A Microgrid Hybrid Energy Storage Optimization Configuration Method and Device

Abstract

The present application provides a method and device for optimizing the configuration of a hybrid energy storage system in a microgrid. The method includes: decomposing the net load of the microgrid into the tie-line power and the total power of the hybrid energy storage system, and using ensemble empirical mode decomposition to decompose the total power of the hybrid energy storage system into a low-frequency component suppressed by a lithium battery and a high-frequency component suppressed by a supercapacitor; constructing an optimization configuration model for the hybrid energy storage system based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the supercapacitor, and the tie-line power; the optimization configuration model for the hybrid energy storage system includes an annual equivalent cost objective function of the hybrid energy storage, a tie-line power suppression objective function, and an energy supply and demand balance objective function; using an adaptive particle swarm optimization algorithm to solve the optimization configuration model for the hybrid energy storage system to obtain an optimized configuration scheme for the hybrid energy storage capacity. The embodiments of the present application can reduce the charge and discharge times of the lithium battery and the supercapacitor, reduce the energy storage cost, and can also achieve good results in suppressing power fluctuations.

Description

Technical Field

[0001] The present application relates to the technical field of optimal configuration of hybrid energy storage in microgrids, and in particular to a method and device for optimal configuration of hybrid energy storage in microgrids. Background Art

[0002] With the increase in the grid connection penetration rate of distributed generation, the stability and power quality of the power grid have been greatly threatened, and the proposal of microgrids provides a new idea for solving this problem. The grid-connected microgrid is connected to the external power grid through a tie line. Due to the characteristics of the output of wind and light and the fluctuation of load power, the fluctuation of the tie line is aggravated. By reasonably configuring the capacity of the energy storage system, the power fluctuation of the tie line can be effectively suppressed, and the friendly access of distributed power sources can be realized.

[0003] The microgrid has broad application prospects, but it also has many problems of its own. Planning and design is the primary task of microgrid construction, and the capacity optimization of energy storage is the core of planning and design. Therefore, domestic and foreign scholars have conducted in-depth research on the configuration of hybrid energy storage capacity in microgrids. Solution 1: Use a low-pass filter to decompose the unbalanced power in the microgrid into high and low frequencies to achieve a better suppression effect. However, the low-pass filter itself has a time delay, resulting in a relatively large configured hybrid energy storage capacity. Solution 2: Considering the cost of the energy storage system, the discrete Fourier transform is used to solve the hybrid energy storage capacity for the net load power in the independent microgrid. However, the discrete Fourier transform is easily affected by noise, making the decomposed spectrum more complex. Solution 3: Use wavelet packets to decompose the grid-connected fluctuating power of photovoltaic output, improving the operating economy of the microgrid. Solution 4: A smoothing control strategy combining wavelet packet decomposition and low-pass filter algorithm is proposed, which prolongs the service life of the hybrid energy storage under grid-connected conditions. However, the wavelet packet decomposition method used in the above solutions is limited by the basis function when decomposing power, which affects the configuration of energy storage capacity. Solution 5: The method of moving average and empirical mode decomposition is used to obtain the power command of the hybrid energy storage system, improving the economic benefits of the system. However, the signal components obtained by the empirical mode decomposition method have the problem of mode mixing, which affects the configuration result of energy storage capacity. In addition, when configuring the capacity of the energy storage system, only the cost of the hybrid energy storage is considered, the power supply reliability of the microgrid is not considered, and the impact of frequent charge and discharge on the life of the energy storage battery is ignored. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a method and device for optimal configuration of hybrid energy storage in microgrids, so as to reduce the charge and discharge times of lithium batteries and supercapacitors, reduce the energy storage cost, and achieve a better effect in suppressing power fluctuations.

[0005] In a first aspect, an embodiment of the present application provides a method for optimal configuration of hybrid energy storage in microgrids using ensemble empirical mode decomposition, including:

[0006] The net load of the microgrid is decomposed into the tie-line power and the total power of the hybrid energy storage system, and the total power of the hybrid energy storage system is decomposed into the low-frequency component suppressed by the lithium battery and the high-frequency component suppressed by the supercapacitor by using ensemble empirical mode decomposition;

[0007] Based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the supercapacitor, and the tie-line power, a hybrid energy storage optimal configuration model is constructed; wherein, the hybrid energy storage optimal configuration model includes the annual equivalent cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function;

[0008] The adaptive particle swarm optimization algorithm is used to solve the hybrid energy storage optimal configuration model to obtain the optimal configuration scheme of the hybrid energy storage capacity.

[0009] In a possible implementation manner, the tie-line power suppression objective function is constructed by the following method:

[0010] The tie-line power suppression objective function is constructed with the goal of minimizing the sum of squares of the adjusted tie-line power change differences.

[0011] In a possible implementation manner, the energy supply and demand balance objective function is constructed by the following method:

[0012] The energy supply and demand balance objective function is constructed with the output power variances of the lithium battery and the supercapacitor as the goal.

[0013] In a possible implementation manner, it further includes:

[0014] The deviation ranking method is used to determine the weights of the annual equivalent cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function respectively;

[0015] Based on the weights of the annual equivalent cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function, the annual equivalent cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function are aggregated into a single objective function.

[0016] In a possible implementation manner, the hybrid energy storage optimal configuration model further includes: the remaining capacity constraint condition of the hybrid energy storage system, the charge and discharge power constraint condition of the hybrid energy storage system, and the state of charge constraint condition of the hybrid energy storage system; the charge and discharge power constraint condition of the hybrid energy storage system includes the upper and lower limits of the charge and discharge power of the lithium battery and the upper and lower limits of the charge and discharge power of the supercapacitor;

[0017] After obtaining the optimal configuration scheme of the hybrid energy storage capacity, the method further includes:

[0018] According to the state of charge of the hybrid energy storage, a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the lithium battery and the charging and discharging power of the super capacitor.

