A power supply management system based on distributed grid energy storage devices

Abstract

The application discloses a power supply management system based on distributed power grid energy storage equipment, and relates to the technical field of power supply management. The system collects and pre-processes light intensity and temperature data in a fixed time period, then reconstructs a phase space, obtains a maximum Lyapunov exponent through a small data amount method, compares the maximum Lyapunov exponent with a threshold value, selects an MPPT algorithm or an LSTM-MPPT algorithm to track the maximum output power of solar energy, and effectively improves the accuracy of maximum output power tracking. The maximum output power is sorted by time, and the slope k between the two points is obtained. Whether to start the standby energy storage equipment is determined by the size of k, and the charging and discharging conditions of the standby energy storage equipment are determined by the positive and negative of k. After starting the standby energy storage equipment, the loss of electric energy transmission between solar energy and the standby energy storage equipment is compared with the electric energy consumed by moving the standby energy storage equipment to solar energy, so as to select wire transmission or move the standby energy storage equipment, thereby avoiding electric energy loss.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power supply management, in particular to a power supply management system based on distributed power grid energy storage equipment. BACKGROUND

[0002] With the continuous growth of energy demand and the environmental challenges faced by traditional energy structure, distributed power grid as an efficient, flexible and sustainable power supply mode is increasingly valued, especially in solar power generation, distributed power grid combines multiple solar energy not only can maximize the use of sunlight, but also avoids the environmental pollution and other problems caused by traditional power generation.

[0003] In the traditional distributed power grid, the selection of the maximum output power of a single solar power generation does not consider the influence of the surrounding environment, resulting in inaccurate maximum output power at different times and low power generation rate. When the maximum output power at a certain moment changes sharply, the solar energy storage device cannot store excess power or discharge insufficient power. The existing technology often considers distributing or borrowing power to the surrounding solar energy storage device. At this time, the distance between the surrounding storage devices and the storage capacity of the surrounding devices need to be considered. If the surrounding suitable storage device cannot be accurately found, it will cause serious loss of electric energy or damage to the storage device. SUMMARY

[0004] In view of the defects of the prior art, the present application provides a power supply management system based on distributed power grid energy storage equipment, which solves the problems of not being able to accurately track the maximum output power according to the surrounding environment during solar power generation and the storage of electric energy in extreme conditions of solar power generation.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a power supply management system based on distributed power grid energy storage equipment, comprising:

[0006] A source data acquisition end collects source data of light intensity and temperature changing with time in a fixed time period, and transmits the collected source data to a source data processing end;

[0007] The source data processing end performs outlier rejection and denoising on the collected source data, and performs phase space reconstruction on the obtained one-dimensional time sequence. The maximum Lyapunov exponent of the light intensity and temperature sequence is calculated by using the small data method, and the maximum Lyapunov exponent of the light intensity and temperature sequence is Lyapunov1 and Lyapunov2, respectively. Lyapunov1 and Lyapunov2 are transmitted to the strategy selection end;

[0008] The strategy selection end determines to select a starting MPPT algorithm or an LSTM-MPPT algorithm to track the maximum output power of solar energy according to Lyapunov1 and Lyapunov2, and transmits the maximum output power sequence to the energy storage decision end;

[0009] The energy storage decision end calculates the slope k between two points before and after the maximum output power sequence, determines whether to start the standby energy storage device according to k, generates a decision result, and transmits the decision result to the adaptive standby energy storage end;

[0010] The adaptive standby energy storage end determines the movement of the standby energy storage device according to the electric energy consumed by the movement of the standby energy storage device to solar energy and the electric energy loss amount transmitted by solar energy and the standby energy storage device, and keeps the original state if the standby energy storage device is not started.

[0011] As a further scheme of the present application, the method for denoising the collected source data is:

[0012] The mean μ and the standard deviation σ of the collected data are calculated, if no less than 99.73% of the data points fall between (μ-3σ, μ+3σ), it indicates that the data has Gaussian noise, and Gaussian filtering is used for denoising;

[0013] The reasonable range of the data is determined as [Imin, lmax], the data lower than lmin and exceeding lmax is regarded as an abnormal value, the number of abnormal values is m1, the abnormal value ratio is calculated according to the formula r=m1 / n1, if r>γ, it indicates that the data contains salt and pepper noise, and median filtering is used for denoising, wherein n1 is the total number of data, and γ is the abnormal value ratio threshold;

