Microgrid group intelligent coordination control method and device based on deep model prediction

Abstract

The present invention relates to a method and device for intelligent coordinated control of a microgrid group based on deep model prediction. In order to solve the problem that the centralized control of a microgrid group is seriously affected in weak communication scenarios, resulting in poor control effect, the method proposes to generate an external system equivalent droop coefficient prediction model based on historical regulation information training; each microgrid controller predicts the external system equivalent droop coefficient based on the acquired measurement information; the power regulation amount is proportionally allocated according to its own droop coefficient and the predicted value of the external system equivalent droop coefficient to complete coordinated control, and then a microgrid group intelligent coordinated control device based on deep model prediction is designed. The present invention improves the intelligent control effect of a microgrid group in the event of a communication failure.

Description

Technical Field

[0001] The present application relates to the field of microgrid control technology, and in particular to a method and device for intelligent coordinated control of a microgrid group based on deep model prediction. Background Art

[0002] In recent years, the gradual depletion of fossil energy has led to an increasingly severe energy crisis, and clean, renewable distributed energy (DG) has developed rapidly. In order to give full play to the advantages of DG and reduce the impact on the power system, scholars have proposed the concept of microgrid, which is a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. Microgrid is an autonomous system that can achieve self-control, protection and management. It can not only improve the utilization rate of renewable energy, but also ensure the power supply of key loads in the event of a grid failure and improve the reliability of power supply. Microgrids contain various types of components such as wind power, photovoltaics, batteries, capacitors, etc., with various control methods, and the output power of micro sources such as wind and light is intermittent and uncertain. These factors make it difficult for the safe and stable operation of microgrids.

[0003] In the related technologies, the coordinated control of microgrid groups mainly focuses on two aspects: (1) from the perspective of power balance, the comprehensive coordinated control strategy of source, storage and load is studied to maintain the real-time power balance of the microgrid group; (2) from the perspective of regulation characteristics, a virtual synchronous machine control strategy of solar / wind power station with energy storage is proposed to meet the frequency and voltage regulation requirements of the microgrid group. The current microgrid controller mainly considers the optimization of the operation safety and economy of the microgrid, and rarely involves the multi-coordinated interaction problem when there is a communication failure.

[0004] At present, the operation and control of microgrids adopts a centralized energy management system. Each unit such as photovoltaic, wind turbine, energy storage, and load is connected to the microgrid centralized control through a communication network. Functions such as power balance control, system operation optimization, fault detection and protection, and power quality management are all realized by the centralized controller. The use of centralized control alone has the following shortcomings: (1) It is highly dependent on the communication network, and has high requirements for the real-time and reliability of the communication network, requiring large investments. Once the communication network fails or the communication data is wrong, the effect of microgrid operation and control cannot be guaranteed, which may affect the power quality at the least and threaten the stability of microgrid operation at the worst. (2) It has high requirements for the computing power and reliability of the centralized controller, and cannot give full play to the local rapid processing capabilities of each control unit.

[0005] Therefore, in the relevant technology, relying solely on a single control method can no longer meet the requirements of microgrid operation reliability, intelligence and economy, which is not conducive to the promotion and application of microgrids and the development of smart distribution networks. Therefore, in the event of a communication failure, power distribution and frequency regulation cannot be completed quickly and accurately. Summary of the invention

[0006] Based on this, it is necessary to provide a microgrid group intelligent coordination control method and device based on deep model prediction that can quickly and accurately complete power allocation and frequency regulation in response to the above technical problems.

[0007] A microgrid group intelligent coordinated control method based on deep model prediction, the method comprising:

[0008] Step 1, the controllers of each microgrid in the microgrid group respectively obtain the measurement information on the common bus in real time;

[0009] Step 2: Analyze the measurement information to determine the current droop coefficient.

