Optical storage power station control method, device and equipment and storage medium

Abstract

The application discloses a kind of light storage power station control method, device, equipment and storage medium, it is related to energy technology field, can let light storage power station have stable inertia support in different extreme operating scenarios, realize the stability of light storage linkage, improve energy utilization efficiency.The method comprises: constructing the occurrence probability set of light storage extreme scene, and the light storage extreme scene is the extreme operating scenario of photovoltaic energy storage system, including the scene that photovoltaic power and energy storage charge state are in different extreme operating interval;The occurrence probability set of light storage extreme scene is used as feedforward input, when the occurrence probability of different extreme operating scenarios changes, the inertia parameter of light storage power station is adjusted, and the target inertia parameter of light storage power station is obtained;According to the target inertia parameter of light storage power station, light storage power station is controlled, so that in different extreme operating scenarios, light storage power station uses target inertia parameter of light storage power station to regulate and control energy storage charge and discharge and energy release.

Description

Technical Field

[0001] This application relates to the field of energy technology, and in particular to a control method, device, equipment and storage medium for a photovoltaic power station. Background Technology

[0002] As the penetration rate of new energy sources continues to increase, traditional synchronous generator units are being largely replaced, leading to a continuous decline in the equivalent inertia of the power grid and increasingly prominent frequency stability issues. Photovoltaic-storage power stations, as an important power source, can participate in grid frequency regulation through virtual inertia control. However, their output is fluctuating due to weather conditions, and their energy storage capacity is limited. In extreme scenarios, such as continuous rain causing a sharp drop in photovoltaic power and insufficient energy storage, or a surge in photovoltaic power generation coupled with near-full energy storage, the inertia support capability of photovoltaic-storage power stations will be significantly weakened or even fail, potentially triggering frequency instability risks. They do not possess the robust grid support capability to maintain stability under complex operating environments.

[0003] Most existing control methods for photovoltaic and energy storage power stations are designed based on the current operating point or typical scenarios. For example, some methods use fixed thresholds or simple segmentation strategies, which are difficult to adapt to the dynamic relationship between photovoltaic power and energy storage charge state. While the probabilistic optimal control strategy takes into account uncertainty, it often assumes that the photovoltaic and energy storage states are independent of each other, ignoring the strong correlation between the two in extreme scenarios. This results in insufficient inertia support capability under high-risk combinations of different extreme operating scenarios. Summary of the Invention

[0004] In view of this, this application provides a control method, device, equipment and storage medium for a photovoltaic power station. The main purpose is to solve the problem that the existing technology has insufficient inertia support capability under high-risk combinations such as low photovoltaic power-low SOC or high photovoltaic power-high SOC, which limits the frequency regulation robustness of the system under real complex operating conditions.

[0005] According to the first aspect of this application, a control method for a photovoltaic-storage power station is provided, comprising:

[0006] Construct a probability set of extreme photovoltaic-storage scenarios, where extreme photovoltaic-storage scenarios are extreme operating scenarios of photovoltaic energy storage systems, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges;

[0007] Using the probability set of the occurrence of the extreme photovoltaic-storage scenarios as the feedforward input, the inertia parameters of the photovoltaic-storage power station are adjusted when the probability of the occurrence of different extreme operating scenarios changes, so as to obtain the inertia parameters of the target photovoltaic-storage power station.

[0008] The photovoltaic-storage power station is controlled based on the inertia parameters of the target photovoltaic-storage power station, so that the photovoltaic-storage power station can regulate the energy storage charging and discharging and energy release using the inertia parameters of the target photovoltaic-storage power station in different extreme operating scenarios.

[0009] Furthermore, the probability set for constructing extreme optical-storage scenarios includes:

[0010] Acquire historical operating data of the photovoltaic-storage power station, including photovoltaic power data and energy storage charge status data;

[0011] Based on the historical operating data of the photovoltaic-storage power station, the probability of occurrence of extreme photovoltaic-storage scenarios is quantified using variable correlation coefficients to obtain a set of probability of occurrence of extreme photovoltaic-storage scenarios.

[0012] Furthermore, based on the historical operating data of the photovoltaic-storage power station, the probability of occurrence of extreme photovoltaic-storage scenarios is quantified using variable correlation coefficients to obtain a probability set of extreme photovoltaic-storage scenarios, including:

[0013] Based on the historical operating data of the photovoltaic and energy storage power station, sample pairs of data for different extreme operating scenarios are constructed. The sample pairs of data include ordered sample pairs formed by synchronously associating photovoltaic power data with core variable data.

[0014] Based on the sample pairs of data from the different extreme operating scenarios, the observed linkage values ​​between photovoltaic power and energy storage charge state are calculated using the variable correlation coefficient. These observed linkage values ​​are used to reflect the basic linkage strength between photovoltaic power and energy storage charge state.

[0015] If the observed linkage values ​​meet the linkage condition, the local linkage values ​​under different power thresholds are calculated by iteratively setting the photovoltaic power threshold, so as to obtain the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0016] For different extreme operating scenarios, the probability set of the occurrence of extreme photovoltaic-storage scenarios is calculated based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence.

[0017] Furthermore, the step of calculating the observed linkage value between photovoltaic power and energy storage charge state using variable correlation coefficients based on sample pairs of data from different extreme operating scenarios includes:

[0018] Based on the sample pairs of data from the different extreme operating scenarios, statistically analyze the consistent and inconsistent valid data pairs in the sample pairs of data.

[0019] The correlation between photovoltaic power and energy storage charge state is quantified using the consistent valid pair data and the inconsistent valid pair data to obtain the observed correlation values ​​between photovoltaic power and energy storage charge state.

[0020] Furthermore, the step of using iteratively set photovoltaic power thresholds to calculate local linkage values ​​under different power thresholds, and obtaining linkage value sequences that change with power thresholds in different extreme operating scenarios, includes:

[0021] A photovoltaic power threshold sequence is set in ascending order, starting from the lowest photovoltaic power and increasing in fixed steps until the highest photovoltaic power.

[0022] Based on each photovoltaic power threshold, sub-sample pairs with photovoltaic power greater than or equal to the corresponding photovoltaic power threshold are selected from the sample pairs data of different extreme operating scenarios to obtain sub-sample pairs data of different extreme operating scenarios;

[0023] Based on the subsample pairs of data from the different extreme operating scenarios, the local linkage values ​​under different power thresholds are calculated using the variable correlation coefficient, thus obtaining the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0024] Furthermore, for different extreme operating scenarios, based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence, the probability of occurrence of extreme photovoltaic-storage scenarios is calculated, including:

[0025] For different extreme operating scenarios, the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence is taken as the core photovoltaic power threshold of the corresponding extreme operating scenario. Risk sample pairs with photovoltaic power greater than or equal to the corresponding core photovoltaic power threshold are selected from the sample pair data of the different extreme operating scenarios to obtain risk sample pair data of different extreme operating scenarios.

