CN114123279B - Method and system for predicting energy storage state of charge reference value of wind power energy storage hybrid system - Google Patents
Method and system for predicting energy storage state of charge reference value of wind power energy storage hybrid system Download PDFInfo
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Abstract
The application discloses a method and a system for predicting an energy storage charge state reference value of a wind power energy storage hybrid system, wherein the method comprises the steps of obtaining an average power predicted value of a wind power energy storage hybrid system in a last front continuous period and a last rear continuous period; and inputting the average power predicted value of the front and rear continuous periods into a two-input-single-output machine learning network model which is trained in advance, so as to obtain the energy storage charge state reference value of the energy storage device in the wind power energy storage hybrid system, wherein the two-input-single-output machine learning network model is trained in advance to establish a mapping relation between the average power predicted value of the front and rear continuous periods and the energy storage charge state reference value of the energy storage device. According to the application, on the premise that the power and capacity of the battery energy storage system are configured to be certain, the optimal smoothing effect on the power fluctuation of the wind power plant is realized, and meanwhile, the battery energy storage system can be prevented from being overcharged or deeply discharged.
Description
Technical Field
The application relates to the technical field of power systems, in particular to a method and a system for predicting an energy storage state of charge reference value of a wind power energy storage hybrid system.
Background
The wind power and energy storage hybrid system is a wind power generation system with an energy storage device. The wind power generation system can not continuously and stably output electric energy due to the change of natural conditions such as wind speed, wind direction and the like, and the electric energy quality and the stability of a power grid can be greatly influenced under the condition of higher wind power penetrating power. Therefore, the energy storage device with certain capacity is configured in the system to play roles in smoothing power fluctuation, maintaining dynamic balance of power generation/load and keeping voltage/frequency stable, so that the wind power generation system can safely, economically, efficiently and high-quality operate. The larger the capacity of the energy storage system is configured, the better the smoothing effect on the power fluctuation of the wind power system is, but the investment cost of the system is increased, and the economic requirement cannot be well met. Therefore, how to improve the technical performance of an energy storage system in a wind power system through the self optimal control of the energy storage system with a certain capacity has become a problem which needs to be solved urgently at present.
Disclosure of Invention
The application aims to solve the technical problems: aiming at the problems in the prior art, the application provides the method and the system for predicting the reference value of the energy storage state of charge of the wind power energy storage hybrid system, which can realize the optimal smoothing effect on the power fluctuation of the wind power plant on the premise of certain power and capacity configuration of the battery energy storage system, and simultaneously can enable the battery energy storage system to avoid the occurrence of the condition of overcharge or deep discharge.
In order to solve the technical problems, the application adopts the following technical scheme:
a method for predicting an energy storage charge state reference value of a wind power energy storage hybrid system comprises the following steps:
1) Obtaining an average power predicted value of a nearest front continuous period and a nearest rear continuous period of the wind power energy storage hybrid system;
2) And inputting the average power predicted value of the front and rear continuous periods into a two-input-single-output machine learning network model which is trained in advance, so as to obtain the energy storage charge state reference value of the energy storage device in the wind power energy storage hybrid system, wherein the two-input-single-output machine learning network model is trained in advance to establish a mapping relation between the average power predicted value of the front and rear continuous periods and the energy storage charge state reference value of the energy storage device.
Optionally, the two-input-single-output machine learning network model is a two-input-single-output single hidden layer BP network.
Optionally, the single hidden layer BP network includes an input layer, a hidden layer and an output layer, wherein the input layer includes two neurons for inputting average power prediction values of two consecutive periods, and the hidden layer includes 6 neurons for outputting X 1 ～X 6 The output layer is used for outputting the obtained energy storage charge state reference value of the energy storage device.
Optionally, the input layer and the hidden layer are connected by using a hyperbolic tangent excitation function tansig, and the function expression of the hidden layer is:
in the above formula, tanh represents a hyperbolic tangent excitation function tansig,and->Two sets of weighting parameters, P, of 6 neurons respectively mean(k) And P mean(k+1) The average power predicted value of the two continuous time periods before and after being input to the input layer.
