An emergency resource pre-disaster deployment strategy optimization method and system for supply load-oriented load

Abstract

The application discloses an emergency resource pre-disaster deployment strategy optimization method and system for supply load. Based on the power system important load supply during a disaster, the system minimizes the system load loss during the disaster. Considering the demand of the supply load in power supply reliability and power quality, a node power supply constraint for the supply load is proposed on the basis of traditional load power supply constraints. Then, considering the support capacity of the emergency resource during the disaster, an emergency resource pre-disaster deployment model is established, which takes into account the economic and reliability demands. Further, the nonlinear flow constraint and the bilinear constraint in the model are linearized, and the original model is converted into a mixed integer linear programming problem. Finally, the effectiveness of the proposed model is verified in an improved IEEE 33-node distribution network system. The operation strategy obtained by the method fully considers the power supply demand of the supply load, the model is simple and easy to solve, and has certain theoretical value and engineering value.

Description

Technical Field

[0001] This invention belongs to the field of power system emergency control technology, and in particular, it is a method and system for optimizing emergency resource pre-disaster deployment strategies for power supply load. Background Technology

[0002] The power system is a crucial link in modern society, maintaining industrial production and ensuring people's livelihoods. Its operational status directly affects the national economy and social order. With the continuous expansion of the power system, extreme disasters such as typhoons and ice storms, characterized by "low probability but high impact," pose a severe challenge to the safe and stable operation of the system. These disasters make transmission lines and power equipment prone to failure, even leading to regional power outages, directly impacting the normal functioning of society and people's basic lives. Especially for critical loads with a high degree of impact, a lack of stable power supply during disasters can easily threaten personal safety and cause significant accident losses.

[0003] To improve the stability of power supply and the efficiency of load recovery during disasters, many scholars are currently researching power grid emergency response strategies. Based on predicted scenarios, emergency resources are deployed to critical points in advance to ensure power supply to loads. However, the strategy formulation process overlooks the fact that emergency response strategies may neglect some low-demand but important loads (such as important sporting events, emergency command centers, and medical rescue facilities), making it impossible to guarantee priority power supply and power quality for them during disasters. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by providing an optimization method for emergency resource pre-disaster deployment strategies oriented towards supply load.

[0005] The technical solution to achieve the purpose of this invention is: an optimization method for pre-disaster deployment strategy of emergency resources oriented towards supply guarantee load, the method comprising the following steps:

[0006] Step 1: Considering the requirements of the load for power supply reliability and power quality, construct a node power supply constraint model for the load to be guaranteed, based on the traditional load power supply constraints.

[0007] Step 2: Consider the fuel consumption process of the mobile emergency power supply and establish a deployment, output and fuel consumption model for the mobile emergency power supply.

[0008] Step 3: Consider the support capacity of emergency resources for supply load during a disaster, and establish a pre-disaster deployment model for emergency resources that takes into account both economic and reliability requirements.

[0009] Step 4: Linearize the nonlinear terms in the models of Steps 2 and 3 to transform the original model into a mixed-integer linear programming model.

[0010] Step 5: Solve the mixed-integer linear programming model (using commercial software GUROBI) to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply load.

[0011] Furthermore, step 1 specifically includes:

[0012] Step 1-1 involves modeling the power supply constraints for conventional loads, specifically including:

[0013] The power supply constraints of the load nodes are modeled and represented as follows:

[0014]

[0015] In the formula, A 0-1 variable, representing Time-based load nodes Is the device powered on? If yes, the value is 1; otherwise, the value is 0. Represents the set of all load nodes. The total simulation time steps are set;

[0016] The voltage constraints of the load nodes are modeled as follows:

[0017]

[0018] In the formula, for Time-based load nodes The square of the voltage, These represent the upper and lower limits of the square of the voltage at a conventional load node, respectively.

[0019] The power demand constraints of the load nodes are modeled and expressed as follows:

[0020]

[0021]

[0022] In the formula, , They represent Time-based load nodes The size of the active and reactive power demand, Representing load nodes Upper and lower limits of active power under normal operating conditions; Representing load nodes Upper and lower limits of reactive power under normal operating conditions;

[0023] The load reduction range of the load nodes is modeled and represented as follows:

[0024]

[0025]

[0026] In the formula, The limiting factor representing the fluctuation of node power within one time step; express Time-based load nodes The size of the active power demand;

[0027] Steps 1-2: Based on the power supply constraint modeling of conventional loads, construct the power supply demand model for guaranteed loads;

[0028] The specific modeling of power supply status and load status is as follows:

[0029]

[0030]

[0031] In the formula, Represents the set of nodes that guarantee supply load;

[0032] Steps 1-3: Model the node voltages of the loads required to maintain power supply during a disaster.

