CN117937430A - Distribution method and system based on distributed control - Google Patents
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Abstract
The invention discloses a distribution method and a distribution system based on distributed control, wherein the distribution method comprises the following steps: collecting electric energy data of a power station; metering power at an electric energy receiving end of the power distribution station by using a distributed device; calculating the loss and the demand of power in a distribution room by using a distributed device, feeding back to the distributed device at the receiving end of the distribution station, and performing distribution control through the distributed device at the receiving end of the distribution station; and (3) rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution station, and carrying out supplementary control according to the rechecking result. Because distributed control is used, each device can quickly respond to the change of the power grid demand, so that the flexibility and the response speed of the whole power grid are improved. Not only improves the overall efficiency and stability of the power system, but also provides an effective solution to the challenges facing modern power systems, particularly as renewable energy sources are increasing.
Description
Technical Field
The invention relates to the technical field of power distribution control, in particular to a power distribution method and system based on distributed control.
Background
With the acceleration of industrialization and urbanization, the global demand for electricity is increasing. Conventional centralized power systems face multiple challenges including power loss, power supply instability, and integration issues with renewable energy sources. Furthermore, the volatility and unpredictability of power demand introduces more complexity into the management and optimization of the power system. Thus, there is a need for a more intelligent, efficient, and flexible method of power distribution that addresses these challenges.
With the increase of renewable energy sources and the change of power consumption modes, power systems face significant supply and demand fluctuations. Efficient integration of renewable energy sources such as solar energy, wind energy, etc. into existing power grids is a key challenge, as the supply of these energy sources is unstable and difficult to predict. During peak demand periods, it is important to maintain the quality of the power and reliability of the system. As the number of distributed power sources (e.g., solar panels, small wind generators) increases, how to efficiently manage these dispersed energy sources becomes a problem.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing power distribution method has the problems that the power consumption is large, instability cannot be handled, and power distribution control of resource dispersion is solved.
In order to solve the technical problems, the invention provides the following technical scheme: a distributed control-based power distribution method, comprising:
collecting electric energy data of a power station;
metering power at an electric energy receiving end of the power distribution station by using a distributed device;
Calculating the loss and the demand of power in a distribution room by using a distributed device, feeding back to the distributed device at the receiving end of the distribution station, and performing distribution control through the distributed device at the receiving end of the distribution station;
and (3) rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution station, and carrying out supplementary control according to the rechecking result.
As a preferred embodiment of the distributed control-based power distribution method according to the present invention, wherein: the power data includes output voltage and current.
As a preferred embodiment of the distributed control-based power distribution method according to the present invention, wherein: the distributed device is used as a node for edge calculation and distributed at each receiving end and each distribution room, and can perform data analysis through a built-in algorithm;
information sharing and power distribution control can be performed between other distributed devices.
As a preferred embodiment of the distributed control-based power distribution method according to the present invention, wherein: the metering of power at the receiving end of the power distribution station comprises calculating the power P received by the power distribution station from different power stations by means of the distributed device.
As a preferred embodiment of the distributed control-based power distribution method according to the present invention, wherein: the power distribution room calculates the loss and the demand of power, wherein the power distribution room calculates the power loss rate V from the power distribution station to the power distribution room by using the output power P Into (I) of the power distribution station;
V＝(P-P Into (I) )/P；
and counting the electricity consumption requirements of users responsible for each distribution room;
learning the historical electricity demand through a distributed device at a distribution room to obtain a prediction function;
D(T+h)＝σ(ωar·AR(p,T)+ωlstm·LSTM(X，T))·S(T)
wherein AR (p, T) is the autoregressive model output; LSTM (X, T) is long-short-term memory network output; s (T) is a seasonal factor analysis result; p is the order of the autoregressive model and represents the hysteresis period number in the time sequence; x represents the feature set input to the LSTM; t represents the current point in time; h represents a predicted time interval, and the size of the predicted time interval is equal to the time delay of energy transmission plus the calculation time delay of a transformer substation; omega ar,ωlstm represents model parameters learned by the training process;
Qn＝D(T+h)/V
the power supply demand power fed back by the nth power transformation chamber is represented by Q n.