[0019] In a possible implementation manner, according to the state of charge of the hybrid energy storage, using a fuzzy control algorithm to perform secondary correction on the charging and discharging power of the lithium battery and the charging and discharging power of the super capacitor includes:

[0020] If the state of charge of the lithium battery is less than a first preset value and the discharge power command of the lithium battery is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the lithium battery;

[0021] If the state of charge of the super capacitor is less than a first preset value and the discharge power command of the super capacitor is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the super capacitor.

[0022] In a possible implementation manner, according to the state of charge of the hybrid energy storage, using a fuzzy control algorithm to perform secondary correction on the charging and discharging power of the lithium battery and the charging and discharging power of the super capacitor includes:

[0023] If the state of charge of the lithium battery is greater than a first preset value and the charging power command of the lithium battery is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the lithium battery;

[0024] If the state of charge of the super capacitor is greater than a first preset value and the charging power command of the super capacitor is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the super capacitor.

[0025] In a second aspect, a microgrid hybrid energy storage optimal configuration device using ensemble empirical mode decomposition includes:

[0026] A decomposition module, configured to decompose the microgrid net load into the tie line power and the total power of the hybrid energy storage system, and use ensemble empirical mode to decompose the total power of the hybrid energy storage system into a low-frequency component suppressed by the lithium battery and a high-frequency component suppressed by the super capacitor;

[0027] A construction module, configured to construct a hybrid energy storage optimal configuration model based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the super capacitor, and the tie line power; wherein, the hybrid energy storage optimal configuration model includes an annual equivalent cost objective function of the hybrid energy storage, a tie line power suppression objective function, and an energy supply and demand balance objective function;

[0028] A solution module, configured to use an adaptive particle swarm optimization algorithm to solve the hybrid energy storage optimal configuration model to obtain an optimal configuration scheme for the hybrid energy storage capacity.

[0029] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps in the above first aspect or any possible implementation manner in the first aspect are executed.

[0030] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps in the above first aspect or any possible implementation manner in the first aspect are executed.

[0031] A method for optimizing the configuration of a hybrid energy storage system in a microgrid using ensemble empirical mode decomposition provided by an embodiment of the present application. First, the net load of the microgrid is decomposed into the tie-line power and the total power of the hybrid energy storage system. The total power of the hybrid energy storage system is decomposed into a low-frequency component suppressed by a lithium battery and a high-frequency component suppressed by a supercapacitor using ensemble empirical mode. Adaptive decomposition of the unbalanced power in the microgrid using ensemble empirical mode can achieve the complementary advantages of energy-type energy storage and power-type energy storage. Second, a hybrid energy storage optimization configuration model is constructed based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the supercapacitor, and the tie-line power. Among them, considering the cost of the hybrid energy storage and the power supply reliability of the microgrid, and the impact of frequent charge and discharge on the life of the energy storage battery, the hybrid energy storage optimization configuration model includes an equivalent annual cost objective function of the hybrid energy storage, a tie-line power suppression objective function, and an energy supply and demand balance objective function. Finally, an adaptive particle swarm optimization algorithm is used to solve the hybrid energy storage optimization configuration model to obtain an optimized configuration scheme for the hybrid energy storage capacity, which is simple to operate and has a fast convergence speed. The embodiment of the present application can reduce the charge and discharge times of the lithium battery and the supercapacitor, reduce the energy storage cost, and can also achieve a good effect in suppressing power fluctuations.

[0032] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specific preferred embodiments are given in conjunction with the accompanying drawings and are described in detail as follows. Description of the Drawings

[0033] To more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1Shows a flowchart of a microgrid hybrid energy storage optimization configuration method using ensemble empirical mode decomposition provided by an embodiment of the present application;

[0035] Figure 2 Shows a microgrid system and its connection diagram with the external power grid;

[0036] Figure 3 Shows a schematic diagram of the charge and discharge power control of the hybrid energy storage;

[0037] Figure 4 Shows the typical daily wind turbine, photovoltaic output and load data;

[0038] Figure 5 Shows the system net load, tie line protocol power and total power of the hybrid energy storage;

[0039] Figure 6 Shows the ensemble empirical mode decomposition result;

[0040] Figure 7 Shows the power commands of the lithium iron phosphate battery and the supercapacitor;

[0041] Figure 8 Shows a schematic diagram of the smoothing effect of the hybrid energy storage;

[0042] Figure 9 Shows the fuzzy optimization of the supercapacitor power;

[0043] Figure 10 Shows the fuzzy optimization of the lithium iron phosphate battery power;

[0044] Figure 11 Shows the fuzzy optimization of the supercapacitor SOC;

[0045] Figure 12 Shows the fuzzy optimization of the lithium iron phosphate battery SOC;

[0046] Figure 13 Shows a schematic diagram of the structure of a microgrid hybrid energy storage optimization configuration device using ensemble empirical mode decomposition provided by an embodiment of the present application;

[0047] Figure 14 Shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part rather than all of the embodiments of this application. Components of the embodiments of this application described and illustrated in the drawings here usually can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of this application claimed, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts fall within the scope of protection of this application.

