Construction method and device of light storage equivalent model and electronic equipment

Abstract

The invention discloses an optical storage equivalent model construction method and device and electronic equipment, is applied to an optical storage system, and comprises the following steps: determining a second parameter set according to a pre-acquired first parameter set of the optical storage system; determining a simulation step size and a target function according to the pre-acquired output power of the optical storage system; determining fitness between the first parameter set and the second parameter set based on the objective function; judging whether the fitness and the current number of iterations meet a preset threshold value or not; if the fitness does not meet the preset threshold value or the current number of iterations does not meet the preset threshold value, the second parameter set is updated based on the simulation step length; and constructing a light storage equivalent model according to the current second parameter set. According to the method, the core mechanism of the moss optimization algorithm is combined with the requirements of light-storage combined frequency modulation equivalent modeling, so that accurate optimization of equivalent model parameters is realized.

Description

Technical Field

[0001] This application relates to the field of model building technology, and in particular to a method, apparatus and electronic device for building an equivalent optical storage model. Background Technology

[0002] Large-scale clustered photovoltaic and energy storage systems are playing an increasingly important role in the practical application of distribution networks. One of the key issues in the group control and dispatch of distribution networks is the adaptability of cluster equivalent modeling, which is related to the accuracy of the group control and dispatch optimization results.

[0003] Currently, the parameter optimization process in cluster equivalent modeling does not consider the constraints of the objective function and optimization function in the model, which affects the optimization accuracy of group control and adjustment.

[0004] To obtain cluster equivalent modeling that is more suitable for group control and dispatch optimization, this invention proposes a method, device and electronic equipment for constructing a light- and energy-storage equivalent model, which helps the distribution network group control and dispatch obtain more accurate optimization results and improve the economy and reliability of distribution network operation. Summary of the Invention

[0005] Therefore, the purpose of this application is to provide a method, apparatus and electronic equipment for constructing a light-storage equivalent model, which helps to obtain more accurate optimization results in the group control and dispatch of the distribution network, and improve the economy and reliability of the distribution network operation.

[0006] In a first aspect, embodiments of the present invention provide a method for constructing an equivalent model of photovoltaic-storage systems, applied to a photovoltaic-storage system. The method includes: S102: determining a second parameter set based on a pre-acquired first parameter set of the photovoltaic-storage system; S104: determining a simulation step size and an objective function based on the pre-acquired output power of the photovoltaic-storage system; S106: determining the fitness between the first parameter set and the second parameter set based on the objective function; S108: determining whether the fitness and the current iteration number meet a preset threshold; S110: if the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold, updating the second parameter set based on the simulation step size and returning to S106; S112: constructing an equivalent model of photovoltaic-storage systems based on the current second parameter set.

[0007] Furthermore, S102 includes: based on a preset proportional relationship, performing random proportional processing on the first parameter set to generate a second parameter set.

[0008] Furthermore, S102 also includes: eliminating the dimensional differences between the first parameter set and the second parameter set; and unifying the metric space of the first parameter set and the second parameter set.

[0009] Further, S104 includes: S104-2: determining the simulation step size based on the pre-acquired output power of the optical storage system before and after frequency modulation; S104-4: determining the objective function based on the pre-acquired output power of the optical storage system before and after frequency modulation.

[0010] Further, S104-2 includes: S104-2-2: pre-acquiring the output power of the optical storage system before and after frequency modulation based on a preset time interval; S104-2-4: determining the power difference value based on the output power before and after frequency modulation; S104-2-6: determining the simulation step size based on the power difference value.

[0011] Further, S104-4 includes: S104-4-2: acquiring the output power of the optical storage system before and after frequency modulation based on a preset time interval, and determining the power sequence before and after frequency modulation; S104-4-4: performing derivative processing on the power sequence before and after frequency modulation respectively; S104-4-6: calculating the loss coefficient between the power sequence before and after frequency modulation after derivative processing; S104-4-8: determining the transient loss weighting coefficient based on the preset regression coefficient and the loss coefficient; S104-4-10: determining the objective function between the optical storage system before and after frequency modulation based on the transient loss weighting coefficient.

[0012] Further, S106 includes: determining the fitness between the first parameter set and the second parameter set based on preset weights, the objective function between the pre-modulation optical storage system and the post-modulation optical storage system, the first parameter set, and the current second parameter set.

[0013] Furthermore, S110 is implemented based on the MGO algorithm, and S110 includes: S110-2: determining the parameter evolution direction of the second parameter set; S110-4: performing a global spore diffusion search and cryptic mechanism processing on the second parameter set based on the parameter evolution direction and the simulation step size to obtain multiple sets of processed parameter sets; S110-6: calculating the fitness between each set of processed parameter sets and the first parameter set; S110-8: combining the optimal individual iteration rule of the MGO algorithm, taking the processed parameter set with the smallest fitness as the current second parameter set.

[0014] Furthermore, the photovoltaic-storage system includes a photovoltaic system and an energy storage system; the first set of parameters includes: the load shedding reserve fitting coefficient of the photovoltaic system, the load shedding self-reserve rate of the photovoltaic system, the up-modulation trigger threshold of the photovoltaic system, the down-modulation trigger threshold of the photovoltaic system, the upper boundary of the frequency modulation dead zone of the photovoltaic system, the lower boundary of the frequency modulation dead zone of the photovoltaic system, the upper boundary of the frequency modulation of the photovoltaic system, the lower boundary of the frequency modulation of the photovoltaic system, the short-circuit current coefficient of the photovoltaic system, the left frequency modulation control gain of the photovoltaic system, the right frequency modulation control gain of the photovoltaic system, the upper boundary of the energy storage system to activate the SOC self-regulation function, the lower boundary of the energy storage system to activate the SOC self-regulation function, the equivalent impedance of the transformer in the photovoltaic-storage system, the equivalent impedance of the collector line of the transformer in the photovoltaic-storage system, and the equivalent irradiance of the transformer in the photovoltaic-storage system.

