A Multi-Period Dynamic Power Supply Restoration Method Based on the Coupling of Power Distribution, Information, and Transportation Networks

Abstract

This invention discloses a multi-period dynamic power supply restoration method based on the coupling of power distribution, information, and transportation networks, comprising the following steps: constructing a fault recovery model of the power distribution network based on a multi-source collaborative restoration strategy, and obtaining the objective function of the fault recovery model; based on the objective function, obtaining the optimal access node of MESS in each time period by constructing a power distribution network restoration constraint model, a MESS operation constraint model, and an information network constraint model, and establishing a MESS scheduling model taking into account traffic flow; and performing fault recovery on the power distribution network based on the fault recovery model and the MESS scheduling model. This invention utilizes local distributed power sources for collaborative power generation, restoring as much load as possible while considering the impact of information networks on load restoration, ensuring the reliability of load restoration, compensating for insufficient power demand by scheduling MESS, and dynamically optimizing its location, thereby maximizing load restoration and further improving the resilience of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of emergency recovery technology for smart distribution networks, and more specifically, to a multi-period dynamic power supply recovery method based on the coupling of power distribution, information, and transportation networks. Background Technology

[0002] To cope with prolonged and widespread power outages, a "resilient grid" is needed. With the integration of new energy sources such as wind and solar power, and the development of energy storage systems (ESS) and mobile energy storage systems (MESS), the emergency planning, response, and recovery capabilities of distribution networks are constantly improving. How to fully leverage the flexibility of these new resources to ensure the rapid restoration of the distribution network during major power outages is a pressing issue that needs to be addressed.

[0003] To address the aforementioned issues, existing technologies have developed emergency recovery strategies for distribution networks from various perspectives. For example, strategies that coordinate the recovery of multiple power sources improve resource utilization, but they do not consider the power supply support role of MESS (Mechanical Energy Utilization System). Another approach focuses on the active power spatiotemporal support role of MESS in restoring distribution network loads, but this method does not consider the impact of transportation networks, resulting in overly idealistic results. Therefore, further research is needed to coordinate the restoration of power from multiple sources while fully leveraging the spatiotemporal mobility of MESS.

[0004] On the other hand, in practice, major power outages caused by information network failures occur frequently. For example, a major power grid outage was partly caused by the risk of unauthorized access and information theft from the information network, leading to abnormal operation or interruption of information network equipment, which in turn affects the power supply system. Therefore, the normal operation of the terminal communication equipment corresponding to the power nodes plays a crucial role in the power grid fault recovery process. With the rapid development of power system intelligence, the interaction mechanism between the power network of the distribution network and the information network of the communication terminals is becoming increasingly complex. Existing models require that the connectivity status of the information system and the distribution network be consistent. However, in reality, when a power line is disconnected, the information nodes corresponding to the power nodes at both ends of the line may not be disconnected. Therefore, further in-depth research into the impact of information networks on power restoration and ensuring the reliability of power restoration remains an unresolved issue. Summary of the Invention

[0005] To address the aforementioned issues, the purpose of this invention is to coordinate the load recovery capabilities of distributed energy sources and MESS (Mechanical Energy Storage System) in scenarios involving prolonged and widespread power outages in distribution networks. This approach aims to fully leverage the spatiotemporal power support capabilities of MESS while also considering the bidirectional interaction between the information network and the distribution network to ensure the reliability and effectiveness of load recovery. A mathematical model for this strategy is established to further enhance the emergency recovery capabilities and resilience of the distribution network.

[0006] To achieve the above technical objectives, this application provides a multi-period dynamic power supply restoration method based on the coupling of power distribution, information, and transportation networks, including the following steps:

[0007] Based on a multi-source collaborative recovery strategy, a fault recovery model for the distribution network is constructed, and the objective function of the fault recovery model is obtained. The objective function is used to obtain the fault recovery objective of maximizing load recovery and minimizing network loss.

[0008] Based on the objective function, by constructing a distribution network restoration constraint model, a MESS operation constraint model, and an information network constraint model, the optimal access node for MESS in each time period is obtained, and a MESS scheduling model taking into account traffic flow is established.

