A method and system for preventing reverse power flow at the grid connection point of an energy storage power station

Abstract

The present application relates to the technical field of energy storage systems, and in particular to a method and system for preventing backflow of power at a power grid interface of an energy storage power station, which includes: periodically sampling first window data containing a number of gateway powers at the power grid interface; performing nonlinear filtering on the first window data to obtain characteristic gateway powers; obtaining a preset anti-backflow margin, and comparing the characteristic gateway power with the anti-backflow margin to select a corresponding strategy weight based on the comparison result; based on the strategy weight, calling the corresponding calculation strategy and calling the algorithm to calculate the expected execution power, and feeding back the expected execution power to the energy storage power station. The present application is used to achieve a stable anti-backflow effect for any peak-to-peak load environment.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage systems, and more particularly to a method and system for preventing reverse power flow at the grid connection point of an energy storage power station. Background Art

[0002] In the new energy power generation scenario, when the output power of the energy storage system is greater than the user's electricity demand, it will cause excess electric energy to flow back to the grid, resulting in a reverse power flow phenomenon. This will lead to instability of the grid system and even cause the grid to collapse when there is a large amount of reverse power. To avoid this phenomenon, current energy storage systems are equipped with a reverse power flow prevention function, which takes corresponding measures when necessary by real-time monitoring the operating state of the power generation system.

[0003] In the current technology, the measure for preventing reverse power flow is to obtain the power adjustment decision value of the energy storage power station through the change of the load. However, due to the unpredictability of the instantaneous change of the load power in the actual scenario, the method 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 reverse power flow optimization adjustment of any peak-to-peak load. Summary of the Invention

[0004] To achieve a stable reverse power flow prevention effect in any peak-to-peak load environment, the present application provides a method and system for preventing reverse power flow at the grid connection point of an energy storage power station.

[0005] In a first aspect, the present application provides a method for preventing reverse power flow at the grid connection point of an energy storage power station, adopting the following technical solution:

[0006] A method for preventing reverse power flow at the grid connection point of an energy storage power station includes the following steps:

[0007] Periodically sample the first window data including a plurality of connection point powers at the grid connection point;

[0008] Perform non-linear filtering on the first window data to obtain the characteristic connection point power;

[0009] Obtain a preset reverse power flow margin, and compare the characteristic connection point power with the reverse power flow margin to select a corresponding strategy weight according to the comparison result;

[0010] Based on the strategy weight, retrieve the corresponding calculation strategy and call an algorithm to calculate the expected execution power, and feedback the expected execution power to the energy storage power station.

[0011] In some of these embodiments, performing non-linear filtering on the first window data to obtain the characteristic connection point power includes the following steps:

[0012] Perform sliding window minimum filtering on the first window data based on an order statistic filter to retain the gateway power with the minimum value in the first window data as the characteristic gateway power.

[0013] In some embodiments, compare the characteristic gateway power with the anti-counterflow margin to select a corresponding policy weight according to the comparison result, including the following steps:

[0014] If the characteristic gateway power is less than 0, the policy weight is a high weight;

[0015] Among the high weights, immediately call an algorithm to calculate the expected execution power based on the characteristic gateway power and erase and overwrite the first window data where the characteristic gateway power is located;

[0016] If the characteristic gateway power is not less than 0 and less than the anti-counterflow margin, the policy weight is a secondary weight;

[0017] Among the secondary weights, keep acquiring the characteristic gateway power in the next first window data until the secondary weight is continuously recorded M times, and then call an algorithm to calculate the expected execution power based on the characteristic gateway data in the last sampled first window data;

[0018] If the characteristic gateway power is not less than the anti-counterflow margin, the policy weight is a low weight;

[0019] Among the low weights, keep acquiring the characteristic gateway power in the next first window data until the low weight is continuously recorded N times, and then call an algorithm to calculate the expected execution power based on the smallest characteristic gateway data in N first window data;

[0020] Wherein, N is greater than M.