[0049] Considering that the microgrid has broad application prospects but also has many problems of its own. Planning and design is the primary task of microgrid construction, and the capacity optimization of energy storage is the core of planning and design. Therefore, scholars at home and abroad have conducted in-depth research on the configuration of the hybrid energy storage capacity of the microgrid. Solution 1: Use a low-pass filter to decompose the unbalanced power in the microgrid into high and low frequencies to achieve a better smoothing effect. However, the low-pass filter itself has a time delay, resulting in a relatively large configured hybrid energy storage capacity. Solution 2: Considering the cost of the energy storage system, the discrete Fourier transform is used to solve the hybrid energy storage capacity for the net load power in the independent microgrid. However, the discrete Fourier transform is easily affected by noise, making the decomposed spectrum more complex. Solution 3: The wavelet packet is used to decompose the grid-connected fluctuating power of the photovoltaic output, improving the operating economy of the microgrid. Solution 4: A smoothing control strategy combining wavelet packet decomposition and low-pass filter algorithm is proposed. Under grid-connected conditions, the service life of the hybrid energy storage is extended. However, the wavelet packet decomposition method used in the above solutions is limited by the basis function when decomposing power, which affects the configuration of the energy storage capacity. Solution 5: The method of moving average and empirical mode decomposition is used to obtain the power command of the hybrid energy storage system, improving the economic benefits of the system. However, there is a mode mixing problem in the signal components obtained by the empirical mode decomposition method, which affects the configuration result of the energy storage capacity. In addition, when configuring the capacity of the energy storage system, only the cost of the hybrid energy storage is considered, the power supply reliability of the microgrid is not considered, and the impact of frequent charge and discharge on the life of the energy storage battery is ignored. Based on this, the embodiments of this application provide a method and device for optimizing the configuration of a microgrid hybrid energy storage, which will be described below through embodiments.

[0050] To facilitate the understanding of this embodiment, first, a method for optimizing the configuration of a microgrid hybrid energy storage using ensemble empirical mode decomposition disclosed in the embodiments of this application will be introduced in detail.

[0051] Please refer to Figure 1 , Figure 1 which is a flowchart of a method for optimizing the configuration of a microgrid hybrid energy storage using ensemble empirical mode decomposition provided by the embodiments of this application. AsFigure 1 As shown, the method may include the following steps:

[0052] S101. Decompose the microgrid net load into the tie-line power and the total power of the hybrid energy storage system, and decompose the total power of the hybrid energy storage system into the low-frequency component suppressed by the lithium battery and the high-frequency component suppressed by the super capacitor by using the ensemble empirical mode;

[0053] S102. Construct an optimal configuration model for the hybrid energy storage based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the super capacitor, and the tie-line power; wherein, the optimal configuration model for the hybrid energy storage includes the equivalent annual cost objective function of the hybrid energy storage, the objective function of suppressing the tie-line power, and the objective function of energy supply and demand balance;

[0054] S103. Solve the optimal configuration model for the hybrid energy storage by using the adaptive particle swarm optimization algorithm to obtain the optimal configuration scheme for the hybrid energy storage capacity.

[0055] In step S101, as Figure 2 shown, the microgrid system includes wind power generation, photovoltaic power generation and load power consumption. The external power grid of the microgrid system includes a hybrid energy storage system and a large power grid. The microgrid is connected to the distribution network through a common tie-line. The microgrid net load refers to the unbalanced power generated by wind power generation, photovoltaic power generation and load power consumption in the microgrid. For the net load generated by the system in the grid-connected microgrid, it is jointly compensated by the energy storage system and the large power grid.

[0056] P J (t) = P L (t) - P W (t) - P PV (t) (1)

[0057] P HESS (t) = P Li (t) + P SC (t) (2)

[0058] P HESS (t) = P J (t) - P G (t) (3)

[0059] Wherein, P W and P PV respectively represent the output powers of the wind turbine and the photovoltaic, P L represents the local load power, P J represents the net load power, P HESS represents the hybrid energy storage power, P Li (t) and P SC (t) respectively represent the powers of the lithium battery and the super capacitor, P G(t) represents the tie-line protocol power, P G It usually takes the 60-minute average value of the net load power of the microgrid system without energy storage and makes adjustments according to the actual power conditions of the microgrid and the distribution network. The power transmitted from the grid to the microgrid is positive, the power transmitted from the microgrid to the grid is negative, the discharge of the energy storage system is positive, and the charge is negative.

[0060] Ensemble Empirical Mode Decomposition (EEMD) is developed on the basis of the Fourier transform. It decomposes according to the time-scale characteristics of the original signal itself without the need to preset any basis functions, and has significant advantages in dealing with non-linear and non-stationary time series. EEMD can decompose a complex signal into a finite number of Intrinsic Mode Functions (IMFs) and a residual component. The IMF contains the local characteristic information of different time scales in the original signal. After the original signal passes through EEMD, it can be obtained.

[0061]

[0062] In the formula, P(t) is the signal component to be decomposed, that is, the instruction for the hybrid energy storage to suppress power fluctuations; c i (t) is the i-th IMF after decomposition; r n (t) is the trend term after decomposition.

[0063] According to the characteristics of the lithium battery and the supercapacitor to suppress power fluctuations respectively, the IMF is reconstructed into high and low frequencies. Select an appropriate filtering order j. The sum of the IMF components with orders less than or equal to j is determined as the high-frequency component, which is suppressed by the supercapacitor, that is, the power instruction P SC.ref (t) of the supercapacitor. The sum of the IMF components with orders greater than j and the remainder is the low-frequency component, which is suppressed by the lithium battery, that is, the power instruction P Li.ref (t) of the lithium battery. It can be expressed as:

[0064]

[0065] Among them, j is the filtering order, 0 ≤ j ≤ n. When selecting j, the smaller j is, the more components are included in the low-frequency part, and the larger the capacity of the lithium battery to be configured is. The larger j is selected, the more high-frequency components there will be, and the larger the capacity of the supercapacitor to be configured will be.