[0033]

[0034] In the formula, These represent the upper and lower limits of the square of the voltage at the load node to ensure power supply.

[0035] Furthermore, step 2 specifically includes:

[0036] Step 2-1, Construct a pre-deployment model for mobile emergency power supplies:

[0037]

[0038]

[0039]

[0040] In the formula, A 0-1 variable, representing a load node. Whether to deploy a mobile emergency power supply, 1 indicates to deploy, 0 indicates not to deploy; Indicates load node The number of mobile emergency power supplies that need to be deployed; Indicates the number of mobile emergency power supplies that can be dispatched; Indicates a maximum value;

[0041] Step 2-2, Construct the output model of the mobile emergency power supply:

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, A 0-1 variable, representing a load node. The deployed mobile emergency power supply The indicator of whether or not to exert effort at any given moment: 1 indicates to exert effort, and 0 indicates not to exert effort. , Representing load nodes The deployed mobile emergency power supply The extent of effort exerted, whether or not it yields results; , These represent the upper limits of active and reactive power output of the mobile emergency power supply, respectively.

[0047] Steps 2-3: Construct a model relating the fuel consumption and output of the mobile emergency power supply.

[0048] In the formula, Indicates that the portable emergency power supply is in Fuel consumption at any given moment; , , , These represent the fuel consumed per unit output of the mobile emergency power supply within different output ranges;

[0049] Steps 2-4: Construct a fuel consumption status model for the mobile emergency power supply at each hourly step:

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] In the formula, Indicates the maximum fuel capacity of the mobile emergency power supply. , These respectively represent the mobile emergency power supply in time, Fuel consumption at any time , These respectively indicate that the mobile emergency power vehicle is in time, Fuel remaining at any given time; This indicates the remaining fuel of the mobile emergency power supply at the first moment.

[0056] Furthermore, step 3 specifically includes:

[0057] Step 3-1: Establish the objective function of the emergency resource pre-disaster deployment model. :

[0058]

[0059] In the formula, Indicates the cost of resource deployment; This indicates the cost of emergency repairs; Indicates the cost of load reduction; Indicates the cost of line movement;

[0060] Step 3-2, establish the emergency repair team model, including:

[0061] Only critical power supply lines will be repaired, while remaining faults will be repaired after the disaster to reduce the number of repairs required. The specific modeling is as follows:

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] In the formula, The variable is 0-1, representing the emergency repair team. exist Whether the time slot is deployed on the line The value is 1 if deployed, otherwise 0. The variable is 0-1, representing the emergency repair team. Whether it is deployed on the line at the first moment The value is 1 if deployed, otherwise 0. The variable is 0-1, representing the emergency repair team. exist Is the time slot deployed on the line? The value is 1 if deployed, otherwise 0. A 0-1 variable, representing the line exist The fault status at any given time; 0 indicates a fault, and 1 indicates otherwise. A 0-1 variable, representing the line The fault status at the first moment; The variable is 0-1, representing the repair team's... The value is 1 if emergency repairs have started, and 0 otherwise. This indicates the travel time of the route; This represents the set of all routes and outposts; It represents the set of all lines in the system; This represents the set of all faulty lines in the system. This indicates the assembly of all emergency repair teams; Represents the set of all outposts;

[0070] Define the workload required for each line repair operation and the efficiency of the repair team:

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] In the formula, , They represent the lines respectively. exist time, The remaining workload that still needs to be fixed; Indicates the line The remaining workload that still needs to be repaired in the first instance; Indicates the line Total workload required to resolve the fault; The variable is 0-1, representing the repair team's... The value is 1 if emergency repairs have started, and 0 otherwise. To improve the work efficiency of the construction team; 0-1 variables represent Timetable The on / off state is represented by 1 if connected and 0 otherwise. A collection of lines connected to the power supply load; It is the minimum value;

[0077] Step 3-3: Model the wind-storage power source in the power grid;

[0078] The wind turbine output model is as follows:

[0079]

[0080]

[0081]

[0082] In the formula, express Time-based load nodes The active power output of the wind power at the location; This indicates the upper limit of active power output of wind power; express Time-based load nodes The amount of reactive power output of the wind power at the location; This indicates the upper limit of reactive power output of wind power; , These represent the upper and lower limits of the power factor of the wind power system, respectively. This represents the set of load nodes where the wind power system is located;