[0021] In some embodiments, call an algorithm to calculate the expected execution power, including the following steps:

[0022] Obtain the sampling period corresponding to the first window data, and collect second window data containing several electricity powers with the same sampling period;

[0023] When calling the algorithm, obtain the time period point corresponding to the selected characteristic gateway data, and select the electricity power matching the time period point;

[0024] Subtract the anti-counterflow margin from the selected electricity power to calculate the expected execution power.

[0025] In some embodiments, call an algorithm to calculate the expected execution power, including the following steps:

[0026] Obtain the sampling period corresponding to the first window data, and collect second window data containing a plurality of electrical powers with the same sampling period;

[0027] Perform non-linear filtering on the second window data to obtain the minimum value in the second window data and use it as the characteristic electrical power;

[0028] When the algorithm is called, obtain the time period point corresponding to the selected characteristic checkpoint data, and select the characteristic electrical power in the second window data that matches the time period point;

[0029] Subtract the anti-counterflow margin from the selected characteristic electrical power to calculate the expected execution power.

[0030] In some of the embodiments, after performing non-linear filtering on the second window data to obtain the minimum value in the second window data and using it as the characteristic electrical power, the following steps are further included:

[0031] Set a dead zone threshold range, and determine whether the difference between consecutive characteristic electrical powers is within the dead zone threshold range;

[0032] If it is within, filter the subsequent characteristic electrical power and keep the previous characteristic electrical power for dead zone filtering.

[0033] In some of the embodiments, when performing the dead zone filtering, the characteristic electrical powers all correspond to the low weights.

[0034] In some of the embodiments, the following steps are further included:

[0035] When sampling the first window data and calculating the characteristic checkpoint power, compare it with the characteristic checkpoint power corresponding to the previous first window data;

[0036] If the values are the same, return the expected execution power calculated from the previous characteristic checkpoint power;

[0037] If the values are different, record the current characteristic checkpoint power to select the corresponding strategy weight.

[0038] In some of the embodiments, if the values are the same, when returning the expected execution power calculated from the previous characteristic checkpoint power, the following steps are further included:

[0039] Generate a timeout threshold based on the sampling period;

[0040] If the time when the new characteristic grid connection point power is not entered exceeds the timeout threshold, the characteristic grid connection point power with the current same value is forcibly entered to select the corresponding policy weight.

[0041] Second, this application provides a reverse power flow prevention system for the grid connection point power of an energy storage power station, adopting the following technical solutions:

[0042] A reverse power flow prevention system for the grid connection point power of an energy storage power station is used to implement the above method.

[0043] The technical solutions provided in the embodiments of this application have the following technical effects:

[0044] First, filter the grid connection point window data sampled periodically to filter out high-frequency and spike signals to reduce system oscillation. By solving the problem of the unpredictability of the load power consumption, select to switch to the grid connection point data associated with the power consumption for sampling and analysis to perform a weighted strategy. By comparing the sampled values with the set reverse power flow margin in different power consumption scenarios, obtain the urgency and sensitivity of the reverse power flow prevention decision, and select the corresponding processing algorithm according to the decision to calculate the expected execution power and feedback it 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 reverse power flow. Description of the Drawings

[0045] Figure 1 It is a step schematic diagram of the reverse power flow prevention method for the grid connection point power of the energy storage power station provided in this embodiment.

[0046] Figure 2 It is a schematic diagram corresponding to the policy weight in the reverse power flow prevention method for the grid connection point power of the energy storage power station provided in the embodiments of this application.

[0047] Figure 3 It is a reverse power flow prevention expected diagram in the embodiments of this application.

[0048] Figure 4 It is a schematic diagram of the filtering object corresponding to the dead zone filtering in the embodiments of this application.

[0049] Figure 5 It is an actual on-site operation diagram of the reverse power flow prevention method in the embodiments of this application. Detailed Embodiments

[0050] To understand the purpose, technical solution, and advantages of this application more clearly, the following describes and explains this application in combination with the accompanying drawings and embodiments. However, those of ordinary skill in the art should understand that this application can be implemented without these details. In some cases, to avoid unnecessary descriptions from obscuring various aspects of this application, well-known methods, processes, systems, components, and / or circuits that have been described at a higher level will not be elaborated further. For those of ordinary skill in the art, it is obvious that various changes can be made to the disclosed embodiments of this application, and without departing from the principles and scope of this application, the general principles defined in this application can be applied to other embodiments and application scenarios. Therefore, this application is not limited to the shown embodiments, but conforms to the broadest scope consistent with the scope claimed in this application.

