An intelligent monitoring and management system for operating state of a photovoltaic power station

Abstract

The present application relates to photovoltaic power station management technical field, especially to a kind of photovoltaic power station operating state intelligent monitoring management system, in active prediction light rejection power limiting aspect, by running perception module, historical power limiting data, current operating data and power grid real-time data are continuously obtained, according to this, historical power limiting characteristics, output acceptance parameter and light rejection early warning parameter are generated by light rejection prediction module, then light rejection decision module determines power station operating state according to early warning parameter, combined with the remaining capacity of energy storage and the difference of predicted light rejection power, different strategies are formulated, and power regulation timing is planned in advance to improve the traditional passive response light rejection mode, realize active prediction and accurate regulation of light rejection power limiting, improve regulation efficiency and reduce power generation loss.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power plant management technology, and in particular to an intelligent monitoring and management system for the operating status of photovoltaic power plants. Background Technology

[0002] With the large-scale development of photovoltaic power plants, the problem of curtailment caused by fluctuations in grid acceptance capacity has become increasingly prominent, becoming a core bottleneck restricting the utilization rate of photovoltaic energy. Existing curtailment technologies are mostly passive responses, relying on grid dispatch instructions to initiate curtailment, lacking a mechanism for advance prediction, and often causing excess power to impact the grid due to delayed response.

[0003] Chinese Patent Publication No. CN120342092B discloses a distributed monitoring and control method for photovoltaic power plants, belonging to the field of photovoltaic power plant monitoring technology. The method specifically includes the following steps: S1, when any local monitoring unit detects abnormal fluctuations in parameters that meet preset trigger conditions, it is marked as an event initiating unit; S2, the event initiating unit, based on a preset topology diagram, identifies other local monitoring units within a defined surrounding range as associated monitoring units and sends a collaborative diagnostic activation command; S3, the associated monitoring units perform enhanced data acquisition, combining local historical operating mode data to generate a lightweight pre-diagnostic report; S4, each associated monitoring unit sends the report to the event initiating unit, which constructs a propagation characteristic model of the abnormal fluctuations; S5, based on the abnormal fluctuation event type output by the model, a preliminary handling command is generated and sent to the affected local monitoring units for execution. This invention improves the flexibility and reliability of photovoltaic power plants in complex operating environments. Therefore, it is evident that the existing technology has the following problems:

[0004] Without combining real-time output of photovoltaic arrays with dynamic coupling analysis of real-time grid capacity, prediction accuracy is poor and the risk of curtailment is easily misjudged. Moreover, the curtailment strategy is fixed and mostly adopts single power regulation without linking energy storage absorption capacity and time sequence optimization. This results in either excessive curtailment causing power generation losses or incomplete curtailment affecting grid stability, making it difficult to meet the dual requirements of efficient photovoltaic utilization and safe grid operation. Summary of the Invention

[0005] Therefore, the present invention provides an intelligent monitoring and management system for the operation status of photovoltaic power plants to overcome the problems of the prior art.

[0006] To achieve the above objectives, the present invention provides an intelligent monitoring and management system for the operation status of a photovoltaic power station. The photovoltaic power station includes a photovoltaic array, a grid-connected inverter, and an energy storage device. The operating parameters of the energy storage device include the remaining capacity and the actual charging power. The operating parameters of the grid-connected inverter include the power adjustment step size and the power curtailment response delay.

[0007] The intelligent monitoring and management system for the operation status of photovoltaic power plants includes:

[0008] The operation sensing module is used to continuously acquire historical power curtailment data, current operation data and real-time grid data of the photovoltaic power station. The historical power curtailment data includes historical power curtailment instructions and historical power curtailment rates. The current operation data includes the real-time output power of the photovoltaic array and the real-time grid data includes the real-time grid capacity.

[0009] The curtailment prediction module is used to determine the historical curtailment characteristics and power output acceptance parameters based on historical curtailment data, current operating data, and real-time grid data, and to determine the curtailment early warning parameters based on the historical curtailment characteristics and power output acceptance parameters.

[0010] The curtailment decision module is used to determine the operating status of the photovoltaic power station based on the curtailment early warning parameters and to formulate a curtailment strategy based on the remaining capacity of the energy storage device, including:

[0011] Adjust the power regulation step size of the grid-connected inverter to limit the power regulation strategy;

[0012] Alternatively, an energy storage consumption strategy and a power regulation strategy can be developed based on the remaining capacity of the energy storage device to calculate energy storage scheduling parameters.

