Power distribution network mobile energy storage scheduling method and device based on rolling look-ahead early warning, and storage medium

Abstract

The invention provides a power distribution network mobile energy storage scheduling method and device based on rolling look-ahead early warning and a storage medium, and belongs to the technical field of power system operation scheduling, and the method comprises the steps: inputting historical operation data of a selected date into a power distribution network linearization network power flow model, and obtaining a power distribution network node impedance matrix; predicting voltage reduction power flow of each distribution transformer, a distributed power supply output prediction value, a node load prediction value and balance node voltage according to the historical operation data in a rolling look-ahead manner; obtaining a conjugate complex matrix of the total injection power of each node; inputting the power distribution network node impedance matrix, the balance node voltage predicted value and the conjugate complex matrix of the total injection power of each node into a power distribution network linearization network power flow model, and predicting a node voltage phasor; and the line load rate and the distribution transformer load rate between the nodes are predicted, so that an optimal scheduling scheme of the mobile energy storage in the power distribution network is obtained, the prospective layout of the mobile energy storage is realized, and the reliability of the power distribution network is improved by reasonably using the resources of the mobile energy storage system.

Description

Technical Field

[0001] The present invention relates to a distribution network mobile energy storage dispatching method, equipment and storage medium based on rolling forward warning, belonging to the technical field of power system operation dispatching. Background Art

[0002] Under the background of building a new power system, the network structure of the distribution network has become more complex, the number of distributed power sources (wind power, photovoltaics, etc.) in the distribution network has increased, and the power supply reliability requirements of the distribution network in scenarios such as emergency supply, peak load, and rural power grid upgrade and transformation are also constantly improving. The application of mobile energy storage technology is also increasing.

[0003] With the gradual enrichment of loads, power sources and other elements in the distribution network, the load and distributed power output directly affect the power flow and voltage level in the distribution network. At the same time, the load and distributed power output fluctuate greatly, and the difficulty of predicting the operating status of the distribution network and prejudging risks is gradually increasing.

[0004] Mobile energy storage technology can provide users with safe, reliable and convenient power supply solutions with strong flexibility. However, the investment cost of mobile energy storage systems is relatively high, which limits their large-scale application. For emergency power supply and other scenarios, how to reasonably use mobile energy storage system resources, give full play to the role of mobile energy storage in improving the reliability of distribution networks, combine forward-looking predictions of the operating status of distribution networks, and optimize the layout of mobile energy storage to reduce the risks of line flow exceeding limits, excessive load rates of distribution transformers, and system voltage exceeding limits in distribution networks is a problem worth studying. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a distribution network mobile energy storage scheduling method based on rolling forward warning, which makes a forward-looking prediction of the distribution network operation status through a rolling forward prediction model, and then realizes the reasonable configuration of mobile energy storage in combination with the mobile energy storage early warning optimization model.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a method for dispatching mobile energy storage in a distribution network based on rolling forward warning, comprising:

[0008] Obtain the actual operation data of the distribution network within a preset historical period, collect the operation data at the current moment in real time, and obtain the historical actual operation data set based on all the operation data;

[0009] One day's actual operation data is selected from the historical actual operation data set, and input into a pre-built distribution network linearized network power flow model to calculate and obtain a distribution network node impedance matrix;

[0010] Input the historical actual operation data within a preset day a before the current date into the pre-trained rolling forward prediction model, and make rolling predictions on the voltage reduction flow of each distribution transformer, the output prediction value of the distributed power source, the node load prediction value and the balanced node voltage within a preset day b after the current date;

[0011] According to the predicted values ​​of the step-down power flows of each distribution transformer, the predicted values ​​of the node loads and the predicted values ​​of the output of distributed power sources, a conjugate complex matrix of the total injected power of each node in the distribution network within a preset b days after the current date is obtained, wherein the total injected power of the load node in the distribution network is the same as the predicted value of the node load of the load node, and the total injected power of the power node in the distribution network is the same as the sum of the predicted values ​​of the output of the distributed power sources connected to the power node;

[0012] The distribution network node impedance matrix, the balanced node voltage prediction value and the total injected power conjugate complex matrix of each node are input into the distribution network linearized network power flow model to calculate the node voltage phasor within b days after the current date;

[0013] According to the node voltage phasor, obtain the line load rate and distribution transformer load rate between nodes in the distribution network within b days after the current date;

[0014] According to the line load rate, distribution transformer load rate and node voltage phasor between nodes in the distribution network, an optimized dispatching scheme of mobile energy storage in the distribution network is obtained.

