A post-disaster distribution network and microgrid collaborative recovery method and device

Abstract

The application discloses a kind of post-disaster distribution network and microgrid collaborative recovery method and device, the present application is with post-disaster distribution network and microgrid collaborative recovery as research object, consider flexible resource such as maintenance team, distributed generator, energy storage and so on and network reconfiguration and other recovery means, consider the autonomy of microgrid operation, proposed distribution network and microgrid collaborative recovery method, based on linear DistFlow model establishes two-layer mixed integer linear programming model, using based on relaxation double-layer reconfiguration and decomposition algorithm solution.The present application considers flexible resource such as distributed generator, energy storage, under the premise of guaranteeing each microgrid autonomous operation, make full use of the resources in distribution network and microgrid, formulate optimal post-disaster recovery scheme.Distribution network cannot directly intervene the operation of microgrid, the resources in microgrid are all autonomously scheduled, solve the problem of different resource ownership, improve the efficiency of distribution network and microgrid collaboration.

Description

Technical Field

[0001] This invention belongs to the field of distribution network and microgrid coordination technology, specifically relating to a method and apparatus for post-disaster distribution network and microgrid coordinated recovery. Background Technology

[0002] The power system plays a vital role in modern society, and its security is fundamental to social development. In recent years, natural disasters have caused numerous power outages. For example, in 2016, a tornado caused power outages for 135,000 households in Jiangsu Province, China. Statistical analysis of wildfires from 2000 to 2016 shows that wildfires caused over $700 million in utility losses in parts of California's transmission and distribution system. Due to climate change, the incidence of such disasters will increase. These natural disasters are low-probability, high-loss extreme events. Traditional reliability indicators only address high-probability, low-loss events and cannot guarantee the safety of the power system in the face of extreme events. Therefore, building a resilient power grid is becoming a key issue in the energy sector.

[0003] Compared to transmission systems, distribution systems are more vulnerable. Extensive research in recent decades has focused on improving the resilience of distribution networks. Distribution networks are equipped with abundant flexible resources, such as distributed generation, electric vehicles, and energy storage, meaning there are numerous ways to enhance their resilience. Many researchers combine network reconfiguration with flexible resources to improve recovery, proposing post-disaster recovery schemes that consider the coordination of distributed generation, energy storage, and network reconfiguration. Furthermore, some researchers use network reconfiguration to divide the distribution network into multiple microgrids to improve its resilience. Regarding microgrid formation, some studies have proposed new distribution network operation methods, namely forming multiple microgrids powered by distributed generation, and controlling the ON / OFF states of switching devices and distributed generation to meet the self-sufficiency and operational constraints of the microgrids. To address potential risks in subsequent events, some studies have proposed incorporating adaptive microgrid formation as part of critical load recovery. Considering the uncertainty of line faults, some studies have proposed a robust microgrid formation method. Besides distributed generation, energy storage systems also play a crucial role in distribution network recovery; some scholars have proposed a method to enhance distribution network resilience through the cooperation of microgrids and mobile energy storage devices.

[0004] Existing research proposes a recovery strategy that divides the distribution network into microgrids powered by distributed generation or energy storage systems after a disaster to restore critical loads. Flexible resources are centrally dispatched without needing to protect the privacy of microgrid operators. However, with the large-scale integration of distributed renewable energy sources such as rooftop solar photovoltaics, numerous microgrids will form under normal operation, each with its own operating plan. Differences in microgrid resource ownership hinder the coordination of recovery resources across different microgrids. Therefore, a collaborative method for post-disaster distribution networks and microgrids is needed, while ensuring the autonomy of microgrid operation. Summary of the Invention

[0005] This invention provides a method and apparatus for the coordinated recovery of distribution networks and microgrids after a disaster. Under the premise of ensuring the autonomous operation of each microgrid, it makes full use of the resources in the distribution network and microgrids to formulate the optimal post-disaster recovery plan.

[0006] To achieve the above objectives, the present invention provides a method for the coordinated recovery of a distribution network and microgrid after a disaster, comprising the following steps: Step 1: Establish a two-layer post-disaster recovery model, which includes an upper-layer model and a lower-layer model; Step 2: Solve the two-layer post-disaster recovery model established in Step 1 to obtain the optimal solution; Step 3: Control the distribution network and microgrid based on the optimal solution; In step 1, the upper-level model includes an upper-level objective function, an upper-level linear power flow model, a maintenance team dispatch model, an upper-level radial model, and a distribution network-microgrid coupling model; the lower-level model includes a lower-level objective function, a lower-level linear power flow model, a lower-level radial model, and a distribution network-microgrid coupling model. The upper-layer radial model and the distribution network-microgrid coupling model, as well as the lower-layer radial model and the distribution network-microgrid coupling model, all include the power demand sent from the distribution network to the microgrid.

[0007] Furthermore, the upper-level objective function is: (1) in A collection of typical scenarios for contributing to new energy. For a set of distribution network nodes, A set of time intervals For the scene s The probability of occurrence For nodes j The importance of the load, For the scene s Mid-moment t Time distribution network nodes j The amount of load loss at the location.

