Power supply management system based on distributed power grid energy storage equipment

Abstract

The invention 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 preprocesses light intensity and temperature data in a fixed time period, then carries out phase-space reconstruction, obtains a maximum Lyapunov index through a small data volume method, compares the maximum Lyapunov index with a threshold value, and determines whether the maximum Lyapunov index is smaller than the threshold value. And the maximum output power of the solar energy is tracked by selecting the MPPT algorithm or the LSTM-MPPT algorithm, so that the tracking accuracy of the maximum output power is effectively improved. The maximum output power is sequenced according to time, the slope k between the front point and the rear point is solved, whether the standby energy storage device is started or not is judged according to the magnitude of k, and the charging and discharging conditions of the standby energy storage device are judged according to the positive and negative of k. After the standby energy storage device is started, the loss of the electric energy transmitted between the solar energy and the standby energy storage device is compared with the electric energy consumed when the standby energy storage device is moved to the solar energy, so that the wire transmission or movement of the standby energy storage device is selected, and the electric energy loss is avoided.

Description

Technical Field

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

[0002] With the continuous growth of energy demand and the environmental challenges faced by traditional energy structures, distributed power grids are increasingly valued as an efficient, flexible and sustainable power supply model, especially in solar power generation. Distributed power grids combine multiple solar energy sources to not only maximize the use of sunlight, but also avoid environmental pollution and other problems caused by traditional power generation.

[0003] In traditional distributed power grids, the selection of the maximum output power for a single solar power generation does not take into account the impact of the surrounding environment, resulting in inaccurate maximum output power at different times and low power generation rate. When the maximum output power changes drastically at a certain moment, the solar energy storage device cannot store all the excess power or the released power is insufficient. The existing technology often considers distributing or borrowing power from the surrounding solar energy storage devices. At this time, factors such as the distance between the surrounding storage devices and the storage capacity of the surrounding devices need to be considered. If the surrounding suitable storage devices cannot be found more accurately, it will cause serious power loss or damage to the storage devices. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a power supply management system based on distributed power grid energy storage equipment, which solves the problem of the inability to accurately track the maximum output power according to changes in the surrounding environment during solar power generation and the problem of energy storage in extreme situations of solar power generation.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A power supply management system based on distributed power grid energy storage equipment, comprising:

[0006] The source data collection end collects the source data of light intensity and temperature changes over time within a fixed time period, and transmits the collected source data to the source data processing end;

[0007] At the source data processing end, outliers are removed and denoised for the collected source data, and the phase space of the obtained one-dimensional time series is reconstructed. The maximum Lyapunov exponents of the light intensity and temperature series are calculated using the small data method, which are Lyapunov1 and Lyapunov2, respectively. Lyapunov1 and Lyapunov2 are transmitted to the strategy selection end;

[0008] The strategy selection end determines whether to start the MPPT algorithm or the LSTM-MPPT algorithm to track the maximum output power of solar energy based on 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 the two points before and after the maximum output power sequence, determines whether to turn on the backup energy storage device based on k, generates a decision result, and transmits the decision result to the adaptive backup energy storage end;

[0010] At the adaptive backup energy storage end, if the backup energy storage device is turned on, the movement of the backup energy storage device is determined according to the electric energy consumed by the backup energy storage device when moving to the solar energy and the electric energy loss transmitted between the solar energy and the backup energy storage device. If the backup energy storage device is not turned on, it remains as it is.

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

[0012] Obtain 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 has Gaussian noise, and Gaussian filtering is used to remove the noise.

[0013] The reasonable range of data is determined to be [Imin, lmax]. Data below lmin and above lmax are considered as outliers. The number of outliers is m1. The outlier ratio is obtained 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 denoising, where n1 is the total number of data and γ is the outlier ratio threshold.

[0014] Obtain the autocorrelation function R(w) of the data at different lag orders w. 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, where Q is the peak value.

[0015] As a further solution of the present invention, the specific steps of obtaining the maximum Lyapunov exponent using the small data method are as follows:

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

[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 as follows:

[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 thresholds of the maximum Lyapunov exponent.

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

[0024] If |k| > nbd and k < 0, turn on the backup energy storage device and discharge it; if |k| > nbd and k > 0, turn on the backup energy storage device and charge it; if |k| < nbd, do not turn on 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 electric energy consumed by the movement of the backup energy storage device to the solar energy is related to its own power P and movement time t. The consumed electric energy 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 electric energy loss during the transmission between the 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.

[0027] As a further solution of the present invention, 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 the solar energy and directly select wire transmission; if W < W1, it is necessary to move the backup energy storage device to the location of the solar energy.

[0028] The present invention provides a power supply management system based on distributed power grid energy storage equipment, which has the following beneficial effects compared with the prior art:

[0029] (1) The present invention collects light intensity and temperature data within a fixed time period T, reconstructs their phase space respectively, obtains the maximum Lyapunov exponents of the two by a small data method, and accurately judges whether the surrounding environment is in a steady state based on whether the maximum Lyapunov exponent exceeds the threshold. Then, MPPT or LSTM-MPPT is used to track the maximum output power of solar energy under different environments, so that the efficiency of solar power generation is significantly improved.

