for determining the operation mode of a photovoltaic power plant according to claim 1, characterized in that: Determining the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply in each pricing period includes: For the current pricing period, if the target photovoltaic power supply amount for the current pricing period is not zero, determining that the operation mode of the photovoltaic power plant for the current pricing period is surplus power access to the grid; If the target photovoltaic power supply in the current pricing period is zero and the target energy storage power supply is not zero, it is determined that the operation mode of the photovoltaic power plant in the current pricing period is charging energy storage.

7. The method for determining the operation mode of a photovoltaic power plant according to claim 1, characterized in that: After determining the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply in each pricing period, the method further includes: The operation mode of each pricing period is sent to the terminal held by the staff.

8. A device for determining an operation mode of a photovoltaic power plant, characterized in that: The device comprises: A target conversion module, used for converting the minimum cost function of the photovoltaic power plant into a quantum target function according to a preset conversion matrix, wherein the minimum cost target function is determined by the power supply cost and power supply of the photovoltaic power plant; A constraint conversion module, used to convert the constraint conditions corresponding to the photovoltaic power plant into quantum constraint conditions according to the conversion matrix, wherein the constraint conditions include the constraint conditions of the required power and the power supply and the constraint conditions of the photovoltaic power supply; A mode determination module is used to determine the target photovoltaic power supply and the target energy storage power supply of each pricing period according to the quantum constraint condition, the quantum objective function, the target period, and the cost parameters and required electricity corresponding to each pricing period within the target period, and determine the operation mode of the photovoltaic power plant in each pricing period according to the target photovoltaic power supply and the target energy storage power supply of each pricing period.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the operating mode of a photovoltaic power plant as described in any one of claims 1-7.

10. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, the method for determining the operation mode of a photovoltaic power plant according to any one of claims 1 to 7 is implemented.

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