[0066] The ensemble empirical mode is used to adaptively decompose the unbalanced power in the microgrid, and the respective compensation powers of the lithium iron phosphate battery and the supercapacitor are obtained, realizing the complementary advantages of energy-type energy storage and power-type energy storage.

[0067] In step S102, the capacity configuration of the hybrid energy storage system needs to be reasonable. Otherwise, it is difficult to achieve the optimal operation effect during actual operation, and it may cause waste of funds. In this step, the hybrid energy storage capacity will be configured from the aspects of economy and reliability.

[0068] The hybrid energy storage optimization configuration model includes the annual equivalent cost objective function of the hybrid energy storage, the objective function of suppressing the tie-line power, and the objective function of energy supply and demand balance. The following is a specific description of each objective function.

[0069] 1) Annual equivalent cost objective function of the hybrid energy storage

[0070] minf 1 =C Li +C SC (6)

[0071] Where:

[0072]

[0073]

[0074] Among them, f 1 is the annual comprehensive cost of the energy storage system (converted to the annual equivalent value); C Li , C SC are the investment and operation costs of the lithium battery and the supercapacitor respectively; Y Li , Y SC are the service lives of the lithium battery and the supercapacitor respectively; k LiP , k LiE , k LiY are the unit power cost, unit capacity cost, and operation and maintenance cost of the lithium battery respectively; k SCP , k SCE , k SCY are the unit power cost, unit capacity cost, and operation and maintenance cost of the supercapacitor respectively.

[0075] 2) Objective function of suppressing the tie-line power

[0076] To characterize the suppression effect of the hybrid energy storage on the net load power fluctuation of the microgrid, an objective function of suppressing the tie-line power is constructed with the minimum sum of the squares of the difference in the tie-line power change after regulation as the goal.

[0077]

[0078] Among them, the Δt interval is 1 min; P Li (t), P SC (t) represent the actual output powers of the lithium battery and the supercapacitor respectively; P J (t) is the net load power; P g(t) is the tie-line power after being smoothed by energy storage.

[0079] 3) Energy supply and demand balance objective function

[0080] To minimize the changes in the charging and discharging powers of lithium batteries and supercapacitors and ensure system stability, an energy supply and demand balance objective function is constructed with the variances of the output powers of lithium batteries and supercapacitors as the objectives.

[0081]

[0082] The hybrid energy storage optimal configuration model further includes: the remaining capacity constraint condition of the hybrid energy storage system, the charging and discharging power constraint condition of the hybrid energy storage system, and the state of charge constraint condition of the hybrid energy storage. To avoid overcharging and over-discharging of the energy storage system and extend its service life, there are the following constraints:

[0083] 1) Remaining capacity constraint condition of the hybrid energy storage system

[0084]

[0085] where E Li.max 、E Li.min 、E SC.max 、E SC.min are the upper and lower limits of the remaining power of the lithium iron phosphate battery and the supercapacitor respectively.

[0086] 2) Charging and discharging power constraint condition of the hybrid energy storage system

[0087] When the remaining capacities of the lithium battery and the supercapacitor cannot meet the charging and discharging powers at the next moment, the charging and discharging powers of the lithium battery and the supercapacitor should be adjusted. The charging and discharging power constraint condition of the hybrid energy storage system includes the upper and lower limits of the charging and discharging powers of the lithium battery and the upper and lower limits of the charging and discharging powers of the supercapacitor.

[0088]

[0089] where η Li 、η SC are the charging and discharging efficiencies of the lithium battery and the supercapacitor.

[0090] 3) State of charge constraint condition of the hybrid energy storage

[0091]

[0092] where SOC Li.max 、SOC Li.min 、SOC SC.max 、SOC SC.min are the upper and lower limits of the SOC of the lithium battery and the supercapacitor respectively.

[0093] In a possible implementation, the root mean square deviation of the energy between the smoothed tie-line power of the energy storage system and the tie-line protocol power is used as an index to evaluate the power fluctuation smoothing of the energy storage system, and is expressed as follows:

[0094]

[0095] Among them, R max represents the maximum root mean square deviation value of energy.

[0096] The weight of the objective function reflects the relative importance of each objective function. In a possible implementation, the method further includes: using the deviation ranking method to respectively determine the weights of the equal annual value cost objective function of the hybrid energy storage, the tie-line power smoothing objective function, and the energy supply and demand balance objective function; based on the weights of the equal annual value cost objective function of the hybrid energy storage, the tie-line power smoothing objective function, and the energy supply and demand balance objective function, aggregating the equal annual value cost objective function of the hybrid energy storage, the tie-line power smoothing objective function, and the energy supply and demand balance objective function into a single objective function.

[0097]

[0098] Among them, f(x) is a single objective function aggregated from multiple objective functions.

[0099] In step S103, the particle swarm algorithm is an algorithm for solving optimization problems proposed by observing the foraging behavior of birds, and has the advantages of simple operation and fast convergence speed.

[0100] The particle swarm formula is as follows:

[0101]

[0102] Among them, i is the particle number; k is the iteration number of the particle; ω is the inertia weight, which controls the influence of the previous velocity on the current velocity and is automatically adjusted according to the values of each objective function; c 1 , c 2 are learning factors, which respectively adjust the step sizes of the particle flying towards its own optimal position P best.i and the global optimal position G best ; r 1 , r 2 are independent random numbers, following a uniform distribution.