[0009] in,

[0010] The MESS scheduling model is used to obtain the optimal scheduling path and scheduling time, while the distribution network recovery constraint model is used to generate the load of the distribution network, the charging and discharging characteristics of the energy storage system, radial topology constraints, and branch power flow constraints.

[0011] The MESS runtime constraint model is used to generate MESS transmission location constraints.

[0012] The information network constraint model is used to generate information node power supply constraints, information network traffic constraints, and information node control action constraints.

[0013] Based on the fault recovery model and the MESS scheduling model, the multi-period load recovery results, information network effectiveness, and distribution network topology of the distribution network are obtained, and fault recovery of the distribution network is carried out.

[0014] Preferably, in the process of obtaining the objective function of the fault recovery model, the objective function is used to satisfy the convex relaxation condition in the subsequent branch power flow constraints, and the expression of the objective function is:

[0015]

[0016] in, The value is an integer variable between 0 and 1, indicating whether the load at node i has been restored during time period t; This indicates the load importance of node i; This represents the recovery power of node i during time period t; This is the network loss coefficient, ensuring that the solution to the primary objective is not affected by the secondary objective; The line flowing through during time period t The current; For the line The resistance; Represents the set of load nodes; All available lines within the target island; This is the reference power.

[0017] Preferably, in the process of constructing the distribution network restoration constraint model, the distribution network restoration constraint model includes:

[0018] A load recovery power model is used to obtain the load of the distribution network;

[0019] Energy storage charge and discharge constraint model is used to obtain the charge and discharge characteristics of energy storage system;

[0020] Radial topological constraint model, used to generate radial topological constraints;

[0021] Branch flow constraint model, used to generate branch flow constraints.

[0022] Preferably, in the process of constructing the distribution network restoration constraint model, the distribution network restoration constraint model also includes a line current thermal limit safety constraint model, which is used to obtain the power output and node voltage by correlating the current magnitude with whether the branch is restored.

[0023] Preferably, in the process of constructing the MESS operational constraint model, the MESS transmission location constraints include:

[0024] When the MESS is not docked at a distribution network node, it cannot perform charging and discharging operations.

[0025] A given MESS can only be located at one distribution network node or be in transmission state at any given time.

[0026] If the j-th MESS is in the transmission route from the first node to the second node, then the access flag bits are all 0;

[0027] If the difference between the access flag bits of adjacent time periods is positive, then transmission is performed; otherwise, no transmission is performed.

[0028] When a certain MESS is connected to the power grid, and the expression contains a 0-1 variable multiplied by a continuous variable, the Big M linearization process is used.

[0029] Preferably, in the process of constructing the information network constraint model, the coupling effect between the distribution network and the information network is modeled based on the cyber-physical network background to obtain the information network constraint model;

[0030] Information network constraint models include:

[0031] Information node power supply constraint model, used to generate information node power supply constraints;

[0032] Information network traffic constraint model, used to generate information network traffic constraints;

[0033] The information node control action constraint model is used to generate control action constraints for information nodes, which are used to ensure that information nodes remain in an effective state when the load is restored.

[0034] Preferably, in the process of constructing the MESS scheduling model, the MESS scheduling model includes:

[0035] The flow-speed model is used to determine the direct impact of road capacity and flow on transmission speed.

[0036] The path-time model is used to obtain the shortest path between different origin-destination node pairs using the Floyd algorithm.

[0037] Preferably, in the process of constructing the MESS scheduling model, the flow-speed model is as follows:

[0038]

[0039] in, Indicates adjacent nodes in time period t Inter-transmission speed; Indicates adjacent nodes Zero flow velocity between; Adjacent nodes in time period t Traffic; Adjacent nodes The traffic capacity is mainly affected by the road grade; and The ratio of adjacent nodes in time period t The saturation level; a, b, and n are coefficients for different road grades. Different road grades correspond to different coefficients; a road refers to the transportation road corresponding to the geographical location of the power distribution network.

[0040] Preferably, in the process of constructing the MESS scheduling model, the path-time model is as follows:

[0041]

[0042] in, This represents the total transmission time between nodes i and j; The time required for the h-th road segment; Indicates the length of the h-th road segment; The transmission speed of the h-th directly connected road is obtained from the flow-speed model.

