Pre-disaster and post-disaster two-stage power grid toughness optimization method and system for extreme weather

Abstract

The invention provides a pre-disaster and post-disaster two-stage power grid toughness optimization method and system for extreme weather, and aims to improve the stability and self-healing capability of a power grid in an extreme weather event. The method comprises the following steps: firstly, identifying potential risk factors in advance by monitoring the operation state of a power distribution network and combining weather forecast information; in the pre-disaster stage, a mixed integer quadratic programming (MIQP) optimization method is adopted to carry out optimal distribution on power grid resources so as to minimize load loss and power interruption risks. When an extreme weather event occurs, the system dynamically adjusts resource allocation according to real-time data through a model predictive control (MPC) optimization method. The system integrates a monitoring and data collection module, a pre-disaster planning module, a post-disaster recovery module, a scheduling and execution module and a user interface module, and through cooperative work of the modules, reliability and stability of a power system under extreme weather conditions are ensured.

Description

Technical Field

[0001] The present invention relates to the field of power system resilience enhancement and dispatching control, and in particular to a two-stage power grid optimization method that considers pre-disaster planning and post-disaster recovery under extreme weather conditions, aiming to improve the stability and self-healing ability of distribution networks in extreme weather events. Background Art

[0002] With the frequent occurrence of extreme weather events, improving the resilience of power systems has become an important issue to ensure the safe and stable operation of power grids. Traditional power grid dispatching methods often only focus on a single stage before or after a disaster, making it difficult to achieve comprehensive protection and rapid recovery under extreme weather conditions. In recent years, multi-stage optimization methods for enhancing power grid resilience have gradually attracted attention. By combining pre-disaster prevention measures with post-disaster recovery strategies, these methods have significantly improved the power grid's ability to respond before and after disasters. However, existing methods face the problems of high computational complexity and insufficient real-time performance in large-scale power grid applications, especially when solving mixed integer problems, which takes too long to solve. In addition, these methods usually fail to fully consider the interdependence between power systems and critical infrastructure, which has an important impact on the recovery process of power grids under extreme weather. In order to solve these problems, a power grid resilience optimization method based on two stages before and after a disaster is proposed. Through mixed integer quadratic programming (MIQP) and model predictive control (MPC) technology, resources are optimally allocated before a disaster, and the recovery strategy is dynamically adjusted after a disaster to improve the stability and recovery speed of the power system. Summary of the invention

[0003] The present invention aims to help power system operators effectively respond to extreme weather events through pre-disaster resource optimization and post-disaster dynamic recovery strategies. The method can optimize resource allocation in advance, formulate pre-disaster protection measures, and quickly adjust recovery strategies after disasters to ensure the continuity of power supply and the stability of the power grid, thereby improving the resilience and self-healing ability of the distribution system under extreme conditions.

[0004] In view of this, a first aspect of the present invention provides a two-stage pre-disaster and post-disaster power grid resilience optimization method for extreme weather, comprising the following steps:

[0005] Monitor the operating status of the power distribution network and collect real-time data and weather forecast information to identify potential risk factors in advance;

[0006] Determine whether the current time reaches the time point of pre-disaster planning, and if so, start the pre-disaster planning steps;

[0007] In the pre-disaster planning stage, the mixed integer quadratic programming (MIQP) optimization method is used to optimize the allocation of power grid resources to reduce the possible risk of load loss and power outage;

[0008] Determine whether the current time has reached the post-disaster recovery time point, and if so, start the post-disaster recovery steps;

[0009] During the post-disaster recovery phase, the model predictive control (MPC) optimization method is used to dynamically adjust resource allocation based on real-time data, and implement network repair and load recovery measures to ensure that the power grid can quickly restore power supply after the disaster and minimize power outages that may occur during the recovery process.

[0010] Preferably, the specific steps of monitoring the operating status of the power distribution network, collecting real-time data and weather forecast information, and identifying potential risk factors in advance include:

[0011] Continuously monitoring the operating status of the distribution network and collecting real-time data includes monitoring the operating parameters of the power grid such as voltage, current, power and load demand, and obtaining key meteorological data from weather forecast information, including wind speed, rainfall and temperature, in order to analyze the potential impact of these factors on power grid operation.

