Electric field power control method and system based on multi-source data fusion and networking collaboration

Abstract

The present invention discloses an electric field power control method and system based on multi-source data fusion and networking collaboration, the method comprising: calculating the maximum total power of the electric field, taking the ratio of the maximum power of the new energy power station partition to the maximum total power as the adjustable capacity ratio of each new energy power station partition; multiplying the total power demand by the adjustable capacity ratio of each new energy power station partition, and taking the obtained amount as the power instruction of the new energy power station partition; within the new energy power station partition, taking the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition as the adjustable capacity ratio of each new energy generator; determining the power demand according to the power instruction of the new energy power station partition, multiplying the power demand by the adjustable capacity ratio of each new energy generator, and taking the obtained amount as the power instruction of the new energy generator. The above technical scheme is adopted to reduce the calculation burden while ensuring that the output power reaches the target power demand.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation and power grid control technology, and in particular to an electric field power control method and system based on multi-source data fusion and networking collaboration. Background Art

[0002] Renewable energy generation is currently a hot topic. The scale of wind power and photovoltaic power generation is increasing, and large-capacity new energy bases are emerging in my country. On the one hand, large-capacity new energy (wind power, photovoltaic) power fields have become the power supply that cannot be ignored by the power grid. The requirements for steady-state and dynamic control of the power field are constantly increasing. On the other hand, the control devices and control methods of new energy power stations are also increasing.

[0003] As we all know, renewable energy power generation has strong uncertainty and is easily affected by the environment. Therefore, this requires that the renewable energy power field connected to the power grid has strong control capabilities to meet the power demand at different times and avoid the negative impact caused by insufficient power supply.

[0004] In the prior art, two methods are usually adopted. First, energy storage batteries are introduced into the power grid to ensure the supply of electricity in different time periods; second, new energy power stations are controlled one by one to ensure that the overall power generation meets the power demand. The problem is that energy storage batteries have limitations on charge and discharge rates, cycles and lifespan. Although it is easy to accurately control each new energy power station by controlling each new energy power station one by one, it will lead to too frequent control times, high system calculation pressure, and no control focus. After one or more new energy power stations are controlled, the target power may have changed, which may easily lead to a certain deviation between the final overall output and the target power demand. Summary of the invention

[0005] Purpose of the invention: The present invention provides an electric field power control method and system based on multi-source data fusion and networking collaboration, aiming to solve the technical problems existing in the prior art, such as low accuracy of overall control of new energy electric fields, heavy control calculation burden, and difficulty in ensuring that the output power reaches the target power requirement.

[0006] Technical solution: The present invention provides an electric field power control method based on multi-source data fusion and networking collaboration, including: based on current environmental data, power grid data and new energy generator parameters, predicting the maximum power of each new energy generator in the electric field, and calculating the maximum power of each new energy power station partition; the electric field includes multiple new energy power station partitions, and each new energy power station partition includes multiple new energy generators; calculating the maximum total power of the electric field, and taking the ratio of the maximum power of the new energy power station partition to the maximum total power as the adjustable capacity ratio of each new energy power station partition; obtaining the total power demand according to the total power instruction received by the electric field, multiplying the total power demand by the adjustable capacity ratio of each new energy power station partition, and taking the obtained amount as the power instruction of the new energy power station partition, and each new energy power station partition executes according to the corresponding power instruction; in the new energy power station partition, taking the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition as the adjustable capacity ratio of each new energy generator; determining the power demand according to the power instruction of the new energy power station partition, multiplying the power demand by the adjustable capacity ratio of each new energy generator, and taking the obtained amount as the power instruction of the new energy generator, and each new energy generator executes according to the corresponding power instruction.

[0007] Specifically, the maximum active power and maximum reactive power of each renewable energy generator in the electric field are predicted, and the maximum active power and maximum reactive power of each renewable energy power station partition are calculated; the maximum power is the sum of the maximum active power and the maximum reactive power.

[0008] Specifically, historical environmental data, power grid data and new energy generator parameters, as well as the corresponding active power and reactive power of the new energy generator are obtained as training data to train the power prediction model; the current environmental data, power grid data and new energy generator parameters are input into the trained power prediction model to obtain the maximum active power and maximum reactive power of the new energy generator.

[0009] Specifically, the corresponding total active power demand is obtained according to the total power command received by the electric field, and the corresponding total reactive power demand is calculated based on the proportional relationship between the active power and reactive power of the new energy generator; the total power demand is the sum of the total active power demand and the total reactive power demand.

