CN119171539A

CN119171539A - Multi-distributed resource aggregation operation optimization method and system

Info

Publication number: CN119171539A
Application number: CN202411605768.1A
Authority: CN
Inventors: 冯华; 陈晓刚; 章寒冰; 叶吉超; 黄慧; 徐永海; 朱利锋; 吴晓刚; 丁宁; 张程翔; 胡鑫威; 季奥颖; 娄冰; 汪华; 陈冰恽
Original assignee: Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd; Zhejiang Huayun Information Technology Co Ltd; Lishui Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Current assignee: Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd; Zhejiang Huayun Information Technology Co Ltd; Lishui Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date: 2024-11-12
Filing date: 2024-11-12
Publication date: 2024-12-20

Abstract

The invention relates to the technical field of power and discloses a multi-distributed resource aggregate operation optimization method and system. The method comprises the steps of simulating hydrodynamic performance changes of all power generation equipment to obtain instantaneous power output characteristic curves of all the power generation equipment, determining deviation of rated output power and actual output power of a tidal power generation system based on the instantaneous power output characteristic curves of all the power generation equipment, constructing an objective function for optimizing energy storage capacity of all the energy storage units based on characteristic parameters of all the energy storage units in the tidal power generation system, solving an optimal value of the objective function to obtain a target energy storage capacity configuration scheme, and determining a charging and discharging strategy of the tidal power generation system based on deviation of rated output power and actual output power of the tidal power generation system, a new energy output prediction result in a first time period in the future and the target energy storage capacity configuration scheme to control work of all the power generation equipment and all the energy storage units.

Description

Multi-distributed resource aggregate operation optimization method and system

Technical Field

The disclosure relates to the technical field of power, in particular to a multi-distributed resource aggregate operation optimization method, a system, electronic equipment and a storage medium.

Background

A multi-distributed resource aggregate is an aggregate system composed of various types of distributed energy sources, and tidal power generation equipment plays an important role in the system as one of the important resources. Tidal power generation equipment runs for a long time in a marine environment, and various microorganisms are inevitably attached to the surface of the tidal power generation equipment to form a biological film. The microbial communities in different sea areas have large composition difference, and environmental conditions such as temperature, salinity, nutrient content and the like are different, and the factors influence the growth rate of microorganisms on the surface of the tidal power generation equipment together, so that the growth rate of microorganisms on the surface of the equipment in different sea areas is obviously different. Microorganism adhesion growth can change roughness and frictional resistance of the surface of the tidal power generation equipment, further influence the hydrodynamic performance of the blades and the rotating shaft, and finally change the output power and response characteristics of the generator set, so that the overall performance of the multi-distributed resource polymer is directly influenced.

When the power grid needs a multi-distributed resource aggregate to provide millisecond frequency modulation service, the transient variation characteristic of the output power of the tidal power generation device is important because the transient variation characteristic is directly related to the overall frequency modulation capability of the aggregate. If microorganism attached to the surface of the tidal power generation equipment affects the original transient characteristics, so that the power output response of the tidal power generation equipment becomes slow or unstable, the frequency modulation capability of the multi-distributed resource polymer is greatly weakened, and the requirement of the power grid on millisecond response of the power grid is difficult to meet, so that the stability and reliability of the power grid are affected.

Therefore, the growth rule of the microbiota of different sea areas on the surface of the tidal power generation equipment is researched, the influence mechanism of the microbiota on the dynamic output characteristic of the equipment is revealed, and the method has important significance for grasping the uncertainty factor of the tidal power and improving the grid-connected frequency modulation capability of the tidal power generation equipment, and has positive effects for improving the overall performance and reliability of the multi-distributed resource polymer.

