Information-physical coupling modeling method for distribution network distributed collaborative control

Abstract

The application discloses a kind of information physical coupling modeling methods for distribution network distributed collaborative control, it is related to distribution network distributed collaborative control technical field, solve how to realize distributed communication control in distribution network by information physical system architecture, to improve the problem of distributed collaborative control effect under non-ideal communication condition;Adopt the form of multiple group and adjacency matrix, construct the physical layer of distribution network under information physical system architecture, the information layer of proxy network and the coupling layer model of coupling network;Select power data and information instruction as the data communication of coupling layer model, adopt hybrid computing method to extract communication delay component in coupling layer model;Based on the physical layer of distribution network, the information layer of proxy network and the coupling layer model of coupling network and data communication constructed, establish cost increment model;Distributed control is used to carry out the control variable processing of primary and secondary proxy nodes, realize the coordination between distribution network and the convergence function within distribution network.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of distributed cooperative control of power distribution networks, and relates to an information-physical coupling modeling method for distributed cooperative control of power distribution networks. BACKGROUND

[0002] With the construction and development of smart grids and energy internet, the number of measurement devices, routing transmission and control decision units in the power grid and the scale of distributed power sources are rapidly increasing and expanding. Modern power distribution networks have evolved into a complex, multi-dimensional and heterogeneous information-physical system (CPS, Cyber-Physical System) coupled by power networks and communication networks. The traditional centralized control method uses a central controller for unified information processing and instruction regulation. Once a single point failure occurs, a communication accident of data unable to flow will occur, and only waiting for repair can be done, which cannot adapt to the scene of a large number of distributed power sources (DG, Distributed Generation) access. In order to overcome the limitations of the centralized control method, the distributed cooperative control method (DCC, Distributed Cooperative Control) is used in the research of power distribution network communication.

[0003] In the prior art, most scholars focus on the distributed cooperative control strategy of the consensus algorithm, and excavate the state recovery potential of the disturbed power distribution network. Based on the ideal communication condition, the cooperative effect of each intelligent agent is adjusted in real time to ensure the effective transmission of power information. For example, the document "Vulnerability Assessment of Power Distribution Network CPS under Distributed Cooperative Control Mode" (Li Peikai, Zhejiang University, 2018) proposes a vulnerability assessment method of power distribution network CPS under distributed cooperative control mode based on dynamic attack and defense game. Through the equal output ratio power source cooperative control strategy, the risk assessment model of power distribution network CPS is analyzed to cope with the ability of network attack. However, the above-mentioned document has the following shortcomings: 1) the risk assessment model under network attack only considers the power shortage information in distributed control, but does not consider the delay of actual non-ideal communication operation on the lag of adjustment and control effect; 2) the cooperative control strategy only realizes the equal ratio power regulation of each DG in the power distribution network, does not have the cooperative distribution ability of multiple power distribution networks, and cannot reasonably schedule and optimize in real time. SUMMARY

[0004] The technical problem to be solved by the present application is how to realize distributed communication control in power distribution networks through information-physical system architecture to improve the distributed cooperative control effect under non-ideal communication conditions.

[0005] The present application solves the above technical problems by the following technical solutions:

[0006] The information physical coupling modeling method for distribution network distributed collaborative control comprises the following steps:

[0007] S1, a multi-element group and an adjacency matrix are used to construct a physical layer model of a distribution network, an information layer model of an agent network and a coupling layer model of a coupling network under an information physical system architecture;

[0008] S2, power data and information instructions are selected as data communication of the coupling layer model, and a hybrid calculation method is used to extract a communication time delay component in the coupling layer model;

[0009] S3, based on the constructed physical layer model of the distribution network, the information layer model of the agent network and the coupling layer model of the coupling network and data communication, a cost increment model of a control variable of an agent node is established; a distributed control strategy is used for control variable processing of primary and secondary agent nodes, and coordination between distribution networks and convergence functions in the distribution network are realized.

