Multi-distributed resource aggregate operation optimization method and system

Abstract

The invention relates to the technical field of electric power, and discloses a multi-distributed resource aggregate operation optimization method and system. According to the specific scheme, the method comprises the steps that hydrodynamic performance changes of all power generation devices are simulated, and instantaneous power output characteristic curves of all the power generation devices are obtained; based on the instantaneous power output characteristic curve of each power generation device, the deviation between the rated output power and the actual output power of the tidal power generation system is determined; based on the characteristic parameters of each energy storage unit in the tidal power generation system, constructing a target function for optimizing the energy storage capacity of each energy storage unit, and solving the optimal value of the target function to obtain a target energy storage capacity configuration scheme; and on the basis of 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 in the first time period in the future and the target energy storage capacity configuration scheme, determining a charging and discharging strategy of the tidal energy power generation system so as to control the work of each power generation device and each energy storage unit.

Description

Technical Field

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

[0002] The multi-distributed resource aggregate is an aggregate system composed of various types of distributed energy. As one of the important resources, tidal power generation equipment plays a vital role in this system. Tidal 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 tidal power generation equipment, resulting in significant differences in the growth rate of microorganisms on the surface of equipment in different sea areas. The attachment and growth of microorganisms will change the roughness and friction resistance of the surface of tidal power generation equipment, thereby affecting the fluid dynamics of the blades and shafts, 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 multiple distributed resource aggregates to provide millisecond-level frequency regulation services, the instantaneous change characteristics of the output power of tidal power generation equipment are crucial because it is directly related to the overall frequency regulation capability of the aggregate. If the microbial attachment on the surface of tidal power generation equipment affects its original transient characteristics, making its power output response slow or unstable, it will greatly weaken the frequency regulation capability of the multiple distributed resource aggregates, making it difficult to meet the millisecond-level response requirements of the power grid, thereby affecting the stability and reliability of the power grid.

[0004] Therefore, studying the growth patterns of microbial communities in different sea areas on the surface of tidal power generation equipment and revealing their influence mechanism on the dynamic output characteristics of the equipment are not only of great significance for mastering the uncertainty factors of tidal energy and improving its grid-connected frequency regulation capabilities, but also have a positive effect on improving the overall performance and reliability of multi-distributed resource aggregates. 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, comprising: 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; Determining 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 of the power generation devices; Based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, construct 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; Determine the charging and discharging 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 first future time period, and the target energy storage capacity configuration scheme; Based on the charging strategy of the tidal energy power generation system, the operation of each of the power generation equipment and each of the energy storage units is controlled.

[0007] According to another aspect of the present disclosure, a multi-distributed resource aggregate operation optimization system is provided, comprising: A characteristic curve determination module is used to determine a mathematical model of the surface roughness variation over time of each power generation device based on the biofilm growth model of each power generation device in the tidal energy power generation system, and simulate the hydrodynamic performance variation 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 determination module, used 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 of the power generation devices; An objective function construction module, used 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 use a genetic algorithm to solve the optimal value of the objective function to obtain a target energy storage capacity configuration scheme for the tidal power generation system; A charging and discharging strategy determination module, configured to determine the charging and discharging 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 first future time period, and the target energy storage capacity configuration scheme; The power detection module is used to control the operation of each power generation device and energy storage unit in the tidal energy power generation system based on the charging strategy of the tidal energy power generation system.

[0008] According to another aspect of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, 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 any multi-distributed resource aggregate operation optimization method in the embodiments of the present disclosure.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any multi-distributed resource aggregate operation optimization method in the embodiments of the present disclosure.

[0010] The technical solution disclosed in the present invention is used to determine the mathematical model of the surface roughness change over time of each power generation device based on the biofilm growth model of each power generation device in the tidal 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; 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 power generation system. This deviation can evaluate the impact of microbial attachment on the output power of the energy storage system.

