Method and System for Optimizing the Operation of a Multi-Distributed Resource Aggregate

Abstract

The present disclosure relates to the field of power technologies, and discloses a method and a system for optimizing the operation of a multi-distributed resource aggregation body. The specific solution is as follows: Simulate the changes in the hydrodynamic performance of each power generation device to obtain the instantaneous power output characteristic curve of each power generation device; Based on the instantaneous power output characteristic curves of each power generation device, determine the deviation between the rated output power and the actual output power of the tidal energy power generation system; Based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, construct an objective function for optimizing the energy storage capacity of each energy storage unit, and solve the optimal value of the objective function to obtain the target energy storage capacity configuration scheme; Based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the predicted new energy output result in the first future time period, and the target energy storage capacity configuration scheme, determine the charge and discharge strategy of the tidal energy power generation system to control the operation of each power generation device and each energy storage unit.

Description

Technical Field

[0001] The present disclosure relates to the field of power technology, and particularly to a method, system, electronic device and storage medium for optimizing the operation of a multi-distributed resource aggregate. Background Art

[0002] A multi-distributed resource aggregate is an aggregation system composed of various types of distributed energy sources. As an important resource among them, tidal energy power generation equipment plays a crucial role in this system. The tidal energy power generation equipment operates in the marine environment for a long time, and various microorganisms will inevitably adhere to its surface to form a biofilm. The composition of microbial communities in different sea areas varies greatly, and environmental conditions such as temperature, salinity, and nutrient content are also different. These factors jointly affect the growth rate of microorganisms on the surface of the tidal energy power generation equipment, resulting in significant differences in the growth rate of surface microorganisms of equipment in different sea areas. The attachment growth of microorganisms will change the roughness and frictional resistance of the surface of the tidal energy power generation equipment, thereby affecting the hydrodynamic performance of the blades and the rotating shaft, and ultimately changing the output power and response characteristics of the generator set, which will directly affect the overall performance of the multi-distributed resource aggregate.

[0003] When the power grid requires the multi-distributed resource aggregate to provide millisecond-level frequency modulation services, the instantaneous change characteristics of the output power of the tidal energy power generation equipment are crucial because it directly relates to the overall frequency modulation ability of the aggregate. If the attachment of microorganisms on the surface of the tidal energy power generation equipment affects its original transient characteristics and makes its power output response sluggish or unstable, it will greatly weaken the frequency modulation ability of the multi-distributed resource aggregate and be difficult to meet the requirements of the power grid for its millisecond-level response, thus affecting the stability and reliability of the power grid.

[0004] Therefore, studying the growth laws of microbial communities in different sea areas on the surface of tidal energy power generation equipment and revealing the influence mechanism on the dynamic output characteristics of the equipment not only have important significance for mastering the uncertain factors of tidal energy and improving its grid-connected frequency modulation ability, but also play a positive role in enhancing the overall performance and reliability of the multi-distributed resource aggregate. Summary of the Invention

[0005] The present disclosure provides a method, system, electronic device and storage medium for optimizing the operation of a multi-distributed resource aggregate, which can solve at least one of the above problems.

[0006] The present disclosure provides a method for optimizing the operation of a multi-distributed resource aggregate, including:

[0007] Based on the biofilm growth model of each power generation device in the tidal energy power generation system, determine the mathematical model of the surface roughness of each power generation device changing with time, and based on the mathematical models corresponding to each power generation device, simulate the hydrodynamic performance changes of each power generation device to obtain the instantaneous power output characteristic curves of each power generation device;

[0008] Based on the instantaneous power output characteristic curves of each power generation device, determine the deviation between the rated output power and the actual output power of the tidal energy power generation system;

[0009] Based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, construct an objective function for the tidal energy power generation system to optimize the energy storage capacity of each energy storage unit;

[0010] Adopt a genetic algorithm to solve the optimal value of the objective function to obtain the target energy storage capacity configuration plan of the tidal energy power generation system;

[0011] Based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the prediction result of the new energy output of the tidal energy power generation system in the first future time period, and the target energy storage capacity configuration plan, determine the charge-discharge strategy of the tidal energy power generation system;

[0012] Based on the charging strategy of the tidal energy power generation system, control the operation of each power generation device and each energy storage unit.

[0013] According to another aspect of the present disclosure, there is provided a multi-distributed resource aggregation operation optimization system, including:

[0014] A characteristic curve determination module, configured to determine the mathematical model of the surface roughness of each power generation device changing with time based on the biofilm growth model of each power generation device in the tidal energy power generation system, and based on the mathematical models corresponding to each power generation device, simulate the hydrodynamic performance changes of each power generation device to obtain the instantaneous power output characteristic curves of each power generation device;

[0015] A power deviation determination module, configured to determine the deviation between the rated output power and the actual output power of the tidal energy power generation system based on the instantaneous power output characteristic curves of each power generation device;

[0016] An objective function construction module, configured to construct an objective function for the tidal energy power generation system to optimize the energy storage capacity of each energy storage unit based on the characteristic parameters of each energy storage unit in the tidal energy power generation system;

[0017] The objective function solving module is used to solve the optimal value of the objective function by using the genetic algorithm, and obtain the target energy storage capacity configuration scheme of the tidal energy power generation system;

[0018] The charge-discharge strategy determination module is used to determine the charge-discharge strategy of the tidal energy power generation system based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the prediction result of the new energy output of the tidal energy power generation system in the future first time period, and the target energy storage capacity configuration scheme;

[0019] The power detection module is used to control each power generation device and energy storage unit in the tidal energy power generation system to work based on the charging strategy of the tidal energy power generation system.