[0010] The technical scheme of the application is based on the construction of an information physical system architecture and a distributed collaborative control strategy, and an information physical coupling modeling method for distribution network distributed collaborative control is constructed; a multi-element group and an adjacency matrix are used to construct a distribution network model containing physical information of a distribution network, a coupling interface model and an agent network model, and original power data in operation and power adjustment instructions are collected and transmitted through a remote terminal unit; the coupling model fuses a distributed collaborative control algorithm, and power information between and inside the distribution network is interacted and updated in real time, adjustment instructions are generated in multiple scenes and multiple states, and are fed back to the physical system through a communication network and a coupling interface, distributed power output is dynamically optimized and adjusted, and optimal control of power cost of the distribution network is realized under the condition of considering communication time delay, and the safe and stable operation of the power grid is ensured.

[0011] Further, the construction method of the physical layer model of the distribution network in step S1 is as follows:

[0012] The distribution network comprises power nodes and power transmission lines; the power nodes comprise distributed power sources, transformers and power consumption loads;

[0013] The multi-element group model G of the power node is constructed as follows: i As follows:

[0014] G i =[N,K] (1)

[0015] Wherein, N represents the number of adjacent power nodes around, and the value of K is 0, 1 and 2, respectively representing a distributed power source, a transformer and a power consumption load;

[0016] The adjacency matrix model G of the distribution network is constructed as follows:

[0017]

[0018] Wherein, the main diagonal elements of matrix G are power nodes, and the rest elements are the connection relationship between power nodes, and the value of element L ij is 0 or 1, which respectively represents that there is no or there is a power transmission line connected between power nodes i and j.

[0019] Further, the construction method of the information layer model of the proxy network in step S1 is as follows:

[0020] The proxy network comprises proxy nodes and communication lines; the proxy nodes comprise primary proxy nodes and secondary proxy nodes, the primary proxy nodes adopt switches, and the secondary proxy nodes adopt routers; the proxy nodes are modeled by combining communication delay and buffer state characteristics, and the information flow transmission process in the communication line is unified from the perspective of optical fiber and carrier communication;

[0021] The multi-tuple model of the proxy node is constructed as follows:

[0022] D i =[A i (m in ),T i (h(t),q(t)),Cst i ] (3)

[0023] Wherein, A i (m in ) represents the processing of the collected information m in , T i represents the delay, which is composed of processing delay h(t) and queuing delay q(t), and Cst i represents the buffer state, and the value of 0 or 1 represents not full or full;

[0024] The multi-tuple model C of the communication line is constructed as follows:

[0025] C=[T C ,P B ,P M ] (4)

[0026] Wherein, T c represents the delay of the communication line, P B represents the probability of communication line interruption, and P M represents the error rate of the communication line;

[0027] Based on the constructed multi-tuple model of the proxy node and the multi-tuple model of the communication line, for the proxy network composed of n proxy nodes and their communication lines, the adjacent matrix model D of the proxy network is as follows:

[0028]

[0029] wherein the main diagonal elements of matrix D represent the agent nodes, and the elements C o,ij represent the carrier communication. c,ij

[0030] Further, the method for constructing the coupling layer model of the coupling network in step S1 is as follows:

[0031] The coupling network adopts the remote terminal unit to realize the coupling of the power distribution network and the agent network.

[0032] The multi-element group model r of the remote terminal unit is constructed as follows:

[0033] r = [TP G-D ,T,P B ] (6)

[0034] wherein TP G-D represents the association relationship between the power nodes and the agent nodes, and is the unit matrix e, that is, one power node corresponds to one agent node; T represents the transmission delay, and P B represents the transmission interruption probability.

[0035] The adjacent matrix model R of the coupling network is constructed as follows:

[0036]

[0037] wherein the main diagonal elements of matrix R represent the remote terminal units on the power nodes, and the remaining elements represent the remote terminal units on the power transmission lines.

