Photovoltaic power station power generation scheduling method based on multi-source data

Abstract

The invention relates to the technical field of power generation scheduling, and discloses a photovoltaic power station power generation scheduling method based on multi-source data, which comprises the following steps: dividing a photovoltaic power station into a plurality of photovoltaic string units, and obtaining expected operation data and cloud layer data of the photovoltaic power station; generating a first power curve of each photovoltaic string unit according to the expected operation data and cloud layer data; identifying a section meeting a power change condition in the first power curve, and marking the section meeting the power change condition as a dangerous section; dividing the danger level of each danger section according to the power data of the danger sections; and setting an adjustment scheme according to the danger level. According to the method, power station resources are flexibly allocated, the influence of external factors on the power station is refined to each photovoltaic string unit, and the operation condition of the power station is flexibly scheduled according to different characteristics of the photovoltaic string units.

Description

Technical Field

[0001] This application relates to the field of power generation dispatching technology, and in particular to a photovoltaic power plant power generation dispatching method based on multi-source data. Background Technology

[0002] As the proportion of photovoltaic power generation in the energy structure continues to increase, the large-scale grid-connected operation of photovoltaic power plants poses a severe challenge to the stability of the power system. The output power of photovoltaic power generation has significant intermittent and fluctuating characteristics. Especially under cloudy weather conditions, the rapid movement of clouds can cause drastic changes in the output power of photovoltaic power plants, and such power fluctuations bring great difficulties to grid dispatch.

[0003] Existing photovoltaic power plant power dispatching methods suffer from the following technical shortcomings: First, insufficient power prediction accuracy. Traditional methods are mostly based on a single data source or simple weather forecasts, failing to accurately predict the refined impact of cloud movement on individual photovoltaic string units, resulting in a lack of foresight and accuracy in dispatching decisions. Second, rigid and inflexible dispatching strategies. Existing technologies often employ fixed dispatching patterns, unable to provide tiered responses based on the severity of power fluctuations, easily leading to under- or over-dispatching. Third, inadequate resource allocation. The lack of a comprehensive evaluation mechanism for the status of photovoltaic string units prevents intelligent allocation of dispatching tasks based on actual equipment operating conditions, impacting equipment lifespan and dispatching effectiveness.

[0004] Especially when dealing with sudden power fluctuations, existing technologies struggle to respond effectively and in a timely manner. When power anomalies are detected, manual intervention is usually required, resulting in significant response delays and failing to meet the grid's stringent power quality requirements. Furthermore, insufficient coordination between different dispatch resources leads to low utilization rates of these resources. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a photovoltaic power plant power generation scheduling method based on multi-source data, aiming to obtain a technical solution that can reasonably allocate the power resources of the inverter and flexibly formulate scheduling plans based on the impact of the external environment on each photovoltaic string unit.

[0006] In some embodiments of this application, a photovoltaic power plant power generation scheduling method based on multi-source data is provided, characterized by comprising:

[0007] The photovoltaic power station is divided into multiple photovoltaic string units, and the expected operating data of the photovoltaic power station and cloud data are obtained.

[0008] The first power curve of each photovoltaic string unit is generated based on the expected operating data and cloud data.

[0009] Identify the segments in the first power curve that meet the power change conditions, and mark the segments that meet the power change conditions as dangerous segments;

[0010] Based on the power data of the dangerous sections, the danger level of each dangerous section is classified.

[0011] Adjustment schemes are set according to the aforementioned hazard levels.

[0012] In some embodiments of this application, generating the first power curve for each photovoltaic string unit includes:

[0013] Obtain cloud data for the forecast period;

[0014] Multiple data collection points are set within the prediction period;

[0015] Acquire cloud movement speed and direction data at each data collection point;

[0016] The cloud projection position at each data collection point is generated based on the cloud movement speed data and cloud movement direction data.

[0017] The shading area of ​​each photovoltaic string unit at each data acquisition point is determined based on the projection position.

[0018] The first power curve of each photovoltaic string unit is generated based on the shading area and irradiance data.

[0019] In some embodiments of this application, the division of the hazard levels of each hazardous section includes:

[0020] Obtain the power change rate data for each of the first power curves;

[0021] Set the primary amplitude threshold and the secondary amplitude threshold;

[0022] The intervals with a power change rate greater than the first-level amplitude threshold are designated as first-level dangerous zones; the intervals with a power change rate less than the first-level amplitude threshold but greater than the second-level amplitude threshold are designated as second-level dangerous zones; and the intervals with a power change rate less than the second-level amplitude threshold are designated as third-level dangerous zones.