[0008] Furthermore, the upper-level linear power flow model is as follows: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) in For the collection of routes, For a set of nodes, For generator sets, For nodes j A collection of routes originating from a given point. For nodes j A collection of routes ending at a specific destination. A set of nodes connected to a microgrid. For the scene s middle t Timetable l Active power flowing upstream For the scene s middle t Timetable l The reactive power flowing upstream, For the scene s middle t The active power output of the generator at all times. For the scene s middle t The reactive power output of the generator at all times, For nodes j Active load at the location, For reactive load, For the scene s Mid-moment t Time node j The amount of active power loss at the location, For the scene s Mid-moment t Time node j The amount of reactive power loss at the location, For the scene s Mid-moment t Time node iVoltage at that point For the scene s Mid-moment t Time node j Voltage at that point This is the reference voltage for the power distribution network. For the line l The resistance, For the line l Reactance, For the line l Capacity constraints, for t Timetable l state, For nodes j The power factor at that point, and For nodes j The minimum and maximum voltage values ​​that can be achieved at that location. and For the minimum and maximum active power output of the generator, For the scene s Mid-moment t Time generator g Contributing to the scene and For the minimum and maximum reactive power output of the generator, For the scene s Mid-moment t Time generator g Unproductive efforts M It is a constant.

[0009] Furthermore, the upper-layer radial model is as follows: (25) (26) (27) (28) in, For the collection of routes, R This is the set of potential root nodes, which includes the nodes at both ends of the damaged line and the node where the generator is located. The number of nodes, a 0 / 1 variable. Determine whether a potential root node becomes a root node. The virtual power flowing through the line. M It is a constant.

[0010] Furthermore, the upper-level distribution network-microgrid coupling model is as follows: (29) (30) The lower-level distribution network-microgrid coupling model is as follows: (53) in For microgrid collection, For micro-network m A collection of generators, For micro-network m A collection of energy storage systems, For micro-network m The set of nodes, For the scene s From distribution network to microgrid m The power requirement for transmission. For micro-network m medium generator g The predicted output value, For micro-network m Energy storage system in e Maximum discharge power, For micro-network m Middle node j The load; To cooperate with microgrids m The nodes connected to the distribution network in the middle, For micro-network m Nodes connected to the distribution network The load, In the scene s MicroNet m Nodes connected to the distribution network The load shedding capacity, the electrical energy that the distribution network requires to be transmitted is considered a load in the microgrid.

[0011] Furthermore, the objective function of the lower-level model is: (31) in For micro-network m Middle node j The importance of the load, For the scene s Mid-moment t Shiwei.com m node j The amount of load loss at the location.

[0012] Furthermore, the linear power flow model for the lower-level model is as follows: (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) in For micro-network m The nodes connected to the distribution network. For micro-network m Central route l The damaged state, and Scenes s time t Shiwei.com m Middle node j The discharge power and charging power of the energy storage system at the location For micro-network m A collection of routes, For micro-network m A collection of generators, For nodes j A collection of routes originating from a given point. For nodes j A collection of routes ending at a specific destination. For the scene s middle t Moments Micro Network m Central route l Active power flowing upstream For the scene s middle t Moments Micro Network m Central route l The reactive power flowing upstream, For the scene s middle t Moments Micro Network m medium generator g Those who have made contributions For the scene s middle t Moments Micro Network m medium generator g Unproductive efforts For micro-network m Middle node j Active load at the location, For reactive load, For the scene s Mid-moment t Shiwei.com m Middle node j The amount of active power loss at the location, For the scene s Mid-moment t Shiwei.com m Middle node j The amount of reactive power loss at the location, For the scene s Mid-moment t Shiwei.com m Middle node i Voltage at that point For the scene s Mid-moment t Shiwei.com m Middle node j Voltage at that point For micro-network m The reference voltage in the middle, For micro-network m Central route l The resistance, For micro-network m Central route l Reactance, For micro-network m Central route l Capacity constraints, For the scene s middle t Moments Micro Network m Central route l state, For micro-network m Middle node j The power factor at that point, and For micro-network m Middle node j The minimum and maximum voltage values ​​that can be achieved at that location. For micro-network m medium generator g The minimum and maximum active power output, For the scene s Mid-moment t Shiwei.com m medium generator g Contributing to the scene and For the minimum and maximum reactive power output of the generator, For the scene s Mid-moment t Shiwei.com m medium generatorg Unproductive efforts M It is a constant.

