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

Abstract

The invention relates to a power distribution method and device of a photovoltaic energy storage system, computer equipment, a storage medium and a program product, and the method comprises the steps: determining an initial filtering step length and a filtering step length increment, and obtaining photovoltaic historical active power before a target time period and photovoltaic predicted active power after the target time period according to the filtering step length; determining the current energy storage power of the target time period based on the photovoltaic historical active power and the photovoltaic predicted active power; and under the condition that the current energy storage power does not exceed the preset energy storage operation boundary condition, the filtering step length is updated according to the filtering step length increment, the photovoltaic historical active power and the photovoltaic predicted active power are obtained again according to the new filtering step length, and circulation is ended until the determined current energy storage power exceeds the energy storage operation boundary condition. And obtaining the energy storage target power of the target time period. According to the method, the problem of lag of a photovoltaic grid-connected target power curve can be avoided by combining the photovoltaic historical power data and the short-term prediction power data.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a power distribution method, device, 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" goal. However, photovoltaic power generation is constrained by weather factors and has the characteristics of intermittent, random and uncertain, which will have an adverse impact on the safe and stable operation of the power system. Therefore, it is of great significance to smooth out the fluctuations in photovoltaic power to reduce the impact on the power grid. Energy storage equipment has the ability to transfer and flexibly dispatch electric energy in time and space. Configuring energy storage equipment on the photovoltaic field side is a feasible solution to photovoltaic fluctuations.

[0003] Regarding the energy storage optimization scheduling strategy for smoothing photovoltaic fluctuations, the existing power allocation methods mainly include the first-order low-pass filtering method, the moving average filtering (MAF) method and the empirical mode decomposition (EMD) method. In actual engineering, the future photovoltaic raw power data cannot be obtained in advance during the current period, and the energy storage smoothing power can only be calculated based on the photovoltaic historical data.

[0004] However, the existing power allocation method does 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 power distribution method for 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 length, the photovoltaic historical active power before the target period and the photovoltaic predicted 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 of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power;

[0010] In the case that 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 step of obtaining the photovoltaic historical active power before the target time period and the photovoltaic predicted active power after the target time period according to the filter step size is returned until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target time 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] Determine, 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 of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power includes:

[0015] Determining a current value of the photovoltaic grid-connected power after smoothing in 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 photovoltaic grid-connected power after smoothing in 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 step of determining the current value of the photovoltaic grid-connected power after smoothing in the target period based on the photovoltaic historical active power and the photovoltaic predicted active power includes:

[0019] The historical active power of each photovoltaic array is summed with the predicted active power of each photovoltaic array to obtain the total power;

[0020] The ratio of the total power to the filtering step length 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 of the target period and the photovoltaic original power includes:

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

[0023] In one of the embodiments, 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 an initial filtering step size and a filtering step size increment for each iteration;

[0028] A power acquisition module, used to acquire the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step length; the target period is a period for smoothing photovoltaic fluctuations;

[0029] An energy storage determination module, configured to determine the current energy storage power of the target period based on the photovoltaic historical active power and the photovoltaic predicted 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 conditions, 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 conditions, the cycle is terminated, and the energy storage target power of the target period is obtained.

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

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

[0033] According to the filtering step length, the photovoltaic historical active power before the target period and the photovoltaic predicted 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 of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power;

[0035] In the case that 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 step of obtaining the photovoltaic historical active power before the target time period and the photovoltaic predicted active power after the target time period according to the filter step size is returned until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target time period is obtained.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

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

[0038] According to the filtering step length, the photovoltaic historical active power before the target period and the photovoltaic predicted 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 of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power;

[0040] In the case that 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 step of obtaining the photovoltaic historical active power before the target time period and the photovoltaic predicted active power after the target time period according to the filter step size is returned until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target time 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, and when the computer program is executed by a processor, the following steps are implemented:

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

[0043] According to the filtering step length, the photovoltaic historical active power before the target period and the photovoltaic predicted 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 of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power;

[0045] In the case that 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 step of obtaining the photovoltaic historical active power before the target time period and the photovoltaic predicted active power after the target time period according to the filter step size is returned until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target time period is obtained.