[0023] In some embodiments of this application, the setting adjustment scheme includes:

[0024] A priority sequence for each photovoltaic string unit is generated based on the first power curve and expected operating data of each photovoltaic string unit.

[0025] A first scheduling method is set based on the hazard level and the expected operational data;

[0026] The second scheduling method is determined based on the expected operating data of each photovoltaic string unit and the priority sequence.

[0027] In some embodiments of this application, generating the priority sequence of each photovoltaic string unit includes:

[0028] Obtain the damage level S and power margin Y of each photovoltaic string unit. i ;

[0029] Generate a priority score P for each photovoltaic string unit;

[0030] P i =k1*Y i +(1-k2*S i );

[0031] Among them, P i The priority score for the i-th photovoltaic string unit; k1 is the first threshold; Y i S represents the power margin of the i-th photovoltaic string unit; k2 represents the second threshold; S i Let represent the damage level of the i-th photovoltaic string unit;

[0032] All photovoltaic string units are sorted from high to low according to priority scores to generate a priority sequence.

[0033] In some embodiments of this application, the method for setting a first scheduling method based on the hazard level and the expected operating data includes:

[0034] Obtain the power margin data for each photovoltaic string unit;

[0035] When the power margin of a photovoltaic string unit in a Class I hazardous section is less than the margin threshold, a Class I scheduling is implemented.

[0036] When the power margin of a photovoltaic string unit with a first-level danger zone is greater than the margin threshold, second-level scheduling is performed.

[0037] For photovoltaic string units with secondary hazardous sections, Class I scheduling is implemented;

[0038] For photovoltaic string units with level-three danger zones, category-two scheduling is implemented;

[0039] Photovoltaic string units subject to Category I scheduling are designated as Category I units, and photovoltaic string units subject to Category II scheduling are designated as Category II units.

[0040] In some embodiments of this application, the second type of scheduling includes:

[0041] Obtain power change and power margin data for the two types of cells;

[0042] The two types of units are sorted according to a priority sequence;

[0043] Based on the power margin data, the power change is allocated to the two types of units.

[0044] In some embodiments of this application, the scheduling includes:

[0045] To obtain the power change of a class of cells;

[0046] Obtain the electrical energy storage capacity of the reserve module;

[0047] For units with energy storage greater than the integral value of power change, a reserve module is used for scheduling; for units with energy storage less than the integral value of power change, a reserve module and an inverter are used together for scheduling.

[0048] In some embodiments of this application, the degree of damage includes:

[0049] Obtain the expected lifetime M0 of each photovoltaic string unit;

[0050] Obtain the actual lifetime M1 of each photovoltaic string unit;

[0051] Generate the damage degree S of each photovoltaic string unit;

[0052] S=M1 / M0;

[0053] Where S represents the damage level of the photovoltaic string unit; M1 represents the actual lifespan of the photovoltaic string unit; and M0 represents the expected lifespan of the photovoltaic string unit.

[0054] In some embodiments of this application, the second scheduling method includes:

[0055] Obtain the active power and reactive power data of the photovoltaic string unit where the first scheduling occurs;

[0056] Obtain the change in active power of the photovoltaic string unit that has undergone the first scheduling;

[0057] Obtain the power ratio and power margin of the photovoltaic string units that have undergone the first scheduling;

[0058] Based on the power ratio, the power change of the photovoltaic string unit that has undergone the first scheduling is calculated to generate the reactive power change.

[0059] When the change in reactive power is less than the power margin, the photovoltaic string unit is pre-scheduled; when the change in reactive power is greater than the power margin, the photovoltaic string unit is not pre-scheduled.

[0060] Compared with existing technologies, the photovoltaic power plant power generation scheduling method based on multi-source data in this application has the following advantages:

[0061] A smart power generation dispatching system integrating power prediction, risk assessment, and adaptive dispatching was constructed. This method achieves accurate prediction of power fluctuations at the unit level of photovoltaic strings by fusing expected power plant operating data with cloud motion data. After power prediction, the system not only identifies potential power fluctuation risks but also accurately determines the severity and scope of impact of power fluctuations through an established hazard level assessment model. This significantly improves the stability and safety of power plant operation.