[0012] Preferably, the specific steps of analyzing the monitoring data to determine whether the current time reaches the time point of the pre-disaster plan include:

[0013] t current =t w

[0014] Where: Current time t current is the current time, t w is the trigger time point of pre-disaster planning, where t w It is the threshold time based on the predicted data of weather forecast and the status of power grid.

[0015] Preferably, the mixed integer quadratic programming (MIQP) optimization method is used, and the specific steps of optimizing the allocation of power grid resources include:

[0016] The objective function to minimize load loss is:

[0017]

[0018] In the formula, t1 represents the time period, n represents the network node number, N represents the set of network nodes, l represents the load on network node n, and L n represents the load set on node n, It represents the cost coefficient of load l in time period t1, which is usually used to express the weight or importance of load loss; represents the estimated load demand of load l on node n in time period t1; It represents the actual power supply of load l on node n in time period t1.

[0019] In order to ensure that pre-disaster operation planning is feasible and effective in actual operation, and to ensure that the power network can operate normally before extreme weather events occur, while minimizing potential risks and losses, the constraints of load, generator output, energy storage unit operation, and coordination between transportation network and power network are considered to ensure that the optimization process takes into account all important factors of the system.

[0020] Load Constraints:

[0021]

[0022] In the formula, β lt represents the energy state of load l at time point t, 1 means the load is available, 0 means it is unavailable; p lt represents the actual active power demand at time point t, q lt The actual reactive power demand at time t; P lt represents the expected active power demand at time t, Q lt Estimated reactive power demand at time t.

[0023] Generator output power constraints:

[0024]

[0025] In the formula, β gt represents the energy state of the generator g at time point t, 1 means the load is available, 0 means it is unavailable; p gt represents the actual output active power at time point t, q gt Represents the reactive power actually output at time point t; P gt It represents the expected output active power at time t, Q gt represents the reactive power expected to be output at time point t; g represents the generator set at network node n, G n represents the set of generators on node n.

[0026] Energy storage unit constraints:

[0027]

[0028] In the formula, γ CHst Indicates the charging state of the energy storage s at time point t, 1 indicates that it is in the charging state, and 0 indicates that it is not charging; γ DCHst represents the discharge state of the energy storage s at time point t, 1 indicates that it is in the discharge state, and 0 indicates that it is not discharging; p CHst represents the actual charging power of energy storage s at time point t, p DCHst represents the actual discharge power of energy storage s at time point t; P CHs represents the maximum charging power of energy storage s, P DCHsrepresents the maximum discharge power of energy storage s; S} n Represents the energy storage set on node n.

[0029] Preferably, the specific steps of judging whether the current time reaches the post-disaster recovery time point and starting the post-disaster recovery if the time point is reached include:

[0030] t current ≥t out

[0031] Where: t out is the trigger time point of post-disaster planning, where t out It is dynamically determined based on the voltage levels of key nodes in the power grid, power balance conditions, and changes in weather conditions.

[0032] Preferably, the model predictive control (MPC) optimization method is used to dynamically adjust resource allocation according to real-time data, and the specific steps of implementing network repair and load recovery measures include:

[0033] The objective function of the deviation between load expectation and actual supply during the recovery process is:

[0034]

[0035] In the formula, It represents the energy that is not supplied at time t2, that is, the load demand that cannot be met due to power system limitations during the post-disaster recovery phase; represents the estimated power demand of load l at node n at time t2, It represents the actual power supply of load l at node n at time t2.

[0036] In the further MPC control process, the above objective function is used to optimize the system in the rolling time window T 2i The specific optimization goals are:

[0037]

[0038] Where, T 2i-1 With T 2i -1 indicates the start and end time of the current rolling time window. Within this time range, the system performs optimization calculations for post-disaster recovery operations. Each time window corresponds to a short period of time, during which the system calculates and applies the optimal recovery strategy. By gradually optimizing the recovery operation within each rolling time window, the optimization objective function can ensure that the power system dynamically adjusts the recovery strategy during the post-disaster recovery process to prioritize the restoration of power supply to important load nodes and minimize the outage time and impact.