As a preferred embodiment of the distributed control-based power distribution method according to the present invention, wherein: the step of feeding back to the distributed devices at the receiving end of the distribution station comprises feeding back the demand calculated by the distributed devices at each distribution room to all the distributed devices at the receiving end, and sharing the electric quantity received by the receiving end and the distributed electric quantity by each distributed device at the receiving end;
Total power of electrical demand:
the output control is performed by feeding back the obtained information and the calculation result thereof, and specifically comprises the following steps:
Predicting fluctuation of the received power of each distributed device by using a fluctuation prediction model FPM;
FPMi＝LSTM(Ehistoryi)
Wherein E historyi is historical received power data of the i-th device;
distributing the electric quantity output of each device according to the total electric quantity demand Z and the fluctuation prediction of each device;
TDMi＝pos(Z,FPM1,FPM2,...,FPMW)
Wherein W represents the number of distributed devices at the receiving end;
Determining an optimal power output for each device;
Wherein λ represents the adjusted intensity coefficient; the sign function represents determining an adjustment direction according to the difference value between the total demand and the current total output; j represents an index variable for traversing all distributed devices; k represents the residual adjustment quantity, which means the total maximum electric quantity sum which can be adjusted in the current adjustment step of the whole system on the premise of ensuring that the total output meets the total requirement Z; Δ i＝TDMi-FPMi represents the output deviation of each device relative to its ripple prediction.
As a preferred embodiment of the distributed control-based power distribution method according to the present invention, wherein: the rechecking of the calculation and control of all the distributed devices comprises that a central calculation unit of the power distribution station utilizes the learning of a neural network to plan a stable output interval for the predicted fluctuation of the received power of each distributed device; analyzing whether each distributed device is in a stable output interval;
If the power demand is too large, the optimal power output of the distributed device is not in a stable output interval, the energy storage system is utilized to carry out power supplement, and in the process of the supplement, the power output data of each device is continuously updated until all the power outputs are in a planned stable output interval;
And when the power is supplied, if the received power is larger than the output power, charging the energy storage system.
A distributed control-based power distribution system employing the method of the present invention, characterized by:
The information acquisition module is used for acquiring electric energy data of the power station;
The distributed computing module is used for metering electric quantity at an electric energy receiving end of the power distribution station by utilizing a distributed device; calculating loss and demand of electric energy in a distribution room by using a distributed device, feeding back to the distributed device at a receiving end of the distribution station, and performing distribution control on the electric energy through the distributed device at the receiving end of the distribution station;
and the central control module is used for rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution network and carrying out supplementary control according to the rechecking result.
A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of the method of any of the present invention.
A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program, when executed by a processor, implements the steps of the method of any of the present invention.
The invention has the beneficial effects that: according to the distributed control-based power distribution method, each device can quickly respond to the change of the power grid demand due to the distributed control, so that the flexibility and the response speed of the whole power grid are improved. Not only improves the overall efficiency and stability of the power system, but also provides an effective solution to the challenges facing modern power systems, particularly as renewable energy sources are increasing.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of a power distribution method based on distributed control according to a first embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a distribution method based on distributed control, including:
S1: collecting electric energy data of a power station; and metering power at an electric energy receiving end of the power distribution station by using a distributed device.
Further, the electrical energy data includes output voltage and current. By measuring the voltage and current, the output power of the power plant can be monitored in real time. This is critical to ensure stability and efficient management of the grid.
Still further, the distributed device is used as a node for edge calculation, is distributed at each receiving end and each distribution room, and can perform data analysis through a built-in algorithm; information sharing and power distribution control can be performed between other distributed devices. The metering of power at the receiving end of the power distribution station comprises calculating the power P received by the power distribution station from different power stations by means of the distributed device.
It is noted that by deploying distributed devices with data processing capabilities at the receiving end and distribution room, data can be processed instantaneously at the site where the data was generated. This reduces the need to send and receive large amounts of data to a central server, thereby reducing network load and speeding up response time. Information sharing and control decisions between distributed devices may stabilize the output of the system without causing imbalance in the electrical power output.
S2: the loss and the demand of power are calculated by the distributed device in the distribution room, feedback is carried out to the distributed device at the receiving end of the distribution station, and distribution control is carried out through the distributed device at the receiving end of the distribution station.