[0038] Further, the power data and information instructions are selected as the data communication of the coupling layer model in step S2, and the communication delay component in the coupling layer model is extracted by using the hybrid calculation method, which is specifically as follows:

[0039] The active information P of the power node is selected as the data collected by the remote terminal unit, and the corresponding information received by the agent node is obtained by using the hybrid calculation method as follows:

[0040]

[0041] wherein P rcve is the active information received by the communication substation;

[0042] The active information P is uniformly packaged and processed by the master agent node and delivered to the outside world, and the power information matrix M rcve received by the master agent node is as follows:

[0043] ​

[0044] Subsequently, the master agent node obtains the supply and demand imbalance degree from the outside to coordinate the distribution among the distribution networks, and issues active adjustment instructions to the secondary agent nodes in the distribution network, and the instruction matrix M send is expressed as:

[0045]

[0046] Wherein, P adj is the active adjustment value sent by the master agent node;

[0047] Finally, through the distributed consensus control strategy of the secondary agent node, the stability of the internal distribution network is reached.

[0048] Further, the cost increment model of the agent node control variable in step S3 includes: a power generation cost objective function, a power generation constraint condition and a cost function of the production unit index active power;

[0049] The power generation cost objective function is as follows:

[0050] W(t)=aP 2 (t)+bP(t)+c (11)

[0051] Wherein, a, b, c are fuel cost coefficient, maintenance cost coefficient and constant term respectively; P(t) is the power generation power of the node at t time;

[0052] The power generation constraint condition is as follows:

[0053] P min ≤P≤P max (12)

[0054] Wherein, P min and P max are the upper and lower limits of the power generation power of DG;

[0055] The cost function of the production unit index active power is as follows:

[0056]

[0057] Wherein, w(t) is the cost increment.

[0058] Further, the control variable processing of the master and secondary agent nodes in step S3 is carried out by using distributed control to realize the coordination among the distribution networks and the consensus in the distribution network, which is as follows:

[0059] 1) Control variable processing of the master agent node:

[0060] The distributed coordination control of the master agent node is based on the active power imbalance ΔP caused by the supply-demand deviation. The active power imbalance ΔP is allocated proportionally, and the ΔP to be borne by each distribution network is calculated. i The formula for the share is as follows:

[0061]

[0062] Among them, S i For the power generation capacity of distribution network i, S T B represents the total power generation capacity of the distribution network. i B represents the bandwidth of the communication distribution network i. T This refers to the total bandwidth of the communication distribution network.

[0063] The master agent nodes of different distribution networks achieve the power transfer task of the overloaded distribution network through mutual information exchange; the distributed control algorithm of the master agent nodes is as follows:

[0064]

[0065] in, w is the information variable after the (k+1)th iteration. j α is the information variable sent by agent node j to i, τ is the communication weight between agent nodes i and j, ε is the time delay parameter, and ε is the active power bearing convergence coefficient, which is used to adjust the information update speed and effect.

[0066] 2) Control variable processing for secondary proxy nodes: The updates to the distributed convergence control variables of secondary proxy nodes are related to their own and their neighbors' states; secondary proxy nodes use a consensus equilibrium algorithm to update their respective states and make them converge to the same state, as shown in the following formula:

[0067]

[0068] in, This refers to the information variables after the k-th iteration.

[0069] The advantages of this invention are:

[0070] The technical scheme of the present application is based on constructing an information physical system architecture and a distributed collaborative control strategy, and constructs an information physical coupling modeling method for distributed collaborative control of a power distribution network; a multi-element group and an adjacency matrix are used to construct a power distribution network model containing physical information of the power distribution network, a coupling interface model and an agent network model, and original power data in operation are collected and power adjustment instructions are transmitted through a remote terminal unit; the coupling model fuses a distributed collaborative control algorithm, interacts and updates power information between and inside the power distribution network in real time, generates adjustment instructions in multiple scenarios and multiple states, feeds back to the physical system through a communication network and a coupling interface, dynamically optimizes and adjusts distributed power output, and realizes optimal control of power cost of the power distribution network under consideration of communication time delay, thereby guaranteeing safe and stable operation of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 is a flowchart of the information physical coupling modeling method for distributed collaborative control of a power distribution network of the present application embodiment one;

