Power grid gateway power countercurrent prevention method and system of energy storage power station

Abstract

The invention relates to the technical field of energy storage systems, in particular to a power grid gateway power countercurrent prevention method and system for an energy storage power station, and the method comprises the steps: periodically sampling first window data, containing a plurality of gateway powers, on a power grid gateway; performing nonlinear filtering on the first window data to obtain characteristic gateway power; acquiring a preset anti-reflux margin, and comparing the characteristic gateway power with the anti-reflux margin to select a corresponding strategy weight according to a comparison result; and calling a corresponding calculation strategy based on the strategy weight, calling an algorithm to calculate expected execution power, and feeding back the expected execution power to the energy storage power station. The method and the device are used for realizing a stable anti-reflux effect on any peak-to-peak value load environment.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage systems, and in particular to a method and system for preventing power backflow at a power gateway of an energy storage power station. Background Art

[0002] In the scenario of renewable energy power generation, when the output power of the energy storage system is greater than the user's power demand, it will cause the excess power to flow back to the grid, resulting in reverse flow, which will cause the grid system to be unstable and cause the grid to collapse when the reverse power is too much. To avoid this phenomenon, current energy storage systems are equipped with an anti-reverse flow function, which monitors the operating status of the power generation system in real time and takes corresponding measures when necessary.

[0003] In current technology, the measure to prevent backflow is to obtain the power adjustment decision value of the energy storage power station through the change of load. However, due to the unpredictability of the instantaneous change of load power in actual scenarios, the way of adjusting the energy supply of the energy storage power station is not stable and agile. At the same time, the existing technology cannot be applied to the optimization adjustment of the anti-backflow of any peak-to-peak load. Summary of the invention

[0004] In order to achieve a stable anti-backflow effect in any peak-to-peak load environment, the present application provides a power backflow prevention method and system for a power grid gateway of an energy storage power station.

[0005] In a first aspect, the present application provides a method for preventing power backflow at a power grid interface of an energy storage power station, which adopts the following technical solution: A method for preventing power backflow at a power grid interface of an energy storage power station comprises the following steps: Periodically sampling the first window data including a plurality of gateway powers on the electrical gateway; Performing nonlinear filtering on the first window data to obtain characteristic threshold power; Obtaining a preset backflow prevention margin, and comparing the characteristic gateway power with the backflow prevention margin to select a corresponding strategy weight according to the comparison result; Based on the strategy weight, the corresponding calculation strategy is retrieved and the algorithm is called to calculate the expected execution power, and the expected execution power is fed back to the energy storage power station.

[0006] In some embodiments, performing nonlinear filtering on the first window data to obtain characteristic threshold power comprises the following steps: The first window data is subjected to sliding window minimum filtering based on an order statistical filter to retain the threshold power with the smallest value in the first window data as the characteristic threshold power.

[0007] In some embodiments, comparing the characteristic gateway power with the anti-backflow margin to select a corresponding strategy weight according to the comparison result includes the following steps: If the characteristic threshold power is less than 0, the strategy weight is a high weight; In the high weight, an algorithm is immediately called to calculate the expected execution power based on the characteristic gate power and the first window data where the characteristic gate power is located is erased and overwritten; If the characteristic threshold power is not less than 0 and less than the anti-backflow margin, the strategy weight is a secondary weight; In the secondary weight, keep acquiring the next characteristic gate power in the first window data until the secondary weight is recorded M times continuously, and then call the algorithm to calculate the expected execution power based on the characteristic gate data in the first window data sampled last; If the characteristic threshold power is not less than the anti-backflow margin, the strategy weight is a low weight; In the low weight, keep acquiring the feature gate power in the next first window data until the low weight is recorded N times continuously, and then call the algorithm to calculate the expected execution power based on the smallest feature gate data in the N first window data; Among them, N is greater than M.

[0008] In some of the embodiments, calling an algorithm to calculate the expected execution power includes the following steps: Acquire a sampling period corresponding to the first window data, and collect second window data containing a number of power consumptions with the same sampling period; When the algorithm is called, the time period point corresponding to the selected characteristic threshold data is obtained, and the power consumption matching the time period point is selected; The desired execution power is calculated by subtracting the anti-backflow margin from the selected power consumption.