[0010] Step 3: Using the collected historical adjustment information as samples for training, a prediction model for the equivalent droop coefficient of the external system is generated;

[0011] Step 4: Input the current measurement information into the trained external system equivalent droop coefficient prediction model, and output the external system equivalent droop coefficient prediction value;

[0012] Step 5: The power regulation amount is allocated in proportion according to the self-droop coefficient and the predicted value of the equivalent droop coefficient, and the self-power regulation amount is calculated;

[0013] Step 6: Perform power adjustment according to the power adjustment amount to determine whether the current frequency reaches the qualified range. If the frequency does not reach the qualified range, return to step 1.

[0014] Furthermore, in the step 2, the controller of each microgrid obtains its own droop coefficient at the current moment by fitting the output power change value in the measurement information with the corresponding frequency change value.

[0015] Furthermore, in step 3, the historical adjustment information collection process includes:

[0016] The controller of each microgrid in the microgrid group obtains the measurement information on the common bus in real time;

[0017] The controller of each microgrid analyzes the measurement information and determines the current preliminary power regulation amount:

[0018]

[0019] Where Δf is the frequency deviation, k i is the self-droop coefficient, ΔP i 0 is the initial power adjustment amount;

[0020] The controller of each microgrid performs power regulation in real time according to the current preliminary power regulation amount, and collects regulation information, wherein the regulation information includes its own droop coefficient, the power regulation amount corresponding to each regulation, and the frequency change amount.

[0021] Furthermore, in step 3, the training process of the external system equivalent droop coefficient prediction model includes:

[0022] The system is divided into two parts: the internal system and the external system. The internal system is the microgrid itself, and the external system is the equivalent system of other parts of the power system connected to the microgrid.

[0023] Perform historical adjustments and preset the number of adjustments. When the number of adjustments reaches the preset number, the collected adjustment information is used as a sample and input into the long short-term memory artificial neural network model to be trained to obtain the corresponding external system equivalent droop coefficient prediction model.

[0024] Furthermore, in step 5, each microgrid controller allocates the power regulation amount in proportion according to its own droop coefficient and the predicted value of the external system droop coefficient, and the calculation formula of its own power regulation amount is:

[0025]

[0026] Where ΔP is the system power shortage, k i is the droop coefficient of microgrid i itself, k' is the predicted value of the equivalent droop coefficient of the external system, ΔP i is the corrected power regulation amount.

[0027] Furthermore, the external system equivalent droop coefficient prediction model includes an input layer, a hidden layer, a memory layer and an output layer;

[0028] The input layer is implemented by Sigmoid and Tanh activation functions;

[0029] The hidden layer is used to perform nonlinear transformation on all input information to generate a prediction value;

[0030] The memory layer is used to store the state information output by the hidden layer at the last moment;

[0031] The output layer compresses the input information through the hidden layer and then outputs it through an activation function.

[0032] A microgrid group intelligent coordination control device based on deep model prediction, the device comprising:

[0033] The measurement acquisition module is used for each microgrid controller in the microgrid group to obtain the measurement information on the common bus in real time;

[0034] An analysis module, used to analyze the measurement information to determine the current self-droop coefficient;

[0035] A prediction module is used to train and generate an external system equivalent droop coefficient prediction model and obtain an external system equivalent droop coefficient prediction value based on current measurement information;

[0036] The power regulation module is used to allocate the power regulation amount in proportion according to its own droop coefficient and the predicted value of the external system droop coefficient, calculate its own power regulation amount and perform power regulation.

[0037] The judgment module is used to judge whether the system has reached the qualified frequency range. If the frequency has not reached the qualified frequency range, it returns to the measurement information acquisition link and proceeds to the next cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is an application environment diagram of the microgrid group intelligent coordinated control method based on deep model prediction of the present invention;

[0039] Figure 2 It is a flow chart of a microgrid group intelligent coordinated control method based on deep model prediction of the present invention;

[0040] Figure 3 It is a structural diagram of a microgrid group intelligent coordination control device based on deep model prediction of the present invention;

[0041] Figure 4 It is a frequency waveform diagram of the simulation process in one embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0043] The microgrid group intelligent coordinated control method based on deep model prediction provided in this application can be applied to Figure 1 In the application environment shown in the figure, each microgrid in the microgrid group is connected to the same bus. The microgrid group includes each microgrid. Photovoltaic, wind power, fuel cells, micro gas turbines, etc. can all be regarded as distributed power sources in the microgrid. Through the microgrid group intelligent coordination control method based on deep model prediction, each microgrid in the microgrid group is intelligently coordinated and controlled together. In the event of a communication failure, the intelligent coordination control method solves the technical problem of being unable to quickly and accurately complete power distribution and frequency regulation.