[0015] Secondly, embodiments of the present invention provide an apparatus for constructing an equivalent model of a photovoltaic-storage system. The apparatus includes: a first construction module, configured to determine a second parameter set based on a pre-acquired first parameter set of the photovoltaic-storage system; a second construction module, configured to determine a simulation step size and an objective function based on the pre-acquired output power of the photovoltaic-storage system; a third construction module, configured to determine the fitness between the first parameter set and the second parameter set based on the objective function; a fourth construction module, configured to determine whether the fitness and the current iteration number meet a preset threshold; a fifth construction module, configured to update the second parameter set based on the simulation step size and return to step S106 if the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold; and a sixth construction module, configured to construct an equivalent model of a photovoltaic-storage system based on the current second parameter set.

[0016] Furthermore, the first construction module is also used to generate a second parameter set by randomly proportionally processing the first parameter set based on a preset proportional relationship.

[0017] Furthermore, the second construction module is also used to determine the simulation step size based on the pre-acquired output power of the optical storage system before and after frequency modulation; and to determine the objective function based on the pre-acquired output power of the optical storage system before and after frequency modulation.

[0018] Furthermore, the third construction module is also used to determine the fitness between the first parameter set and the second parameter set based on the preset weights, the objective function between the optical storage system before and after frequency modulation, the first parameter set, and the current second parameter set.

[0019] Furthermore, the fifth construction module is also used to determine the parameter evolution direction of the second parameter set; perform a global spore diffusion search and cryptic mechanism processing on the second parameter set based on the parameter evolution direction and the simulation step size to obtain multiple sets of processed parameter sets; calculate the fitness between each set of processed parameter sets and the first parameter set; and combine the optimal individual iteration rule of the MGO algorithm to take the set of processed parameter sets with the smallest fitness as the current second parameter set.

[0020] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor executes the machine-executable instructions to implement the method for constructing the optical storage equivalent model.

[0021] Fourthly, embodiments of the present invention provide a machine-readable storage medium storing machine-executable instructions. When the machine-executable instructions are invoked and executed by a processor, the machine-executable instructions cause the processor to implement the method for constructing the optical storage equivalent model.

[0022] The beneficial effects of the embodiments of the present invention are as follows:

[0023] This application discloses a method, apparatus, and electronic device for constructing an equivalent model of photovoltaic (PV) and energy storage systems, applied to PV-energy storage systems. The method includes: determining a second parameter set based on a pre-acquired first parameter set of the PV-energy storage system; determining a simulation step size and an objective function based on the pre-acquired output power of the PV-energy storage system; determining the fitness between the first parameter set and the second parameter set based on the objective function; determining whether the fitness and the current iteration number meet a preset threshold; if the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold, updating the second parameter set based on the simulation step size; and constructing an equivalent model of PV-energy storage based on the current second parameter set. This application combines the core mechanism of the moss optimization algorithm with the requirements of PV-energy storage joint frequency modulation equivalent modeling to achieve accurate optimization of the equivalent model parameters.

[0024] Other features and advantages of this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described techniques of this application.

[0025] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating the construction method of the first optical-storage equivalent model provided in this application;

[0028] Figure 2 A flowchart illustrating the construction method of the second optical-storage equivalent model provided in this application;

[0029] Figure 3 A flowchart of an MGO algorithm provided in this application;

[0030] Figure 4 This application provides a schematic diagram of typical IU and PU curves for a photovoltaic array.

[0031] Figure 5 This application provides a schematic diagram of the primary frequency regulation frequency-power of a photovoltaic system.

[0032] Figure 6 A waveform diagram of the active power difference value of a model provided in this application;

[0033] Figure 7 A schematic diagram of a device for constructing an equivalent optical storage model provided in this application;

[0034] Figure 8 A schematic diagram of an electronic device provided in this application. Detailed Implementation

[0035] The technical solutions of this application will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] This application is applied to the combined frequency modulation scenario of photovoltaic and energy storage.

[0037] This application addresses the problem of equivalent modeling for active support of new energy power plants. Taking photovoltaic-storage joint frequency regulation as a scenario, it proposes a refined equivalent clustering method driven by measured data.

[0038] The specific content protected by this application includes:

[0039] 1) On the Python platform, based on the real-time operation data of the frequency modulation of the photovoltaic storage system, a refined real-time equivalent modeling process based on the MGO algorithm is proposed.

[0040] 2) A specific method for determining the time step parameter of the model is proposed.

[0041] 3) An optimization method for the objective function is proposed.

[0042] 4) An optimization method for the optimization function is proposed.

[0043] Example 1

[0044] like Figure 1 The diagram shown is a flowchart of the method for constructing the first optical storage equivalent model provided in this application; Figure 2 This is a flowchart illustrating the construction method of the second optical-storage equivalent model provided in this application. This embodiment is described based on this flowchart.

[0045] Before detailing the technical solution, the principles of the photovoltaic-storage frequency modulation system and its equivalent model will first be explained:

[0046] (1.1) Principle of frequency regulation and load shedding backup in photovoltaic systems

[0047] Although photovoltaic cells can generate electricity, the power output of a single photovoltaic cell is too low to serve as an effective power generation unit for a photovoltaic power station. Connecting multiple photovoltaic cells (standard quantities are 60 or 72 cells) together creates a photovoltaic module.

[0048] Photovoltaic modules are the most basic power generation unit in a photovoltaic power station. To build a photovoltaic power station, photovoltaic modules must first be assembled in a certain way to form a larger DC power generation unit, namely a "photovoltaic array". Then, a large number of photovoltaic arrays are connected to photovoltaic inverters, distribution cabinets and other equipment, and after being connected through a central control system, a photovoltaic power station that can be built and put into actual use can be constructed.

[0049] Typical IU and PU curves of a photovoltaic array are as follows: Figure 4 As shown, Ur and Pr are the output power at the standby operating point, UOC represents the output open-circuit voltage, and Ump and Imp represent the maximum power output voltage and current, respectively.

[0050] analyze Figure 4It can be seen that the current of the photovoltaic array is not significantly affected by voltage changes in the initial stage. After reaching the maximum power point voltage, the current of the photovoltaic array decreases sharply as the voltage increases. The active power of the photovoltaic array initially exhibits a positive linear relationship with voltage, and similarly decreases rapidly after reaching the maximum power point voltage. When the temperature remains constant, both the current and voltage of the photovoltaic array increase with increasing irradiance, and the active power output also increases accordingly; when the irradiance remains constant, both the current and voltage of the photovoltaic array decrease with increasing temperature, and the active power output also decreases accordingly.