Optionally, the hidden layer and the output layer are connected by adopting a linear function purelin, and the function expression of the output layer is:
in the above equation, tanh represents the linear function purelin,six weight parameters connected between the output layer and 6 neurons of the hidden layer respectively.
Optionally, the step 2) further includes a step of performing offline training of the single hidden layer BP network in advance:
s1) generating training data and dividing the training data into a training data set and a verification data set, wherein the training data set and the verification data set comprise average power predicted values of a front continuous period and a rear continuous period after normalization processing and labels of energy storage charge state reference values of corresponding energy storage devices;
s2) a Levenberg-Marquardt back propagation optimization algorithm is selected, a training data set is adopted to train a single hidden layer BP network, and a selected momentum gradient descent learning function Learngdm is adopted to adjust weight parameters of an hidden layer and an output layer;
and S3) verifying the single hidden layer BP network which completes the round of training by adopting a verification data set, judging that the training is completed if the error of the single hidden layer BP network is smaller than a preset threshold value, and saving the weight parameters of the hidden layer and the output layer as a finally obtained training result, otherwise, executing the step S1) to continue the training.
Optionally, step 2) further comprises: acquiring an actual value of the energy storage state of charge of the energy storage device and a difference between the actual value and a reference value of the energy storage state of charge of the energy storage device to obtain an energy storage state of charge difference delta SOC; and taking the difference value delta SOC of the energy storage state and the charge and discharge state of the battery energy storage system at the moment as input of a fuzzy controller, and obtaining the current smooth time constant T by the fuzzy controller according to the difference value delta SOC of the energy storage state and the charge and discharge state by using a fuzzy rule.
Optionally, the step of obtaining the current smoothing time constant T further comprises the step of outputting the power P of the wind farm G Obtaining a wind power grid-connected power reference value P through a first-order low-pass filter T * When the wind farm outputs power P G Higher than the reference value P of wind power grid-connected power T * Storing excess energy in the energy storage device; when the wind farm outputs power P G Is lower than the reference value P of wind power grid-connected power T * When the energy storage device is used, the stored energy in the energy storage device is released to provide power support for the power grid; and the transfer function of the first order low pass filter has the following functional expression:
in the above formula, T is a smoothing time constant.
In addition, the application also provides an energy storage charge state reference value prediction system of the wind power energy storage hybrid system, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the energy storage charge state reference value prediction method of the wind power energy storage hybrid system.
Furthermore, the application also provides a computer readable storage medium, wherein a computer program is stored in the computer readable storage medium and is used for being executed by computer equipment to implement the steps of the method for predicting the energy storage charge state reference value of the wind power energy storage hybrid system.
Compared with the prior art, the application has the following advantages: the method comprises the steps of obtaining an average power predicted value of a wind power energy storage hybrid system in a last front continuous period and a last rear continuous period; and inputting the average power predicted value of the front and rear continuous periods into a two-input-single-output machine learning network model which is trained in advance, so as to obtain the energy storage charge state reference value of the energy storage device in the wind power energy storage hybrid system, wherein the two-input-single-output machine learning network model is trained in advance to establish a mapping relation between the average power predicted value of the front and rear continuous periods and the energy storage charge state reference value of the energy storage device. According to the application, the prediction of the energy storage state of charge reference value of the energy storage device is performed by utilizing a two-input-single-output machine learning network model based on the mapping relation between the average power prediction value of the front and rear continuous time periods and the energy storage state of charge reference value of the energy storage device, so that the prediction accuracy of the energy storage state of charge reference value of the energy storage device can be improved through a large number of data sample training, the optimal smoothing effect on the power fluctuation of the wind power plant can be realized on the premise of certain power and capacity configuration of the battery energy storage system, and meanwhile, the battery energy storage system can be prevented from overcharging or deep discharging.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present application.
Fig. 2 is a schematic diagram illustrating the division of input signals of a machine learning network model according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a single hidden layer BP network according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a hybrid wind-electric energy storage system according to an embodiment of the present application.
Fig. 5 is a block diagram of a basic smoothing control strategy for a battery energy storage system in accordance with an embodiment of the present application.
Fig. 6 is a schematic diagram of a grid-connected power curve of the wind power energy storage system according to an embodiment of the present application.
