Power distribution method, device, computer equipment, storage medium and program product for photovoltaic energy storage system

Abstract

The present application relates to a power allocation method, apparatus, computer equipment, storage medium, and program product for a photovoltaic energy storage system, including: determining an initial filtering step size and filtering step size increment, obtaining the photovoltaic historical active power before a target period and the photovoltaic predicted active power after the target period based on the filtering step size; determining the current energy storage power for the target period based on the photovoltaic historical active power and the photovoltaic predicted active power; updating the filtering step size based on the filtering step size increment when the current energy storage power does not exceed a preset energy storage operation boundary condition, and re-obtaining the photovoltaic historical active power and the photovoltaic predicted active power based on the new filtering step size until the determined current energy storage power exceeds the energy storage operation boundary condition, terminating the cycle, and obtaining the energy storage target power for the target period. By combining photovoltaic historical power data with short-term predicted power data, this method can avoid the problem of lagging photovoltaic grid-connected target power curve.

Description

Technical Field

[0001] The present application relates to the field of new energy technologies, and in particular to a power distribution method, apparatus, computer equipment, computer-readable storage medium, and computer program product for a photovoltaic energy storage system. Background Art

[0002] In today's power system, vigorously developing new energy sources such as photovoltaics is an effective way to achieve sustainable energy development and the "dual carbon" goals. However, photovoltaic power generation is subject to weather factors and is characterized by intermittent, random, and uncertain characteristics, which can negatively impact the safe and stable operation of the power system. Therefore, smoothing photovoltaic power fluctuations to reduce the impact on the power grid is of great significance. Energy storage devices have the ability to transfer electricity in time and space and flexibly dispatch it. Deploying energy storage devices on the photovoltaic power field side is a feasible solution to address photovoltaic power fluctuations.

[0003] Regarding energy storage optimization scheduling strategies to smooth out PV fluctuations, existing power allocation methods primarily include first-order low-pass filtering, moving average filtering (MAF), and empirical mode decomposition (EMD). In practical engineering, future PV raw power data is unavailable in advance for the current period, so energy storage smoothing power can only be calculated based on historical PV data.

[0004] However, existing power allocation methods do not consider the target power lag problem caused by the use of historical data, which will significantly increase the pressure on the energy storage system during periods of rapid changes in photovoltaic power. Summary of the Invention

[0005] Based on this, it is necessary to provide a power allocation method, device, computer equipment, computer-readable storage medium and computer program product for a photovoltaic energy storage system to address the technical problem that the above-mentioned power allocation method does not take into account the target power lag caused by the use of historical data, which will significantly increase the pressure on the energy storage system during periods of rapid photovoltaic power changes.

[0006] In a first aspect, the present application provides a method for distributing power to a photovoltaic energy storage system. The method comprises:

[0007] Determine the initial filter step size and the filter step size increment for each iteration;

[0008] According to the filtering step size, the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period are obtained; the target period is the period for smoothing photovoltaic fluctuations;

[0009] Determining the current energy storage power during the target period based on the historical photovoltaic active power and the predicted photovoltaic active power;

[0010] In the case that the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the process returns to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the determined current energy storage power exceeds the energy storage operation boundary condition, the cycle is terminated, and the energy storage target power for the target period is obtained.

[0011] In one embodiment, obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size includes:

[0012] Determining, according to the filtering step length, a first number of photovoltaic historical active powers to be acquired and a second number of photovoltaic predicted active powers to be acquired; the sum of the first number and the second number is equal to the filtering step length;

[0013] A first number of photovoltaic active powers before the target period and closest to the target period is obtained as the photovoltaic historical active power; and a second number of photovoltaic active powers after the target period and closest to the target period is predicted as the photovoltaic predicted active power.

[0014] In one embodiment, determining the current energy storage power in the target period based on the historical photovoltaic active power and the predicted photovoltaic active power includes:

[0015] Determining a current value of the stabilized photovoltaic grid-connected power during the target period based on the photovoltaic historical active power and the photovoltaic predicted active power;

[0016] The photovoltaic original power of the target period is obtained, and the current energy storage power of the target period is obtained according to the current value of the smoothed photovoltaic grid-connected power of the target period and the photovoltaic original power.