[0009] In some of the embodiments, calling an algorithm to calculate the expected execution power includes the following steps: Acquire a sampling period corresponding to the first window data, and collect second window data containing a number of power consumptions with the same sampling period; Performing nonlinear filtering on the second window data to obtain a minimum value in the second window data and using the minimum value as characteristic power consumption; When the algorithm is called, the time period point corresponding to the selected characteristic gate data is obtained, and the characteristic power consumption in the second window data matching the time period point is selected; The expected execution power is calculated by subtracting the anti-backflow margin from the selected characteristic electric power.

[0010] In some embodiments, the second window data is subjected to nonlinear filtering to obtain a minimum value in the second window data and use the minimum value as the characteristic power, and the following steps are also included: Setting a dead zone threshold range, and determining whether the difference between the consecutive characteristic electric powers is within the dead zone threshold range; If so, the characteristic power consumption at the rear is filtered and the characteristic power consumption at the front is maintained to perform dead zone filtering.

[0011] In some of the embodiments, when performing the dead zone filtering, the characteristic power consumption all corresponds to the low weight.

[0012] In some of the embodiments, the following steps are also included: After sampling the first window data and calculating the characteristic threshold power, the characteristic threshold power is compared with the characteristic threshold power corresponding to the previous first window data; If the values ​​are the same, the expected execution power calculated from the previous characteristic gate power is returned; If the values ​​are different, the current characteristic threshold power is entered to select the corresponding strategy weight.

[0013] In some embodiments, if the values ​​are the same, the expected execution power calculated by the previous characteristic gate power is returned, and the following steps are also included: generating a timeout threshold based on the sampling period; If the time for which the new characteristic gateway power is not recorded exceeds the timeout threshold, the characteristic gateway power of the same current value is forcibly recorded to select the corresponding strategy weight.

[0014] In a second aspect, the present application provides a power backflow prevention system for a power grid gateway of an energy storage power station, which adopts the following technical solution: A power backflow prevention system for a power grid gateway of an energy storage power station is used to implement the above method.

[0015] The technical solution provided by the embodiment of the present application has the following technical effects: First, the threshold window data sampled periodically is filtered to remove high frequencies and spikes to reduce system oscillations. By solving the problem of unpredictability of load power consumption, the threshold data associated with power consumption is sampled and analyzed to perform a weighted strategy. The urgency and sensitivity of the anti-backflow decision are obtained by comparing the sampling values ​​with the set anti-backflow margin under different power consumption scenarios. The expected execution power is calculated based on the corresponding decision-making processing algorithm and fed back to the energy storage power station. The energy storage power station adjusts the current energy storage power according to the obtained expected execution power to achieve optimization, optimize the system energy efficiency ratio and prevent the occurrence of backflow. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the steps of the method for preventing power backflow at the power gateway of the energy storage power station provided in this embodiment.

[0017] Figure 2 It is a schematic diagram of the corresponding strategy weights in the power backflow prevention method for the power grid interface of the energy storage power station provided in the embodiment of the present application.

[0018] Figure 3 It is the expected anti-backflow diagram in the embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of the filtering object corresponding to the dead zone filtering in the embodiment of the present application.

[0020] Figure 5 It is an actual on-site operation diagram of the backflow prevention method in the embodiment of the present application. DETAILED DESCRIPTION

[0021] To more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. However, it should be understood by those of ordinary skill in the art that the present application can be implemented without these details. In some cases, in order to avoid unnecessary descriptions that make various aspects of the present application obscure, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be described in detail. For those of ordinary skill in the art, it is obvious that various changes can be made to the embodiments disclosed in the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the embodiments shown, but conforms to the broadest scope consistent with the scope claimed for protection of the present application.

[0022] It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as there is no conflict between them.

[0023] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used to distinguish the technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0024] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples.