[0043] Preferably, the multi-time dynamic power restoration system for implementing the multi-time dynamic power restoration method includes:

[0044] The fault recovery strategy module is used to construct a fault recovery model for the distribution network based on a multi-source collaborative recovery strategy and obtain the objective function of the fault recovery model.

[0045] The scheduling strategy module is used to obtain the best access node for MESS in each time period based on the objective function by constructing a distribution network recovery constraint model, a MESS operation constraint model, and an information network constraint model, and to establish a MESS scheduling model that takes traffic flow into account.

[0046] The fault recovery module is used to obtain the multi-period load recovery results, information network effectiveness, and distribution network topology of the distribution network based on the fault recovery model and the MESS scheduling model, and to perform fault recovery on the distribution network.

[0047] Compared with existing technologies, the multi-period dynamic power supply restoration strategy proposed in this invention, which considers the power distribution, information, and transportation networks, has the following advantages:

[0048] By utilizing local distributed power sources for collaborative generation, the system restores as much load as possible while considering the impact of information networks on load restoration, ensuring the reliability of load restoration. By scheduling MESS (Mechanical Service Assistance System) to compensate for insufficient power demand and dynamically optimizing its location, the system can restore load to the maximum extent and further improve the resilience of the distribution network. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is the multi-time-period dynamic power supply restoration process described in this invention;

[0051] Figure 2 This is a topology diagram of the multi-period dynamic power supply restoration system considering the coupling of power distribution, information, and transportation networks as described in this invention.

[0052] Figure 3 The load ratio change curve of the multi-period dynamic power supply restoration system considering the coupling of power distribution, information and transportation networks as described in this invention;

[0053] Figure 4This is the solution result of the first time period dynamic power supply restoration strategy considering the coupling of power distribution, information and transportation networks as described in this invention;

[0054] Figure 5 This is the solution result of the second-period dynamic power supply restoration strategy considering the coupling of power distribution, information and transportation networks as described in this invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0056] like Figure 1-5 As shown, Figure 1 The flowchart illustrates the specific implementation of the recovery strategy proposed in this invention. As can be seen, the multi-period dynamic power supply recovery scheme includes a distribution-information network coupled recovery model and a MESS scheduling model that considers traffic flow. The main contents are as follows:

[0057] S1: Under the multi-source collaborative recovery approach, the objective function of the fault recovery model is established. The fault recovery objectives are to maximize the load recovery amount and minimize network loss, with the former being the primary objective, as follows:

[0058] (1)

[0059] In the formula, The value is an integer variable between 0 and 1, indicating whether the load at node i has been restored during time period t; This indicates the load importance of node i; This represents the recovery power of node i during time period t; This is the network loss coefficient, ensuring that the solution to the primary objective is not affected by the secondary objective; The line flowing through during time period t The current; For the line The resistance; Represents the set of load nodes; All available lines within the target island; The baseline power is used; network losses are taken into account in the objective function, which can also satisfy the convex relaxation condition in the subsequent branch power flow constraints.

[0060] S21: Traditional distribution network restoration constraints require modeling the load of the distribution network, the charging and discharging characteristics of the energy storage system, the radial topology constraints, and the branch power flow constraints separately.

[0061] S211: Load Restoration Power Model

[0062] (2)

[0063] In the formula, Let be the load recovery coefficient at node i during time period t, assuming the load is controllable. , and when When the value is 1, the load is equivalent to an uncontrollable load; Let be the load amount of load i during time period t.

[0064] S212: Energy storage charging and discharging constraints:

[0065] (3)

[0066] (4)

[0067] (5)

[0068] (6)

[0069] In the formula, ; , These are the charging and discharging state variables of the j-th ESS during time period t; For the ESS collection. , Let represent the charging and discharging power of the j-th ESS during time period t, respectively; , These represent the maximum charging and discharging power of the j-th ESS, respectively; , These represent the charge states of the j-th ESS in time period t and time period t+1, respectively. , These represent the maximum and minimum charge states of the j-th ESS, respectively; , Let represent the charging and discharging efficiencies of the j-th ESS, respectively;

[0070] Equation (3) is the charge / discharge state constraint; Equation (5) is the power constraint; Equation (6) is the state of charge constraint.