[0015] Furthermore, the obtaining of the historical actual operation data set based on all the operation data includes: preprocessing all the operation data; the preprocessing includes: abnormal data removal, missing data filling and normalization processing.

[0016] Furthermore, the linearized network power flow model of the distribution network is: ; In the formula, is the node voltage phasor, , is the number of nodes, The balancing nodes connected to the upper power grid are not included; is the unit phasor, and all elements in the phasor are 1; is the distribution network node impedance matrix, which represents the impedance between connected nodes; is the conjugate complex matrix of the total injected power of each node; , the conjugate complex number is: ; , are the voltage amplitude and phase angle of the equilibrium node respectively.

[0017] Furthermore, the distribution network node impedance matrix is ​​calculated, including: collecting actual operation data of the selected date at a preset collection frequency to obtain historical data of c time nodes; inputting the historical data of the c time nodes into the distribution network linearized network flow model respectively to calculate c node impedance matrices; averaging the node impedance matrices of the c time nodes to obtain the distribution network node impedance matrix Z.

[0018] Furthermore, the construction of the rolling forward prediction model includes:

[0019] Establish BP neural network model and select neural network structure;

[0020] Determine the number and connection mode of neurons in the input layer, hidden layer and output layer, as well as the learning rate, number of iterations, loss function and activation function; the input layer parameters include: the voltage step-down flow of each distribution transformer in the distribution network, the actual output of the distributed power source and the amplitude and phase angle of all node voltages;

[0021] The output layer parameters include: the step-down power flow of each distribution transformer in the distribution network, the output prediction value of the distributed power source, the node load prediction value and the balanced node voltage;

[0022] The training of the rolling forward prediction model includes:

[0023] The actual operation data of the distribution network within a preset historical period is divided into a training set and a validation set;

[0024] Calculate the output of the rolling forward forecast model using the data in the training set and calculate the forecast error based on the actual value;

[0025] According to the prediction error, adjust the weight and bias parameters of the rolling forward prediction model until the model converges;

[0026] Use the data in the validation set to evaluate the trained rolling forward forecast model and perform parameter optimization.

[0027] Further, the optimization scheduling scheme of mobile energy storage in the distribution network is obtained according to the line load rate, distribution transformer load rate and node voltage phasor between nodes in the distribution network, including: early warning of the distribution network operation status according to the line load rate, distribution transformer load rate or node voltage phasor between nodes in the distribution network, and the corresponding preset limit values; wherein the levels of the early warning include: red warning, yellow warning and green warning;

[0028] Based on the principle of highest priority configuration in red warning areas and second priority configuration in yellow warning areas, an optimized dispatching plan for mobile energy storage in the distribution network is generated according to the warning results of the distribution network operation status.

[0029] Furthermore, according to the line load rate, distribution transformer load rate or node voltage phasor between nodes in the distribution network, and the corresponding preset limit values, an early warning is given to the operation status of the distribution network, including:

[0030] According to the preset upper limit of the distribution transformer load rate and lower limit Early warning of the load rate of the distribution transformer, including: responding to the load rate of the distribution transformer being less than , generating a green warning of the distribution transformer load rate, in response to the distribution transformer load rate being greater than or equal to and less than or equal to , generating a yellow warning for the distribution transformer load rate; in response to the distribution transformer load rate being greater than , generating a red warning for the distribution transformer load rate.

[0031] According to the preset line load rate upper limit and lower limit Early warning of line load rate, including: responding to the line load rate being less than , generates a green line load rate warning, in response to the line load rate being greater than or equal to and less than or equal to , generating a yellow warning of line load rate; in response to the line load rate being greater than , generating a red warning for line load rate.

[0032] Calculate the node voltage deviation ratio based on the node voltage phasor and the preset node voltage deviation ratio first upper limit value , the first lower limit , the second upper limit and the second lower limit Early warning of voltage amplitude, including:

[0033] Calculate the node voltage deviation ratio, the formula is: ; In the formula, is the magnitude of the node voltage, is the rated voltage corresponding to the distribution network.

[0034] Response to node voltage deviation ratio or , generates a voltage amplitude red warning, responding to the node voltage deviation ratio or , generates a yellow voltage amplitude warning, in response to the node voltage deviation ratio , generating a green warning of voltage amplitude.