[0026] Based on the risk sample pairs of data from different extreme operating scenarios, a joint distribution model is constructed for each extreme operating scenario to calculate the probability of occurrence of extreme photovoltaic-storage scenarios.

[0027] Furthermore, after constructing the probability set of extreme optical-storage scenarios, the method further includes:

[0028] Extract the occurrence probability values ​​of different extreme operating scenarios from the occurrence probability of the optical storage extreme scenarios;

[0029] The probability values ​​of the occurrence of the different extreme operating scenarios are weighted according to the set scenario weights to obtain the risk index;

[0030] Based on the aforementioned risk indicators, establish a mapping relationship between different extreme operating scenarios and inertia parameters;

[0031] Accordingly, the probability set of the occurrence of the extreme photovoltaic-storage scenarios is used as the feedforward input. When the probability of the occurrence of different extreme operating scenarios changes, the inertia parameters of the photovoltaic-storage power station are adjusted according to the mapping relationship to obtain the inertia parameters of the target photovoltaic-storage power station.

[0032] According to a second aspect of this application, a photovoltaic-storage power station control device is provided, comprising:

[0033] The construction unit is used to construct the probability set of occurrence of extreme photovoltaic-storage scenarios. The extreme photovoltaic-storage scenarios are extreme operating scenarios of photovoltaic energy storage systems, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges.

[0034] The adjustment unit is used to take the probability set of the occurrence of the extreme photovoltaic-storage scenario as the feedforward input, and adjust the inertia parameters of the photovoltaic-storage power station when the probability of the occurrence of the different extreme operating scenarios changes, so as to obtain the target photovoltaic-storage power station inertia parameters.

[0035] The control unit is used to control the photovoltaic-storage power station according to the inertia parameters of the target photovoltaic-storage power station, so that the photovoltaic-storage power station can regulate the energy storage charging and discharging and energy release using the inertia parameters of the target photovoltaic-storage power station in different extreme operating scenarios.

[0036] Furthermore, the building unit includes:

[0037] The acquisition module is used to acquire historical operating data of the photovoltaic-storage power station, including photovoltaic power data and energy storage charge status data.

[0038] The quantization module is used to quantify the probability of occurrence of extreme scenarios of photovoltaic and energy storage based on the historical operation data of the photovoltaic and energy storage power station using variable correlation coefficients, so as to obtain the probability set of occurrence of extreme scenarios of photovoltaic and energy storage.

[0039] Furthermore, the quantization module includes:

[0040] The construction submodule is used to construct sample pairs of data for different extreme operating scenarios based on the historical operating data of the photovoltaic and energy storage power station. The sample pairs of data include ordered sample pairs formed by synchronously associating photovoltaic power data with core variable data.

[0041] The first calculation submodule is used to calculate the observed linkage value between photovoltaic power and energy storage charge state based on the sample pair data of the different extreme operating scenarios using the variable correlation coefficient. The observed linkage value is used to reflect the basic linkage strength between photovoltaic power and energy storage charge state.

[0042] The second calculation submodule is used to calculate the local linkage values ​​under different power thresholds by iteratively setting the photovoltaic power threshold if the observed linkage values ​​meet the linkage conditions, so as to obtain the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0043] The third calculation submodule is used to calculate the probability set of extreme photovoltaic-storage scenarios based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence for different extreme operating scenarios.

[0044] Furthermore, the first calculation submodule is specifically used for:

[0045] Based on the sample pairs of data from the different extreme operating scenarios, statistically analyze the consistent and inconsistent valid data pairs in the sample pairs of data.

[0046] The correlation between photovoltaic power and energy storage charge state is quantified using the consistent valid pair data and the inconsistent valid pair data to obtain the observed correlation values ​​between photovoltaic power and energy storage charge state.

[0047] Furthermore, the second calculation submodule is specifically used for:

[0048] A photovoltaic power threshold sequence is set in ascending order, starting from the lowest photovoltaic power and increasing in fixed steps until the highest photovoltaic power.

[0049] Based on each photovoltaic power threshold, sub-sample pairs with photovoltaic power greater than or equal to the corresponding photovoltaic power threshold are selected from the sample pairs data of different extreme operating scenarios to obtain sub-sample pairs data of different extreme operating scenarios;

[0050] Based on the subsample pairs of data from the different extreme operating scenarios, the local linkage values ​​under different power thresholds are calculated using the variable correlation coefficient, thus obtaining the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0051] Furthermore, the third calculation submodule is specifically used for:

[0052] For different extreme operating scenarios, the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence is taken as the core photovoltaic power threshold of the corresponding extreme operating scenario. Risk sample pairs with photovoltaic power greater than or equal to the corresponding core photovoltaic power threshold are selected from the sample pair data of the different extreme operating scenarios to obtain risk sample pair data of different extreme operating scenarios.

[0053] Based on the risk sample pairs of data from different extreme operating scenarios, a joint distribution model is constructed for each extreme operating scenario to calculate the probability of occurrence of extreme photovoltaic-storage scenarios.

[0054] Furthermore, the device also includes:

[0055] The extraction unit is used to extract the occurrence probability values ​​of different extreme operating scenarios from the occurrence probability of the extreme optical and energy storage scenarios after constructing the occurrence probability set of extreme optical and energy storage scenarios.

[0056] The calculation unit is used to perform weighted calculations on the probability values ​​of the occurrence of different extreme operating scenarios according to the set scenario weights to obtain risk indicators;

[0057] A unit is established to create a mapping relationship between different extreme operating scenarios and inertia parameters based on the risk indicators.

[0058] Accordingly, the adjustment unit is specifically used to take the probability set of the occurrence of the extreme photovoltaic-storage scenario as a feedforward input, and adjust the inertia parameter of the photovoltaic-storage power station according to the mapping relationship when the probability of the occurrence of different extreme operating scenarios changes, so as to obtain the target photovoltaic-storage power station inertia parameter.

[0059] According to a third aspect of this application, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect above.