[0044] In one embodiment, Figure 2As shown, a microgrid group intelligent coordinated control method based on deep model prediction is provided, comprising the following steps:

[0045] Step S1, the controller of each microgrid in the microgrid group respectively obtains the measurement information on the common bus in real time.

[0046] The measurement information includes the measurement information of voltage, current and frequency. The microgrid group includes n microgrids.

[0047] Step S2, analyzing the measurement information to determine the current self-droop coefficient.

[0048] Specifically, the step S2 includes: the controller of each microgrid obtains its own droop coefficient at the current moment by fitting the output power change value in the measurement information with the corresponding frequency change value.

[0049] Among them, each microgrid in the microgrid group determines the initial state of each microgrid according to the measurement information of the common bus and its own state quantity. The initial state of microgrid i is expressed as:

[0050] i∈1,2,3…n

[0051] Where n is the total number of microgrids, V is the voltage of the common bus, f is the frequency of the common bus, P i is the active power output of microgrid i, k i is the self-droop coefficient of microgrid i, which is determined by the output power value P of microgrid i. i The frequency f of the common bus is obtained by fitting, and t is the time variable.

[0052] Step S3, training is performed using the collected historical adjustment information as samples to generate an external system equivalent droop coefficient prediction model.

[0053] Specifically, step S3 includes:

[0054] S31: Collection of historical adjustment information.

[0055] The historical adjustment information collection process includes:

[0056] The controller of each microgrid in the microgrid group obtains the measurement information on the common bus in real time;

[0057] The controller of each microgrid analyzes the measurement information and determines the current preliminary power regulation amount:

[0058]

[0059] Where Δf is the frequency deviation, k i is the self-droop coefficient, ΔPi 0 is the initial power adjustment amount.

[0060] The controller of each microgrid performs power regulation in real time according to the current preliminary power regulation amount, and collects regulation information, wherein the regulation information includes its own droop coefficient, the power regulation amount corresponding to each regulation, and the frequency change amount.

[0061] S32: External system equivalent droop coefficient prediction model training.

[0062] The training process of the external system equivalent droop coefficient prediction model includes:

[0063] The system is divided into two parts: the internal system and the external system. The internal system is the microgrid itself, and the external system is the equivalent system of other parts of the power system connected to the microgrid.

[0064] Perform historical adjustments and preset the number of adjustments. When the number of adjustments reaches the preset number, the collected adjustment information is used as a sample and input into the long short-term memory artificial neural network model to be trained to obtain the corresponding external system equivalent droop coefficient prediction model.

[0065] Step S4, inputting the current moment measurement information into the trained external system equivalent droop coefficient prediction model, and outputting the external system equivalent droop coefficient prediction value.

[0066] Step S5, the power regulation amount is allocated in proportion according to the self-droop coefficient and the predicted value of the equivalent droop coefficient, and the self-power regulation amount is calculated.

[0067] Specifically, step S5 includes:

[0068] Each microgrid controller shares the power regulation amount in proportion to its own droop coefficient and the predicted value of the external system droop coefficient. The calculation formula for its own power regulation amount is:

[0069]

[0070] Where ΔP is the system power shortage, k i is the droop coefficient of microgrid i itself, k' is the predicted value of the equivalent droop coefficient of the external system, ΔP i is the corrected power regulation amount.

[0071] Step S6, perform power adjustment according to the power adjustment amount, and determine whether the current frequency reaches the qualified range. If the frequency does not reach the qualified range, return to step S1.

[0072] Among them, the qualified frequency range in step S6 is 50±0.1 Hz.