[0013] The execution feedback module is used to issue execution commands based on the judgment result of switching to the curtailment state to complete the power curtailment adjustment and / or energy storage consumption operation, and to feed back the actual curtailment rate data after the strategy is executed to the curtailment prediction module to update the early warning parameters.

[0014] As a preferred technical solution for the intelligent monitoring and management system for the operation status of photovoltaic power plants, the curtailment prediction module determines the distribution of release time periods based on the historical curtailment instructions, determines the numerical change trend of the historical curtailment rate based on the historical curtailment rate, and determines the historical curtailment feature map by associating the distribution of release time periods with the numerical change trend.

[0015] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, the curtailment prediction module determines the power output and acceptance parameters based on the ratio of the real-time output power of the photovoltaic array to the real-time grid acceptance capacity.

[0016] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, the curtailment prediction module determines the corresponding sub-historical curtailment feature map according to the prediction period, and determines the curtailment risk parameter according to the ratio of curtailment duration to total duration in each curtailment rate interval to characterize the curtailment risk.

[0017] Additionally, historical power supply data for the predicted time period is obtained to predict the amount of curtailed solar power.

[0018] Among them, the risk of curtailment of solar power is negatively correlated with the parameter of curtailment of solar power.

[0019] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, the curtailment prediction module determines the curtailment warning parameter based on the ratio of the output acceptance parameter to the curtailment risk parameter, and the curtailment decision module determines the operation status of the photovoltaic power plant based on the relationship between the curtailment warning parameter and the standard warning parameter, including:

[0020] In response to a curtailment warning parameter being greater than or equal to a standard warning parameter, the photovoltaic power station's operating status will be switched to curtailment mode.

[0021] In response to the curtailment warning parameter being less than the standard warning parameter, the photovoltaic power station will maintain its normal operating status.

[0022] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, based on the determination result of switching the operation status of the photovoltaic power plant to a curtailment state, the curtailment decision module formulates a curtailment strategy according to the remaining capacity of the energy storage device, including:

[0023] When the remaining capacity of the energy storage device is greater than or equal to the preset capacity, the curtailment strategy for solar power is determined to be a combined energy storage consumption strategy and a power regulation strategy.

[0024] When the remaining capacity of the energy storage device is less than the preset capacity, the curtailment strategy is determined to be a power regulation strategy.

[0025] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, the curtailment decision module determines whether to implement a power regulation strategy based on the remaining capacity of the energy storage device and the predicted curtailment amount.

[0026] Based on the determination that the remaining capacity is greater than or equal to the predicted curtailment of electricity, it is determined that no power regulation strategy will be implemented.

[0027] Based on the determination that the remaining capacity is less than the predicted amount of abandoned electricity, the power adjustment strategy is determined and the start time of power adjustment is determined in combination with the actual charging power.

[0028] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, the curtailment decision module determines the power adjustment step size based on the output acceptance parameters;

[0029] The power adjustment step size is negatively correlated with the output receiving parameter.

[0030] As a preferred technical solution for the intelligent monitoring and management system of photovoltaic power plant operation status, the curtailment decision module determines the curtailment response delay based on the curtailment risk parameters;

[0031] The power curtailment response delay is positively correlated with the power curtailment risk parameter.

[0032] Compared with existing technologies, the beneficial effects of this invention are as follows: First, it enables early identification and dynamic warning of curtailment risks. By analyzing historical patterns and real-time status, it can predict the contradiction between power fluctuations and grid acceptance capacity in advance, gaining valuable time for proactive intervention. Second, it formulates differentiated optimal curtailment strategies based on the real-time status of energy storage devices, realizing intelligent switching between simply limiting grid-connected power and utilizing energy storage to absorb excess power, thereby minimizing energy waste while ensuring grid security. Third, the system has continuous self-optimization capabilities. By feeding back the actual effects of strategy execution to the prediction model, it continuously calibrates and improves the accuracy of early warning and decision-making, forming a virtuous cycle of increasing accuracy with use. Fourth, the system transforms the original passive curtailment response into proactive and intelligent operation management, significantly enhancing the power plant's ability to cope with grid dispatch and its own output fluctuations.