[0060] According to a fourth aspect of this application, a readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0061] By employing the above technical solution, this application provides a photovoltaic-storage power station control method, device, equipment, and storage medium. Compared with the existing technology that uses a probabilistically optimal control strategy to control the photovoltaic-storage power station, this application constructs a probability set of extreme photovoltaic-storage scenarios. These extreme scenarios are extreme operating conditions of the photovoltaic energy storage system, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges. The probability set of extreme photovoltaic-storage scenarios is used as a feedforward input. When the probability of different extreme operating scenarios changes, the inertia parameters of the photovoltaic-storage power station are adjusted to obtain the target photovoltaic-storage power station inertia parameters. The photovoltaic-storage power station is controlled according to the target photovoltaic-storage power station inertia parameters, so that the photovoltaic-storage power station can regulate energy storage charging and discharging and energy release using the target photovoltaic-storage power station inertia parameters in different extreme operating scenarios. The entire process uses extreme scenario probability sets to predict the changing trends of different photovoltaic-storage extreme scenarios, and dynamically adjusts the inertia parameters to adapt to different extreme scenarios based on different changing trends. This enables the photovoltaic-storage power station to improve inertia support when photovoltaic energy storage is insufficient and to reserve adjustment space when photovoltaic energy storage is saturated. This allows the photovoltaic-storage power station to have stable inertia support in different extreme operating scenarios, realize the stability of photovoltaic-storage linkage, and improve energy utilization efficiency.

[0062] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0063] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0064] Figure 1 This is a flowchart illustrating a photovoltaic-storage power station control method in one embodiment of this application;

[0065] Figure 2 yes Figure 1 A flowchart illustrating a specific implementation method of step 101;

[0066] Figure 3 yes Figure 2 A flowchart illustrating a specific implementation method for step 202;

[0067] Figure 4 yes Figure 3 A flowchart illustrating a specific implementation method for step 302;

[0068] Figure 5 yes Figure 3 A flowchart illustrating a specific implementation method for step 303;

[0069] Figure 6 yes Figure 3 A flowchart illustrating a specific implementation method for step 304;

[0070] Figure 7 This is a flowchart illustrating the control method for a photovoltaic power station in another embodiment of this application;

[0071] Figure 8 This is a schematic diagram of the structure of a photovoltaic power station control device in one embodiment of this application;

[0072] Figure 9 This is a schematic diagram of the device structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0073] The invention will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are described merely to enable those skilled in the art to better understand and thus implement the invention, and are not intended to imply any limitation on the scope of the invention.

[0074] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "one embodiment" and "an embodiment" are to be interpreted as "at least one embodiment". The term "another embodiment" is to be interpreted as "at least one other embodiment".

[0075] Most existing control methods for photovoltaic and energy storage power stations are designed based on the current operating point or typical scenarios. For example, some methods use fixed thresholds or simple segmentation strategies, which are difficult to adapt to the dynamic relationship between photovoltaic power and energy storage charge state. While the probabilistic optimal control strategy takes into account uncertainty, it often assumes that the photovoltaic and energy storage states are independent of each other, ignoring the strong correlation between the two in extreme scenarios. This results in insufficient inertia support capability under high-risk combinations of different extreme operating scenarios.

[0076] To address this issue, this embodiment provides a control method for a photovoltaic-storage power station, such as... Figure 1 As shown, it includes the following steps:

[0077] 101. Construct a probability set for extreme scenarios of photovoltaic and energy storage.

[0078] Among them, the extreme scenarios of photovoltaic energy storage refer to the extreme operating scenarios of photovoltaic energy storage systems, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges. At least, they include extreme operating scenarios where both photovoltaic power and energy storage charge state are at low levels and extreme operating scenarios where both photovoltaic power and energy storage charge state are at high levels. For the extreme operating scenarios with low levels, both photovoltaic power and energy storage charge state need to be less than the set value. This scenario is prone to causing inertia support failure. For the extreme operating scenarios with high levels, both photovoltaic power and energy storage charge state need to be greater than the set value. This scenario is prone to causing photovoltaic curtailment and insufficient energy storage adjustment space.

[0079] It should be noted that the two core scenarios with the highest linkage risks, namely the dual-low extreme operation scenario and the dual-high extreme operation scenario, can also include other derivative scenarios for photovoltaic extreme scenarios. For example, extreme operation scenarios where photovoltaic power is low and energy storage charge is high, and extreme operation scenarios where photovoltaic power is high and energy storage charge is low, are not limited in this embodiment.

[0080] In this embodiment, the probability set of extreme photovoltaic-storage scenarios is a collection of probabilities of different extreme operating scenarios. Specifically, valid historical samples can be screened based on the historical operating data of the photovoltaic-storage power station. Then, based on pre-set extreme thresholds for photovoltaic power and energy storage charge, target samples that meet different extreme scenarios are selected from the valid historical samples. Probability quantification is performed on the target samples for different extreme scenarios to obtain the probability set of extreme photovoltaic-storage scenarios. Here, probability quantification can be determined by the ratio of target samples of extreme photovoltaic-storage scenarios to valid historical samples. It is understood that, in order to ensure the validity of the probability set, historical operating data can be updated and supplemented periodically to avoid the problems caused by outdated historical data. Then, the linkage values ​​calculated by the correlation variables are used to eliminate randomly superimposed extreme samples to ensure that the probability reflects the true linkage risk.

[0081] The execution entity in this embodiment can be a photovoltaic power station control device or equipment, which can be configured on the server side of the photovoltaic power station control. By constructing a probability set of extreme photovoltaic and energy storage scenarios, abstract extreme risks can be transformed into quantifiable and predictable data signals to capture risk trends in extreme scenarios in advance, guide the rational allocation of resources, and thus provide accurate basis for the inertia control, risk prevention and control and operation optimization of photovoltaic and energy storage power stations.

[0082] 102. Using the probability set of the occurrence of the extreme photovoltaic-storage scenarios as the feedforward input, the inertia parameters of the photovoltaic-storage power station are adjusted when the probability of the occurrence of the different extreme operating scenarios changes, so as to obtain the inertia parameters of the target photovoltaic-storage power station.

[0083] In this embodiment, the probability set of extreme scenarios for photovoltaic and energy storage is not a static value, but a risk signal library that is dynamically updated based on historical operating data and changes in environmental conditions. These probability values ​​quantify the occurrence trends of various extreme scenarios. When the system detects a significant change in the probability of a certain type of extreme scenario or when the comprehensive risk index exceeds the risk level threshold due to probability changes, the probability set serves as a feedforward input to trigger an adjustment mechanism. This process does not require waiting for the extreme scenario to actually occur, but rather predicts the direction of risk evolution through probability changes and initiates inertia parameter adjustments in advance, avoiding grid fluctuations or equipment impacts in traditional post-event remediation modes. For example, if the fluctuation range of the probability of occurrence under dual-low extreme operating scenarios exceeds ±0.5% of a set threshold, the adjustment mechanism for the inertia parameters of the photovoltaic and energy storage power station is triggered.