[0025] like Figure 1 As shown, the embodiment of the present application discloses a method for preventing power backflow at a power grid interface of an energy storage power station, comprising the following steps: S100, periodically sampling first window data including a plurality of gateway powers on a power supply gateway.

[0026] Obtain window data containing a number of sampled powers before the current time of the electrical network interface. In this application, the sampling period is 1s, and each window contains N sampled data.

[0027] S200, performing nonlinear filtering on the first window data to obtain characteristic threshold power.

[0028] Firstly, the first window data is filtered, and after filtering, a gate power with a predetermined characteristic is selected from a plurality of gate power data in each window as a characteristic gate power to characterize the sampling state of the entire window.

[0029] S300, obtaining a preset backflow prevention margin, and comparing the characteristic gateway power with the backflow prevention margin to select a corresponding strategy weight according to the comparison result.

[0030] Obtain the anti-backflow margin set by the user based on the actual power consumption scenario. The anti-backflow margin is the expected grid power reference value. The control purpose of the system is to make the filtered gateway power close to or equal to this reference value to achieve effective control of the grid power.

[0031] Then, based on the numerical relationship between the characteristic gateway power and the anti-backflow margin, the overall state relationship between the current load power consumption, the power gateway power, and the energy storage power can be analyzed, and different strategy weights can be selected according to different results.

[0032] Different strategy weights correspond to different processing logics, different processing response speeds, and different processing frequencies, thereby making different control decisions in different power usage scenarios.

[0033] S400, based on the strategy weight, the corresponding calculation strategy is retrieved and the algorithm is called to calculate the expected execution power, and the expected execution power is fed back to the energy storage power station.

[0034] According to the analyzed strategy weight, the corresponding calculation strategy is selected to calculate the expected execution power by combining the characteristic gate power with the corresponding frequency and the corresponding processing method. The expected execution power is represented as the final control decision value.

[0035] In the electricity consumption scenario, p_grid(t) (power of the power grid interface) = p_load(t) (user power consumption) - p_systemd(t) (energy storage power), where p_grid(t) is the instantaneous power of the power grid interface at time t, p_load(t) is the instantaneous power of the user power consumption at time t, and p_systemd(t) is the instantaneous power output of the energy storage system at time t. From the above formula, it can be found that when the energy storage power is greater than the power consumption at time t, it will cause excess power to flow back into the power grid.

[0036] By sending the expected execution power to the energy storage power station so that the energy storage power station can adjust its current energy storage output power according to the expected execution power, it is avoided that the output power of the energy storage is too large, resulting in excessive electricity remaining to flow back to the grid after meeting the electricity demand and causing backflow.

[0037] Through the above method, the threshold window data sampled in a periodic manner is first filtered to filter out high frequencies and spikes to reduce system oscillations. By solving the problem of unpredictability of load power consumption, the threshold data associated with power consumption is selected for sampling and analysis to perform a weighted strategy. The urgency and sensitivity of the anti-backflow decision are obtained by comparing the sampling values ​​with the set anti-backflow margin under different power consumption scenarios, and the expected execution power is calculated based on the corresponding decision selection processing algorithm and fed back to the energy storage power station. The energy storage power station adjusts the current energy storage power according to the obtained expected execution power to achieve optimization, optimize the system energy efficiency ratio and prevent the occurrence of backflow.

[0038] In some other embodiments, performing nonlinear filtering on the first window data to obtain characteristic threshold power includes the following steps: S210 , performing sliding window minimum filtering on the first window data based on an order statistical filter to retain a threshold power with the smallest value in the first window data as a characteristic threshold power.

[0039] Specifically, a sliding window minimum filtering method of an order statistical filter is used to suppress positive high-frequency fluctuations (such as sudden increase noise) by retaining the lowest point in each window data, and to maintain a low-frequency trend or baseline.

[0040] When the window size is N (there are N sampling points in the window), the output after filtering is the minimum value of p_grid, and the formula is: p_grid_filtered(t)=min(p_grid(t-N+1),p_grid(t-N+2),...,p_grid(t)).