[0033] In particular, by statistically analyzing the aggregation of historical power curtailment orders over time, the periodic patterns of grid dispatching behavior or local load changes can be identified, which can predict the probability of power curtailment occurring in a specific future period. By analyzing the dynamic changes in historical power curtailment rates, the intensity evolution characteristics of power curtailment events from occurrence to development to end can be grasped. This not only allows for the prediction of whether power curtailment will occur but also for the estimation of its possible severity and development trajectory. This lays the foundation for subsequent decision-making modules to formulate differentiated control strategies that match the expected power curtailment intensity, thus avoiding insufficient or excessive response.

[0034] In particular, by matching the predicted time period with the sub-historical curtailment feature map, the judgment of curtailment risk becomes more targeted. The curtailment risk parameter is quantified by the proportion of curtailment time within the curtailment rate range, realizing a quantifiable representation of risk and replacing subjective experience judgment, which greatly improves the accuracy of risk judgment. Based on the sub-historical curtailment feature map, the historical power supply is calculated and corrected with the current operating conditions, making the curtailment power prediction more in line with the actual operating conditions and highly practical. In addition, the construction of a risk and power linkage analysis system enables curtailment decision-making to formulate strategies based on the degree of risk and the scale of power supply, effectively optimizing the scientific and comprehensive nature of photovoltaic power plant curtailment control, and providing a reliable quantitative basis for the efficient operation of the power plant.

[0035] In particular, by determining the curtailment warning parameter by the ratio of the power input and output parameters to the curtailment risk parameters, the determination of the photovoltaic power plant's operating status is transformed into a quantifiable numerical comparison, replacing the traditional subjective judgment method and significantly improving the accuracy of curtailment status switching. At the same time, using the standard warning parameter as a unified judgment benchmark clarifies the critical conditions for operating status switching, avoiding control errors caused by ambiguous judgment standards. In addition, curtailment strategies are formulated based on the differences in the remaining capacity of energy storage devices. When energy storage capacity is sufficient, energy storage resources are fully utilized to coordinate power regulation, reducing the power generation loss caused by simple power curtailment. When energy storage capacity is insufficient, power regulation is used to ensure the curtailment effect, realizing the quantification, standardization, and flexibility of curtailment control, and significantly improving the scientificity and adaptability of photovoltaic power plant operation control. Attached Figure Description

[0036] Figure 1 This is a connection diagram of the intelligent monitoring and management system for the operation status of a photovoltaic power plant, as described in an embodiment of the present invention.

[0037] Figure 2 This is a flowchart of the light abandonment decision module in an embodiment of the present invention. Detailed Implementation

[0038] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0039] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0040] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0041] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0042] Please see Figure 1 The diagram shown is a connection diagram of the intelligent monitoring and management system for the operation status of a photovoltaic power station according to the present invention. This invention provides an intelligent monitoring and management system for the operation status of a photovoltaic power station. The photovoltaic power station includes a photovoltaic array, a grid-connected inverter, and an energy storage device. The operating parameters of the energy storage device include remaining capacity and actual charging power. The operating parameters of the grid-connected inverter include power adjustment step size and power curtailment response delay.

[0043] The intelligent monitoring and management system for the operation status of photovoltaic power plants includes:

[0044] The operation sensing module is used to continuously acquire historical power curtailment data, current operating data, and real-time grid data of the photovoltaic power plant. The historical power curtailment data includes historical power curtailment commands and historical power curtailment rates. The current operating data includes the real-time output power of the photovoltaic array, and the real-time grid data includes the real-time grid capacity. In implementation, the operation sensing module is responsible for comprehensive data acquisition. It continuously acquires the power plant's historical power curtailment records, real-time power generation, and the grid's real-time capacity through sensors and grid interfaces, providing a reliable data foundation for system decision-making. In one implementation, based on the system's processing capacity and data timeliness requirements, the data sampling interval is set to 1 second, and the specific value is usually determined after preliminary testing and calibration.

[0045] The curtailment prediction module is used to determine historical curtailment characteristics and power output acceptance parameters based on historical curtailment data, current operating data, and real-time grid data, and to determine curtailment early warning parameters based on these characteristics and parameters. In practice, the curtailment prediction module analyzes curtailment patterns in historical data and calculates a quantitative curtailment early warning parameter by combining current photovoltaic output and grid acceptance capacity, thus establishing a correlation model between data and risk.

[0046] The curtailment decision module is used to determine the operating status of the photovoltaic power station based on the curtailment early warning parameters and to formulate a curtailment strategy based on the remaining capacity of the energy storage device, including:

[0047] Adjust the power regulation step size of the grid-connected inverter to limit the power regulation strategy;

[0048] Alternatively, an energy storage consumption strategy and a power regulation strategy can be developed based on the remaining capacity of the energy storage device to calculate energy storage scheduling parameters.