[0084] Specifically, during the adjustment process, the system will adjust the inertia parameters based on the preset mapping relationship between extreme scenarios, risk levels, and inertia parameters, combined with the magnitude and direction of changes in the probability of occurrence. For example, if the probability of occurrence under the dual-low extreme operating scenario increases significantly, the inertia support coefficient needs to be increased to enhance the response strength of energy storage discharge, while shortening the energy storage response delay time, ensuring that it can still provide stable inertia to the grid when there is insufficient sunlight and low energy storage capacity. As another example, if the probability of occurrence under the dual-high extreme operating scenario continues to rise, the upper limit of the inertia support coefficient needs to be lowered to prevent parameter failure after energy storage saturation, while simultaneously advancing the energy storage fast charging threshold to reserve space for energy regulation during high photovoltaic output. The final target photovoltaic-energy storage power station inertia parameters are the optimal solution adapted to the probability distribution of the current extreme scenario. At this point, the photovoltaic-energy storage power station will neither suffer excessive equipment wear or increased energy consumption due to excessively high parameters, nor will it allow risks to accumulate due to insufficient parameters. Understandably, in order to ensure the control effect of the photovoltaic-storage power station, the inertia parameters of the target photovoltaic-storage power station, as a parameter combination to cope with the current risk situation, not only include core control indicators such as inertia support coefficient and response speed, but may also involve auxiliary parameters such as energy storage charging and discharging depth limit and standby capacity activation threshold.

[0085] 103. Control the photovoltaic-storage power station according to the inertia parameters of the target photovoltaic-storage power station, so that the photovoltaic-storage power station can regulate the energy storage charging and discharging and energy release by using the inertia parameters of the target photovoltaic-storage power station in different extreme operating scenarios.

[0086] In this embodiment, the process of controlling the photovoltaic-storage power station based on the target inertia parameters involves converting the dynamically optimized target inertia parameters into precise execution commands. These commands are applied throughout the entire operation of the photovoltaic-storage system, ultimately achieving adaptive regulation of energy storage charging and discharging and energy release under different extreme scenarios. This control process features real-time feedback and dynamic calibration mechanisms. The photovoltaic-storage power station control system continuously monitors the actual operating status under extreme scenarios and compares it with the preset control effect of the target inertia parameters. If the deviation between the actual operating indicators and the expected values ​​exceeds a threshold, the control commands are promptly fine-tuned to ensure that energy storage charging and discharging and energy release always meet the needs of the current extreme scenario.

[0087] Specifically, in extreme low-energy-density (LDH) operating scenarios, the target inertia parameter will focus on enhancing the energy storage discharge support capability. On the one hand, by increasing the inertia support coefficient, the sensitivity of the energy storage system to grid frequency fluctuations is improved, ensuring that the energy storage can quickly release energy to replenish the inertia gap when photovoltaic output is insufficient. On the other hand, the energy storage discharge curve is optimized to avoid deep discharge of the energy storage while ensuring grid stability, balancing the inertia support requirements with the lifespan of the energy storage equipment. At this time, the system will strictly adhere to the upper limit of discharge power and the response delay threshold set by the target parameters to ensure timely and controllable energy release.

[0088] Specifically, in extreme high-voltage and high-efficiency operating scenarios, the focus of controlling the target inertia parameter shifts to reserving adjustment space and optimizing energy consumption. By lowering the fast-charging threshold of energy storage in advance, the system guides energy storage to reserve some capacity before the peak of photovoltaic output, avoiding photovoltaic curtailment due to energy storage saturation. At the same time, the dynamic range of the inertia support coefficient is appropriately adjusted, allowing energy storage to flexibly switch between absorbing excess photovoltaic energy and providing inertia support. If the grid experiences high-frequency fluctuations, the system will activate the fast-charging mode of energy storage according to the target parameters to efficiently store excess photovoltaic energy, thus smoothing grid fluctuations and improving photovoltaic consumption rate. If the grid frequency is low, the system will quickly switch to the discharge mode to release energy storage to replenish inertia, achieving intelligent linkage between charging and discharging and energy release.

[0089] The photovoltaic-storage power station control method provided in this application differs from existing technologies that use probabilistically optimal control strategies. This application constructs a probability set of extreme photovoltaic-storage scenarios, where extreme scenarios refer to extreme operating conditions of the photovoltaic energy storage system, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges. Using this probability set as a feedforward input, the inertia parameters of the photovoltaic-storage power station are adjusted when the probability of different extreme operating scenarios changes, resulting in target photovoltaic-storage power station inertia parameters. The photovoltaic-storage power station is then controlled based on these target inertia parameters, enabling it to regulate energy storage charging and discharging and energy release in different extreme operating scenarios. The entire process uses extreme scenario probability sets to predict the changing trends of different photovoltaic-storage extreme scenarios, and dynamically adjusts the inertia parameters to adapt to different extreme scenarios based on different changing trends. This enables the photovoltaic-storage power station to improve inertia support when photovoltaic energy storage is insufficient and to reserve adjustment space when photovoltaic energy storage is saturated. This allows the photovoltaic-storage power station to have stable inertia support in different extreme operating scenarios, realize the stability of photovoltaic-storage linkage, and improve energy utilization efficiency.

[0090] In the above embodiments, extreme photovoltaic-storage scenarios are affected by multiple factors such as sunlight, weather, and demand. The coupling relationship between these factors cannot be fully simulated through theoretical derivation. To ensure the reliability of probability set statistics, historical operating data can be used to record the linkage between photovoltaic power and energy storage charge state under real-world conditions. This historical operating data can objectively reflect the actual laws governing this linkage, making the probability statistics of extreme scenarios more realistic and reproducing the actual occurrence patterns of extreme scenarios. Specifically, such as... Figure 2 As shown, step 101 includes the following steps:

[0091] 201. Obtain historical operating data of photovoltaic and energy storage power stations.

[0092] 202. Based on the historical operating data of the photovoltaic-storage power station, the probability of occurrence of extreme photovoltaic-storage scenarios is quantified using variable correlation coefficients to obtain the probability set of extreme photovoltaic-storage scenarios.

[0093] In this embodiment, historical operating data includes photovoltaic power data and energy storage state of charge data. Typically, the photovoltaic-energy storage power station control system collects and stores the equipment's operating parameters in real time. This includes photovoltaic power data, i.e., the output power of the photovoltaic inverter, usually sampled on a minute or hourly basis, recording fluctuations in power generation over different time periods. It also includes energy storage state of charge data, i.e., the state of charge of the energy storage battery, reflecting the remaining energy capacity, also sampled on a minute or hourly basis, recording the impact of the charging and discharging process on the energy storage state of charge.