Fig. 7 is a schematic diagram of an output power curve of an energy storage system according to an embodiment of the application.
Fig. 8 is a schematic diagram of a state of charge curve of an energy storage system according to an embodiment of the present application.
Fig. 9 is a schematic block diagram of fuzzy-neural network control forming a smoothed time constant T in an embodiment of the present application.
Fig. 10 is a graph of a control rule of a neural network controller according to an embodiment of the present application.
Fig. 11 shows a characteristic of variation of the grid-connected power reference PT with the smoothing time constant T according to an embodiment of the present application.
Fig. 12 shows a predicted short-term output power of a wind turbine farm according to an embodiment of the present application.
Fig. 13 is an average of short term output power of a wind farm in accordance with an embodiment of the present application.
Fig. 14 is a state of charge reference of a battery energy storage system according to an embodiment of the application.
Fig. 15 is a simulation waveform when the fuzzy-neural network control strategy in the method of the present embodiment is adopted. The method comprises the steps of (a) obtaining grid-connected power of a wind power plant output power/wind power energy storage hybrid system, (b) obtaining a smooth time constant, (c) obtaining output power of a battery energy storage system, and (d) obtaining a state of charge monitoring value of the battery energy storage system.
FIG. 16 is a simulation waveform of a system when a conventional control strategy is adopted, wherein (a) is grid-connected power of a wind power energy storage hybrid system, (b) is output power of a battery energy storage system, and (c) is a state of charge monitoring value of the battery energy storage system.
Fig. 17 is a simulation waveform of the system when the conventional control strategy is adopted and t=150s. Wherein (a) is grid-connected power of a wind farm output power/wind power energy storage hybrid system and (b) is grid-connected power P T The output waveform when the fuzzy-neural network control is adopted and the traditional control is adopted, (c) the output power of the battery energy storage system is adopted, and (d) the state of charge monitoring value of the battery energy storage system is adopted.
Detailed Description
As shown in fig. 1, the method for predicting the energy storage charge state reference value of the wind power and energy storage hybrid system according to the embodiment includes:
1) Obtaining an average power predicted value of a nearest front continuous period and a nearest rear continuous period of the wind power energy storage hybrid system;
2) And inputting the average power predicted value of the front and rear continuous periods into a two-input-single-output machine learning network model which is trained in advance, so as to obtain the energy storage charge state reference value of the energy storage device in the wind power energy storage hybrid system, wherein the two-input-single-output machine learning network model is trained in advance to establish a mapping relation between the average power predicted value of the front and rear continuous periods and the energy storage charge state reference value of the energy storage device.
In this embodiment, the short-term predicted power of the wind farm is divided according to the equal time period, and the average power of each time period is obtained, so as to be used as the input signal of the machine learning network model, that is, the average power predicted value of the last two consecutive time periods, as shown in fig. 2.
As an alternative implementation manner, the two-input-single-output machine learning network model in this embodiment is a two-input-single-output single hidden layer BP network. As shown in fig. 3, the single hidden layer BP network includes an input layer, a hidden layer and an output layer, wherein the input layer includes two neurons for inputting average power prediction values for two consecutive periods, and the hidden layer includes 6 neurons for outputting X 1 ～X 6 The output layer is used for outputting the obtained energy storage charge state reference value of the energy storage device.
In this embodiment, the input layer and the hidden layer are connected by using a hyperbolic tangent excitation function tansig, and the function expression of the hidden layer is:
in the above formula, tanh represents a hyperbolic tangent excitation function tansig,and->Two sets of weighting parameters, P, of 6 neurons respectively mean(k) And P mean(k+1) The average power predicted value of the two continuous time periods before and after being input to the input layer.
In this embodiment, the hidden layer and the output layer are connected by using a linear function purelin, and the functional expression of the output layer is:
in the above equation, tanh represents the linear function purelin,six weight parameters connected between the output layer and 6 neurons of the hidden layer respectively.