[0017] In one embodiment, there are multiple photovoltaic historical active powers and multiple photovoltaic predicted active powers;

[0018] The determining, based on the historical photovoltaic active power and the predicted photovoltaic active power, a current value of the stabilized photovoltaic grid-connected power in the target period includes:

[0019] Summing each of the photovoltaic historical active powers and each of the photovoltaic predicted active powers to obtain a total power;

[0020] A ratio of the total power to the filtering step size is obtained as the current value of the smoothed photovoltaic grid-connected power.

[0021] In one embodiment, obtaining the current energy storage power of the target period according to the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power during the target period includes:

[0022] The current energy storage power of the target period is obtained by subtracting the current value of the smoothed photovoltaic grid-connected power from the original photovoltaic power.

[0023] In one embodiment, the energy storage operation boundary conditions include energy storage quantity constraint conditions and energy storage power constraint conditions;

[0024] The energy storage capacity constraint condition is that the energy storage capacity in the next period of the target period is within a first preset range;

[0025] The energy storage power constraint condition is that the energy storage power in the target time period is within a second preset range.

[0026] In a second aspect, the present application also provides a power distribution device for a photovoltaic energy storage system. The device comprises:

[0027] An initial determination module, used to determine the initial filtering step size and the filtering step size increment for each iteration;

[0028] A power acquisition module, configured to acquire, based on the filtering step size, the historical photovoltaic active power before a target period and the predicted photovoltaic active power after the target period; the target period being a period for smoothing photovoltaic fluctuations;

[0029] An energy storage determination module is used to determine the current energy storage power of the target period based on the historical photovoltaic active power and the predicted photovoltaic active power;

[0030] The target determination module is used to update the filtering step size according to the filtering step size increment to obtain a new filtering step size when the current energy storage power does not exceed the preset energy storage operation boundary condition, and return to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the current energy storage power is determined to exceed the energy storage operation boundary condition, terminate the loop, and obtain the energy storage target power for the target period.

[0031] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are performed:

[0032] Determine the initial filter step size and the filter step size increment for each iteration;

[0033] According to the filtering step size, the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period are obtained; the target period is the period for smoothing photovoltaic fluctuations;

[0034] Determining the current energy storage power during the target period based on the historical photovoltaic active power and the predicted photovoltaic active power;

[0035] In the case that the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the process returns to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the determined current energy storage power exceeds the energy storage operation boundary condition, the cycle is terminated, and the energy storage target power for the target period is obtained.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the following steps:

[0037] Determine the initial filter step size and the filter step size increment for each iteration;

[0038] According to the filtering step size, the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period are obtained; the target period is the period for smoothing photovoltaic fluctuations;

[0039] Determining the current energy storage power during the target period based on the historical photovoltaic active power and the predicted photovoltaic active power;

[0040] In the case that the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the process returns to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the determined current energy storage power exceeds the energy storage operation boundary condition, the cycle is terminated, and the energy storage target power for the target period is obtained.

[0041] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the following steps:

[0042] Determine the initial filter step size and the filter step size increment for each iteration;

[0043] According to the filtering step size, the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period are obtained; the target period is the period for smoothing photovoltaic fluctuations;

[0044] Determining the current energy storage power during the target period based on the historical photovoltaic active power and the predicted photovoltaic active power;

[0045] In the case that the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the process returns to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the determined current energy storage power exceeds the energy storage operation boundary condition, the cycle is terminated, and the energy storage target power for the target period is obtained.

[0046] The power allocation method, apparatus, computer equipment, storage medium and computer program product of the photovoltaic energy storage system described above are based on a sliding average filtering method to determine an initial filtering step size and a filtering step size increment for each iteration; based on the filtering step size, the historical photovoltaic active power before the target period for smoothing photovoltaic fluctuations and the predicted photovoltaic active power after the target period are obtained; based on the historical photovoltaic active power and the predicted photovoltaic active power, the current energy storage power of the target period is determined; if the current energy storage power does not exceed a preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the process returns to the step of obtaining the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period according to the filtering step size, until the determined current energy storage power exceeds the energy storage operation boundary condition, the loop ends and the energy storage target power of the target period is obtained. By combining historical photovoltaic power data with short-term predicted power data, this method can avoid the problem of lagging photovoltaic grid-connected target power curve. Furthermore, by deeply considering the random characteristics of photovoltaics, by judging in real time whether the energy storage power exceeds the energy storage operation boundary, the filtering step size is iteratively adjusted in real time. This can maximize the peak-shaving potential of energy storage for photovoltaics with different fluctuation conditions, reduce the pressure on the energy storage system, and achieve the effect of adaptive adjustment according to different fluctuation conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 1 is a flow chart of a power distribution method for a photovoltaic energy storage system according to an embodiment;