[0011] 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; a genetic algorithm is used to solve the optimal value of the objective function to obtain a target energy storage capacity configuration scheme 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 power generation system, the prediction results of the new energy output of the tidal power generation system in the first time period in the future, and the target energy storage capacity configuration plan, the charging and discharging strategy of the tidal power generation system is determined. Therefore, the charging and discharging strategy of the system can be optimized by combining the impact of microbial attachment on the output power of the energy storage system and the target energy storage capacity configuration plan.

[0012] Subsequently, based on the charging strategy of the tidal energy power generation system, the operation of each power generation equipment and each energy storage unit is controlled to ensure the stable operation of the tidal energy power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of a method for optimizing operation of a multi-distributed resource aggregate according to an embodiment of the present disclosure; 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; Figure 3 is a block diagram of an electronic device for implementing an embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may 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 in the following description.

[0015] Figure 1 It is a flowchart of a method for optimizing operation of a multi-distributed resource aggregate according to an embodiment of the present disclosure.

[0016] like Figure 1 As shown, the optimization method comprises: S110, based on the biofilm growth model of each power generation equipment in the tidal power generation system, determine the mathematical model of the surface roughness of each power generation equipment changing with time, and based on the mathematical model corresponding to each power generation equipment, simulate the change of the hydrodynamic performance of each power generation equipment to obtain the instantaneous power output characteristic curve of each power generation equipment.

[0017] S120, determining a deviation between a rated output power and an actual output power of the tidal power generation system based on the instantaneous power output characteristic curve of each power generation device.

[0018] S130, constructing an objective function of the tidal energy power generation system for optimizing 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.

[0019] S140, using a genetic algorithm to solve the optimal value of the objective function and obtain a target energy storage capacity configuration plan for the tidal power generation system.

[0020] S150, determining a charging and discharging strategy of the tidal energy power generation system based on a deviation between a rated output power and an actual output power of the tidal energy power generation system, a prediction result of a new energy output of the tidal energy power generation system in a first future time period, and a target energy storage capacity configuration plan.

[0021] S160, based on the charging strategy of the tidal power generation system, controlling the operation of each power generation equipment and each energy storage unit.

[0022] 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, which is not specifically limited here.

[0023] For example, 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 charging and discharging strategy is adjusted again to ensure stable operation of the tidal power generation system.

[0024] It can be understood that the biofilm growth model can characterize the nonlinear relationship function between environmental factors and microbial growth rate or thickness.

[0025] Exemplarily, based on the non-line relationship function, the surface roughness at each time point can be obtained.

[0026] For example, a partial function can be used to fit the surface roughness at each time point to obtain a curve of surface roughness variation over time of the power generation equipment, that is, a mathematical model of surface roughness variation over time. The fitting process can be performed using the least squares method.

[0027] Exemplarily, the instantaneous power output characteristic curve represents the change of the instantaneous output power over time.

[0028] Exemplarily, the characteristic parameters of the energy storage unit may include an upper limit of the energy storage capacity of the energy storage unit, a lower limit and an upper limit of the charge and discharge efficiency, a charge and discharge time limit, etc.

[0029] Exemplarily, based on the deviation between the rated output power and the actual output power of the tidal power generation system and the prediction result of the new energy output of the tidal power generation system in the first time period in the future, the target energy storage capacity configuration scheme is optimized, and the charging and discharging strategy of the tidal power generation system is determined using the optimized target energy storage capacity configuration scheme.

[0030] Exemplarily, the charging and discharging strategy of the energy storage system may include: charging start time, charging power curve, discharging start time and discharging power curve. For example, considering the characteristics of the tidal cycle, charging is performed during the trough period of tidal power generation and discharging is performed during the peak period, ensuring that the energy storage system can smooth out the fluctuation of new energy output and improve economic benefits under optimized capacity.

[0031] For example, considering the tidal cycle of about 12.4 hours, the charging strategy is implemented during the tidal low period (1-3 hours and 13-15 hours), and the discharging strategy is implemented during the peak period (6-8 hours and 18-20 hours). The final solution has an annualized rate of return of 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.