In this embodiment, the step 2) further includes the step of performing offline training on the single hidden layer BP network in advance:
s1) generating training data and dividing the training data into a training data set and a verification data set, wherein the training data set and the verification data set comprise average power predicted values of a front continuous period and a rear continuous period after normalization processing and labels of energy storage charge state reference values of corresponding energy storage devices;
s2) a Levenberg-Marquardt back propagation optimization algorithm is selected, a training data set is adopted to train a single hidden layer BP network, and a selected momentum gradient descent learning function Learngdm is adopted to adjust weight parameters of an hidden layer and an output layer;
and S3) verifying the single hidden layer BP network which completes the round of training by adopting a verification data set, judging that the training is completed if the error of the single hidden layer BP network is smaller than a preset threshold value, and saving the weight parameters of the hidden layer and the output layer as a finally obtained training result, otherwise, executing the step S1) to continue the training.
In this embodiment, step 2) further includes: acquiring an actual value of the energy storage state of charge of the energy storage device and a difference between the actual value and a reference value of the energy storage state of charge of the energy storage device to obtain an energy storage state of charge difference delta SOC; and taking the difference value delta SOC of the energy storage state and the charge and discharge state of the battery energy storage system at the moment as input of a fuzzy controller, and obtaining the current smooth time constant T by the fuzzy controller according to the difference value delta SOC of the energy storage state and the charge and discharge state by using a fuzzy rule. The fuzzy controller is used for giving out proper smooth time constant values according to the charge state of the battery energy storage system and the charge and discharge states of the battery energy storage system, so that the wind power grid-connected power reference value is dynamically regulated in real time, and the charge state of the battery energy storage system is controlled in real time. When the state of charge of the battery energy storage system is higher, if the state of charge is in the state of charge, the smoothing time constant is reduced, so that the tracking speed of the grid-connected power reference value to the output power of the wind power plant is increased, the charging power of the battery energy storage system is relatively reduced, the speed of increasing the state of charge is reduced, and the overcharge condition of the battery energy storage system is prevented; if the power is in a discharging state, the time constant is increased, so that the tracking speed of the grid-connected power reference value to the output power of the wind power plant is reduced, and the discharging power of the battery energy storage system is relatively increased, so that the reduction of the charge state of the battery energy storage system is accelerated, and the battery energy storage system is changed towards a proper charge state. Vice versa, when the state of charge of the battery energy storage system is low, if the state of charge is in the state of charge, the time constant is increased, so that the tracking speed of the grid-connected power reference value to the output power of the wind power plant is reduced, the charging power of the battery energy storage system is relatively increased, the return of the state of charge is accelerated, and the state of charge is changed towards the proper state of charge; if the power is in a discharging state, the time constant is reduced, so that the tracking speed of the grid-connected power reference value to the output power of the wind power plant is increased, the discharging power of the battery energy storage system is relatively reduced, the speed of reducing the state of charge of the battery energy storage system is reduced, and the condition of deep discharging of the battery energy storage system is prevented.
In this embodiment, according to the above characteristics of the battery energy storage system control system, the fuzzy controller adopts a two-input-single-output two-dimensional structure, and the input and output of the fuzzy controller include: input 1: the reference value of the charge state output by the neural network controller is SOC, the real-time charge state of the battery energy storage system is SOC, the deviation DeltaSOC of the charge state is equal to SOC-SOC, the language variable is E2, and the basic domain is [ -100%,100%]The fuzzy arguments are { -3, -2, -1,0, +1, +2, +3}, and the corresponding fuzzy subsets are { NB, NM, NS, ZO, PS, PM, PB }, which respectively indicate that the state of charge of the current battery energy storage system is { very low, moderate, very high }, relative to the set value. Input 2: and taking the language variable of the charge and discharge state of the battery energy storage system as E3.N represents the battery energy storage system in a discharged state and P represents the battery energy storage system in a charged state. The charge and discharge states of the battery energy storage system can be output by the wind farm G And the actual grid-connected power P T Is of the size of (a)Determining if P G ＜P T Indicating that the energy storage system is in a discharge state, if P G ＞P T Indicating that the energy storage system is in a charged state. And (3) outputting: smoothing time constant T, whose fundamental domain is [0s,3000s ]]The ambiguity domains are {0,1,2,3,4,5,6}, and the corresponding ambiguity subsets are { EL, VL, RL, ZO, RB, VB, EB }, which respectively represent the output time constants as { very small, medium, very large }. The input and output membership functions of the fuzzy controller are Gaussian membership functions with high sensitivity, and the defuzzification method is a gravity center method. The control rules of the fuzzy controller are given as shown in table 1 according to the above-described deviation of the state of charge and the relation of smooth time constant output corresponding to the charge and discharge states of the battery energy storage system.