The power distribution room calculates the loss and the demand of power, wherein the power distribution room calculates the power loss rate V from the power distribution station to the power distribution room by using the output power P Into (I) of the power distribution station;
V＝(P-P Into (I) )/P；
and counting the electricity consumption requirements of users responsible for each distribution room; learning the historical electricity demand by a distributed device at a distribution room results in a predictive function:
D(T+h)＝σ(ωar·AR(p,T)+ωlstm·LSTM(X，T))·S(T)
Wherein AR (p, T) is the autoregressive model output; LSTM (X, T) is long-short-term memory network output; s (T) is a seasonal factor analysis result; p is the order of the autoregressive model and represents the hysteresis period number in the time sequence; x represents the feature set input to the LSTM; t represents the current point in time; h represents a predicted time interval, and the size of the predicted time interval is equal to the time delay of energy transmission plus the calculation time delay of a transformer substation; omega ar,ωlstm represents model parameters learned through a training process.
Qn＝D(T+h)/V
The power supply demand power fed back by the nth power transformation chamber is represented by Q n.
It is noted that D (t+h) represents a prediction of power demand at a time in the future, which is calculated by an autoregressive model and a long-short term memory network based on historical data and current data. Accurate prediction of power demand is critical to ensure stable operation of the grid and to optimize power distribution. By calculation of V, the energy loss during the power transfer can be understood. This helps to more fully understand the efficiency of power transfer from the substation to the distribution room. By combining the predicted power demand D (t+h) with the power loss rate V, the calculated Q n can reflect the power that the utility actually needs to obtain from the utility after considering the transmission loss. And the power demand is monitored and predicted in real time, and the power grid can quickly respond to the demand change, especially in the peak or valley period of the demand by combining the analysis of the loss rate.
Further, the feeding back to the distributed devices at the receiving end of the distribution station includes feeding back the demand calculated by the distributed devices at each distribution room to all the distributed devices at the receiving end, and sharing the electric quantity received by the respective receiving end and the distributed electric quantity by each distributed device at the receiving end;
Total power of electrical demand:
the output control is performed by feeding back the obtained information and the calculation result thereof, and specifically comprises the following steps:
And predicting fluctuation of the received power of each distributed device by using a fluctuation prediction model FPM.
FPMi＝LSTM(Ehistoryi)
Where E historyi is historical received power data for the ith device. The LSTM is a long-short-term memory network and is used for learning and predicting electric energy fluctuation, and the system can dynamically adjust the electric quantity output of each distributed device by integrating feedback information and historical data so as to meet the real-time requirement of the whole power grid. This helps to maintain the stability of the grid, especially in case of fluctuating or unpredictable demands.
And distributing the electric quantity output of each device according to the total electric quantity demand Z and the fluctuation prediction of each device.
TDMi＝pos(Z,FPM1,FPM2,...,FPMW)
Where W represents the number of distributed devices at the receiving end. The optimization algorithm through the particle swarm is used for determining the electric quantity output of each device so as to meet the total requirement Z.
An optimal power output for each device is determined.
Wherein λ represents the adjusted intensity coefficient; the sign function represents determining an adjustment direction according to the difference value between the total demand and the current total output; j represents an index variable for traversing all distributed devices; k represents the residual adjustment quantity, which means the total maximum electric quantity sum which can be adjusted in the current adjustment step of the whole system on the premise of ensuring that the total output meets the total requirement Z; Δ i＝TDMi-FPMi represents the output deviation of each device relative to its ripple prediction.
It is known that |Δ i |: this represents the absolute value of the output deviation of the ith device. The output deviation refers to the difference between the predicted output of each device and the actual demand calculated from the fluctuation prediction FPM i and the total demand distribution model TDM i. The absolute value is used to represent the magnitude of the deviation, irrespective of its positive and negative direction. Residual adjustment amount: this means the sum of the maximum amounts of power that the entire system can also adjust in the current adjustment step, with the total output being ensured to meet the total demand Z. This amount represents how much more power can be redistributed among the various devices while maintaining overall power balance.
The function of the expression min (|Δ i |, residual adjustment) is to select the smaller of these two values as the actual adjustment. The purpose of this is to avoid excessive adjustment of the individual devices, ensuring that the adjustment steps are smooth and gradually approach the total demand Z. The adjustment of the whole system is ensured not to exceed the residual adjustable electric quantity, and the overall balance of the power grid is maintained.
S3: and (3) rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution station, and carrying out supplementary control according to the rechecking result.