[0035] In a second aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the distribution network mobile energy storage scheduling method based on rolling forward warning as described in the first aspect is implemented.

[0036] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for dispatching mobile energy storage in a distribution network based on rolling forward warning as described in the first aspect is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The distribution network mobile energy storage scheduling method provided by the present invention is based on the historical operation data of the distribution network. It extracts the operation characteristics of the distribution network through a rolling forward prediction model established based on the BP neural network, and predicts the future operation status of the distribution network in real time. According to the real-time prediction results of the rolling forward prediction model, an optimized scheduling plan for mobile energy storage in the distribution network is generated, the forward-looking layout of mobile energy storage is realized, and the resources of the mobile energy storage system are reasonably used to improve the reliability of the distribution network.

[0039] (2) The distribution network mobile energy storage scheduling method provided by the present invention monitors the distribution network according to the line load rate, distribution transformer load rate and node voltage phasor between nodes in the distribution network, and issues an early warning for the line load rate, distribution transformer load rate or node voltage phasor in the distribution network through a mobile energy storage early warning optimization model. According to the early warning result, the mobile energy storage is first configured to the red early warning area, followed by the yellow early warning area, and finally the remaining mobile energy storage is configured to the green early warning area, so as to monitor the operating status of the distribution network and reduce the risks of line flow exceeding the limit, distribution transformer load rate being too high and system voltage exceeding the limit in the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flow chart of a method for dispatching mobile energy storage in a distribution network based on rolling forward warning provided in Example 1 of the present invention;

[0041] Figure 2 A flow chart of classification of distribution transformer load rate warning levels provided in Example 2 of the present invention;

[0042] Figure 3 A flow chart of line flow warning level classification provided in Example 2 of the present invention;

[0043] Figure 4 This is a flow chart of voltage amplitude warning level classification provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0044] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0045] Example 1

[0046] This embodiment provides a distribution network mobile energy storage scheduling method based on rolling forward warning, such as Figure 1 As shown, the method comprises the following steps:

[0047] Step 1: Obtain the actual operation data of the distribution network within a preset historical time period, collect the operation data at the current moment in real time, and obtain the historical actual operation data set based on all the operation data.

[0048] Specifically, the current time node is defined as t, and the historical voltage amplitude, phase angle, distribution transformer step-down flow and distributed power output data of the distribution network within (t-30) days are obtained from the dispatching system at time intervals of 15 minutes. The abnormal data are first eliminated, and then the interpolation method is used to fill the missing data. Finally, the data is normalized to obtain the historical actual operation data set of the distribution network for study.

[0049] It should be noted that the historical actual operation data set is a data set updated in real time.

[0050] Step 2: Select one day's actual operation data from the historical actual operation data set, input it into the pre-built distribution network linearized network power flow model, and calculate the distribution network node impedance matrix.

[0051] Specifically, according to the historical actual operation data of the distribution network on the (t-1)th day, the actual operation data of the (t-1)th day is collected at a preset collection frequency to obtain historical data of c time nodes; the node voltage phasor of the distribution network at each time node, the node voltage amplitude and phase angle of the balancing node, and the conjugate complex number of the apparent power flowing into each node are input into the linearized network flow model of the distribution network to calculate c node impedance matrices; the node impedance matrices of the c time nodes are averaged to obtain the node impedance matrix of the distribution network system.

[0052] The linearized network power flow model of the distribution network is: ; In the formula, is the node voltage phasor, , is the number of nodes, The balancing nodes connected to the upper power grid are not included; is the unit phasor, and all elements in the phasor are 1; is the distribution network node impedance matrix, which represents the impedance between connected nodes; is the conjugate complex matrix of the total injected power of each node; , the conjugate complex number is: ; , are the voltage amplitude and phase angle of the equilibrium node respectively.

[0053] In this embodiment, 96 sets of data can be collected for 24 hours a day at a time interval of 15 minutes. 96 node impedance matrices are calculated based on the historical data of these 96 time nodes, and the distribution network node impedance matrix Z can be obtained by averaging.

[0054] Step 3: Input the historical actual operation data within 30 days before the current date into the pre-trained rolling forward prediction model, and make rolling predictions on the voltage reduction flow of each distribution transformer, the output forecast value of distributed generation, the node load forecast value and the balanced node voltage within the preset 3 days after the current date.