[0046] The power allocation method, device, computer equipment, storage medium and computer program product of the photovoltaic energy storage system are based on the sliding average filtering method to determine the initial filtering step size and the filtering step size increment of each iteration; according to the filtering step size, the photovoltaic historical active power before the target period for smoothing photovoltaic fluctuations and the photovoltaic predicted active power after the target period are obtained; based on the photovoltaic historical active power and the photovoltaic predicted 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 conditions, 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 photovoltaic historical active power before the target period and the photovoltaic predicted 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 conditions, the cycle is terminated, and the energy storage target power of the target period is obtained. This method can avoid the problem of lagging photovoltaic grid-connected target power curve by combining photovoltaic historical power data and short-term predicted power data; further, by deeply considering the characteristics of photovoltaic randomness, by real-time judgment of whether the energy storage power exceeds the energy storage operation boundary, the filter step size is iteratively adjusted in real time, so that photovoltaics with different fluctuation conditions can maximize the peak-shaving potential of energy storage, 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] FIG1 is a schematic flow chart of a power distribution method for a photovoltaic energy storage system in one embodiment;

[0048] FIG2 is a topological diagram of a photovoltaic energy storage system in one embodiment;

[0049] FIG3 is a schematic flow chart of a power distribution method for a photovoltaic energy storage system in another embodiment;

[0050] FIG4 is a schematic diagram of a photovoltaic grid-connected power curve before and after stabilization in one embodiment;

[0051] FIG5 is a power curve diagram of an energy storage system in one embodiment;

[0052] FIG6 is a structural block diagram of a power distribution device of a photovoltaic energy storage system in one embodiment;

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

[0054] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0056] In the related technologies, regarding the energy storage optimization scheduling strategy for smoothing photovoltaic fluctuations, the existing power allocation methods mainly include the first-order low-pass filtering method, the moving average filtering (MAF) method and the empirical mode decomposition (EMD) method. In actual engineering, the future photovoltaic raw power data cannot be obtained in advance during the current period, and the energy storage smoothing power can only be calculated based on the photovoltaic historical data. However, the existing power allocation method does 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 when the photovoltaic power changes rapidly. In addition, most power allocation methods do not deeply consider the characteristics of photovoltaic randomness, and the smoothing effect cannot be adaptively adjusted according to different fluctuation conditions.

[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, iteratively adjusts the filtering step size, and fully taps the peak-shaving potential of energy storage.

[0058] In one embodiment, as shown in FIG1 , a power distribution method for a photovoltaic energy storage system is provided. This embodiment is illustrated by applying the method to a terminal. 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, tablet computers, 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 consisting 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, the present application performs power allocation of a photovoltaic energy storage system based on a sliding average filtering method, wherein the filtering step size refers to the number of power data involved in calculating the average value each time when performing a sliding average filtering process. The filtering step size increment represents the filtering step size increased each time during the cycle. The power allocation of a photovoltaic energy storage system based on a sliding average filtering method is simple to operate, has a small amount of calculation, and can quickly process real-time data.

[0061] In practical applications, in addition to determining the initial filter step size and the filter 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, according to the filtering step length, the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period are obtained; the target period is the period for smoothing photovoltaic fluctuations.

[0063] In a specific implementation, the filter step size indicates the number of power data involved in calculating the average value each time. Therefore, the first number of photovoltaic historical active powers and the second number of photovoltaic predicted active powers need to be determined based on the filter step size. Then, the first number of photovoltaic active powers before the target period is obtained as the photovoltaic historical active power, and the second number of photovoltaic active powers after the target period is predicted as the photovoltaic predicted 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, which is a topological diagram of the photovoltaic energy storage system. When the loss in the system is ignored, the power flow has the following relationship:

[0066]

[0067] in, express The photovoltaic grid-connected power in each period, express The original photovoltaic power of the period, express The energy storage power of the time period.