[0103] Specifically, step S103 includes the following sub-steps:

[0104] S1031. Read the power data and system parameters, obtain the total power of the hybrid energy storage using formulas (1)-(3), and decompose it using EEMD;

[0105] S1032. Determine the power dividing line according to the power smoothing characteristics of the lithium battery and the supercapacitor respectively and the spectral characteristics of the total power signal of the hybrid energy storage, using formulas (4)-(5).

[0106] S1033. Randomly initialize the positions and velocities of the particles in the population.

[0107] S1034. Constrain the charge and discharge power and SOC of the hybrid energy storage system according to formulas (11)-(13).

[0108] S1035. Calculate the optimal solutions of each objective function and the fitness values according to formulas (6)-(10).

[0109] S1036. Update the individual extreme values, global extreme values and inertia weights of each objective function.

[0110] S1037. Compare the obtained individual extreme values, global extreme values with the historical data to determine the optimal fitness values and optimal solution capacities of each objective function.

[0111] S1038. Substitute the optimal solutions of each objective function into different objective functions, calculate their corresponding values, and use the deviation ranking method to aggregate the weights of each objective function into a single objective, and solve its optimal fitness value and optimal solution.

[0112] S1039. Determine whether the termination condition (number of iterations or reaching the preset accuracy) is satisfied. If satisfied, output the result; otherwise, return to step S1033 and continue to execute.

[0113] In a possible implementation manner, after obtaining the optimized configuration scheme of the hybrid energy storage capacity, the method further includes: according to the state of charge of the hybrid energy storage, using a fuzzy control algorithm to perform secondary correction on the charge and discharge power of the lithium battery and the charge and discharge power of the supercapacitor.

[0114] Specifically, if the state of charge of the lithium battery is less than the first preset value and the discharge power command of the lithium battery is greater than the second preset value, then use the fuzzy control algorithm to perform secondary correction on the charge and discharge power of the lithium battery. If the state of charge of the supercapacitor is less than the first preset value and the discharge power command of the supercapacitor is greater than the second preset value, then use the fuzzy control algorithm to perform secondary correction on the charge and discharge power of the supercapacitor.

[0115] Or, if the state of charge of the lithium battery is greater than the first preset value and the charge power command of the lithium battery is greater than the second preset value, then use the fuzzy control algorithm to perform secondary correction on the charge and discharge power of the lithium battery. If the state of charge of the supercapacitor is greater than the first preset value and the charge power command of the supercapacitor is greater than the second preset value, then use the fuzzy control algorithm to perform secondary correction on the charge and discharge power of the supercapacitor.

[0116] The charging and discharging power of the above-mentioned fuzzy control hybrid energy storage system will be specifically described below.

[0117] The combination of lithium batteries and supercapacitors extends the action time of the energy storage system to suppress power fluctuations and speeds up the response speed. However, in engineering applications, to prevent overcharging and over-discharging of lithium batteries and supercapacitors from causing insufficient power regulation ability at the next moment and SOC out-of-limit problems, auxiliary strategies are usually required to solve such problems. In this embodiment, a fuzzy control algorithm is selected to constrain the charging and discharging power of the energy storage system.

[0118] The charging and discharging control strategy of the hybrid energy storage is as Figure 3 shown. EEMD decomposes the total power of the hybrid energy storage into power commands for the supercapacitor and the lithium battery. Combining the real-time SOC(t) of the energy storage system, power correction is performed through fuzzy control. Fuzzy controllers 1 and 2 control the power of the supercapacitor and the lithium battery respectively. The controller takes the current SOC(t) value and the normalized value P * (t) of the charging and discharging power as input quantities, and the adjusted power as the output quantity to control the power of the energy storage system, as follows.

[0119] For the supercapacitor:

[0120] (1) When the SOC SC (t) of the supercapacitor is moderate, there is no need to adjust its charging and discharging power;

[0121] (2) When the SOC SC (t) of the supercapacitor is small and the discharge power command is large, or the SOC SC (t) is large and the charging power command is large, controller 1 adjusts the power command of the supercapacitor.

[0122] (3) The deficit part ΔP SC generated after the power command of the supercapacitor is corrected by fuzzy controller 1 is compensated by the lithium battery.

[0123] For the lithium battery:

[0124] (1) When the SOC Li (t) of the lithium battery is moderate, there is no need to adjust its charging and discharging power;

[0125] (2) When the SOC Li (t) of the lithium battery is small and the discharge power command is large, or the SOC Li (t) is large and the charging power command is large, fuzzy controller 2 adjusts the power command of the lithium battery.

[0126] (3) The deficit part ΔP SC generated after the power command of the lithium battery is corrected by fuzzy controller 2 is compensated by the supercapacitor.

[0127] The charging and discharging power and SOC of the lithium iron phosphate battery and the super capacitor are optimized by fuzzy control, so that they are in the state of shallow charge and discharge, and the service life of the hybrid energy storage is extended.

[0128] A method for optimizing the configuration of a hybrid energy storage in a microgrid using ensemble empirical mode decomposition provided by an embodiment of the present application. First, the net load of the microgrid is decomposed into the tie line power and the total power of the hybrid energy storage system, and the total power of the hybrid energy storage system is decomposed into a low-frequency component suppressed by the lithium battery and a high-frequency component suppressed by the super capacitor using ensemble empirical mode. Adaptive decomposition of the unbalanced power in the microgrid using ensemble empirical mode can achieve the complementary advantages of energy-type energy storage and power-type energy storage. Second, a hybrid energy storage optimization configuration model is constructed based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the super capacitor, and the tie line power; among them, comprehensively considering the cost of the hybrid energy storage and the power supply reliability of the microgrid, as well as the impact of frequent charging and discharging on the life of the energy storage battery, the hybrid energy storage optimization configuration model includes an equivalent annual cost objective function of the hybrid energy storage, a tie line power suppression objective function, and an energy supply and demand balance objective function. Finally, an adaptive particle swarm optimization algorithm is used to solve the hybrid energy storage optimization configuration model to obtain an optimized configuration scheme for the hybrid energy storage capacity, which is simple to operate and has a fast convergence speed. The embodiment of the present application can reduce the charging and discharging times of the lithium battery and the super capacitor, reduce the energy storage cost, and can also achieve good results in suppressing power fluctuations.