[0062] Meanwhile, this method enables dynamic optimization of scheduling strategies and rational allocation of resources. Based on the real-time operating status and equipment health of each photovoltaic string unit, the system intelligently generates a priority sequence, ensuring that scheduling tasks are preferentially allocated to units with sufficient power margin and good equipment condition. This decision-making mechanism based on multi-dimensional evaluation significantly improves the utilization efficiency of scheduling resources, effectively smoothing power fluctuations while maximizing the power plant's generation revenue. Attached Figure Description

[0063] Figure 1 This application presents a photovoltaic power plant power generation scheduling method based on multi-source data. Detailed Implementation

[0064] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0065] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0066] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0067] In the description of this application, it should be noted that, unless otherwise expressly 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 between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0068] like Figure 1 As shown in the figure, a photovoltaic power plant power generation scheduling method based on multi-source data according to an embodiment of this application includes:

[0069] The photovoltaic power station is divided into multiple photovoltaic string units to obtain the expected operating data of the photovoltaic power station and cloud data;

[0070] The first power curve of each photovoltaic string unit is generated based on the expected operating data and cloud data.

[0071] Identify the sections in the first power curve that meet the power change conditions, and mark these sections as dangerous sections.

[0072] Based on the power data of the dangerous sections, the danger level of each dangerous section is classified.

[0073] Adjustment plans are set according to the level of danger.

[0074] Specifically, the expected operating data includes the power data of each photovoltaic string during the forecast period, the damage level of each photovoltaic string unit, and the power data including active power data, reactive power data, power ratio, and power margin data.

[0075] Specifically, a photovoltaic power plant is a pre-planned collection of multiple photovoltaic modules called a photovoltaic string unit; a photovoltaic string unit is connected to an inverter, which controls the power changes of the two photovoltaic string units.

[0076] Specifically, the expected operating data of a photovoltaic power plant includes the active and reactive power of each photovoltaic string unit and the amount of electricity generated.

[0077] Specifically, cloud data includes the spatial location of clouds, the location of the shadows cast by clouds on photovoltaic modules, the duration of the shadows, the impact of the shadows on light intensity, the speed and direction of cloud movement, and the cloud coverage area.

[0078] Specifically, the first power curve represents the power data within the forecast period, which is a three-hour period in the future.

[0079] In some embodiments of this application, generating the first power curve for each photovoltaic string unit includes:

[0080] Obtain cloud data for the forecast period;

[0081] Set up multiple data collection points within the forecast period;

[0082] Acquire cloud movement speed and direction data at each data collection point;

[0083] The projected position of the cloud layer at each data collection point is generated based on cloud layer movement speed data and cloud layer movement direction data.

[0084] The shading area of ​​each photovoltaic string unit at each data acquisition point is determined based on the projection position.

[0085] The first power curve for each photovoltaic string unit is generated based on the shading area and irradiance data.

[0086] Specifically, each photovoltaic string unit corresponds to a first power curve.

[0087] Specifically, a data collection point is set up every 10 minutes.

[0088] Specifically, the shading area of ​​each photovoltaic string unit at each data collection point is determined based on the projection position. This involves obtaining the position of the data collection point and then obtaining the position information of each photovoltaic string unit, thereby obtaining the shading area of ​​each cloud projection on the photovoltaic string unit.

[0089] Specifically, the first power curve for each photovoltaic string unit is generated based on the shading area and irradiance data. This involves: acquiring irradiance data for each photovoltaic string unit using sensors, obtaining power data from the inverter at each data acquisition point, and then proportionally interpolating the calculated power data to obtain the complete first power curve. This irradiance data represents the overall irradiance of the entire photovoltaic string after cloud shading. After obtaining the irradiance, the DC power generated by the photovoltaic string is calculated using the formula: Pd = Ge * At * ηp * [1 - α * (Tc - TS)]; where: Pd is the DC output power of the photovoltaic string (W); At is the total light-receiving area of ​​the photovoltaic string (m²); ηp is the conversion efficiency of the photovoltaic panel under standard test conditions, preferably 0.25; ηp is the power temperature coefficient of the photovoltaic cell, preferably -0.5% / °C. It is usually negative, indicating that as temperature increases, the output power decreases; Tc is the average operating temperature of the photovoltaic cell during the re-prediction period (°C); TS is the standard test temperature, preferably 25°C; and then the AC power output of the inverter is calculated. Pa = Pd * ηi; where: Pa: AC active power output by the inverter (W) Pd: DC power of the photovoltaic string (W) ηi: inverter conversion efficiency, preferably 95%, thereby obtaining the power data of each data acquisition point, and setting this AC power as the data of the first power curve.