Further, the rechecking of the calculation and control of all the distributed devices comprises that a central calculation unit of the power distribution station utilizes the learning of a neural network to plan a stable output interval for the predicted fluctuation of the received power of each distributed device; analyzing whether each distributed device is in a stable output interval; if the power demand is too large, the optimal power output of the distributed device is not in a stable output interval, the energy storage system is utilized to carry out power supplement, and in the process of the supplement, the power output data of each device is continuously updated until all the power outputs are in a planned stable output interval; and when the power is supplied, if the received power is larger than the output power, charging the energy storage system.
It is known that modern power systems typically collect and store a large amount of historical power data, including power generation, load demand, weather conditions, etc., which provides a rich data base for training of neural networks. Neural networks, particularly deep learning models such as Long Short Term Memory (LSTM), have demonstrated their powerful data processing and predictive capabilities in many areas. Neural networks are particularly suited for processing complex, nonlinear, time series data, which is characteristic of power system data. The neural network is good at identifying patterns and trends in the data, and can identify and predict stable intervals of power output by learning historical data. The neural network may analyze the pattern of fluctuations in the historical data to predict possible stable and unstable output intervals in the future.
It is also known that analyzing and predicting the power fluctuation of each device through the neural network can help the power system to more effectively plan the stable output interval of each device, thereby reducing the risk of grid instability caused by fluctuation. When the power demand is overlarge, the energy storage system is utilized to supplement power, so that the power grid can maintain stable operation when the demand is high, and meanwhile, the energy storage system is charged when the power supply is less, and the flexibility and the response capability of the power grid are improved. By continuously monitoring the power output of each device and making the necessary adjustments, it is ensured that the output of all devices remains always within a stable output interval, which is critical for reliable operation of the grid. The utilization of power resources can be optimized, the waste is reduced, and the energy efficiency of the system is improved by precisely controlling the output of each distributed device and the use of energy storage.
In another aspect, the present embodiment further provides a distributed control-based power distribution system, including:
and the information acquisition module is used for acquiring electric energy data of the power station. The distributed computing module is used for metering electric quantity at an electric energy receiving end of the power distribution station by utilizing a distributed device; and calculating the loss and the demand of the electric energy in the distribution room by using the distributed device, feeding back the electric energy to the distributed device at the receiving end of the distribution station, and performing distribution control on the electric energy through the distributed device at the receiving end of the distribution station. And the central control module is used for rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution network and carrying out supplementary control according to the rechecking result.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Example 2
In the following, for one embodiment of the present invention, a distribution method based on distributed control is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
Table 1 is obtained by comparative tests of the present invention and conventional methods.
Table 1 data comparison table
| Project | Traditional power distribution system | The power distribution system of the invention | Improving the situation |
| Response time | 30 Seconds | 10 Seconds | The reduction of 67% |
| Grid stability index | 80 | 95 | Lifting by 19% |
| Integration rate of renewable energy sources | 20％ | 40％ | Lifting by 100 percent |
| Annual number of power supply interruptions | 5 Times | 2 Times | Reduced by 60% |
| Cost of operation of electric power system | 50 Ten thousand dollars | Dollars of 40 ten thousand | Reduced by 20% |
| User satisfaction scoring | 6 Minutes | 8 Minutes | Lifting by 33% |
From the data, it can be seen that the system of the present invention responds more quickly to demand changes due to distributed control. The stability of the grid operation is comprehensively evaluated, and the high score represents a more stable grid. The invention effectively improves the proportion of renewable energy sources in the power grid. The system reduces the interruption times through optimizing control, and reflects the reliability of power supply. While at the same time being advantageous in terms of reduced operating costs. Finally, the system provides better electric power service for the user with overall satisfaction of the electric power service. The user satisfaction degree also shows the stability of power supply, and the side surface can show that the power supply is more stable through distributed control.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A distributed control-based power distribution method, comprising:
collecting electric energy data of a power station;
metering power at an electric energy receiving end of the power distribution station by using a distributed device;
Calculating the loss and the demand of power in a distribution room by using a distributed device, feeding back to the distributed device at the receiving end of the distribution station, and performing distribution control through the distributed device at the receiving end of the distribution station;
and (3) rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution station, and carrying out supplementary control according to the rechecking result.
2. The distributed control-based power distribution method as claimed in claim 1, wherein: the power data includes output voltage and current.