[0071] S213: Radial topological constraints:

[0072] (7)

[0073] (8)

[0074] (9)

[0075] (10)

[0076] In the formula, N is the number of nodes in the distribution network; It is an integer variable between 0 and 1, representing the line during time period t. Whether it has been restored; As a continuous variable, representing the line The size of the virtual current; This represents the virtual requirement of a non-root node; r is the root node number; M is a very large positive real number.

[0077] S214: Branch flow constraint:

[0078] (11)

[0079] (12)

[0080] (13)

[0081] (14)

[0082] In the formula, ; , The lines for time period t are respectively The active and reactive power flowing through a single phase; Line for time period t The square of the amplitude of the current flowing through the single phase. The square of the single-phase voltage amplitude at node t during time period t is... ; , A constant, representing the circuit. Resistance and reactance; , Let be the active and reactive power of the power supply connected to node i during time period t, respectively; Equation (11) represents the active and reactive power balance equation of the node. For the line voltage drop balance equation, Equation (12) is an auxiliary variable introduced using the "Big M method". The magnitude of the voltage drop and the line restoration status If the lines are connected, Disconnect, then for No restrictions, or vice versa. =0; Equation (14) is the standard second-order cone form of Equation (13).

[0083] In addition, it also includes power output, node voltage, and line current thermal limit safety constraints:

[0084] (15)

[0085] (16)

[0086] (17)

[0087] In the formula, S represents the set of power nodes. , , , These are the upper and lower limits of the active power and reactive power of the power source at node i, respectively. , These represent the minimum and maximum voltage amplitudes at node i, respectively. The maximum allowable value of the square of the current amplitude is given by equation (17), which indicates that the magnitude of the current is related to whether the branch is restored.

[0088] S22: The MESS operation constraint model, in addition to including the charging and discharging constraints of the energy storage elements, also needs to include the MESS transmission location constraints:

[0089] (18)

[0090] (19)

[0091] (20)

[0092] (twenty one)

[0093] (twenty two)

[0094] (twenty three)

[0095] In the formula: This is the flag bit of the j-th MESS at the distribution network access node s during time period t. ; It is the set of accessible nodes; Access node for time period t and Transmission time parameters between; This is a MESS transmission indicator variable for time period t; a value of 1 indicates transmission. Let be the active power of the j-th MESS in time period t; M is a very large positive number.

[0096] Equation (18) indicates that if the MESS is not docked at a distribution network node, it cannot perform charging and discharging operations; Equation (19) stipulates that the j-th MESS can only be located at one distribution network node (value equal to 1) or in transmission state (value equal to 0) in each time period; Equation (20) describes if the j-th MESS is at a node and During transmission, a flag is accessed. All are 0. Equation (21) indicates that if the difference between the access flag bits of adjacent time periods is positive, transmission will be carried out; otherwise, no transmission will be carried out. Equation (22) describes the output power relationship of the j-th MESS accessing the power grid. In this equation, there is a case where 0-1 variables are multiplied by continuous variables. The large M linearization process is used as shown in equation (23).

[0097] S23: The information network constraint model needs to be based on the cyber-physical network context, modeling the coupling effect between the power distribution network and the information network. Based on relevant concepts in graph theory, the information network topology is defined as follows: ,in Represents the set of all information nodes. This represents the set of connections between all information nodes.

[0098] S231: Power supply constraints for information nodes:

[0099] (twenty four)

[0100] (25)

[0101] (26)

[0102] In the formula: Information node for time period t The energy supply effectiveness flag; Representation and information nodes A collection of connected power nodes; It is a parameter that defines the coupling strength between information networks and power networks. The larger the value, the stronger the correlation. The above constraints cannot be solved directly; therefore, auxiliary variables are introduced to linearize the constraints:

[0103] (27)

[0104] (28)

[0105] In the formula: , They are respectively The upper and lower limits; Let it be a very small positive number. Here we let... , This linearizes the aforementioned constraints.

[0106] S232: Information Network Traffic Constraints

[0107] (29)

[0108] (30)

[0109] (31)

[0110] (32)

[0111] In the formula: ; Information node for time period t Work status; For the inflow of information nodes Communication traffic; For information nodes outbound communication traffic; For the destination node set of information flow; This refers to the set of information flow source nodes, i.e., the control center node; The number of information terminals; A set of information network links; For nodes The required bandwidth is 1 for each destination node in this invention. .