[0068] Based on the above relationship, it can be known that the current energy storage power in the target period needs to be determined based on the photovoltaic grid-connected power and the photovoltaic original power in the target period. In order to ensure the safe operation of the power system, the photovoltaic grid-connected power is smoothed, and the smoothing of photovoltaic power is specifically achieved through the design of energy storage power. Therefore, the photovoltaic grid-connected power after smoothing is used to determine the current energy storage power in the target period, and the smoothing method used is the sliding average filtering method. Therefore, the photovoltaic historical active power and photovoltaic predicted active power obtained based on the filtering step size can be filtered to obtain the current value of the smoothed photovoltaic grid-connected power, and then the photovoltaic original power in the target period is obtained. According to the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power in the target period, the current energy storage power in the target period is determined.

[0069] 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 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 is returned until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target period is obtained.

[0070] In a specific implementation, after obtaining the current energy storage power of the target period, it is further determined whether the current energy storage power exceeds the preset energy storage operation boundary conditions. If exceeded, the current energy storage power is output as the energy storage target power of the target period to smooth out photovoltaic fluctuations. If not exceeded, the filter step length is updated according to the filter step increment, that is, the current filter step length is added to the filter step increment to obtain a new filter step length, and return to step S120, and the new filter step length is used to obtain the new photovoltaic historical active power before the target period and the new photovoltaic predicted active power after the target period. Based on the new photovoltaic historical active power and the new photovoltaic predicted active power, the new current energy storage power of the target period is determined, and it is determined again whether the new current energy storage power exceeds the preset energy storage operation boundary conditions. If exceeded, the new filter step length is added to the filter step increment again, and then returned to step S120 for calculation, and so on, until the loop end condition is reached, that is, the current energy storage power obtained the most recently exceeds the energy storage operation boundary conditions, the loop ends, and the current energy storage power obtained the most recently is output as the energy storage target power of the target period.

[0071] It can be understood that if an upper limit on the number of iterations is set before the start of the loop, the loop end condition includes not only the current energy storage power obtained most recently 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 of the photovoltaic energy storage system mentioned above, based on the sliding average filtering method, the initial filtering step size and the filtering step size increment of each iteration are determined; according to the filtering step size, the photovoltaic historical active power before the target period for smoothing photovoltaic fluctuations and the photovoltaic predicted active power after the target period are obtained; based on the photovoltaic historical active power and the photovoltaic predicted 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 conditions, 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 photovoltaic historical active power before the target period and the photovoltaic predicted 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 conditions, the cycle is terminated, and the energy storage target power of the target period is obtained. This method can avoid the problem of lagging photovoltaic grid-connected target power curve by combining photovoltaic historical power data and short-term predicted power data; further, by deeply considering the characteristics of photovoltaic randomness, by real-time judgment of whether the energy storage power exceeds the energy storage operation boundary, the filter step size is iteratively adjusted in real time, so that photovoltaics with different fluctuation conditions can maximize the peak-shaving potential of energy storage, 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, the above step S120 obtains the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step length, 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 the 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 indicates the number of power data involved in calculating the average value each time, which can be understood as a window for smoothing photovoltaic fluctuations, and the photovoltaic historical active power and photovoltaic predicted active power are used to calculate the photovoltaic grid-connected power within the smoothing window. Therefore, the sum of the first number of photovoltaic historical active power and the second number of photovoltaic predicted active power should be equal to the filtering step size. Then, the first number of photovoltaic active power before the target period and closest to the target period can be obtained as the photovoltaic historical active power; and the second number of photovoltaic active power after the target period and closest to the target period can be predicted as the photovoltaic predicted active power.

[0077] More specifically, in one implementation, 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, the above step S130 determines the current energy storage power in the target period based on the photovoltaic historical active power and the photovoltaic predicted active power, and specifically includes:

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

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

[0082] In the specific implementation, the current value of the photovoltaic grid-connected power after the target period is determined based on the photovoltaic historical active power and the photovoltaic predicted active power. The sliding average filtering method can be used to average the photovoltaic historical active power and the photovoltaic predicted active power to obtain the current value of the photovoltaic grid-connected power after the target period. Then, after obtaining the photovoltaic original power of the target period, the current energy storage power of the target period can be obtained based on the current value of the photovoltaic grid-connected power after the target period and the photovoltaic original power.