Table 1: a fuzzy controller rule table.
The fuzzy rule of table 1 sufficiently reflects the variation characteristics of the smoothing time constant T at different state of charge deviations Δsoc and its charge-discharge states. The following 2 rules are selected from table 1 for illustration.
Rule 1: IF E2 is PB and E3 is P, THEN T is EL;
rule 2: IF E2 is NB and E3 is P, THEN T is EB.
Rule 1 is explained as: when the SOC deviates from the reference SOC by Δsoc positive electrode by a large amount (PB) and the battery energy storage system is in the state of charge (P), the smoothing time constant output by the fuzzy controller is a minimum value (EL). The minimum value of the smoothing time constant can be 0s, and at the moment, the charge and discharge power of the battery energy storage system is 0, and the charge state of the battery energy storage system is unchanged; rule 2 is explained as: when the SOC deviates from the SOC by Δsoc negative electrode by a reference value (NB) and the battery energy storage system is in the state of charge (P), the output of the smoothing time constant T given by the fuzzy controller is a maximum value (EB).
In this embodiment, the current smoothing time constant T is obtained and then the power P output by the wind farm is also included G Obtaining a wind power grid-connected power reference value P through a first-order low-pass filter T * When the wind farm outputs power P G Higher than the reference value P of wind power grid-connected power T * Storing excess energy in the energy storage device; when the wind farm outputs power P G Is lower than the reference value P of wind power grid-connected power T * When the energy storage device is used, the stored energy in the energy storage device is released to provide power support for the power grid; and the transfer function of the first order low pass filter has the following functional expression:
in the above formula, T is a smoothing time constant.
As shown in fig. 4, the wind power and energy storage hybrid system of the embodiment incorporates a battery energy storage system between the wind farm outlet and the grid, and reasonably controls the output power P of the battery energy storage system F Smoothing wind farm output power P G Fluctuation of the wind power energy storage hybrid system is enabled to lead to grid-connected power P T Realizing smoother output; when the wind farm outputs power P G Higher than the reference value P of wind power grid-connected power T * When, i.e. P G >P T * When the energy storage device is in the closed state, the redundant energy can be stored in the energy storage device; the method comprises the steps of carrying out a first treatment on the surface of the When the wind farm outputs power P G Is lower than the reference value P of wind power grid-connected power T * When, i.e. P G <P T * When the energy storage device is used, the energy stored in the energy storage device can be released to provide power support for the power grid.
Fig. 5 is a block diagram of a basic smoothing control strategy of a battery energy storage system in the wind power energy storage composite system according to the present embodiment. The magnitude of the grid-connected power reference value PT is obtained by filtering the wind farm output power PG by a first-order low-pass filter, and the time constant T of the first-order low-pass filter is referred to as a smoothing time constant T. Fig. 6 to 8 are graphs of power and state of charge of the wind power energy storage system according to the embodiment of the present application, as can be seen from fig. 6 to 8: the smaller the smoothing time constant T is, the faster the grid-connected power PT can track the output power PG of the wind power plant, the lower the requirements on the maximum instantaneous power and capacity required to be achieved by the battery energy storage system are, but the poorer the smoothing effect on the power fluctuation of the wind power plant is. Conversely, the larger the smoothing time constant T, the better the smoothing effect of the battery energy storage system on wind power fluctuation will be, but at the same time, the battery energy storage system is required to have a larger power output capability and configure a larger energy storage capacity. According to the relation between the smoothing time constant T and the smoothing wind power plant power fluctuation effect and the configuration relation between the smoothing time constant and the battery energy storage system power and capacity, the requirements that the smoothing effect on the wind power plant power fluctuation is good and the configuration of the battery energy storage system power and capacity is low can not be met at the same time when the fixed smoothing time constant is set.