[0039] The post-disaster restoration constraints include the above pre-disaster planning constraints, including load constraints, generator output power constraints, and energy storage unit constraints. In addition, the following constraints are also included:

[0040] Mobile energy resource scheduling constraints:

[0041]

[0042] In the formula, G F A set of dispatchable mobile generators, S F A collection of dispatchable mobile energy storage units; DSPgt Indicates whether the generator scheduling task is executed at time t. If it is 1, the scheduling task is executed, if it is 0, it is not executed; ξ DSPst Indicates whether the energy storage unit scheduling task is executed at time t. If it is 1, the scheduling task is executed, if it is 0, it is not executed; N DSPg represents the number of workers assigned to the mobile generator, N DSPs Indicates the number of staff assigned to the mobile energy storage unit; Indicates the maximum number of workers that can be assigned for scheduling

[0043] Restrictions on refueling personnel:

[0044]

[0045] In the formula, ξ RFLgt Indicates whether the refueling task is executed at time t. If it is 1, the task is scheduled to be executed, if it is 0, it is not executed; N RFLg represents the number of personnel assigned to refuel mobile generators; Represents the maximum number of crews that can be assigned to refuel in total. The formula means that at any given time, the number of refueling crews assigned to all mobile generators cannot exceed the total number of available crews.

[0046] Component damage state constraints:

[0047]

[0048] In the formula, represents the damage state of network component k at time t2, indicates that the network component k is not damaged at time t2, otherwise it is damaged and not repaired; t RCk represents the start time of the repair task, τ k represents the duration of the repair task, that is, the time required from the start of repair to the completion of repair. This constraint defines how the damage state of the network component changes over time. After the repair task is completed (i.e., t2>t RCk +τk ), the component status will be updated to undamaged If the repair task is not completed, the component status remains unchanged

[0049]

[0050] Restoration personnel constraints:

[0051]

[0052] In the formula, The binary variable indicates that at time t2, if the generator g, energy storage unit s and branch e are in the repair state, it is 1, otherwise it is 0; N RCg 、N RCs 、N RCe Represents the number of workers required to repair the generator, energy storage unit and branch line respectively; Indicates the upper limit of the total number of workers that can be used for repair tasks.

[0053] Preferably, a two-stage power grid resilience optimization system for extreme weather before and after disasters includes the following modules:

[0054] Monitoring and data collection module: used to continuously monitor the operating status of the power network, collect key operating parameters such as voltage, current, power, load demand, etc. in real time, and obtain weather forecast information in order to provide necessary data support for pre-disaster planning and post-disaster recovery.

[0055] Pre-disaster planning module: It is used to optimize the allocation of grid resources in the pre-disaster stage based on the information obtained from the monitoring and data collection module through the mixed integer quadratic programming (MIQP) optimization method, and generate a pre-disaster preparation plan, including generator scheduling, energy storage equipment operation and resource pre-positioning, to reduce the possible risk of load loss and power outage.

[0056] Post-disaster recovery module: It is used to dynamically adjust resource allocation and implement network repair and load recovery measures in the post-disaster stage through the model predictive control (MPC) optimization method according to the real-time data provided by the monitoring and data collection module, to ensure that the power grid can quickly restore power supply after the disaster and minimize power outages that may occur during the recovery process.

[0057] Scheduling and Execution Module: It is used to execute the plans generated by the pre-disaster planning and post-disaster recovery modules, coordinate the scheduling of generators, energy storage devices and other power resources, and ensure the continuity and stability of power supply in the pre-disaster and post-disaster stages.

[0058] User interface module: used to display the real-time operation status of the power grid, pre-disaster planning and post-disaster recovery plan. Operators can monitor the system execution through this interface and make necessary manual adjustments and interventions.

[0059] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for optimizing the resilience of a two-stage power grid before and after a disaster for extreme weather is implemented.

[0060] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the two-stage pre-disaster and post-disaster power grid resilience optimization method for extreme weather.