[0049] During implementation, the curtailment decision module executes corresponding strategies based on the remaining capacity of the energy storage device: if the remaining capacity is low, a power limiting regulation strategy is activated to adjust the power regulation step size of the inverter to reduce grid-connected power; if the energy storage device has sufficient space, an energy storage consumption strategy is activated, which will determine the remaining energy storage capacity and coordinate its consumption.

[0050] The execution feedback module is used to issue execution commands based on the judgment result of switching to the curtailment state to complete the power curtailment adjustment and / or energy storage consumption operation, and to feed back the actual curtailment rate data after the strategy is executed to the curtailment prediction module to update the early warning parameters. In implementation, the execution feedback module sends the instructions of the decision module to the inverter and energy storage device to complete the power adjustment and charging operation. At the same time, the system monitors the actual curtailment rate and feeds back the result to the curtailment prediction module for calibration and updating of the early warning model for continuous improvement.

[0051] Specifically, the curtailment prediction module determines the distribution of release periods based on the historical power curtailment instructions, determines the numerical change trend of the historical power curtailment rate based on the historical power curtailment rate, and determines the historical power curtailment feature map by associating the distribution of release periods with the numerical change trend.

[0052] In implementation, the curtailment prediction module constructs a historical power curtailment feature map to extract representative patterns from past power curtailment events, providing a basis for accurately predicting future power curtailment risks. First, it analyzes historical power curtailment orders to determine the typical time periods in which they occur. Statistical methods are used to identify that power curtailment orders are issued most frequently during the morning and afternoon peak periods. At the same time, the module analyzes the sequence data of historical power curtailment rates to calculate their numerical change trends over a certain period. Subsequently, these two dimensions are correlated, and linear regression is used to establish a quantitative relationship between time period distribution and power curtailment rate trends, thereby generating a feature map that can comprehensively reflect the power curtailment patterns.

[0053] Understandably, the historical power rationing feature map transforms scattered historical data into a systematic cognitive model, enabling the system to identify the intrinsic relationship between specific time periods and the severity of power rationing.

[0054] Specifically, the curtailment prediction module determines the power acceptance parameters based on the ratio of the real-time output power of the photovoltaic array to the real-time grid acceptance capacity.

[0055] Understandably, the output-to-acceptance parameter reflects the ratio between the real-time power generation capacity (output) of a photovoltaic power plant and the grid's receiving capacity (acceptance), and can intuitively reflect the matching degree between the two. The larger the output-to-acceptance parameter, the higher the proportion of the real-time output power of the photovoltaic array relative to the grid's receiving capacity, which means that the grid has less margin to accept photovoltaic power and the power plant faces a higher risk of curtailment. The smaller the output-to-acceptance parameter, the lower the proportion of the real-time output power of the photovoltaic array relative to the grid's receiving capacity. At this time, the grid has sufficient margin to accept photovoltaic power and the power plant faces a lower risk of curtailment.

[0056] Understandably, by integrating temporal and intensity patterns in multiple dimensions, a spatiotemporally correlated power rationing profile that is closer to the real scenario is constructed. This reveals the intrinsic connection between different time periods and specific power rationing modes, making the final prediction model no longer a simple superposition of two independent dimensions, but an organic whole. This greatly enhances the system's understanding of complex power rationing scenarios and simulation accuracy, thereby outputting more reliable early warning parameters.

[0057] Specifically, the curtailment prediction module determines the corresponding sub-historical curtailment feature map based on the predicted time period, and determines the curtailment risk parameter based on the ratio of curtailment duration in each curtailment rate interval to the total duration to characterize the curtailment risk. In implementation, the proportion of a curtailment rate interval is determined based on the ratio of curtailment duration in a single curtailment rate interval to the total duration and is denoted as ai. The median of a single curtailment rate interval is denoted as bi. The curtailment risk for the predicted time period is calculated as a1×(1-b1)+a2×(1-b2)+……+ai×(1-bi)+……+an×(1-bn), where n is the number of curtailment rate intervals.

[0058] In addition, the historical power supply corresponding to the predicted time period is obtained to predict the amount of curtailed solar power; in practice, the power supply for the predicted time period can be calculated based on the current power supply parameters, and the difference between it and the historical power supply is determined as the predicted amount of curtailed solar power.