[0013] Furthermore, step 3 includes the following steps: Step 1: Rewrite the two-layer post-disaster recovery model in matrix form: (54) (55) (56) in The coefficient matrix of the objective function of the upper-level problem. This represents all continuous variables in the lower-level model. Represents all 0 / 1 variables in the lower-level model. In addition to the upper-level model and All variables, For the scene s MicroNet m The coefficient matrix of the objective function. , , and In the upper-level model respectively , , The coefficient matrix and the constant coefficient matrix, , , and In the lower-level model respectively , , The coefficient matrix and the constant coefficient matrix, A collection of scenes; Step 2: Divide the two-level problem into a main problem and sub-problems; The main question takes the following form: (57) (58) (59) (60) in It is the penalty coefficient. It is a coefficient matrix containing only 0s and 1s. For the scene s Middle problem m All combinations of 0 / 1 variables contained therein For a fixed combination of 0 / 1 variables, For the 0 / 1 variable combination of the lower-level model The values ​​of continuous variables in the current layer model, For the 0 / 1 variable combination of the lower-level model The penalty variable at that time , , and For the scene s Lower-level problems m middle , , The coefficient matrix and the constant coefficient matrix; In obtaining optimal solution The specific form of the subproblem is as follows: (61) (62) Step 3: Solve the main problem and subproblems, including the following steps: Step 3.1: Input the system topology of the distribution network and microgrid, as well as the parameters of each component. The parameters of each component include the impedance of the line, the range of generator output, the load size of each node, the acceptable voltage range of each node, the maximum output of energy storage, and the capacity of energy storage. Step 3.2: Initialize the number of iterations i and iteration termination error : For all Set the iteration count variable as well as , ; Step 3.3: Solve the main problem to obtain the optimal solution, represented as follows: Let the objective function value of the lower-level problem be denoted as ,Will Passed to the subproblem; where for The optimal value; Step 3.4: Solve the subproblems, and denote the optimal solution to the subproblems as... Let the optimal value of the subproblem be denoted as ; Step 3.5: Judgment Is it true or false? If true, return the optimal solution. The process is complete; If not, then and combine Add to In, and update Then set Return to Step 3.2.

[0014] A post-disaster power distribution network and microgrid collaborative restoration device includes: The input module is used to input the system topology of the distribution network and microgrid, as well as the parameters of each component. The processing module is used to solve the preset two-layer post-disaster recovery model based on the system topology of the distribution network and microgrid and the parameters of each component, obtain the optimal control strategy, and transmit the control strategy to the distribution network and microgrid.

[0015] Furthermore, the two-layer post-disaster recovery model includes an upper-layer model and a lower-layer model; the upper-layer model includes an upper-layer objective function, an upper-layer linear power flow model, a maintenance team dispatch model, an upper-layer radial model, and a distribution network-microgrid coupling model; the lower-layer model includes a lower-layer objective function, a lower-layer linear power flow model, a lower-layer radial model, and a distribution network-microgrid coupling model. The upper-layer radial model and the distribution network-microgrid coupling model, as well as the lower-layer radial model and the distribution network-microgrid coupling model, all include the power demand sent from the distribution network to the microgrid.

[0016] Compared with existing technologies, this invention has at least the following beneficial technical effects: This invention takes the collaborative recovery of distribution networks and microgrids after disasters as its research object, considering flexible resources such as maintenance teams, distributed generators, and energy storage, as well as recovery methods such as network reconfiguration. Taking into account the autonomy of microgrid operation, it proposes a collaborative recovery method for distribution networks and microgrids, and establishes a two-layer mixed-integer linear programming model based on a linear power flow model. It has the following advantages: The method proposed in this invention takes into account flexible resources such as maintenance teams, distributed generators, and energy storage. While ensuring the autonomous operation of each microgrid, it fully utilizes resources within the distribution network and microgrids to formulate optimal post-disaster recovery plans. In this collaborative recovery method, the distribution network operator cannot directly interfere with the operation of the microgrid; it only needs to transmit the power required from the microgrid. All resources within the microgrid are autonomously scheduled, resolving the issue of conflicting resource ownership and improving the efficiency of cooperation between the distribution network and microgrids. Furthermore, the proposed method requires only minimal communication between the distribution network operator and the microgrid operator, reducing the demands on post-disaster communication.

[0017] Furthermore, the method of this invention uses stochastic programming to model the uncertainty of renewable energy, and the established two-level mixed integer linear programming model can be solved in a distributed manner, reducing the complexity of optimization and control for distribution network operators.

[0018] Furthermore, this invention employs a relaxation-based two-layer reconstruction and decomposition algorithm to solve two-layer mixed-integer linear programming models that traditional single-layer reconstruction methods cannot solve. During the solution process, the two layers of the model exchange information during iteration, ensuring a globally optimal control strategy is obtained. This guarantees the autonomy of the microgrid operation while minimizing the load loss in the distribution network, thus reducing economic losses to a minimum. For complex examples requiring a large number of iterations, this algorithm can significantly reduce the number of iterations and computation time while ensuring the optimal solution is found. Moreover, as the number of lower-level problems increases, the number of iterations and computation time do not increase significantly, allowing the method proposed in this invention to be extended to solve more complex situations. Attached Figure Description

[0019] Figure 1 A schematic diagram illustrating the collaborative restoration method of power distribution network and microgrid after a disaster; Figure 2 This is a structural diagram of a two-layer collaborative recovery model; Figure 3 Flowchart for solving the model; Figure 4 A schematic diagram of the module structure of the post-disaster power distribution network and microgrid collaborative recovery device provided by the present invention; Figure 5 A schematic diagram of the structure of the computer device provided by the present invention. Detailed Implementation

[0020] To make the objectives and technical solutions of this invention clearer and easier to understand, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0021] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, 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. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more. In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] Example 1 The post-disaster collaborative recovery method for distribution networks and microgrids proposed in this invention considers the autonomy of each microgrid's operation, fully utilizes the resources in both the distribution network and the microgrid, and formulates the optimal post-disaster recovery plan. Its flowchart is as follows: Figure 1 As shown in the diagram. At the upper layer, the distribution network control center is responsible for maintaining the power supply within the distribution network. After a disaster, the distribution network control center optimizes the scheduling of available resources, restoring as many critical loads as possible through maintenance personnel dispatch, distributed generator scheduling, and network reconfiguration. Furthermore, protocols are established between the distribution network control center and the microgrid control center to enable the microgrid to participate in collaborative post-disaster recovery.