[0090] In some embodiments of this application, the hazard levels of each hazardous section are classified, including:

[0091] Obtain the power change rate data for each of the first power curves;

[0092] Set the primary amplitude threshold and the secondary amplitude threshold;

[0093] The intervals with a power change rate greater than the first-level amplitude threshold are designated as first-level dangerous zones; the intervals with a power change rate less than the first-level amplitude threshold but greater than the second-level amplitude threshold are designated as second-level dangerous zones; and the intervals with a power change rate less than the second-level amplitude threshold are designated as third-level dangerous zones.

[0094] Specifically, the power change rate data of the first power curve is the slope data of the entire power curve, that is, the power curve is a graph with time on the x-axis and power data on the y-axis.

[0095] Specifically, for the interval where the power change rate is greater than a preset amplitude threshold, the corresponding independent variable segment is divided into segments with different risk levels.

[0096] Specifically, the preferred first-level amplitude threshold is 2 kW / s; the preferred second-level amplitude threshold is 0.5 kW / s.

[0097] In some embodiments of this application, an adjustment scheme is set, including:

[0098] A priority sequence for each photovoltaic string unit is generated based on the first power curve and expected operating data of each photovoltaic string unit.

[0099] The first scheduling method is set based on the hazard level and expected operational data;

[0100] The second scheduling method is determined based on the expected operating data and priority sequence of each photovoltaic string unit.

[0101] Specifically, active power is the electrical power required to maintain the normal operation of the equipment, which is the conversion of electrical energy into other forms of energy; reactive power is the energy used to establish and maintain the magnetic and electric fields of electrical equipment, which does not consume electrical energy but is necessary to exist; active power data and reactive power data are the data of the inverter corresponding to each photovoltaic string unit.

[0102] Specifically, the first scheduling method is dynamically selected based on the hazard level. For Level 1 hazard sections, equipment safety is given priority. When the inverter temperature is too high or the power margin is low, reserve module scheduling is used; otherwise, inverter scheduling is used.

[0103] In some embodiments of this application, the priority sequence of each photovoltaic string unit is generated, including:

[0104] Obtain the damage level S and power margin Y of each photovoltaic string unit. i ;

[0105] Generate a priority score P for each photovoltaic string unit;

[0106] P i =k1*Y i +(1-k2*S i );

[0107] Among them, P i The priority score for the i-th photovoltaic string unit; k1 is the first threshold; Y i S represents the power margin of the i-th photovoltaic string unit; k2 represents the second threshold; S i Let represent the damage level of the i-th photovoltaic string unit;

[0108] All photovoltaic string units are sorted from high to low according to priority scores to generate a priority sequence.

[0109] Specifically, the damage level is used to measure the proportion of the total working time of the inverter and its corresponding photovoltaic string unit hardware.

[0110] Specifically, k1 is preferably 0.8; k2 is preferably 0.4.

[0111] Specifically, the priority score is an analysis of the performance of photovoltaic string units, which takes into account both the degree of damage and the power margin. The higher the score, the better the performance, and these strings are given priority in scheduling.

[0112] In some embodiments of this application, a first scheduling method is set based on the hazard level and expected operational data, including:

[0113] Obtain the power margin data for each photovoltaic string unit;

[0114] When the power margin of a photovoltaic string unit in a Class I hazardous section is less than the margin threshold, a Class I scheduling is implemented.

[0115] When the power margin of a photovoltaic string unit with a first-level danger zone is greater than the margin threshold, second-level scheduling is performed.

[0116] For photovoltaic string units with secondary hazardous sections, Class I scheduling is implemented;

[0117] For photovoltaic string units with level-three danger zones, category-two scheduling is implemented;

[0118] Photovoltaic string units subject to Category I scheduling are designated as Category I units, and photovoltaic string units subject to Category II scheduling are designated as Category II units.

[0119] Specifically, one type of scheduling utilizes reserve modules for power scheduling; the other type uses inverters for power scheduling.