[0048] Figure 2 A topological diagram of a photovoltaic energy storage system in one embodiment;

[0049] Figure 3 Schematic diagram of a flow chart of a power distribution method for a photovoltaic energy storage system in another embodiment;

[0050] Figure 4 Schematic diagram of photovoltaic grid-connected power curves before and after leveling in one embodiment;

[0051] Figure 5 is a power curve diagram of an energy storage system in one embodiment;

[0052] Figure 6 is a structural block diagram of a power distribution device of a photovoltaic energy storage system in one embodiment;

[0053] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0055] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, such that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0056] Among related technologies, existing power allocation methods for optimizing energy storage scheduling strategies to smooth out PV fluctuations primarily include first-order low-pass filtering, moving average filtering (MAF), and empirical mode decomposition (EMD). In practical engineering, future raw PV power data cannot be obtained in advance for the current period, and energy storage smoothing power can only be calculated based on historical PV data. However, existing power allocation methods fail to account for the target power lag caused by using historical data, significantly increasing the pressure on the energy storage system during periods of rapid PV power fluctuations. Furthermore, most power allocation methods fail to fully consider the random nature of PV power, and the smoothing effect cannot be adaptively adjusted to varying fluctuations.

[0057] Based on this, this application proposes an improved sliding average filtering method for power allocation of photovoltaic energy storage systems. By combining photovoltaic historical power data and short-term predicted power data, it avoids the target power lag problem caused by using only historical power data, and judges in real time whether it exceeds the energy storage operation boundary, it iteratively adjusts the filtering step size, and fully taps the peak-shaving potential of energy storage.

[0058] In one embodiment, Figure 1 As shown, a power distribution method for a photovoltaic energy storage system is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented as an independent server or a server cluster composed of multiple servers. In this embodiment, the method includes the following steps:

[0059] Step S110 , determining an initial filtering step size and a filtering step size increment for each iteration.

[0060] Specifically, this application uses a sliding average filter method to allocate power to a photovoltaic energy storage system. The filter step size refers to the number of power data points involved in calculating the average value during each sliding average filter process. The filter step size increment represents the increment in the filter step size during each iteration. This method is simple to use, requires minimal computation, and can quickly process real-time data.

[0061] In practical applications, in addition to determining the initial filtering step size and the filtering step size increment for each iteration, an upper limit on the number of iterations can also be set to save time and resources and improve the power allocation efficiency of the photovoltaic energy storage system.

[0062] Step S120 , obtaining the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period according to the filtering step size; the target period is the period for smoothing photovoltaic fluctuations.

[0063] In a specific implementation, the filter step size represents the number of power data points involved in each average calculation. Therefore, a first number of historical PV active power values ​​and a second number of predicted PV active power values ​​are determined based on the filter step size. The first number of PV active power values ​​before the target period is obtained as the historical PV active power, and the second number of PV active power values ​​after the target period is predicted as the predicted PV active power.

[0064] Step S130 : determining the current energy storage power in the target period based on the historical photovoltaic active power and the predicted photovoltaic active power.

[0065] refer to Figure 2 , is the structural topology diagram of the photovoltaic energy storage system. When the loss in the system is negligible, its power flow has the following relationship:

[0066]

[0067] in, express PV grid-connected power in the period, express The original photovoltaic power of the period, express Energy storage power during the period.

[0068] Based on the above relationship, determining the current energy storage power during the target period requires considering both the PV grid-connected power and the PV raw power during the target period. To ensure the safe operation of the power system, the PV grid-connected power is smoothed. This PV power smoothing is achieved through the design of the energy storage power. Therefore, the current energy storage power during the target period is determined based on the smoothed PV grid-connected power, using a sliding average filter. Therefore, the historical PV active power and the predicted PV active power, obtained based on the filter step size, are filtered to obtain the current value of the smoothed PV grid-connected power. The PV raw power during the target period is then obtained. The current energy storage power during the target period is determined based on the smoothed current value of the PV grid-connected power and the PV raw power.