[0010] Specifically, if the actual power of a new energy power station partition is inconsistent with the corresponding power instruction, a new energy power station partition is selected from multiple new energy power station partitions as a corrected new energy power station partition; the difference between the actual active power of the corrected new energy power station partition and the active power instruction is calculated, and the difference is input into a PI controller. The output of the PI controller is superimposed on the power instruction of the corrected new energy power station partition to obtain a corrected power instruction, and the corrected new energy power station partition is executed according to the corrected power instruction.

[0011] Specifically, the maximum power of each new energy generator in multiple power time periods is predicted; and the new energy power station partition with the highest maximum power in the current power time period is selected as the revised new energy power station partition.

[0012] Specifically, the active power demand and the reactive power demand are determined according to the power instructions of the new energy power station zones; the power demand is the sum of the active power demand and the reactive power demand.

[0013] Specifically, the fast power instructions and slow power instructions issued by the electric field to each new energy power station partition are stored in the data buffer in order; in each reading time cycle, the first number of fast power instructions and the second number of slow power instructions are queried from the data buffer and conveyed to the corresponding new energy power station partition.

[0014] The present invention also provides an electric field control system based on multi-source data fusion and networking collaboration, including: an electric field control center and a district-level control station, wherein: the electric field control center is used to predict the maximum power of each new energy generator in the electric field based on current environmental data, power grid data and new energy generator parameters, and thereby calculate the maximum power of each new energy power station partition; the electric field includes multiple new energy power station partitions, and each new energy power station partition includes multiple new energy generators; the maximum total power of the electric field is calculated, and the ratio of the maximum power of the new energy power station partition to the maximum total power is used as the adjustable capacity ratio of each new energy power station partition; the district-level control station is used to calculate the maximum power of each new energy power station partition according to the data received by the electric field. The total power instruction obtains the total power demand, and the total power demand is multiplied by the adjustable capacity ratio of each new energy power station partition, and the obtained amount is used as the power instruction of the new energy power station partition, and each new energy power station partition executes according to the corresponding power instruction; within the new energy power station partition, the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition is used as the adjustable capacity ratio of each new energy generator; the power demand is determined according to the power instruction of the new energy power station partition, and the power demand is multiplied by the adjustable capacity ratio of each new energy generator, and the obtained amount is used as the power instruction of the new energy generator, and each new energy generator executes according to the corresponding power instruction.

[0015] Specifically, the electric field control system also includes: a communication network unit, which is used to store the fast power instructions and slow power instructions issued by the electric field to each new energy power station partition in a data buffer in order; within each reading time period, a first number of fast power instructions and a second number of slow power instructions are queried from the data buffer and conveyed to the corresponding new energy power station partition.

[0016] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: while reducing the computational burden, it ensures that the output power reaches the target power requirement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of the electric field based on multi-source data fusion and networking collaboration provided by the present invention;

[0018] Figure 2 The architecture diagram of the electric field power control method based on multi-source data fusion and networking collaboration provided by the present invention;

[0019] Figure 3 A flow chart of an electric field power control method based on multi-source data fusion and networking collaboration provided by the present invention;

[0020] Figure 4 A flow chart for distinguishing fast and slow communication speeds provided by the present invention. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0022] See also Figure 1 , which is a structural schematic diagram of the electric field based on multi-source data fusion and networking collaboration provided by the present invention.

[0023] In specific implementation, the power grid dispatching system includes automatic voltage control system (AGC and AVC), fast power control device, wind power control standby device, and stabilization device, etc., which are used to control power parameters such as voltage to ensure the safe operation of the power grid. The power grid dispatching system communicates with the new energy power station through the communication network in the power station and the wind power control interface device, thereby issuing control instructions. Figure 1 In the figure, feeder PV 1 represents a new energy power station partition, and feeder PV 2 represents another new energy power station partition.

[0024] In the specific implementation, since the number of fast communication transceivers at the substation end is limited (GOOSE is usually 96), the controlled wind turbines (new energy generators) of the power station are controlled in partitions, and each area (new energy power station partition) does not exceed the maximum number of fast power parallel distribution control. After the wind turbines in the partition form a partition ring network through fast communication, they are communicated with the wind power interface device.

[0025] See also Figure 2 , which is an architecture diagram of the electric field power control method based on multi-source data fusion and networking collaboration provided by the present invention.

[0026] In the embodiment of the present invention, based on the current environmental data, power grid data and new energy generator parameters, the maximum power of each new energy generator in the electric field is predicted, and the maximum power of each new energy power station partition is calculated accordingly.