[0041] like Figure 2 and Figure 3 As shown, in other embodiments, the characteristic gateway power is compared with the backflow prevention margin to select a corresponding strategy weight according to the comparison result, including: Assuming that the load device changes with a sinusoidal wave period, the first window data corresponding to the p_grid sampling should be a triangular wave. The anti-reverse flow margin is used to cope with the power attenuation when the load changes non-periodically. Then, the minimum value of the load power that changes with a sinusoidal wave should fall within the anti-reverse flow margin. This allows the algorithm to adapt to any load environment with peak and valley data values.

[0042] S310, if the characteristic gate power is less than 0, the strategy weight is a high weight.

[0043] S320 , in high weight, immediately call the algorithm to calculate the expected execution power based on the characteristic gate power and erase and overwrite the first window data where the characteristic gate power is located.

[0044] When p_grid is less than 0, it indicates that the power supply of the energy storage is already greater than the power demand of the electricity consumption. At this time, it is certain that there will be excess electric energy flowing back to the power grid. Therefore, in this case, p_grid corresponds to the highest weight. Immediately call the algorithm to calculate p_systemd_exp (expected execution power) immediately to adjust the current energy storage power of the energy storage system to prevent the occurrence of backflow.

[0045] At the same time, after calling the algorithm to calculate the expected execution power immediately, the window data corresponding to the highest weight is patterned. In this way, the next p_grid covers all the data of the entire window. If the highest weight is still triggered afterwards, loop the above steps until p_grid obtained once breaks away from the high weight.

[0046] S330, if the characteristic gateway power is not less than 0 and less than the anti-backflow margin, the policy weight is the secondary weight.

[0047] S340, in the secondary weight, keep obtaining the characteristic gateway power in the next first window data until the secondary weight is continuously recorded M times, and then call the algorithm to calculate the expected execution power based on the characteristic gateway data in the last sampled first window data.

[0048] When 0 ≤ p_grid < p_margin (anti-backflow margin), the secondary weight is assigned to the characteristic gateway power, which means that the power output by the energy storage power station is less than the user's electricity consumption power. At this time, the power grid and the energy storage power station supply power to the user load together. When 0 ≤ p_grid < p_margin is continuously recorded M times, it is regarded as the attenuation of the load demand power. Since the energy storage power supply power has always been below the demand parameters of the energy storage power station, the algorithm is called again at this time to calculate p_systemd_exp, and the energy storage power is adjusted through the calculated expected execution power to attenuate the energy storage power.

[0049] Among them, when calling the algorithm to calculate p_systemd_exp here, the calculation parameters selected are the characteristic gateway data corresponding to the last window among the M consecutive same-weight data, because when calculating the expected execution power, the data in the last window has the closest sampling time to the current time and the strongest representativeness of the current power grid state.

[0050] S350, if the characteristic gateway power is not less than the anti-backflow margin, the policy weight is the low weight.

[0051] S360, in the low weight, keep obtaining the characteristic gateway power in the next first window data until the low weight is continuously recorded N times, and then call the algorithm to calculate the expected execution power based on the smallest characteristic gateway data among the N first window data.

[0052] If p_margin≤p_grid is detected at present, a low weight is given to the characteristic threshold power, which is characterized by the fact that the power consumed by the user load is much greater than the power output by the energy storage power station. At this time, the possibility of reverse flow is greatly reduced, and it is regarded as an increase in load demand power. Then, after recording p_margin≤p_grid for N consecutive times, the expected execution power is calculated based on the characteristic threshold data corresponding to the valley value in the window data of these N samples.

[0053] Since the reverse flow risk corresponding to the low weight is the smallest, the lowest value in the window is selected to calculate the expected execution power. On the one hand, it can adapt to any load environment with peak-to-peak data, and on the other hand, it can adjust the energy storage power to a stable state as much as possible based on the valley value.

[0054] Among them, N is greater than M.

[0055] Specifically, for the risk of reverse flow in unpredictable electric energy changes, the risk in the low weight is smaller than the risk in the secondary weight. Therefore, when a low weight situation occurs, the number of times the algorithm is not called after the gateway power is collected to ensure stable power supply of the energy storage power station is greater than the number of times allowed in the secondary weight scenario.