[0020] According to another aspect of the present disclosure, there is provided an electronic device, including:

[0021] At least one processor; and

[0022] A memory communicatively connected to the at least one processor; wherein,

[0023] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any one of the multi-distributed resource aggregation body operation optimization methods in the embodiments of the present disclosure.

[0024] According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute any one of the multi-distributed resource aggregation body operation optimization methods in the embodiments of the present disclosure.

[0025] By adopting the technical solution of the present disclosure, based on the biofilm growth models of each power generation device in the tidal energy power generation system, a mathematical model of the surface roughness change of each power generation device with time is determined, and based on the mathematical models corresponding to each power generation device, the hydrodynamic performance change of each power generation device is simulated, and the instantaneous power output characteristic curve of each power generation device is obtained; based on the instantaneous power output characteristic curves of each power generation device, the deviation between the rated output power and the actual output power of the tidal energy power generation system is determined. This deviation can evaluate the influence of microbial attachment on the output power of the energy storage system.

[0026] Then, based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, an objective function for optimizing the energy storage capacity of each energy storage unit of the tidal energy power generation system is constructed; the genetic algorithm is used to solve the optimal value of the objective function, and the target energy storage capacity configuration scheme of the tidal energy power generation system is obtained;

[0027] Furthermore, based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the new energy output prediction result of the tidal energy power generation system in the first future time period, and the target energy storage capacity configuration scheme, the charge-discharge strategy of the tidal energy power generation system is determined. Thus, the charge-discharge strategy of the system can be optimized by combining the influence of microorganism attachment on the output power of the energy storage system and the target energy storage capacity configuration scheme.

[0028] Subsequently, based on the charging strategy of the tidal energy power generation system, the operation of each power generation device and each energy storage unit is controlled to ensure the stable operation of the tidal energy power generation system. Description of the Drawings

[0029] Figure 1 is a flowchart of an operation optimization method for a multi-distributed resource aggregation body according to an embodiment of the present disclosure;

[0030] Figure 2 is a structural block diagram of an operation optimization system for a multi-distributed resource aggregation body according to an embodiment of the present disclosure;

[0031] Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present disclosure. Detailed Embodiments

[0032] The following describes exemplary embodiments of the present disclosure in conjunction with the accompanying drawings. Various details of the embodiments of the present disclosure are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0033] Figure 1 is a flowchart of an operation optimization method for a multi-distributed resource aggregation body according to an embodiment of the present disclosure.

[0034] As Figure 1 shown, the optimization method includes:

[0035] S110. Based on the biofilm growth model of each power generation device in the tidal energy power generation system, determine the mathematical model of the surface roughness change of each power generation device over time, and based on the mathematical models corresponding to each power generation device, simulate the change of the hydrodynamic performance of each power generation device to obtain the instantaneous power output characteristic curve of each power generation device.

[0036] S120. Based on the instantaneous power output characteristic curve of each power generation device, determine the deviation between the rated output power and the actual output power of the tidal energy power generation system.

[0037] S130. Based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, construct an objective function for optimizing the energy storage capacity of each energy storage unit in the tidal energy power generation system.

[0038] S140. Use the genetic algorithm to solve the optimal value of the objective function and obtain the target energy storage capacity configuration plan of the tidal energy power generation system.

[0039] S150. Based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the prediction result of the new energy output of the tidal energy power generation system in the first future time period, and the target energy storage capacity configuration plan, determine the charge-discharge strategy of the tidal energy power generation system.

[0040] S160. Based on the charging strategy of the tidal energy power generation system, control the operation of each power generation device and each energy storage unit.

[0041] Exemplarily, the above steps are not necessarily executed in sequence. Some steps can be executed in parallel, and some steps need to be executed in sequence. No specific limitation is made here.

[0042] Exemplarily, while executing the above step S160, the real-time output power of each power generation device can be detected, and then based on the real-time output power of each power generation device, the charge-discharge strategy can be adjusted again to make the tidal energy power generation system operate stably.

[0043] It can be understood that the biofilm growth model can characterize the non-linear relationship function between environmental factors and microbial growth rate or thickness.

[0044] Exemplarily, based on this non-linear relationship function, the surface roughness at each time point can be obtained.

[0045] Exemplarily, a piecewise function can be used to fit the surface roughness at each time point to obtain the curve of the surface roughness of the power generation device changing with time, that is, the mathematical model of the surface roughness changing with time. The fitting process can be carried out by the least square method.

[0046] Exemplarily, the instantaneous power output characteristic curve characterizes the change of the instantaneous output power with time.

[0047] Exemplarily, the characteristic parameters of the energy storage unit can include the upper limit of the energy storage capacity of the energy storage unit, the lower and upper limits of the charge-discharge efficiency, the charge-discharge time limit, etc.

[0048] Exemplarily, based on the deviation between the rated output power and the actual output power of the tidal energy power generation system and the prediction result of the new energy output of the tidal energy power generation system in the first future time period, optimize the target energy storage capacity configuration plan, and use the optimized target energy storage capacity configuration plan to determine the charge-discharge strategy of the tidal energy power generation system.

[0049] Exemplarily, the charge-discharge strategy of the energy storage system may include: charge start time, charge power curve, discharge start time, and discharge power curve. For example, considering the characteristics of the tidal cycle, charging is carried out during the low tide period of tidal energy power generation, and discharging is carried out during the high tide period, ensuring that the energy storage system smooths the fluctuations of new energy output under the optimized capacity and improves economic benefits.

[0050] Exemplarily, considering that the tidal cycle is about 12.4 hours, the charging strategy is carried out during the low tide period (the 1st - 3rd hours and the 13th - 15th hours), and the discharging strategy is executed during the high tide period (the 6th - 8th hours and the 18th - 20th hours). The annualized rate of return of the final plan reaches 8.5%, the standard deviation of power fluctuation is reduced by 40%, and the expected life of the energy storage system reaches 8.5 years.