[0112] Equation (29) defines the working state of the information node; Equation (30) indicates that the communication traffic on the information link is affected by the total traffic volume. and the power supply status of the nodes , Constraints; Equation (31) indicates that the source node only transmits outbound traffic and cannot exceed the number of destination nodes in the information flow; Equation (32) indicates that, apart from the source node, the inbound traffic of other nodes includes outbound traffic and traffic required by the terminal equipment. When a node only plays the role of service transmission, i.e. The inflow and outflow of the corresponding information flow should be equal.

[0113] S233: Constraints on the Control Role of Information Nodes:

[0114] The failure of information nodes will render the corresponding power nodes unobservable and uncontrollable. In distribution networks, the main considerations are the normal operation and control of power equipment such as power loads, energy storage, and distributed energy sources.

[0115] (33)

[0116] (34)

[0117] (35)

[0118] In the formula, The maximum output power of the power supply connected to node i; Equation (33) indicates that when performing load restoration, if the information node corresponding to the load node fails, due to uncontrollability, the node cannot perform the corresponding load restoration operation. Similarly, Equation (34) indicates that power supply equipment can only be put into operation when its corresponding information node is working normally.

[0119] S3: Based on the optimization models in steps S1 and S2, the optimal access nodes for each time period of MESS can be obtained, and a MESS scheduling model can be established, including the flow-speed model and path-time model under the traffic network. The Floyd algorithm is used to obtain the optimal scheduling path and scheduling time for MESS.

[0120] S31: Flow-Voltage Model

[0121] Capacity and throughput have a direct impact on transmission speed, so this impact must be considered when calculating transmission speed, as shown in the following model.

[0122] (36)

[0123] In the formula: Indicates adjacent nodes in time period t Inter-transmission speed; Indicates adjacent nodes Zero flow velocity between; Adjacent nodes in time period t Traffic; Adjacent nodes The traffic capacity is mainly affected by the road grade; and The ratio of adjacent nodes in time period t The saturation level; a, b, and n are coefficients for different road grades. Different road grades correspond to different coefficients; a road refers to the transportation road corresponding to the geographical location of the power distribution network.

[0124] S32: Path-Time Model

[0125] Considering that there are often multiple feasible paths between the starting node and the destination node, this invention uses the Floyd algorithm to find the shortest path between different starting-destination node pairs. Assuming that there are H direct road segments between the starting point i and the destination point j, the transmission time model is as follows.

[0126] (37)

[0127] (38)

[0128] In the formula, This represents the total transmission time between nodes i and j; The time required for the h-th road segment; Indicates the length of the h-th road segment; The transmission speed of the h-th directly connected road is obtained from the flow-speed model.

[0129] S4: The established multi-period power supply restoration model and MESS scheduling method are used for restoration to obtain the multi-period load restoration results, information network effectiveness, and distribution network topology of the distribution network.

[0130] Figure 2 This is a topology diagram of a multi-period dynamic power supply restoration system considering the coupling of power distribution, information, and transportation networks proposed in this embodiment of the invention. The distribution network has 33 nodes. The substation is out of power, and branch 5 of the distribution network is faulty and isolated, resulting in a major power outage for the entire distribution network. Only the control center located at node 1 of the distribution network is functioning normally in the information network. Node 1 of the transportation network is the MESS pre-positioning center, i.e., the initial parking position of the MESS. Road levels are set as main roads (I) and secondary roads (II). The road parameters a, b, and n of the main road are 1.726, 3.15, and 3, respectively, while those of the secondary road are 2.076, 2.870, and 3, respectively. The MESS zero-current velocity is 70 km / h. The load is divided into three levels: a weighting factor of 10 for primary important loads, 5 for secondary important loads, and 1 for ordinary loads. The load power of each node varies as a percentage, such as... Figure 3 As shown.

