A power system source-grid-load-storage collaborative multi-objective planning method and application thereof

Abstract

A kind of power system source network load storage collaborative multi-objective planning method and its application, comprising the following steps: the change of new energy incorporation into power grid after new type power system form is analyzed, and analysis content includes high power electronic and strong uncertainty in multiple time, space;For planning area, obtain the content such as load demand in planning period, power grid operation, power development, power system safety;According to the relevant information obtained in planning area, the characteristics of new type power system are combined to build the multi-objective planning model of source network load storage collaboration;According to the multi-objective planning model of source network load storage collaboration established, the new type power system source network load storage collaborative planning result containing high proportion of clean energy is obtained.The application further reduces the cost of power grid operation, makes its operation more smooth, maximizes the consumption of new energy power generation, improves the operation efficiency and safety of power system, promotes the development of new energy wind and light power generation and protects ecological environment.

Description

Technical Field

[0001] This invention relates to a planning method, and more particularly to a multi-objective planning method for the coordinated operation of power generation, grid, load and storage in a power system and its application. Background Technology

[0002] New energy power generation has significant advantages in terms of carbon emissions, with photovoltaic and wind power being prime examples. Clean energy plays a crucial role in the power system transformation, serving as a significant alternative to traditional power generation methods. New energy generating units differ greatly from traditional units in terms of unit inertia and output characteristics. The participation of new energy power generation has brought about a qualitative change in the power system, resulting in a "dual-high" characteristic: a high proportion of power electronics and a high proportion of new energy. Building a new type of power system with new energy as the mainstay is the development direction of my country's power system.

[0003] Existing technologies, such as patent application number CN202011379111.X (An Active Distribution Network Scheduling Method Considering Source-Storage-Load Interaction), involve an active distribution network scheduling method that considers source-storage-load interaction. The optimization objective is to minimize the daily comprehensive operating cost of the distribution network. It comprehensively considers the costs of various stages, including power purchase, generation, energy storage, demand response, and network losses, as well as the fluctuation costs introduced to quantify the negative impact of tie-line power changes on the grid. Solving the model requires considering constraints, mainly including power balance constraints, distributed generation (DG) output constraints, energy storage operation constraints, and demand response constraints. The model is then solved using a particle swarm optimization algorithm improved based on dynamic inertia weights and random mutation factors. This invention can effectively reduce the operating cost of the distribution network and improve the operating characteristics of the distribution network system. However, this existing technology lacks systematic research on the power balance problem in the source-grid-load-storage coordination of new power systems.

[0004] Existing technologies, such as patent application number CN202111035449.8 (Real-time Optimization Scheduling Method, Device, and Medium for High-Proportion New Energy Power Systems), disclose a real-time optimization scheduling method, device, and medium for high-proportion new energy power systems. This includes: acquiring control unit parameters, node parameters, and line parameters of the high-proportion new energy power system; constructing a first optimization scheduling model based on the control unit parameters, node parameters, and line parameters; converting the first optimization scheduling model into a feedback control system to perform dynamic distributed calculations on the first optimization scheduling model to obtain a second optimization scheduling model; obtaining the control unit dynamic feedback equation, node phase angle dynamic feedback equation, and Lagrange multiplier dynamic feedback equation based on the second optimization scheduling model; and using the ode15s solver to simulate and solve the control unit dynamic feedback equation, node phase angle dynamic feedback equation, and Lagrange multiplier dynamic feedback equation to obtain the optimal scheduling scheme for the high-proportion new energy power system. However, this existing technology does not consider practical methods for planning new power systems that include a high proportion of new energy; it only focuses on the scheduling level of power system operation optimization under the condition of new energy grid integration. Summary of the Invention

[0005] The purpose of this invention is to provide a novel integrated planning method for power system generation, grid, load, and storage. This method aims at safety, economy, and low carbon emissions, and conducts integrated planning for generation, grid, load, and storage under these objectives. This planning is based on the coordinated and optimized operation of new energy sources and carbon capture power plants, the safe operation of high-proportion new energy transmission, and the low-carbon operation of the power system considering safety and stability constraints, thus developing a more optimized new power system operation mode.