[0051] According to the above embodiments, by using the deviation between the rated output power and the actual output power of the tidal energy power generation system, the influence of microbial attachment on the output power of the energy storage system can be determined. Then, based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, a target function for optimizing the energy storage capacity of each energy storage unit in the tidal energy power generation system is constructed; the genetic algorithm is used to solve the optimal value of the target function to obtain the target energy storage capacity configuration plan of the tidal energy power generation system. Furthermore, based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the prediction result of new energy output in the first future time period of the tidal energy power generation system, and the target energy storage capacity configuration plan, the charge-discharge strategy of the tidal energy power generation system is determined. Thus, the charge-discharge strategy of the system can be optimized by combining the influence of microbial attachment on the output power of the energy storage system and the target energy storage capacity configuration plan. Subsequently, based on the charging strategy of the tidal energy power generation system, the operation of each power generation device and each energy storage unit is controlled to ensure the stable operation of the tidal energy power generation system and reduce the influence of microbial attachment on the output power of the energy storage system.

[0052] In one embodiment, the above method further includes: determining the characteristic vector of the marine microbial growth environment corresponding to each power generation device based on the temperature and salinity environment change parameters of the sea area where each power generation device is located; determining the theoretical growth rate of the microorganisms on each power generation device based on the characteristic vector of the marine microbial growth environment corresponding to each power generation device; performing spectral analysis on the biofilm morphological characteristics in the surface biofilm growth video of each power generation device to obtain the actual growth rate of the microorganisms on each power generation device; correcting the preset biofilm growth model of each power generation device based on the comparison result of the theoretical growth rate and the actual growth rate of the microorganisms on each power generation device to obtain the biofilm growth model of each power generation device.

[0053] Exemplarily, the marine environmental sensor network consists of 20 buoy-type sensors, each equipped with temperature and salinity measurement instruments, with a sampling frequency of once every 10 minutes. The sensors transmit data to the data processing center in real time via a 4G wireless network. In the data preprocessing stage, first, outliers beyond the normal range are removed, such as data points with a temperature lower than -2°C or higher than 35°C, and a salinity lower than 28‰ or higher than 40‰. Then, the 5-point median filtering algorithm is used to smooth the data, effectively removing short-term fluctuations and noise. In the data fusion stage, the Dempster-Shafer evidence theory is adopted to assign confidence levels to the temperature and salinity data. For example, the confidence level of the temperature data is 0.6, and the confidence level of the salinity data is 0.4. The fused environmental parameters are calculated through the D-S combination rule. Thus, the environmental change parameters of temperature and salinity are obtained.

[0054] Exemplarily, principal component analysis is performed on the environmental change parameters of temperature and salinity, and the principal component eigenvalues with a cumulative contribution greater than 85% are extracted; based on the extracted principal component eigenvalues, a characteristic vector of the marine microbial growth environment is constructed. For example, the characteristic vector of the marine microbial growth environment can include 10 parameters such as temperature, salinity, pH value, dissolved oxygen, etc.

[0055] Exemplarily, the microbial species that match the characteristic vector of the marine microbial growth environment are screened from the pre-established microbial community growth model database. For example, the cosine algorithm can be used to calculate the matching degree, and the microbial species with a matching degree greater than the preset threshold are extracted to obtain the list information of microbial species. For example, the preset threshold is 0.85, and 50 candidate microorganisms are screened out.

[0056] Exemplarily, the generalized additive model is adopted, with the characteristic vector of the marine microbial growth environment as the independent variable and the microbial growth index as the dependent variable, to establish a non-linear relationship function between the microbial growth rate and environmental factors, thereby determining the dominant environmental factors that have the most significant impact on microbial growth.

[0057] Exemplarily, the dominant microbial species are determined from the list information of microbial species by using the dominant environmental factors.

[0058] Exemplarily, the growth kinetic parameters such as the maximum specific growth rate, half-saturation constant, and lag phase time of the dominant microbial species are extracted from the pre-established growth kinetic parameter database. The environmental characteristic vector and the extracted growth kinetic parameters are input into the Gompertz equation to calculate the theoretical growth rate of the microorganisms on the surface of the device.

[0059] For example, the maximum specific growth rates of three microorganisms, 0.15 h^-1, 0.22 h^-1, 0.18 h^-1, the half-saturation constants, 0.5 g / L, 0.8 g / L, 0.6 g / L, and the lag phase times, 2 h, 1.5 h, 2.5 h, are extracted from the growth kinetics parameter database. These parameters are substituted into the modified Gompertz equation, with the initial colony count set at 10^3 CFU / cm^2 and the simulation time at 72 hours. The growth curves of the microorganisms on the surface of the device are calculated, and the final biomass reaches 10^7 CFU / cm^2, 10^8 CFU / cm^2, and 10^6 CFU / cm^2 respectively.

[0060] Exemplarily, the surface biofilm growth video includes a sequence of biofilm images collected continuously.

[0061] Exemplarily, the structured light three-dimensional reconstruction technique is adopted. Through the sliding window method, the thickness of the biofilm in the sequence of biofilm images is calculated to obtain the actual biofilm growth rate curve.

[0062] For example, the structured light three-dimensional reconstruction uses sinusoidal fringe projection with a spatial resolution of 0.1 mm. The images collected hourly within 72 hours are processed, and it is estimated that the biofilm thickness increases from an initial 0.2 mm to 1.8 mm. The sliding window size is set at 6 hours, and the calculated average growth rate is 0.022 mm / h.

[0063] Exemplarily, the comparison result can be the root mean square error.