[0056] In some other embodiments, calling an algorithm to calculate the expected execution power includes the following steps: S410, obtaining a sampling period corresponding to the first window data, and collecting second window data including a plurality of power consumptions with the same sampling period.

[0057] S411, when the algorithm is called, the time period point corresponding to the selected characteristic threshold data is obtained, and the power consumption matching the time period point is selected.

[0058] S412, subtract the anti-backflow margin from the selected power consumption to calculate the expected execution power.

[0059] In order to calculate the expected execution power, it is first necessary to perform periodic window sampling on the user load power according to the same period as the sampling of the gateway power to obtain the second window data corresponding to the sampling time. That is to say, in the embodiment of the present application, the sampling period of the second window data is also 1s, and each window summary contains N sampling data.

[0060] At the same time, when the algorithm is called according to the strategy weight, if a certain characteristic threshold data is selected for calculation, the sampling time t corresponding to the characteristic threshold data is obtained, and the power p_load (t) matching the sampling time t is selected at the same time.

[0061] At the same time, according to the above analysis, the most ideal power state between energy storage, load and power grid is that the power grid gateway power is the same as the anti-reverse flow margin. At this time, not only is the backflow risk low, but it also prevents the energy storage power station from experiencing excessive fluctuations due to power adjustment.

[0062] Therefore, in order to suppress the filtered grid power to the optimal state, it is necessary to meet the following conditions: p_grid_filtered(t)=p_margin.

[0063] Therefore, the energy storage power station power (expected execution power) expected by the system is: p_systemd_exp=p_load(t)-p_margin.

[0064] In some other embodiments, calling an algorithm to calculate the expected execution power includes the following steps: S420, obtaining a sampling period corresponding to the first window data, and collecting second window data including a plurality of power consumptions with the same sampling period.

[0065] S421, performing nonlinear filtering on the second window data to obtain the minimum value in the second window data and use it as the characteristic power consumption.

[0066] S422, when the algorithm is called, the time period point corresponding to the selected characteristic gate data is obtained, and the characteristic power consumption in the second window data matching the time period point is selected.

[0067] S423, subtract the anti-backflow margin from the selected characteristic electric power to calculate the expected execution power.

[0068] Ideally, the expected execution power can be calculated by p_systemd_exp=p_load(t)-p_margin, but in the actual system, due to the hysteresis of the sampling data, p_load is not real-time, and the load is unpredictable, so p_load will still fluctuate. The fluctuation of p_load will be reflected on p_systemd_exp, resulting in the expected execution power superimposed on the delayed p_load fluctuation caused by the control delay. The fluctuation of both will cause the system to oscillate, which will affect the energy efficiency ratio at the least, and the oscillation will be uncontrollable and impact the power grid at the worst.

[0069] Therefore, in the embodiment of the present application, after obtaining the second window data, the second window data is subjected to nonlinear filtering to obtain the minimum value in each window data as the characteristic power consumption, specifically: p_load_min(t)=min(p_load(t-N+1),...,p_load(t)).

[0070] By filtering the collected power consumption, eliminating high frequencies and spikes, and reducing system oscillations, the impact of p_load fluctuations can be reduced in the calculated p_systemd_exp.

[0071] In this way, the expected execution power is calculated by the filtered p_load_min(t), specifically: p_systemd_exp=p_load_min(t)-p_margin.

[0072] like Figure 4 As shown, in some other embodiments, the second window data is subjected to nonlinear filtering to obtain the minimum value in the second window data and use it as the characteristic power consumption, and the following steps are also included: S4211, setting a dead zone threshold range, and determining whether the difference between consecutive characteristic electric powers is within the dead zone threshold range.

[0073] S4212, if in, filter the subsequent characteristic power consumption and keep the previous characteristic power consumption to perform dead zone filtering.

[0074] After filtering the high frequency and cutting edge of the power consumption, a relatively stable expected execution power can be obtained. However, due to the small fluctuations in the characteristic power consumption (such as Figure 4 The boxed part in the figure), then the small fluctuations will still be superimposed on the expected execution power finally calculated and interfere with the waveform. Therefore, in order to reduce the impact of small fluctuations on the expected execution power, the embodiment of the present application also needs to perform further dead-zone filtering on the filtered characteristic power.