[0069] In step S140, when the current energy storage power does not exceed the preset energy storage operation boundary conditions, the filter step size is updated according to the filter step size increment to obtain a new filter step size, and the process returns to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filter step size, until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop ends, and the energy storage target power for the target period is obtained.

[0070] In a specific implementation, after obtaining the current energy storage power for the target period, it is further determined whether the current energy storage power exceeds the preset energy storage operating boundary conditions. If so, the current energy storage power is output as the target energy storage power for the target period to smooth out photovoltaic fluctuations. If not, the filter step size is updated based on the filter step size increment. That is, the filter step size increment is added to the current filter step size to obtain a new filter step size. The process then returns to step S120. Using the new filter step size, the new historical photovoltaic active power before the target period and the new predicted photovoltaic active power after the target period are again obtained. Based on the new historical photovoltaic active power and the new predicted photovoltaic active power, the new current energy storage power for the target period is determined. It is then again determined whether the new current energy storage power exceeds the preset energy storage operating boundary conditions. If so, the filter step size increment is added to the new filter step size again, and the process returns to step S120 for calculation. This process continues until the loop termination condition is met, i.e., the most recently obtained current energy storage power exceeds the energy storage operating boundary conditions. The loop then terminates, and the most recently obtained current energy storage power is output as the target energy storage power for the target period.

[0071] It is understandable that if an upper limit on the number of iterations is set before the loop starts, the loop end condition includes not only the most recently obtained current energy storage power exceeding the energy storage operation boundary condition, but also the number of iterations reaching the preset upper limit on the number of iterations.

[0072] In the power allocation method for the photovoltaic energy storage system described above, the initial filtering step size and the filtering step size increment for each iteration are determined based on the sliding average filtering method; based on the filtering step size, the historical photovoltaic active power before the target period for smoothing photovoltaic fluctuations and the predicted photovoltaic active power after the target period are obtained; based on the historical photovoltaic active power and the predicted photovoltaic active power, the current energy storage power of the target period is determined; when the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the step of obtaining the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period according to the filtering step size is returned until the determined current energy storage power exceeds the energy storage operation boundary condition, the loop is terminated, and the energy storage target power of the target period is obtained. By combining historical photovoltaic power data with short-term predicted power data, this method can avoid the problem of lagging photovoltaic grid-connected target power curve. Furthermore, by deeply considering the random characteristics of photovoltaics, by judging in real time whether the energy storage power exceeds the energy storage operation boundary, the filtering step size is iteratively adjusted in real time. This can maximize the peak-shaving potential of energy storage for photovoltaics with different fluctuation conditions, reduce the pressure on the energy storage system, and achieve the effect of adaptive adjustment according to different fluctuation conditions.

[0073] In an exemplary embodiment, step S120 acquires the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period according to the filtering step size, specifically including:

[0074] Step S121: determining a first number of photovoltaic historical active powers to be acquired and a second number of photovoltaic predicted active powers to be acquired according to a filtering step length; the sum of the first number and the second number is equal to the filtering step length;

[0075] Step S122: obtaining a first number of photovoltaic active powers before the target period and closest to the target period as the photovoltaic historical active power; and predicting a second number of photovoltaic active powers after the target period and closest to the target period as the photovoltaic predicted active power.

[0076] Specifically, the filtering step size represents the number of power data points involved in each average calculation, which can be understood as the window for smoothing PV fluctuations. The historical PV active power and the predicted PV active power are used to calculate the PV grid-connected power within the smoothing window. Therefore, the sum of the first number of historical PV active power and the second number of predicted PV active power should be equal to the filtering step size. The first number of PV active power points before and closest to the target period is then obtained as the historical PV active power; and the second number of PV active power points after and closest to the target period is predicted as the predicted PV active power.

[0077] More specifically, in one embodiment, the first number and the second number may be equal, that is, both are equal to half of the filtering step size.