[0059] The risk of curtailment of solar power is negatively correlated with the parameter of curtailment risk; that is, the smaller the parameter of curtailment risk, the higher the risk of curtailment.

[0060] It is understood that the active switching of the operating state in this invention is considered when the output receiving parameter is less than 1. When the output receiving parameter is ≥1, it indicates that the output has exceeded the receiving capacity. At this time, the operating state is passively switched rather than actively predicted and switched.

[0061] Please see Figure 2 The diagram shows the workflow of the curtailment decision-making module in an embodiment of the present invention. Specifically, the curtailment prediction module determines the curtailment warning parameter based on the ratio of the output acceptance parameter to the curtailment risk parameter, and the curtailment decision-making module determines the operating status of the photovoltaic power station based on the relationship between the curtailment warning parameter and the standard warning parameter, including:

[0062] In response to a curtailment warning parameter being greater than or equal to a standard warning parameter, the photovoltaic power station's operating status will be switched to curtailment mode.

[0063] In response to the curtailment warning parameter being less than the standard warning parameter, the photovoltaic power station will maintain its normal operating status.

[0064] In practice, the standard early warning parameter is usually 1.5 to 2. At this time, the risk parameter of curtailment of solar power is relatively small, that is, the historical risk of curtailment of solar power is very large, so it is necessary to actively switch the operating status; preferably, it is set to 1.8.

[0065] Specifically, based on the determination result of switching the photovoltaic power plant's operating state to a curtailment state, the curtailment decision module formulates a curtailment strategy according to the remaining capacity of the energy storage device, including:

[0066] When the remaining capacity of the energy storage device is greater than or equal to the preset capacity, the curtailment strategy for solar power is determined to be a combined energy storage consumption strategy and a power regulation strategy.

[0067] When the remaining capacity of the energy storage device is less than the preset capacity, the curtailment strategy for solar power is determined to be a power regulation strategy.

[0068] In implementation, the preset capacity is 5% to 10% of the standard energy storage capacity of the energy storage device, usually 5%. Below this value, the remaining energy storage capacity is no longer sufficient to effectively buffer the curtailed solar power, and deep discharge will significantly damage the cycle life of the energy storage battery. Above this value, the energy storage still has usable absorption space to participate in the storage of curtailed solar power. This makes the switching between energy storage absorption coordinated power regulation and pure power regulation strategies more operable, improves the efficiency of regulation response, and can make full use of energy storage resources to absorb part of the curtailed solar power when the remaining energy storage capacity meets the standard, reducing the power regulation range of the grid-connected inverter and reducing the power generation loss caused by simple power limiting. Setting the usual value to 5% can also maximize the absorption potential of energy storage equipment while ensuring its safety, and achieve a balance between energy storage protection and power plant power generation benefits.

[0069] Specifically, based on the curtailment strategy for solar power and the coordinated power regulation strategy for energy storage consumption, the curtailment decision module determines whether to implement the power regulation strategy based on the remaining capacity of the energy storage device and the predicted amount of curtailed solar power.

[0070] Based on the determination that the remaining capacity is greater than or equal to the predicted curtailment of electricity, it is determined that no power regulation strategy will be implemented.

[0071] Based on the determination that the remaining capacity is less than the predicted amount of abandoned electricity, the power adjustment strategy is determined and the start time of power adjustment is determined in combination with the actual charging power.

[0072] Understandably, the remaining capacity of an energy storage device directly determines the upper limit of curtailed solar power it can absorb. When the remaining capacity is greater than or equal to the predicted curtailed solar power, the energy storage can independently absorb all the excess power without requiring inverter power limiting to complete the curtailment process. When the remaining capacity is less than the predicted curtailed solar power, the energy storage can only absorb a portion of the curtailed solar power, requiring power regulation to supplement the absorption of the remaining curtailed solar power. Furthermore, the actual charging power determines the time it takes for the energy storage to absorb the predicted curtailed solar power. By calculating the estimated time for the energy storage to reach full capacity using this parameter, the start time of power regulation can be accurately determined, ensuring that power regulation matches the rhythm of energy storage charging and avoiding premature or late power regulation. In practice, the start time of power regulation is usually earlier than the estimated time to avoid the impact of charging interruption at the moment the energy storage device reaches full capacity. Additionally, the energy storage device... The actual charging process of the device is affected by factors such as battery internal resistance and temperature, and its charging power is not an absolutely constant value. If the power regulation is started exactly at the expected time when the energy storage is fully charged, the charging circuit will be cut off instantly if the energy storage is fully charged in advance due to sudden operating conditions. If the inverter does not reduce the power in time, the excess waste power will directly impact the power grid. At the same time, the energy storage battery will also generate voltage spikes due to the sudden stop of charging, which will accelerate the aging of the battery plates. However, starting the power regulation in advance allows the inverter to gradually reduce the power as the energy storage approaches full capacity, so that the energy storage charging power decreases smoothly with the reduction of the inverter output power until the energy storage is fully charged, achieving a seamless connection between charging and power regulation, and completely avoiding equipment impact and power fluctuations caused by charging interruption. Preferably, the start time of power regulation is usually 10 minutes earlier than the expected time.