[0075] Dead zone filtering is when the system detects that the floating amplitude of p_load_min(t) is within the preset threshold range, and the system maintains the output at the value of the previous moment. At this time, the system enters the "no action interval", which can further filter out the impact of small fluctuations.

[0076] In other embodiments, when performing dead zone filtering, characteristic power consumption corresponds to a low weight.

[0077] Furthermore, the dead zone filter value acts on the low weight area. Since the probability of reverse flow in this area is extremely small, the impact of the change in the expected execution power finally calculated by the overall system due to the sampling change of the electric power after filtering is small and within an acceptable range.

[0078] In the secondary weight and high weight areas, the reverse flow phenomenon has occurred or is close to the reverse flow phenomenon. At this time, the expected execution power needs to be given accurately, and dead zone filtering cannot be used. If dead zone filtering is performed on the characteristic power consumption corresponding to the secondary weight or high weight area, the expected execution power may not correspond to the current time or the current power consumption scenario.

[0079] In some other embodiments, the following steps are also included: S500: After sampling the first window data and calculating the characteristic threshold power, compare it with the characteristic threshold power corresponding to the previous first window data.

[0080] S510: If the values ​​are the same, the expected execution power calculated by the previous characteristic gate power is returned.

[0081] S520: If the values ​​are different, the current characteristic gate power is entered to select a corresponding strategy weight.

[0082] When calling the algorithm to calculate the expected execution power, in theory the algorithm should be called once every time the data is sampled to obtain the expected execution power to control the energy storage system. However, the coupling of the sampling period and the calling period will cause the algorithm calculation period to be heavily dependent on the sampling rate, thereby affecting the control period and control effect of the system's expected execution power.

[0083] Therefore, in an embodiment of the present application, the obtained p_grid is compared with the latest data of the window queue. If they are different, they are entered to calculate the new expected execution power to update the control of the energy storage system. If they are consistent, the data is skipped and the expected execution power corresponding to the previous window data is directly returned.

[0084] In some other embodiments, if the values ​​are the same, the expected execution power calculated by the previous characteristic gate power is returned, and the following steps are also included: S511, generating a timeout threshold based on a sampling period.

[0085] S512: If the time for which no new characteristic gate power is recorded exceeds a timeout threshold, the characteristic gate power of the same value is forcibly recorded to select a corresponding strategy weight.

[0086] At the same time, in order to avoid the problem of no update of the expected execution power due to the static system being idle for a long time, a timeout threshold is set according to the sampling period. When the time for which no new p_grid is entered continuously exceeds the threshold time, the same data is forced to be entered the next time the data with the same value is received, so as to drive the algorithm to perform the next calculation.

[0087] In the embodiment of the present application, the optimal timeout threshold is 1 / 2 * p_grid sampling period.

[0088] The present application also discloses a power backflow prevention system for the power gateway of an energy storage power station, which is used to implement the above method.

[0089] The implementation principle is: like Figure 1 and Figure 5 As shown, first, the threshold window data sampled in a periodic manner is filtered to filter out high frequencies and spikes to reduce system oscillations. By solving the problem of unpredictability of load power consumption, the threshold data associated with power consumption is sampled and analyzed to perform a weighted strategy. The urgency and sensitivity of the anti-backflow decision are obtained by comparing the sampling values ​​with the set anti-backflow margin under different power consumption scenarios. The expected execution power is calculated based on the corresponding decision-making selection processing algorithm and fed back to the energy storage power station. The energy storage power station adjusts the current energy storage power according to the obtained expected execution power to achieve optimization, optimize the system energy efficiency ratio and prevent the occurrence of backflow.

[0090] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps is not strictly limited in order and can be performed in other orders.