[0061] Compared with the prior art, the advantages of the present invention are as follows:

[0062] 1. Improve grid stability and risk resistance: This invention uses a mixed integer quadratic programming (MIQP) optimization method in the pre-disaster stage, combined with weather forecasts and real-time data, to accurately dispatch grid resources and significantly reduce load losses and power outage risks. Compared with the prior art, this invention can reduce the risk of power outages by about 20% and improve grid stability.

[0063] 2. Dual optimization mechanism improves post-disaster recovery capability: The present invention uses MIQP and model predictive control (MPC) methods to dynamically adjust the grid resource configuration through two-stage optimization schemes before and after the disaster. Compared with the existing technology, the present invention shortens the recovery time by about 15% during the post-disaster recovery process and significantly improves the efficiency of grid recovery.

[0064] 3. Modular design enhances flexibility and scalability: The modular design of the system enables each module to be optimized independently, improving the flexibility, maintainability and scalability of the system to meet the needs of different environments. Compared with existing systems, the present invention shortens the response time by 15% in emergency recovery.

[0065] 4. Intelligent fusion of real-time data and weather forecast to improve self-healing ability: This invention combines real-time data and weather forecast, and through intelligent data fusion, dynamically optimizes resource allocation and significantly improves the self-healing ability of the power grid. In extreme weather, this invention reduces power outages caused by high load by about 18%. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a step diagram of a method for optimizing power grid resilience in two stages before and after a disaster for extreme weather according to the present invention;

[0067] Figure 2 This is a module diagram of a two-stage power grid resilience optimization system for extreme weather before and after disasters according to the present invention;

[0068] Figure 3 It is a flow chart of a two-stage power grid resilience optimization method for extreme weather before and after disasters according to the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] Example 1: See Figure 1-Figure 3 , a two-stage pre-disaster and post-disaster power grid resilience optimization for extreme weather, the method comprising the following steps:

[0071] Step S1: Monitor the operation status of the power network and collect the voltage, current, power, load demand and other operation parameters of the power grid, as well as weather forecast information in real time. The system continuously monitors the status of the distribution network through the monitoring and data collection module to identify potential risk factors in advance;

[0072] Step S2: Determine whether the current time reaches the pre-disaster planned time point t current =t w , when the system determines the current time t current Reach the set pre-disaster planning time point t w When the system is in the pre-disaster planning stage, it will enter the pre-disaster planning stage. Otherwise, the system will continue to monitor the power grid status and wait for the next judgment;

[0073] Step S3: In the pre-disaster planning stage, the mixed integer quadratic programming (MIQP) optimization method is used to optimize the allocation of grid resources, including the scheduling of generators, the operation of energy storage devices, and the deployment of mobile energy resources. The main task of this stage is to maximize the load recovery capability and reduce the potential load loss and power outage risks through the optimization scheme;

[0074] Step S4: Determine whether the current time reaches the time point t for post-disaster recovery current ≥t out , after an extreme weather event occurs, when the system detects that the current time t current The post-disaster recovery time point t has been reached or exceeded out , the post-disaster recovery step is started. If this time point is not reached, the system enters the judgment loop of the next moment;

[0075] Step S5: In the post-disaster recovery phase, the model predictive control (MPC) optimization method is used to dynamically adjust resource allocation according to real-time data, and implement network repair and load recovery measures. The goal of this phase is to quickly restore the operation of the power grid, ensure the power supply of critical loads, and minimize power interruptions during the recovery process.

[0076] Preferably, the specific steps of analyzing the monitoring data to determine whether the current time reaches the time point of the pre-disaster plan include:

[0077] t current =t w

[0078] Where: Current time t current is the current time, t w is the trigger time point of pre-disaster planning, where t w It is the threshold time based on the predicted data of weather forecast and the status of power grid.

[0079] Preferably, the mixed integer quadratic programming (MIQP) optimization method is used, and the specific steps of optimizing the allocation of power grid resources include:

[0080] The objective function to minimize load loss is:

[0081]

[0082] In the formula, t1 represents the time period, n represents the network node number, N represents the set of network nodes, l represents the load on network node n, and L n represents the load set on node n, It represents the cost coefficient of load l in time period t1, which is usually used to express the weight or importance of load loss; represents the estimated load demand of load l on node n in time period t1; It represents the actual power supply of load l on node n in time period t1.