[0023] When a disaster occurs, the microgrid control center reports its remaining generation capacity to the distribution network control center, which then sends an assistance command to the microgrid control center based on this information. Under this mechanism, distribution network operators can quickly formulate post-disaster recovery plans, significantly improving the efficiency of post-disaster recovery. At the lower level, each microgrid is controlled by its own control center, which, like the distribution network control center, is responsible for maintaining the power supply within the microgrid. Upon receiving an assistance command from the distribution network control center, the microgrid operator, while ensuring its own needs are met, dispatches distributed generators and energy storage to transmit the remaining power to the distribution network. The method proposed in this invention allows microgrid operators to autonomously dispatch their own resources, protecting microgrid privacy and resolving issues arising from differing resource ownership. Furthermore, the autonomous operation of the microgrid significantly reduces the complexity of optimization and control for distribution network operators.

[0024] This invention first establishes a two-layer post-disaster recovery model, using stochastic programming to model the uncertainty of renewable energy, and constructs a two-layer mixed-integer linear programming model for the coordinated recovery of the distribution network and microgrids. Through maintenance team scheduling, distributed generator scheduling, network reconfiguration, and microgrid assistance, the optimal post-disaster recovery plan is formulated. The model structure diagram is shown below. Figure 2 As shown, the upper-level model is the key load in the distribution network restoration process. The problem of the upper-level model is to minimize the expected value of the power supply shortage (load loss) in the distribution network. A mixed-integer linear programming model is established based on the linear power flow model (hereinafter referred to as the linear DistFlow model). It includes four parts: the linear DistFlow model, the maintenance team dispatch model, the radial model, and the distribution network-microgrid coupling model.

[0025] The lower-level model involves microgrid operators scheduling microgrid resources to meet their own load and complete the assistance command after receiving it from the distribution network. This takes into account distributed generators, energy storage systems, and network reconfiguration. A mixed-integer linear programming model is established to complete the assistance command issued by the distribution network while ensuring the supply of their own load. The lower-level problem aims to minimize the microgrid's load loss and consists of four parts: a linear DistFlow model, a radial model, an energy storage operation model, and a microgrid-distribution network coupling model. A relaxation-based two-level reconfiguration and decomposition algorithm is used to solve the two-level mixed-integer programming model. The details of each part are as follows.

[0026] Reference Figure 1 A method for the coordinated recovery of power distribution networks and microgrids after a disaster includes the following: Step 1: Establish a two-layer post-disaster recovery model, which includes an upper-layer model and a lower-layer model. 1. Upper-level model (1) Objective function (1) in Typical scenarios for contributing to new energy sources, such as scenarios where the prediction error reaches its maximum or the prediction error is zero. For a set of distribution network nodes, A set of time intervals For the scene s The probability of occurrence For nodes j The importance of the load, For the scene s Mid-moment t Time distribution network nodes j The amount of load loss at the location.

[0027] (2) Linear DistFlow Model (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) in For the collection of routes, For a set of nodes, For generator sets, For nodes j A collection of routes originating from a given point. For nodes j A collection of routes ending at a specific destination. A set of nodes connected to a microgrid. For the scene s middle t Timetable l Active power flowing upstream For the scene s middle t Timetable l The reactive power flowing upstream, For the scene s middle t The active power output of the generator at all times. For the scenes middle t The reactive power output of the generator at all times, For nodes j Active load at the location, For reactive load, For the scene s Mid-moment t Time node j The amount of active power loss at the location, For the scene s Mid-moment t Time node j The amount of reactive power loss at the location, For the scene s Mid-moment t Time node i Voltage at that point For the scene s Mid-moment t Time node j Voltage at that point This is the reference voltage for the power distribution network. For the line l The resistance, For the line l Reactance, For the line l Capacity constraints, for t Timetable l state, For nodes j The power factor at that point, and For nodes j The minimum and maximum voltage values ​​that can be achieved at that location. and For the minimum and maximum active power output of the generator, For the scene s Mid-moment t Time generator g Contributing to the scene and For the minimum and maximum reactive power output of the generator, For the scene s Mid-moment t Time generator g Unproductive efforts M It is a very large constant, which can be taken as 10000.

[0028] Equations (2) and (3) are power balance constraints, constraints (4) and (5) are the relationship between the voltage at both ends of the line and the power flowing through the line, constraints (6) and (7) limit the active and reactive power flowing through the line, constraints (8) and (9) limit the load shedding, constraint (9) ensures that the power factor of each node remains unchanged, constraint (10) limits the voltage level of each node, and constraints (11) and (12) limit the generator output.