[0032] According to the above implementation, the deviation between the rated output power and the actual output power of the tidal power generation system can be used to determine the impact of microbial attachment on the output power of the energy storage system. Then, based on the characteristic parameters of each energy storage unit in the tidal power generation system, the objective function of the tidal power generation system for optimizing the energy storage capacity of each energy storage unit is constructed; the genetic algorithm is used to solve the optimal value of the objective function to obtain the target energy storage capacity configuration scheme of the tidal power generation system. Furthermore, based on the deviation between the rated output power and the actual output power of the tidal power generation system, the prediction result of the new energy output of the tidal power generation system in the first time period in the future, and the target energy storage capacity configuration scheme, the charging and discharging strategy of the tidal power generation system is determined. Thus, the charging and discharging strategy of the system can be optimized by combining the impact of microbial attachment on the output power of the energy storage system and the target energy storage capacity configuration scheme. Subsequently, based on the charging strategy of the tidal power generation system, the operation of each power generation equipment and each energy storage unit is controlled to ensure the stable operation of the tidal power generation system and reduce the impact of microbial attachment on the output power of the energy storage system.

[0033] In one embodiment, the above method also includes: determining the characteristic vector of the marine microbial growth environment corresponding to each power generation equipment based on the temperature and salinity environmental change parameters of the sea area where each power generation equipment is located; determining the theoretical growth rate of the microorganisms on each power generation equipment based on the characteristic vector of the marine microbial growth environment corresponding to each power generation equipment; performing spectral analysis on the biofilm morphological characteristics in the surface biofilm growth video of each power generation equipment to obtain the actual growth rate of the microorganisms on each power generation equipment; based on the comparison results of the theoretical growth rate and the actual growth rate of the microorganisms on each power generation equipment, correcting the preset biofilm growth model of each power generation equipment to obtain the biofilm growth model of each power generation equipment.

[0034] Exemplarily, the marine environment sensor network consists of 20 buoy sensors, each equipped with temperature and salinity measuring instruments, with a sampling frequency of once every 10 minutes. The sensor transmits data to the data processing center in real time via a 4G wireless network. In the data preprocessing stage, first remove abnormal values ​​that exceed the normal range, such as data points with temperatures below -2°C or above 35°C, and salinities below 28‰ or above 40‰. Then use the 5-point median filter algorithm to smooth the data to effectively remove short-term fluctuations and noise. In the data fusion stage, the Dempster-Shafer evidence theory is used to assign confidence levels to temperature and salinity data, such as a confidence level of 0.6 for temperature data and a confidence level of 0.4 for salinity data, and the fused environmental parameters are calculated using the DS combination rule. Thus, the temperature and salinity environmental change parameters are obtained.

[0035] For example, the temperature and salinity environmental change parameters are subjected to principal component analysis, and the principal component eigenvalues ​​with cumulative contributions 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 marine microbial growth environment characteristic vector may include 10 parameters such as temperature, salinity, pH value, and dissolved oxygen.

[0036] Exemplarily, microbial species that match the characteristic vector of the marine microbial growth environment are screened from a pre-established microbial community growth model database. For example, a cosine algorithm can be used to calculate the matching degree, and microbial species with a matching degree greater than a preset threshold are extracted to obtain microbial species list information. For example, the preset threshold is 0.85, and 50 candidate microorganisms are screened out.

[0037] Exemplarily, a generalized additive model is used, 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 nonlinear 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.

[0038] Exemplarily, dominant environmental factors are used to determine dominant microorganism species in the microorganism species list information.

[0039] For example, the maximum specific growth rate, half-saturation constant, lag time and other growth kinetic parameters of the dominant microorganism species are extracted from a pre-established growth kinetic parameter database. The environmental feature vector and the extracted growth kinetic parameters are input into the Gompertz equation to calculate the theoretical growth rate of the microorganism on the device surface.