[0073] In one embodiment, the external system equivalent droop coefficient prediction model includes an input layer, a hidden layer, a memory layer and an output layer; the input layer is implemented by Sigmoid and Tanh activation functions; the hidden layer is used to perform nonlinear transformation on all input information to generate a prediction value; the memory layer is used to store the state information output by the hidden layer at the previous moment; the output layer compresses the input information through the hidden layer and then outputs it through the activation function.

[0074] The long short-term memory artificial neural network model is mainly composed of input layer, hidden layer, memory layer and output layer. The input layer is the input data or the output of the previous neural network layer, which is realized by Sigmoid and Tanh activation functions. The input layer extracts information as needed to control the sequence input. The calculation formula is:

[0075] a t =σ(W a h t-1 +U a x t +b a )

[0076]

[0077] Among them, a t is the input unit at time t, is the input information at time t; σ is the Sigmoid activation function, tanh is the Tanh activation function; U a Enter the weights for the input units, U c Input weight for cell state; W a is the input unit recurrent weight, W c is the cell state cycle weight; b a is the input unit bias, b c Bias for cell state.

[0078] The hidden layer, also known as the excitation layer, performs nonlinear transformation on all inputs to generate prediction values. Its excitation function is:

[0079]

[0080] in, is the output value of the jth neuron at time t; is the state information of the jth neuron at time t; S t-1 is the vector composed of the output values ​​of all hidden layer neurons at time t-1; U a is the jth row of the weight matrix of the input matrix X; W a For S t-1 The jth row of the weight matrix of b a is the bias value of the jth hidden layer neuron.

[0081] The memory layer is used to store the state information output by the hidden layer at the previous moment. The calculation formula is:

[0082] f t =σ(W f h t-1 +U f x t +b f )

[0083] Among them, f t is the forgotten value at time t; σ is the Sigmoid activation function; U f Input weights for the memory layer; W f is the recurrent weight of the memory layer; h t-1 is the state information output by the hidden layer at time t-1; x t is the hidden layer input; b f Bias for the memory layer.

[0084] The output layer compresses the input data through the hidden layer and then through the activation function. The calculation formula is:

[0085] o t =σ(W o h t-1 +U o x t +b o )

[0086] h t =o t *tanh(C t )

[0087] Among them, t is the output value at time t; σ is Sigmoid, tanh is Tanh activation function; U o Input weights for the output layer; W o is the output layer recurrent weight; h t-1 is the state information output by the hidden layer at time t-1; x t is the hidden layer input; b o is the output layer bias; h t is the state information output by the hidden layer at time t; C t is the cell state; * is the Hadamard product.

[0088] Cell State C t Used to record the current state of the LSTM neural network. The calculation formula is:

[0089]

[0090] Among them, at is the input unit at time t, Input information at time t; f t is the forgotten value at time t; C t-1 is the cell state at the previous moment; * is the Hadamard product.

[0091] In one embodiment, the microgrid group coordinated control method based on deep model prediction also includes taking the external system equivalent droop coefficient prediction value output at the previous moment as feedback data at the current moment, inputting it into the external system equivalent droop coefficient prediction model together with the adjustment information collected at the current moment, and outputting the external system equivalent droop coefficient prediction value at the current moment.

[0092] Among them, each prediction is added with the external system equivalent droop coefficient prediction value output at the previous moment as feedback data, and enters the next round of external system equivalent droop coefficient prediction value prediction, making the prediction of the external system equivalent droop coefficient prediction model more accurate.

[0093] The above-mentioned microgrid group coordinated control method based on deep model prediction obtains the measurement information on the common bus in real time through the controller of each microgrid in the microgrid group; analyzes according to the measurement information to determine the current moment's own droop coefficient; uses the collected historical adjustment information as a sample for training to generate an external system equivalent droop coefficient prediction model; inputs the current moment's measurement information into the trained external system equivalent droop coefficient prediction model, and outputs the external system equivalent droop coefficient prediction value; allocates the power adjustment amount in proportion according to the self-droop coefficient and the equivalent droop coefficient prediction value, and calculates the self-power adjustment amount; performs power adjustment according to the self-power adjustment amount, and determines whether the current frequency reaches the qualified range. If the frequency does not reach the qualified range, returns to the measurement information acquisition step, completes the power optimization allocation and frequency adjustment, optimizes the coordinated control effect of the microgrid group under communication failure conditions, and improves the safety and efficiency of microgrid operation.