[0006] This invention provides a novel multi-objective programming method for power system source-grid-load-storage coordination that incorporates a high proportion of clean energy, comprising the following steps:

[0007] S1: Analyze the changes in the form of the new power system after the integration of new energy sources into the power grid. The analysis includes high power electronics and strong uncertainties in multiple time and space. For the planning area, obtain information on load demand, power grid operation, power source development, and power system security within the planning period.

[0008] S2: Based on the relevant information already obtained in the planning area and combined with the characteristics of the new power system, construct a multi-objective planning model for source-grid-load-storage coordination.

[0009] S3: Based on the established multi-objective programming model for source-grid-load-storage coordination, the planning results for source-grid-load-storage coordination of a new type of power system containing a high proportion of clean energy are obtained.

[0010] Beneficial effects

[0011] Compared with existing technologies, the beneficial effects achieved by this invention are:

[0012] (1) Although existing inventions can effectively reduce the operating costs of distribution networks and improve the operating characteristics of distribution network systems, this invention further optimizes them. While meeting the requirements of new power system operation, it also considers the problem of wind and solar power curtailment, further reducing the operating costs of the power grid and making its operation smoother.

[0013] (2) In view of the spatiotemporal uncertainty caused by the intermittency of high proportion of new energy output in the existing technology and the power system transformation caused by the high proportion of power electronics due to the grid connection method, the present invention establishes a new power system planning with high proportion of new energy on the basis of safety and reliability, and optimizes the high proportion of new energy with a more comprehensive and scientific solution.

[0014] (3) Existing technologies do not take into account practical methods for planning new power systems with a high proportion of renewable energy, and only focus on the dispatch level of power system operation optimization under the condition of renewable energy grid connection. This invention proposes a practical and feasible scheme for optimizing the operation of new power systems with a high proportion of renewable energy, which maximizes the absorption of renewable energy generation, improves the operating efficiency and safety of the power system, promotes the development of renewable energy wind and solar power generation, and protects the ecological environment. Attached Figure Description

[0015] Figure 1 The following is a flowchart of the steps of this invention.

[0016] Figure 2 This invention provides a novel optimized operation mode for power systems. Detailed Implementation

[0017] Combination Figure 1 The content includes a multi-objective programming method for source-grid-load-storage coordination in a new type of power system with a high proportion of clean energy, comprising the following steps:

[0018] Step 1: Analyze the changes in the form of the new power system after new energy sources are integrated into the power grid:

[0019] Grey relational analysis method was used:

[0020] Step 1: For the indicator system, establish an initial data column X0, including the ideal data column, which is the maximum value of each column.

[0021]

[0022] In the formula: i = 1, 2, ..., 12; j = 1, 2, ..., 9; Xj (j = 1...9) represents the sample set of the j-th index.

[0023] Step 2: Data standardization, the formula is as follows:

[0024]

[0025] Step 3: Perform principal component analysis to obtain the principal component score formula.

[0026]

[0027] Where: x1, x2, ..., x p There are p original indicators; F1, F2, F3, ..., Fm are m independent new composite indicators, F = (a ij ) mxp =(F1,F2,F3,L,F m ), m depends on the requirement that the variance contribution rate is greater than or equal to 85%.

[0028] Step 4: Using the principal component scores of the ideal sample as the reference data column, denoted as a0, perform grey relational analysis. Calculate the grey relational coefficient using the following formula.

[0029]

[0030] In the formula: a i (k) represents the score of the k-th principal component of the i-th sample; ρ∈[0,+∞) is the resolution coefficient, ρ=0.5;

[0031] Step 5: Calculate the correlation degree, using the following formula.

[0032]

[0033] In the formula: e k This represents the variance contribution rate of the k-th principal component.

[0034] Based on the above formulas and methods, we analyze the factors and indicators that affect the changes in the morphology of power systems, such as high power electronics, multi-time and multi-space strong uncertainties.

[0035] Using data query methods, load demand in the planning years of the planning area is obtained and statistically analyzed from government websites. The power grid operation status and power source development status are obtained based on model calculations. Based on power security requirements, the power system security content is determined.

[0036] The high-proportion new energy power system is a "two-sided random system". "Two-sided" refers to the load side and the power supply side. It changes the structure of the grid, uses power electronic devices to integrate new energy into the grid, and transmits it through AC and DC lines. Power electronic devices are also applied on the load side.