[0040] For example, the maximum specific growth rates of three microorganisms, 0.15 h^-1, 0.22 h^-1, 0.18 h^-1, half-saturation constants of 0.5 g / L, 0.8 g / L, 0.6 g / L, and lag times of 2 h, 1.5 h, and 2.5 h, were extracted from the growth kinetic parameter database. These parameters were substituted into the modified Gompertz equation, the initial colony count was set to 10^3 CFU / cm^2, the simulation time was 72 hours, and the growth curves of microorganisms on the equipment surface were calculated. The final biomass reached 10^7 CFU / cm^2, 10^8 CFU / cm^2, and 10^6 CFU / cm^2, respectively.

[0041] Illustratively, the surface biofilm growth video includes a sequence of continuously acquired biofilm images.

[0042] Exemplarily, structured light 3D reconstruction technology is used to calculate the biofilm thickness in the biofilm image sequence through a sliding window method to obtain an actual biofilm growth rate curve.

[0043] For example, the structured light 3D reconstruction uses sinusoidal fringe projection with a spatial resolution of 0.1 mm. The images collected every hour for 72 hours are processed to estimate the biofilm thickness from the initial 0.2 mm to 1.8 mm. The sliding window size is set to 6 hours, and the average growth rate is calculated to be 0.022 mm / h.

[0044] Exemplarily, the comparison result may be a root mean square error.

[0045] Exemplarily, the root mean square error is used as the error function, and a genetic algorithm is used to optimize the parameters of the preset biofilm growth model to obtain a revised biofilm growth model.

[0046] According to the above-described embodiment, a biofilm growth model of each power generation device can be accurately obtained.

[0047] In one embodiment, based on the mathematical model corresponding to each power generation device, the change in the hydrodynamic performance of each power generation device is simulated to obtain the instantaneous power output characteristic curve of each power generation device, including: converting the mathematical model of the surface roughness of the power generation device changing with time into the boundary conditions of fluid mechanics simulation, and using the equivalent sand grain roughness method to process the boundary conditions to obtain the surface state of the power generation device at each time point, wherein 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: by surface integration, the surface pressure distribution at the time point is calculated to obtain the working pressure resistance of the power generation device at the time point, by wall shear stress integration, the surface velocity field distribution at the time point is calculated to obtain the working friction resistance 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, the instantaneous output power of the power generation device at the time point is determined; based on the instantaneous output power of the power generation device at each of the time points, the instantaneous power output characteristic curve of the power generation device is determined.

[0048] Exemplarily, the equivalent sand roughness method converts the surface roughness into computational fluid dynamics boundary conditions, and the roughness coefficient gradually increases from an initial 0.02 to 0.15 after 168 hours.

[0049] For example, the flow field around the power generation equipment 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 a three-dimensional unsteady flow field numerical simulation. Based on the numerical simulation results, the surface pressure distribution and surface velocity field distribution of the equipment are extracted.

[0050] For example, the unstructured grid generation adopts the Delaunay triangulation algorithm, and 10 layers of encrypted grids are set in the near-wall area, with a total number of grids of about 2 million. The turbulent stress transport equation in the Reynolds stress model is discretized using the second-order upwind scheme, with the time step set to 0.1s and the total simulation time being 600s. At different time points of 0h, 24h, 72h, 168h, different flow velocities of 0.5m / s, 1.0m / s, 1.5m / s, 2.0m / s and different flow directions of 0°, 45°, 90°, a total of 48 working conditions, the corresponding simulations were carried out to obtain the numerical simulation results.

[0051] Exemplarily, the instantaneous output power corresponding to the working pressure resistance, working friction resistance and lift at a specified time point is determined in combination with the energy conversion efficiency curve obtained in advance through experiments. The energy conversion efficiency curve records the instantaneous output power corresponding to different working pressure resistance, working friction resistance and lift.

[0052] Exemplarily, the instantaneous output power at each time point is arranged in chronological order to obtain a corresponding sequence, the sequence is interpolated, and then the interpolated sequence is curve fitted to obtain an instantaneous power output characteristic curve of the power generation equipment.

[0053] Exemplarily, the instantaneous output power at each time point is arranged in chronological order to obtain a corresponding sequence, and then a curve fitting is performed on the sequence to obtain an instantaneous power output characteristic curve of the power generation equipment.