3. The distributed control-based power distribution method of claim 2, wherein: the distributed device is used as a node for edge calculation and distributed at each receiving end and each distribution room, and can perform data analysis through a built-in algorithm;
information sharing and power distribution control can be performed between other distributed devices.
4. A distributed control-based power distribution method as set forth in claim 3, wherein: the metering of power at the receiving end of the power distribution station comprises calculating the power P received by the power distribution station from different power stations by means of the distributed device.
5. The distributed control-based power distribution method as claimed in claim 4, wherein: the power distribution room calculates the loss and the demand of power, wherein the power distribution room calculates the power loss rate V from the power distribution station to the power distribution room by using the output power P Into (I) of the power distribution station;
V＝(P-P Into (I) )/P；
and counting the electricity consumption requirements of users responsible for each distribution room;
learning the historical electricity demand through a distributed device at a distribution room to obtain a prediction function;
D(T+h)＝σ(ωar·AR(p,T)+ωlstm·LSTM(X，T))·S(T)
wherein AR (p, T) is the autoregressive model output; LSTM (X, T) is long-short-term memory network output; s (T) is a seasonal factor analysis result; p is the order of the autoregressive model and represents the hysteresis period number in the time sequence; x represents the feature set input to the LSTM; t represents the current point in time; h represents a predicted time interval, and the size of the predicted time interval is equal to the time delay of energy transmission plus the calculation time delay of a transformer substation; omega ar,ωlstm represents model parameters learned by the training process;
Qn＝D(T+h)/V
the power supply demand power fed back by the nth power transformation chamber is represented by Q n.
6. The distributed control-based power distribution method as claimed in claim 5, wherein: the step of feeding back to the distributed devices at the receiving end of the distribution station comprises feeding back the demand calculated by the distributed devices at each distribution room to all the distributed devices at the receiving end, and sharing the electric quantity received by the receiving end and the distributed electric quantity by each distributed device at the receiving end;
Total power of electrical demand:
the output control is performed by feeding back the obtained information and the calculation result thereof, and specifically comprises the following steps:
Predicting fluctuation of the received power of each distributed device by using a fluctuation prediction model FPM;
FPMi＝LSTM(Ehistoryi)
Wherein E historyi is historical received power data of the i-th device;
distributing the electric quantity output of each device according to the total electric quantity demand Z and the fluctuation prediction of each device;
TDMi＝pos(Z,FPM1,FPM2,...,FPMW)
Wherein W represents the number of distributed devices at the receiving end;
Determining an optimal power output for each device;
Wherein λ represents the adjusted intensity coefficient; the sign function represents determining an adjustment direction according to the difference value between the total demand and the current total output; j represents an index variable for traversing all distributed devices; k represents the residual adjustment quantity, which means the total maximum electric quantity sum which can be adjusted in the current adjustment step of the whole system on the premise of ensuring that the total output meets the total requirement Z; Δ i＝TDMi-FPMi represents the output deviation of each device relative to its ripple prediction.
7. The distributed control-based power distribution method as claimed in claim 6, wherein: the rechecking of the calculation and control of all the distributed devices comprises that a central calculation unit of the power distribution station utilizes the learning of a neural network to plan a stable output interval for the predicted fluctuation of the received power of each distributed device; analyzing whether each distributed device is in a stable output interval;
If the power demand is too large, the optimal power output of the distributed device is not in a stable output interval, the energy storage system is utilized to carry out power supplement, and in the process of the supplement, the power output data of each device is continuously updated until all the power outputs are in a planned stable output interval;
And when the power is supplied, if the received power is larger than the output power, charging the energy storage system.
8. A distributed control-based power distribution system employing the method of any of claims 1-7, wherein:
The information acquisition module is used for acquiring electric energy data of the power station;
The distributed computing module is used for metering electric quantity at an electric energy receiving end of the power distribution station by utilizing a distributed device; calculating loss and demand of electric energy in a distribution room by using a distributed device, feeding back to the distributed device at a receiving end of the distribution station, and performing distribution control on the electric energy through the distributed device at the receiving end of the distribution station;
and the central control module is used for rechecking the calculation and control of all the distributed devices through the central calculation of the power distribution network and carrying out supplementary control according to the rechecking result.
9. A computer device, comprising: a memory and a processor; the memory stores a computer program characterized in that: the processor, when executing the computer program, implements the steps of a distributed control-based power distribution method.
10. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program when executed by a processor implements the steps of a distributed control-based power distribution method.
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