[0037] The output of a high proportion of new energy power systems is affected by a variety of factors, including natural conditions, randomness, and intermittency. The reliability, environmental benefits, and economic efficiency of the power grid are the main aspects of power system operation and related safety.

[0038] Step Two: Based on relevant information within the planning area and the characteristics of a high-proportion new energy power system, construct a source-grid-load-storage coordinated planning model. This model is a hierarchical model for grid and power source coordinated planning. The upper-level model is a grid construction cost model, and the lower-level model is a new energy power generation cost model. The upper-level model's objective function is to minimize the sum of grid peak-shaving power plant construction costs, grid line construction costs, grid loss costs, carbon emission costs, and electricity purchase costs. The lower-level model's objective function is to minimize the sum of new energy power generation maintenance costs and curtailment loss costs. Therefore, the constraints mentioned above are considered.

[0039] This model is a collaborative planning model for renewable energy generation, grid, load, and storage with a high proportion of renewable energy. The objective function is to minimize the cost of renewable energy generation and the construction cost of the power grid. Constraints include the penetration power of clean energy generation at load nodes, power balance, and branch power flow. The model is as follows:

[0040] (1) Objective function:

[0041]

[0042] In the formula, the construction cost of peak-shaving power plants for the power grid is L. NCG The construction cost of power grid lines is L NL The grid loss cost is L LS The cost of carbon emissions is L C The cost of purchasing electricity is L NE The maintenance cost of new energy power generation is L NG The cost of curtailing renewable energy is L NQ .

[0043] (2) The construction cost of a peak-shaving power plant is:

[0044]

[0045] Wherein, the decision variable is x i The value is 0-1, representing the operational status of the power grid investment used for peak-shaving generating units; 0 represents outage and 1 represents operation. The annual value of the investment cost of the i-th generating unit is represented by C. NCG,i express.

[0046] The construction cost of transmission lines in the power grid is:

[0047]

[0048] In the formula, the decision variable is represented by y.j z k The value is represented by 0-1, signifying the operating status of the wind and solar power plant lines; the number of wind and solar power lines to be added and connected to the grid is represented by b and c; the annual value of the corresponding investment cost of the grid-connected lines is represented by C. NWTL,j C NPVL,k express.

[0049] The calculation method for power grid loss costs is as follows:

[0050]

[0051] In the formula, the electricity price per unit of grid loss is The total number of system transmission lines already in use is d; the current I on the line during the corresponding time period t is... l I j I k The resistance on the corresponding line is R. l R j R k The total number of time periods throughout the year is T.

[0052] The cost of carbon emissions is calculated in the following way:

[0053]

[0054] In the formula, the market price of carbon emissions is λ; the carbon emission intensity per unit of electricity generated by the i-th unit is e. ncg,i It represents the active power output of the i-th generating unit during time period t, expressed as P. NCG,i,t express.

[0055] Calculate the cost of electricity using the following formula:

[0056] L NE =αE NE

[0057] In the formula, the electricity price is α, and the amount of electricity purchased is E. NE .

[0058] The maintenance cost of renewable energy power generation is calculated using the following formula:

[0059]

[0060] In the formula, the power generation and maintenance cost of each wind and solar generator unit is ρ. WT ρ PV During time period t, the actual active power output of the j-th wind turbine and the k-th photovoltaic generator is represented by P. WT,j,t P PV,k,t express.

[0061] The following formula can be used to calculate the cost of losses from the curtailment of renewable energy:

[0062]

[0063] In the formula, the cost of per unit of abandoned wind and light power loss is represented by σ WT , σ PV . During the t period, the planned active power outputs of the j-th wind turbine and the k-th photovoltaic power generation unit are represented by P WT0,j,t , P PV0,k,t . During the t period, the actual active power outputs of the j-th wind turbine and the k-th photovoltaic power generation unit are represented by P WT,j,t , P PV,k,t .

[0064] (3) Constraint conditions:

[0065] Power balance of constraint conditions:

[0066] P t = A t θ t

[0067] In the formula, during the t period, the power injection vector of the node is represented by P t ; during the t period, the admittance matrix of the node is represented by A t .