[0078] In this embodiment, by obtaining photovoltaic historical power data and short-term predicted power data, so that both can be used together as the basis for calculating the energy storage smoothing power, and by making the first number of photovoltaic historical active power and the second number of photovoltaic predicted active power equal, the balance between historical data and predicted data can be further achieved, thereby achieving a better photovoltaic smoothing effect.

[0079] In an exemplary embodiment, step S130 determines the current energy storage power in the target period based on the historical photovoltaic active power and the predicted photovoltaic active power, specifically including:

[0080] Step S131, determining the current value of the photovoltaic grid-connected power after stabilization in the target period based on the photovoltaic historical active power and the photovoltaic predicted active power;

[0081] Step S132: Obtain the original photovoltaic power in the target period, and obtain the current energy storage power in the target period according to the current value of the smoothed photovoltaic grid-connected power and the original photovoltaic power in the target period.

[0082] In specific implementations, the current value of the stabilized PV grid-connected power for the target period is determined based on the historical and predicted PV active power. This is done by averaging the historical and predicted PV active power using a sliding average filter to obtain the current value of the stabilized PV grid-connected power for the target period. After obtaining the raw PV power for the target period, the current energy storage power for the target period is calculated based on the current value of the stabilized PV grid-connected power and the raw PV power.

[0083] Furthermore, in an exemplary embodiment, there are multiple photovoltaic historical active powers and photovoltaic predicted active powers; in the above-mentioned step S131, based on the photovoltaic historical active power and the photovoltaic predicted active power, the current value of the photovoltaic grid-connected power after smoothing in the target period is determined, including: summing each photovoltaic historical active power and each photovoltaic predicted active power to obtain the total power; obtaining the ratio of the total power to the filtering step size as the current value of the photovoltaic grid-connected power after smoothing.

[0084] In the specific implementation, since the total number of PV historical active power and PV predicted active power is equal to the filter step size, when averaging each PV historical active power and each PV predicted active power through the sliding average filter method, the historical active power of each PV and each PV predicted active power are first summed to obtain the total power, and then the total power is divided by the filter step size to obtain the current value of the smoothed PV grid-connected power.

[0085] For example, if the filtering step is N, the first number of photovoltaic historical active power and the second number of photovoltaic predicted active power are equal, both N / 2, and the target period is ,but The current value of the photovoltaic grid-connected power after smoothing in the period can be expressed by the formula:

[0086]

[0087] in, express The current value of the photovoltaic grid-connected power after smoothing obtained at the Kth iteration of the period, express The historical photovoltaic active power of the time period, express The predicted PV active power for each time period.

[0088] In this embodiment, by combining the historical photovoltaic power data and the short-term predicted power data as a basis for calculating the energy storage smoothing power, the problem of lagging photovoltaic grid-connected target power curve can be avoided.

[0089] Furthermore, in an exemplary embodiment, in the above step S132, the current energy storage power of the target period is obtained based on the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power of the target period, including: taking the difference between the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power to obtain the current energy storage power of the target period.

[0090] Specifically, based on Figure 2 The relationship between energy storage power, PV grid-connected power, and raw PV power can be determined as follows: PV grid-connected power = raw PV power + energy storage power. Therefore, when determining the current energy storage power for the target period based on the current value of the smoothed PV grid-connected power and the raw PV power, the current energy storage power for the target period can be obtained by taking the difference between the current value of the smoothed PV grid-connected power and the raw PV power.

[0091] In this embodiment, the current energy storage power during the target period is obtained by taking the difference between the current value of the smoothed photovoltaic grid-connected power and the original photovoltaic power, so as to facilitate subsequent judgment and adjustment of the current energy storage power, thereby maximizing the peak-shaving potential of energy storage and reducing the pressure on the energy storage system.

[0092] In an exemplary embodiment, the energy storage operation boundary conditions in the above step S140 include an energy storage power constraint condition and an energy storage power constraint condition; wherein, the energy storage power constraint condition is that the energy storage power in the next period of the target period is within a first preset range; and the energy storage power constraint condition is that the energy storage power in the target period is within a second preset range.

[0093] Specifically, the energy storage capacity constraint, that is, the energy storage SOC (State of Charge) condition, can be expressed by the following relationship:

[0094]

[0095] in, express Energy storage capacity during the period (energy storage SOC value), and Respectively represent the minimum and maximum values ​​of energy storage capacity, that is, the first preset range is [ , ]. E represents the maximum capacity of energy storage, and T represents the scheduling period.