[0083] Furthermore, in an exemplary embodiment, there are multiple photovoltaic historical active powers and photovoltaic predicted active powers; in the above 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 length as the current value of the photovoltaic grid-connected power after smoothing.

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

[0085] For example, assuming that the filtering step size 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 time period can be expressed by the formula:

[0086]

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

[0088] In this embodiment, by combining the photovoltaic historical 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] Further, in an exemplary embodiment, in the above step S132, the current energy storage power of the target period is obtained according to the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power of the target period, including: subtracting 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, photovoltaic grid-connected power and photovoltaic original power can be determined as follows: photovoltaic grid-connected power = photovoltaic original power + energy storage power. Therefore, when determining the current energy storage power of the target period according to the current value of the photovoltaic grid-connected power after leveling and the photovoltaic original power during the target period, the current energy storage power of the target period can be obtained by subtracting the current value of the photovoltaic grid-connected power after leveling and the photovoltaic original power.

[0091] In this embodiment, the current energy storage power in the target period is obtained by subtracting the current value of the smoothed photovoltaic grid-connected power from 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 energy storage power constraint conditions and energy storage power constraint conditions; 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 (energy storage SOC value) during the period, and Respectively represent the minimum and maximum value 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 by the following relationship:

[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 two dimensions: energy storage quantity and energy storage power. This can ensure the rationality of the energy storage target power in the target period finally determined, more effectively realize 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 in conjunction with specific examples of the accompanying drawings. Referring to FIG3, a specific flow chart of a power allocation method for a photovoltaic energy storage system based on a sliding filter average method is shown. Assuming that for a period of 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 PV grid-connected power at the Kth iteration in period t.

[0103] Specifically, the current value of the PV grid-connected power after stabilization is calculated by combining the PV historical active power and the short-term PV 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 time period is: express The historical photovoltaic active power of the time period, express The predicted PV active power for each 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 Kth iteration in period t can be expressed as The energy storage power is obtained by subtracting the original photovoltaic power of the time period, which is 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 it is determined 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 conditions, 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 carried out 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; further, 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 lagging photovoltaic grid-connected target power curve can be avoided; further, this method deeply considers the characteristics of photovoltaic randomness, and by real-time judgment of 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 curve before and after smoothing obtained by the method of this application is shown in Figure 4. It can be seen from the figure that compared with the traditional sliding mean filtering method, the target power lag problem of the photovoltaic grid-connected power after the improved sliding mean filtering method of this application is significantly improved. In addition, due to the real-time iterative adjustment of the filtering step size, the peak-shaving potential of energy storage is deeply excavated, which can more effectively smooth the fluctuation of photovoltaic output compared with the traditional sliding mean filtering method.

[0116] In addition, see Figure 5, which is a power curve diagram of an energy storage system shown in an embodiment. It can be seen that the energy storage system after adopting the improved sliding mean filtering method of the present application, whether it is the power peak or the peak-shaving power, is significantly smaller than the traditional sliding mean filtering method. This is due to the obvious improvement of the target power lag problem by adopting the improved sliding mean filtering method, so that all the output of the energy storage is basically used to smooth the photovoltaic fluctuations.

[0117] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed 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 part of the steps or stages in other steps.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a power distribution device for a photovoltaic energy storage system for implementing the power distribution method of the photovoltaic energy storage system involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the power distribution device for one or more photovoltaic energy storage systems provided below can refer to the limitations of the power distribution method for the photovoltaic energy storage system above, and will not be repeated here.

[0119] In one embodiment, as shown in FIG6 , a power distribution device of a photovoltaic energy storage system is provided, comprising:

[0120] An initial determination module 610, for determining an initial filtering step size and a filtering step size increment for each iteration;

[0121] The power acquisition module 620 is used to acquire the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step length; 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 photovoltaic historical active power and the photovoltaic predicted active power;

[0123] The target determination module 640 is used to update the filter step size according to the filter step size increment to obtain a new filter step size when the current energy storage power does not exceed the preset energy storage operation boundary conditions, 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 filter step size, until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target period is obtained.