[0068] Branch power flow of constraint conditions:

[0069] |P n,t | ” P n,max

[0070] In the formula, the active power flow on the n-th branch is represented by P n,t ; during the t period, the upper limit value of the power that the n-th branch can carry is represented by P n,max .

[0071] Penetration power of new energy generation in load nodes:

[0072]

[0073] In the formula, the maximum output power of the j-th wind turbine is represented by P WT,j,max , and the maximum output power of the k-th photovoltaic power generation unit is represented by P PV,k,max ; the maximum penetration power of the load node f is represented by P Nf,max .

[0074] Output of generator sets of constraint conditions:

[0075] P NGG,min ≤ P NCG,t ≤ P NCG,max

[0076] 0 to P WT,t P WT,max

[0077] 0 to P PV,t P PV,max

[0078] In the formula, the lower limit value of the active power output of the power grid peak shaving unit is represented by P NGG,min and the upper limit value of the active power output of the power grid peak shaving generator set is represented by P NCG,max The upper limit values of the output powers of the wind-solar units are P WT,max and P PV,max .

[0079] Step 3: Use the source-network-load-storage collaborative planning model to calculate and obtain the planning results of the new power system containing a high proportion of new energy.

[0080] (1) On the basis of Steps 1 and 2, comprehensively considering the constraint conditions such as power balance, branch power flow, penetration power of new energy power generation in load nodes, and output of generator sets, substitute the data into the multi-objective planning model of source-network-load-storage collaboration, and use MATLAB2019Rb programming to solve.

[0081] The planning results obtained by solving the above model are the planning results of a new power system that is safe and reliable and contains a high proportion of clean energy under the best economic conditions.

[0082] Combined with Figure 2 the content, the optimized operation mode of the new power system provided by the present invention is as follows:

[0083] Step 2-1: When the power system operates, consider the security constraints and stability constraints. For the problem of different types of new energy并入电网 (it seems there is a wrong expression here, assuming it should be "connected to the grid"), determine the key constraints affecting system security and stability through model-driven and data-driven methods, and organically combine them with the economic dispatch model and the steady-state unit commitment of the power system.

[0084] Step 2-2: Build a model for the coordinated operation of intermittent new energy such as wind power and photovoltaic power with carbon capture power plants, so that the intermittent renewable energy and carbon capture power plants operate in a coordinated and optimized manner.

[0085] Step 2-3: Consider the constraints such as system frequency stability, inertia level and system emission reduction potential, and coordinate the operation of the high proportion of new energy transmission while ensuring safety and controllability.

[0086] Example 1

[0087] It should be noted that there may be an incorrect expression "并入电网" in the original text. If it is not a misspelling, it needs to be further clarified for a more accurate translation.The "Integrated Generation, Grid, Load, and Storage" comprehensive application demonstration base project in City A has a construction scale of 2 million kilowatts, including 1.7 million kilowatts of wind power, 300,000 kilowatts of photovoltaic power, and supporting 550,000 kilowatts × 2-hour energy storage, with a total investment of 14.1 billion yuan. By adding energy storage to the traditional "generation, grid, and load" operation mode of the power system, an integrated "generation, grid, load, and storage" solution is achieved. This allows for precise control of interruptible power loads and energy storage resources, improving the safety of the power grid. After adding energy storage to the power system, when renewable energy is abandoned, it automatically stores the energy, using the abandoned portion to recharge the storage, avoiding waste. When load curtailment occurs, the storage is activated to supplement the power supply, making up for the power shortage. Energy storage also participates in power system peak shaving, ensuring stable load operation and preventing load curtailment. Energy storage in the power system acts like a large power bank, constantly providing backup power to the loads in the power system.