[0096] The energy storage power constraint can be expressed as follows:

[0097]

[0098] in, and Respectively represent the minimum and maximum values ​​of the energy storage power, that is, the second preset range is [ , ].

[0099] In this embodiment, the energy storage operation boundary conditions are set from the two dimensions of energy storage quantity and energy storage power, which can ensure the rationality of the energy storage target power in the final target period, more effectively achieve the stabilization of photovoltaic power generation, and improve the reliability and economy of the entire power system.

[0100] In one embodiment, in order to facilitate those skilled in the art to understand the embodiments of the present application, the following will be described with reference to specific examples in conjunction with the accompanying drawings. Figure 3 , shows a specific flow chart of a method for implementing power allocation for a photovoltaic energy storage system based on a sliding filter averaging method. Assuming that during period t, photovoltaic fluctuations are smoothed by energy storage, the specific process is as follows:

[0101] (1) Set the initial filter step size N, the upper limit of the number of iterations M, and the filter step size increment L for each iteration, and record the current number of iterations K = 0.

[0102] (2) Calculate the smoothed photovoltaic grid-connected power at the Kth iteration in period t.

[0103] Specifically, the current value of the stabilized photovoltaic grid-connected power is calculated by combining the historical photovoltaic active power and the short-term photovoltaic predicted active power to avoid the target power lag caused by relying solely on historical data. The specific calculation formula is as follows:

[0104]

[0105] in, express The smoothed photovoltaic grid-connected power obtained at the Kth iteration of the period is, express The historical photovoltaic active power of the time period, express The predicted PV active power for each time period.

[0106] (3) Calculate the energy storage power at the Kth iteration in period t.

[0107] Specifically, the smoothed photovoltaic grid-connected power and the K-th iteration in period t can be expressed as follows: The energy storage power is obtained by subtracting the original photovoltaic power of the time period, which can be expressed as:

[0108]

[0109] in, represents the energy storage power at the Kth iteration in period t, Represents the photovoltaic raw power during period t.

[0110] (4) If the upper limit of the number of iterations M = 0, that is, only one calculation is performed, the currently calculated energy storage power can be directly stored as the energy storage target power, that is, .

[0111] (5) If the upper limit of the number of iterations M≠0, then the energy storage power obtained in this iteration is Make a judgment to determine whether it exceeds the energy storage operation boundary conditions.

[0112] If the energy storage power does not exceed the energy storage operation boundary conditions, K = K + 1 is executed to update the number of iterations and determine whether the updated number of iterations exceeds the upper limit of the number of iterations. If it exceeds the upper limit of the number of iterations, the current latest energy storage power is output as the energy storage target power. If it does not exceed the upper limit of the number of iterations, the filter step size is updated, that is, N = N + L is executed, and the process returns to step (2) to recalculate the energy storage power.

[0113] If the energy storage power exceeds the energy storage operation boundary condition, the energy storage power calculated in the previous iteration (i.e., K-1) is output as the energy storage target power.

[0114] In this method, the power allocation of the photovoltaic energy storage system is performed based on the sliding average filtering method, which is simple to operate, has a small amount of calculation, and can quickly process real-time data. Furthermore, on the basis of the sliding average filtering method, by combining the photovoltaic historical power data and the short-term predicted power data, the problem of the lag of the photovoltaic grid-connected target power curve can be avoided. Furthermore, this method deeply considers the characteristics of photovoltaic randomness, and by judging in real time whether the energy storage power exceeds the energy storage operation boundary, the filtering step size is iteratively adjusted in real time, which can maximize the peak-shaving potential of energy storage for photovoltaics with different fluctuation conditions, and achieve the effect of adaptive adjustment according to different fluctuation conditions.

[0115] Furthermore, the effectiveness of this method has been verified through testing. Taking the operating data of a typical day of a photovoltaic power station as an example, it includes 96-point output data and photovoltaic short-term forecast data, and energy storage is configured according to 10% of the photovoltaic installed capacity. The photovoltaic grid-connected power curves before and after the smoothing process obtained by the method of this application are as follows: Figure 4 As shown in the figure, compared with the traditional sliding mean filtering method, the improved sliding mean filtering method of this application significantly improves the target power lag problem of photovoltaic grid-connected power. In addition, due to the real-time iterative adjustment of the filtering step size, the peak-shaving potential of energy storage is deeply explored, and compared with the traditional sliding mean filtering method, it can more effectively smooth out the fluctuations of photovoltaic output.