[0124] In one embodiment, the power acquisition module 620 is also 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 according to a filtering step; the sum of the first number and the second number is equal to the filtering step; the first number of photovoltaic active powers before the target time period and closest to the target time period is acquired as the photovoltaic historical active power; and the second number of photovoltaic active powers after the predicted target time period and closest to the target time period is predicted as the photovoltaic predicted active power.

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

[0126] In one of the embodiments, 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 length as the current value of the smoothed photovoltaic grid-connected power.

[0127] In one of the embodiments, the energy storage determination module 630 is further used to make a difference between the current value of the smoothed photovoltaic grid-connected power and the photovoltaic original power to obtain the current energy storage power in the target period.

[0128] In one embodiment, the energy storage operation boundary conditions include energy storage quantity constraint conditions and energy storage power constraint conditions; the energy storage quantity constraint conditions are that the energy storage quantity in the next period of the target period is within a first preset range; the energy storage power constraint conditions are 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 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0130] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG7. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, 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 an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a power distribution method for a photovoltaic energy storage system is implemented. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, a trackball or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0131] Those skilled in the art will understand that the structure shown in FIG. 7 is merely a block diagram of a partial 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 arrangement of components.

[0132] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, 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 relevant laws, regulations and standards of relevant countries and regions.

[0136] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium 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), magnetoresistive 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0137] The technical features of the above embodiments may be combined arbitrarily. 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 only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached 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; According to the filtering step length, the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period are obtained; the target period is the period for smoothing photovoltaic fluctuations; Determining the current energy storage power of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power; In the case that 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 step of obtaining the photovoltaic historical active power before the target time period and the photovoltaic predicted active power after the target time period according to the filter step size is returned until the current energy storage power is determined to exceed the energy storage operation boundary conditions, the loop is terminated, and the energy storage target power of the target time period is obtained.

2. The method according to claim 1, characterized in that The step of acquiring the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step length includes: Determine, 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; 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.

3. The method according to claim 1, characterized in that The determining the current energy storage power of the target period based on the photovoltaic historical active power and the photovoltaic predicted active power includes: Determining a current value of the photovoltaic grid-connected power after smoothing in the target period based on the photovoltaic historical active power and the photovoltaic predicted active power; 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 photovoltaic grid-connected power after smoothing in the target period and the photovoltaic original power.

4. The method according to claim 3, characterized in that There are multiple photovoltaic historical active powers and multiple photovoltaic predicted active powers; The step of determining the current value of the photovoltaic grid-connected power after smoothing in the target period based on the photovoltaic historical active power and the photovoltaic predicted active power includes: The historical active power of each photovoltaic array is summed with the predicted active power of each photovoltaic array to obtain the total power; The ratio of the total power to the filtering step length is obtained as the current value of the smoothed photovoltaic grid-connected power.

5. The method according to claim 3, characterized in that: The step of obtaining the current energy storage power of the target period according to the current value of the photovoltaic grid-connected power after smoothing and the photovoltaic original power of the target period includes: The current energy storage power of the target time period is obtained by subtracting the current value of the smoothed photovoltaic grid-connected power from the photovoltaic original power.

6. The method according to any one of claims 1 to 5, 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.

7. A power distribution device for a photovoltaic energy storage system, characterized in that: The device comprises: An initial determination module, used to determine an initial filtering step size and a filtering step size increment for each iteration; A power acquisition module, used to acquire the photovoltaic historical active power before the target period and the photovoltaic predicted active power after the target period according to the filtering step length; the target period is a period for smoothing photovoltaic fluctuations; An energy storage determination module, configured to determine the current energy storage power of the target period based on the photovoltaic historical active power and the photovoltaic predicted active 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 conditions, 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 conditions, the cycle is terminated, and the energy storage target power of the target period is obtained.

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 6 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 6 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 described in any one of claims 1 to 6 are implemented.

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