[0064] Exemplarily, with the root mean square error as the error function, the genetic algorithm is used to optimize the parameters of the preset biofilm growth model to obtain the modified biofilm growth model.

[0065] According to the above embodiments, the biofilm growth models of each power generation device can be accurately obtained.

[0066] In one embodiment, based on the mathematical models corresponding to each power generation device, the hydrodynamic performance changes of each power generation device are simulated to obtain the instantaneous power output characteristic curves of each power generation device, including: transforming the mathematical model of the surface roughness change of the power generation device over time into the boundary conditions of fluid mechanics simulation, and processing the boundary conditions using the equivalent sand grain roughness method to obtain the surface state of the power generation device at each time point, where the surface state includes the surface pressure distribution and the surface velocity field distribution; for the surface state at each time point, the following operations are performed: through area integration, calculate the surface pressure distribution at this time point to obtain the working pressure resistance of the power generation device at this time point, through wall shear stress integration, calculate the surface velocity field distribution at this time point to obtain the working friction resistance of the power generation device at this time point, and based on the working pressure resistance and the working friction resistance of the power generation device at this time point, determine the instantaneous output power of the power generation device at this time point; based on the instantaneous output powers of the power generation device at each of these time points, determine the instantaneous power output characteristic curve of the power generation device.

[0067] Exemplarily, the equivalent sand grain roughness method transforms the surface roughness into the boundary conditions of computational fluid dynamics, and the roughness coefficient gradually increases from the initial 0.02 to 0.15 after 168 hours.

[0068] Exemplarily, the surrounding flow field of the power generation device is discretized using unstructured grids, the Reynolds stress model is used to describe the fluid motion characteristics, and the surface roughness at different time points is set as the wall boundary condition to perform three-dimensional unsteady flow field numerical simulation. Based on the numerical simulation results, the surface pressure distribution and the surface velocity field distribution of the device are extracted.

[0069] For example, the unstructured grid generation adopts the Delaunay triangulation algorithm, 10 layers of encrypted grids are set in the near-wall region, and the total number of grids is about 2 million. The turbulent stress transport equation in the Reynolds stress model is discretized using the second-order upwind scheme, the time step is set to 0.1 s, and the total simulation time is 600 s. Corresponding simulations are carried out under 48 working conditions at different time points 0h, 24h, 72h, 168h, different flow velocities 0.5 m / s, 1.0 m / s, 1.5 m / s, 2.0 m / s, and different flow directions 0°, 45°, 90°, and the numerical simulation results are obtained.

[0070] Exemplarily, in combination with the energy conversion efficiency curve obtained through experiments in advance, determine the instantaneous output power corresponding to the working pressure resistance, the working friction resistance, and the lift at the specified time point. The energy conversion efficiency curve records the corresponding instantaneous output powers under different working pressure resistances, working friction resistances, and lifts.

[0071] Exemplarily, arrange the instantaneous output powers at each time point in chronological order to obtain a corresponding sequence, perform interpolation processing on the sequence, and then perform curve fitting on the interpolated sequence to obtain the instantaneous power output characteristic curve of the power generation device.

[0072] Exemplarily, arrange the instantaneous output powers at each time point in chronological order to obtain a corresponding sequence, and then perform curve fitting on the sequence to obtain the instantaneous power output characteristic curve of the power generation device.

[0073] According to the above embodiments, the instantaneous power output characteristic curves of each power generation device can be accurately obtained.

[0074] In one embodiment, based on the instantaneous power output characteristic curves of each power generation device, determine the deviation between the rated output power and the actual output power of the tidal energy power generation system, including: for each power generation device, respectively perform the following operations: segment the instantaneous power output characteristic curve of the power generation device to obtain multiple curve segments, perform power spectral density estimation on each curve segment to obtain the power density function corresponding to each curve segment, and construct a power fluctuation frequency spectrum model of the power generation device based on the peaks in each power density function; determine the frequency range of high-frequency noise based on the power fluctuation frequency spectrum models of each power generation device; filter the actual output power curve of the tidal energy power generation system based on the frequency range of high-frequency noise to obtain the effective output power curve of the tidal energy power generation system; calculate the root mean square error and the maximum absolute deviation of the actual output power of the tidal energy power generation system at each time point relative to its rated output power, so as to be used as the deviation between the rated output power and the actual output power of the tidal energy power generation system.

[0075] Exemplarily, take 1 / 4 of the tidal period as the time length to segment the instantaneous power output characteristic curve. In this way, the statistical characteristics within each segment can be calculated, such as mean, variance, skewness, kurtosis, etc. At the same time, the power fluctuation time-domain characteristic sequence can also be obtained by analyzing the power fluctuation of each curve segment.

[0076] Exemplarily, use the Welch method to perform power spectral density estimation on the power fluctuation time-domain characteristic sequence corresponding to the curve segment to obtain the power density function corresponding to the curve segment.

[0077] Exemplarily, determine the main frequency component corresponding to each curve segment and the power corresponding to the main frequency component according to the peaks in the power density functions of each curve segment. Thus, construct a power fluctuation frequency spectrum model based on the main frequency components corresponding to each curve segment and the powers corresponding to the main frequency components.

[0078] Exemplarily, if the power fluctuation spectrum model shows that the high-frequency noise is mainly concentrated above 2 Hz, the initial filtering parameter is set such that the cut-off frequency of the low-pass filter is 1.5 Hz and the filter order is 32. Then, using this low-pass filter, the actual output power curve of the tidal energy power generation system is filtered to obtain the effective output power curve of the tidal energy power generation system. Thus, the high-frequency noise components in the actual output power curve are removed to obtain an effective power output curve.