[0051] In existing photovoltaic power plant frequency regulation control strategies, a certain proportion of energy storage is required to provide frequency regulation capacity space. However, the maximum power and capacity of the energy storage system are limited. Therefore, it is also necessary to allow the photovoltaic equipment to operate at a given self-reserve rate to provide a larger frequency regulation space (the self-reserve rate of the photovoltaic power plant can be considered unknown, and we use a data-driven algorithm to regress it).

[0052] Basic load shedding frequency regulation principle: To reserve some power as a self-backup for the photovoltaic power station, the maximum power operating point can be shifted to another operating point. Generally, considering that the downslope of the characteristic curve is very steep, its current variation range is very small; even a small current change can cause drastic power fluctuations, leading to instability. On the upslope, voltage and output power have a roughly linear relationship, and voltage adjustments have a relatively small impact on power output. Therefore, the current at the self-backup operating point... and short-circuit current The interval can be obtained by fitting a piecewise function (assuming the self-sustaining rate of the photovoltaic converter is unknown, this function is the target we regress on):

[0053] (1)

[0054] In the formula These are the coefficients of the piecewise fitting function. The self-reserve ratio is expressed as the sum of the self-reserve point power and the maximum power. The ratio.

[0055] The real-time maximum power estimation expression can be obtained through equation (1). as follows:

[0056] (2)

[0057] Assuming target self-reserve rate Once known, the target current from the standby operating point is... Given the target self-sustaining rate Substituting into formula (2), the maximum power can be calculated. Based on the current maximum power estimate at time i. and photovoltaic array output power The self-reserve rate can be estimated. .

[0058] (1.2) Principle of frequency modulation of photovoltaic and energy storage system

[0059] A photovoltaic-storage system mainly consists of a photovoltaic cluster and an energy storage system.

[0060] A photovoltaic (PV) cluster is composed of multiple PV power plants connected in series and parallel on the grid side. This application assumes that a PV cluster contains... A photovoltaic power station, set up Let represent the operating power of the photovoltaic cluster when the grid frequency remains unchanged. Then, the steady-state operating power of the photovoltaic cluster can be expressed as: The sum of the power of each photovoltaic power station.

[0061] (3)

[0062] In the formula, This represents the steady-state operating power of the i-th power station in the photovoltaic cluster before frequency regulation.

[0063] like Figure 5 As shown, the current frequency of the power grid is f, and the rated frequency f0 is the power frequency of 50Hz. The upper and lower boundaries of the frequency regulation dead zone of the photovoltaic cluster are fdead+ and fdead-, respectively. The upper and lower boundary frequencies for the photovoltaic cluster to participate in the system frequency regulation are fmax and fmin, respectively. In the figure, Kf1 and Kf2 represent the left and right frequency regulation control gains of the photovoltaic system during the primary frequency regulation process, respectively. In the figure, Psolar,max and Psolar,min represent the power limit values ​​of the photovoltaic system during the frequency up and down regulation processes, respectively.

[0064] When a photovoltaic (PV) system participates in frequency regulation, it is required to operate under reduced load with a certain reserve capacity. Therefore, the PV system needs to operate in reduced load mode. This represents the theoretical power reserve retained when the photovoltaic cluster is operating under reduced load. This indicates the power absorbed or released by the photovoltaic cluster at that moment during operation. Let represent the theoretical maximum power of the photovoltaic cluster under the current temperature and light intensity conditions. Then, the following power relationship can be easily obtained.

[0065] (4)

[0066] This is equivalent to the sum of the theoretical reserve power of all photovoltaic power plants at that moment; Equivalent to the sum of the current operating power of all photovoltaic power plants

[0067] (5) (6)

[0068] In the formula, This represents the power demand reserved by the i-th photovoltaic power station in the photovoltaic cluster during its load reduction operation at that moment. Let represent the operating power of the i-th photovoltaic power station. Additionally, to specifically distinguish between frequency upsampling and downsampling processes, let This represents the power retained by the i-th photovoltaic power station during the frequency reduction process at that moment; This represents the power retained by the i-th photovoltaic power station during the frequency upscaling process at that moment when it is operating at reduced load.

[0069] Since the theoretical maximum power of the photovoltaic power station under its current state cannot be directly obtained... , excluding self-reserve rate In addition, load reduction rate can also be used. To measure the reserve capacity of a photovoltaic power plant during off-load operation, it is generally measured using... This represents the load reduction rate of the i-th photovoltaic power station.

[0070] (7)

[0071] In the formula This indicates the rated operating power of the photovoltaic power station.

[0072] When the grid frequency fluctuates, assuming the photovoltaic-storage combined system outputs positive power to the grid and absorbs negative power, and the primary frequency regulation power demand of the photovoltaic-storage combined system at that moment is... If the frequency needs to be increased, then it is... If the frequency needs to be lowered, then... . This indicates the power demand allocated to the photovoltaic system during primary frequency regulation. This represents the power demand allocated to the energy storage system during a primary frequency regulation process. (Given...) This represents the power absorbed or released by the photovoltaic system at that moment during operation, assuming... This indicates the power absorbed or released by the energy storage system at that moment during operation.

[0073] Let the operating power of the photovoltaic cluster system at that moment be... The primary frequency regulation power of the photovoltaic cluster at the current moment Steady-state operating power before frequency modulation composition

[0074] (8)

[0075] When a frequency regulation operation occurs, the photovoltaic system and the energy storage system jointly regulate the frequency, that is, they share the frequency regulation power demand.