[0088] City B is my country's first demonstration zone integrating power generation, grid, load, and energy storage. On the power supply side, a company's 11,973 kW distributed photovoltaic power generation project has been connected to the grid in City B, with an estimated annual power generation of 11 million kWh. On the grid side, the State Grid City B Power Supply Company is committed to creating a leading demonstration project for a diversified, integrated, and highly resilient power grid. This includes 18 application scenarios such as autonomous and collaborative control of distributed photovoltaic clusters, high-quality sharing of energy storage power stations at distribution network hubs, and millisecond-level self-healing of distributed multi-distribution networks, as well as 12 special projects such as active distribution networks, distribution network resource integration and optimization, and combined cooling, heating, and power (CCHP). These projects conduct in-depth research and application of new power systems from multiple perspectives, including theoretical frameworks, technical methods, and business models. On the load side, 347 enterprises in City B have signed demand-side response agreements, achieving efficient interaction between enterprises and the grid; 8 enterprises have achieved 2.17 MW of multi-system collaborative millisecond-level interruptible load control; and on the energy storage side, the requirement that 10% of the installed capacity of new energy projects be equipped with energy storage is being fully implemented. In terms of energy storage stations deployed on the photovoltaic side, City B already has a large-scale energy storage station with a capacity of 1 MW / 2 MWh; the next phase will complete the full coverage of V2G charging facilities and deploy energy storage in proportion. In addition, integrated systems empowered by digital technologies such as the active coordination and control system for source-grid-load-storage are integrating and efficiently utilizing resources from all aspects.

[0089] Two specific case studies demonstrate the following: First, the multi-objective planning model integrating power generation, grid, load, and storage largely solves the high-cost problem in traditional power grid planning, maximizing the absorption of renewable energy generation while improving the operational efficiency and security of the power system, promoting the development of renewable wind and solar power, and protecting the ecological environment. Second, unified planning addresses the shortcomings of existing research methods that did not consider practical approaches to planning new power systems with a high proportion of renewable energy, focusing only on the scheduling level of power system operation optimization under renewable energy grid integration. It achieves a solution that meets the operational requirements of new power systems. Third, it considers the issue of wind and solar curtailment and, based on safety and reliability, establishes a plan for new power systems with a high proportion of renewable energy.

[0090] To address the shortcomings of existing technologies that employ particle swarm optimization algorithms improved based on dynamic inertia weights and random mutation factors, and methods using ode15s solvers to solve dynamic feedback equations for control units, node phase angles, and Lagrange multipliers, this invention builds upon these methods by constructing a model for the coordinated operation of intermittent renewable energy sources such as wind and solar power with carbon capture power plants. This model enables optimized coordinated operation of intermittent renewable energy sources and carbon capture power plants, while considering constraints such as system frequency stability, inertia levels, and system emission reduction potential. Under the premise of ensuring safety and controllability, it coordinates the high-proportion transmission of renewable energy. Therefore, this invention further reduces the cost of grid operation, makes its operation smoother, maximizes the absorption of renewable energy generation, improves the operating efficiency and safety of the power system, promotes the development of renewable energy wind and solar power, and protects the ecological environment.

[0091] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A multi-objective programming method for power system source-grid-load-storage coordination, wherein the method is a novel multi-objective programming method for power system source-grid-load-storage coordination that includes a high proportion of clean energy, characterized by: Includes the following steps: S1: Analyze the changes in the form of the new power system after the integration of new energy sources into the grid. The analysis includes high power electronics and strong uncertainties in multiple time and space. For the planning area, obtain the load demand, grid operation status, power source development status, and power system security information within the planning period. S2: Based on the relevant information already obtained in the planning area and combined with the characteristics of the new power system, construct a multi-objective planning model for source-grid-load-storage coordination; S3: Based on the established multi-objective planning model for source-grid-load-storage coordination, the planning results for source-grid-load-storage coordination of a new power system with a high proportion of clean energy are obtained. Step S1 further includes the following steps: (1) Using grey relational analysis, the factors influencing the changes in the power system morphology are analyzed from the perspectives of high power electronics, multi-time, and multi-space uncertainty: Step 1: For the indicator system, establish an initial data column X0, including the ideal data column, i.e., the maximum value of each column: In the formula: i = 1, 2, ..., 12; j = 1, 2, ..., 9; Xj (j = 1...9) represents the sample set of the j-th index; Step 2: Data standardization, the formula is as follows: Step 3: Perform principal component analysis to obtain the principal component score formula. Where: x1, x2, ..., x p There are p original indicators; F1, F2, F3, ..., Fm are m independent new composite indicators, F = (a ij ) mxp =(F1,F2,F3,L,F m ), m depends on the requirement that the variance contribution rate is greater than or equal to 85%; Step 4: Using the principal component scores of the ideal sample as the reference data column, denoted as a0, perform grey relational analysis and calculate the grey relational coefficient. The formula is as follows: In the formula: a i (k) represents the score of the k-th principal component of the i-th sample; ρ∈[0,+∞) is the resolution coefficient, ρ=0.5; Step 5: Calculate the correlation degree, using the following formula. In the formula: e k The variance contribution rate of the k-th principal component; (2) Using data query and descriptive statistics methods, obtain and statistically analyze the load demand, power grid operation, power source development, and power system security in the planning period of the planning area; Step S2 further includes the following: The target programming model is: (1) Objective function: In the formula, the construction cost of peak-shaving power plants for the power grid is L. NCG The construction cost of power grid lines is L NL The grid loss cost is L LS The cost of carbon emissions is L C The cost of purchasing electricity is L NE The maintenance cost of new energy power generation is L NG The cost of curtailing renewable energy is L NQ .