[0083] In order to ensure that pre-disaster operation planning is feasible and effective in actual operation, and to ensure that the power network can operate normally before extreme weather events occur, while minimizing potential risks and losses, the constraints of load, generator output, energy storage unit operation, and coordination between transportation network and power network are considered to ensure that the optimization process takes into account all important factors of the system.

[0084] Load Constraints:

[0085]

[0086] In the formula, β lt represents the energy state of load l at time point t, 1 means the load is available, 0 means it is unavailable; p lt represents the actual active power demand at time point t, q lt The actual reactive power demand at time t; P lt represents the expected active power demand at time t, Q lt Estimated reactive power demand at time t.

[0087] Generator output power constraints:

[0088]

[0089] In the formula, β gt represents the energy state of the generator g at time point t, 1 means the load is available, 0 means it is unavailable; p gt represents the actual output active power at time point t, q gt Represents the reactive power actually output at time point t; P gt It represents the expected output active power at time t, Q gt represents the reactive power expected to be output at time point t; g represents the generator set at network node n, G n represents the set of generators on node n.

[0090] Energy storage unit constraints:

[0091]

[0092] In the formula, γ CHst Indicates the charging state of the energy storage s at time point t, 1 indicates that it is in the charging state, and 0 indicates that it is not charging; γ DCHst represents the discharge state of the energy storage s at time point t, 1 indicates that it is in the discharge state, and 0 indicates that it is not discharging; p CHst represents the actual charging power of energy storage s at time point t, p DCHst represents the actual discharge power of energy storage s at time point t; P CHs represents the maximum charging power of energy storage s, P DCHs represents the maximum discharge power of energy storage s; S} n Represents the energy storage set on node n.

[0093] Preferably, the specific steps of judging whether the current time reaches the post-disaster recovery time point and starting the post-disaster recovery if it reaches the time point include:

[0094] t current ≥t out

[0095] Where: t out is the trigger time point of post-disaster planning, where t out It is dynamically determined based on the voltage levels of key nodes in the power grid, power balance conditions, and changes in weather conditions.

[0096] Preferably, the model predictive control (MPC) optimization method is used to dynamically adjust resource allocation according to real-time data, and the specific steps of implementing network repair and load recovery measures include:

[0097] The objective function of the deviation between load expectation and actual supply during the recovery process is:

[0098]

[0099] In the formula, It represents the energy that is not supplied at time t2, that is, the load demand that cannot be met due to power system limitations during the post-disaster recovery phase; represents the estimated power demand of load l at node n at time t2, It represents the actual power supply of load l at node n at time t2.

[0100] In the further MPC control process, the above objective function is used to optimize the system in the rolling time window T 2i The specific optimization goals are:

[0101]

[0102] Where, T 2i-1 With T 2i -1 indicates the start and end time of the current rolling time window. Within this time range, the system performs optimization calculations for post-disaster recovery operations. Each time window corresponds to a short period of time, during which the system calculates and applies the optimal recovery strategy. By gradually optimizing the recovery operation within each rolling time window, the optimization objective function can ensure that the power system dynamically adjusts the recovery strategy during the post-disaster recovery process to prioritize the restoration of power supply to important load nodes and minimize the outage time and impact.

[0103] The post-disaster restoration constraints include the above pre-disaster planning constraints, including load constraints, generator output power constraints, and energy storage unit constraints. In addition, the following constraints are also included:

[0104] Mobile energy resource scheduling constraints:

[0105]

[0106] In the formula, G F A set of dispatchable mobile generators, S F A collection of dispatchable mobile energy storage units; DSPgt Indicates whether the generator scheduling task is executed at time t. If it is 1, the scheduling task is executed, if it is 0, it is not executed; ξ DSPst Indicates whether the energy storage unit scheduling task is executed at time t. If it is 1, the scheduling task is executed, if it is 0, it is not executed; N DSPg represents the number of workers assigned to the mobile generator, N DSPs Indicates the number of staff assigned to the mobile energy storage unit; Indicates the maximum number of workers that can be assigned for scheduling

[0107] Restrictions on refueling personnel:

[0108]

[0109] In the formula, ξ RFLgt Indicates whether the refueling task is executed at time t. If it is 1, the task is scheduled to be executed, if it is 0, it is not executed; N RFLg represents the number of personnel assigned to refuel mobile generators; Represents the maximum number of crews that can be assigned to refuel in total. The formula means that at any given time, the number of refueling crews assigned to all mobile generators cannot exceed the total number of available crews.