[0079] Exemplarily, the obtained effective power output curve is compared with the system rated power curve obtained based on historical data statistical analysis, and the root mean square error and the maximum absolute deviation between the two can be calculated. Then, the root mean square error and the maximum absolute deviation are used as the deviation between the rated output power and the actual output power of the tidal energy power generation system.

[0080] According to the above embodiments, the deviation between the rated output power and the actual output power of the tidal energy power generation system can be accurately calculated.

[0081] In one embodiment, based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, a target function for optimizing the energy storage capacity of each energy storage unit in the tidal energy power generation system is constructed, including: taking the energy storage capacity of each energy storage unit in the tidal energy power generation system as an independent variable, and taking maximizing the charge-discharge power of the tidal energy power generation system, minimizing the charge-discharge time and charge-discharge cost of the tidal energy power generation system as dependent variables to construct the target function; based on the upper limit of the energy storage capacity of the energy storage unit, the lower and upper limits of the charge-discharge efficiency, and the charge-discharge time limit, the constraint conditions of the target function are determined.

[0082] It can be understood that the target function is a multi-objective function, and its objectives include maximizing the charge-discharge power, minimizing the charge-discharge time, and minimizing the charge-discharge cost.

[0083] Exemplarily, taking the energy storage capacity of the energy storage unit as the target, the required charging power, charge-discharge time and charge-discharge cost are determined, and then summation is performed for all energy storage units to obtain the corresponding target function.

[0084] According to the above embodiments, the corresponding target function can be constructed.

[0085] In one embodiment, a genetic algorithm is used to solve the optimal value of the objective function to obtain a target energy storage capacity configuration plan for the tidal energy power generation system, including: constructing a population based on the preset energy storage values of each energy storage unit in the tidal energy power generation system, where each individual in the population corresponds to an energy storage capacity configuration plan of the tidal energy power generation system; calculating the fitness values of each individual in the population based on the objective function; in the case where the fitness values of each individual do not meet the preset fitness value conditions, updating the population based on the constraint conditions of the objective function, and based on the updated population, returning to continue executing the step of calculating the fitness values of each individual in the population based on the objective function; in the case where the fitness values of each individual meet the preset fitness value conditions, determining the target energy storage capacity configuration plan based on the energy storage capacity configuration plan corresponding to the individual with the smallest fitness value determined in the population.

[0086] Exemplarily, the energy storage capacity configuration plan includes the energy storage capacity of each energy storage unit.

[0087] Exemplarily, the output value of the objective function is used as the fitness value of the corresponding individual.

[0088] Exemplarily, when the fitness values of all individuals are obtained, the smallest fitness value is selected from them. If the smallest fitness value does not meet the preset fitness value conditions, for example, the smallest fitness value is greater than the preset threshold, then each individual in the population is updated under the condition of meeting the constraint conditions of the objective function.

[0089] Exemplarily, if the smallest fitness value meets the preset fitness value conditions, the target energy storage capacity configuration plan is determined based on the energy storage capacity configuration plan corresponding to the individual with the smallest fitness value determined in the population.

[0090] According to the above embodiment, a particle swarm optimization algorithm can be used to solve the optimal solution of the objective function and use the optimal solution as the target energy storage capacity configuration plan.

[0091] In one embodiment, the above method may further include: constructing a training sample set based on the historical operation data of the tidal energy power generation system, where each training sample in the training sample set includes the meteorological forecast data of the historical first time period, the historical tidal data, and the new energy output result of the tidal energy power generation system; training a neural network based on the training sample set to obtain a new energy output prediction model; inputting the meteorological forecast data of the future first time period and the historical tidal data of the historical second time period corresponding to the future first time period into the new energy output prediction model to obtain the new energy output prediction result of the tidal energy power generation system within the future first time period output by the new energy output prediction model.

[0092] Exemplarily, the historical first time period corresponding to each training sample in the training sample set may be different time periods, but their durations are the same.

[0093] Exemplarily, feature extraction can be performed on meteorological forecast data and historical tidal data. For example, fast Fourier transform is used to extract the periodic features in the meteorological forecast data and historical tidal data, obtaining a data set with a unified format; time features, statistical features, and frequency domain features are extracted from the data set; and the optimal feature subset is selected from the time features, statistical features, and frequency domain features through correlation analysis to obtain a feature vector.

[0094] Exemplarily, the new energy output result can be the output power.

[0095] Exemplarily, the training process can be multiple iterations. In each iteration, the following operations are performed: the feature vector in the training sample in the training sample set is output to the neural network to obtain the output power of its output, the output power is compared with the output power in the training sample, and then the neural network is parameter - adjusted using the comparison result to obtain the neural network for the next iteration.

[0096] Exemplarily, the meteorological forecast data for the first future time period and the historical tidal data for the corresponding historical second time period in the first future time period are converted into corresponding feature vectors. Then, the feature vectors are input into the new energy output prediction model, and the output power output by the model can be obtained, and this output power is used as the new energy output prediction result of the tidal energy power generation system in the first future time period.

[0097] According to the above - mentioned embodiments, using a neural network model can accurately obtain the new energy output prediction result of the tidal energy power generation system in the first future time period.

[0098] Figure 2 It is a structural block diagram of a multi - distributed resource aggregation operation optimization system according to an embodiment of the present disclosure.