[0027] In the embodiment of the present invention, the electric field includes a plurality of new energy power station sections, and each new energy power station section includes a plurality of new energy generators.

[0028] In a specific implementation, predicting the maximum power of each renewable energy generator in an electric field refers to predicting the maximum power that each renewable energy generator in the electric field can output in a certain period of time in the future. The maximum power can be represented by a time-dependent curve. By superimposing the maximum power of each renewable energy generator, the maximum power of the renewable energy power station partition can be obtained.

[0029] In specific implementation, the new energy power station can be a wind power station or a photovoltaic power station.

[0030] In an embodiment of the present invention, the maximum active power and the maximum reactive power of each renewable energy generator in the electric field are predicted, thereby calculating the maximum active power and the maximum reactive power of each renewable energy power station partition; the maximum power is the sum of the maximum active power and the maximum reactive power.

[0031] In an embodiment of the present invention, historical environmental data, power grid data and new energy generator parameters, as well as the corresponding active power and reactive power of the new energy generator are obtained as training data to train the power prediction model; the current environmental data, power grid data and new energy generator parameters are input into the trained power prediction model to obtain the maximum active power and maximum reactive power of the new energy generator.

[0032] In specific implementations, historical environmental data is generally environmental data at the same time in the past, such as the same time yesterday or the same time last week. Environmental data is generally environmental data related to renewable energy power generation, such as wind speed, light intensity, etc. Grid data is generally power parameters related to the power output of renewable energy power stations in the grid, such as grid-connected voltage and load. New energy generator parameters, taking wind power as an example, are generally parameters related to power output, such as wind turbine blade parameters and rated voltage.

[0033] In the specific implementation, the real-time adjustable capacity of wind power in the sub-zone is estimated. Based on the meteorological data such as wind speed, temperature and humidity near the sub-zone, based on the ultra-short-term power prediction function of the substation and combined with the power tracking control method of the wind turbine group, the local adjustable capacity of the wind turbine group in the sub-zone is obtained and sent to the communication layer (upper layer) via fast communication.

[0034] See also Figure 3 , a flow chart of the electric field power control method based on multi-source data fusion and networking collaboration provided by the present invention.

[0035] In the embodiment of the present invention, the maximum total power of the electric field is calculated, and the ratio of the maximum power of the new energy power station partition to the maximum total power is used as the adjustable capacity ratio of each new energy power station partition.

[0036] In specific implementation, the maximum total power of the electric field can be calculated by superimposing the maximum power of all new energy power station partitions. That is, after summarizing the real-time adjustable capacity of each partition, the total adjustable capacity of the entire station is obtained. After summarizing the real-time active and reactive data of each partition, the total operating status of the entire station is obtained. The indicator deviation of each regulation performance is calculated in combination with the electrical data of the grid connection point.

[0037] In the specific implementation, the adjustable capacity ratio of the new energy power station partition, that is, the ratio of the maximum power of the new energy power station partition to the maximum total power, represents the maximum proportion that a new energy power station partition can account for in the total output power, that is, the adjustable capacity ratio K of the new energy power station partition. fi .

[0038] In an embodiment of the present invention, a total power demand is obtained based on a total power instruction received by the electric field, and the total power demand is multiplied by the adjustable capacity ratio of each new energy power station partition. The obtained amount is used as the power instruction of the new energy power station partition, and each new energy power station partition executes according to the corresponding power instruction.

[0039] In an embodiment of the present invention, the corresponding total active power demand is obtained according to the total power instruction received by the electric field, and the corresponding total reactive power demand is calculated based on the proportional relationship between the active power and reactive power of the new energy generator; the total power demand is the sum of the total active power demand and the total reactive power demand.

[0040] In specific implementation, the total power command issued by the power grid dispatching system is usually the total active power demand, and in order to maintain the normal operation of the power grid, corresponding reactive power needs to be generated. The ratio between active power and reactive power can refer to the ratio between the (average) active power and (average) reactive power of the new energy generator, thereby calculating the total reactive power demand and the corresponding total power demand. Based on the total power demand and the adjustable capacity ratio of each new energy power station partition, the power output that each new energy power station partition should provide can be calculated by multiplying them.