[0051] It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but do not constitute a limitation on the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0052] In the description of this application, the meaning of "several" is one or more, the meaning of "multiple" is more than two, "greater than", "less than", "exceeding", etc. are understood as not including the number itself, and "above", "below", "within", etc. are understood as including the number itself. If there is a description of "first" and "second", it is only used to distinguish technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or implicitly indicating the sequence relationship of the indicated technical features.

[0053] In the description of this application, the descriptions with reference to terms such as "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a combined manner.

[0054] As Figure 1 shown, the embodiments of this application disclose a method for preventing reverse power flow at the grid connection point of an energy storage power station, including the following steps:

[0055] S100, periodically sample the first window data including several grid connection point powers at the grid connection point.

[0056] Obtain the window data including several sampled powers before the current time at the grid connection point. In this application, the sampling period is 1 s, and each window contains N sampled data.

[0057] S200, perform non - linear filtering on the first window data to obtain the characteristic grid connection power.

[0058] First, filter the first window data. After filtering, select a grid connection power with a predetermined characteristic from several grid connection power data in each window as the characteristic grid connection power and characterize the sampling state of the entire window.

[0059] S300, obtain the preset anti - backflow margin, and compare the characteristic grid connection power with the anti - backflow margin to select the corresponding strategy weight according to the comparison result.

[0060] Obtain the anti - backflow margin formulated by the user according to the actual power consumption scenario. The anti - backflow margin is the desired grid power reference value. The control purpose of the system is to make the filtered grid connection power close to or equal to this reference value to achieve effective control of the grid power.

[0061] Then, based on the numerical relationship between the characteristic grid connection power and the anti - backflow margin, the overall state relationship of the current load power consumption, grid connection power, and energy storage power can be analyzed, and different strategy weights can be selected according to different results.

[0062] Different strategy weights correspond to different processing logics, different processing response speeds, and different processing frequencies, so as to make different control decisions in different power consumption scenarios.

[0063] S400, based on the strategy weight, retrieve the corresponding calculation strategy and call the algorithm to calculate the desired execution power, and feedback the desired execution power to the energy storage power station.

[0064] Select the corresponding calculation strategy according to the analyzed strategy weight to calculate the desired execution power by combining the characteristic grid connection power with the corresponding frequency and corresponding processing method. The desired execution power is characterized as the final control decision value.

[0065] In the power consumption scenario, p_grid(t) (grid connection power) = p_load(t) (user power consumption) - p_systemd(t) (energy storage power), where p_grid(t) is the instantaneous power of the grid connection at time t, p_load(t) is the instantaneous power of the user at time t, and p_systemd(t) is the instantaneous power output by the energy storage system at time t. Then, it can be found from the above formula that when the energy storage power is greater than the power consumption at time t, it will cause excess electric energy to flow back to the grid.

[0066] By sending the expected execution power to the energy storage power station so that the energy storage power station adjusts its current energy storage output power according to the expected execution power, it is possible to avoid excessive energy storage output power, resulting in too much remaining electric energy flowing back to the power grid after meeting the electricity demand and causing a countercurrent phenomenon.

[0067] Through the above method, first, filter the gateway window data sampled at intervals to filter out high-frequency and spikes to reduce system oscillation. By solving the problem of unpredictable load power consumption, select to switch to sampling and analyzing the gateway data associated with the power consumption to perform a weighted strategy. By comparing the sampled values with the set anti-countercurrent margin in different electricity consumption scenarios, obtain the urgency and sensitivity of the anti-countercurrent decision, and select the processing algorithm according to the corresponding decision to calculate the expected execution power and feedback it 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 countercurrent.

[0068] In some other embodiments, non-linearly filter the first window data to obtain the characteristic gateway power, including the following steps:

[0069] S210, based on the order statistic filter, perform sliding window minimum filtering on the first window data to retain the gateway power with the smallest value in the first window data as the characteristic gateway power.