[0029] (3) Maintenance team dispatch model (13) (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) in, Let the route selection variable be the one used by the repair team when it departs from the repair station. This is a path selection variable for the repair team to return to the repair station after completing the repair task. RC represents the set of repair teams, and DN represents the set of damaged components and repair stations. For the repair station, 0 / 1 variables For the maintenance team c Path selection variables, 0 / 1 variables Assign variables to the repair team for the damaged components. For the maintenance team c Reaching the damaged component m Time, For the maintenance team c Repair damaged components m Time required For the maintenance team c Reaching the damaged component n Time required For the repair team c from components m To components n The passage time, M is a constant, taken as 100, 0 / 1 variable. This is the repair status variable for the component; a value of 1 indicates that the component... m At any moment t Fixed, 0 / 1 variables To complete the repair, if the value is 1, it represents a component. m At any moment t Previously repaired Take 0.01, represent t Time element m Whether it has been repaired, if it is 1, it has been repaired, if it is 0, it has not been repaired. Constraint (13) ensures that the repair team starts from the repair station, constraint (14) ensures that the repair team returns to the repair station, constraint (15) ensures that the repair team will leave the component after repairing it, constraints (16) and (17) ensure that a repair team will go to repair each damaged component, constraints (18) and (19) determine the time when the repair team arrives at each damaged component, constraint (20) ensures that each damaged component must be repaired, constraints (21)-(23) determine the repair status of the component at each time, and constraint (24) represents the change of the component status.

[0030] (4) Radial model (25) (26) (27) (28) in, R This is the set of potential root nodes, which includes the nodes at both ends of the damaged line and the node where the generator is located. The number of nodes, a 0 / 1 variable. Determine whether a potential root node becomes a root node. The virtual power flowing through the line. M It is a very large constant, which can be taken as 100. According to graph theory, a radial graph must satisfy: (a) the number of edges equals the number of nodes minus the number of subgraphs; (b) each subgraph must be connected. Constraint (25) satisfies condition (a), and constraints (26)-(28) satisfy condition (b).

[0031] (5) Distribution network-microgrid coupling model (29) (30) in For microgrid collection, For micro-network m A collection of generators, For micro-network mA collection of energy storage systems, For micro-network m The set of nodes, For the scene s From distribution network to microgrid m The power requirement for transmission. For micro-network m medium generator g The predicted output value, For micro-network m Energy storage system in e Maximum discharge power, For micro-network m Middle node j The load. Constraint (29) indicates that the electrical energy transmitted by the microgrid can be regarded as a generator, and constraint (30) limits the transmitted energy.

[0032] 2. Lower-level model (1) Objective function (31) in For micro-network m Middle node j The importance of the load, For the scene s Mid-moment t Shiwei.com m node j The amount of load loss at the location.

[0033] (2) Linear DistFlow Model (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) in For micro-network m The nodes connected to the distribution network. For micro-network m Central route l The damaged state, and Scenes s time t Shiwei.com m Middle node j The discharge power and charging power of the energy storage system at the location For micro-network m A collection of routes, For micro-network m A collection of generators, For nodes j A collection of routes originating from a given point. For nodes j A collection of routes ending at a specific destination. For the scene s middle t Moments Micro Network m Central route l Active power flowing upstream For the scene s middle t Moments Micro Network m Central route l The reactive power flowing upstream, For the scene s middle t Moments Micro Network m medium generator g Those who have made contributions For the scene s middle t Moments Micro Network m medium generator g Unproductive efforts For micro-network m Middle node j Active load at the location, For reactive load, For the scene s Mid-moment t Shiwei.com m Middle node j The amount of active power loss at the location, For the scene s Mid-moment t Shiwei.com m Middle node j The amount of reactive power loss at the location, For the scene s Mid-moment t Shiwei.com m Middle node i Voltage at that point For the scene s Mid-momentt Shiwei.com m Middle node j Voltage at that point For micro-network m The reference voltage in the middle, For micro-network m Central route l The resistance, For micro-network m Central route l Reactance, For micro-network m Central route l Capacity constraints, For the scene s middle t Moments Micro Network m Central route l state, For micro-network m Middle node j The power factor at that point, and For micro-network m Middle node j The minimum and maximum voltage values ​​that can be achieved at that location. For micro-network m medium generator g The minimum and maximum active power output, For the scene s Mid-moment t Shiwei.com m medium generator g Contributing to the scene and For the minimum and maximum reactive power output of the generator, For the scene s Mid-moment t Shiwei.com m medium generator g Unproductive efforts M It is a very large constant, which can be taken as 10000.

[0034] Equations (32) and (33) are power balance constraints, constraints (34) and (35) are the relationship between the voltage at both ends of the line and the power flowing through the line, constraints (36) and (37) limit the active and reactive power flowing through the line, constraints (38) and (39) limit the load shedding, constraint (39) ensures that the power factor of each node remains unchanged, constraint (40) limits the voltage level of each node, and constraints (41) and (42) limit the generator output.

[0035] (3) Radial model (44) (45) (46) (47) in, For micro-network m The set of nodes, For micro-network m The set of potential root nodes, which includes the nodes at both ends of the damaged line and the node where the generator is located. For micro-network m The number of nodes, a 0 / 1 variable. Used to determine the scene s MicroNet m Potential root node j Whether to become the root node For the scene s MicroNet m The route l The virtual power flowing upwards, M These are all very large numbers, such as 100. According to graph theory, a radial graph must satisfy: (a) the number of edges equals the number of nodes minus the number of subgraphs; (b) each subgraph must be connected. Constraint (44) satisfies condition (a), and constraints (45)-(46) satisfy condition (b).