[0072] Figure 2 is a power distribution network information physical coupling architecture based on CPS of the present application embodiment one;

[0073] Figure 3 is a structure diagram of the information physical coupling modeling simulation model for distributed collaborative control of a power distribution network of the present application embodiment one;

[0074] Figure 4 is a communication line time delay calculation value of the information physical coupling modeling for distributed collaborative control of a power distribution network of the present application embodiment one;

[0075] Figure 5 is a total power comparison diagram of a power distribution network under ideal and non-ideal conditions after the information physical coupling modeling for distributed collaborative control of a power distribution network of the present application embodiment one;

[0076] Figure 6 is a first internal incremental cost comparison diagram of a power distribution network under ideal and non-ideal conditions after the information physical coupling modeling for distributed collaborative control of a power distribution network of the present application embodiment one;

[0077] Figure 7 is a total power comparison diagram of a power distribution network under non-ideal and communication optimization conditions after the information physical coupling modeling for distributed collaborative control of a power distribution network of the present application embodiment one;

[0078] Figure 8 is a first internal incremental cost comparison diagram of a power distribution network under non-ideal and communication optimization conditions after the information physical coupling modeling for distributed collaborative control of a power distribution network of the present application embodiment one. DETAILED DESCRIPTION

[0079] The technical solutions of the embodiments of the present application will be further described below in conjunction with the drawings of the specification and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work belong to the scope of protection of the present application.

[0080] The technical solutions of the present application will be further described below in conjunction with the drawings of the specification and specific embodiments:

[0081] Embodiment one

[0082] As shown in Figure 1 and Figure 2 , the information-physical coupling modeling for power distribution network distributed collaborative control includes the following steps:

[0083] Step 1, based on the CPS technology architecture, the physical layer, coupling layer and information layer in the power distribution network are modeled by using multiple groups and adjacency matrix, reflecting the mapping relationship between physical and information;

[0084] Step 1.1, considering the distributed power supply, transformer, power transmission line equipment and power load in the power production process, the multiple group model G i and the adjacency matrix model G of the power distribution network are as follows:

[0085] G i = [N, K]

[0086]

[0087] In the power node model, N represents the number of adjacent power nodes around, and K takes the value of 0, 1, 2, representing distributed power supply, transformer and power load respectively; in the power distribution network model, the diagonal elements are power nodes; the non-diagonal elements represent the connection relationship between nodes, and the value of element L ij is 0 or 1, representing no or having power line connected between nodes i and j respectively.

[0088] Step 1.2, considering the routers, switches and other primary and secondary proxy nodes and communication lines near the power equipment, referring to the modeling form of the power distribution network, the proxy nodes in the information layer are modeled by combining the communication delay and buffer state characteristics, and the information flow transmission process in the communication line is unified modeled from the carrier and optical fiber communication angle:

[0089] D i = [A i (min ),T i (h(t),q(t)),Cst i ]

[0090] C=[T C ,P B ,P M ]

[0091] where D i is the proxy node model, A i (m in ) represents the processing of the collected information m in , T i represents the time delay, which is composed of the processing time delay h(t) and the queuing time delay q(t), and Cst i represents the buffer state, whose value of 0 or 1 represents not full or full;

[0092] In the communication line model C, T c represents the time delay of the communication line, P B represents the probability of communication line interruption, and P M represents the error rate of the communication line. The error rate of optical fiber is usually less than 10e-9, while the carrier is greatly affected by the signal-to-noise ratio.