[0014] The autocorrelation function R(w) of the data under different lag orders w is calculated, if there is a period q such that R(w)=R(w+q)=R(w+2q)=....=Q, it indicates that there is periodic noise, and adaptive filtering is used for denoising, wherein Q is the peak value;

[0015] As a further scheme of the present application, the specific steps for obtaining the maximum Lyapunov exponent by using the small data amount method are:

[0016] The optimal embedding dimension m and time delay τ of the light intensity and temperature data {x(n), n=1, 2,..., N} are obtained according to the C-C method;

[0017] The reconstructed phase space vector is X(n)=[x(n), x(n+τ),..., x(n+(m-1)τ)];

[0018] For each point X(n) in the phase space, the nearest neighbor point X(v) is found through the formula d0(n) = min||X(n) - X(v)||, where |||| represents the Euclidean distance and v ≠ n;

[0019] After s steps of time, the reference point becomes X(n + s), and its nearest neighbor point becomes X(v + s). Calculate the distance between them d(s,n) = ||X(n + s) - X(v + s)||;

[0020] According to the formula Calculate the average maximum Lyapunov exponent, where M is the number of selected initial points.

[0021] As a further solution of the present invention, the method for determining whether to start the MPPT algorithm or the LSTM-MPPT algorithm according to Lyapunov1 and Lyapunov2 is:

[0022] If Lyapunov1 > a1 and Lyapunov2 > a2, start the MPPT algorithm to track the maximum output power of solar energy; otherwise, start the LSTM-MPPT algorithm to track the maximum output power of solar energy, where a1 and a2 represent the maximum Lyapunov exponent thresholds.

[0023] As a further solution of the present invention, the method for determining whether to activate the backup energy storage device according to k is:

[0024] If |k| > nbd and k < 0, activate the backup energy storage device and discharge it; if |k| > nbd and k > 0, activate the backup energy storage device and charge it; if |k| < nbd, do not activate the backup energy storage device, where nbd represents the threshold of the calculated slope between two adjacent points.

[0025] As a further solution of the present invention, the power consumed by the backup energy storage device moving to the solar energy is related to its own power P and moving time t. The consumed power is calculated according to the formula W = Pt / η, where η represents the energy conversion efficiency and η ∈ (0,1).

[0026] As a further solution of the present invention, the power loss during the transmission between solar energy and the backup energy storage device can be obtained through the formula W1 = V^2*S*t_1 / ρ*L, where ρ is the wire resistivity, L is the transmission distance, S is the wire cross-sectional area, t_1 is the transmission time, and V is the voltage. <00​​

[0028] The application provides a power supply management system based on a distributed power grid energy storage device, and has the following beneficial effects compared with the prior art:

[0029] (1) The application collects light intensity and temperature data within a fixed time period T, respectively reconstructs the phase space, obtains the maximum Lyapunov exponent of both by the small data method, and accurately determines whether the surrounding environment is in a steady state according to whether the maximum Lyapunov exponent exceeds a threshold value, and then respectively uses MPPT or LSTM-MPPT to track the maximum output power of solar energy under different environments, so that the solar power generation efficiency is significantly improved.

[0030] (2) The application sets up a standby energy storage device, which can be started to charge or discharge in time when the maximum output power of solar energy changes extremely, and at the same time, the standby energy storage device can also select solar energy and standby energy storage device according to W and W1 to select whether to transmit power by wire or to adaptively move the standby energy storage device to the place where the solar energy is located to transmit power, which effectively reduces the power loss caused by traditional wire transmission. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 The system diagram of the application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0033] As Figure 1 , the application provides a power supply management system based on a distributed power grid energy storage device, comprising:

[0034] A source data acquisition end collects light intensity and temperature data every 5 seconds within a fixed time T using a light intensity illuminometer and a thermometer, respectively obtains two groups of source data of light intensity and temperature changing with time, and transmits the collected source data to a source data processing end.

[0035] The source data processing end performs outlier rejection and denoising on the collected source data, reconstructs the obtained one-dimensional time sequence into a phase space, calculates the maximum Lyapunov exponents of the light intensity and temperature sequences by the small data method, and transmits Lyapunov1 and Lyapunov2 to a strategy selection end.