[0094] Specifically, based on the rated parameters and operating characteristics of the power plant, the threshold for determining extreme scenarios can be determined. Data meeting these thresholds can be selected from historical operating data as candidate samples for extreme scenarios. For example, in a low-power (low efficiency) extreme operating scenario, historical operating data with photovoltaic power ≤ 20% of rated power and energy storage charge state ≤ 30% can be selected as candidate samples; in a high-power (high efficiency) extreme operating scenario, historical operating data with photovoltaic power ≥ 80% of rated power and energy storage charge state ≥ 80% can be selected as candidate samples. Then, for each candidate sample, the correlation coefficient between photovoltaic power and energy storage charge state under different extreme scenarios can be calculated using variable correlation coefficients. Kendall's τ coefficient or Spearman's rank correlation coefficient can be used. Samples with correlation coefficients greater than a set threshold are then selected as valid samples. Valid samples are considered true extreme samples with systemic linkage, while samples with coefficients close to zero are removed to avoid interfering with the accuracy of the probability. Finally, the valid sample data for different extreme scenarios after linkage screening are statistically analyzed, and the probability of occurrence of each extreme scenario is calculated.

[0095] Specifically, such as Figure 3 As shown, step 202 above includes the following steps:

[0096] 301. Based on the historical operating data of the photovoltaic-storage power station, construct sample pairs of data for different extreme operating scenarios.

[0097] 302. Based on the sample pairs of data from the different extreme operating scenarios, use the variable correlation coefficient to calculate the observed linkage values ​​between photovoltaic power and energy storage charge state.

[0098] 303. If the observed linkage values ​​meet the linkage conditions, the local linkage values ​​under different power thresholds are calculated by iteratively setting the photovoltaic power threshold, so as to obtain the linkage value sequence that changes with the power threshold in different extreme operating scenarios.

[0099] 304. For different extreme operating scenarios, calculate the probability set of the occurrence of extreme photovoltaic and energy storage scenarios based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence.

[0100] It is understandable that extreme scenarios involving photovoltaic power and energy storage are key high-risk scenarios that need to be considered in the inertia control decision of photovoltaic power and energy storage power plants. Typically, when the photovoltaic power is lower than the set threshold, the photovoltaic power and the state of charge of the energy storage exhibit a strong upper tail dependence structure, that is, a significant linkage state occurs where the photovoltaic power is low and the state of charge of the energy storage is low.

[0101] To identify high-risk, uncertain scenarios involving the linkage between photovoltaic (PV) and energy storage in extreme operating conditions, a correlation coefficient can be introduced to calculate observed linkage values. Then, when the observed linkage values ​​meet the linkage conditions, an iterative setting of the PV power threshold is used to calculate the local linkage threshold, forming a sequence of linkage values ​​that vary with the power threshold in different extreme operating scenarios. Finally, the maximum linkage value is selected in different extreme operating scenarios to characterize the joint distribution structure of high-risk scenarios involving the linkage between PV power and energy storage charge state. Based on the joint distribution structure of high-risk scenarios, the probability set of occurrence of extreme PV-energy storage scenarios is calculated.

[0102] Specifically, such as Figure 4 As shown, step 302 above includes the following steps:

[0103] 401. Based on the sample pairs of data from the different extreme operating scenarios, count the consistent valid pairs and inconsistent valid pairs of data in the sample pairs of data.

[0104] 402. Using the consistent valid pair data and inconsistent valid pair data, the correlation between photovoltaic power and energy storage charge state is quantified to obtain the observed correlation values ​​between photovoltaic power and energy storage charge state.

[0105] Specifically, in the process of statistically analyzing the consistent and inconsistent valid data pairs in the aforementioned sample data, if the photovoltaic power variable's first... The observed value is greater than the first. If the nth observation is valid, the photovoltaic power coordination index is 1; otherwise, the photovoltaic power coordination index is -1. Correspondingly, if the nth observation of the energy storage charge state... The observation value is less than the first. If there are 1 observation, the coherence index of the energy storage charge state is 1; otherwise, the coherence index of the energy storage charge state is -1. Refer to the following formula for details:

[0106]

[0107] Where, 1≤ < ≤ , ≠ , The number of sample pairs for different extreme operating scenarios. As a synergistic indicator of photovoltaic power, It is a coordinating index of the state of stored charge.

[0108] Accordingly, the correlation between photovoltaic power and energy storage charge state is quantified based on the statistically obtained synergy index of photovoltaic power and energy storage charge state. This quantification process involves using the correlation coefficient of variables to calculate the observed correlation value between photovoltaic power and energy storage charge state. Please refer to the following formula for details:

[0109]

[0110] Specifically, such as Figure 5 As shown, step 303 above includes the following steps:

[0111] 501. Set the photovoltaic power threshold sequence in ascending order.

[0112] 502. Based on each photovoltaic power threshold, select sub-sample pairs from the sample pairs data of different extreme operating scenarios where the photovoltaic power is greater than or equal to the corresponding photovoltaic power threshold to obtain sub-sample pairs data of different extreme operating scenarios.

[0113] 503. Based on the subsample pairs of data from the different extreme operating scenarios, use the variable correlation coefficient to calculate the local linkage values ​​under different power thresholds, and obtain the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0114] In this embodiment, setting the photovoltaic power threshold sequence in ascending order can be described as follows: Correlation coefficients were used to calculate local linkage values ​​under different power thresholds, resulting in linkage value sequences that vary with power thresholds in different extreme operating scenarios. The process can be referred to the following formula:

[0115]

[0116] in, Subsample pairs whose photovoltaic power is greater than or equal to the corresponding photovoltaic power threshold. and Refer to the above text for the specific calculation process.

[0117] Specifically, such as Figure 6 As shown, step 304 above includes the following steps:

[0118] 601. For different extreme operating scenarios, the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence is taken as the core photovoltaic power threshold of the corresponding extreme operating scenario. Risk sample pairs with photovoltaic power greater than or equal to the corresponding core photovoltaic power threshold are selected from the sample pair data of the different extreme operating scenarios to obtain risk sample pair data of different extreme operating scenarios.

[0119] 602. Based on the risk sample pairs of the different extreme operating scenarios, construct a joint distribution model for the different extreme operating scenarios, and calculate the probability of occurrence of extreme photovoltaic-storage scenarios through the joint distribution model.

[0120] In this embodiment, in the linkage value sequence of each extreme scenario, the maximum linkage value signifies the strongest linkage between photovoltaic power and energy storage charge state at that threshold, essentially representing the core dividing point for systemic risk linkage. For each extreme scenario, sample pairs with photovoltaic power greater than or equal to the core photovoltaic power threshold are selected from its sample pair data, resulting in the risk sample pair data for that extreme scenario.