[0094] In order to prove the effect of the above-mentioned microgrid group coordinated control method based on deep model prediction in the case of communication failure, a system consisting of three microgrids is taken as an example for simulation verification, wherein the simulation duration is set to 2100s, within 0s to 600s, microgrid 1 has a communication failure; within 600s to 1500s, microgrid 1 and microgrid 2 both have communication failures; within 1500s to 2100s, microgrid 3 has a communication failure.

[0095] Among them, the communication failure conditions include: there is a communication failure between the distributed power supply in the microgrid and the microgrid controller; there is a communication failure between the microgrids; there is a communication failure between the microgrid and the microgrid group master controller or the distribution network DMS system.

[0096] The system frequency waveform during the simulation is as follows Figure 3 As shown, the simulation results are statistically obtained: the average convergence steps are 3.34, the average calculation time is 0.0433s, and the average frequency deviation is 0.06178.

[0097] This application is based on the LSTM network and uses a deep learning method to train the long short-term memory artificial neural network model of microgrid group control. It has a faster convergence speed and solves the technical problem in related technologies that power distribution and frequency regulation cannot be completed quickly and accurately by relying solely on a single control strategy in the event of a communication failure. It can also control the frequency deviation of the microgrid group within a smaller range, which is beneficial to improving the reliability and efficiency of the microgrid operation.

[0098] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0099] In one embodiment, a microgrid group intelligent coordination control device based on deep model prediction is provided, and the device includes: a measurement acquisition module, an analysis module, a prediction module, a power regulation module and a judgment module.

[0100] The measurement acquisition module is used for each microgrid controller in the microgrid group to obtain the measurement information on the common bus in real time;

[0101] An analysis module, used to analyze the measurement information to determine the current self-droop coefficient;

[0102] A prediction module is used to train and generate an external system equivalent droop coefficient prediction model and obtain an external system equivalent droop coefficient prediction value based on current measurement information;

[0103] The power regulation module is used to allocate the power regulation amount in proportion according to its own droop coefficient and the predicted value of the external system droop coefficient, calculate its own power regulation amount and perform power regulation.

[0104] The judgment module is used to judge whether the system has reached the qualified frequency range. If the frequency has not reached the qualified frequency range, it returns to the measurement information acquisition link and proceeds to the next cycle.

[0105] For the specific limitations of the microgrid group intelligent coordination control device based on deep model prediction, please refer to the limitations of the microgrid group intelligent coordination control method based on deep model prediction in the above text, which will not be repeated here. Each module in the above-mentioned microgrid group intelligent coordination control device based on deep model prediction can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0106] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A microgrid group intelligent coordination control method based on deep model prediction, characterized in that: The method comprises: Step 1, the controllers of each microgrid in the microgrid group respectively obtain the measurement information on the common bus in real time; Step 2: Analyze the measurement information to determine the current droop coefficient. Step 3: Using the collected historical adjustment information as samples for training, a prediction model for the equivalent droop coefficient of the external system is generated; Step 4: Input the current measurement information into the trained external system equivalent droop coefficient prediction model, and output the external system equivalent droop coefficient prediction value; Step 5: The power regulation amount is allocated in proportion according to the self-droop coefficient and the predicted value of the equivalent droop coefficient, and the self-power regulation amount is calculated; Step 6: Perform power adjustment according to the power adjustment amount to determine whether the current frequency reaches the qualified range. If the frequency does not reach the qualified range, return to step 1. In step 3, the historical adjustment information collection step includes: The controller of each microgrid in the microgrid group obtains the measurement information on the common bus in real time; The controller of each microgrid analyzes the measurement information and determines the current preliminary power regulation amount: Where Δf is the frequency deviation, k i is the self-droop coefficient, ΔP i 0 is the initial power adjustment amount; The controller of each microgrid performs power regulation in real time according to the current preliminary power regulation amount, and collects regulation information, wherein the regulation information includes its own droop coefficient, the power regulation amount corresponding to each regulation, and the frequency change amount; In step 3, the training process of the external system equivalent droop coefficient prediction model includes: The system is divided into two parts: the internal system and the external system. The internal system is the microgrid itself, and the external system is the equivalent system of other parts of the power system connected to the microgrid. Perform historical adjustments and preset the number of adjustments. When the number of adjustments reaches the preset number, the collected adjustment information is used as a sample and input into the long short-term memory artificial neural network model to be trained to obtain the corresponding external system equivalent droop coefficient prediction model; In step 5, each microgrid controller allocates the power regulation amount in proportion according to its own droop coefficient and the predicted value of the external system droop coefficient. The calculation formula of its own power regulation amount is: Where ΔP is the system power shortage, k i is the droop coefficient of microgrid i itself, k' is the predicted value of the equivalent droop coefficient of the external system, ΔP i is the corrected power regulation amount.