[0129] The following analyzes and verifies the method for optimizing the configuration of a hybrid energy storage in a microgrid using ensemble empirical mode decomposition through specific examples.

[0130] Taking a microgrid in a certain place as an example, according to the historical data of 100 MW of wind power, 50 MW of photovoltaic power, and typical loads in this place, the capacity of the grid-connected microgrid hybrid energy storage system is optimized. The relevant parameters are shown in Table 1.

[0131] Table 1 System parameters

[0132]

[0133] 1) Verification and analysis of power distribution based on ensemble empirical mode

[0134] The ensemble empirical mode is used to decompose the fluctuating power required by the hybrid energy storage system. To verify the effectiveness of the EEMD decomposed power, the wind, light, and load data of a typical day are collected to configure the microgrid hybrid energy storage. The sampling data time is one day, and the sampling interval is 1 min, as Figure 4 shown.

[0135] From Figure 4It can be seen that the wind and light power data fluctuate greatly. According to the wind, light, and load data, the net load power of the microgrid system can be obtained, and its range is between -18.0 MW and 26.4 MW. Considering the protocol power of the tie line and the actual situation of grid dispatching, the tie line power is determined, as shown in Figure 5 . By calculation, the total power that the hybrid energy storage needs to suppress can be obtained.

[0136] Perform adaptive mode decomposition on the total power of the hybrid energy storage in Figure 5 , and the results are shown in Figure 6 . There are a total of 10 components. Among them, IMF1-IMF9 are the intrinsic modes of the total power of the hybrid energy storage, and r9 is the signal trend term. As the order of the mode components increases, the frequency change of each mode gradually decreases. According to equations (4)-(5), reconstruct the total power of the hybrid energy storage, select IMF1-IMF2 as the power command of the supercapacitor, and IMF3-IMF9 and their remainders as the power command of the lithium battery, as shown in Figure 7 .

[0137] As can be seen from Figure 7, the frequency change of the power command of the lithium battery is relatively gentle and the amplitude is slightly larger, which is 8.90 MW. The frequency change of the power command of the supercapacitor is relatively intense and the amplitude is smaller, which is 5.02 MW. This will be used as the rated power of the lithium battery and the supercapacitor for capacity optimization of the hybrid energy storage later.

[0138] 2) Analysis of energy storage configuration results

[0139] To optimize the capacity of the hybrid energy storage using multi-objective optimization, it is necessary to first find the optimal solutions of each objective function, substitute the optimal solutions into different objective functions to solve their corresponding values, use the deviation ranking method to determine the weights of each objective function, and then aggregate them into a single objective to solve its optimal fitness value and optimal solution. Here, the number of particle populations is 30; the number of iterations is 500; the value range of ω is [0.4, 0.9]; the learning factors c 1 and c 2 are both 2.

[0140] After solving, the rated capacities of the lithium battery and the supercapacitor obtained by the objective function f 1 are 1.08 MWh and 3.54 MWh, respectively. The rated capacities of the lithium battery and the supercapacitor obtained by the objective function f 2 are 41.40 MWh and 3.26 MWh, respectively. The rated capacities of the lithium battery and the supercapacitor obtained by the objective function f 3 are 1.0 MWh and 16.28 MWh, respectively. Substitute the above calculation results into different objective functions, and the results are shown in Table 2.

[0141] Table 2 Target fitness values under different optimal solutions

[0142]

[0143] The objective function values in Table 2 have different dimensions, and directly solving the weights of each objective function has low accuracy. Therefore, the data in Table 2 is normalized, and the results are shown in Table 3.

[0144] Table 3 Normalization of the target values

[0145]

[0146] Using the data in Table 3 and according to the deviation sorting method, the weight coefficients λ 1 、λ 2 、λ 3 of each objective function are 0.245, 0.184, and 0.571 respectively. Different objectives have different requirements for the energy storage capacity. According to the importance of each objective function, they are recombined, and the formula is as follows:

[0147] minf(x) = 0.571f 1 +0.245f 2 +0.184f 3 (17)

[0148] The rated capacities of the lithium battery and the supercapacitor obtained by solving are 16.94 MWh and 3.21 MWh respectively, and the cost is 12.951 million yuan.

[0149] Traditional filtering methods have problems with time delay, and the configured energy storage system has a large capacity. The ensemble empirical mode decomposition adaptively decomposes according to different time scales of the original data, thereby obtaining the power commands of the lithium battery and the supercapacitor, reducing the hybrid energy storage capacity. In this embodiment, the cost of the hybrid energy storage, suppressing the tie-line power, and the energy supply and demand balance are used as the objective functions, and the particle swarm optimization algorithm is used to solve the hybrid energy storage capacity of the grid-connected microgrid. The configuration results are as follows:

[0150] Table 4 Comparison of the results of low-pass filtering and EEMD

[0151]

[0152] It can be seen from the above results that the rated powers of the lithium battery and the supercapacitor obtained by EEMD decomposition are smaller than those of the low-pass filter. The rated capacity of the lithium battery obtained by low-pass filter decomposition is smaller than that of EEMD, while the rated capacity of the supercapacitor obtained by decomposition is larger than that of EEMD. This is because there is a time delay in the low-pass filtering during the power decomposition, resulting in a smaller capacity of the lithium battery obtained by decomposition than the actual value and a larger rated capacity of the supercapacitor allocated, ultimately leading to a higher total energy storage cost than the cost calculated by the EEMD method; the hybrid energy storage power commands obtained by the EEMD and the low-pass filtering methods can equally suppress the tie-line power, and the energy deviation is within a reasonable range, and the difference between the two is small; in terms of the number of charge and discharge cycles, EEMD is better than the low-pass filter.