[0076] (9)

[0077] Current operating power of the energy storage system It is divided into two parts: one part is the electrical energy purchased from the grid when maintaining its own SOC balance. Or the power of electricity sold The other part is the power output of the energy storage system participating in primary frequency regulation at that moment. For ease of representation, when up-modulating the frequency, use This indicates that when down-modulating the frequency, use If we express this as an expression, then the current operating power of the energy storage system is expressed as:

[0078] (10)

[0079] When frequency increases and decreases occur, the energy storage system needs to determine whether to participate in the primary frequency regulation based on its own State of Charge (SOC). The power allocated during the primary frequency regulation process is as follows:

[0080] (11) (12)

[0081] The frequency regulation strategy of an energy storage system varies depending on its state of charge (SOC). This represents the rated power of the energy storage system. Let the upper limit of the energy storage system's SOC self-regulation function be... The lower bound is Shared energy storage charges when participating in down-regulation, i.e., absorbs power from the grid; and discharges when participating in up-regulation, i.e., outputs power back to the grid. To maintain the SOC of the energy storage system within its normal operating range, when the SOC of the energy storage system is at... At that time, the energy storage system has a demand for electricity purchase; when At that time, energy storage systems have a need to sell electrical energy.

[0082] (13) (14)

[0083] (1.3) Equivalent principle of physical and operating parameters of photovoltaic power station

[0084] (1.3.1) Equivalent power calculation of photovoltaic array

[0085] First, to maintain the same output voltage before and after the photovoltaic array is equivalent, the number of photovoltaic array modules connected in series remains unchanged. Second, to ensure a constant output power before and after the equivalent, the output current of the photovoltaic array after the equivalent needs to be increased to a times the original value, that is:

[0086] (15)

[0087] In the formula, This refers to the number of photovoltaic array modules connected in parallel. This represents the number of photovoltaic array modules connected in parallel after being equivalent. 'a' represents the ratio of the total capacity Ssum of all photovoltaic power generation units in this category to the capacity Scen of the photovoltaic power generation unit at the category center, i.e., a = Ssum / Scen.

[0088] To ensure consistent power output before and after frequency regulation, the change in active power during the frequency regulation process must be consistent, assuming the photovoltaic array capacity remains the same before and after the regulation. According to the formula, this requires considering the current output of the photovoltaic array. With a fixed short-circuit current value under the current irradiance It can calculate the current self-sustaining rate of photovoltaic modules. It can be solved =0 is calculated as shown in the formula. .

[0089] (16)

[0090] For the maximum output power of the photovoltaic array before and after the equivalence, and combining with formula (2-20), the short-circuit current is directly used to determine the equivalent output power. Summing them together gives us formula (2-30).

[0091] (17)

[0092] Therefore, by combining the principle of the same change in frequency modulation power mentioned in formula (17), we get formula (18) and formula (19).

[0093] (18) (19) (20) (twenty one) (twenty two) (twenty three)

[0094] For the solar irradiance of photovoltaic power generation units within the same category, the equivalent irradiance for this category is used. .

[0095] (twenty four)

[0096] (1.3.2) Equivalent calculation of transformer parameters

[0097] For unit transformers in wind farms and photovoltaic farms, based on the principle of equal power loss before and after frequency regulation equivalence, the total power loss before equivalence is:

[0098] (25)

[0099] The total transformer loss after equivalent conversion is

[0100] (26)

[0101] Obtain the equivalent impedance

[0102] (27)

[0103] Its equivalent capacity is

[0104] (28)

[0105] (1.3.3) Equivalent calculation of collector line parameters

[0106] Following the principle that the total loss remains unchanged before and after the equivalent loss, for a radial topology, where each generating unit is connected in parallel at the grid connection point, considering n generating units, the line power loss before the equivalent loss is:

[0107] (29)

[0108] In the formula, Let I be the impedance of the i-th collector line. Under ideal natural environmental conditions, such as the wind speed in a wind farm or the same light and temperature conditions in a photovoltaic field, the output current of all photovoltaic arrays is I. The equivalent line power loss is...

[0109] (30)

[0110] Ensure that the transmission line losses are the same The total impedance of the collector line after equivalence is

[0111] (31)

[0112] like Figure 1 and Figure 2 The diagram shown is a flowchart of a method for constructing an equivalent model of photovoltaic energy storage, applied to a photovoltaic energy storage system. The method includes:

[0113] S102: Determine the second parameter set based on the first parameter set of the pre-acquired optical storage system.

[0114] S102 includes: generating a second parameter set by randomly proportionalizing the first parameter set based on a preset proportional relationship.

[0115] S102 further includes: eliminating the dimensional differences between the first parameter set and the second parameter set; and unifying the metric space of the first parameter set and the second parameter set.

[0116] like Figure 2 As shown, in order to adjust the control parameter 2 of the light-storage equivalent model to be consistent with the control parameter 1 of the actual physical object, a closed-loop process based on the moss growth optimization algorithm (MGO) was designed to gradually approximate the control parameter 1 of the light-storage equivalent model to the baseline control parameter 1 of the actual physical object.

[0117] S102 includes:

[0118] S1: Obtain “Actual Physical Parameters 1” (i.e., the first set of parameters of the “Actual Model”).

[0119] S2: Using the baseline control parameters of 1 (i.e. the first set of parameters of the actual model) as a reference, generate the "initial candidate solution" (i.e. the second set of parameters of the "equivalent model") according to the rule that "the initial equivalent model 2 is 1.1 times the "baseline parameter 1").

[0120] S3: Standardize the relevant parameters of the "equivalent model" and the "actual model" to eliminate dimensional differences and unify the measurement space.

[0121] S104: Determine the simulation step size and objective function based on the pre-acquired output power of the photovoltaic storage system.

[0122] S104 includes:

[0123] S104-2: Determine the simulation step size based on the pre-acquired output power of the optical storage system before and after frequency modulation.

[0124] S104-4: Determine the objective function based on the pre-acquired output power of the optical storage system before and after frequency modulation.

[0125] S104-2 includes: S104-2-2: Pre-acquiring the output power of the optical storage system before and after frequency modulation based on a preset time interval. S104-2-4: Determining the power difference value based on the output power before and after frequency modulation. S104-2-6: Determining the simulation step size based on the power difference value.