[0093] Based on formula (2), the proxy network adjacency matrix model D in the information layer can be expressed as:

[0094]

[0095] where the diagonal elements are communication nodes; the lower left triangular part C o,ij describes the optical fiber communication, and the upper right triangular part C c,ij describes the carrier communication.

[0096] Step 1.3, considering the various sensors, communication devices and execution units equipped by the RTU (Remote Terminal Unit), the functions of physical layer information collection and information layer instruction sending can be realized, so the coupling interface is realized by means of the RTU. The multi-element model r of the RTU in the coupling layer and the adjacency matrix model R of the coupling interface can be expressed as:

[0097] r=[TP G-D ,T,P B ]

[0098]

[0099] where in the RTU model, TP G-D represents the association relationship between power and proxy nodes, which is generally a unit matrix e, that is, one power node corresponds to one communication node, T represents the transmission time delay, and PB Represent transmission interruption probability; in the coupling interface model, the diagonal elements represent the RTU devices on the power equipment; the non-diagonal elements represent the RTU devices on the distribution network line.

[0100] Step 2, adopt hybrid calculation method to extract communication delay component in coupling model, and select relevant power data and information instruction as data communication of coupling modeling.

[0101] Hybrid calculation method example: let T Ai and T Bi be the delay component of matrix A and B respectively, Represent hybrid calculation, adopt matrix "and" calculation for delay component in the model by hybrid calculation method to extract communication delay value in coupling model, as follows:

[0102]

[0103] Consider relevant power data and information instruction as data communication of coupling modeling, select active type information P of power node as RTU collection data, and get corresponding information received by agent node through hybrid calculation as follows:

[0104]

[0105] Among them, P rcve is the active information received by the communication substation.

[0106] The main agent node uniformly packs and transmits the active type information to the outside world, and the power information matrix M rcve received by the main agent node is as follows:

[0107]

[0108] Then, the main agent node obtains the supply-demand imbalance degree from the outside world to carry out distributed coordination distribution among distribution network, and issues active adjustment instruction to the secondary agent node in the distribution network, and the instruction matrix M send is expressed as:

[0109]

[0110] Among them, P adj is the active adjustment value sent by the main agent node. Finally, through the distributed convergence control strategy of the secondary agent node, the stability of the distribution network is achieved.

[0111] Step 3, based on the models and data communication of each layer built in step S1, establish the cost increment model of the control variable of the agent node; adopt distributed control strategy to process the control variable of the main and secondary agent nodes, and realize the coordination among distribution networks and the convergence function in the distribution network;

[0112] Step 3.1, the distributed power generation cost objective function and power generation constraints are as follows:

[0113] W(t) = aP 2 (t) + bP(t) + c

[0114] P min ≤ P ≤ P max

[0115] Wherein, a, b, c are fuel cost coefficient, maintenance cost coefficient and constant term; P(t) is the power generation of the node at time t, P min and P max are the upper and lower limits of the power generation of DG.

[0116] The cost of producing one unit of active power at the next time can be expressed as the derivative of the objective function:

[0117]

[0118] Wherein, w(t) is the cost increment, which is an important information in the communication process of the secondary agent node.

[0119] Step 3.2, the distributed coordination control of the master agent node is based on the active imbalance ΔP generated by the supply-demand deviation. In order to realize the interactive distribution between distribution networks, the master agent node ΔP adopts the principle of equal proportion allocation, calculates the share of ΔP i each distribution network needs to bear, which is related to the power capacity configuration and communication bandwidth flow level of the distribution network:

[0120]

[0121] S i is the power generation capacity of distribution network i, S T is the total power generation capacity of distribution network, B i is the bandwidth size of communication distribution network i, B T is the total bandwidth size of communication distribution network.

[0122] In order to prevent some distribution network from bearing too heavy, the master agent nodes of different distribution networks realize the power transfer task of overloaded distribution network through mutual information interaction.