[0099] As Figure 2 shown, the optimization system includes:

[0100] A characteristic curve determination module 210, configured to determine a mathematical model of the surface roughness change with time of each power generation device in the tidal energy power generation system based on the biofilm growth model of each power generation device in the tidal energy power generation system, and simulate the change of the hydrodynamic performance of each power generation device based on the mathematical model corresponding to each power generation device to obtain the instantaneous power output characteristic curve of each power generation device;

[0101] A power deviation determination module 220, configured to determine the deviation between the rated output power and the actual output power of the tidal energy power generation system based on the instantaneous power output characteristic curve of each power generation device;

[0102] An objective function construction module 230, configured to construct an objective function for optimizing the energy storage capacity of each energy storage unit in the tidal energy power generation system based on the characteristic parameters of each energy storage unit in the tidal energy power generation system;

[0103] An objective function solving module 240, configured to use a genetic algorithm to solve the optimal value of the objective function to obtain an objective energy storage capacity configuration scheme for the tidal energy power generation system;

[0104] A charge-discharge strategy determination module 250, configured to determine a charge-discharge strategy for the tidal energy power generation system based on the deviation between the rated output power and the actual output power of the tidal energy power generation system, the predicted result of new energy output in the tidal energy power generation system in a first future period, and the objective energy storage capacity configuration scheme;

[0105] A power detection module 260, configured to control each power generation device and energy storage unit in the tidal energy power generation system to work based on the charging strategy of the tidal energy power generation system, and detect the real-time output power of each power generation device;

[0106] A strategy adjustment module 270, configured to readjust the charge-discharge strategy based on the real-time output power of each power generation device to enable the stable operation of the tidal energy power generation system.

[0107] In one implementation, the above system may further include:

[0108] An environmental vector determination module, configured to determine a marine microbial growth environment characteristic vector corresponding to each power generation device based on the temperature and salinity environment change parameters of the sea area where each power generation device is located;

[0109] A theoretical rate determination module, configured to determine the theoretical growth rate of microorganisms on each power generation device based on the marine microbial growth environment characteristic vector corresponding to each power generation device;

[0110] An actual rate determination module, configured to perform spectral analysis on the biofilm morphological characteristics in the surface biofilm growth video of each power generation device to obtain the actual growth rate of microorganisms on each power generation device;

[0111] A model correction module, configured to correct the preset biofilm growth model of each power generation device based on the comparison result between the theoretical growth rate and the actual growth rate of microorganisms on each power generation device to obtain the biofilm growth model of each power generation device.

[0112] In one implementation, the characteristic curve determination module 210 includes:

[0113] A surface state determination unit, configured to convert a mathematical model of the change of the surface roughness of the power generation device over time into boundary conditions for fluid dynamics simulation, and process the boundary conditions by using an equivalent sand grain roughness method to obtain the surface state of the power generation device at each time point, where the surface state includes a surface pressure distribution and a surface velocity field distribution;

[0114] A power determination unit, configured to perform the following operations for the surface state at each of the time points: calculate the working pressure resistance of the power generation device at the time point by area integration of the surface pressure distribution at the time point, calculate the working friction resistance of the power generation device at the time point by wall shear stress integration of the surface velocity field distribution at the time point, and determine the instantaneous output power of the power generation device at the time point based on the working pressure resistance and the working friction resistance of the power generation device at the time point;

[0115] A curve determination unit, configured to determine an instantaneous power output characteristic curve of the power generation device based on the instantaneous output power of the power generation device at each of the time points.

[0116] In one embodiment, the power deviation determination module 220 includes:

[0117] A spectrum model determination unit, configured to perform the following operations for each power generation device: segment the instantaneous power output characteristic curve of the power generation device to obtain a plurality of curve segments; perform power spectral density estimation on each of the curve segments to obtain a power density function corresponding to each of the curve segments; construct a power fluctuation spectrum model of the power generation device based on the peaks in each of the power density functions;

[0118] A noise range determination unit, configured to determine a frequency range of high-frequency noise based on the power fluctuation spectrum models of each of the power generation devices;

[0119] A filtering unit, configured to filter the actual output power curve of the tidal energy power generation system based on the frequency range of the high-frequency noise to obtain an effective output power curve of the tidal energy power generation system;

[0120] A deviation determination unit, configured to calculate a root mean square error and a maximum absolute deviation of the actual output power of the tidal energy power generation system at each time point with respect to its rated output power based on the effective output power curve, so as to obtain a deviation between the rated output power and the actual output power of the tidal energy power generation system.

[0121] In one embodiment, the objective function construction module 230 includes:

[0122] An objective function determination unit for constructing the objective function with the energy storage capacity of each energy storage unit in the tidal energy power generation system as an independent variable, and with maximizing the charge-discharge power of the tidal energy power generation system, minimizing the charge-discharge time and charge-discharge cost of the tidal energy power generation system as dependent variables;

[0123] A function condition determination unit for determining the constraint conditions of the objective function based on the upper limit of the energy storage capacity of the energy storage unit, the lower and upper limits of the charge-discharge efficiency, and the charge-discharge time limit.

[0124] In one embodiment, the objective function solving module 240 includes:

[0125] A population initialization unit for constructing a population based on the preset energy storage values of each energy storage unit in the tidal energy power generation system, where each individual in the population corresponds to an energy storage capacity configuration scheme of the tidal energy power generation system;

[0126] An adaptation value calculation unit for calculating the adaptation values of each individual in the population based on the objective function;

[0127] A population update unit for updating the population based on the constraint conditions of the objective function when the adaptation values of each individual do not meet the preset adaptation value conditions, and based on the updated population, returning to continue executing the step of calculating the adaptation values of each individual in the population based on the objective function;

[0128] An objective scheme determination unit for determining the target energy storage capacity configuration scheme based on the energy storage capacity configuration scheme corresponding to the individual with the minimum adaptation value determined from the population when the adaptation values of each individual meet the preset adaptation value conditions.