[0030] (2) The present invention sets up a backup energy storage device. When the maximum output power of solar energy changes extremely, the backup energy storage device can be turned on for timely charging or discharging. At the same time, the backup energy storage device can also choose whether to transmit power between the solar energy and the backup energy storage device by wire or to adaptively move the backup energy storage device to the location of the solar energy for power transmission according to W and W1. This method effectively reduces the problem of power loss caused by traditional transmission relying on wires. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a system diagram of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] like Figure 1 The present application provides a power supply management system based on a distributed power grid energy storage device, including:

[0034] At the source data collection end, a light intensity illumination meter and a thermometer are used to collect light intensity and temperature data every 5 seconds within a fixed time T, and two sets of source data of light intensity and temperature changing with time are obtained respectively, and the collected source data are transmitted to the source data processing end;

[0035] At the source data processing end, outliers are removed and denoised for the collected source data, and the phase space of the obtained one-dimensional time series is reconstructed. The maximum Lyapunov exponents of the light intensity and temperature series are calculated using the small data method, which are Lyapunov1 and Lyapunov2, respectively. Lyapunov1 and Lyapunov2 are transmitted to the strategy selection end;

[0036] The interquartile range method is mainly used to eliminate outliers. For example, a set of data is 12, 5, 9, 18, 3, 21, 7. First, sort them to get: 3, 5, 7, 9, 12, 18, 21. Since the number of this set of data is an odd number, the first quartile Q1 = 5.5, the third quartile Q3 = 20.25, and the interquartile range IQR = Q3-Q1 = 14.75 are calculated by linear interpolation to determine the boundaries of outliers. The lower limit = Q1-1.5*IQR = -16.625, the upper limit = Q3+1.5*IQR = 42.375, and the values ​​less than the lower limit and greater than the upper limit are classified as outliers and eliminated;

[0037] When denoising the source data, we first need to determine the type of noise contained in the source data, including Gaussian noise, salt and pepper noise, and periodic noise, and then select the corresponding denoising method for different noises;

[0038] Collect the mean μ and standard deviation σ of the source data. If no less than 99.73% of the data points fall between (μ-3σ, μ+3σ), it indicates that the data has Gaussian noise. Use Gaussian filtering to denoise, because Gaussian filtering has its unique advantages in dealing with Gaussian noise, including excellent smoothing effect, flexible parameter adjustment ability, strong ability to filter noise of normal distribution, wide applicability and linear separability.

[0039] Set the reasonable range of data to [Imin, lmax], and regard the data below lmin and beyond lmax as outliers, and obtain the number of outliers as 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 ability, simple and efficient implementation, and good denoising ability when processing salt and pepper noise. Among them, n1 is the total number of data, and γ is the outlier ratio threshold;

[0040] Obtain the autocorrelation function R(w) of the data at different lag orders w. 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 it has the advantages of dynamic adaptability, no need for prior knowledge, efficient periodic noise suppression, stability and robustness when processing periodic noise. Q is the peak value.

[0041] CC is used to find the optimal embedding dimension m and time delay τ of the light intensity and temperature series, in preparation for the subsequent phase space reconstruction. The general method of obtaining m and τ is often to use separate methods to obtain them. For example, m can be obtained by the false nearest neighbor method and the Cao method, while τ can be obtained by the complex autocorrelation method, the autocorrelation method, and the mutual information method. Although both can be obtained, they need to be obtained by different methods, which is troublesome and time-consuming. The CC method can calculate m and τ at the same time, saving calculation time.

[0042] The steps of CC method to obtain m and τ are as follows:

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

[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, and find the value of τ that makes S2(m,τ) reach the global minimum, denoted 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, m is the optimal embedding dimension;

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

[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 point X(v) by the formula d0(n)=min||X(i)-X(v)||, where |||| represents the Euclidean distance and v≠i;

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

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

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

[0053] The maximum Lyapunov exponent calculated based on the light intensity data sequence is Lyapunov1, while the maximum Lyapunov exponent calculated based on 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 perturbation observation method and the conductivity increment method) is used to track the maximum output power of solar energy, which is good 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 at the same time, it indicates that the light intensity or temperature changes in time T are complex, and the LSTM-MPPT algorithm is used to track the maximum output power of solar energy. The effect is better because LSTM-MPPT can use its processing ability 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. Among them, a1 and a2 represent the thresholds 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 turn on the backup energy storage device based on k, generates a decision result, and transmits the decision result to the adaptive backup energy storage end;

[0055] |k|>nbd means that the output power of solar energy during this period is very small or very large. At this time, the energy storage device that comes with solar energy can no longer meet the demand, and the backup energy storage device needs to be turned on for charging and discharging operations;

[0056] When |k|>nbd and k<0, it means that the sunlight that solar energy contacts is affected by clouds or the surrounding environment, resulting in very little electricity generated at this time. The power of solar energy and energy storage equipment alone cannot meet the load needs. Therefore, the backup energy storage equipment should be turned on for discharge operation at this time;

[0057] When |k|>nbd and k>0, it means that the solar energy is fully exposed to sunlight at this time, generating excess electricity, 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, it is necessary to turn on the backup energy storage device for charging.