[0041] In the specific implementation, the new energy electric field is controlled in a hierarchical and zoned manner, the total power demand is decomposed into each new energy power station zone, the power output of the new energy power station zone is controlled, and then the new energy power station within the new energy power station zone is controlled. Among them, the technical effect that the zone can produce is that multiple new energy power stations are controlled as a whole (new energy power station zone), which can significantly reduce the number of control objects, and achieve a significant reduction in the calculation burden while reducing the control frequency. And because the number of control objects is reduced, it is only necessary to control the total output power of multiple new energy power stations as a whole, and the number of new energy power station zones is obviously small (relative to the total number of new energy power stations). Therefore, the total error after the power error is accumulated is obviously smaller, which can effectively improve the control accuracy.

[0042] In the embodiment of the present invention, if the actual power of a new energy power station partition is inconsistent with the corresponding power command (the total power demand multiplied by the adjustable capacity ratio of the new energy power station partition), a new energy power station partition is selected from multiple new energy power station partitions as a modified new energy power station partition; the difference between the actual active power of the modified new energy power station partition and the active power command is calculated, and the difference is input into the PI controller, and the output K of the PI controller is cof The power instructions of the modified new energy power station partitions are superimposed to obtain the modified power instructions, and the modified new energy power station partitions are executed according to the modified power instructions.

[0043] In the embodiment of the present invention, the maximum power of each new energy generator in multiple power time periods is predicted; and the new energy power station partition with the highest maximum power in the current power time period is selected as the revised new energy power station partition.

[0044] In the specific implementation, the active and reactive partition output, power factor and other electrical quantity data are calculated in real time based on the voltage / current sampling data of the partition grid connection point. The calculated data is sent to the upper communication network for the upper controller to perform the whole station control closed loop or auxiliary reference. According to the power control instructions issued, the periodic error accuracy analysis is performed with the real-time power response value of the partition to obtain the control accuracy, speed, contribution power and other indicators, and the test results are sent through the fast communication network.

[0045] In the specific implementation, one new energy power station partition is selected from multiple new energy power station partitions for control, so that the open-loop control method that only controls the input end can be changed to a closed-loop control that controls both the input end and the output end. The advantage of selecting only one new energy power station partition for control is that, on the one hand, when only one new energy power station partition is selected for closed-loop control, the calculation burden can also be significantly reduced. In addition, by controlling the increase or decrease of the output power of one new energy power station partition, it is also sufficient to adjust the total output power of the entire electric field to be close to the target power without paying attention to the power error of all new energy power stations. In addition, the closed-loop control of only one new energy power station partition is obviously faster and higher than controlling multiple ones, so that the power output of the entire electric field can be quickly and effectively controlled by closed-loop control of one new energy power station partition, that is, while improving the control accuracy, the control rate can also be improved.

[0046] Furthermore, in order to make the power output of the new energy power station partition under closed-loop control approach and eventually be consistent with the target power after adjustment, the output power capacity of the new energy power station partition is required to be high, that is, it has a high maximum output power, so as to make up for the power errors of other partitions. Therefore, the new energy power station partition with the highest maximum output power is dynamically selected as the corrected new energy power station partition. For example, within the next 24 hours, it can be predicted which new energy power station partition has the highest maximum output power in each hour (by calculating the total maximum power of the new energy generators), and the new energy power station partition with the highest maximum output power is used as the corrected new energy power station partition for closed-loop control. The time period can be set according to the actual application scenario.

[0047] In general, since there are a large number of new energy power stations in a new energy power field, the present invention divides multiple new energy power stations into a new energy power station partition, thereby obtaining multiple new energy power station partitions. The control scheme provided by the present invention is to perform overall power control on the new energy power station partition, and select a new energy power station partition from the new energy power station partition for closed-loop control, which significantly reduces the calculation burden, and improves the power control accuracy while also improving the control rate.

[0048] In the embodiment of the present invention, within the new energy power station partition, the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition is used as the adjustable capacity ratio of each new energy generator.

[0049] In the specific implementation, the adjustable capacity ratio of the new energy generator, that is, the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition, represents the maximum proportion that a new energy power station can occupy in the partition output power, that is, the adjustable capacity ratio K of the new energy power station partition. gi .

[0050] In an embodiment of the present invention, the power demand is determined according to the power instruction of the new energy power station partition, the power demand is multiplied by the adjustable capacity ratio of each new energy generator, and the obtained amount is used as the power instruction of the new energy generator. Each new energy generator executes according to the corresponding power instruction.

[0051] In the embodiment of the present invention, the active power demand and the reactive power demand are determined according to the power instructions of the new energy power station partitions; the power demand is the sum of the active power demand and the reactive power demand.