In this embodiment, the execution body of the machine learning network model is a neural network controller, and the neural network controller is used for providing a suitable state of charge reference value SOC of the energy storage device in the previous period of time according to the predicted value of the average output power of the wind power plant in the previous period and the next period. When the average power predicted value of the wind power plant in the later period is greatly increased compared with that of the wind power plant in the former period, the charge state reference value of the energy storage device in the former period is reduced, so that the energy storage device is converted to a lower charge state in the former period, a larger charge capacity space which is possibly required is provided for the later period, and the power fluctuation of the wind power plant can be smoothed to the maximum extent in the later period by the energy storage device. On the contrary, when the average output power predicted value of the wind power plant in the later period is reduced more than that of the wind power plant in the earlier period, the charge state reference value of the energy storage device in the earlier period is increased, so that the energy storage device is converted towards a higher charge state in the earlier period, a possibly required larger discharge capacity space is provided for the later period, and smooth output of grid-connected power in the later period is realized to the greatest extent. Fig. 9 is a schematic block diagram of fuzzy-neural network control forming a smoothed time constant T in the present embodiment. Firstly, the average power predicted value P of the wind power plant in the front and the rear time periods mean(k) 、P mean(k+1) As input of a neural network controller, obtaining a reference value SOC of the state of charge of the energy storage system in the previous period through a neural network control algorithm; and then the deviation between the real-time state of charge (SOC) of the battery energy storage system and the reference value (SOC) of the battery energy storage system is calculatedThe charge and discharge state of the energy storage system at the moment is used as input of the fuzzy controller, and the magnitude of the smoothing time constant T is dynamically adjusted in real time by utilizing a fuzzy rule according to the deviation degree and the charge and discharge state of the energy storage system. Therefore, the optimal smooth effect on the power fluctuation of the wind power plant is achieved on the premise that the power and capacity of the battery energy storage system are configured to be certain, and the battery energy storage system can be prevented from being overcharged or deeply discharged.
In this embodiment, a single hidden layer BP network shown in fig. 3 is specifically built by using Matlab neural network toolbox. The training function selects a Levenberg-Marquardt back propagation optimization algorithm (Trainlm), the number of hidden layer neurons is 6, and the weight adjustment rule selects a gradient descent learning function (Learngdm). According to the corresponding relation between the average power predicted value and the charge state reference value of the wind turbine generator in the front and rear time periods, 9 groups of typical input and target data can be determined to serve as training samples. In order to correct the network error performance and improve the generalization capability of the network, each training sample is normalized before training. The training error change curve based on the neural network is shown in fig. 9, the training error is less than 0.0001 in training 160 steps, and the training result is shown in table 2. And (3) online using the offline trained single hidden layer BP network in real-time control of the wind power energy storage system, wherein the control rule of the single hidden layer BP network is shown in figure 10.
Table 2: training results of the single hidden layer BP network.
FIG. 11 is a power P output by a wind farm G When=30mw, grid-connected power reference value P T * Waveform diagram as a function of the smoothing time constant T. As can be seen from the foregoing, the smaller the smoothing time constant T is, the grid-connected power reference value P T * To wind farm output power P G The faster the tracking speed of (c), and vice versa, the slower.
In order to verify the effectiveness of the proposed control strategy, a simulation analysis is performed on the wind power and energy storage hybrid system in the embodiment by using MATLAB/SIMULINK. Specific simulationThe parameters are as follows: rated output power of the wind power plant is 30MW, rated capacity of the battery energy storage system is 15MW/2.5 MW.h, and grid-connected power P of the wind power system is equal to or higher than that of the wind power plant T The initial value at time 0s is 15MW; the initial state of charge of the battery energy storage system at the moment of 0s is 85%; and presume the normal working range of the state of charge of the battery energy storage system is 10% -100%; if this range is exceeded, the battery energy storage system will cease to operate. The smoothing time constant T varies from 0s to 3000s. The simulation results and analysis of the system when the fuzzy-neural network control strategy and the traditional control strategy are adopted are as follows.