[0153] Figure 8 After the optimization of the hybrid energy storage capacity, the lithium iron phosphate battery and the supercapacitor jointly output power to suppress the power of the tie line. After calculation, the energy deviation is less than 0.1, and the suppression effect is good, reducing the impact of the tie line power on the power grid.

[0154] 3) Charging and discharging power of the fuzzy control hybrid energy storage system

[0155] The power and SOC of the energy storage system optimized by the fuzzy control algorithm are as Figures 9 - 12 shown. It can be seen from the figure that after the fuzzy control is added to the hybrid energy storage system, the charging and discharging power and SOC of the lithium iron phosphate battery and the supercapacitor are significantly controlled compared with the unconstrained conditions, effectively avoiding the impact of overcharging and over-discharging of the supercapacitor and lithium battery on their service life. It can also be seen from the figure that after the fuzzy control, the charging and discharging power of the lithium battery changes violently. This is because the shortfall of the supercapacitor suppressing the high-frequency power fluctuation is compensated by the lithium battery, so the charging and discharging power of the lithium battery changes violently.

[0156] Based on the same technical concept, the embodiment of the present application also provides a microgrid hybrid energy storage optimization configuration device, an electronic device, and a computer storage medium using ensemble empirical mode decomposition. For details, please refer to the following embodiments.

[0157] Please refer to Figure 13 , Figure 13 which is a schematic structural diagram of a microgrid hybrid energy storage optimization configuration device provided by the embodiment of the present application. As Figure 13 shown, the device includes:

[0158] A decomposition module 10, configured to decompose the microgrid net load into tie line power and the total power of the hybrid energy storage system, and decompose the total power of the hybrid energy storage system into a low-frequency component suppressed by the lithium battery and a high-frequency component suppressed by the supercapacitor by using ensemble empirical mode;

[0159] A construction module 20, configured to construct a hybrid energy storage optimization configuration model based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the supercapacitor, and the tie line power; wherein, the hybrid energy storage optimization configuration model includes an annual equivalent cost objective function of the hybrid energy storage, a tie line power suppression objective function, and an energy supply and demand balance objective function;

[0160] A solving module 30, configured to solve the hybrid energy storage optimization configuration model by using an adaptive particle swarm algorithm to obtain a hybrid energy storage capacity optimization configuration scheme.

[0161] In a possible implementation manner, the construction module 20 is specifically configured to construct a tie line power suppression objective function in the following manner:

[0162] Construct a target function for suppressing the tie-line power with the minimum sum of squares of the adjusted difference in tie-line power changes as the goal.

[0163] In a possible implementation manner, the construction module 20 is specifically configured to construct an energy supply-demand balance target function in the following manner:

[0164] Construct an energy supply-demand balance target function with the output power variances of the lithium battery and the supercapacitor as the goal.

[0165] In a possible implementation manner, the device further includes:

[0166] A weight determination module 40, configured to respectively determine the weights of the equal annual value cost target function of the hybrid energy storage, the target function for suppressing the tie-line power, and the energy supply-demand balance target function by using the deviation ranking method;

[0167] A function aggregation module 50, configured to aggregate the equal annual value cost target function of the hybrid energy storage, the target function for suppressing the tie-line power, and the energy supply-demand balance target function into a single target function based on the weights of the equal annual value cost target function of the hybrid energy storage, the target function for suppressing the tie-line power, and the energy supply-demand balance target function.

[0168] In a possible implementation manner, the hybrid energy storage optimal configuration model further includes: the remaining capacity constraint condition of the hybrid energy storage system, the charge-discharge power constraint condition of the hybrid energy storage system, and the state of charge constraint condition of the hybrid energy storage; the charge-discharge power constraint condition of the hybrid energy storage system includes the upper and lower limits of the charge-discharge power of the lithium battery and the upper and lower limits of the charge-discharge power of the supercapacitor;

[0169] The device further includes:

[0170] A power correction module 60, configured to, after obtaining the optimal configuration scheme of the hybrid energy storage capacity, perform secondary correction on the charge-discharge power of the lithium battery and the charge-discharge power of the supercapacitor according to the state of charge of the hybrid energy storage by using a fuzzy control algorithm.

[0171] In a possible implementation manner, the power correction module 60 is specifically configured to:

[0172] If the state of charge of the lithium battery is less than a first preset value and the discharge power command of the lithium battery is greater than a second preset value, then use a fuzzy control algorithm to perform secondary correction on the charge-discharge power of the lithium battery;

[0173] If the state of charge of the supercapacitor is less than a first preset value and the discharge power command of the supercapacitor is greater than a second preset value, then use a fuzzy control algorithm to perform secondary correction on the charge-discharge power of the supercapacitor.

[0174] In a possible implementation, the power correction module 60 is specifically configured to:

[0175] If the state of charge of the lithium battery is greater than a first preset value and the charging power command of the lithium battery is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the lithium battery;

[0176] If the state of charge of the super capacitor is greater than a first preset value and the charging power command of the super capacitor is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the super capacitor.