2. The microgrid group intelligent coordinated control method based on deep model prediction according to claim 1 is characterized in that: In the step 2, the controller of each microgrid obtains its own droop coefficient at the current moment by fitting the output power change value in the measurement information with the corresponding frequency change value.

3. According to claim 1, the microgrid group intelligent coordinated control method based on deep model prediction is characterized in that: The external system equivalent droop coefficient prediction model includes an input layer, a hidden layer, a memory layer and an output layer; The input layer is implemented by Sigmoid and Tanh activation functions; The hidden layer is used to perform nonlinear transformation on all input information to generate a prediction value; The memory layer is used to store the state information output by the hidden layer at the last moment; The output layer compresses the input information through the hidden layer and then outputs it through an activation function.

4. A microgrid group intelligent coordination control device based on deep model prediction, characterized in that: The device comprises: The measurement acquisition module is used for each microgrid controller in the microgrid group to obtain the measurement information on the common bus in real time; An analysis module, used to analyze the measurement information to determine the current self-droop coefficient; A prediction module is used to train and generate an external system equivalent droop coefficient prediction model and obtain an external system equivalent droop coefficient prediction value based on current measurement information; A power regulation module is used to allocate the power regulation amount in proportion according to its own droop coefficient and the predicted value of the external system droop coefficient, calculate its own power regulation amount and perform power regulation; The judgment module is used to judge whether the system has reached the qualified frequency range. If the frequency has not reached the qualified frequency range, it returns to the measurement information acquisition link and proceeds to the next cycle; The steps for collecting historical adjustment information include: The controller of each microgrid in the microgrid group obtains the measurement information on the common bus in real time; The controller of each microgrid analyzes the measurement information and determines the current preliminary power regulation amount: Where Δf is the frequency deviation, k i is the self-droop coefficient, ΔP i 0 is the initial power adjustment amount; The controller of each microgrid performs power regulation in real time according to the current preliminary power regulation amount, and collects regulation information, wherein the regulation information includes its own droop coefficient, the power regulation amount corresponding to each regulation, and the frequency change amount; The training process of the external system equivalent droop coefficient prediction model includes: The system is divided into two parts: the internal system and the external system. The internal system is the microgrid itself, and the external system is the equivalent system of other parts of the power system connected to the microgrid. Perform historical adjustments and preset the number of adjustments. When the number of adjustments reaches the preset number, the collected adjustment information is used as a sample and input into the long short-term memory artificial neural network model to be trained to obtain the corresponding external system equivalent droop coefficient prediction model; Each microgrid controller shares the power regulation amount in proportion to its own droop coefficient and the predicted value of the external system droop coefficient. The calculation formula for its own power regulation amount is: Where ΔP is the system power shortage, k i is the droop coefficient of microgrid i itself, k' is the predicted value of the equivalent droop coefficient of the external system, ΔP i is the corrected power regulation amount.

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