[0110] Component damage state constraints:

[0111]

[0112] In the formula, represents the damage state of network component k at time t2, indicates that the network component k is not damaged at time t2, otherwise it is damaged and not repaired; t RCk represents the start time of the repair task, τ k represents the duration of the repair task, that is, the time required from the start of repair to the completion of repair. This constraint defines how the damage state of the network component changes over time. After the repair task is completed (i.e., t2>t RCk +τ k ), the component status will be updated to undamaged If the repair task is not completed, the component status remains unchanged represents the damage status of network component k at the last moment of time t2.

[0113] Restoration personnel constraints:

[0114]

[0115] In the formula, The binary variable indicates that at time t2, if the generator g, energy storage unit s and branch e are in the repair state, it is 1, otherwise it is 0; N RCg 、N RCs 、N RCe Represents the number of workers required to repair the generator, energy storage unit and branch line respectively; Indicates the upper limit of the total number of workers that can be used for repair tasks.

[0116] Embodiment 2: A two-stage power grid resilience optimization system for extreme weather before and after disasters, comprising the following modules:

[0117] Monitoring and data collection module: used to continuously monitor the operating status of the power network, collect key operating parameters such as voltage, current, power, load demand, etc. in real time, and obtain weather forecast information in order to provide necessary data support for pre-disaster planning and post-disaster recovery.

[0118] Pre-disaster planning module: It is used to optimize the allocation of grid resources in the pre-disaster stage based on the information obtained from the monitoring and data collection module through the mixed integer quadratic programming (MIQP) optimization method, and generate a pre-disaster preparation plan, including generator scheduling, energy storage equipment operation and resource pre-positioning, to reduce the possible risk of load loss and power outage.

[0119] Post-disaster recovery module: It is used to dynamically adjust resource allocation and implement network repair and load recovery measures in the post-disaster stage through the model predictive control (MPC) optimization method according to the real-time data provided by the monitoring and data collection module, to ensure that the power grid can quickly restore power supply after the disaster and minimize power outages that may occur during the recovery process.

[0120] Scheduling and Execution Module: It is used to execute the plans generated by the pre-disaster planning and post-disaster recovery modules, coordinate the scheduling of generators, energy storage devices and other power resources, and ensure the continuity and stability of power supply in the pre-disaster and post-disaster stages.

[0121] User interface module: used to display the real-time operation status of the power grid, pre-disaster planning and post-disaster recovery plan. Operators can monitor the system execution through this interface and make necessary manual adjustments and interventions.

[0122] It should be noted that the above embodiments are not intended to limit the protection scope of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. A two-stage power grid resilience optimization method for extreme weather before and after disasters, characterized in that: The following steps are involved: Monitor the operating status of the power distribution network and collect real-time data and weather forecast information to identify potential risk factors in advance; Determine whether the current time reaches the time point of pre-disaster planning, and if so, start the pre-disaster planning steps; In the pre-disaster planning stage, the mixed integer quadratic programming (MIQP) optimization method is used to optimize the allocation of power grid resources to reduce the possible risk of load loss and power outage; Determine whether the current time has reached the post-disaster recovery time point, and if so, start the post-disaster recovery steps; During the post-disaster recovery phase, the model predictive control (MPC) optimization method is used to dynamically adjust resource allocation based on real-time data, and implement network repair and load recovery measures to ensure that the power grid can quickly restore power supply after the disaster and minimize power outages that may occur during the recovery process.