[0126] Specifically, S104-2 refers to the process of determining the optimal time step in the optical-storage equivalent model. This includes the following:

[0127] Based on the Python platform, this study focuses on the equivalent model of photovoltaic-storage systems and observes the output power of the photovoltaic-storage system at the field station before and after frequency modulation over a certain period of time. and By analyzing the waveform of the difference value, the optimal time step of the equivalent model is determined, and the equivalent model parameter 2 (i.e., the second parameter set) is optimized based on the optimal time step as the model simulation time.

[0128] This represents the operating power of the photovoltaic cluster when the grid frequency remains unchanged. This indicates the power absorbed or released by the energy storage system at that moment during operation.

[0129] Let t be a time node, P1,t be the output power value of the field station at time node t before frequency modulation, and P2,t be the output power value of the field station at time node t after frequency modulation. Then, P2,t represents the difference in output power of the photovoltaic energy storage system at the field station before and after frequency modulation at time node t. for:

[0130] (32)

[0131] The results obtained at each time point Plot as a waveform (e.g.) Figure 6 As shown), according to Figure 6 Determine the optimal time step.

[0132] This application selects multiple sets of output power difference values ​​over time, resamples the time series data of each parameter difference value, obtains parameter difference value data under different time steps, and plots corresponding waveforms. By comparing and analyzing the characteristics of the waveforms under different time steps, the rationality of the time step is judged.

[0133] The waveform diagram of the difference in active power between Model 1 and Model 2 is shown below. Figure 6 As shown. One minute was selected as the time length for the difference value test, and the waveform was analyzed. Figure 6 It can be seen that the difference value reaches its peak at 7s and then gradually stabilizes, reaching a relatively stable state at 8s. The test results show that the time step of the model is 8s.

[0134] S104-4 includes: S104-4-2: Pre-acquiring the output power of the optical-storage system before and after frequency modulation based on a preset time interval, and determining the power sequence before and after frequency modulation. S104-4-4: Performing derivative processing on the power sequence before and after frequency modulation, respectively. S104-4-6: Calculating the loss coefficient between the power sequence before and after frequency modulation after derivative processing. S104-4-8: Determining the transient loss weighting coefficient based on the preset regression coefficient and the loss coefficient. S104-4-10: Determining the objective function between the optical-storage system before and after frequency modulation based on the transient loss weighting coefficient.

[0135] Specifically, S104-4 is the process of determining the objective function of the optical-storage equivalent model. This invention uses TLDTW as the objective function of the optical-storage equivalent model. The core idea of ​​the Time Weighted Extended Dynamic Time Warping (TLDTW) algorithm is that during the registration process, instead of directly comparing the original amplitudes of the samples, the first derivatives of the sequences (i.e., the trend of change) are compared, thus focusing more on the consistency of signal shape rather than absolute value differences, resulting in a more accurate objective function.

[0136] Let there be two sequences X=(x1,…,xn) (i.e., the power sequence before frequency modulation, where any value is represented by i, and i represents different times within the sampling period) and Y=(y1,…,ym) (i.e., the power sequence after frequency modulation, where any value is represented by j), where n=m. First, calculate their derivative approximations to describe the local trend.

[0137] (33)

[0138] Define local distance in derivative space And introduce transient loss weighting coefficient Both are referred to as loss coefficients. :

[0139] (34)

[0140] in, = , These are the time points at time i and time j, respectively. It represents the continuous time between two time points.

[0141] To calculate the transient loss weighting coefficient Define a mathematical model that satisfies both linear regression and logistic regression, where the point is β and the steepness is α, for example:

[0142] (35)

[0143] Then, we continue using the dynamic programming recursion of TLDTW:

[0144] (36)

[0145] For comparability, normalization is performed according to a preset path length K:

[0146] (37)

[0147] The objective function is the relationship between the "response sequence of the actual model" and the "response sequence of the current equivalent model". TLDTW reflects the shape similarity of time series better than traditional similarity algorithms and is more robust to amplitude offset and noise disturbances.

[0148] S106: Determine the fitness between the first parameter set and the second parameter set based on the objective function.

[0149] S108: Determine whether the fitness and the current iteration number meet the preset threshold.

[0150] S110: If the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold, then update the second parameter set based on the simulation step size and return to S106.

[0151] S112: Construct an equivalent model of optical storage based on the current set of the second parameters.

[0152] S106-S112 refers to the process of determining the optimization function of the optical-storage equivalent model, i.e., the implementation process of the MGO algorithm (such as...). Figure 3 As shown in the figure, this part combines the core mechanism of the moss optimization algorithm with the requirement of photoelectric storage joint frequency modulation equivalent modeling, and determines the optimization function to achieve accurate optimization of the equivalent model parameters.

[0153] S106 includes:

[0154] The fitness between the first parameter set and the second parameter set is determined based on the preset weights, the objective function between the pre-modulation and post-modulation optical-storage systems, the first parameter set, and the current second parameter set.

[0155] S110 is implemented based on the MGO algorithm, and S110 includes:

[0156] S110-2: Determine the parameter evolution direction of the second parameter set.

[0157] S110-4: Based on the parameter evolution direction and the simulation step size, perform a global spore diffusion search and cryptobiosis mechanism processing on the second parameter set to obtain multiple sets of processed parameter sets.

[0158] S110-6: Calculate the fitness between each processed parameter set and the first parameter set.

[0159] S110-8: Combining the optimal individual iteration rule of the MGO algorithm, the set of parameters with the minimum fitness after processing is used as the current second parameter set.

[0160] like Figure 3 As shown, the MGO algorithm includes:

[0161] Figure 3 S101, S102: Initialization, determining the simulation step size and objective function, performing population initialization and standardization. Each individual corresponds to a set of optimization parameters. The initial parameters were randomly generated based on 1.1 times the baseline parameters of the actual system. All parameters were then normalized. Eliminate dimensional differences and unify the measurement space.

[0162] Set of key parameters for the photovoltaic-storage equivalent model :

[0163] (38)

[0164] As optimization variables for the MGO algorithm, they include:

[0165] The photovoltaic-energy storage system includes a photovoltaic system and an energy storage system.