[0070] Specifically, adopt the sliding window minimum filtering method of the order statistic filter. By retaining the lowest point in each window data, suppress the positive high-frequency fluctuations (such as sudden increase noise) and maintain the low-frequency trend or baseline.

[0071] When the window size is N (there are N sampling points in the window), the filtered output is the minimum value of p_grid, and the formula is:

[0072] p_grid_filtered(t)=min(p_grid(t-N+1),p_grid(t-N+2),...,p_grid(t)).

[0073] As Figure 2 and Figure 3 shown, in some other embodiments, compare the characteristic gateway power with the anti-countercurrent margin to select the corresponding strategy weight according to the comparison result, including:

[0074] Assume that the load device changes in a sine wave period. The first window data corresponding to the p_grid sampling should be a triangular wave. The anti-countercurrent margin is used to cope with the power attenuation during the non-periodic change of the load. Then, the lowest value of the sine wave-changing load power should fall within the anti-countercurrent margin, so that the algorithm can adapt to any load environment with peak and valley data values.

[0075] S310, if the power at the characteristic checkpoint is less than 0, the policy weight is a high weight.

[0076] S320, among the high weights, immediately call the algorithm to calculate the expected execution power based on the power at the characteristic checkpoint and erase and overwrite the first window data where the power at the characteristic checkpoint is located.

[0077] When p_grid is less than 0, it indicates that the power supplied by the energy storage has exceeded the power demand of the electricity consumption. At this time, it is determined that there will be excess electrical energy flowing back to the power grid. Therefore, in this case, p_grid corresponds to the highest weight. Immediately call the algorithm to immediately calculate p_systemd_exp (expected execution power) to adjust the current energy storage power of the energy storage system to prevent the occurrence of backflow.

[0078] Meanwhile, when calling the algorithm to immediately calculate the expected execution power, the window data corresponding to the highest weight is patterned. In this way, the next p_grid overwrites all the data in the entire window. If the highest weight is still triggered later, loop the above steps until p_grid obtained once breaks away from the high weight.

[0079] S330, if the power at the characteristic checkpoint is not less than 0 and less than the anti-backflow margin, the policy weight is a secondary weight.

[0080] S340, among the secondary weights, keep obtaining the power at the characteristic checkpoint 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 checkpoint data in the last sampled first window data.

[0081] When 0 ≤ p_grid < p_margin (anti-backflow margin), the secondary weight is assigned to the power at the characteristic checkpoint, which means that the power output by the energy storage power station is less than the power consumption of the user. 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 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 by the calculated expected execution power to attenuate the energy storage power.

[0082] Among them, when calling the algorithm to calculate p_systemd_exp here, the calculation parameter selected is the characteristic checkpoint 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 has the strongest representativeness of the current power grid state.

[0083] S350. If the power at the characteristic checkpoint is not less than the anti-counterflow margin, the policy weight is a low weight.

[0084] S360. In the low weight, keep obtaining the power at the characteristic checkpoint 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 checkpoint data among the N first window data.

[0085] If it is currently detected that p_margin ≤ p_grid, assign a low weight to the power at the characteristic checkpoint, which indicates 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 counterflow is greatly reduced. At this time, it is regarded as an increase in the load demand power. Then, when p_margin ≤ p_grid is continuously recorded N times, calculate the expected execution power based on the characteristic checkpoint data corresponding to the valley value in the window data of these N samplings.

[0086] Since the counterflow risk corresponding to the low weight is the smallest, at this time, select the lowest value in the window to calculate the expected execution power. On the one hand, it can adapt to the load environment of any peak-to-peak data. On the other hand, based on the valley value, adjust the energy storage power to a stable state as much as possible.

[0087] Among them, N is greater than M.

[0088] Specifically, for the risk magnitude of the occurrence of counterflow in the unpredictable power change, the risk in the low weight is less than the risk in the secondary weight. Therefore, when the low weight situation occurs, the number of allowable times for not calling the algorithm to stabilize the power supply of the energy storage power station after collecting the checkpoint power is greater than the allowable number of times in the secondary weight scenario.