[0036] The quartile range method is mainly used for outlier rejection. For example, a set of data is 12, 5, 9, 18, 3, 21, 7. First, the data is sorted to obtain 3, 5, 7, 9, 12, 18, 21. Since the number of data in this set is odd, the first quartile Q1 = 5.5 and the third quartile Q3 = 20.25 are calculated by linear interpolation. The interquartile range IQR = Q3-Q1 = 14.75 is calculated to determine the outlier limit, the lower limit = Q1-1.5*IQR = -16.625, and the upper limit = Q3+1.5*IQR = 42.375. Values less than the lower limit and greater than the upper limit are classified as outliers and are removed;

[0037] When denoising the source data, it is necessary to first determine the type of noise contained in the source data, including Gaussian noise, salt and pepper noise, and periodic noise. Then, different denoising methods are selected for different noises.

[0038] The mean μ and standard deviation σ of the source data are collected. If no less than 99.73% of the data points fall within (μ-3σ, μ+3σ), it indicates that the data contains Gaussian noise. Gaussian filtering is used for denoising because Gaussian filtering has unique advantages in processing Gaussian noise, including excellent smoothing effect, flexible parameter adjustment capability, strong filtering capability for normal distribution noise, wide applicability, and linear separability.

[0039] Set a reasonable range [lmin, lmax] for the data, and consider data below lmin and data exceeding lmax as outliers, obtaining the number of outliers m1. According to the formula r = m1 / n1, the outlier ratio is obtained. If r>γ, it indicates that the data contains salt and pepper noise. Median filtering is used for denoising because median filtering has good edge preservation capability, simple and efficient implementation, and good denoising capability when processing salt and pepper noise. Here, n1 is the total number of data, and γ is the outlier ratio threshold.

[0040] The autocorrelation function R(w) of the data under different lag orders w is obtained. If there is a period q such that R(w) = R(w+q) = R(w+2q) =... = Q, it indicates that there is periodic noise. Adaptive filtering is used for denoising because adaptive filtering has dynamic adaptability, does not require prior knowledge, has efficient periodic noise suppression capability, and has stability and robustness when processing periodic noise. Here, Q is the peak value.

[0041] The C-C method is used to calculate the optimal embedding dimension m and time delay τ of the light intensity and temperature sequence, so as to prepare for the subsequent phase space reconstruction. Generally, m and τ are calculated by separate methods. For example, m can be calculated by the false nearest neighbor method and Cao method, and τ can be calculated by the complex autocorrelation method, autocorrelation method and mutual information method. Although all of them can be calculated, they need to be calculated by different methods, which is troublesome and time-consuming. The C-C method can calculate m and τ at the same time, saving calculation time.

[0042] The steps of calculating m and τ by the C-C method are as follows:

[0043] For a given time sequence {x(i)}, according to the formula Calculate the correlation integral under different m and τ, where θ(x) is the Heaviside function, X i (m,τ) is the i-th m-dimensional phase space of the time sequence;

[0044] Define two difference statistics: S1(m,τ) = C(m,τ)-C(m+1,τ), S2(m,τ) = C(m,2τ)-C(m+1,2τ);

[0045] Fix the value of m, calculate the value of S2(m,τ) under all possible values of τ, find the value of τ that makes S2(m,τ) reach the global minimum, and record it as τopt(m);

[0046] Fix τopt(m), gradually increase the value of m, calculate S1(m,τopt(m)), and when its value no longer increases significantly with m, the value of m at this time is the optimal embedding dimension;

[0047] After the phase space is constructed according to m and τ, the specific method for calculating the maximum Lyapunov exponent by the small data method is as follows:

[0048] The reconstructed phase space vector is X(i) = [x(i),x(i+τ),...,x(i+(m-1)τ)];

[0049] For each point X(i) in the phase space, find the nearest neighbor X(v) by the formula d0(n) = min||X(i)-X(v)||, where |||| represents the Euclidean distance, and v≠i;

[0050] After s steps, the reference point becomes X(i+s), and its nearest neighbor becomes X(v+s). Calculate the distance between them d(s,i) = ||X(i+s)-X(v+s)||;

[0051] According to the formula The average maximum Lyapunov exponent is calculated, wherein M is the number of selected initial points;

[0052] The strategy selection end determines to select the MPPT algorithm or the LSTM-MPPT algorithm to track the maximum output power of the solar energy according to Lyapunov1 and Lyapunov2, and transmits the maximum output power sequence to the energy storage decision end;