[0054] According to the above embodiment, the instantaneous power output characteristic curve of each power generation device can be accurately obtained.

[0055] In one embodiment, based on the instantaneous power output characteristic curve of each power generation device, determining the deviation between the rated output power and the actual output power of the tidal energy power generation system includes: for each power generation device, performing the following operations respectively: segmenting the instantaneous power output characteristic curve of the power generation device to obtain multiple curve segments, performing power spectrum density estimation on each curve segment to obtain the power density function corresponding to each curve segment, and constructing a power fluctuation spectrum model of the power generation device based on the peak value in each power density function; determining the frequency range of high-frequency noise based on the power fluctuation spectrum model of each power generation device; 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 the effective output power curve of the tidal energy power generation system; and calculating 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 based on the effective output power curve, as the deviation between the rated output power and the actual output power of the tidal energy power generation system.

[0056] For example, the instantaneous power output characteristic curve is segmented with a time length of 1 / 4 of the tidal cycle, so that the statistical characteristics in each segment can be calculated, such as mean, variance, skewness and kurtosis, etc. At the same time, the power fluctuation of each curve segment can be analyzed to obtain the power fluctuation time domain feature sequence.

[0057] Exemplarily, the Welch method is used 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.

[0058] Exemplarily, based on the peak value in the power density function of each curve segment, the main frequency component corresponding to each curve segment and the power corresponding to the main frequency component are determined, and thus, based on the main frequency component corresponding to each curve segment and the power corresponding to the main frequency component, a power fluctuation spectrum model is constructed.

[0059] For example, if the power fluctuation spectrum model shows that the high-frequency noise is mainly concentrated above 2 Hz, the initial filter parameters are set to set the low-pass filter cutoff frequency to 1.5 Hz and the filter order to 32. Then, the actual output power curve of the tidal power generation system is filtered using the low-pass filter to obtain the effective output power curve of the tidal power generation system. Thus, the high-frequency noise component in the actual output power curve is removed to obtain an effective power output curve.

[0060] Exemplarily, the obtained effective power output curve is compared with the system rated power curve obtained based on statistical analysis of historical data, and the root mean square error and maximum absolute deviation between the two can be calculated. Then, the root mean square error and maximum absolute deviation are used as the deviation between the rated output power and the actual output power of the tidal power generation system.

[0061] According to the above implementation, the deviation between the rated output power and the actual output power of the tidal energy power generation system can be accurately calculated.

[0062] In one embodiment, 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, including: taking the energy storage capacity of each energy storage unit in the tidal energy power generation system as an independent variable, and maximizing the charging and discharging power of the tidal energy power generation system, and minimizing the charging and discharging time and charging and discharging cost of the tidal energy power generation system as dependent variables, constructing 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 charging and discharging efficiency, and the charging and discharging time limit, determining the constraints of the objective function.

[0063] It can be understood that the objective function is a multi-objective function, whose objectives include maximizing the charge and discharge power, minimizing the charge and discharge time, and minimizing the charge and discharge cost.

[0064] Exemplarily, the energy storage capacity of the energy storage unit is taken as the target, the required charging power, charging and discharging time and charging and discharging cost are determined, and then the sum is taken for all energy storage units to obtain the corresponding objective function.

[0065] According to the above implementation, a corresponding objective function can be constructed.

[0066] 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 scheme for the tidal power generation system, including: constructing a population based on preset energy storage values ​​of each energy storage unit in the tidal power generation system, wherein each individual in the population corresponds to an energy storage capacity configuration scheme for the tidal 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 meet the preset fitness value condition, updating the population based on the constraint condition of the objective function, and returning to continue to execute the step of calculating the fitness value of each individual in the population based on the objective function based on the updated population; when the fitness value of each individual meets the preset fitness value condition, determining the target energy storage capacity configuration scheme based on the energy storage capacity configuration scheme corresponding to the individual corresponding to the minimum fitness value determined in the population.

[0067] Exemplarily, the energy storage capacity configuration scheme includes the energy storage capacity of each energy storage unit.