[0073] Specifically, the curtailment decision module determines the power adjustment step size based on the output acceptance parameters;

[0074] The power adjustment step size is negatively correlated with the output receiving parameter.

[0075] In implementation, the output-to-grid parameter reflects the supply-demand matching degree between the real-time output of the photovoltaic array and the grid's capacity. The larger this parameter is, the higher the proportion of photovoltaic output relative to grid capacity, and the more prominent the grid supply-demand contradiction. Based on the supply-demand matching logic, a smaller power adjustment step size is needed to achieve fine-tuning of grid-connected power and avoid sudden changes in grid power caused by excessive adjustment in a single step. Conversely, the smaller the output-to-grid parameter is, the more moderate the supply-demand contradiction is, and a larger adjustment step size can be used to improve the efficiency of power curtailment control, thereby ensuring that the power adjustment range is precisely matched with the degree of grid supply-demand imbalance.

[0076] During implementation, historical data from the past 12 months was collected, and grid voltage fluctuation and inverter regulation efficiency data corresponding to different output parameters were screened. The mapping relationship between these data and the optimal step size (the optimal step size refers to the step size that avoids voltage fluctuations exceeding grid standards while enabling rapid regulation) was established, and the power regulation step size was matched according to the mapping relationship.

[0077] Specifically, the curtailment decision module determines the power curtailment response delay based on the curtailment risk parameters;

[0078] The power curtailment response delay is positively correlated with the power curtailment risk parameter.

[0079] In practice, when the actual risk of curtailment is high (small parameters), the power curtailment response delay needs to be shortened to allow the inverter to execute the power curtailment command quickly and reduce the excess grid-connected power. When the actual risk of curtailment is low (large parameters), the response delay can be extended to give the inverter more time to adjust and avoid power fluctuations caused by excessively fast response, thus achieving the adaptation of power curtailment response speed to the level of curtailment risk.

[0080] During implementation, historical curtailment risk parameters and time delay-grid connection power deviation data are statistically analyzed to screen the safe time delay range (safe time delay refers to the range that avoids both excessive time delay leading to over-grid connection and excessive time delay causing power fluctuation). The historical curtailment risk parameters at each level are divided into several levels (usually 4 levels). At least 3 field tests are conducted at each level to test the excess grid connection corresponding to each curtailment response time delay, and the time delay corresponding to the minimum excess grid connection is selected as the curtailment response time delay for that level.

[0081] Understandably, by dynamically adjusting the step size of the power input parameters, the power adjustment range can be made to match the actual supply and demand of the power grid, avoiding overly aggressive dispatching or insufficient control. By combining the risk parameters of curtailment with the setting of response delays, the curtailment execution rhythm can be matched with the risk level, significantly reducing the deviation of subjective control and making curtailment operations more quantitatively based. When the supply and demand contradiction is eased, a large step size adjustment can quickly complete the curtailment and reduce the number of adjustments and the power generation loss caused by ineffective equipment operation. When the supply and demand contradiction is tense, a small step size adjustment can avoid a cliff drop in grid-connected power and reduce the impact on grid voltage and frequency. When the risk is low, extending the delay can maximize the utilization of energy storage or the grid's temporary acceptance capacity, reduce unnecessary curtailment, and improve the effective utilization of photovoltaic power while meeting curtailment requirements.