[0131] The fault occurred during two time periods, from 11:00 to 13:00. The solution was obtained by following the strategy and process outlined in this invention, and the results are as follows: Figure 4 , Figure 5 As shown. Figure 4 , Figure 5The weighted load power recovery ratios were 65.2% and 71.7%, respectively. The results show that the dynamic recovery strategy of this invention is reflected in two aspects: First, the load power of each node fluctuates over time, thus the distribution network topology differs at different times. Dynamic changes in the distribution network topology are achieved through the opening and closing of circuit breakers and tie switches. Second, based on the correspondence between the distribution network and transportation network nodes, multi-time-period dynamic scheduling of the MESS is performed. Figure 4 , 5 The paths shown are all shortest paths between the origin and destination nodes. Combined with traffic flow information at different times, real-time travel speeds can be obtained, leading to the scheduling time. Figure 4 , 5 As indicated by the annotations, the specific recovery results are shown in Table 1.

[0132] Table 1

[0133]

[0134] Note: All other load nodes in the distribution network are partially restored.

[0135] The validity of the corresponding information nodes is taken into account in this invention. Figure 4 , 5 As shown in Table 1, load restoration can only be performed on the corresponding power nodes when the information nodes are valid. In order to restore as many loads as possible, all information nodes corresponding to the power access nodes are valid. Failed information nodes act as relay nodes to transmit information without consuming information traffic. The terminal equipment corresponding to the valid information nodes plays a monitoring and control role. Among the fully restored load nodes, all are first-level and second-level important loads, and partial restoration of controllable loads has been achieved.

[0136] In summary, the embodiments of the present invention can ensure that load restoration is performed while the information node corresponding to the power node is in a valid state, thereby improving the reliability and effectiveness of load restoration. Furthermore, it can fully leverage the spatiotemporal power support function of MESS and enhance the load restoration capability of the distribution network.

[0137] In scenarios where extreme conditions lead to prolonged power outages in the distribution network, this invention focuses on the emergency response phase. Following a multi-source collaborative recovery approach, it utilizes interconnected local power sources to form as large an island as possible, enhancing system stability during power restoration. With load restoration as the primary recovery objective, a MESS transmission configuration model is incorporated into the model to dynamically optimize configuration nodes. Simultaneously, from a cyber-physical network perspective, a coupling model between the information network and the distribution network is established, considering the impact of different coupling degrees on load restoration to ensure its effectiveness. Furthermore, a MESS scheduling model is established, taking into account the impact of the transportation network on MESS scheduling, optimizing scheduling paths, and reducing scheduling time. Ultimately, a multi-period dynamic recovery strategy integrating the distribution, information, and transportation networks is obtained.

[0138] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0140] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-period dynamic power supply restoration method based on the coupling of power distribution, information, and transportation networks, characterized in that, Includes the following steps: Based on a multi-source collaborative recovery strategy, a fault recovery model for the distribution network is constructed, and the objective function of the fault recovery model is obtained. The objective function is used to obtain the fault recovery objective of maximizing load recovery and minimizing network loss. Based on the objective function, by constructing a distribution network restoration constraint model, a MESS operation constraint model, and an information network constraint model, the optimal access node for MESS in each time period is obtained, and a MESS scheduling model considering traffic flow is established. in, The MESS scheduling model is used to obtain the optimal scheduling path and scheduling time, and the distribution network recovery constraint model is used to generate the load of the distribution network, the charging and discharging characteristics of the energy storage system, the radial topology constraints, and the branch power flow constraints. The MESS operational constraint model is used to generate MESS transmission location constraints. The information network constraint model is used to generate information node power supply constraints, information network traffic constraints, and information node control action constraints. Based on the fault recovery model and the MESS scheduling model, the multi-period load recovery results, information network effectiveness, and distribution network topology of the distribution network are obtained, and fault recovery of the distribution network is performed. In constructing the MESS scheduling model, the MESS scheduling model includes: The flow-speed model is used to determine the direct impact of road capacity and flow on transmission speed. The path-time model is used to obtain the shortest path between different origin-destination node pairs using the Floyd algorithm. In constructing the MESS scheduling model, the flow-speed model is as follows: Among them, v ij (t) represents the transmission speed between adjacent nodes (i,j) during time period t; v ij.0 q represents the zero-flow velocity between adjacent nodes (i,j); ij (t) represents the flow of adjacent node (i,j) in time period t; C ij The traffic capacity of adjacent nodes (i,j) is mainly affected by road grade; q ij (t) and C ij The ratio is the saturation of adjacent nodes (i,j) in time period t; a, b, and n are coefficients under different road levels, and the corresponding coefficients are different for different road levels; the road represents the traffic road corresponding to the geographical location of the distribution network; In constructing the MESS scheduling model, the path-time model is as follows: Where, ΔT ij ΔT represents the total transmission time between nodes i and j. h The time required for the h-th road segment; d h V represents the length of the h-th road segment; h (t) is the transmission speed of the h-th directly connected road obtained from the flow-speed model.