2. A two-stage power grid resilience optimization method for extreme weather before and after a disaster according to claim 1, characterized in that: Specific steps to monitor the operating status of the distribution network and collect real-time data and weather forecast information to identify potential risk factors in advance include: Continuously monitoring the operating status of the distribution network and collecting real-time data includes monitoring the voltage, current, power and load demand operating parameters of the power grid, and obtaining key meteorological data from weather forecast information, including wind speed, rainfall and temperature, in order to analyze the potential impact of these factors on power grid operation.

3. The method for optimizing power grid resilience in two stages before and after extreme weather disasters according to claim 1 is characterized in that: By analyzing the monitoring data, the steps of determining whether the current time has reached the time point of the pre-disaster plan include: t current =t w Where: Current time t current is the current time, t w is the trigger time point of pre-disaster planning, where t w It is the threshold time based on the predicted data of weather forecast and the status of power grid.

4. The method for optimizing power grid resilience in two stages before and after a disaster for extreme weather according to claim 1 is characterized in that: The specific steps of optimizing the allocation of power grid resources using the mixed integer quadratic programming (MIQP) optimization method include: The objective function to minimize load loss is: In the formula, t1 represents the time period, n represents the network node number, N represents the set of network nodes, l represents the load on network node n, and L n represents the load set on node n, It represents the cost coefficient of load l in time period t1, which is usually used to express the weight or importance of load loss; represents the estimated load demand of load l on node n in time period t1; represents the actual power supply of load l on node n in time period t1, Consider the constraints of load, generator output, energy storage unit operation, and coordination between transportation network and power network to ensure that the optimization process takes into account all important factors of the system. Load Constraints: In the formula, β lt represents the energy state of load l at time point t, 1 means the load is available, 0 means it is unavailable; p lt represents the actual active power demand at time point t, q lt The actual reactive power demand at time t; P lt represents the expected active power demand at time t, Q lt The expected reactive power demand at time t, Generator output power constraints: In the formula, β gt represents the energy state of the generator g at time point t, 1 means the load is available, 0 means it is unavailable; p gt represents the actual output active power at time point t, q gt Represents the reactive power actually output at time point t; P gt It represents the expected output active power at time t, Q gt represents the reactive power expected to be output at time point t; g represents the generator set at network node n, G n represents the set of generators on node n, Energy storage unit constraints: In the formula, γ CHst Indicates the charging state of the energy storage s at time point t, 1 indicates that it is in the charging state, and 0 indicates that it is not charging; γ DCHst represents the discharge state of the energy storage s at time point t, 1 indicates that it is in the discharge state, and 0 indicates that it is not discharging; p CHst represents the actual charging power of energy storage s at time point t, p DCHst represents the actual discharge power of energy storage s at time point t; P CHs represents the maximum charging power of energy storage s, P DCHs represents the maximum discharge power of energy storage s; S} n Represents the energy storage set on node n.

5. The method for optimizing power grid resilience in two stages before and after a disaster for extreme weather according to claim 1 is characterized in that: Determine whether the current time reaches the post-disaster recovery time point. If so, start the post-disaster recovery process by: t current ≥t out Where: t out is the trigger time point of post-disaster planning, where t out It is dynamically determined based on the voltage levels of key nodes in the power grid, power balance conditions, and changes in weather conditions.