Disclosure of Invention

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

The present disclosure provides a multi-distributed resource aggregate operation optimization method, comprising:

Determining a mathematical model of the surface roughness of each power generation device along with the time change based on a biological film growth model of each power generation device in the tidal power generation system, and simulating the hydrodynamic performance change 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;

Determining deviation of rated output power and actual output power of the tidal power generation system based on the instantaneous power output characteristic curves of the power generation devices;

constructing an objective function of the tidal 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 power generation system;

adopting a genetic algorithm to solve an optimal value of the objective function to obtain a target energy storage capacity configuration scheme of the tidal power generation system;

Determining a charging and discharging strategy of the tidal power generation system based on a deviation of rated output power and actual output power of the tidal power generation system, a new energy output prediction result of the tidal power generation system in a first time period in the future, and the target energy storage capacity configuration scheme;

And controlling the operation of each power generation device and each energy storage unit based on the charging strategy of the tidal power generation system.

According to another aspect of the present disclosure, there is provided a multi-distributed resource aggregate operation optimization system, comprising:

The characteristic curve determining module is used for determining a mathematical model of the change of the surface roughness of each power generation device along with time based on a biological film growth model of each power generation device in the tidal power generation system, and simulating the hydrodynamic performance change 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;

The power deviation determining module is used for determining deviation between rated output power and actual output power of the tidal power generation system based on the instantaneous power output characteristic curves of the power generation equipment;

the objective function construction module is used for constructing an objective function of the tidal 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 power generation system;

The objective function solving module is used for solving an optimal value of the objective function by adopting a genetic algorithm to obtain a target energy storage capacity configuration scheme of the tidal power generation system;

The charging and discharging strategy determining module is used for determining a charging and discharging strategy of the tidal power generation system based on a deviation of rated output power and actual output power of the tidal power generation system, a new energy output prediction result of the tidal power generation system in a first time period in the future and the target energy storage capacity configuration scheme;

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

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

At least one processor, and

A memory communicatively coupled to the at least one processor, wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform any one of the multiple distributed resource aggregate operation optimization methods of the disclosed embodiments.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform any of the multiple distributed resource aggregate operation optimization methods of the embodiments of the present disclosure.

According to the technical scheme, the mathematical model of the change of the surface roughness of each power generation device along with time is determined based on the biological film growth model of each power generation device in the tidal power generation system, the hydrodynamic performance change of each power generation device is simulated based on the mathematical model corresponding to each power generation device, the instantaneous power output characteristic curve of each power generation device is obtained, and the deviation of rated output power and actual output power of the tidal power generation system is determined based on the instantaneous power output characteristic curve of each power generation device. The deviation can evaluate the effect of microorganism attachment on the output power of the energy storage system.

Then, based on characteristic parameters of each energy storage unit in the tidal power generation system, constructing an objective function of the tidal power generation system for optimizing the energy storage capacity of each energy storage unit;

Furthermore, based on the deviation of the rated output power and the actual output power of the tidal power generation system, the new energy output prediction result of the tidal power generation system in the future first time period and the target energy storage capacity configuration scheme, 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 influence of microorganism adhesion on the output power of the energy storage system and the target energy storage capacity configuration scheme.

Subsequently, based on a charging strategy of the tidal power generation system, the work of each power generation device and each energy storage unit is controlled, and the stable operation of the tidal power generation system is ensured.

Drawings

FIG. 1 is a flow chart of a multi-distributed resource aggregate operation optimization method in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram of a multi-distributed resource aggregate operation optimization system in accordance with an embodiment of the present disclosure;

fig. 3 is a block diagram of an electronic device used to implement an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

FIG. 1 is a flow chart of a multi-distributed resource aggregate operation optimization method in accordance with an embodiment of the present disclosure.

As shown in fig. 1, the optimization method includes:

S110, determining a mathematical model of the surface roughness of each power generation device along with the time change based on a biological film growth model of each power generation device in the tidal power generation system, and simulating the hydrodynamic performance change 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.

S120, determining deviation of rated output power and actual output power of the tidal power generation system based on the instantaneous power output characteristic curves of the power generation devices.

S130, constructing an objective function of the tidal 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 power generation system.

And S140, solving an optimal value of the objective function by adopting a genetic algorithm to obtain a target energy storage capacity configuration scheme of the tidal power generation system.