[0166] For the load shedding reserve fitting coefficient of the photovoltaic system, To reduce the self-sustaining rate of photovoltaic systems,

[0167] For the up-modulation trigger threshold of the photovoltaic system, For the down-modulation trigger threshold of the photovoltaic system,

[0168] The upper boundary of the frequency modulation dead zone of the photovoltaic system, The lower boundary of the frequency modulation dead zone of the photovoltaic system,

[0169] For the upper limit of frequency regulation of photovoltaic systems, For the lower boundary of frequency regulation of photovoltaic systems,

[0170] For the left frequency modulation control gain of the photovoltaic system, For the right-hand frequency modulation control gain of the photovoltaic system,

[0171] For the short-circuit current coefficient of the photovoltaic system,

[0172] The upper limit for the energy storage system to activate its SOC self-regulation function. The lower bound for the energy storage system to activate its SOC self-regulation function.

[0173] The equivalent impedance of the transformer in the photovoltaic-storage system, The equivalent impedance of the collector line of the transformer in the photovoltaic-storage system, This represents the equivalent irradiance of the transformer in the photovoltaic-storage system.

[0174] After determining the optimization objective and optimization variables, the MGO algorithm is then executed.

[0175] Figure 3 -S103: To minimize the difference in dynamic response between the optical storage equivalent model and the actual physical model, the derivative dynamic time warping (TLDTW) similarity value (i.e., the objective function of formula 37) is used as the core optimization index to make the frequency modulation power response and frequency following characteristics of the optimized equivalent model consistent with the actual system.

[0176] Calculate fitness value:

[0177] Using the TLDTW similarity function (result of Formula 37) as the core of the objective function, a fitness function is constructed by integrating Euclidean distance and cosine similarity to quantify the consistency between the equivalent model and the actual model, and the fitness value is calculated:

[0178] (39)

[0179] in, This is the fitness value;

[0180] A, B, and C are preset weights, which can be set to 0.6, 0.2, and 0.2 respectively.

[0181] The result is obtained from the processing of formulas 33-37;

[0182] This is the frequency modulation power response sequence of an actual photovoltaic-storage system;

[0183] Output a sequence for the current equivalent model;

[0184] These are the baseline parameters for the actual system, i.e., the parameters in the standard model, i.e., the first set of parameters;

[0185] These are the parameters in the current optical-storage equivalent model, i.e., the second set of parameters;

[0186] The angle between the parameter vector and the reference vector:

[0187] (40)

[0188] The smaller the fitness value The stronger the consistency between the equivalent model and the actual model, the better the set of the second parameters.

[0189] Figure 3-S104: Determine if the number of iterations has been reached.

[0190] Figure 3 -S106: If not achieved, then based on the wind speed determination mechanism, a set of parameter combinations to be optimized is divided according to the specific optimization objective. That is, based on the wind speed determination mechanism, the parameter set of the current formula 38 is used to generate a new set of key parameters, group num. .

[0191] Randomly select a single parameter dimension for updating:

[0192] .

[0193] Figure 3 -S107: Calculate the main population (i.e., Figure 3 -S106 generates a set of key parameters The most realistic combination of parameters (i.e.) Figure 2 The average distance of the actual physical parameter 1) of S1 According to the average distance Determine the direction of parameter evolution Avoid blind searches;

[0194] Formula 41;

[0195] Formula 42;

[0196] Where D_wind represents the calculated evolutionary wind direction, which is related to the individual (i.e., the set of key parameters). The variables and have the same dimension. The variable num represents the total number of individuals in dirX. dirX is calculated as shown in Equation 42. The reason for calculating the average distance between the main individuals and Mbest (i.e., the parameter set) is that this method helps smooth the path of individuals approaching Mbest, thereby improving the optimization capability of MGO. Here, dirX represents the set of distances between individuals within divX and Mbest.

[0197] Figure 3 -S108: Based on D_wind, perform spore diffusion search. The spore diffusion search is used for global exploration; a large step size is used in sunny weather with gentle winds (r1>0.2), and a small step size is used in cloudy / unpredictable weather (r1≤0.2). This yields multiple sets of individuals (i.e., sets of key parameters). )

[0198] Figure 3 -S109: Judgment That is, to determine whether the cryptic mechanism has been entered.

[0199] Specifically, rand is the distance between multiple groups of individuals (i.e., multiple "light-storage equivalent models"). Alternatively, it is the area of ​​the swath diffusion search.

[0200] Figure 3 -S110, S111: When Trigger: Dual reproductive search is used for local development, (r4>0.5) is for sexual reproduction: it belongs to the hidden mechanism of the MGO algorithm. The core is to record the historical optimal state of the individual during the iteration process, avoid getting trapped in local optima, and update to obtain a new set of parameters.

[0201] Formula 43

[0202] Formula 44;

[0203] ;

[0204] Integrate current parameters with optimal parameters (i.e.) Figure 3 The core feature of S1 actual physical parameters 1) is that it is asexual reproduction otherwise. To enhance local fine search, the hidden generation mechanism records the 10 optimal parameter solutions of each individual during the iteration process. When the number of iterations reaches the threshold n or the record is full, the current individual is replaced with the historical optimal state to avoid getting trapped in local optima.

[0205] Figure 3 -S112: This is the fitness evaluation mechanism of the MGO algorithm. Its core is to quantify model consistency through fitness values. It determines whether the fitness meets the preset requirements.

[0206] Figure 3 -S113: This is the optimal individual iteration rule of the MGO algorithm. If Fitness(Mnew) < Fitness(Mbest), then an update is performed.

[0207] Mbest=Mnew,b_cost=Fitness(Mnew) formula 45;

[0208] In the formula, Mbest is the current global best individual (i.e., parameter set), and b_cost is the optimal fitness value (i.e., fitness value).

[0209] Figure 3 -S114: Based on the physical properties of the key parameters of the light-storage equivalent model, the main population divX is selected and grouped. The mean distance between this population and the current global optimal parameter Θ_best is calculated. By generating an evolutionary direction vector D_wind that satisfies engineering constraints, clear guidance is provided for subsequent parameter optimization.

[0210] Figure 3-S115: Finally, determine the termination condition, and after the iteration terminates, select the fitness ( The parameters corresponding to the smallest individual (i.e., the set of key parameters) ) is used as the optimal equivalent model parameter 2 to obtain the "optical-storage equivalent model", and then de-standardized to restore it to the actual engineering parameters.