[0055] Specifically, the rolling forward prediction model is used to predict future operating data based on the distribution transformer step-down flow, node voltage amplitude, phase angle (including nodes connected to the main grid) and actual output of distributed power sources in historical actual operating data, including: distribution transformer step-down flow, voltage amplitude and phase angle of balancing nodes, node load forecast value and distributed power source output forecast value.

[0056] Step 4: According to the predicted values ​​of the step-down power flows of the distribution transformers, the predicted values ​​of the node loads and the predicted values ​​of the output of the distributed generation, the conjugate complex matrix of the total injected power of each node in the distribution network within a preset b days after the current date is obtained.

[0057] Specifically, the nodes in the distribution network include: load nodes and power supply nodes. The total injected power of the load node is the same as the node load prediction value of the load node, and the total injected power of the power supply node is the same as the sum of the output prediction values ​​of the distributed power sources connected to the power supply node.

[0058] Step 5: Input the distribution network node impedance matrix, the balanced node voltage prediction value and the total injected power conjugate complex matrix of each node into the distribution network linearized network power flow model to calculate the node voltage phasor 3 days after the current date.

[0059] Step 6: According to the node voltage phasor and based on the conventional power flow calculation method of the power system, obtain the line load rate and distribution transformer load rate between nodes in the distribution network 3 days after the current date;

[0060] Step 7: Obtain an optimized dispatching scheme for mobile energy storage in the distribution network according to the line load rate, distribution transformer load rate or node voltage phasor between nodes in the distribution network.

[0061] Example 2

[0062] Based on Example 1, this example provides a process for obtaining an optimized scheduling scheme for mobile energy storage in a distribution network according to line load rates, distribution transformer load rates, and node voltage phasors between nodes in the distribution network.

[0063] The mobile energy storage early warning optimization model is used to issue early warnings on the distribution network operation status based on the line load rate, distribution transformer load rate or node voltage phasor between nodes in the distribution network, as well as the corresponding preset limit values.

[0064] In this embodiment, the warning levels include: red warning, yellow warning and green warning;

[0065] According to the preset line load rate upper limit and lower limit Early warning of line load rate, including: responding to the line load rate being less than , generates a green line load rate warning, in response to the line load rate being greater than or equal to and less than or equal to , generating a yellow warning of line load rate; in response to the line load rate being greater than , generating a red warning for line load rate.

[0066] Specifically, Figure 2As shown, the upper limit of the line load rate is 100% and the lower limit is 80%. The warning rules of the line load rate include: in response to the line load rate being less than 80%, a green line load rate warning is generated; in response to the line load rate being greater than or equal to 80% and less than or equal to 100%, a yellow line load rate warning is generated; in response to the line load rate being greater than 100%, a red line load rate warning is generated.

[0067] According to the preset upper limit of the distribution transformer load rate and lower limit Early warning of the load rate of the distribution transformer, including: responding to the load rate of the distribution transformer being less than , generating a green warning of the distribution transformer load rate, in response to the distribution transformer load rate being greater than or equal to and less than or equal to , generating a yellow warning for the distribution transformer load rate; in response to the distribution transformer load rate being greater than , generating a red warning for the distribution transformer load rate.

[0068] Specifically, Figure 3 As shown in the figure, the upper limit of the distribution transformer load rate is 100% and the lower limit is 80%. The warning rules of the distribution transformer load rate include: in response to the distribution transformer load rate being less than 80%, a green warning of the distribution transformer load rate is generated; in response to the distribution transformer load rate being greater than or equal to 80% and less than or equal to 100%, a yellow warning of the distribution transformer load rate is generated; in response to the distribution transformer load rate being greater than 100%, a red warning of the distribution transformer load rate is generated.

[0069] Calculate the node voltage deviation ratio based on the node voltage phasor and the preset node voltage deviation ratio first upper limit value , the first lower limit , the second upper limit and the second lower limit Early warning of voltage amplitude, including:

[0070] Calculate the node voltage deviation ratio, the formula is: ; In the formula, is the magnitude of the node voltage, is the rated voltage corresponding to the distribution network.

[0071] like Figure 4 As shown, in response to the node voltage deviation ratio or , generates a voltage amplitude red warning, responding to the node voltage deviation ratio or , generates a yellow voltage amplitude warning, in response to the node voltage deviation ratio , generating a green warning of voltage amplitude.