[0123] The distributed control algorithm of the master agent node is as follows:

[0124]

[0125] Wherein, is the information variable after the k+1 iteration, w jis the information variable sent by the agent j to i, a is the communication weight between agent i and j, τ is the time delay parameter, ε is the active engagement convergence coefficient, which is used to adjust the information update speed and effect.

[0126] Step 3.3, the control variable update of the distributed convergence of the secondary agent node is related to its own and neighbor states. In order to realize the distributed control in the power distribution network, the secondary agent node uses the consensus balancing algorithm to update and converge the state, as shown in formula (13):

[0127]

[0128] wherein, is the information variable after the kth iteration.

[0129] As shown in Figure 3 , a physical information simulation model of the power distribution network is established, which is composed of 6 distributed power sources, 2 primary agent nodes and 4 secondary agent nodes, and the specific parameters are shown in Table 1. The communication distances of P1 to S1 and S2 in the first power distribution network are 10km and 15km respectively, and the communication distance between S1 and S2 is 3km; the communication distances of P2 to S3 and S4 in the second power distribution network are 20km and 15km respectively, and the communication distance between S3 and S4 is 2km. Considering the high reliability of optical fiber communication, the influence of communication interruption and error code on time delay is ignored in the simplified calculation, and the time delay of optical signal on the optical fiber communication is considered to be 5μs / km. The time delay of short distance carrier communication mainly considers the influence of communication line frequency band, and the data transmission rates of high frequency band and low frequency band are 0.1Mb / s and 20Mb / s respectively. In the simulation model, the S1-S2 line adopts high frequency band carrier, the S3-S4 line adopts low frequency band carrier, and the other lines adopt optical fiber. The average processing data time of the primary and secondary agent nodes is 5ms and 0.5ms respectively. The average time delay of RTU data acquisition is 100ms. In the initial stage, the total power generation is 90kW, which is equal to the load demand; at 1s, the load surge scenario is set, and the load increase is 30kW, i.e. the load demand rises to 120kW.

[0130] Table 1

[0131]

[0132] As shown in Figure 4 , the time delay calculation value of each communication line is obtained by using hybrid calculation, and it can be seen from the figure that the time delay of the line using optical fiber and low frequency band carrier communication is relatively low, in the interval of 105ms-112ms, while the communication mode between S1 and S2 uses high frequency band, and the slow data transmission speed produces a time delay of 241ms, which is more than twice that of other lines.

[0133] As shown in Figure 5and Figure 6 As shown in Figs. 1 and 2, the power distribution network successfully converges under ideal communication conditions, verifying the effectiveness of the proposed distributed collaborative control strategy, but oscillates and fails to converge under non-ideal communication conditions with time delay. Then the most likely cause of the fluctuation is the introduction of exact time delay data. The greatest impact of time delay is to change the synchronization of communication data arrival. Once the sampling value lags, the update between agents will be slow. By analyzing the time delay data in Fig. 3, it can be found that the time delay between agents D1-D2 is much higher than that of other lines due to the use of high-frequency carrier, which greatly increases the data transmission time and reduces the efficiency of distributed collaborative control. Figure 5

[0134] As shown in Figs. 1 and 2, the power distribution network successfully converges under ideal communication conditions, verifying the effectiveness of the proposed distributed collaborative control strategy, but oscillates and fails to converge under non-ideal communication conditions with time delay. Then the most likely cause of the fluctuation is the introduction of exact time delay data. The greatest impact of time delay is to change the synchronization of communication data arrival. Once the sampling value lags, the update between agents will be slow. By analyzing the time delay data in Fig. 3, it can be found that the time delay between agents D1-D2 is much higher than that of other lines due to the use of high-frequency carrier, which greatly increases the data transmission time and reduces the efficiency of distributed collaborative control. Figure 7 and Figure 8 As shown in Figs. 1 and 2, the power distribution network successfully converges under ideal communication conditions, verifying the effectiveness of the proposed distributed collaborative control strategy, but oscillates and fails to converge under non-ideal communication conditions with time delay. Then the most likely cause of the fluctuation is the introduction of exact time delay data. The greatest impact of time delay is to change the synchronization of communication data arrival. Once the sampling value lags, the update between agents will be slow. By analyzing the time delay data in Fig. 3, it can be found that the time delay between agents D1-D2 is much higher than that of other lines due to the use of high-frequency carrier, which greatly increases the data transmission time and reduces the efficiency of distributed collaborative control.