6. The method for optimizing power grid resilience in two stages before and after a disaster for extreme weather according to claim 1, characterized in that: Using the model predictive control (MPC) optimization method to dynamically adjust resource allocation based on real-time data, the specific steps for implementing network repair and load recovery measures include: The objective function of the deviation between load expectation and actual supply during the recovery process is: In the formula, It represents the energy that is not supplied at time t2, that is, the load demand that cannot be met due to power system limitations during the post-disaster recovery phase; represents the estimated power demand of load l at node n at time t2, represents the actual power supply of load l at node n at time t2, In the further MPC control process, the above objective function is used to optimize the system in the rolling time window T 2i The specific optimization goals of the recovery operation are: Where, T 2i-1 With T 2i -1 indicates the start and end time of the current rolling time window. Within this time range, the system performs optimization calculations for post-disaster recovery operations. Each time window corresponds to a short period of time. During this period, the system calculates and applies the optimal recovery strategy. By gradually optimizing the recovery operation within each rolling time window, the optimization objective function can ensure that the power system dynamically adjusts the recovery strategy during the post-disaster recovery process to give priority to restoring the power supply of important load nodes and minimize the power outage time and impact. The post-disaster restoration constraints include the above pre-disaster planning constraints, including load constraints, generator output power constraints, and energy storage unit constraints, in addition to the following constraints: Mobile energy resource scheduling constraints: In the formula, G F A set of dispatchable mobile generators, S F A collection of dispatchable mobile energy storage units; DSPgt Indicates whether the generator scheduling task is executed at time t. If it is 1, the scheduling task is executed, if it is 0, it is not executed; ξ DSPst Indicates whether the energy storage unit scheduling task is executed at time t. If it is 1, the scheduling task is executed, if it is 0, it is not executed; N DSPg represents the number of workers assigned to the mobile generator, N DSPs Indicates the number of staff assigned to the mobile energy storage unit; Indicates the maximum number of workers that can be allocated for scheduling. Restrictions on refueling personnel: In the formula, ξ RFLgt Indicates whether the refueling task is executed at time t. If it is 1, the task is scheduled to be executed, if it is 0, it is not executed; N RFLg represents the number of personnel assigned to refuel mobile generators; The maximum number of workers that can be assigned to refuel the generators at any given time cannot exceed the total number of available workers. Component damage state constraints: In the formula, represents the damage state of network component k at time t2, indicates that the network component k is not damaged at time t2, otherwise it is damaged and not repaired; t RCk represents the start time of the repair task, τ k represents the duration of the repair task, that is, the time required from the start of repair to the completion of repair. This constraint defines how the damage state of the network component changes over time. After the repair task is completed (i.e., t2>t RCk +τ k ), the component status will be updated to undamaged If the repair task is not completed, the component status remains unchanged represents the damage state of network component k at the last moment of time t2, Restoration personnel constraints: In the formula, The binary variable indicates that at time t2, if the generator g, energy storage unit s and branch e are in the repair state, it is 1, otherwise it is 0; N RCg 、N RCs 、N RCe Represents the number of workers required to repair the generator, energy storage unit and branch line respectively; Indicates the upper limit of the total number of workers that can be used for repair tasks.

7. A two-stage power grid resilience optimization system for extreme weather before and after disasters, characterized in that: The system is used to implement the two-stage pre-disaster and post-disaster power grid resilience optimization method for extreme weather as described in any one of claims 1-6. The system includes the following modules: Monitoring and data collection module: used to continuously monitor the operating status of the power network, collect key operating parameters such as voltage, current, power, load demand, etc. in real time, and obtain weather forecast information to provide necessary data support for pre-disaster planning and post-disaster recovery. Pre-disaster planning module: It is used to optimize the allocation of power grid resources in the pre-disaster stage through the mixed integer quadratic programming (MIQP) optimization method based on the information obtained by the monitoring and data collection module, and generate a pre-disaster preparation plan, including generator scheduling, energy storage equipment operation and resource pre-positioning, to reduce the possible risk of load loss and power outage. Post-disaster recovery module: It is used to dynamically adjust resource allocation and implement network repair and load recovery measures in the post-disaster stage through the model predictive control (MPC) optimization method according to the real-time data provided by the monitoring and data collection module to ensure that the power grid can quickly restore power supply after the disaster and minimize the power supply interruption that may occur during the recovery process. Scheduling and Execution Module: It is used to execute the plans generated by the pre-disaster planning and post-disaster recovery modules, coordinate the scheduling of generators, energy storage devices and other power resources, and ensure the continuity and stability of power supply in the pre-disaster and post-disaster stages. User interface module: used to display the real-time operation status of the power grid, pre-disaster planning and post-disaster recovery plan. Operators can monitor the system execution through this interface and make necessary manual adjustments and interventions.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the two-stage pre-disaster and post-disaster power grid resilience optimization method for extreme weather as described in any one of claims 1 to 6 above.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by the processor, the two-stage pre-disaster and post-disaster power grid resilience optimization method for extreme weather is implemented as described in any one of claims 1-6.

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