[0082] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent monitoring and management system for the operation status of a photovoltaic power station, wherein the photovoltaic power station includes a photovoltaic array, a grid-connected inverter and an energy storage device, wherein the operating parameters of the energy storage device include the remaining capacity and the actual charging power, and the operating parameters of the grid-connected inverter include the power adjustment step size and the power curtailment response delay; Its features are, The intelligent monitoring and management system for the operation status of photovoltaic power plants includes: The operation sensing module is used to continuously acquire historical power curtailment data, current operation data and real-time grid data of the photovoltaic power station. The historical power curtailment data includes historical power curtailment instructions and historical power curtailment rates. The current operation data includes the real-time output power of the photovoltaic array and the real-time grid data includes the real-time grid capacity. The curtailment prediction module is used to determine the historical curtailment characteristics and power output acceptance parameters based on historical curtailment data, current operating data, and real-time grid data, and to determine the curtailment early warning parameters based on the historical curtailment characteristics and power output acceptance parameters. The curtailment prediction module determines the distribution of release periods based on the historical power curtailment instructions, determines the numerical change trend of the historical power curtailment rate based on the historical power curtailment rate, and determines the historical power curtailment feature map by associating the distribution of release periods with the numerical change trend. The curtailment prediction module determines the corresponding sub-historical curtailment feature map based on the prediction period, and determines the curtailment risk parameter based on the ratio of curtailment duration to total duration in each curtailment rate interval to characterize the curtailment risk. In addition, historical power supply for the predicted period is obtained to predict the amount of curtailed solar power. Among them, the risk of curtailment of solar power is negatively correlated with the parameter of curtailment of solar power; The curtailment decision module is used to determine the operating status of the photovoltaic power station based on the curtailment early warning parameters and to formulate a curtailment strategy based on the remaining capacity of the energy storage device, including: Power regulation strategy that adjusts the power regulation step size of grid-connected inverters; Alternatively, an energy storage consumption strategy and a power regulation strategy can be developed based on the remaining capacity of the energy storage device to calculate energy storage scheduling parameters. The curtailment prediction module determines the curtailment warning parameter based on the ratio of the output acceptance parameter to the curtailment risk parameter. The curtailment decision module determines the operating status of the photovoltaic power station based on the relationship between the curtailment warning parameter and the standard warning parameter, including: In response to a curtailment warning parameter being greater than or equal to a standard warning parameter, the photovoltaic power station's operating status will be switched to curtailment mode. In response to the curtailment warning parameter being less than the standard warning parameter, the photovoltaic power station will maintain its normal operating status. The execution feedback module is used to issue execution commands based on the judgment result of switching to the curtailment state to complete power regulation and / or energy storage consumption operations, and to feed back the actual curtailment rate data after the strategy is executed to the curtailment prediction module to update the early warning parameters.

2. The intelligent monitoring and management system for the operation status of a photovoltaic power station according to claim 1, characterized in that, The curtailment prediction module determines the power input parameters based on the ratio of the real-time output power of the photovoltaic array to the real-time grid capacity.

3. The intelligent monitoring and management system for the operation status of a photovoltaic power station according to claim 1, characterized in that, Based on the determination result of switching the photovoltaic power plant's operating status to curtailment status, the curtailment decision module formulates a curtailment strategy according to the remaining capacity of the energy storage device, including: When the remaining capacity of the energy storage device is greater than or equal to the preset capacity, the curtailment strategy for solar power is determined to be a combined energy storage consumption strategy and a power regulation strategy. When the remaining capacity of the energy storage device is less than the preset capacity, the curtailment strategy is determined to be a power regulation strategy.

4. The intelligent monitoring and management system for the operation status of a photovoltaic power station according to claim 3, characterized in that, The curtailment decision module determines whether to implement a power regulation strategy based on the remaining capacity of the energy storage device and the predicted amount of curtailed solar power. Based on the determination that the remaining capacity is greater than or equal to the predicted curtailment of electricity, it is determined that no power regulation strategy will be implemented. Based on the determination that the remaining capacity is less than the predicted amount of abandoned electricity, the power adjustment strategy is determined and the start time of power adjustment is determined in combination with the actual charging power.

5. The intelligent monitoring and management system for the operation status of a photovoltaic power station according to claim 3, characterized in that, The curtailment decision module determines the power adjustment step size based on the output acceptance parameters. The power adjustment step size is negatively correlated with the output receiving parameter.

6. The intelligent monitoring and management system for the operation status of a photovoltaic power station according to claim 3, characterized in that, The curtailment decision module determines the power curtailment response delay based on the curtailment risk parameters; The power curtailment response delay is positively correlated with the power curtailment risk parameter.

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