[0089] In some other embodiments, calling the algorithm to calculate the expected execution power includes the following steps:

[0090] S410. Obtain the sampling period corresponding to the first window data, and collect the second window data containing several power consumptions with the same sampling period.

[0091] S411. When calling the algorithm, obtain the time period point corresponding to the selected characteristic checkpoint data, and select the power consumption that matches the time period point.

[0092] S412. Subtract the anti-counterflow margin from the selected power consumption to calculate the expected execution power.

[0093] In order to calculate the expected execution power, first, it is also necessary to perform periodic window sampling on the user load power consumption according to the same period as the sampling of the checkpoint power to obtain the second window data corresponding to the sampling time. That is to say, in the embodiments of the present application, the sampling period of the second window data is also 1s, and each window summary contains N sampling data.

[0094] Meanwhile, when calling an algorithm according to the policy weight, if a certain characteristic checkpoint data is selected for calculation, the sampling time t corresponding to the characteristic checkpoint data is obtained, and the power consumption p_load(t) matching the sampling time t is selected simultaneously.

[0095] Meanwhile, according to the above analysis, the most ideal state of the power among the energy storage, the load, and the power grid is that the power at the grid checkpoint is the same as the anti-counterflow margin. At this time, not only is the risk of counterflow relatively low, but it also prevents the energy storage power station from having excessive fluctuations due to power adjustment.

[0096] Therefore, in order to suppress the filtered grid power to the optimal situation, it is necessary to satisfy:

[0097] p_grid_filtered(t) = p_margin.

[0098] Therefore, the power of the energy storage power station expected by the system (expected execution power) is:

[0099] p_systemd_exp = p_load(t) - p_margin.

[0100] In some other embodiments, calling an algorithm to calculate the expected execution power includes the following steps:

[0101] S420, obtaining the sampling period corresponding to the first window data, and collecting the second window data containing a plurality of power consumptions with the same sampling period.

[0102] S421, performing non-linear filtering on the second window data to obtain the minimum value in the second window data and using it as the characteristic power consumption.

[0103] S422, when calling the algorithm, obtaining the time period point corresponding to the selected characteristic checkpoint data, and selecting the characteristic power consumption in the second window data that matches the time period point.

[0104] S423, subtracting the anti-counterflow margin from the selected characteristic power consumption to calculate the expected execution power.

[0105] Ideally, the expected execution power only needs to be calculated through p_systemd_exp = p_load(t) - p_margin. However, in an actual system, due to the hysteresis of the sampled data, p_load is not real-time, and the load has unpredictability. Therefore, p_load will still fluctuate. The fluctuation of p_load will be mapped onto p_systemd_exp, resulting in the expected execution power being superimposed with the lagging p_load fluctuation caused by the control delay. The two fluctuations will cause the system to oscillate, which will affect the energy efficiency ratio at best and may even be unable to control the oscillating counterflow impact on the power grid at worst.

[0106] Therefore, in the embodiments of the present application, after obtaining the second window data, non-linear filtering is performed on the second window data to obtain the minimum value in each window data as the characteristic power consumption, specifically:

[0107] p_load_min(t)=min(p_load(t-N+1),...,p_load(t)).

[0108] By filtering the collected power consumption, high frequencies and spikes are eliminated, system oscillations are reduced, and the influence brought by the fluctuations of p_load in the calculated p_systemd_exp can be reduced.

[0109] In this way, the expected execution power is calculated through the filtered p_load_min(t), specifically:

[0110] p_systemd_exp=p_load_min(t)-p_margin.

[0111] As Figure 4 shown, in some other embodiments, after non-linear filtering is performed on the second window data to obtain the minimum value in the second window data and use it as the characteristic power consumption, the following steps are further included:

[0112] S4211, set the dead zone threshold range, and judge whether the difference between consecutive characteristic power consumptions is within the dead zone threshold range.

[0113] S4212, if it is, filter the subsequent characteristic power consumption and keep the previous characteristic power consumption for dead zone filtering.