[0121] Specifically, in the process of constructing joint distribution models for different extreme operating scenarios, an appropriate joint distribution model can be selected based on data characteristics to calculate the probability of occurrence of extreme photovoltaic-storage scenarios.

[0122] If photovoltaic power and energy storage charge state approximately follow a normal distribution and are linearly correlated, the probability of extreme photovoltaic-energy storage scenarios can be calculated using a multivariate normal distribution model. If the data distribution is complex or there is nonlinear linkage, the probability of extreme photovoltaic-energy storage scenarios can be calculated using a Copula model.

[0123] Taking the calculation of the probability of extreme scenarios in photovoltaic-storage systems using the Copula model as an example, the following formula is used for risk sample pairs of data from different extreme operating scenarios:

[0124]

[0125]

[0126] in, , It is the normalized value of photovoltaic power and energy storage state of charge. It is the probability corresponding to photovoltaic power and energy storage charge state, which is estimated and generated based on the distribution of historical operating data that is not higher than the photovoltaic power threshold range; These are the structural parameters of a copula, reflecting the strength of the dependency between variables, which can be obtained from the linked numerical sequences mentioned above. express.

[0127] In practical applications, different extreme scenarios pose varying degrees of threat to the power grid or power plant. Applying the same weight to all extreme scenarios when adjusting the inertia parameters of photovoltaic-storage power plants would mask the true risks of high-impact scenarios, leading to resource misallocation. To improve the ability to control high-risk scenarios, further, such as... Figure 7 As shown, after step 101, the above method further includes the following steps:

[0128] 701. Extract the occurrence probability values ​​of different extreme operating scenarios from the occurrence probability of the optical storage extreme scenarios.

[0129] 702. The probability values ​​of the occurrence of the different extreme operating scenarios are weighted according to the set scenario weights to obtain the risk index.

[0130] 703. Establish a mapping relationship between different extreme operating scenarios and inertia parameters based on the risk indicators.

[0131] Accordingly, in step 102, the probability set of the occurrence of the extreme photovoltaic-storage scenario is used as the feedforward input, and the inertia parameter of the photovoltaic-storage power station is adjusted according to the mapping relationship when the probability of the occurrence of the different extreme operating scenarios changes, so as to obtain the inertia parameter of the target photovoltaic-storage power station.

[0132] In this embodiment, the probability value of the occurrence of the dual-low extreme operating scenario can be described as P_low, and the probability value of the occurrence of the dual-high extreme operating scenario can be described as P_high. The risk index R obtained by weighting the probability values ​​of the two extreme operating scenarios according to the set scenario weights can be described by the following formula:

[0133] , where α and β are weighting coefficients. Since the dual low extreme operating scenario poses a greater threat to the frequency stability of the power grid, the weighting coefficients corresponding to the dual low extreme operating scenario are usually much larger than those corresponding to the dual high extreme operating scenario.

[0134] Accordingly, the inertia parameters of the photovoltaic-storage power station are adjusted according to the mapping relationship, and the resulting target inertia parameter H of the photovoltaic-storage power station can be described by the following formula:

[0135] , where k is the attenuation coefficient, H_max is the maximum virtual inertia coefficient, and H_min is set to 0.

[0136] Specifically, establishing a mapping relationship between different extreme operating scenarios and inertia parameters based on risk indicators can be in the form of a function or a table. Through this mapping relationship, the system can adaptively adjust the inertia output when an increased probability of extreme scenarios is detected. For example, appropriately reducing the inertia coefficient to reserve energy margin ensures that the power station can still provide reliable but not excessive inertia support when extreme scenarios actually occur, significantly improving the robustness and reliability of the entire photovoltaic-storage power station's frequency support function under real and complex operating conditions.

[0137] In this embodiment, by embedding the probability of extreme scenarios of photovoltaic and energy storage as a key decision factor into the photovoltaic and energy storage control system, the photovoltaic and energy storage power station can transform from passively responding to the current state to actively preventing future risks. This makes the control process of the photovoltaic and energy storage power station no longer just a response to the current situation, but includes forward-looking prevention of future risks based on historical patterns, thereby providing more stable and reliable grid inertia support throughout the entire operation life cycle.

[0138] Furthermore, as Figure 1-7 To specifically implement the method, this application provides a photovoltaic-storage power station control device, such as... Figure 8 As shown, the device includes: a construction unit 81, an adjustment unit 82, and a control unit 83.

[0139] Construction unit 81 is used to construct the probability set of occurrence of extreme photovoltaic-storage scenarios. The extreme photovoltaic-storage scenarios are extreme operating scenarios of photovoltaic energy storage systems, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges.

[0140] The adjustment unit 82 is used to take the probability set of the occurrence of the extreme photovoltaic-storage scenario as the feedforward input, and adjust the inertia parameter of the photovoltaic-storage power station when the probability of the occurrence of the different extreme operating scenarios changes, so as to obtain the inertia parameter of the target photovoltaic-storage power station.

[0141] The control unit 83 is used to control the photovoltaic-storage power station according to the inertia parameters of the target photovoltaic-storage power station, so that the photovoltaic-storage power station can use the inertia parameters of the target photovoltaic-storage power station to regulate the energy storage charging and discharging and energy release in different extreme operating scenarios.

[0142] The photovoltaic-storage power station control device provided in this invention, compared with the existing technology that uses a probability-optimal control strategy to control the photovoltaic-storage power station, constructs a probability set of occurrence of extreme photovoltaic-storage scenarios. These extreme scenarios are extreme operating conditions of the photovoltaic energy storage system, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges. Using this probability set as a feedforward input, the inertia parameters of the photovoltaic-storage power station are adjusted when the probability of occurrence of different extreme operating scenarios changes, resulting in target photovoltaic-storage power station inertia parameters. The photovoltaic-storage power station is then controlled based on these target inertia parameters, enabling the photovoltaic-storage power station to regulate energy storage charging and discharging and energy release in different extreme operating scenarios. The entire process uses extreme scenario probability sets to predict the changing trends of different photovoltaic-storage extreme scenarios, and dynamically adjusts the inertia parameters to adapt to different extreme scenarios based on different changing trends. This enables the photovoltaic-storage power station to improve inertia support when photovoltaic energy storage is insufficient and to reserve adjustment space when photovoltaic energy storage is saturated. This allows the photovoltaic-storage power station to have stable inertia support in different extreme operating scenarios, realize the stability of photovoltaic-storage linkage, and improve energy utilization efficiency.

[0143] In specific application scenarios, the building unit includes:

[0144] The acquisition module is used to acquire historical operating data of the photovoltaic-storage power station, including photovoltaic power data and energy storage charge status data.