[0053] The maximum Lyapunov exponent calculated according to the light intensity data sequence is Lyapunov1, and the maximum Lyapunov exponent calculated according to the temperature data sequence is Lyapunov2. If Lyapunov1>a1 and Lyapunov2>a2, it indicates that the light intensity and temperature are relatively stable within time T, and the MPPT algorithm (such as the perturb and observe method and the conductance increment method) performs well when tracking the maximum output power of the solar energy, because the MPPT algorithm is simple to implement and does not require a large amount of computing resources and complex hardware support. If both conditions cannot be met, it indicates that the light intensity or temperature changes complexly within time T, and the LSTM-MPPT algorithm performs better when tracking the maximum output power of the solar energy, because the LSTM-MPPT can use its processing capability for time series data to quickly and accurately predict the change of the maximum power point by learning the relationship between historical light, temperature and power data, thereby achieving efficient tracking. Wherein, a1 and a2 represent the threshold value of the maximum Lyapunov exponent.

[0054] The energy storage decision end calculates the slope k between the two points before and after the maximum output power sequence, determines whether to start the standby energy storage device according to k to generate a decision result, and transmits the decision result to the adaptive standby energy storage end.

[0055] |k|>nbd indicates that the output power of the solar energy is particularly small or particularly large at this time, and the energy storage device of the solar energy cannot meet the needs at this time, and the standby energy storage device needs to be started to perform charging and discharging operations.

[0056] When |k|>nbd and k<0, it indicates that the sunlight contacted by the solar energy is affected by clouds or the surrounding environment, resulting in little electricity generated at this time, and the power of the solar energy and the energy storage device cannot meet the needs of the load, so the standby energy storage device should be started to perform discharging operation at this time.

[0057] When |k|>nbd and k>0, it indicates that the solar energy is fully contacted with the sunlight at this time, and excess electricity is generated, and the energy storage device cannot completely store the excess electricity. In order to avoid the excess electricity exceeding the limit of the energy storage device and causing damage to the energy storage device, the standby energy storage device needs to be started to perform charging operation at this time.

[0058] |k|<nbd represents that the output power of the solar energy in this period is within the normal output power, and no matter how much the solar energy generates, it can be regulated by the energy storage device carried by itself, so the standby energy storage device does not need to be started;

[0059] The adaptive standby energy storage end, if the standby energy storage device is started, the movement of the standby energy storage device is determined according to the amount of electric energy consumed by the solar energy and the amount of electric energy loss transmitted between the solar energy and the standby energy storage device, and if the standby energy storage device is not started, it remains unchanged;

[0060] If the standby energy storage device is started, in order to save as much electric energy as possible, the electric energy loss W1 caused by the wire transmission between the solar energy and the standby energy storage device and the electric energy W consumed by the movement of the standby energy storage device to the solar energy need to be considered, if W1>W, the standby energy storage device is moved to the place where the solar energy is located and then the electric energy transmission is performed, if W<W1, the wire transmission between the two is selected to perform the electric energy transmission;

[0061] The electric energy consumed by the movement of the standby energy storage device to the solar energy is related to the power P of the standby energy storage device and the movement time t, and the consumed electric energy is calculated according to the formula W=Pt / η, wherein η represents the energy conversion efficiency, and η∈(0,1);

[0062] The electric energy loss W1 between the solar energy and the standby energy storage device during the transmission can be calculated by the formula W1=V^2*S*t1 / ρ*L, wherein ρ is the wire resistivity, L is the transmission distance, S is the wire cross-sectional area, t1 is the transmission time, and V is the voltage;

[0063] In addition, if the wire is severely damaged or in extreme weather conditions such as high temperature and rainfall, the standby energy storage device is selected to be moved to the solar energy for electric energy transmission, because in the above conditions, the wire is easy to cause electric leakage, which will seriously threaten the entire circuit;

[0064] The method for judging the damage degree of the wire is as follows:

[0065] The maintenance time twe i of the wire history is collected, and the average value mean and the standard deviation std of the maintenance time are calculated, and the damage degree of the wire is divided according to the combination of mean and std, if twe i∈(0,mean-std), it belongs to slight damage, if twe i∈(mean-std,mean+std), it belongs to moderate damage, if twe i∈(mean+std,∞), it belongs to serious damage;

[0066] Some data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0067] The above examples are only used to illustrate the technical method of the present application but not to limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present application.