[0116] Also, see Figure 5, which shows a power curve of an energy storage system according to an embodiment. It can be seen that the energy storage system using the improved sliding mean filtering method of this application achieves significantly lower peak power and peak load than the traditional sliding mean filtering method. This is due to the significant improvement in the target power lag problem achieved by the improved sliding mean filtering method, allowing the entire output of the energy storage system to be used to smooth out photovoltaic fluctuations.

[0117] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0118] Based on the same inventive concept, embodiments of the present application also provide a power distribution device for a photovoltaic energy storage system, which is used to implement the aforementioned power distribution method for a photovoltaic energy storage system. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the following embodiments of the power distribution device for one or more photovoltaic energy storage systems can be found in the aforementioned limitations of the power distribution method for photovoltaic energy storage systems, and will not be further elaborated here.

[0119] In one embodiment, Figure 6 As shown, a power distribution device for a photovoltaic energy storage system is provided, comprising:

[0120] An initial determination module 610 is used to determine an initial filtering step size and a filtering step size increment for each iteration;

[0121] The power acquisition module 620 is used to obtain the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period according to the filtering step size; the target period is the period for smoothing photovoltaic fluctuations;

[0122] The energy storage determination module 630 is used to determine the current energy storage power in the target period based on the historical photovoltaic active power and the predicted photovoltaic active power;

[0123] The target determination module 640 is used to update the filtering step size according to the filtering step size increment when the current energy storage power does not exceed the preset energy storage operation boundary conditions, obtain a new filtering step size, and return to the step of obtaining the historical photovoltaic active power before the target period and the predicted photovoltaic active power after the target period according to the filtering step size, until the current energy storage power is determined to exceed the energy storage operation boundary conditions, end the loop, and obtain the energy storage target power for the target period.

[0124] In one embodiment, the power acquisition module 620 is further used to determine a first number of photovoltaic historical active powers to be acquired and a second number of photovoltaic predicted active powers to be acquired based on a filtering step size; the sum of the first number and the second number is equal to the filtering step size; the first number of photovoltaic active powers before the target period and closest to the target period is acquired as the photovoltaic historical active power; and the second number of photovoltaic active powers after the target period and closest to the target period is predicted as the photovoltaic predicted active power.

[0125] In one embodiment, the energy storage determination module 630 is further used to determine the current value of the smoothed photovoltaic grid-connected power in the target period based on the historical photovoltaic active power and the predicted photovoltaic active power; obtain the photovoltaic raw power in the target period, and obtain the current energy storage power in the target period based on the current value of the smoothed photovoltaic grid-connected power and the photovoltaic raw power in the target period.

[0126] In one embodiment, there are multiple photovoltaic historical active powers and photovoltaic predicted active powers; the energy storage determination module 630 is also used to sum each photovoltaic historical active power and each photovoltaic predicted active power to obtain the total power; obtain the ratio of the total power to the filtering step size as the current value of the smoothed photovoltaic grid-connected power.

[0127] In one embodiment, the energy storage determination module 630 is further configured to obtain the current energy storage power in the target period by subtracting the current value of the smoothed photovoltaic grid-connected power from the original photovoltaic power.

[0128] In one embodiment, the energy storage operation boundary conditions include energy storage power constraint conditions and energy storage power constraint conditions; the energy storage power constraint condition is that the energy storage power in the next period of the target period is within a first preset range; the energy storage power constraint condition is that the energy storage power in the target period is within a second preset range.

[0129] Each module in the power distribution device of the photovoltaic energy storage system described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0130] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal via wired or wireless communication. Wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (near-field communication), or other technologies. When executed by the processor, the computer program implements a power distribution method for a photovoltaic energy storage system. The display screen of the computer device can be a liquid crystal display or an electronic ink display. The input device of the computer device can be a touch layer covering the display screen, keys, trackball, or touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse.