[0068] Exemplarily, the output value of the objective function is used as the fitness value of the corresponding individual.

[0069] Exemplarily, when the fitness values ​​of all individuals are obtained, the minimum fitness value is selected. If the minimum fitness value does not meet the preset fitness value condition, for example, the minimum fitness value is greater than a preset threshold, then each individual in the population is updated while meeting the constraint condition of the objective function.

[0070] Exemplarily, if the minimum fitness value satisfies a preset fitness value condition, 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.

[0071] According to the above implementation, a particle swarm algorithm may be used to find an optimal solution to the objective function, and the optimal solution may be used as a target energy storage capacity configuration solution.

[0072] In one embodiment, the above method may also include: constructing a training sample set based on the historical operation data of the tidal power generation system, wherein each training sample in the training sample set includes weather forecast data, historical tidal data and new energy output results of the tidal power generation system in a first historical time period; training a neural network based on the training sample set to obtain a new energy output prediction model; inputting 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 into the new energy output prediction model to obtain the new energy output prediction results of the tidal power generation system in the first future time period output by the new energy output prediction model.

[0073] Exemplarily, the historical first time period corresponding to each training sample in the training sample set may be different time periods, but have the same duration.

[0074] Exemplarily, feature extraction can be performed on weather forecast data and historical tidal data, for example: using fast Fourier transform to extract periodic features in weather forecast data and historical tidal data to obtain a data set with a unified format; extracting time features, statistical features, and frequency domain features from the data set; and selecting the optimal feature subset from the time features, statistical features, and frequency domain features through correlation analysis to obtain a feature vector.

[0075] Exemplarily, the new energy output result may be output power.

[0076] Exemplarily, the training process can be multiple iterations, and in each iteration, the following operations are performed: the feature vectors in the training samples in the training sample set are 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 samples, and then the comparison results are used to adjust the parameters of the neural network to obtain the neural network for the next iteration.

[0077] Exemplarily, the weather forecast data for a first time period in the future and the historical tidal data for a second time period in the past corresponding to the first time period in the future are converted into corresponding feature vectors. Then, the feature vectors are input into a new energy output prediction model to obtain the output power of the model output, and the output power is used as the new energy output prediction result of the tidal power generation system in the first time period in the future.

[0078] According to the above implementation mode, the neural network model can be used to accurately obtain the prediction result of the new energy output of the tidal power generation system in the first time period in the future.

[0079] Figure 2 It is a structural block diagram of a multi-distributed resource aggregate operation optimization system according to an embodiment of the present disclosure.

[0080] like Figure 2 As shown, the optimization system comprises: The characteristic curve determination module 210 is used to determine the mathematical model of the surface roughness change of each power generation device over time 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; A power deviation determination module 220, configured to determine a deviation between a rated output power and an actual output power of the tidal power generation system based on an instantaneous power output characteristic curve of each of the power generation devices; An objective function construction module 230, for constructing 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; The objective function solving module 240 is used to solve the optimal value of the objective function by using a genetic algorithm to obtain a target energy storage capacity configuration scheme of the tidal power generation system; A charging and discharging strategy determination module 250 is used to determine the charging and discharging 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 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 power detection module 260 is used to control each power generation device and energy storage unit in the tidal power generation system to work based on the charging strategy of the tidal power generation system, and detect the real-time output power of each power generation device; The strategy adjustment module 270 is used to adjust the charging and discharging strategy again based on the real-time output power of each of the power generation devices, so as to ensure the stable operation of the tidal power generation system.

[0081] In one embodiment, the above system may further include: An environmental vector determination module, used to determine the marine microbial growth environment characteristic vector corresponding to each of the power generation equipment based on the temperature and salinity environmental change parameters of the sea area where each of the power generation equipment is located; A theoretical rate determination module, used to determine the theoretical growth rate of microorganisms on each of the power generation devices based on the marine microbial growth environment characteristic vectors corresponding to each of the power generation devices; An actual rate determination module is used to perform spectral analysis on the biofilm morphological characteristics in the surface biofilm growth video of each of the power generation equipment to obtain the actual growth rate of the microorganisms on each of the power generation equipment; The model correction module is used to correct 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.