[0058] |k| < nbd indicates that the output power of solar energy during this period is within the normal output power. Whether the electric energy generated by solar energy is more or less at this time, it can be regulated by the energy storage device it carries, so there is no need to turn on the backup energy storage device;

[0059] Adaptive backup energy storage terminal. If the backup energy storage device is turned on, the movement of the backup energy storage device is determined according to the electric energy consumed by the solar energy and the power loss of the electric energy transmitted between the solar energy and the backup energy storage device. If the backup energy storage device is not turned on, it remains unchanged;

[0060] If the backup energy storage device is turned on, in order to save electric energy as much as possible, it is necessary to consider the electric energy loss W1 caused by the wire transmission between the solar energy and the backup energy storage device and the electric energy W consumed by moving the backup energy storage device to the solar energy. If W1 > W, then move the backup energy storage device to the location of the solar energy and then perform electric energy transmission. If W < W1, then choose to use the wire to transmit electric energy between the two;

[0061] The electric energy consumed by moving the backup energy storage device to the solar energy is related to its own power P and moving time t. The consumed electric energy is calculated according to the formula W = Pt / η, where η represents the energy conversion efficiency, and η ∈ (0, 1);

[0062] The electric energy loss during the transmission between the solar energy and the backup energy storage device can be obtained by 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;

[0063] In addition, if the wire is severely damaged or in extreme weather conditions such as high temperature and rainfall, choose to move the backup energy storage device to the solar energy for electric energy transmission, because choosing the wire in the above situations is extremely likely to cause electric leakage, which will pose a serious threat to the entire circuit;

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

[0065] Collect the historical repair times and repair time twe i of the wire, and calculate the average value mean and standard deviation std of the repair time. Divide the degree of wire damage according to the combination of mean and std. If twe i ∈ (0, mean - std), it belongs to minor damage. If twe i ∈ (mean - std, mean + std), it belongs to moderate damage. If twe i ∈ (mean + std, ∞), it belongs to severe damage;

[0066] Some of the data in the above formulas are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0067] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power supply management system based on distributed power grid energy storage equipment, characterized in that: include: The source data collection end collects the source data of light intensity and temperature changes over time within a fixed time period, and transmits the collected source data to the source data processing end; At the source data processing end, outliers are removed and denoised for the collected source data, and the phase space of the obtained one-dimensional time series is reconstructed. The maximum Lyapunov exponents of the light intensity and temperature series are calculated using the small data method, which are Lyapunov1 and Lyapunov2, respectively. Lyapunov1 and Lyapunov2 are transmitted to the strategy selection end; The strategy selection end determines whether to start the MPPT algorithm or the LSTM-MPPT algorithm to track the maximum output power of solar energy based on Lyapunov1 and Lyapunov2, and transmits the maximum output power sequence to the energy storage decision end; The energy storage decision end calculates the slope k between the two points before and after the maximum output power sequence, determines whether to turn on the backup energy storage device based on k, generates a decision result, and transmits the decision result to the adaptive backup energy storage end; At the adaptive backup energy storage end, if the backup energy storage device is turned on, the movement of the backup energy storage device is determined according to the electric energy consumed by the backup energy storage device when moving to the solar energy and the electric energy loss transmitted between the solar energy and the backup energy storage device. If the backup energy storage device is not turned on, it remains as it is.

2. A power supply management system based on distributed power grid energy storage equipment according to claim 1, characterized in that: The method for denoising the collected source data is: Obtain 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 has Gaussian noise, and Gaussian filtering is used to remove the noise. The reasonable range of data is determined to be [Imin, lmax]. Data below lmin and above lmax are considered as outliers. The number of outliers is m1. The outlier ratio is obtained 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 denoising, where n1 is the total number of data and γ is the outlier ratio threshold. Obtain the autocorrelation function R(w) of the data at different lag orders w. If there is a period q such that R(w)=R(w+q)=R(w+2q)=....=Q, it indicates the existence of periodic noise. Adaptive filtering is used for denoising, where Q is the peak value.

3. A power supply management system based on distributed power 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: According to the CC method, the optimal embedding dimension m and time delay τ of the light intensity and temperature data {x(n), n=1,2,...,N} are obtained; 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, find the nearest neighbor point X(v) by the formula d0(n)=min||X(n)-X(v)||, where |||| represents the Euclidean distance and v≠n; After s time steps, the reference point becomes X(n+s), and its nearest neighbor becomes X(v+s). The distance between them is calculated as 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 power grid energy storage equipment according to claim 1, characterized in that: The method for determining whether to start the MPPT algorithm or the LSTM-MPPT algorithm according to Lyapunov1 and Lyapunov2 is as follows: 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.

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

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

7. A power supply management system based on distributed power 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.

8. A power supply management system based on distributed power 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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