[0129] In one embodiment, the above method may further include:

[0130] A training set determination module for constructing a training sample set based on the historical operation data of the tidal energy power generation system, where each training sample in the training sample set includes meteorological forecast data for a historical first time period, historical tidal data, and the new energy output result of the tidal energy power generation system;

[0131] A model training module for training a neural network based on the training sample set to obtain a new energy output prediction model;

[0132] A model prediction module, configured to input the meteorological forecast data for the first future time period and the historical tide data for the corresponding historical second time period of the first future time period into the new energy output prediction model, and obtain the new energy output prediction result of the tidal power generation system within the first future time period output by the new energy output prediction model.

[0133] According to an embodiment of the present disclosure, the above method of the present disclosure can be applied to an electronic device and a readable storage medium.

[0134] Figure 3 FIG. shows a schematic block diagram of an exemplary electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0135] As Figure 3 shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0136] Multiple components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, an optical disc, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0137] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 executes the various methods and processes described above, such as a method for optimizing the operation of a multi-distributed resource aggregate. For example, in some embodiments, a method for optimizing the operation of a multi-distributed resource aggregate can be implemented as a computer software program that is tangibly included in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for optimizing the operation of a multi-distributed resource aggregate described above can be executed. Alternatively, in other embodiments, the computing unit 601 can be configured to execute a method for optimizing the operation of a multi-distributed resource aggregate by any other suitable means (e.g., by means of firmware).

[0138] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0139] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0140] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0141] For providing interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0142] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0143] A computer system may include a client and a server. The client and the server are generally far away from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, or a server of a distributed system, or a server combined with a blockchain.

[0144] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and no limitation is imposed herein.

[0145] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for optimizing the operation of a multi-distributed resource aggregate, characterized in that: include: Based on the biofilm growth model of each power generation device in the tidal energy power generation system, a mathematical model of the surface roughness change of each power generation device over time is determined, and based on the mathematical model corresponding to each power generation device, the hydrodynamic performance change of each power generation device is simulated to obtain the instantaneous power output characteristic curve of each power generation device; Based on the instantaneous power output characteristic curve of each of the power generation devices, determining the deviation between the rated output power and the actual output power of the tidal energy power generation system, comprising: for each power generation device, performing the following operations respectively: segmenting the instantaneous power output characteristic curve of the power generation device to obtain a plurality of curve segments; performing power spectrum density estimation on each of the curve segments to obtain a power density function corresponding to each of the curve segments; constructing a power fluctuation spectrum model of the power generation device based on the peak value in each of the power density functions; determining the frequency range of high-frequency noise based on the power fluctuation spectrum model of each of the power generation devices; filtering the actual output power curve of the tidal energy power generation system based on the frequency range of the high-frequency noise to obtain an effective output power curve of the tidal energy power generation system; and calculating, based on the effective output power curve, the root mean square error and the maximum absolute deviation of the actual output power of the tidal energy power generation system at each time point relative to its rated output power as the deviation between the rated output power and the actual output power of the tidal energy power generation system; Based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, constructing an objective function of the tidal energy power generation system for optimizing the energy storage capacity of each energy storage unit; Using a genetic algorithm to solve the optimal value of the objective function, and obtain a target energy storage capacity configuration scheme for the tidal power generation system; Optimizing a target energy storage capacity configuration scheme based on a deviation between a rated output power and an actual output power of the tidal energy power generation system and a prediction result of a new energy output of the tidal energy power generation system in a first future time period, and determining a charge and discharge strategy of the tidal energy power generation system using the optimized target energy storage capacity configuration scheme; Based on the charging and discharging strategy of the tidal energy power generation system, controlling the operation of each of the power generation equipment and each of the energy storage units; The method also includes: Determining the marine microbial growth environment characteristic vector corresponding to each power generation device based on the temperature and salinity environmental change parameters of the marine area where each power generation device is located; determining a theoretical growth rate of microorganisms on each of the power generation devices based on a characteristic vector of the marine microbial growth environment corresponding to each of the power generation devices; Performing spectral analysis on the biofilm morphological characteristics in the biofilm growth video on the surface of each power generation device to obtain the actual growth rate of microorganisms on each power generation device; Based on the comparison results of the theoretical growth rate and the actual growth rate of the microorganisms on each of the power generation devices, the preset biofilm growth model of each of the power generation devices is corrected to obtain the biofilm growth model of each of the power generation devices; The step of simulating the hydrodynamic performance changes of each power generation device based on the mathematical model corresponding to each power generation device to obtain the instantaneous power output characteristic curve of each power generation device includes: Converting a mathematical model of the surface roughness of the power generation device over time into boundary conditions for fluid dynamics simulation, and processing the boundary conditions using an equivalent sand grain roughness method to obtain the surface state of the power generation device at each time point, wherein the surface state includes surface pressure distribution and surface velocity field distribution; For each of the surface states at the time points, the following operations are performed: calculating the surface pressure distribution at the time point by surface integration to obtain the working pressure resistance of the power generation device at the time point; calculating the surface velocity field distribution at the time point by wall shear stress integration to obtain the working friction resistance of the power generation device at the time point; and determining the instantaneous output power of the power generation device at the time point based on the working pressure resistance and working friction resistance of the power generation device at the time point; Based on the instantaneous output power of the power generation equipment at each of the time points, an instantaneous power output characteristic curve of the power generation equipment is determined.

2. The method according to claim 1, characterized in that The constructing of an objective function for optimizing the energy storage capacity of each energy storage unit of the tidal energy power generation system based on the characteristic parameters of each energy storage unit in the tidal energy power generation system comprises: The objective function is constructed by taking the energy storage capacity of each energy storage unit in the tidal energy power generation system as an independent variable, and maximizing the charge and discharge power of the tidal energy power generation system and minimizing the charge and discharge time and charge and discharge cost of the tidal energy power generation system as dependent variables; The constraint conditions of the objective function are determined based on the upper limit of the energy storage capacity of the energy storage unit, the lower limit and upper limit of the charge and discharge efficiency, and the charge and discharge time limit.