[0082] In one implementation, the characteristic curve determination module 210 includes: A surface state determination unit, used to convert a mathematical model of the surface roughness of the power generation equipment changing with time into a boundary condition for fluid mechanics simulation, and process the boundary condition using an equivalent sand grain roughness method to obtain the surface state of the power generation equipment at each time point, wherein the surface state includes a surface pressure distribution and a surface velocity field distribution; The power determination unit is used to perform the following operations for the surface state at each of the time points: calculating the surface pressure distribution at the time point by surface integration to obtain the working pressure resistance of the power generation equipment 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 equipment at the time point, and determining the instantaneous output power of the power generation equipment at the time point based on the working pressure resistance and working friction resistance of the power generation equipment at the time point; The curve determination unit is used to determine the instantaneous power output characteristic curve of the power generation equipment based on the instantaneous output power of the power generation equipment at each of the time points.

[0083] In one implementation, the power deviation determination module 220 includes: The spectrum model determination unit is used 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 determination unit, used to determine the 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; The deviation determination unit is used to 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 based on the effective output power curve, as the deviation between the rated output power and the actual output power of the tidal energy power generation system.

[0084] In one implementation, the objective function construction module 230 includes: An objective function determination unit, configured to construct the objective function by taking the energy storage capacity of each of the energy storage units in the tidal energy power generation system as an independent variable, 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 function condition determination unit is used to determine the constraint conditions of the objective function 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.

[0085] In one implementation, the objective function solving module 240 includes: A population initialization unit, configured to construct a population based on preset energy storage values ​​of each energy storage unit in the tidal energy power generation system, wherein each individual in the population corresponds to an energy storage capacity configuration scheme of the tidal energy power generation system; A fitness value calculation unit, used to calculate the fitness value of each individual in the population based on the objective function; A population updating unit, configured to update the population based on the constraint conditions of the objective function when the fitness values ​​of each of the individuals do not meet the preset fitness value conditions, and return to continue to execute the step of calculating the fitness value of each individual in the population based on the objective function based on the updated population; The target scheme determining unit is used to determine the target energy storage capacity configuration scheme based on the energy storage capacity configuration scheme corresponding to the individual corresponding to the minimum fitness value in the population when the fitness values ​​of each of the individuals meet the preset fitness value conditions.

[0086] In one embodiment, the above method may further include: A training set determination module, used to construct a training sample set based on the historical operation data of the tidal energy power generation system, 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; A model training module, used to train a neural network based on the training sample set to obtain a new energy output prediction model; The model prediction module is used to input the weather forecast data of the first time period in the future and the historical tidal data of the second time period in the history corresponding to the first time period in the future into the new energy output prediction model, and obtain the new energy output prediction result of the tidal power generation system in the first time period in the future output output of the new energy output prediction model.

[0087] According to an embodiment of the present disclosure, the above method of the present disclosure may be applied to an electronic device and a readable storage medium.

[0088] Figure 3 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present disclosure is shown. 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 examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0089] like Figure 3 As 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 via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0090] A number of 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 disk, an optical disk, 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.

[0091] The computing unit 601 may be a variety of general and / or special 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, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as a multi-distributed resource aggregate operation optimization method. For example, in some embodiments, a multi-distributed resource aggregate operation optimization method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on 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 multi-distributed resource aggregate operation optimization method described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute a multi-distributed resource aggregate operation optimization method in any other appropriate manner (for example, by means of firmware).

[0092] Various implementations of the systems and techniques described above herein 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations 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 that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0094] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0095] To provide 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types 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).

[0096] The systems and techniques described herein may be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may 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.

[0097] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0098] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0099] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in 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; Determining 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 of the power generation devices; Based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, construct 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; Determine the charging and discharging 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 first future time period, and the target energy storage capacity configuration scheme; Based on the charging strategy of the tidal energy power generation system, the operation of each of the power generation equipment and each of the energy storage units is controlled.