[0211] Figure 2 The general process is as follows:

[0212] S1: Obtain "Actual physical parameters 1".

[0213] S2: The process takes the baseline control parameters of 1 as a reference and generates the "initial candidate solution" (i.e. "initial parameter 2") according to the principle that "the initial equivalent model 2 is 1.1 times the "baseline parameter 1"".

[0214] S3: Standardize the relevant parameters of the "equivalent model" and the "actual model" to eliminate dimensional differences and unify the measurement space.

[0215] The equivalent model is the model constructed from the initial candidate solutions in S2; the actual model is the model constructed from the actual physical parameters 1 in S1. The relevant parameters are the actual physical parameters 1 and the initial candidate solutions.

[0216] S4: Then, a similarity algorithm (such as the fusion of Euclidean distance and cosine similarity) is used to calculate the fitness value. The closer the value is to 1, the more similar the two models are.

[0217] S5: Determine if the number of iterations is greater than n.

[0218] S8: If the current iteration count has reached the preset upper limit n, output the optimized equivalent model parameter 2 and terminate, thus constructing the "optical storage equivalent model".

[0219] S6: Otherwise, proceed with the MGO update.

[0220] The steps for updating MGO include:

[0221] S6-1: Based on mechanisms such as "irradiance determination mechanism, spore diffusion search, dual reproduction search, and cryptobiosis mechanism", a better combination of parameters is generated.

[0222] S6-2: (i.e. S7) and perform boundary truncation to satisfy physical and security constraints;

[0223] S6-3: (i.e., return to S3) The truncated parameters are standardized again and enter the next round of iteration until the termination condition is met, thereby achieving the continuous approximation of the equivalent model parameter 2 to the benchmark parameter 1 (i.e., "actual physical parameter 1").

[0224] This embodiment discloses a method for constructing an equivalent model of photovoltaic-storage systems, applied to a photovoltaic-storage system. The method includes: determining a second parameter set based on a pre-acquired first parameter set of the photovoltaic-storage system; determining a simulation step size and an objective function based on the pre-acquired output power of the photovoltaic-storage system; determining the fitness between the first parameter set and the second parameter set based on the objective function; determining whether the fitness and the current iteration number meet a preset threshold; if the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold, updating the second parameter set based on the simulation step size; and constructing an equivalent model of photovoltaic-storage systems based on the current second parameter set. This application combines the core mechanism of the moss optimization algorithm with the requirements of photovoltaic-storage joint frequency modulation equivalent modeling to achieve accurate optimization of the equivalent model parameters.

[0225] Example 2

[0226] like Figure 7 As shown, this embodiment of the invention provides a device for constructing an equivalent model of a photovoltaic-storage system. The device includes: a first construction module, used to determine a second parameter set based on a pre-acquired first parameter set of the photovoltaic-storage system; a second construction module, used to determine a simulation step size and an objective function based on the pre-acquired output power of the photovoltaic-storage system; a third construction module, used to determine the fitness between the first parameter set and the second parameter set based on the objective function; a fourth construction module, used to determine whether the fitness and the current iteration number meet a preset threshold; a fifth construction module, used to update the second parameter set based on the simulation step size if the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold; and a sixth construction module, used to construct an equivalent model of the photovoltaic-storage system based on the current second parameter set.

[0227] The first construction module is also used to generate a second parameter set by randomly proportionally processing the first parameter set based on a preset proportional relationship.

[0228] The second construction module is also used to determine the simulation step size based on the pre-acquired output power of the optical storage system before and after frequency modulation; and to determine the objective function based on the pre-acquired output power of the optical storage system before and after frequency modulation.

[0229] The third construction module is also used to determine the fitness between the first parameter set and the second parameter set based on the preset weights, the objective function between the optical storage system before and after frequency modulation, the first parameter set, and the current second parameter set.

[0230] The fifth construction module is also used to determine the parameter evolution direction of the second parameter set; perform global spore diffusion search and cryptic mechanism processing on the second parameter set based on the parameter evolution direction and the simulation step size to obtain multiple sets of processed parameter sets; calculate the fitness between each set of processed parameter sets and the first parameter set; and combine the optimal individual iteration rule of the MGO algorithm to take the set of processed parameter sets with the smallest fitness as the current second parameter set.

[0231] The optical storage equivalent model construction device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned optical storage equivalent model construction device embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned device embodiment.

[0232] Example 3

[0233] This application also provides an electronic device, see [link to relevant documentation] Figure 8 As shown, it includes a processor 100 and a memory 200. The memory 200 stores machine-executable instructions that can be executed by the processor. The processor executes the machine-executable instructions to implement the above-mentioned method for constructing the optical storage equivalent model.

[0234] Furthermore, Figure 8 The electronic device shown also includes a bus 300 and a communication interface 400, with the processor 100, communication interface 400 and memory 200 connected via the bus 300.

[0235] The memory 200 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 400 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 300 may be an ISA bus, PCI bus, or EISA bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0236] The processor 100 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 100 or by instructions in software form. The processor 100 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams of the application in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method applied in conjunction with the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 200, and processor 100 reads information from memory 200 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0237] This application also provides a machine-readable storage medium storing machine-executable instructions. When these machine-executable instructions are called and executed by a processor, they cause the processor to implement the above-described method for constructing the optical-storage equivalent model. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0238] The computer program product of the method, apparatus and electronic device for constructing the optical storage equivalent model provided in the embodiments of this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the apparatus in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0239] If this function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the 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 to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0240] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for constructing an equivalent optical-storage model, characterized in that, Applied to photovoltaic energy storage systems, the method includes: S102: Determine the second parameter set based on the first parameter set of the pre-acquired photovoltaic energy storage system; S104: Determine the simulation step size and objective function based on the pre-acquired output power of the photovoltaic energy storage system; S106: Determine the fitness between the first parameter set and the second parameter set based on the objective function; S108: Determine whether the fitness and the current iteration number meet the preset threshold; S110: If the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold, then update the second parameter set based on the simulation step size and return to S106. S112: Construct an equivalent model of optical storage based on the current set of the second parameters.