2. The multi-period dynamic power supply restoration method based on power distribution-information-transportation network coupling according to claim 1, characterized in that: In obtaining the objective function of the fault recovery model, the objective function is used to satisfy the convex relaxation condition in the subsequent branch power flow constraints, and the expression of the objective function is: Among them, y i,t The value is an integer variable between 0 and 1, representing whether the load at node i has been restored during time period t; w i This indicates the load importance of node i; represents the recovery power of node i during time period t; w0 is the network loss coefficient, ensuring that the solution of the primary objective is not affected by the secondary objective; I ij,t R is the current flowing through line i→j during time period t; ij Let represent the resistance of line i→j; D represents the set of load nodes; ε represents all available lines within the target island; S base This is the reference power.

3. The multi-period dynamic power supply restoration method based on power distribution-information-transportation network coupling according to claim 2, characterized in that: In constructing the distribution network restoration constraint model, the distribution network restoration constraint model includes: A load recovery power model is used to obtain the load of the distribution network; An energy storage charge-discharge constraint model is used to obtain the charge-discharge characteristics of the energy storage system; A radial topological constraint model is used to generate the radial topological constraints; A branch power flow constraint model is used to generate the branch power flow constraints.

4. The multi-period dynamic power supply restoration method based on power distribution-information-transportation network coupling according to claim 3, characterized in that: In the process of constructing the distribution network restoration constraint model, the distribution network restoration constraint model also includes a line current thermal limit safety constraint model, which is used to obtain the power output and node voltage by correlating the current magnitude with whether the branch is restored.

5. The multi-period dynamic power supply restoration method based on power distribution-information-transportation network coupling according to claim 4, characterized in that: In constructing the MESS operational constraint model, the MESS transmission location constraints include: When the MESS is not docked at a distribution network node, it cannot perform charging and discharging operations. A given MESS can only be located at one distribution network node or be in transmission state at any given time. If the j-th MESS is in the transmission route from the first node to the second node, then the access flag bits are all 0; If the difference between the access flag bits of adjacent time periods is positive, then transmission is performed; otherwise, no transmission is performed. When a certain MESS is connected to the power grid, and the formula contains a 0-1 variable multiplied by a continuous variable, the Big M linearization process is adopted.

6. The multi-period dynamic power supply restoration method based on power distribution-information-transportation network coupling according to claim 5, characterized in that: In the process of constructing the information network constraint model, the coupling effect between the distribution network and the information network is modeled based on the background of cyber-physical networks, and the information network constraint model is obtained. The information network constraint model includes: An information node power supply constraint model is used to generate the information node power supply constraints. An information network traffic constraint model is used to generate the information network traffic constraints. An information node control action constraint model is used to generate control action constraints for the information node, which are used to ensure that the information node remains in an effective state when the load is restored.

7. The multi-period dynamic power supply restoration method based on power distribution-information-transportation network coupling according to claim 6, characterized in that: A multi-time dynamic power restoration system for implementing the multi-time dynamic power restoration method includes: The fault recovery strategy module is used to construct a fault recovery model for the distribution network based on a multi-source collaborative recovery strategy, and to obtain the objective function of the fault recovery model. The scheduling strategy module is used to obtain the best access node for each time period of MESS based on the objective function by constructing a distribution network recovery constraint model, a MESS operation constraint model, and an information network constraint model, and to establish a MESS scheduling model that takes into account traffic flow. The fault recovery module is used to obtain the multi-period load recovery results, information network effectiveness, and distribution network topology of the distribution network based on the fault recovery model and the MESS scheduling model, and to perform fault recovery on the distribution network.

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