2. The method according to claim 1, characterized in that Also includes: Based on the temperature and salinity environmental change parameters of the sea areas where the power generation equipment is located, determining the characteristic vector of the marine microbial growth environment corresponding to each power generation equipment; Determining the theoretical growth rate of microorganisms on each of the power generation equipment based on the marine microbial growth environment characteristic vectors corresponding to each of the power generation equipment; Performing spectral analysis on the biofilm morphological features in the biofilm growth video on the surface of each power generation device to obtain the actual growth rate of the 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.

3. The method according to claim 2, characterized in that The method of simulating the hydrodynamic performance change 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: The mathematical model of the surface roughness of the power generation equipment changing with time is converted into the boundary conditions of fluid mechanics simulation, and the boundary conditions are processed by using the equivalent sand grain roughness method to obtain the surface state of the power generation equipment at each time point, wherein the surface state includes the surface pressure distribution and the surface velocity field distribution; For the surface state at each of the time points, the following operations are performed: the surface pressure distribution at the time point is calculated by surface integration to obtain the working pressure resistance of the power generation equipment at the time point; the surface velocity field distribution at the time point is calculated by wall shear stress integration to obtain the working friction resistance of the power generation equipment at the time point; based on the working pressure resistance and working friction resistance of the power generation equipment at the time point, the instantaneous output power of the power generation equipment at the time point is determined; 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.

4. The method according to claim 2, characterized in that: The step of determining 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 of the power generation devices comprises: For each power generation device, the following operations are performed respectively: the instantaneous power output characteristic curve of the power generation device is segmented to obtain a plurality of curve segments; the power spectrum density of each curve segment is estimated to obtain a power density function corresponding to each curve segment; based on the peak value in each power density function, a power fluctuation spectrum model of the power generation device is constructed; Determining the frequency range of high-frequency noise based on the power fluctuation spectrum model of each of the power generation devices; Based on the frequency range of the high-frequency noise, filtering the actual output power curve of the tidal energy power generation system to obtain an effective output power curve of the tidal energy power generation system; 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 are calculated as the deviation between the rated output power and the actual output power of the tidal energy power generation system.

5. The method according to claim 1, characterized in that The objective function of the tidal energy power generation system for optimizing the energy storage capacity of each energy storage unit is constructed based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, including: 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, 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; 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, the constraint conditions of the objective function are determined.

6. The method according to claim 5, characterized in that The method of using a genetic algorithm to solve the optimal value of the objective function to obtain a target energy storage capacity configuration scheme for the tidal power generation system includes: Based on the preset energy storage value of each energy storage unit 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; Based on the objective function, calculating the fitness value of each individual in the population; In the case where the fitness values ​​of each of the individuals do not meet the preset fitness value conditions, the population is updated based on the constraint conditions 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 continue to be executed; In the case where the fitness values ​​of each 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.

7. The method according to any one of claims 1 to 6, characterized in that Also includes: Based on the historical operation 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 time period in the future and the historical tidal data for the second time period in the history corresponding to the first time period in the future are input into the new energy output prediction model to obtain the new energy output prediction result of the tidal power generation system in the first time period in the future output output by the new energy output prediction model.

8. A multi-distributed resource aggregation operation optimization system, characterized in that: include: A characteristic curve determination module is used to determine a mathematical model of the surface roughness variation over time of each power generation device based on the biofilm growth model of each power generation device in the tidal energy power generation system, and simulate the hydrodynamic performance variation 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 determination module, used 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 of the power generation devices; An objective function construction module, used 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 use a genetic algorithm to solve the optimal value of the objective function to obtain a target energy storage capacity configuration scheme for the tidal power generation system; A charging and discharging strategy determination module, configured to determine the charging and discharging 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 first future time period, and the target energy storage capacity configuration scheme; The power detection module is used to control the operation of each power generation device and energy storage unit in the tidal energy power generation system based on the charging strategy of the tidal energy power generation system.

9. 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 so that the at least one processor can execute the method described in any one of claims 1 to 7.

10. 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 1-7.

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