[0036] (4) Energy storage operation model (48) (49) (50) (51) (52) in It is a micro-network m A collection of medium-sized energy storage systems, 0 / 1 variables For energy storage systems e exist t The charge / discharge state at any given time; a value of 1 indicates that the energy storage is in a discharging state, and a value of 0 indicates that it is in a charging state. For the scene s MicroNet m Energy storage system at time t e The discharge power, For the scene s time t Shiwei.com m China Energy Storage e The charging power, For micro-network m China Energy Storagee Maximum charging power, For the scene s time t Energy storage in microgrids e To restore the initial state of charge, For the scene s time t Energy storage in microgrids e exist t State of charge at time t, For the scene s time t Energy storage in microgrids e exist t+1 State of charge at time t, The time interval is a constant, typically taken as 1 hour. Indicates the initial state of charge of energy storage. and energy storage system e The discharge efficiency and charging efficiency, and These represent the minimum and maximum levels of the energy storage state of charge, respectively. Constraints (48) and (49) limit the charging and discharging power of the energy storage system, while constraints (50)-(52) represent the changes in the energy storage system's state of charge.

[0037] (5) Microgrid-distribution network coupling model (53) To cooperate with microgrids m The nodes connected to the distribution network in the middle, For micro-network m Nodes connected to the distribution network The load, In the scene s MicroNet m Nodes connected to the distribution network The electrical energy transmitted by the distribution network is considered a load in the microgrid.

[0038] Step 2: Solving the model Step 1: Write the upper-level model and the lower-level model in matrix form. The two-level mixed integer linear programming model cannot be solved using the traditional single-level reconstruction method based on KKT conditions. Therefore, this invention uses a relaxation-based two-level reconstruction and decomposition algorithm to solve the established model, and its solution process is shown in Figure (3). To simplify the expression, the upper-level model and the lower-level model are written in matrix form: (54) (55) (56) in The coefficient matrix of the objective function of the upper-level problem. This represents all continuous variables in the lower-level model. Represents all 0 / 1 variables in the lower-level model. In addition to the upper-level model and All variables, For the scene s MicroNet m The coefficient matrix of the objective function. , , and In the upper-level model respectively , , The coefficient matrix and the constant coefficient matrix, , , and In the lower-level model respectively , , The coefficient matrix and the constant coefficient matrix, For the scene set, constraints (55) and (56) represent the constraints of the upper-level model and the constraints of the lower-level model, respectively.

[0039] Step 2: Based on the relaxation-based bi-level reconstruction and decomposition algorithm, the bi-level problem is divided into a main problem and sub-problems. The main problem takes the following form: (57) (58) (59) (60) in It's a very large penalty coefficient, which can be set to 100. It is a coefficient matrix containing only 0s and 1s. For the scene s Middle problem m All combinations of 0 / 1 variables contained therein For a fixed combination of 0 / 1 variables, For the 0 / 1 variable combination of the lower-level model The values ​​of continuous variables in the current layer model, For the 0 / 1 variable combination of the lower-level model The penalty variable at that time , , and For the scene s Lower-level problems m middle , , The coefficient matrix and constant coefficient matrix allow the main problem to be solved using the traditional single-layer reconfiguration method based on KKT conditions, yielding the optimal recovery strategy for the distribution network. The optimal recovery strategy is then passed on to the subproblem.

[0040] In obtaining The specific form of the subproblem is as follows: (61) (62) Solving the subproblems composed of (61) and (62) will yield the operation of each microgrid.

[0041] Step 3: Refer to Figure 3 Solving the main problem and its subproblems involves the following steps: Step 1: Input the system topology of the distribution network and microgrid, as well as the parameters of each component. The parameters of each component include the impedance of the line, the range of generator output, the load size of each node, the acceptable voltage range of each node, the maximum output of energy storage, and the capacity of energy storage. Step 2: Initialize the number of iterations i and iteration termination error : For all Set the iteration count variable as well as , ; Step 3: Solve the main problem (57-60) using a commercial solver, and express the optimal solution as follows: Let the objective function value of the lower-level problem be denoted as ,Will Passed to the subproblem; where for The optimal value; Step 4: Solve subproblems (61)-(62) using a commercial solver, and denote the optimal solution of the subproblems as... Let the optimal value of the subproblem be denoted as ; Step 5: Judgment Is it true or false? If true, return the optimal solution. The process is complete; If it is not true, then and combine Add to In, and update Then set Return to Step 2.

[0042] Step 3: Based on the optimal solution Control the distribution network and microgrid. Before a disaster, the distribution network operator and the microgrid operator reached an agreement to allow the microgrid to participate in collaborative post-disaster recovery. After a disaster, the distribution network operator immediately allocates its resources to restore the distribution network's load. Simultaneously, the microgrid operator reports its remaining capacity to the distribution network. Based on this information, the distribution network operator issues an assistance command to the microgrid. Upon receiving the assistance command, the microgrid operator dispatches distributed generators and energy storage to meet its own load and fulfills the distribution network operator's assistance command.

[0043] Example 2 This invention provides a post-disaster power distribution network and microgrid collaborative recovery device, such as... Figure 4 As shown, it includes an input module and a processing module.