[0145] The quantization module is used to quantify the probability of occurrence of extreme scenarios of photovoltaic and energy storage based on the historical operation data of the photovoltaic and energy storage power station using variable correlation coefficients, so as to obtain the probability set of occurrence of extreme scenarios of photovoltaic and energy storage.

[0146] In specific application scenarios, the quantization module includes:

[0147] The construction submodule is used to construct sample pairs of data for different extreme operating scenarios based on the historical operating data of the photovoltaic and energy storage power station. The sample pairs of data include ordered sample pairs formed by synchronously associating photovoltaic power data with core variable data.

[0148] The first calculation submodule is used to calculate the observed linkage value between photovoltaic power and energy storage charge state based on the sample pair data of the different extreme operating scenarios using the variable correlation coefficient. The observed linkage value is used to reflect the basic linkage strength between photovoltaic power and energy storage charge state.

[0149] The second calculation submodule is used to calculate the local linkage values ​​under different power thresholds by iteratively setting the photovoltaic power threshold if the observed linkage values ​​meet the linkage conditions, so as to obtain the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0150] The third calculation submodule is used to calculate the probability set of extreme photovoltaic-storage scenarios based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence for different extreme operating scenarios.

[0151] In specific application scenarios, the first computing submodule is specifically used for:

[0152] Based on the sample pairs of data from the different extreme operating scenarios, statistically analyze the consistent and inconsistent valid data pairs in the sample pairs of data.

[0153] The correlation between photovoltaic power and energy storage charge state is quantified using the consistent valid pair data and the inconsistent valid pair data to obtain the observed correlation values ​​between photovoltaic power and energy storage charge state.

[0154] In a specific application scenario, the second calculation submodule is specifically used for:

[0155] A photovoltaic power threshold sequence is set in ascending order, starting from the lowest photovoltaic power and increasing in fixed steps until the highest photovoltaic power.

[0156] Based on each photovoltaic power threshold, sub-sample pairs with photovoltaic power greater than or equal to the corresponding photovoltaic power threshold are selected from the sample pairs data of different extreme operating scenarios to obtain sub-sample pairs data of different extreme operating scenarios;

[0157] Based on the subsample pairs of data from the different extreme operating scenarios, the local linkage values ​​under different power thresholds are calculated using the variable correlation coefficient, thus obtaining the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

[0158] In specific application scenarios, the third computing submodule is specifically used for:

[0159] For different extreme operating scenarios, the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence is taken as the core photovoltaic power threshold of the corresponding extreme operating scenario. Risk sample pairs with photovoltaic power greater than or equal to the corresponding core photovoltaic power threshold are selected from the sample pair data of the different extreme operating scenarios to obtain risk sample pair data of different extreme operating scenarios.

[0160] Based on the risk sample pairs of data from different extreme operating scenarios, a joint distribution model is constructed for each extreme operating scenario to calculate the probability of occurrence of extreme photovoltaic-storage scenarios.

[0161] In specific application scenarios, the device further includes:

[0162] The extraction unit is used to extract the occurrence probability values ​​of different extreme operating scenarios from the occurrence probability of the extreme optical and energy storage scenarios after constructing the occurrence probability set of extreme optical and energy storage scenarios.

[0163] The calculation unit is used to perform weighted calculations on the probability values ​​of the occurrence of different extreme operating scenarios according to the set scenario weights to obtain risk indicators;

[0164] A unit is established to create a mapping relationship between different extreme operating scenarios and inertia parameters based on the risk indicators.

[0165] Accordingly, the adjustment unit is specifically used to take the probability set of the occurrence of the extreme photovoltaic-storage scenario as a feedforward input, and adjust the inertia parameter of the photovoltaic-storage power station according to the mapping relationship when the probability of the occurrence of different extreme operating scenarios changes, so as to obtain the target photovoltaic-storage power station inertia parameter.

[0166] It should be noted that other corresponding descriptions of the functional units involved in the photovoltaic-storage power station control device provided in this embodiment can be found in [reference]. Figures 1-7 The corresponding description in [the document] will not be repeated here.

[0167] Based on the above, Figures 1-7 Accordingly, this application embodiment also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method. Figures 1-7 The control method for the photovoltaic-storage power station is shown.

[0168] Based on this understanding, the technical solution of this application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), and includes several instructions to cause a computer device (such as a personal computer, server, or network device) to execute the methods described in the various implementation scenarios of this application.

[0169] Based on the above, Figures 1-7 The method shown, and Figure 8 To achieve the above objectives, this application also provides a physical device for controlling a photovoltaic-storage power station, as illustrated in the virtual device embodiment. Specifically, this physical device can be a computer, smartphone, tablet, smartwatch, server, or network device, etc. The physical device includes a storage medium and a processor; the storage medium stores a computer program; the processor executes the computer program to achieve the above-described objectives. Figures 1-7 The control method for the photovoltaic-storage power station is shown.

[0170] Optionally, the physical device may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0171] In an exemplary embodiment, see Figure 9 The aforementioned physical equipment includes a communication bus, a processor, a memory, and a communication interface. It may also include input / output interfaces and a display device. The various functional units can communicate with each other via the bus. The memory stores computer programs, and the processor executes the programs stored in the memory to perform the photovoltaic-storage power station control method described in the above embodiments.

[0172] Those skilled in the art will understand that the physical equipment structure for controlling a photovoltaic power station provided in this embodiment does not constitute a limitation on the physical equipment, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0173] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the physical devices controlled by the aforementioned photovoltaic-storage power station, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical devices.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented using software plus necessary general-purpose hardware platforms, or it can be implemented through hardware. By applying the technical solution of this application, compared with the existing methods, this application predicts the changing trends of different extreme photovoltaic-storage scenarios through extreme scenario probability sets, and dynamically adjusts the inertia parameters to adapt to different extreme scenarios for different changing trends. This enables the photovoltaic-storage power station to improve inertia support when photovoltaic energy storage is insufficient and to reserve adjustment space when photovoltaic energy storage is saturated. This allows the photovoltaic-storage power station to have stable inertia support in different extreme operating scenarios, realize the stability of photovoltaic-storage linkage, and improve energy utilization efficiency.

[0175] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of a preferred embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing this application. Those skilled in the art will understand that the modules in the apparatus of the embodiment can be distributed within the apparatus of the embodiment as described, or can be modified to be located in one or more apparatuses different from this embodiment. The modules of the above-described embodiment can be combined into one module, or further divided into multiple sub-modules.