2. The method for constructing the optical-storage equivalent model according to claim 1, characterized in that, S102 includes: Based on a preset ratio, the first parameter set is processed by random ratio to generate the second parameter set.

3. The method for constructing the optical-storage equivalent model according to claim 2, characterized in that, S102 also includes: Eliminate the dimensional differences between the first parameter set and the second parameter set; Unify the metric space of the first parameter set and the second parameter set.

4. The method for constructing the optical-storage equivalent model according to claim 1, characterized in that, S104 includes: S104-2: Determine the simulation step size based on the pre-acquired output power of the photovoltaic energy storage system before and after frequency modulation; S104-4: Determine the objective function based on the pre-acquired output power of the optical storage system before and after frequency modulation.

5. The method for constructing the optical-storage equivalent model according to claim 4, characterized in that, S104-2 includes: S104-2-2: The output power of the optical storage system before and after frequency modulation is obtained in advance based on a preset time interval; S104-2-4: Determine the power difference value based on the output power before and after frequency modulation; S104-2-6: Determine the simulation step size based on the power difference value.

6. The method for constructing the optical-storage equivalent model according to claim 5, characterized in that, S104-4 includes: S104-4-2: Based on a preset time interval, the output power of the optical storage system before and after frequency modulation is obtained in advance, and the power sequence before frequency modulation and the power sequence after frequency modulation are determined; S104-4-4: Perform derivative processing on the power sequence before frequency modulation and the power sequence after frequency modulation respectively; S104-4-6: Calculate the loss coefficient between the power sequence before frequency modulation and the power sequence after frequency modulation after derivative processing; S104-4-8: Determine the transient loss weighting coefficient based on the preset regression coefficient and the loss coefficient; S104-4-10: Determine the objective function between the pre-frequency modulation optical-storage system and the post-frequency modulation optical-storage system based on the transient loss weighting coefficient.

7. The method for constructing the optical-storage equivalent model according to claim 6, characterized in that, S106 includes: The fitness between the first parameter set and the second parameter set is determined based on the preset weights, the objective function between the pre-modulation and post-modulation optical-storage systems, the first parameter set, and the current second parameter set.

8. The method for constructing the optical-storage equivalent model according to claim 7, characterized in that, S110 is implemented based on the MGO algorithm, and S110 includes: S110-2: Determine the parameter evolution direction of the second parameter set; S110-4: Based on the parameter evolution direction and the simulation step size, perform a global spore diffusion search and cryptogenic mechanism processing on the second parameter set to obtain multiple sets of processed parameter sets; S110-6: Calculate the fitness between each processed parameter set and the first parameter set; S110-8: Combining the optimal individual iteration rule of the MGO algorithm, the set of parameters with the minimum fitness after processing is used as the current second parameter set.

9. The method for constructing the optical-storage equivalent model according to claim 1, characterized in that, The photovoltaic-energy storage system includes a photovoltaic system and an energy storage system; The first parameter set includes: The following parameters are considered: load shedding reserve fitting coefficient of photovoltaic system, load shedding self-reserve rate of photovoltaic system, frequency up-modulation trigger threshold of photovoltaic system, frequency down-modulation trigger threshold of photovoltaic system, upper boundary of frequency modulation dead zone of photovoltaic system, lower boundary of frequency modulation dead zone of photovoltaic system, upper boundary of frequency modulation of photovoltaic system, lower boundary of frequency modulation of photovoltaic system, short-circuit current coefficient of photovoltaic system, left frequency modulation control gain of photovoltaic system, right frequency modulation control gain of photovoltaic system, upper boundary of SOC self-regulation function of energy storage system, lower boundary of SOC self-regulation function of energy storage system, equivalent impedance of transformer in photovoltaic-energy storage system, equivalent impedance of collector line of transformer in photovoltaic-energy storage system, and equivalent irradiance of transformer in photovoltaic-energy storage system.

10. A device for constructing an equivalent model of optical storage, characterized in that, The device includes: The first construction module is used to determine the second parameter set based on the first parameter set of the pre-acquired optical storage system; The second construction module is used to determine the simulation step size and objective function based on the pre-acquired output power of the optical storage system; The third construction module is used to determine the fitness between the first parameter set and the second parameter set based on the objective function; The fourth construction module is used to determine whether the fitness and the current iteration number meet a preset threshold. The fifth construction module is used to update the second parameter set based on the simulation step size and return to S106 if the fitness does not meet the preset threshold or the current iteration number does not meet the preset threshold. The sixth construction module is used to construct an equivalent model of optical storage based on the current second parameter set.

11. The apparatus for constructing an equivalent optical-storage model according to claim 10, characterized in that, The first construction module is also used to generate a second parameter set by randomly proportionally processing the first parameter set based on a preset proportional relationship.

12. The apparatus for constructing an equivalent optical storage model according to claim 10, characterized in that, The second construction module is also used to determine the simulation step size based on the pre-acquired output power of the optical storage system before and after frequency modulation; and to determine the objective function based on the pre-acquired output power of the optical storage system before and after frequency modulation.

13. The apparatus for constructing an equivalent optical storage model according to claim 10, characterized in that, The third construction module is also used to determine the fitness between the first parameter set and the second parameter set based on the preset weights, the objective function between the optical storage system before and after frequency modulation, the first parameter set, and the current second parameter set.

14. The apparatus for constructing an equivalent optical-storage model according to claim 10, characterized in that, The fifth construction module is also used to determine the parameter evolution direction of the second parameter set; perform global spore diffusion search and cryptic mechanism processing on the second parameter set based on the parameter evolution direction and the simulation step size to obtain multiple sets of processed parameter sets; calculate the fitness between each set of processed parameter sets and the first parameter set; and combine the optimal individual iteration rule of the MGO algorithm to take the set of processed parameter sets with the smallest fitness as the current second parameter set.

15. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor, the processor executing the machine-executable instructions to implement the method for constructing the optical storage equivalent model according to any one of claims 1-9.

16. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores machine-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method for constructing the optical storage equivalent model according to any one of claims 1-9.

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