[0120] Specifically, the first-level danger zone is characterized by a sharp rise and fall in power within a very short period of time. In such cases, the power frequency and voltage are severely impacted, requiring the use of reserve modules for dispatch when their capacity is sufficient; otherwise, inverters are needed. The second-level danger zone is characterized by normal fluctuations, for which the reserve modules have sufficient capacity; the second-level danger zone also involves smaller fluctuations, for which inverter dispatch is sufficient.

[0121] In some embodiments of this application, two types of scheduling are performed, including:

[0122] Obtain power change and power margin data for the two types of cells;

[0123] Sort the two types of units according to the priority sequence;

[0124] Based on power margin data, the power variation is allocated to the two types of cells.

[0125] Specifically, the danger zone includes all, as well as level two and level three danger zones.

[0126] Specifically, power margin is the ratio of a power inverter’s total unused active power to its apparent power.

[0127] Specifically, the power factor is the ratio of the inverter's reactive power to its active power.

[0128] Specifically, the power change is divided according to the power margin of each inverter, with the power change being proportional to the power margin.

[0129] In some embodiments of this application, a type of scheduling is performed, including:

[0130] To obtain the power change of a class of cells;

[0131] Obtain the electrical energy storage capacity of the reserve module;

[0132] For units with energy storage greater than the integral value of power change, a reserve module is used for scheduling; for units with energy storage less than the integral value of power change, a reserve module and an inverter are used together for scheduling.

[0133] Specifically, the storage module is a storage mechanism with a storage limit. It absorbs electrical energy during peak power generation and releases electrical energy during off-peak power generation or when it needs to be discharged.

[0134] Specifically, the power change is the amount by which the power of the corresponding photovoltaic string unit increases or decreases due to cloud cover.

[0135] Specifically, the electrical energy storage is the energy absorbed from the inverter when the power generation is high, and the energy released when the power generation is low, or when dispatch is required.

[0136] Specifically, the third threshold is preferably 0.8 times the rated energy reserve of the reserve module.

[0137] In some embodiments of this application, the degree of damage includes:

[0138] Obtain the expected lifetime M0 of each photovoltaic string unit;

[0139] Obtain the actual lifetime M1 of each photovoltaic string unit;

[0140] Generate the damage degree S of each photovoltaic string unit;

[0141] S=M1 / M0;

[0142] Where S represents the damage level of the photovoltaic string unit; M1 represents the actual lifespan of the photovoltaic string unit; and M0 represents the expected lifespan of the photovoltaic string unit.

[0143] Specifically, the expected lifespan is the preset working time of the entire string, provided by the manufacturer.

[0144] Specifically, the actual lifespan is the duration the string has been in operation, and the degree of damage is obtained by dividing this duration by the preset working time.

[0145] In some embodiments of this application, the second scheduling method includes:

[0146] Obtain the active power and reactive power data of the photovoltaic string unit where the first scheduling occurs;

[0147] Obtain the change in active power of the photovoltaic string unit that has undergone the first scheduling;

[0148] Obtain the power ratio and power margin of the photovoltaic string units that have undergone the first scheduling;

[0149] Based on the power ratio, the power change of the photovoltaic string unit that has undergone the first scheduling is calculated to generate the reactive power change.

[0150] When the change in reactive power is less than the power margin, the photovoltaic string unit is pre-scheduled; when the change in reactive power is greater than the power margin, the photovoltaic string unit is not pre-scheduled.

[0151] Specifically, when the change in reactive power is less than the power margin (i.e., the change in reactive power exceeds the range of the reserve module), no further adjustment is made; otherwise, adjustment is made.

[0152] Specifically, when the active power decreases, the pre-dispatch increases the reactive power of the inverter. The active power P1, reactive power P2, and apparent power P0 satisfy the condition that the square of P1 plus the square of P2 equals the square of P0.

[0153] The above are merely preferred embodiments of this application. It should be noted that, for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A photovoltaic power plant power generation scheduling method based on multi-source data, characterized in that, include: The photovoltaic power station is divided into multiple photovoltaic string units, and the expected operating data of the photovoltaic power station and cloud data are obtained. The first power curve of each photovoltaic string unit is generated based on the expected operating data and cloud data. Identify the segments in the first power curve that meet the power change conditions, and mark the segments that meet the power change conditions as dangerous segments; Based on the power data of the dangerous sections, the danger level of each dangerous section is classified. Adjustment schemes are set according to the aforementioned hazard levels.