[0176] The serial numbers in this application are for descriptive purposes only and do not represent the superiority or inferiority of any particular implementation scenario. The above disclosures are merely a few specific implementation scenarios of this application; however, this application is not limited thereto, and any variations conceived by those skilled in the art should fall within the protection scope of this application.

Claims

1. A control method for a photovoltaic-storage power station, characterized in that, include: Constructing a probability set for extreme photovoltaic-storage scenarios includes: acquiring historical operating data of a photovoltaic-storage power station, including photovoltaic power data and energy storage charge state data; constructing sample pairs of data for different extreme operating scenarios based on the historical operating data, the sample pairs of data including ordered sample pairs formed by synchronously associating photovoltaic power data with core variable data; calculating the observed linkage values ​​of photovoltaic power and energy storage charge state using variable correlation coefficients based on the sample pairs of data for different extreme operating scenarios, the observed linkage values ​​reflecting the basic linkage strength between photovoltaic power and energy storage charge state; if the observed linkage values ​​meet the linkage condition, calculating the local linkage values ​​under different power thresholds using iterative setting of photovoltaic power thresholds to obtain a linkage value sequence that varies with the power threshold in different extreme operating scenarios; for different extreme operating scenarios, calculating the probability set for the occurrence of extreme photovoltaic-storage scenarios based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence; the extreme photovoltaic-storage scenarios are extreme operating scenarios of photovoltaic energy storage systems, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges. Using the probability set of the occurrence of the extreme photovoltaic-storage scenarios as the feedforward input, the inertia parameters of the photovoltaic-storage power station are adjusted when the probability of the occurrence of different extreme operating scenarios changes, so as to obtain the inertia parameters of the target photovoltaic-storage power station. The photovoltaic-storage power station is controlled based on the inertia parameters of the target photovoltaic-storage power station, so that the photovoltaic-storage power station can regulate the energy storage charging and discharging and energy release using the inertia parameters of the target photovoltaic-storage power station in different extreme operating scenarios.

2. The method according to claim 1, characterized in that, The step of calculating the observed linkage values ​​between photovoltaic power and energy storage charge state using variable correlation coefficients based on sample pairs of data from different extreme operating scenarios includes: Based on the sample pairs of data from the different extreme operating scenarios, statistically analyze the consistent and inconsistent valid data pairs in the sample pairs of data. The correlation between photovoltaic power and energy storage charge state is quantified using the consistent valid pair data and the inconsistent valid pair data to obtain the observed correlation values ​​between photovoltaic power and energy storage charge state.

3. The method according to claim 1, characterized in that, The method of using iterative setting of photovoltaic power thresholds to calculate local linkage values ​​under different power thresholds yields a series of linkage values ​​that change with the power threshold in different extreme operating scenarios, including: A photovoltaic power threshold sequence is set in ascending order, starting from the lowest photovoltaic power and increasing in fixed steps until the highest photovoltaic power. Based on each photovoltaic power threshold, sub-sample pairs with photovoltaic power greater than or equal to the corresponding photovoltaic power threshold are selected from the sample pairs data of different extreme operating scenarios to obtain sub-sample pairs data of different extreme operating scenarios; Based on the subsample pairs of data from the different extreme operating scenarios, the local linkage values ​​under different power thresholds are calculated using the variable correlation coefficient, thus obtaining the linkage value sequence that varies with the power threshold in different extreme operating scenarios.

4. The method according to claim 1, characterized in that, For different extreme operating scenarios, the probability of occurrence of extreme photovoltaic-storage scenarios is calculated based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence, including: For different extreme operating scenarios, the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence is taken as the core photovoltaic power threshold of the corresponding extreme operating scenario. Risk sample pairs with photovoltaic power greater than or equal to the corresponding core photovoltaic power threshold are selected from the sample pair data of the different extreme operating scenarios to obtain risk sample pair data of different extreme operating scenarios. Based on the risk sample pairs of data from different extreme operating scenarios, a joint distribution model is constructed for each extreme operating scenario to calculate the probability of occurrence of extreme photovoltaic-storage scenarios.

5. The method according to any one of claims 1-4, characterized in that, After constructing the probability set of extreme optical-storage scenarios, the method further includes: Extract the occurrence probability values ​​of different extreme operating scenarios from the occurrence probability of the optical storage extreme scenarios; The probability values ​​of the occurrence of the different extreme operating scenarios are weighted according to the set scenario weights to obtain the risk index; Based on the aforementioned risk indicators, establish a mapping relationship between different extreme operating scenarios and inertia parameters; Accordingly, the probability set of the occurrence of the extreme photovoltaic-storage scenarios is used as the feedforward input. When the probability of the occurrence of different extreme operating scenarios changes, the inertia parameters of the photovoltaic-storage power station are adjusted according to the mapping relationship to obtain the inertia parameters of the target photovoltaic-storage power station.

6. A control device for a photovoltaic-storage power station, characterized in that, include: A construction unit is used to construct a probability set of extreme photovoltaic-storage scenarios. This includes: acquiring historical operating data of the photovoltaic-storage power station, including photovoltaic power data and energy storage charge state data; constructing sample pairs of data for different extreme operating scenarios based on the historical operating data, where each sample pair consists of ordered sample pairs formed by synchronously associating core variable data with photovoltaic power data; calculating the observed linkage values ​​of photovoltaic power and energy storage charge state using variable correlation coefficients based on the sample pairs of data for different extreme operating scenarios, where the observed linkage values ​​reflect the basic linkage strength between photovoltaic power and energy storage charge state; if the observed linkage values ​​meet the linkage condition, calculating local linkage values ​​under different power thresholds using iterative setting of photovoltaic power thresholds to obtain a linkage value sequence that varies with the power threshold in different extreme operating scenarios; and calculating the probability set of extreme photovoltaic-storage scenarios based on the photovoltaic power threshold corresponding to the maximum linkage value in the linkage value sequence for each extreme operating scenario. The extreme photovoltaic-storage scenarios are extreme operating scenarios of the photovoltaic energy storage system, including scenarios where photovoltaic power and energy storage charge state are in different extreme operating ranges. The adjustment unit is used to take the probability set of the occurrence of the extreme photovoltaic-storage scenario as the feedforward input, and adjust the inertia parameters of the photovoltaic-storage power station when the probability of the occurrence of the different extreme operating scenarios changes, so as to obtain the target photovoltaic-storage power station inertia parameters. The control unit is used to control the photovoltaic-storage power station according to the inertia parameters of the target photovoltaic-storage power station, so that the photovoltaic-storage power station can regulate the energy storage charging and discharging and energy release using the inertia parameters of the target photovoltaic-storage power station in different extreme operating scenarios.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the photovoltaic-storage power station control method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the photovoltaic-storage power station control method according to any one of claims 1 to 5.

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