Claims

1. A power supply management system based on distributed grid energy storage devices, characterized in that, include: The source data acquisition end collects source data on the changes in light intensity and temperature over a fixed period of time, and transmits the collected source data to the source data processing end. At the source data processing end, outlier removal and noise reduction are performed on the collected source data, and the phase space is reconstructed on the obtained one-dimensional time series. The maximum Lyapunov exponents of the light intensity and temperature series are calculated using the small data volume method, namely Lyapunov1 and Lyapunov2, and Lyapunov1 and Lyapunov2 are transmitted to the strategy selection end. At the strategy selection end, if Lyapunov1>a1 and Lyapunov2>a2, the MPPT algorithm is started to track the maximum output power of solar energy; otherwise, the LSTM-MPPT algorithm is started to track the maximum output power of solar energy, and the maximum output power sequence is transmitted to the energy storage decision end. The MPPT algorithm is one of the perturbation observation method and the incremental conductance method, where a1 and a2 represent the maximum Lyapunov exponent threshold. At the energy storage decision end, the slope k between two points before and after the maximum output power sequence is calculated. Based on k, it is determined whether to activate the backup energy storage device, and a decision result is generated and transmitted to the adaptive backup energy storage end. The adaptive backup energy storage device will move according to the amount of electricity consumed by the backup energy storage device when it moves to the solar energy source and the amount of electricity loss transmitted between the solar energy source and the backup energy storage device. If the backup energy storage device is not activated, it will remain unchanged.

2. The power supply management system based on distributed grid energy storage equipment according to claim 1, characterized in that, The method for denoising the collected source data is as follows: Calculate the mean μ and standard deviation σ of the collected data. If no less than 99.73% of the data points fall between (μ-3σ, μ+3σ), it indicates that the data contains Gaussian noise. Use Gaussian filtering to remove the noise. The reasonable range of the data is determined to be [lmin, lmax]. Data below lmin and above lmax are considered outliers, and the number of outliers is m1. The proportion of outliers is calculated according to the formula r = m1 / n1. If r > γ, it indicates that the data contains salt-and-pepper noise. Median filtering is used for noise removal, where n1 is the total number of data and γ is the outlier proportion threshold. The autocorrelation function R(w) of the data under different lag orders w is obtained. If there exists a period q such that R(w) = R(w+q) = R(w+2q) = ... = Q, it indicates the presence of periodic noise. Adaptive filtering is used for noise reduction, where Q is the peak value.

3. A power supply management system based on distributed grid energy storage equipment according to claim 1, characterized in that, The specific steps for obtaining the maximum Lyapunov exponent using the small data method are as follows: The optimal embedding dimension m and time delay τ of the light intensity and temperature data {x(n), n=1,2,...,N} are obtained using the CC method; The reconstructed phase space vector is X(n)=[x(n),x(n+τ),...,x(n+(m-1)τ)]; For each point X(n) in the phase space, the nearest neighbor point X(v) is found by the formula d0(n)=min||X(n)-X(v)||, where |||| represents the Euclidean distance and v≠n; After s steps of elapsed time, the reference point becomes X(n + s), and its nearest neighbor becomes X(v + s). Calculate the distance between them, d(s,n) = ||X(n + s) - X(v + s)||; According to the formula Calculate the average maximum Lyapunov exponent, where M is the number of initial points selected.

4. A power supply management system based on distributed grid energy storage equipment according to claim 1, characterized in that, The method for determining whether to activate the backup energy storage device according to k is as follows: If |k| > nbd and k < 0, activate the backup energy storage device and discharge it. If |k| > nbd and k > 0, activate the backup energy storage device and charge it. If |k| < nbd, do not activate the backup energy storage device, where nbd represents the threshold of the calculated slope between two adjacent points.

5. A power supply management system based on distributed grid energy storage equipment according to claim 1, characterized in that, The electrical energy consumed by the backup energy storage device moving to solar energy is related to its own power P and moving time t. The consumed electrical energy is calculated according to the formula W = Pt / η, where η represents the energy conversion efficiency, and η ∈ (0,1).

6. A power supply management system based on distributed grid energy storage equipment according to claim 1, characterized in that, The electrical energy lost during the transmission between solar energy and the backup energy storage device can be obtained through the formula W1 = V^2*S*t1 / ρ*L, where ρ is the wire resistivity, L is the transmission distance, S is the wire cross-sectional area, t1 is the transmission time, and V is the voltage.

7. A power supply management system based on distributed grid energy storage equipment according to claim 1, characterized in that, The specific method for determining the movement of the backup energy storage device according to W and W1 is as follows: If W > W1, do not move the energy storage device to the location of solar energy and directly choose wire transmission. If W < W1, it is necessary to move the backup energy storage device to the location of solar energy.

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