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

S160, controlling the work of each power generation device and each energy storage unit based on the charging strategy of the tidal power generation system.

For example, the steps described above are not necessarily performed in a sequential order, some steps may be performed in parallel, and some steps may be performed in a sequential order, which is not specifically limited herein.

Illustratively, while the above step S160 is performed, the real-time output power of each power generation device may be detected, and then the charge and discharge strategy is readjusted based on the real-time output power of each power generation device, so that the tidal power generation system stably operates.

It will be appreciated that the biofilm growth model may characterize a nonlinear relationship function between environmental factors and microorganism growth rate or thickness.

Illustratively, based on the non-line relation function, the surface roughness at each point in time can be obtained.

For example, a polynomial function may be used to fit the surface roughness at each time point to obtain a time-varying curve of the surface roughness of the power generation device, i.e., a mathematical model of the surface roughness over time. The fitting process may employ a least squares method for fitting.

Illustratively, the instantaneous power output characteristic characterizes a change in instantaneous output power over time.

Illustratively, the characteristic parameters of the energy storage unit may include an upper limit of an energy storage capacity of the energy storage unit, a lower and upper limit of charge and discharge efficiency, a charge and discharge time limit, and the like.

The method comprises the steps of optimizing a target energy storage capacity configuration scheme based on deviation of rated output power and actual output power of the tidal power generation system and a new energy output prediction result of the tidal power generation system in a first time period in the future, and determining a charging and discharging strategy of the tidal power generation system by utilizing the optimized target energy storage capacity configuration scheme.

By way of example, the charge-discharge strategy of the energy storage system may include a charge start time, a charge power curve, a discharge start time, and a discharge power curve. For example, considering the tidal cycle characteristics, charging is performed in the low valley period and discharging is performed in the peak period of the tidal power generation power, so that the energy storage system is ensured to smooth the fluctuation of new energy output under the optimized capacity, and the economic benefit is improved.

Illustratively, considering a tidal cycle of about 12.4 hours, the charging strategy is performed during the tidal valley period (1-3 hours and 13-15 hours) and the discharging strategy is performed during the peak period (6-8 hours and 18-20 hours). The annual income ratio of the final scheme reaches 8.5%, the standard deviation of power fluctuation is reduced by 40%, and the life expectancy of the energy storage system reaches 8.5 years.

According to the embodiment, the influence of microorganism adhesion on the output power of the energy storage system can be determined by utilizing the deviation of the rated output power and the actual output power of the tidal power generation system. Then, based on characteristic parameters of each energy storage unit in the tidal power generation system, constructing an objective function of the tidal power generation system for optimizing the energy storage capacity of each energy storage unit, and solving an optimal value of the objective function by adopting a genetic algorithm to obtain a target energy storage capacity configuration scheme of the tidal power generation system. Furthermore, based on the deviation of the rated output power and the actual output power of the tidal power generation system, the new energy output prediction result of the tidal power generation system in the future first time period and the target energy storage capacity configuration scheme, 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 influence of microorganism adhesion on the output power of the energy storage system and the target energy storage capacity configuration scheme. Subsequently, based on a charging strategy of the tidal power generation system, the work of each power generation device and each energy storage unit is controlled, the stable operation of the tidal power generation system is ensured, and the influence of microorganism adhesion on the output power of the energy storage system is reduced.

In one embodiment, the method further comprises the steps of determining a sea area microorganism growth environment characteristic vector corresponding to each power generation device based on temperature and salinity environment change parameters of a sea area where each power generation device is located, determining a theoretical growth rate of microorganisms on each power generation device based on the sea area microorganism growth environment characteristic vector corresponding to each power generation device, carrying out spectral analysis on biological film morphological characteristics in a surface biological film growth video of each power generation device to obtain an actual growth rate of the microorganisms on each power generation device, and correcting a preset biological film growth model of each power generation device based on a comparison result of the theoretical growth rate and the actual growth rate of the microorganisms on each power generation device to obtain a biological film growth model of each power generation device.