[0091] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for preventing power backflow at a power grid interface of an energy storage power station, characterized in that: The following steps are involved: Periodically sampling the first window data including a plurality of gateway powers on the electrical gateway; Performing nonlinear filtering on the first window data to obtain characteristic threshold power; Obtaining a preset backflow prevention margin, and comparing the characteristic gateway power with the backflow prevention margin to select a corresponding strategy weight according to the comparison result; Based on the strategy weight, the corresponding calculation strategy is retrieved and the algorithm is called to calculate the expected execution power, and the expected execution power is fed back to the energy storage power station.

2. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 1, characterized in that: Performing nonlinear filtering on the first window data to obtain characteristic threshold power comprises the following steps: The first window data is subjected to sliding window minimum filtering based on an order statistical filter to retain the threshold power with the smallest value in the first window data as the characteristic threshold power.

3. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 1, characterized in that: Comparing the characteristic gateway power with the anti-backflow margin to select a corresponding strategy weight according to the comparison result includes the following steps: If the characteristic threshold power is less than 0, the strategy weight is a high weight; In the high weight, an algorithm is immediately called to calculate the expected execution power based on the characteristic gate power and the first window data where the characteristic gate power is located is erased and overwritten; If the characteristic threshold power is not less than 0 and less than the anti-backflow margin, the strategy weight is a secondary weight; In the secondary weight, keep acquiring the next characteristic gate power in the first window data until the secondary weight is recorded M times continuously, and then call the algorithm to calculate the expected execution power based on the characteristic gate data in the first window data sampled last; If the characteristic threshold power is not less than the anti-backflow margin, the strategy weight is a low weight; In the low weight, keep acquiring the feature gate power in the next first window data until the low weight is recorded N times continuously, and then call the algorithm to calculate the expected execution power based on the smallest feature gate data in the N first window data; Among them, N is greater than M.

4. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 1, characterized in that: Calling the algorithm to calculate the expected execution power includes the following steps: Acquire a sampling period corresponding to the first window data, and collect second window data containing a number of power consumptions with the same sampling period; When the algorithm is called, the time period point corresponding to the selected characteristic threshold data is obtained, and the power consumption matching the time period point is selected; The desired execution power is calculated by subtracting the anti-backflow margin from the selected power consumption.

5. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 1, characterized in that: Calling the algorithm to calculate the expected execution power includes the following steps: Acquire a sampling period corresponding to the first window data, and collect second window data containing a number of power consumptions with the same sampling period; Performing nonlinear filtering on the second window data to obtain a minimum value in the second window data and using the minimum value as characteristic power consumption; When the algorithm is called, the time period point corresponding to the selected characteristic gate data is obtained, and the characteristic power consumption in the second window data matching the time period point is selected; The expected execution power is calculated by subtracting the anti-backflow margin from the selected characteristic electric power.

6. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 5, characterized in that: After performing nonlinear filtering on the second window data, obtaining the minimum value in the second window data and using it as the characteristic power consumption, the method further includes the following steps: Setting a dead zone threshold range, and determining whether the difference between the consecutive characteristic electric powers is within the dead zone threshold range; If so, the characteristic power consumption at the rear is filtered and the characteristic power consumption at the front is maintained to perform dead zone filtering.

7. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 6, characterized in that: When performing the dead zone filtering, the characteristic power consumption all corresponds to the low weight.

8. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 1, characterized in that: The following steps are also included: After sampling the first window data and calculating the characteristic threshold power, the characteristic threshold power is compared with the characteristic threshold power corresponding to the previous first window data; If the values ​​are the same, the expected execution power calculated from the previous characteristic gate power is returned; If the values ​​are different, the current characteristic threshold power is entered to select the corresponding strategy weight.

9. The method for preventing power backflow at the power gateway of an energy storage power station according to claim 8, characterized in that: If the values ​​are the same, the expected execution power calculated by the previous characteristic gate power is returned, and the following steps are also included: generating a timeout threshold based on the sampling period; If the time for which the new characteristic gateway power is not recorded exceeds the timeout threshold, the characteristic gateway power of the same current value is forcibly recorded to select the corresponding strategy weight.

10. A power backflow prevention system for a power grid gateway of an energy storage power station, characterized in that: Used to implement the method described in any one of claims 1 to 9.

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