[0177] The embodiment of the present application discloses an electronic device, such as Figure 14 shown, including: a processor 1401, a memory 1402, and a bus 1403. The memory 1402 stores machine-readable instructions executable by the processor 1401. When the electronic device runs, communication is carried out between the processor 1401 and the memory 1402 through the bus 1403. When the machine-readable instructions are executed by the processor 1401, the methods described in the foregoing method embodiments are executed. For specific implementation, reference can be made to the method embodiments, which will not be elaborated herein.

[0178] A computer program product of a microgrid hybrid energy storage optimization configuration method using ensemble empirical mode decomposition provided by the embodiment of the present application includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, reference can be made to the method embodiments, which will not be elaborated herein.

[0179] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated herein.

[0180] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0181] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0183] If the above function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0184] Finally, it should be noted that the above-described embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for optimizing the configuration of a hybrid energy storage system in a microgrid using ensemble empirical mode decomposition, characterized in that, it includes: Decompose the net load of the microgrid into the tie-line power and the total power of the hybrid energy storage system, and use ensemble empirical mode decomposition to decompose the total power of the hybrid energy storage system into a low-frequency component suppressed by a lithium battery and a high-frequency component suppressed by a supercapacitor; Construct an optimization configuration model for the hybrid energy storage based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the supercapacitor, and the tie-line power; wherein, the optimization configuration model for the hybrid energy storage includes an equivalent annual cost objective function of the hybrid energy storage, a tie-line power suppression objective function, and an energy supply and demand balance objective function; Use an adaptive particle swarm optimization algorithm to solve the optimization configuration model for the hybrid energy storage to obtain an optimized configuration scheme for the hybrid energy storage capacity; Construct the tie-line power suppression objective function in the following way: Construct the tie-line power suppression objective function with the minimum sum of squares of the adjusted tie-line power change differences as the goal; Construct the energy supply and demand balance objective function in the following way: Construct the energy supply and demand balance objective function with the output power variances of the lithium battery and the supercapacitor as the goal.

2. The method according to claim 1, characterized in that, it further includes: Use the deviation ranking method to determine the weights of the equivalent annual cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function respectively; Based on the weights of the equivalent annual cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function, aggregate the equivalent annual cost objective function of the hybrid energy storage, the tie-line power suppression objective function, and the energy supply and demand balance objective function into a single objective function.

3. The method according to claim 1, characterized in that, The optimization configuration model for the hybrid energy storage further includes: a remaining capacity constraint condition of the hybrid energy storage system, a charge and discharge power constraint condition of the hybrid energy storage system, and a state of charge constraint condition of the hybrid energy storage; the charge and discharge power constraint condition of the hybrid energy storage system includes upper and lower limits for restricting the charge and discharge power of the lithium battery and upper and lower limits for restricting the charge and discharge power of the supercapacitor; After obtaining the optimized configuration scheme for the hybrid energy storage capacity, the method further includes: According to the state of charge of the hybrid energy storage, use a fuzzy control algorithm to perform secondary correction on the charge and discharge power of the lithium battery and the charge and discharge power of the supercapacitor.

4. The method according to claim 3, characterized in that, According to the state of charge of the hybrid energy storage, using a fuzzy control algorithm to perform secondary correction on the charge and discharge power of the lithium battery and the charge and discharge power of the supercapacitor includes: If the state of charge of the lithium battery is less than a first preset value and the discharge power command of the lithium battery is greater than a second preset value, then use a fuzzy control algorithm to perform secondary correction on the charge and discharge power of the lithium battery; If the state of charge of the supercapacitor is less than a first preset value and the discharge power command of the supercapacitor is greater than a second preset value, then use a fuzzy control algorithm to perform secondary correction on the charge and discharge power of the supercapacitor.

5. The method according to claim 3, characterized in that, According to the state of charge of the hybrid energy storage, a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the lithium battery and the charging and discharging power of the super capacitor, including: If the state of charge of the lithium battery is greater than a first preset value and the charging power command of the lithium battery is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the lithium battery; If the state of charge of the super capacitor is greater than a first preset value and the charging power command of the super capacitor is greater than a second preset value, then a fuzzy control algorithm is used to perform secondary correction on the charging and discharging power of the super capacitor.

6. A microgrid hybrid energy storage optimal configuration device using ensemble empirical mode decomposition, characterized in that it includes: A decomposition module, configured to decompose the microgrid net load into the tie line power and the total power of the hybrid energy storage system, and use ensemble empirical mode to decompose the total power of the hybrid energy storage system into a low-frequency component suppressed by the lithium battery and a high-frequency component suppressed by the super capacitor; A construction module, configured to construct a hybrid energy storage optimal configuration model based on the low-frequency component suppressed by the lithium battery, the high-frequency component suppressed by the super capacitor, and the tie line power; wherein, the hybrid energy storage optimal configuration model includes an annual equivalent cost objective function of the hybrid energy storage, a tie line power suppression objective function, and an energy supply and demand balance objective function; A solution module, configured to use an adaptive particle swarm algorithm to solve the hybrid energy storage optimal configuration model to obtain an optimal configuration scheme for the hybrid energy storage capacity; The construction module is specifically configured to construct the tie line power suppression objective function in the following manner: Construct a tie line power suppression objective function with the minimum sum of squares of the adjusted tie line power change differences as the target; The construction module is specifically configured to construct the energy supply and demand balance objective function in the following manner: Construct an energy supply and demand balance objective function with the output power variances of the lithium battery and the super capacitor as the target.

7. An electronic device, characterized in that it includes: A processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes the steps of the method according to any one of claims 1 to 5.

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