[0044] The input module is used to input the system topology of the distribution network and microgrid, as well as the parameters of each component; the processing module is used to solve the preset two-layer post-disaster recovery model based on the system topology of the distribution network and microgrid, as well as the parameters of each component, to obtain the optimal control strategy, and then transmit the control strategy to the distribution network and microgrid.

[0045] Example 3 The present invention provides a computer device, such as... Figure 5 As shown, it includes a memory and a processor electrically connected, wherein the memory stores a computer program that can run on the processor, and when the processor executes the computer program, it implements the steps of the recovery method described above.

[0046] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention.

[0047] The recovery device can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The recovery device may include, but is not limited to, a processor and a memory.

[0048] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0049] The memory can be used to store the computer program and / or module. The processor implements various functions of the recovery device / terminal device by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0050] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0051] Example 4 If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for the coordinated recovery of distribution networks and microgrids after a disaster, characterized in that, Includes the following steps: Step 1: Establish a two-layer post-disaster recovery model, which includes an upper-layer model and a lower-layer model; Step 2: Input the system topology of the distribution network and microgrid, as well as the parameters of each component, into the two-layer post-disaster recovery model established in Step 1. Solve the two-layer post-disaster recovery model with the objective of minimizing the expected value of the power shortage in the distribution network to obtain the optimal solution. Step 3: Control the distribution network and microgrid based on the optimal solution; In step 1, the upper-level model includes an upper-level objective function, an upper-level linear power flow model, a maintenance team dispatch model, an upper-level radial model, and an upper-level distribution network-microgrid coupling model; the lower-level model includes a lower-level objective function, a lower-level linear power flow model, a lower-level radial model, and a lower-level distribution network-microgrid coupling model. The upper-layer radial model and the distribution network-microgrid coupling model, as well as the lower-layer radial model and the distribution network-microgrid coupling model, all include the power demand sent from the distribution network to the microgrid. The upper-level objective function is: (1) in A collection of typical scenarios for contributing to new energy. For a set of distribution network nodes, A set of time intervals For the scene s The probability of occurrence For nodes j The importance of the load, For the scene s Mid-moment t Time distribution network nodes j Loss of load at the location; The upper-level linear power flow model is as follows: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) in For the collection of routes, For a set of nodes, For generator sets, For nodes j A collection of routes originating from a given point. For nodes j A collection of routes ending at a specific destination. A set of nodes connected to a microgrid. For the scene s middle t Timetable l Active power flowing upstream For the scene s middle t Timetable l The reactive power flowing upstream, For the scene s middle t The active power output of the generator at all times. For the scene s middle t The reactive power output of the generator at all times, For nodes j Active load at the location, For reactive load, For the scene s Mid-moment t Time node j The amount of active power loss at the location, For the scene s Mid-moment t Time node j The amount of reactive power loss at the location, For the scene s Mid-moment t Time node i Voltage at that point For the scene s Mid-moment t Time node j Voltage at that point This is the reference voltage for the power distribution network. For the line l The resistance, For the line l Reactance, For the line l Capacity constraints, for t Timetable l state, For nodes j The power factor at that point, and For nodes j The minimum and maximum voltage values ​​that can be achieved at that location. and For the minimum and maximum active power output of the generator, For the scene s Mid-moment t Time generator g Contributing to the scene and For the minimum and maximum reactive power output of the generator, For the scene s Mid-moment t Time generator g Unproductive efforts M It is a constant; The maintenance team dispatch model is as follows: (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) in, Let the route selection variable be the one used by the repair team when it departs from the repair station. This is a path selection variable for the repair team to return to the repair station after completing the repair task. RC represents the set of repair teams, and DN represents the set of damaged components and repair stations. For the repair station, 0 / 1 variables For the maintenance team c Path selection variables, 0 / 1 variables Assign variables to the repair team for the damaged components. For the maintenance team c Reaching the damaged component m Time, For the maintenance team c Repair damaged components m Time required For the maintenance team c Reaching the damaged component n Time required For the repair team c from components m To components n The travel time, M is a constant. For the repair state variables of the component, Represents any small positive number. represent t Time element m Has it been repaired? The upper radial model is as follows: (25) (26) (27) (28) in, For the collection of routes, R This is the set of potential root nodes, which includes the nodes at both ends of the damaged line and the node where the generator is located. The number of nodes, a 0 / 1 variable. Determine whether a potential root node becomes a root node. The virtual power flowing through the line. M It is a constant; The upper-layer distribution network-microgrid coupling model is as follows: (29) (30) The lower-level distribution network-microgrid coupling model is as follows: (53) in For microgrid collection, For micro-network m A collection of generators, For micro-network m A collection of energy storage systems, For micro-network m The set of nodes, For the scene s From distribution network to microgrid m The power requirement for transmission. For micro-network m medium generator g The predicted output value, For micro-network m Energy storage system in e Maximum discharge power, For micro-network m Middle node j The load; To cooperate with microgrids m The nodes connected to the distribution network in the middle, For micro-network m Nodes connected to the distribution network The load, In the scene s MicroNet m Nodes connected to the distribution network The load shedding capacity, the electrical energy that the distribution network requires to be transmitted is considered a load in the microgrid; The objective function of the lower-level model is: (31) in For micro-network m Middle node j The importance of the load, For the scene s Mid-moment t Shiwei.com m node j Loss of load at the location; The linear power flow model of the lower-level model is as follows: (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) in For micro-network m The nodes connected to the distribution network. For micro-network m Central route l The damaged state, and Scenes s time t Shiwei.com m Middle node j The discharge power and charging power of the energy storage system at the location For micro-network m A collection of routes, For micro-network m A collection of generators, For nodes j A collection of routes originating from a given point. For nodes j A collection of routes ending at a specific destination. For the scene s middle t Moments Micro Network m Central route l Active power flowing upstream For the scene s middle t Moments Micro Network m Central route l The reactive power flowing upstream, For the scene s middle t Moments Micro Network m medium generator g Those who have made contributions For the scene s middle t Moments Micro Network m medium generator g Unproductive efforts For micro-network m Middle node j Active load at the location, For reactive load, For the scene s Mid-moment t Shiwei.com m Middle node j The amount of active power loss at the location, For the scene s Mid-moment t Shiwei.com m Middle node j The amount of reactive power loss at the location, For the scene s Mid-moment t Shiwei.com m Middle node i Voltage at that point For the scene s Mid-moment t Shiwei.com m Middle node j Voltage at that point For micro-network m The reference voltage in the middle, For micro-network m Central route l The resistance, For micro-network m Central route l Reactance, For micro-network m Central route l Capacity constraints, For the scene s middle t Moments Micro Network m Central route l state, For micro-network m Middle node j The power factor at that point, and For micro-network m Middle node j The minimum and maximum voltage values ​​that can be achieved at that location. For micro-network m medium generator g The minimum and maximum active power output, For the scene s Mid-moment t Shiwei.com m medium generator g Contributing to the scene and For the minimum and maximum reactive power output of the generator, For the scene s Mid-moment t Shiwei.com m medium generator g Unproductive efforts M It is a constant; The lower radial model is as follows: (44) (45) (46) (47) in, For micro-network m The set of nodes, For micro-network m The set of potential root nodes, which includes the nodes at both ends of the damaged line and the node where the generator is located. For micro-network m The number of nodes, a 0 / 1 variable. Used to determine the scene s MicroNet m Potential root node j Whether to become the root node For the scene s MicroNet m The route l The virtual power flowing through; The lower-level distribution network-microgrid coupling model is as follows: (53) To cooperate with microgrids m The nodes connected to the distribution network in the middle, For micro-network m Nodes connected to the distribution network The load, In the scene s MicroNet m Nodes connected to the distribution network The electrical energy transmitted by the distribution network is considered a load in the microgrid.