[0052] In a specific implementation, the total power demand is composed of the total active power demand and the total reactive power demand. The power instructions of the new energy power station partitions obtained after decomposition and calculation also include the active power demand and the reactive power demand accordingly.

[0053] See also Figure 4 , which is a flow chart of fast and slow communication speed discrimination provided by the present invention.

[0054] In an embodiment of the present invention, the fast power instructions and slow power instructions issued by the electric field to each new energy power station partition are stored in a data buffer in order; within each reading time period, a first number of fast power instructions and a second number of slow power instructions are queried from the data buffer and conveyed to the corresponding new energy power station partition.

[0055] In the specific implementation, a fast waiting (stack) area for communication messages is set in the process layer bus service. The wind power control interface device queues and inserts the fast power instructions (sent by the fast power device / system) and slow power instructions (sent by the AGCAVC substation) it receives in the waiting area, and processes them according to the principle of time-sharing processing and fast and slow priority distinction. A fixed cycle (variable) is set to prioritize querying X fast instruction messages and Y slow instruction messages to avoid communication link congestion. Multiple fixed cycles form a step-like cycle reading mechanism. The conventional problems of superposition / locking and other coordination encountered when fast instructions and slow instructions are issued by different substation devices at the same time are avoided. The power instructions are arranged according to the timing coordination of the dispatching steady-state response of the controlled power station system and the fast response under local fluctuations to meet the system's transient steady-state stability requirements.

[0056] In the specific implementation, the data exchanged between the wind power control interface device is real-time control data, which not only connects to the grid dispatching / regional control, but also faces massive data of various controls. Therefore, it is necessary to meet the requirements of data protection information security and reliability, adopt partition encryption algorithm and address authentication mechanism to ensure the safe transmission of GOOSE fast communication, and authenticate the identity of the data object according to the partition and adjust the composite data encryption mechanism of poetry to ensure the accuracy and timeliness of control data in communication interaction. At the same time, the partition distributed trusted module (software) is used to realize the trusted verification and confirmation of the control parameters, control instructions, etc. of the wind power control interface device, improve the communication security and communication confirmation real-time, and isolate the communication with the main grid control system.

[0057] The present invention also provides an electric field control system based on multi-source data fusion and networking collaboration, including: an electric field control center and a district-level control station, wherein: the electric field control center is used to predict the maximum power of each new energy generator in the electric field based on current environmental data, power grid data and new energy generator parameters, and thereby calculate the maximum power of each new energy power station partition; the electric field includes multiple new energy power station partitions, and each new energy power station partition includes multiple new energy generators; the maximum total power of the electric field is calculated, and the ratio of the maximum power of the new energy power station partition to the maximum total power is used as the adjustable capacity ratio of each new energy power station partition; the district-level control station is used to calculate the maximum power of each new energy power station partition according to the data received by the electric field. The total power instruction obtains the total power demand, and the total power demand is multiplied by the adjustable capacity ratio of each new energy power station partition, and the obtained amount is used as the power instruction of the new energy power station partition, and each new energy power station partition executes according to the corresponding power instruction; within the new energy power station partition, the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition is used as the adjustable capacity ratio of each new energy generator; the power demand is determined according to the power instruction of the new energy power station partition, and the power demand is multiplied by the adjustable capacity ratio of each new energy generator, and the obtained amount is used as the power instruction of the new energy generator, and each new energy generator executes according to the corresponding power instruction.

[0058] Specifically, the electric field control system also includes: a communication network unit, which is used to store the fast power instructions and slow power instructions issued by the electric field to each new energy power station partition in a data buffer in order; within each reading time period, a first number of fast power instructions and a second number of slow power instructions are queried from the data buffer and conveyed to the corresponding new energy power station partition.

Claims

1. An electric field power control method based on multi-source data fusion and networking collaboration, characterized in that: include: Based on current environmental data, power grid data and new energy generator parameters, the maximum power of each new energy generator in the electric field is predicted, and the maximum power of each new energy power station partition is calculated accordingly; the electric field includes multiple new energy power station partitions, and each new energy power station partition includes multiple new energy generators; Calculate the maximum total power of the electric field, and use the ratio of the maximum power of the new energy power station partition to the maximum total power as the adjustable capacity ratio of each new energy power station partition; The total power demand is obtained according to the total power command received by the electric field, and the total power demand is multiplied by the adjustable capacity ratio of each new energy power station partition. The obtained amount is used as the power command of the new energy power station partition, and each new energy power station partition executes according to the corresponding power command; In the new energy power station zone, the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station zone is used as the adjustable capacity ratio of each new energy generator; The power demand is determined according to the power instructions of the new energy power station zones, and the power demand is multiplied by the adjustable capacity ratio of each new energy generator. The obtained amount is used as the power instruction of the new energy generator, and each new energy generator executes according to the corresponding power instruction.

2. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 1 is characterized in that: The predicted maximum power of each renewable energy generator in the electric field, and the calculated maximum power of each renewable energy power station partition, include: The maximum active power and maximum reactive power of each renewable energy generator in the electric field are predicted, and the maximum active power and maximum reactive power of each renewable energy power station partition are calculated accordingly; the maximum power is the sum of the maximum active power and the maximum reactive power.

3. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 2 is characterized in that: The method of predicting the maximum power of each renewable energy generator in the electric field based on the current environmental data, power grid data and renewable energy generator parameters includes: Obtain historical environmental data, power grid data, and new energy generator parameters, as well as the corresponding active power and reactive power of the new energy generator as training data to train the power prediction model; The current environmental data, power grid data and new energy generator parameters are input into the trained power prediction model to obtain the maximum active power and maximum reactive power of the new energy generator.

4. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 3 is characterized in that: The obtaining of the total power demand according to the total power instruction received by the electric field comprises: The corresponding total active power demand is obtained according to the total power command received by the electric field, and the corresponding total reactive power demand is calculated based on the proportional relationship between the active power and reactive power of the new energy generator; the total power demand is the sum of the total active power demand and the total reactive power demand.

5. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 4 is characterized in that: Each new energy power station partition is executed according to the corresponding power instruction, including: If the actual power of a new energy power station partition is inconsistent with the corresponding power instruction, a new energy power station partition is selected from multiple new energy power station partitions as a revised new energy power station partition; The difference between the actual active power of the modified new energy power station partition and the active power command is calculated and input into the PI controller. The output of the PI controller is superimposed on the power command of the modified new energy power station partition to obtain the modified power command. The modified new energy power station partition is executed according to the modified power command.

6. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 5 is characterized in that: The step of selecting a new energy power station zone from a plurality of new energy power station zones as a revised new energy power station zone comprises: Predict the maximum power of each renewable energy generator in multiple power time periods; The new energy power station partition with the highest maximum power in the current power time period is selected as the revised new energy power station partition.

7. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 4 is characterized in that: The determining of the power demand according to the power instruction of the new energy power station zone includes: The active power demand and reactive power demand are determined according to the power instructions of the new energy power station zones; the power demand is the sum of the active power demand and the reactive power demand.

8. The electric field power control method based on multi-source data fusion and networking collaboration according to claim 1 is characterized in that: include: The fast power instructions and slow power instructions issued by the electric field to each new energy power station partition are stored in the data buffer in a sequence; In each reading time cycle, a first number of fast power instructions and a second number of slow power instructions are queried from the data buffer and forwarded to the corresponding new energy power station partition.

9. An electric field control system based on multi-source data fusion and networking collaboration, characterized in that: include: Electric field control station and district control station, including: The electric field control main station is used to predict the maximum power of each new energy generator in the electric field based on current environmental data, power grid data and new energy generator parameters, and thereby calculate the maximum power of each new energy power station partition; the electric field includes multiple new energy power station partitions, and each new energy power station partition includes multiple new energy generators; calculate the maximum total power of the electric field, and use the ratio of the maximum power of the new energy power station partition to the maximum total power as the adjustable capacity ratio of each new energy power station partition; The district-level control station is used to obtain the total power demand according to the total power instruction received by the electric field, multiply the total power demand by the adjustable capacity ratio of each new energy power station partition, and use the obtained amount as the power instruction of the new energy power station partition, and each new energy power station partition executes according to the corresponding power instruction; within the new energy power station partition, the ratio of the maximum power of the new energy generator to the maximum power of the new energy power station partition is used as the adjustable capacity ratio of each new energy generator; determine the power demand according to the power instruction of the new energy power station partition, multiply the power demand by the adjustable capacity ratio of each new energy generator, and use the obtained amount as the power instruction of the new energy generator, and each new energy generator executes according to the corresponding power instruction.

10. The electric field control system based on multi-source data fusion and networking collaboration according to claim 9 is characterized in that: Also includes: The communication network unit is used to store the fast power instructions and slow power instructions issued by the electric field to each new energy power station partition in a data buffer in order; in each reading time period, the first number of fast power instructions and the second number of slow power instructions are queried from the data buffer and conveyed to the corresponding new energy power station partition.

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