Illustratively, the marine environmental sensor network consists of 20 float-type sensors, each equipped with temperature and salinity measuring instruments, with a sampling frequency of once every 10 minutes. The sensor transmits the data to the data processing center in real time through the 4G wireless network. In the data preprocessing stage, firstly, abnormal values beyond the normal range, such as data points with the temperature lower than-2 ℃ or higher than 35 ℃ and the salinity lower than 28 permillage or higher than 40 permillage, are removed. And then smoothing the data by using a 5-point median filtering algorithm, so as to effectively remove short-term fluctuation and noise. And in the data fusion stage, a Dempster-Shafer evidence theory is adopted, confidence degrees are allocated to temperature and salinity data, for example, the confidence degree of the temperature data is 0.6, the confidence degree of the salinity data is 0.4, and the fused environmental parameters are calculated through a D-S combination rule. Thus, temperature and salinity environment variation parameters are obtained.

Illustratively, principal component analysis is performed on temperature and salinity environment variation parameters, principal component eigenvalues with accumulated contribution of more than 85% are extracted, and a marine microorganism growth environment eigenvector is constructed based on the extracted principal component eigenvalues. For example, the marine microorganism growth environment characteristic vector may include 10 parameters of temperature, salinity, pH, dissolved oxygen, and the like.

Illustratively, the microorganism species matching the marine microorganism growth environment feature vector are screened from a pre-established microorganism colony growth model database. For example, a cosine algorithm may be used to calculate the matching degree, and the microorganism types with the matching degree greater than a preset threshold value may be extracted to obtain the microorganism type list information. For example, a preset threshold of 0.85 is used to screen 50 candidate microorganisms.

Illustratively, a generalized additive model is adopted, a characteristic vector of a microbial growth environment in a sea area is taken as an independent variable, a microbial growth index is taken as an independent variable, and a nonlinear relation function between a microbial growth rate and the environment factor is established, so that a dominant environment factor with the most obvious influence on microbial growth is determined.

Illustratively, the dominant microorganism species is determined in the microorganism species list information using the dominant environmental factor.

Illustratively, growth kinetic parameters of the dominant microorganism species, such as maximum specific growth rate, half-saturation constant, lag time, etc., are extracted from a pre-established database of growth kinetic parameters. And inputting the environmental characteristic vector and the extracted growth kinetic parameters into a Gompertz equation, and calculating the theoretical growth rate of the microorganism on the surface of the equipment.

For example, the maximum specific growth rate of 3 microorganisms is 0.15 h-1, 0.22 h-1, 0.18 h-1, half-saturation constant 0.5g/L, 0.8g/L, 0.6g/L and lag time 2h, 1.5h, 2.5h from the growth dynamics parameters database. Substituting the parameters into a modified Gompertz equation, setting the initial colony number to be 10 < -3 > CFU/cm < -2 >, simulating for 72 hours, and calculating to obtain a growth curve of the microorganism on the surface of the equipment, wherein the final biomass reaches 10 < -7 > CFU/cm < -2 >, 10 < -8 > CFU/cm < -2 > and 10 < -6 > CFU/cm < -2 > respectively.

Illustratively, the surface biofilm growth video comprises a sequence of continuously acquired biofilm images.

Illustratively, the biofilm thickness in the sequence of biofilm images is calculated by a sliding window method using structured light three-dimensional reconstruction techniques to obtain an actual biofilm growth rate curve.

For example, structured light three-dimensional reconstruction employs sinusoidal fringe projection with a spatial resolution of 0.1mm, processing images acquired per hour over 72 hours, and estimating an increase in biofilm thickness from an initial 0.2mm to 1.8mm. The sliding window size was set to 6 hours and the average growth rate was calculated to be 0.022mm/h.

The comparison may be, for example, a root mean square error.

Illustratively, the parameters of the preset biofilm growth model are optimized by adopting a genetic algorithm by taking root mean square error as an error function, so as to obtain a corrected biofilm growth model.