Fig. 12 is a predicted value of short-term output power of the wind farm in 0min-60min, and assuming that the actual output power of the wind farm is consistent with the predicted value, an average value of 10 min/time period of the output power is shown in fig. 13, and a state of charge reference value SOC of the battery energy storage system corresponding to each time period can be obtained according to a neural network control algorithm, as shown in fig. 14. The average power of the wind power plant is 9.78MW in 0min-10min, the average power of the wind power plant is 22.08MW in 10min-20min, and the average power is increased by 12.3MW relatively. The output of the corresponding state of charge reference value of the battery energy storage system in the period of 0min-10min is 23.14%, so as to control the battery energy storage system to be converted to a lower state of charge in the period of 0min-10min and provide a larger charging capacity space which is possibly required for 10min-20 min. The average power of the wind turbine generator is 12.75MW in 20-30 min, and is reduced by 9.33MW compared with the average power in 10-20 min. The corresponding output of the state of charge reference value of the battery energy storage system in the period of 10min-20min is 72.06%, and the purpose is to control the battery energy storage system to be converted to a higher state of charge in the period of 10min-20min and provide a larger discharge capacity space for 20min-30 min. When the average power change of the front period and the rear period is not large, the output of the charge state reference value of the battery energy storage system is about 55%.
When the battery energy storage system adopts the fuzzy-neural network control strategy proposed by the method of the embodiment, the simulation result is shown in fig. 15. When the battery energy storage system adopts the traditional control strategy, the simulation results are shown in fig. 16 and 17. Generally, the larger the smoothing time constant is, the better the smoothing effect on the power fluctuation of the wind power plant is, but the larger capacity battery energy storage system is required to be configured, so that the economy is not good. Fig. 16 is a simulation result of the battery energy storage system under the conventional control strategy when the smoothing time constant is respectively 200s, 700s, 1100 s, 1700s, 3000s. As can be seen from fig. 16, the battery energy storage system will reach 100% of its state of charge after a period of operation, and will be in an overcharged state after a period of time. As shown in fig. 17, when the smoothing time constant t=150s, although the battery energy storage system can be ensured not to be overcharged or deeply discharged during the simulation period, the smoothing effect on the power fluctuation of the wind farm is not as good as that when the fuzzy-neural network control strategy is adopted, as shown in the sub-graph (b) in fig. 17. Compared with the prior art, when the battery energy storage system adopts the fuzzy-neural network control strategy provided by the method, when the state of charge of the battery energy storage system is larger, if the battery energy storage system is in a charged state, the filtering time constant is reduced, the charging power is reduced, the increasing speed of the state of charge is slowed down, and the occurrence of overcharge is prevented; if the power source is in a discharging state, the filtering time constant is increased, the discharging power is increased, the reduction of the state of charge is accelerated, the power source is converted to a proper state of charge, and meanwhile, a good smoothing effect on power fluctuation of a wind power plant can be achieved. Vice versa, the simulation results better illustrate the effectiveness and correctness of the control method provided by the embodiment.
In addition, the embodiment also provides an energy storage charge state reference value prediction system of the wind power energy storage hybrid system, which comprises a microprocessor and a memory which are connected with each other, wherein the microprocessor is programmed or configured to execute the steps of the energy storage charge state reference value prediction method of the wind power energy storage hybrid system.
Furthermore, the present embodiment also provides a computer readable storage medium having a computer program stored therein, the computer program being for execution by a computer device to implement the steps of the method for predicting an energy storage state of charge reference value of the wind power energy storage hybrid system.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above examples, and all technical solutions belonging to the concept of the present application belong to the protection scope of the present application. It should be noted that modifications and adaptations to the present application may occur to one skilled in the art without departing from the principles of the present application and are intended to be within the scope of the present application.