3. The method according to claim 2, characterized in that The method of using a genetic algorithm to solve the objective function for an optimal value to obtain a target energy storage capacity configuration scheme for the tidal power generation system includes: Based on the preset energy storage values of the energy storage units in the tidal energy power generation system, constructing a population, wherein each individual in the population corresponds to an energy storage capacity configuration scheme of the tidal energy power generation system; Calculating the fitness value of each individual in the population based on the objective function; When the fitness value of each individual does not satisfy a preset fitness value condition, the population is updated based on the constraint condition of the objective function, and based on the updated population, the step of calculating the fitness value of each individual in the population based on the objective function is returned to. In the case that the fitness values of the individuals meet the preset fitness value conditions, a target energy storage capacity configuration scheme is determined based on the energy storage capacity configuration scheme corresponding to the individual corresponding to the minimum fitness value in the population.

4. The method according to any one of claims 1 to 3, characterized in that Also includes: Based on the historical operating data of the tidal energy power generation system, a training sample set is constructed, wherein each training sample in the training sample set includes weather forecast data for a first historical time period, historical tidal data, and a new energy output result of the tidal energy power generation system; Based on the training sample set, a neural network is trained to obtain a new energy output prediction model; The weather forecast data for the first future time period and the historical tidal data for the second historical time period corresponding to the first future time period are input into the new energy output prediction model to obtain a new energy output prediction result of the tidal power generation system in the first future time period output by the new energy output prediction model.

5. A multi-distributed resource aggregation operation optimization system, characterized in that: include: a characteristic curve determination module, configured to determine, based on a biofilm growth model of each power generation device in the tidal energy power generation system, a mathematical model of how the surface roughness of each power generation device changes over time, and simulate changes in the hydrodynamic performance of each power generation device based on the mathematical model corresponding to each power generation device to obtain an instantaneous power output characteristic curve of each power generation device; a power deviation determining module, configured to determine a deviation between a rated output power and an actual output power of the tidal energy power generation system based on an instantaneous power output characteristic curve of each of the power generation devices; An objective function construction module, configured to construct an objective function of the tidal energy power generation system for optimizing the energy storage capacity of each energy storage unit based on characteristic parameters of each energy storage unit in the tidal energy power generation system; An objective function solving module is used to solve the objective function for an optimal value by using a genetic algorithm to obtain a target energy storage capacity configuration scheme for the tidal power generation system; a charge-discharge strategy determination module, configured to optimize a target energy storage capacity configuration scheme based on a deviation between a rated output power and an actual output power of the tidal energy power generation system and a forecast result of a new energy output of the tidal energy power generation system in a first future time period, and determine a charge-discharge strategy for the tidal energy power generation system using the optimized target energy storage capacity configuration scheme; A power detection module, configured to control the operation of each power generation device and energy storage unit in the tidal energy power generation system based on a charge and discharge strategy of the tidal energy power generation system; Wherein, the power deviation determination module includes: a spectrum model determination unit configured to perform the following operations for each power generation device: segmenting the instantaneous power output characteristic curve of the power generation device to obtain a plurality of curve segments; performing power spectrum density estimation on each of the curve segments to obtain a power density function corresponding to each of the curve segments; and constructing a power fluctuation spectrum model of the power generation device based on a peak value in each of the power density functions; a noise range determining unit, configured to determine a frequency range of high-frequency noise based on a power fluctuation spectrum model of each of the power generation devices; a filtering unit, configured to filter an actual output power curve of the tidal energy power generation system based on a frequency range of the high-frequency noise to obtain an effective output power curve of the tidal energy power generation system; a deviation determining unit, configured to calculate, based on the effective output power curve, a root mean square error and a maximum absolute deviation of the actual output power of the tidal energy power generation system at each time point relative to its rated output power, as a deviation between the rated output power and the actual output power of the tidal energy power generation system; Also includes: Determining the marine microbial growth environment characteristic vector corresponding to each power generation device based on the temperature and salinity environmental change parameters of the marine area where each power generation device is located; determining a theoretical growth rate of microorganisms on each of the power generation devices based on a characteristic vector of the marine microbial growth environment corresponding to each of the power generation devices; Performing spectral analysis on the biofilm morphological characteristics in the biofilm growth video on the surface of each power generation device to obtain the actual growth rate of microorganisms on each power generation device; Based on the comparison results of the theoretical growth rate and the actual growth rate of the microorganisms on each of the power generation devices, the preset biofilm growth model of each of the power generation devices is corrected to obtain the biofilm growth model of each of the power generation devices; The step of simulating the hydrodynamic performance changes of each power generation device based on the mathematical model corresponding to each power generation device to obtain the instantaneous power output characteristic curve of each power generation device includes: Converting a mathematical model of the surface roughness of the power generation device over time into boundary conditions for fluid dynamics simulation, and processing the boundary conditions using an equivalent sand grain roughness method to obtain the surface state of the power generation device at each time point, wherein the surface state includes surface pressure distribution and surface velocity field distribution; For each of the surface states at the time points, the following operations are performed: calculating the surface pressure distribution at the time point by surface integration to obtain the working pressure resistance of the power generation device at the time point; calculating the surface velocity field distribution at the time point by wall shear stress integration to obtain the working friction resistance of the power generation device at the time point; and determining the instantaneous output power of the power generation device at the time point based on the working pressure resistance and working friction resistance of the power generation device at the time point; Based on the instantaneous output power of the power generation equipment at each of the time points, an instantaneous power output characteristic curve of the power generation equipment is determined.

6. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-4.

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