According to the above embodiment, the biofilm growth model of each power generation device can be accurately obtained.

In one embodiment, the method comprises the steps of simulating hydrodynamic performance changes of each power generation device based on mathematical models corresponding to the power generation devices to obtain instantaneous power output characteristic curves of the power generation devices, converting the mathematical models of the surface roughness changes of the power generation devices with time into boundary conditions of hydrodynamic simulation, processing the boundary conditions by adopting an equivalent sand roughness method to obtain surface states of the power generation devices at each time point, wherein the surface states comprise surface pressure distribution and surface speed field distribution, calculating the surface pressure distribution at each time point through surface integration to obtain working pressure resistance of the power generation devices at the time point, calculating the surface speed field distribution at the time point through wall shear stress integration to obtain working friction resistance of the power generation devices at the time point, determining the instantaneous output power of the power generation devices at the time point based on the working pressure resistance and the working friction resistance of the power generation devices at the time point, and determining the instantaneous power output characteristic curves of the power generation devices based on the instantaneous output power of the power generation devices at each time point.

Illustratively, the equivalent grit roughness method converts the surface roughness to computational fluid dynamics boundary conditions with a roughness coefficient gradually increasing from an initial 0.02 to 0.15 after 168 hours.

Illustratively, the surrounding flow field of the power generation equipment is discretized by using unstructured grids, a Reynolds stress model is used for describing fluid motion characteristics, surface roughness at different time points is set as wall boundary conditions, and three-dimensional unsteady flow field numerical simulation is performed. Based on the numerical simulation results, the surface pressure distribution and the surface velocity field distribution of the apparatus are extracted.

For example, unstructured grid generation employs the Delaunay triangulation algorithm, with 10 layers of encrypted grids disposed in the near wall region, with a total grid count of about 200 tens of thousands. The turbulent stress transport equation in the Reynolds stress model adopts a second-order windward format for dispersion, the time step is set to be 0.1s, and the total simulation time is 600s. Corresponding simulation is carried out under 48 working conditions of different time points of 0h, 24h, 72h, 168h, different flow rates of 0.5m/s, 1.0m/s, 1.5m/s, 2.0m/s and different flow directions of 0 DEG, 45 DEG and 90 DEG, so that a numerical simulation result is obtained.

Illustratively, the instantaneous output power corresponding to the operating pressure resistance, the operating frictional resistance, and the lift force at a specified point in time is determined in conjunction with an energy conversion efficiency curve obtained in advance through experiments. The energy conversion efficiency curves record the corresponding instantaneous output power at different working pressure resistance, working friction resistance and lift.

The instantaneous output power of each time point is arranged according to time sequence to obtain a corresponding sequence, interpolation processing is carried out on the sequence, and then curve fitting is carried out on the sequence after interpolation processing to obtain an instantaneous power output characteristic curve of the power generation equipment.

The instantaneous output powers at the individual points in time are arranged in chronological order, a corresponding sequence is obtained, and then curve fitting is performed on the sequence, so that an instantaneous power output characteristic curve of the power generation device is obtained.

According to the above embodiment, the instantaneous power output characteristic curves of the respective power generation devices can be accurately obtained.

In one embodiment, the deviation of the rated output power and the actual output power of the tidal power generation system is determined based on the instantaneous power output characteristic curves of all power generation equipment, and the method comprises the steps of segmenting the instantaneous power output characteristic curves of the power generation equipment to obtain a plurality of curve segments, carrying out power spectrum density estimation on all the curve segments to obtain power density functions corresponding to all the curve segments, constructing a power fluctuation spectrum model of the power generation equipment based on peaks in all the power density functions, determining the frequency range of high-frequency noise based on the power fluctuation spectrum model of all the power generation equipment, filtering the actual output power curve of the tidal power generation system based on the frequency range of the high-frequency noise to obtain an effective output power curve of the tidal power generation system, and calculating root mean square error and maximum absolute deviation of the actual output power of the tidal power generation system at all time points relative to the rated output power of the tidal power generation system based on the effective output power curve to serve as deviation of the rated output power and the actual output power of the tidal power generation system.