[0135] The present application is based on CPS architecture and distributed collaborative control strategy, and proposes an information-physical coupling modeling method for distributed collaborative control of power distribution network. A multi-element group and an adjacency matrix are used to construct a power distribution network model containing physical information of the power distribution network, a coupling interface model and an agent network model, and original power data in operation is collected by RTU and power adjustment instructions are transmitted; the coupling model fuses a distributed collaborative control algorithm, interacts and updates power information between and inside the power distribution network in real time, generates adjustment instructions in multiple scenarios and multiple states, feeds back to the physical system through a communication network and a coupling interface, dynamically optimizes and adjusts DG output, and realizes optimal control of power cost of the power distribution network under the condition of considering communication time delay, thereby guaranteeing safe and stable operation of the power grid.

[0136] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.​

Claims

1. A method for information-physical coupling modeling for distribution network distributed collaborative control, characterized in that, The method comprises the following steps: S1, a physical layer model of a power distribution network, an information layer model of an agent network and a coupling layer model of a coupling network under an information physical system architecture are constructed in the form of a multivariate group and an adjacency matrix; S2, power data and information instructions are selected as data communication of the coupling layer model, and a hybrid calculation method is used to extract a communication time delay component in the coupling layer model, and the specific method is as follows: Selecting active power node information P As the data collected by the remote terminal unit, the corresponding information received by the proxy node is obtained by the hybrid calculation method: (8) wherein P rcve active information received by the communication sub-station; The master proxy node will have active class information P The unified package processing and delivery to the outside world, the power information matrix it receives M rcve For: (9) Subsequently, the master agent node obtains the supply and demand imbalance degree from the outside to coordinate the distribution among the distribution networks, and issues active adjustment instructions to the secondary agent nodes in the distribution network M send is expressed as: (10) wherein, P adj active adjustment value sent to the master proxy node; Finally, a stable state is reached in the power distribution network through a distributed convergence control strategy of the secondary agent node; S3, based on the constructed physical layer model of the power distribution network, the information layer model of the agent network and the coupling layer model of the coupling network and data communication, a cost increment model of a control variable of the agent node is established; a distributed control strategy is used for control variable processing of the primary and secondary agent nodes, and coordination between the power distribution networks and convergence in the power distribution network are realized.

2. The cyber-physical coupling modeling method for power distribution network distributed collaborative control according to claim 1, characterized in that, The construction method of the physical layer model of the power distribution network in step S1 is as follows: The power distribution network comprises power nodes and power transmission lines; the power nodes comprise distributed power sources, transformers and power consumption loads; Constructing a power node's tuple model G i As follows: (1) Wherein, N represents the number of surrounding adjacent power nodes, and K is 0, 1 or 2, representing the distributed power source, the transformer and the power consumption load respectively; Constructing an adjacency matrix model of a power distribution network G As follows: (2) wherein the matrix G has the power nodes on the main diagonal and the connection relationships between the power nodes as the remaining elements, and the elements L ij have the value of 0 or 1, respectively indicating that there is no or a power transmission line connected between the power nodes i and j .