2. The method for post-disaster coordinated recovery of distribution networks and microgrids according to claim 1, characterized in that, Step 3 includes the following steps: Step 1: Rewrite the two-layer post-disaster recovery model in matrix form: (54) (55) (56) in The coefficient matrix of the objective function of the upper-level problem. This represents all continuous variables in the lower-level model. Represents all 0 / 1 variables in the lower-level model. In addition to the upper-level model and All variables, For the scene s MicroNet m The coefficient matrix of the objective function. , , and In the upper-level model respectively , , The coefficient matrix and the constant coefficient matrix, , , and In the lower-level model respectively , , The coefficient matrix and the constant coefficient matrix, A collection of scenes; Step 2: Divide the two-level problem into a main problem and sub-problems; The main question takes the following form: (57) (58) (59) (60) in It is the penalty coefficient. It is a coefficient matrix containing only 0s and 1s. For the scene s Middle problem m All combinations of 0 / 1 variables contained therein For a fixed combination of 0 / 1 variables, For the 0 / 1 variable combination of the lower-level model The values ​​of continuous variables in the current layer model, For the 0 / 1 variable combination of the lower-level model The penalty variable at that time , , and For the scene s Lower-level problems m middle , , The coefficient matrix and the constant coefficient matrix; In obtaining optimal solution The specific form of the subproblem is as follows: (61) (62) Step 3: Solve the main problem and subproblems, including the following steps: Step 3.1: Input the system topology of the distribution network and microgrid, as well as the parameters of each component. The parameters of each component include the impedance of the line, the range of generator output, the load size of each node, the acceptable voltage range of each node, the maximum output of energy storage, and the capacity of energy storage. Step 3.2: Initialize the number of iterations i and iteration termination error : For all Set the iteration count variable as well as , ; Step 3.3: Solve the main problem to obtain the optimal solution, represented as follows: Let the objective function value of the lower-level problem be denoted as ,Will Passed to the subproblem; where for The optimal value; Step 3.4: Solve the subproblems, and denote the optimal solution to the subproblems as... Let the optimal value of the subproblem be denoted as ; Step 3.5: Judgment Is it true or false? If true, return the optimal solution. The process is complete; If not, then and combine Add to In, and update Then set Return to Step 3.

2.

3. A post-disaster power distribution network and microgrid collaborative restoration device, used to implement the method of claim 1, characterized in that, include: The input module is used to input the system topology of the distribution network and microgrid, as well as the parameters of each component. The processing module is used to solve the preset two-layer post-disaster recovery model based on the system topology of the distribution network and microgrid and the parameters of each component, obtain the optimal control strategy, and transmit the control strategy to the distribution network and microgrid.

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