Illustratively, the instantaneous power output characteristic is segmented for a period of 1/4 of the tidal period, so that statistical features within each segment, such as mean, variance, skewness, kurtosis, etc., can be calculated. Meanwhile, the analysis of power fluctuation can be carried out on each curve segment, and a power fluctuation time domain characteristic sequence is obtained.

Illustratively, a Welch method is utilized to perform power spectrum density estimation on a power fluctuation time domain feature sequence corresponding to a curve segment, so as to obtain a power density function corresponding to the curve segment.

Illustratively, the main frequency component and the power corresponding to the main frequency component corresponding to each curve segment are determined according to the peak value in the power density function of each curve segment, so that a power fluctuation spectrum model is constructed according to the main frequency component and the power corresponding to the main frequency component corresponding to each curve segment.

Illustratively, if the power fluctuation spectrum model shows that high frequency noise is mainly concentrated above 2Hz, the initial filtering parameters are set to a low pass filter cut-off frequency of 1.5Hz and the filter order of 32. And then, filtering the actual output power curve of the tidal power generation system by adopting 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, and an effective power output curve is obtained.

By way of example, the obtained effective power output curve is compared with the rated power curve of the system obtained based on statistical analysis of historical data, the root mean square error and the maximum absolute deviation between the two can be calculated, and 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 power generation system.

According to the embodiment, the deviation between the rated output power and the actual output power of the tidal power generation system can be accurately calculated.

In one embodiment, the objective function of the tidal power generation system for optimizing the energy storage capacity of each energy storage unit is constructed based on characteristic parameters of each energy storage unit in the tidal power generation system, the objective function is constructed based on the energy storage capacity of each energy storage unit in the tidal power generation system as an independent variable and the maximum charge and discharge power of the tidal power generation system as a dependent variable, and the constraint condition of the objective function is determined based on the upper limit of the energy storage capacity, the lower limit and the upper limit of the charge and discharge efficiency and the limit of the charge and discharge time.

It is understood that the objective function is a multi-objective function, and its objectives include maximizing charge-discharge power, minimizing charge-discharge time, and minimizing charge-discharge cost.

The required charging power, charging and discharging time and charging and discharging cost are determined by taking the energy storage capacity of the energy storage unit as a target, and then all the energy storage units are summed to obtain a corresponding target function.

According to the above embodiment, a corresponding objective function can be constructed.

In one embodiment, a genetic algorithm is adopted to solve an optimal value of an objective function to obtain a target energy storage capacity configuration scheme of the tidal power generation system, the method comprises the steps of constructing a population based on preset energy storage values of all energy storage units in the tidal power generation system, wherein each individual in the population corresponds to one energy storage capacity configuration scheme of the tidal power generation system respectively, calculating an adaptive value of each individual in the population based on the objective function, updating the population based on constraint conditions of the objective function when the adaptive value of each individual does not meet preset adaptive value conditions, returning to continue to execute the step of calculating the adaptive value of each individual in the population based on the updated population, and determining the target energy storage capacity configuration scheme based on the energy storage capacity configuration scheme corresponding to the individual corresponding to the minimum adaptive value determined in the population when the adaptive value of each individual meets preset adaptive value conditions.

Illustratively, the energy storage capacity configuration includes energy storage capacities of the respective energy storage units.

The output value of the objective function is taken as the adaptation value of the corresponding individual.

Illustratively, when the fitness values of all individuals are obtained, the minimum fitness value is selected therefrom. If the minimum fitness value does not meet the preset fitness value condition, for example, the minimum fitness value is larger than the preset threshold value, updating each individual in the population under the condition that the constraint condition of the objective function is met.

For example, if the minimum adaptation value meets a preset adaptation value condition, determining a target energy storage capacity configuration scheme based on the energy storage capacity configuration scheme corresponding to the individual corresponding to the minimum adaptation value in the population.