[0114] After filtering the high frequencies and tips of the power consumption, a relatively stable expected execution power can already be obtained. However, due to the still small fluctuations in the characteristic power consumption (such as Figure 4 the boxed part in), then the small fluctuations will still be superimposed on the finally calculated expected execution power and cause interference to the waveform. Therefore, in order to reduce the influence of small fluctuations on the expected execution power, in the embodiments of the present application, further dead zone filtering needs to be performed on the filtered characteristic power consumption.

[0115] Dead zone filtering is that when it is detected that the floating amplitude of p_load_min(t) is within the preset threshold range, the system will maintain the output at the value of the previous moment, and at this time the system enters the "no-action interval", which can further filter out the influence brought by small fluctuations.

[0116] In some other embodiments, when performing dead zone filtering, the characteristic power consumption all corresponds to low weights.

[0117] Furthermore, the dead zone filtering value acts on the low-weight area. In this area, the probability of reverse current occurrence is extremely low. After filtering, the impact on the overall system's finally calculated expected execution power change due to the sampling change of the power consumption is small and within an acceptable range.

[0118] In the secondary-weight and high-weight areas, reverse current has already occurred or is close to occurring. At this time, the expected execution power needs to be accurately given, 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, it may cause the expected execution power to not correspond to the current time or the current power consumption scenario.

[0119] In some other embodiments, the following steps are further included:

[0120] S500, when sampling the first window data and calculating the characteristic gateway power, compare it with the characteristic gateway power corresponding to the previous first window data.

[0121] S510, if the values are the same, return the expected execution power calculated from the previous characteristic gateway power.

[0122] S520, if the values are different, record the current characteristic gateway power to select the corresponding policy weight.

[0123] When calling the algorithm to calculate the expected execution power, theoretically, the algorithm call should control the energy storage system by executing the algorithm once for each sampled data to obtain the expected execution power. However, the coupling of the sampling period and the call period will cause the algorithm calculation period to strongly depend on the sampling rate, thereby affecting the control period and control effect of the system's expected execution power.

[0124] Therefore, in the embodiments of the present application, the obtained p_grid is compared with the latest data in the window queue. If they are different, record it to calculate the new expected execution power to update and control the energy storage system. If they are the same, skip this data and directly return the expected execution power corresponding to the previous window data.

[0125] In some other embodiments, if the values are the same and the expected execution power calculated from the previous characteristic gateway power is returned, the following steps are further included:

[0126] S511, generate a timeout threshold based on the sampling period.

[0127] S512, if the time without recording a new characteristic gateway power exceeds the timeout threshold, forcefully record the current characteristic gateway power with the same value to select the corresponding policy weight.

[0128] Meanwhile, to avoid the problem that the expected execution power remains unchanged due to the long-term static state of the static system, the timeout threshold is set according to the sampling period. When the time of continuously not inputting new p_grid exceeds the threshold time, the same data is forced to be input when the same data is received next time, so as to drive the algorithm to perform the next calculation.

[0129] In the embodiment of the present application, the optimal timeout threshold is 1 / 2 of the p_grid sampling period.

[0130] The present application also discloses a grid connection power anti-counterflow system for an energy storage power station to implement the above method.

[0131] The implementation principle is as follows:

[0132] As Figure 1 and Figure 5 shown, first, the gateway window data sampled in a cycle is filtered to filter out high frequencies and spikes to reduce system oscillation. By solving the problem of the unpredictability of the load power consumption, the gateway data associated with the power consumption is selected for sampling and analysis to perform the sub-weight strategy. The urgency and sensitivity of the anti-counterflow decision are obtained by comparing the sampled values with the set anti-counterflow margin in different power consumption scenarios, and the corresponding decision is used to select the processing algorithm to calculate the expected execution power and feedback it 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 counterflow.

[0133] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit and can be executed in other orders.