Claims (8)
1. The method for predicting the energy storage state of charge reference value of the wind power energy storage hybrid system is characterized by comprising the following steps of:
1) Obtaining an average power predicted value of a nearest front continuous period and a nearest rear continuous period of the wind power energy storage hybrid system;
2) Inputting the average power predicted value of the front and rear continuous periods into a two-input-single-output machine learning network model which is trained in advance, so as to obtain an energy storage charge state reference value of an energy storage device in the wind power energy storage hybrid system, wherein the two-input-single-output machine learning network model is trained in advance to establish a mapping relation between the average power predicted value of the front and rear continuous periods and the energy storage charge state reference value of the energy storage device;
3) Acquiring an actual value of the energy storage state of charge of the energy storage device and a difference between the actual value and a reference value of the energy storage state of charge of the energy storage device to obtain an energy storage state of charge difference delta SOC; taking the difference value delta SOC of the energy storage state and the charge and discharge state of the battery energy storage system at the moment as the input of a fuzzy controller, and obtaining the current smooth time constant by the fuzzy controller according to the difference value delta SOC of the energy storage state and the charge and discharge state by using a fuzzy ruleT；
4) Power P output by wind farm G Obtaining a wind power grid-connected power reference value P through a first-order low-pass filter T * The transfer function of the first-order low-pass filter has the following functional expression:
in the above-mentioned method, the step of,Tis a smooth time constant; when the wind farm outputs power P G Higher than the reference value P of wind power grid-connected power T * Storing excess energy in the energy storage device; when the wind farm outputs powerP G Is lower than the reference value P of wind power grid-connected power T * When the energy stored in the energy storage device is released, the power support is provided for the power grid.
2. The method for predicting an energy storage state of charge reference value of a wind power and energy storage hybrid system according to claim 1, wherein the two-input-single-output machine learning network model is a two-input-single-output single hidden layer BP network.
3. The method for predicting the energy storage state of charge reference value of a wind power and energy storage hybrid system according to claim 2, wherein the single hidden layer BP network comprises an input layer, an hidden layer and an output layer, wherein the input layer comprises two neurons for inputting average power predicted values of two continuous time periods before and after, and the hidden layer comprises 6 neurons for outputtingX 1 ～X 6 The output layer is used for outputting the obtained energy storage charge state reference value of the energy storage device.
4. The method for predicting the energy storage state of charge reference value of a wind power and energy storage hybrid system according to claim 3, wherein the input layer and the hidden layer are connected by adopting a hyperbolic tangent excitation function tansig, and the function expression of the hidden layer is as follows:
in the above formula, tanh represents a hyperbolic tangent excitation function tansig,~/>and->~/>Two sets of weighting parameters for 6 neurons each,P mean(k) andP mean(k+1) the average power predicted value of the two continuous time periods before and after being input to the input layer.
5. The method for predicting the energy storage state of charge reference value of the wind power and energy storage hybrid system according to claim 4, wherein the hidden layer and the output layer are connected by adopting a linear function purelin, and the function expression of the output layer is:
in the above equation, tanh represents the linear function purelin,~/>six weight parameters connected between the output layer and 6 neurons of the hidden layer respectively.
6. The method for predicting an energy storage state of charge reference value of a wind power and energy storage hybrid system according to claim 5, wherein the step 2) further comprises the step of performing offline training of the single hidden layer BP network in advance:
s1) generating training data and dividing the training data into a training data set and a verification data set, wherein the training data set and the verification data set comprise average power predicted values of a front continuous period and a rear continuous period after normalization processing and labels of energy storage charge state reference values of corresponding energy storage devices;
s2) a Levenberg-Marquardt back propagation optimization algorithm is selected, a training data set is adopted to train a single hidden layer BP network, and a selected momentum gradient descent learning function Learngdm is adopted to adjust weight parameters of an hidden layer and an output layer;
and S3) verifying the single hidden layer BP network which completes the round of training by adopting a verification data set, judging that the training is completed if the error of the single hidden layer BP network is smaller than a preset threshold value, and saving the weight parameters of the hidden layer and the output layer as a finally obtained training result, otherwise, executing the step S1) to continue the training.
7. An energy storage state of charge reference prediction system of a wind power energy storage hybrid system, comprising a microprocessor and a memory connected to each other, characterized in that the microprocessor is programmed or configured to perform the steps of the energy storage state of charge reference prediction method of a wind power energy storage hybrid system according to any one of claims 1 to 6.
8. A computer readable storage medium having a computer program stored therein, the computer program being for execution by a computer device to perform the steps of the method of predicting an energy storage state of charge reference value of a wind power energy storage hybrid system according to any one of claims 1 to 6.
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