According to the embodiment, the particle swarm algorithm can be adopted to solve the optimal solution for the objective function, and the optimal solution is used as a target energy storage capacity configuration scheme.

In one embodiment, the method further comprises the steps of constructing a training sample set based on historical operation data of the tidal power generation system, wherein each training sample in the training sample set comprises weather forecast data of a first time period, historical tidal data and new energy output results of the tidal power generation system, training a neural network based on the training sample set to obtain a new energy output prediction model, inputting the weather forecast data of the first time period in the future and the historical tidal data of a second time period corresponding to the first time period in the future into the new energy output prediction model, and obtaining the new energy output prediction results of the tidal power generation system in the first time period in the future, which are output by the new energy output prediction model.

For example, the historical first time period for each training sample in the training sample set may be a different time period, but the same time period.

The weather forecast data and the historical tidal data can be subjected to feature extraction, for example, the periodic features in the weather forecast data and the historical tidal data are extracted through fast Fourier transformation to obtain a data set with a uniform format, time features, statistical features and frequency domain features are extracted from the data set, and an optimal feature subset is selected from the time features, the statistical features and the frequency domain features through correlation analysis to obtain feature vectors.

For example, the new energy output result may be output power.

The training process may be, for example, a plurality of iterations, in each of which the feature vectors in the training samples in the training sample set are output to the neural network to obtain the output power of the output, the output power is compared with the output power in the training samples, and then the neural network is subjected to parameter adjustment using the comparison result to obtain the neural network for the next iteration.

The weather forecast data of the first time period in the future and the historical tidal data of the second time period 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, and output power output by the model can be obtained and used as a new energy output prediction result of the tidal power generation system in the first time period in the future.

According to the embodiment, the new energy output prediction result of the tidal power generation system in the first time period in the future can be accurately obtained by adopting the neural network model.

FIG. 2 is a block diagram of a multi-distributed resource aggregate operation optimization system in accordance with an embodiment of the present disclosure.

As shown in fig. 2, the optimization system includes:

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

a power deviation determining module 220 for determining a deviation of the rated output power and the actual output power of the tidal power generation system based on the instantaneous power output characteristic curves of the respective power generation devices;

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

The objective function solving module 240 is configured to solve an optimal value for the objective function by adopting a genetic algorithm, so as to obtain a target energy storage capacity configuration scheme of the tidal power generation system;

the charge-discharge strategy determining module 250 is configured to determine a charge-discharge strategy of the tidal power generation system based on a deviation of a rated output power and an actual output power of the tidal power generation system, a new energy output prediction result of the tidal power generation system in a first time period in the future, and the target energy storage capacity configuration scheme;

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

And the strategy adjustment module 270 is used for adjusting the charging and discharging strategy again based on the real-time output power of each power generation device so as to ensure that the tidal power generation system stably operates.

In one embodiment, the system may further include:

The environment vector determining module is used for determining the microbial growth environment characteristic vector of the sea area corresponding to each power generation device based on the temperature and salinity environment change parameters of the sea area where the power generation device is located;

the theoretical rate determining module is used for determining the theoretical growth rate of microorganisms on each power generation device based on the corresponding sea area microorganism growth environment characteristic vector of each power generation device;

The actual rate determining module is used for carrying out spectral analysis on the morphological characteristics of the biological film in the surface biological film growth video of each power generation device to obtain the actual growth rate of the microorganisms on each power generation device;

And the model correction module is used for correcting the preset biological film 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 biological film growth model of each power generation device.

In one embodiment, the characteristic determining module 210 includes:

The surface state determining unit is used for converting a mathematical model of the surface roughness of the power generation equipment with time into a boundary condition of hydrodynamic simulation, and processing the boundary condition by adopting an equivalent sand roughness method to obtain the surface state of the power generation equipment at each time point, wherein the surface state comprises surface pressure distribution and surface speed field distribution;

A power determining unit for calculating the surface pressure distribution at the time point through a surface integral to obtain the working pressure resistance of the power generating equipment at the time point, calculating the surface speed field distribution at the time point through a wall shear stress integral to obtain the working friction resistance of the power generating equipment at the time point, and determining the instantaneous output power of the power generating equipment at the time point based on the working pressure resistance and the working friction resistance of the power generating equipment at the time point;

and a curve determination unit configured to determine an instantaneous power output characteristic curve of the power generation apparatus based on the instantaneous output power of the power generation apparatus at each of the time points.