[0072] In this embodiment, and Depend on and Calculation results: ; ;

[0073] According to the above warning results, mobile energy storage is preferentially allocated to distribution transformers in the red warning area, followed by those in the yellow warning area, and the remaining mobile energy storage is allocated to distribution transformers in the green warning area, thereby generating an optimized scheduling plan for mobile energy storage in the distribution network.

[0074] Example 3

[0075] Based on Example 1, this example provides a process for constructing and training a rolling forward prediction model.

[0076] The rolling forward prediction model is constructed based on the BP neural network. The process includes: establishing the BP neural network model and selecting the neural network structure; determining the number and connection method of neurons in the input layer, hidden layer and output layer, as well as the learning rate, number of iterations, loss function and activation function.

[0077] Specifically, the input layer parameters of the rolling forward prediction model include: the step-down flow of each distribution transformer in the distribution network, the actual output of distributed power sources, and the amplitude and phase angle of all node voltages; the output layer parameters include: the step-down flow of each distribution transformer in the distribution network, the predicted output value of distributed power sources, the predicted value of node load and the balanced node voltage.

[0078] Preferably, the rolling look-ahead prediction model uses a ReLU activation function.

[0079] The training of the rolling forward forecasting model includes:

[0080] The actual operation data of the distribution network in the historical actual operation data set is divided into a training set and a validation set.

[0081] Specifically, 70% of the historical actual operation data are selected as the training set and 30% of the historical actual operation data are selected as the validation set according to the time distance. That is, the historical actual operation data from (t-30) to (t-10) days are the training set, and the data from (t-9) to (t-1) days are the validation set.

[0082] The output of the rolling forward prediction model is calculated using the data in the training set, and the prediction error is calculated based on the actual value. Based on the prediction error, the weight and bias parameters of the rolling forward prediction model are adjusted until the model meets the convergence conditions.

[0083] Use the data in the validation set to evaluate the trained rolling forward forecast model and perform parameter optimization.

[0084] Example 4

[0085] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the distribution network mobile energy storage scheduling method based on rolling forward warning as described in Example 1 or Example 2 is implemented.

[0086] Example 5

[0087] This embodiment provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for dispatching mobile energy storage in a distribution network based on rolling forward warning as described in Embodiment 1 or Embodiment 2 is implemented.

[0088] The storage medium may include, for example, a storage component of a tablet computer, a hard disk of a computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer storage medium may be any combination of one or more computer storage media.

[0089] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dispatching mobile energy storage in a distribution network based on rolling forward warning, characterized in that: include: Obtain the actual operation data of the distribution network within a preset historical period, collect the operation data at the current moment in real time, and obtain the historical actual operation data set based on all the operation data; One day's actual operation data is selected from the historical actual operation data set, and input into a pre-built distribution network linearized network power flow model to calculate and obtain a distribution network node impedance matrix; Input the historical actual operation data within a preset day a before the current date into the pre-trained rolling forward prediction model, and make rolling predictions on the voltage reduction flow of each distribution transformer, the output prediction value of the distributed power source, the node load prediction value and the balanced node voltage within a preset day b after the current date; According to the predicted values ​​of the step-down power flows of each distribution transformer, the predicted values ​​of the node loads and the predicted values ​​of the output of distributed power sources, a conjugate complex matrix of the total injected power of each node in the distribution network within a preset b days after the current date is obtained; wherein the total injected power of the load node in the distribution network is the same as the predicted value of the node load of the load node, and the total injected power of the power node in the distribution network is the same as the sum of the predicted values ​​of the output of the distributed power sources connected to the power node; The distribution network node impedance matrix, the balanced node voltage prediction value and the total injected power conjugate complex matrix of each node are input into the distribution network linearized network power flow model to calculate the node voltage phasor within b days after the current date; According to the node voltage phasor, obtain the line load rate and distribution transformer load rate between nodes in the distribution network within b days after the current date; According to the line load rate, distribution transformer load rate and node voltage phasor between nodes in the distribution network, an optimized dispatching scheme of mobile energy storage in the distribution network is obtained.

2. The method for dispatching mobile energy storage in distribution network based on rolling forward warning according to claim 1 is characterized in that: The obtaining of the historical actual operation data set according to all the operation data includes: preprocessing all the operation data; the preprocessing includes: abnormal data elimination, vacant data filling and normalization processing.