3. The cyber-physical coupling modeling method for distribution network oriented distributed collaborative control according to claim 2, characterized in that, The construction method of the information layer model of the agent network in step S1 is as follows: The agent network comprises agent nodes and communication lines; the agent nodes comprise primary agent nodes and secondary agent nodes, the primary agent nodes adopt switches, and the secondary agent nodes adopt routers; the agent nodes are modeled by combining communication time delay and buffer state characteristics, and the information flow transmission process in the communication lines is unified from the angles of optical fiber and carrier communication; The multivariate group model of the agent node is constructed as follows: (3) wherein, A i ( m in ) represents the processing of the collected information m in , T i represents the time delay, which consists of the processing time delay h ( t ) and the queuing time delay q ( t ), Cst i represents the buffer status, whose value of 0 or 1 represents not full or full. Multituple model for constructing a communication line C As follows: (4) wherein, T c a delay representative of the communication line, P B a probability representative of a communication line interruption, P M an error rate representative of the communication line; Based on the constructed multi-tuple model of the proxy nodes and the multi-tuple model of the communication lines, for the proxy network composed of the proxy nodes and the communication lines therebetween, a neighborhood matrix model is constructed as follows: n D As follows:​ (5) where the main diagonal elements of the matrix D are the proxy nodes, the elements of the lower left triangular part of the matrix D C o,ij describing the optical fiber communication, the elements of the upper right triangular part of the matrix D C c,ij describing the carrier communication.​​ 4. The cyber-physical coupling modeling method for power distribution network distributed collaborative control according to claim 3, characterized in that, The construction method of the coupling layer model of the coupling network in step S1 is as follows: The coupling network realizes coupling of the power distribution network and the agent network by using a remote terminal unit; Constructing a multivariate model of a remote terminal unit r As follows: (6) wherein, TP G-D represents the association relationship between the power nodes and the agent nodes, and is a unit matrix e , that is, one power node corresponds to one agent node; T represents the transmission delay, P B represents the transmission interruption probability; Constructing an adjacency matrix model of a coupling network R As follows: (7) where the main diagonal elements of the matrix R represent the remote terminal units at the power nodes and the remaining elements represent the remote terminal units on the power transmission lines.

5. The cyber-physical coupling modeling method for distribution network oriented distributed collaborative control according to claim 4, characterized in that, The cost increment model of the control variable of the agent node in step S3 comprises a power generation cost objective function, power generation constraint conditions and a cost function of a production unit index active power; The power generation cost objective function is as follows: (11) in, a , b , c These are the fuel cost coefficient, maintenance cost coefficient, and constant term, respectively. P ( t )for t Power generation at any given time point; The power generation constraint conditions are as follows: (12) wherein P min and P max DG power upper and lower limits; The cost function of the production unit index active power is as follows: (13) wherein w ( t ) is the incremental cost.

6. The cyber-physical coupling modeling method for distribution network oriented distributed collaborative control according to claim 5, characterized in that, The control variable processing of the primary and secondary agent nodes by using the distributed control in step S3 realizes coordination between the power distribution networks and convergence in the power distribution network, and the specific method is as follows: 1) Control variable processing of the primary agent node: Distributed coordination control of master agent nodes for active imbalance Δ caused by demand bias P For reference, active imbalance Δ P Taking the principle of equal proportion allocation, the Δ required to be borne by each distribution network is calculated P i The formula for the share is as follows: (14) wherein, S i is the total generation capacity of the power distribution network, i S T is the total generation capacity of the power distribution network, B i is the bandwidth size of the communication power distribution network, i B T is the total bandwidth size of the communication power distribution network;​​ The primary agent nodes of different power distribution networks realize power transfer tasks of overloaded power distribution networks through mutual information interaction; the distributed control algorithm of the primary agent node is as follows: (15) wherein, is the information variable after the k+1th iteration, w j is the agent node j sends to i the information variable, α is the agent node i and j the communication weight between τ is the delay parameter, ε is the active bearing convergence coefficient, used to adjust the information update speed and effect; 2) Control variable processing of the secondary agent node: the update of the control variable of the distributed convergence of the secondary agent node is related to the state of itself and neighbors; the secondary agent node uses a consistency balancing algorithm to update and tend to be the same, and the specific formula is as follows: (13) wherein is the information variable after the k th iteration.

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