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

The frequency spectrum model determining unit is used for respectively executing the following operations of segmenting an instantaneous power output characteristic curve of the power generation equipment to obtain a plurality of curve segments, carrying out power spectrum density estimation on each curve segment to obtain a power density function corresponding to each curve segment;

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;

The filtering unit is used for filtering the actual output power curve of the tidal power generation system based on the frequency range of the high-frequency noise to obtain an effective output power curve of the tidal power generation system;

And the deviation determining unit is used for calculating root mean square error and maximum absolute deviation of the actual output power of the tidal power generation system at each time point relative to the rated output power of the tidal power generation system based on the effective output power curve, and taking the root mean square error and the maximum absolute deviation as the deviation of the rated output power and the actual output power of the tidal power generation system.

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

The objective function determining unit is used for constructing the objective function by taking the energy storage capacity of each energy storage unit in the tidal power generation system as an independent variable and maximizing the charge and discharge power of the tidal power generation system and taking the charge and discharge time and the charge and discharge cost of the minimum tidal power generation system as independent variables;

And the function condition determining unit is used for determining the constraint condition of the objective function based on the upper limit of the energy storage capacity of the energy storage unit, the lower limit and the upper limit of the charge and discharge efficiency and the limit of the charge and discharge time.

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

The group initialization unit is used for constructing a group based on preset energy storage values of all energy storage units in the tidal power generation system, wherein each individual in the group corresponds to one energy storage capacity configuration scheme of the tidal power generation system;

An adaptation value calculation unit, configured to calculate an adaptation 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 condition of the objective function, and return to continuously 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 does not meet the preset fitness value condition;

and the target scheme determining unit is used for determining a target energy storage capacity configuration scheme based on the energy storage capacity configuration scheme corresponding to the individual corresponding to the minimum adaptation value determined in the population under the condition that the adaptation value of each individual meets the preset adaptation value condition.

In one embodiment, the method may further include:

The training set determining module is used for 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 comprises weather forecast data of a first historical time period, historical tidal data and a new energy output result of the tidal power generation system;

the model training module is used for training the neural network based on the training sample set to obtain a new energy output prediction model;

the model prediction module is used for inputting the weather 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 a new energy output prediction result of the tidal power generation system in the future first time period, wherein the new energy output prediction result is output by the new energy output prediction model.

The above-described method of the present disclosure may be applied to an electronic device and a readable storage medium according to an embodiment of the present disclosure.

Fig. 3 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile apparatuses, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing apparatuses. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 3, the apparatus 600 includes a computing unit 601 that can perform various suitable 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 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

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

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized 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 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 tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may 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 RAM 603 and executed by computing unit 601, one or more of the steps of a multi-distributed resource aggregate operation optimization method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform a multi-distributed resource aggregate operation optimization method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be a special or general purpose programmable processor, operable to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may 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. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, 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.

To provide for interaction with a user, the systems and techniques described here 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 types of devices may also be used to provide interaction with the user, for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end 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.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. that are within the principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. 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.

2. The method according to claim 1, further comprising:

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 step of simulating the hydrodynamic performance changes of each of the power generation equipment based on the mathematical model corresponding to each of the power generation equipment to obtain the instantaneous power output characteristic curve of each of the power generation equipment comprises:

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 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, based on the characteristic parameters of each energy storage unit in the tidal energy power generation system, constructing an objective function for optimizing the energy storage capacity of each energy storage unit of 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, 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 use of 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 comprises:

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, further comprising:

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 by 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.

9. An electronic device, comprising:

at least one processor; and

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 to 7.