[0131] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0132] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0134] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0135] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0136] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0137] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A power distribution method for a photovoltaic energy storage system, characterized in that: The method comprises: Determine the initial filter step size and the filter step size increment for each iteration; Determining, according to the filtering step length, a first number of photovoltaic historical active powers to be acquired and a second number of photovoltaic predicted active powers to be acquired; the sum of the first number and the second number is equal to the filtering step length; Acquire a first number of photovoltaic active powers that are closest to a target period before the target period as the photovoltaic historical active power; and predict a second number of photovoltaic active powers that are closest to the target period after the target period as the photovoltaic predicted active power; the target period is a period for smoothing photovoltaic fluctuations; and there are multiple photovoltaic historical active powers and multiple photovoltaic predicted active powers; Summing each of the photovoltaic historical active powers and each of the photovoltaic predicted active powers to obtain a total power; obtaining a ratio of the total power to the filtering step size to obtain a current value of the smoothed photovoltaic grid-connected power in the target period; Obtaining the photovoltaic original power of the target period, and obtaining the current energy storage power of the target period according to the current value of the photovoltaic grid-connected power after smoothing in the target period and the photovoltaic original power; In the case that the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step size is updated according to the filtering step size increment to obtain a new filtering step size, and the process returns to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the determined current energy storage power exceeds the energy storage operation boundary condition, the cycle is terminated, and the energy storage target power for the target period is obtained.

2. The method according to claim 1, characterized in that The step of obtaining the current energy storage power of the target period according to the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power during the target period includes: The current energy storage power of the target period is obtained by subtracting the current value of the smoothed photovoltaic grid-connected power from the original photovoltaic power.

3. The method according to claim 1, characterized in that The energy storage operation boundary conditions include energy storage quantity constraint conditions and energy storage power constraint conditions; The energy storage capacity constraint condition is that the energy storage capacity in the next period of the target period is within a first preset range; The energy storage power constraint condition is that the energy storage power in the target time period is within a second preset range.

4. The method according to claim 1, wherein The first number is equal to the second number.

5. A power distribution device for a photovoltaic energy storage system, characterized in that: The device comprises: An initial determination module, used to determine the initial filtering step size and the filtering step size increment for each iteration; A power acquisition module is configured to determine, based on the filtering step size, a first number of photovoltaic historical active powers to be acquired and a second number of photovoltaic predicted active powers to be acquired; acquire a first number of photovoltaic active powers before a target period and closest to the target period as the photovoltaic historical active power; and predict a second number of photovoltaic active powers after the target period and closest to the target period as the photovoltaic predicted active power; the sum of the first number and the second number is equal to the filtering step size; the target period is a period for smoothing photovoltaic fluctuations; and there are multiple photovoltaic historical active powers and multiple photovoltaic predicted active powers. The energy storage determination module is configured to sum each of the photovoltaic historical active powers and each of the photovoltaic predicted active powers to obtain a total power; obtain a ratio of the total power to the filtering step size to obtain a current value of the photovoltaic grid-connected power after smoothing during the target period; obtain the photovoltaic raw power during the target period, and obtain the current energy storage power during the target period based on the current value of the photovoltaic grid-connected power after smoothing during the target period and the photovoltaic raw power; The target determination module is used to update the filtering step size according to the filtering step size increment to obtain a new filtering step size when the current energy storage power does not exceed the preset energy storage operation boundary condition, and return to the step of obtaining the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step size, until the current energy storage power is determined to exceed the energy storage operation boundary condition, terminate the loop, and obtain the energy storage target power for the target period.

6. The device according to claim 5, characterized in that The energy storage determination module is further configured to obtain the current energy storage power of the target period by subtracting the current value of the stabilized photovoltaic grid-connected power from the original photovoltaic power.

7. The device according to claim 5, characterized in that The energy storage operation boundary conditions include energy storage power constraint conditions and energy storage power constraint conditions; the energy storage power constraint condition is that the energy storage power in the next period of the target period is within a first preset range; the energy storage power constraint condition is that the energy storage power in the target period is within a second preset range.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the power distribution method for the photovoltaic energy storage system according to any one of claims 1 to 4 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power distribution method for a photovoltaic energy storage system according to any one of claims 1 to 4 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the power distribution method for the photovoltaic energy storage system according to any one of claims 1 to 4 are implemented.

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