3. The method for dispatching mobile energy storage in distribution network based on rolling forward warning according to claim 1 is characterized in that: The linearized network power flow model of the distribution network is: ； In the formula, is the node voltage phasor, , is the number of nodes, The balancing nodes connected to the upper power grid are not included; is the unit phasor, and all elements in the phasor are 1; is the node impedance matrix of the distribution network, representing the impedance between connected nodes; is the conjugate complex matrix of the total injected power of each node; , the conjugate complex number is: ; , are the voltage amplitude and phase angle of the equilibrium node respectively.

4. The method for dispatching mobile energy storage in distribution network based on rolling forward warning according to claim 3 is characterized in that: The distribution network node impedance matrix is ​​calculated, including: The actual operation data of the selected date is collected at a preset collection frequency to obtain historical data of c time nodes; The historical data of c time nodes are respectively input into the distribution network linearized network power flow model to calculate c node impedance matrices; The average value of c node impedance matrices is obtained to obtain the distribution network node impedance matrix Z.

5. The method for dispatching mobile energy storage in distribution network based on rolling forward warning according to claim 4 is characterized in that: The construction of the rolling forward prediction model includes: Establish BP neural network model and select neural network structure; Determine the number and connection mode of neurons in the input layer, hidden layer and output layer, as well as the learning rate, number of iterations, loss function and activation function; the input layer parameters include: the voltage step-down flow of each distribution transformer in the distribution network, the actual output of the distributed power source and the amplitude and phase angle of all node voltages; The output layer parameters include: the step-down power flow of each distribution transformer in the distribution network, the output prediction value of the distributed power source, the node load prediction value and the balanced node voltage; The training of the rolling forward prediction model includes: The actual operation data of the distribution network within a preset historical period is divided into a training set and a validation set; Calculate the output of the rolling forward forecast model using the data in the training set and calculate the forecast error based on the actual value; According to the prediction error, adjust the weight and bias parameters of the rolling forward prediction model until the model converges; Use the data in the validation set to evaluate the trained rolling forward forecast model and perform parameter optimization.

6. The method for dispatching mobile energy storage in distribution network based on rolling forward warning according to claim 5 is characterized in that: The method of obtaining an optimized dispatching scheme of mobile energy storage in the distribution network according to the line load rate, distribution transformer load rate and node voltage phasor between nodes in the distribution network includes: Early warning of the operation status of the distribution network is provided according to the line load rate between nodes in the distribution network, the distribution transformer load rate or the node voltage phasor and the corresponding preset limit values; wherein the levels of the early warning include: red early warning, yellow early warning and green early warning; Based on the principle of highest priority configuration in red warning areas and second priority configuration in yellow warning areas, an optimized dispatching plan for mobile energy storage in the distribution network is generated according to the warning results of the distribution network operation status.

7. The method for dispatching mobile energy storage in distribution network based on rolling forward warning according to claim 6 is characterized in that: Early warning of the distribution network operation status is provided based on the line load rate, distribution transformer load rate or node voltage phasor between nodes in the distribution network and the corresponding preset limits, including: According to the preset upper limit of the distribution transformer load rate and lower limit Early warning of distribution transformer load rate, including: In response to the load factor of the distribution transformer being less than , generating a green warning of the distribution transformer load rate, In response to the load factor of the distribution transformer being greater than or equal to and less than or equal to , generating a yellow warning of the distribution transformer load rate; In response to the load factor of the distribution transformer being greater than , generating a red warning for the distribution transformer load rate; According to the preset line load rate upper limit and lower limit Early warning of line load rate, including: In response to the line load rate being less than , generating a green warning of line load rate, In response to a line load factor greater than or equal to and less than or equal to , generating a yellow warning of line load rate; In response to a line load greater than , generating a red warning for line load rate; Calculate the node voltage deviation ratio based on the node voltage phasor and the preset node voltage deviation ratio first upper limit value , the first lower limit , the second upper limit and the second lower limit Early warning of voltage amplitude, including: Calculate the node voltage deviation ratio, the formula is: ； In the formula, is the magnitude of the node voltage, is the rated voltage corresponding to the distribution network; Response to node voltage deviation ratio or , generating a voltage amplitude red warning, Response to node voltage deviation ratio or , generating a yellow voltage amplitude warning, Response to node voltage deviation ratio , generating a green warning of voltage amplitude.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the distribution network mobile energy storage scheduling method based on rolling forward warning according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for dispatching mobile energy storage in a distribution network based on rolling forward warning as described in any one of claims 1 to 7 is implemented.

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