[0134] The above are all the preferred embodiments of the present application, and the protection scope of the present application is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method for preventing reverse power flow at the grid connection point of an energy storage power station, characterized in that, Including the following steps: Periodically sample the first window data including a plurality of gateway powers at the grid gateway; Perform non-linear filtering on the first window data to obtain the characteristic gateway power; Obtain a preset anti-counterflow margin, and compare the characteristic gateway power with the anti-counterflow margin to select a corresponding policy weight according to the comparison result; Based on the policy weight, retrieve the corresponding calculation policy and call an algorithm to calculate the expected execution power, and feedback the expected execution power to the energy storage power station. Specifically, If the characteristic gateway power is less than 0, the policy weight is a high weight; Among the high weights, immediately call an algorithm to calculate the expected execution power based on the characteristic gateway power and erase and cover the first window data where the characteristic gateway power is located; If the characteristic gateway power is not less than 0 and less than the anti-counterflow margin, the policy weight is a secondary weight; Among the secondary weights, keep obtaining the characteristic gateway power in the next first window data until the secondary weight is continuously recorded M times, and then call an algorithm to calculate the expected execution power based on the characteristic gateway power in the last sampled first window data; If the characteristic gateway power is not less than the anti-counterflow margin, the policy weight is a low weight; Among the low weights, keep obtaining the characteristic gateway power in the next first window data until the low weight is continuously recorded N times, and then call an algorithm to calculate the expected execution power based on the minimum characteristic gateway power among the N first window data; Where N is greater than M Obtain the sampling period corresponding to the first window data, and collect the second window data including a plurality of power consumptions with the same sampling period; When calling the algorithm, obtain the time period point corresponding to the selected characteristic gateway power, and select the power consumption that matches the time period point; Subtract the anti-counterflow margin from the selected power consumption to calculate the expected execution power.

2. The method for preventing reverse power flow at the grid connection point of the energy storage power station according to claim 1, characterized in that, Performing non-linear filtering on the first window data to obtain the characteristic gateway power includes the following steps: Based on an order statistic filter, perform sliding window minimum filtering on the first window data to retain the gateway power with the smallest value in the first window data as the characteristic gateway power.

3. The method for preventing reverse power flow at the grid connection point of the energy storage power station according to claim 1, characterized in that Calling an algorithm to calculate the expected execution power includes the following steps: Obtain the sampling period corresponding to the first window data, and collect the second window data including a plurality of power consumptions with the same sampling period; Perform non-linear filtering on the second window data to obtain the minimum value in the second window data and use it as the characteristic power consumption; When calling the algorithm, obtain the time period point corresponding to the selected characteristic gateway power, and select the characteristic power consumption in the second window data that matches the time period point; Subtract the anti-counterflow margin from the selected characteristic power consumption to calculate the expected execution power.

4. The method for preventing reverse power flow at the grid connection point of the energy storage power station according to claim 3, wherein After performing non-linear filtering on the second window data to obtain the minimum value in the second window data and using it as the characteristic power consumption, it further includes the following steps: Set the dead zone threshold range, and determine whether the difference between consecutive feature power consumptions is within the dead zone threshold range; If it is within the range, filter the subsequent feature power consumption and keep the previous feature power consumption for dead zone filtering.

5. The method for preventing reverse power flow at the grid connection point of the energy storage power station according to claim 4, characterized in that, When performing the dead zone filtering, the feature power consumptions all correspond to the low weight.

6. The method for preventing reverse power flow at the grid connection point of the energy storage power station according to claim 3, characterized in that, It further includes the following steps: When sampling the first window data and calculating the feature gateway power, compare it with the feature gateway power corresponding to the previous first window data; If the values are the same, return the expected execution power calculated from the previous feature gateway power; If the values are different, record the current feature gateway power to select the corresponding policy weight.

7. The method for preventing reverse power flow at the grid connection point of the energy storage power station according to claim 6, characterized in that, If the values are the same, return the expected execution power calculated from the previous feature gateway power, and it further includes the following steps: Generate a timeout threshold based on the sampling period; If the time when no new feature gateway power is recorded exceeds the timeout threshold, forcefully record the current feature gateway power with the same value to select the corresponding policy weight.

8. A power grid gateway power reverse flow prevention system for an energy storage power station, characterized in that, Used to implement the method described in any one of claims 1-7.

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