2. The multi-objective planning method for power system source-grid-load-storage coordination according to claim 1, characterized in that: The construction of the above goal programming model needs to consider the following construction costs and constraints: The construction cost of a peak-shaving power plant is: Wherein, the decision variable is x i The value is 0-1, representing the operational status of the power grid investment used for peak-shaving generating units; 0 represents outage and 1 represents operation. The annual value of the investment cost of the i-th generating unit is represented by C. NCG,i express; The construction cost of transmission lines in the power grid is: In the formula, the decision variable is represented by y. j z k The value is represented by 0-1, signifying the operating status of the wind and solar power plant lines; the number of wind and solar power lines to be added and connected to the grid is represented by b and c; the annual value of the corresponding investment cost of the grid-connected lines is represented by C. NWTL,j C NPVL,k express; The calculation method for power grid loss costs is as follows: In the formula, the electricity price per unit of grid loss is The total number of system transmission lines already in use is d; the current I on the line during the corresponding time period t is... l I j I k The resistance on the corresponding line is R. l R j R k The total number of time periods throughout the year is T; The cost of carbon emissions is calculated in the following way: In the formula, the market price of carbon emissions is λ; the carbon emission intensity per unit of electricity generated by the i-th unit is e. ncg,i It represents the active power output of the i-th generating unit during time period t, expressed as P. NCG,i,t express; Calculate the cost of electricity using the following formula: L NE =αE NE In the formula, the electricity price is α, and the amount of electricity purchased is E. NE ; The maintenance cost of renewable energy power generation is calculated using the following formula: In the formula, the power generation and maintenance cost of each wind and solar generator unit is ρ. WT ρ PV During time period t, the actual active power output of the j-th wind turbine and the k-th photovoltaic generator is represented by P. WT,j,t P PV,k,t express; The following formula can be used to calculate the cost of losses from the curtailment of renewable energy: In the formula, the cost per unit of wind and solar power curtailment is represented by σ. WT σ PV This indicates that within time period t, P... WT0,j,t P PV0,k,t P represents the planned active power output of the j-th wind turbine and the k-th photovoltaic power generator. During time period t, P represents the actual active power output of the j-th wind turbine and the k-th photovoltaic power generator. WT,j,t P PV,k,t express; Constraints: Constraints on power balance: P t =A t i t In the formula, the power vector injected by the node during time period t is represented by P. t Indicated by A; the admittance matrix of a node during time interval t. t express; Constraints on branch power flow: |P n,t | ” P n,max In the formula, the active power flow on the nth branch is represented by P. n,t P represents the maximum power that the nth branch can carry during time period t. n,max express; Penetration power of renewable energy generation at load nodes under constraints: In the formula, P represents the maximum output power of the j-th wind turbine. WT,j,max The maximum output power of the k-th photovoltaic generator unit is represented by P. PV,k,max The maximum penetration power of load node f is represented by P. Nf,max express.

3. The multi-objective planning method for power system source-grid-load-storage coordination according to claim 1, characterized in that: Step S3 further includes the following steps: Based on steps S1 and S2, taking into account power balance, branch power flow, penetration power of new energy generation in load nodes, and output constraints of generator sets, the data is substituted into the multi-objective programming model of source-grid-load-storage coordination and solved using MATLAB 2019Rb.

4. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute the method of claim 1.

5. An electronic device, characterized in that, It includes a processor and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform the method of claim 1.

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