2. The photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 1, characterized in that, The generation of the first power curve for each photovoltaic string unit includes: Obtain cloud data for the forecast period; Multiple data collection points are set within the prediction period; Acquire cloud movement speed and direction data at each data collection point; The cloud projection position at each data collection point is generated based on the cloud movement speed data and cloud movement direction data. The shading area of ​​each photovoltaic string unit at each data acquisition point is determined based on the projection position. The first power curve of each photovoltaic string unit is generated based on the shading area and irradiance data.

3. The photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 1, characterized in that, The classification of hazard levels for each hazardous section includes: Obtain the power change rate data for each of the first power curves; Set the primary amplitude threshold and the secondary amplitude threshold; The intervals with a power change rate greater than the first-level amplitude threshold are designated as first-level dangerous zones; the intervals with a power change rate less than the first-level amplitude threshold but greater than the second-level amplitude threshold are designated as second-level dangerous zones; and the intervals with a power change rate less than the second-level amplitude threshold are designated as third-level dangerous zones.

4. The photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 1, characterized in that, The setting adjustment scheme includes: A priority sequence for each photovoltaic string unit is generated based on the first power curve and expected operating data of each photovoltaic string unit. A first scheduling method is set based on the hazard level and the expected operational data; The second scheduling method is determined based on the expected operating data of each photovoltaic string unit and the priority sequence.

5. The photovoltaic power plant power generation dispatching method based on multi-source data as described in claim 4, characterized in that, The generation of the priority sequence for each photovoltaic string unit includes: obtaining a damage degree S and a power margin Y of each photovoltaic string unit i ; Generate a priority score P for each photovoltaic string unit; P i = k1*Y i + (1 - k2*S i ); Among them, P i The priority score for the i-th photovoltaic string unit; k1 is the first threshold; Y i S represents the power margin of the i-th photovoltaic string unit; k2 represents the second threshold; S i Let represent the damage level of the i-th photovoltaic string unit; All photovoltaic string units are sorted from high to low according to priority scores to generate a priority sequence.

6. The photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 4, characterized in that, The method for setting a first scheduling based on the hazard level and the expected operational data includes: Obtain the power margin data for each photovoltaic string unit; When the power margin of a photovoltaic string unit in a Class I hazardous section is less than the margin threshold, a Class I scheduling is implemented. When the power margin of a photovoltaic string unit with a first-level danger zone is greater than the margin threshold, second-level scheduling is implemented. For photovoltaic string units with secondary hazardous sections, Class I scheduling is implemented; For photovoltaic string units with level-three danger zones, category-two scheduling is implemented; Photovoltaic string units subject to Category I scheduling are designated as Category I units, and photovoltaic string units subject to Category II scheduling are designated as Category II units.

7. The photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 6, characterized in that, The second type of scheduling includes: Obtain power change and power margin data for the two types of cells; The two types of units are sorted according to a priority sequence; Based on the power margin data, the power change is allocated to the two types of units.

8. The photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 7, characterized in that, The aforementioned type of scheduling includes: To obtain the power change of a class of cells; Obtain the electrical energy storage capacity of the reserve module; For units with energy storage greater than the integral value of power change, a reserve module is used for scheduling; for units with energy storage less than the integral value of power change, a reserve module and an inverter are used together for scheduling.

9. A photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 5, characterized in that, The degree of damage includes: Obtain the expected lifetime M0 of each photovoltaic string unit; Obtain the actual lifetime M1 of each photovoltaic string unit; Generate the damage degree S of each photovoltaic string unit; S = M1 / M0; Where S represents the damage level of the photovoltaic string unit; M1 represents the actual lifespan of the photovoltaic string unit; and M0 represents the expected lifespan of the photovoltaic string unit.

10. A photovoltaic power plant power generation scheduling method based on multi-source data as described in claim 9, characterized in that, The second scheduling method includes: Obtain the active power and reactive power data of the photovoltaic string unit where the first scheduling occurs; Obtain the change in active power of the photovoltaic string unit that has undergone the first scheduling; Obtain the power ratio and power margin of the photovoltaic string units that have undergone the first scheduling; Based on the power ratio, the power change of the photovoltaic string unit that has undergone the first scheduling is calculated to generate the reactive power change. When the change in reactive power is less than the power margin, the photovoltaic string unit is pre-scheduled; when the change in reactive power is greater than the power margin, the photovoltaic string unit is not pre-scheduled.

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