[0083] The energy storage system model is as follows:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] In the formula, , They represent time, Time-based load nodes The amount of active power output of the energy stored at the location; , These represent the upper and lower limits of the active power output of energy storage, respectively. express time The amount of reactive power output from energy storage at the node; , These represent the upper and lower limits of reactive power output from energy storage, respectively. , These represent the upper and lower limits of the power factor of the energy storage system, respectively. A 0-1 variable, representing a load node. Energy storage Whether the device is connected to the power grid at any given time; if connected, the value is 1; otherwise, the value is 0. , The variables are 0-1, representing the load nodes respectively. Energy storage time, Whether the device is currently charging; if it is charging, the value is 1; otherwise, the value is 0. , The variables are 0-1, representing the load nodes respectively. Energy storage time, The value indicates whether the device is in a discharging state at any given time; if it is, the value is 1, otherwise it is 0. , They represent time, The capacity of energy storage at any given time; , This represents the energy storage charging and discharging efficiency parameter; , These represent the upper and lower limits of energy storage capacity, respectively.

[0092] Steps 3-4: In the radial distribution network, the linearized Distflow power flow model is used to calculate power balance and whether the power flow exceeds limits. The modeling process is as follows:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099] In the formula, , Representing load nodes With load nodes Between lines The magnitude of resistance and reactance; express Timetable The amount of active power flowing through; express Timetable The amount of reactive power flowing through; , They represent Time Node , The amount of active power flowing through; , They represent Timetable , The amount of reactive power flowing through; express Timetable The connection status is indicated by 1 for a connected line and 0 for a disconnected line. These represent the upper and lower limits of the power flowing through the line, respectively. Indicates the line Endpoint set; , Representing load nodes The set of parent and child nodes; Indicates the power provided by the main network. for Time-based load nodes The square of the voltage, This indicates the upper limit of the power provided by the main network.

[0100] Furthermore, step 4 specifically includes:

[0101] Step 4-1: Linearize the model from Step 2-3 to obtain:

[0102]

[0103] In the formula, A binary auxiliary variable;

[0104] Step 4-2, linearize the last formula in the energy storage system model in Step 3-3, the first two formulas in Step 3-4, and the formula in Step 4-1 as follows:

[0105]

[0106]

[0107]

[0108]

[0109] In the formula, , , , , , These are the corresponding bilinear terms. , , , , , , , and Auxiliary variables after linearization.

[0110] On the other hand, an emergency resource pre-disaster deployment strategy optimization system for supply guarantee load is provided, the system comprising:

[0111] The first module is used to implement: considering the needs of the load for power supply reliability and power quality, and based on the traditional load power supply constraints, constructing a node power supply constraint model for the load to be supplied.

[0112] The second module is used to: consider the fuel consumption process of mobile emergency power sources and establish a deployment, output, and fuel consumption model for mobile emergency power sources;

[0113] The third module is used to: consider the support capacity of emergency resources for supply load during disasters, and establish an emergency resource pre-disaster deployment model that takes into account both economic and reliability requirements;

[0114] The fourth module is used to linearize the nonlinear terms in the models of the second and third modules, transforming the original model into a mixed-integer linear programming model.

[0115] The fifth module is used to solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply load.

[0116] On the other hand, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing the pre-disaster deployment strategy of emergency resources for supply guarantee load.

[0117] On the other hand, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method for optimizing the pre-disaster deployment strategy of emergency resources for supply guarantee load.

[0118] Compared with the prior art, the significant advantages of this invention are:

[0119] (1) The method of the present invention takes into account the support capabilities of various physical entities, establishes an emergency resource pre-disaster deployment model that takes into account both economic and reliability requirements, and also enriches the structure of the model.

[0120] (2) The present invention takes into account the power supply demand of the load during the disaster process, and can ensure that the power supply demand and power quality requirements of important loads are met in the anticipated fault scenario.

[0121] (3) This invention can be applied to the pre-disaster deployment of emergency resources for supply loads. Through pre-disaster deployment, the survival capacity of supply loads during disasters can be significantly improved, which has certain theoretical and engineering value.

[0122] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0123] Figure 1 This is a schematic diagram illustrating the principle of an emergency resource pre-disaster deployment strategy optimization method for supply guarantee load in one embodiment.

[0124] Figure 2 This is a simulation scene diagram from one embodiment.

[0125] Figure 3 This is a diagram illustrating the deployment and subsequent scheduling of emergency resources in one embodiment.

[0126] Figure 4 This is a node load recovery diagram in one embodiment.

[0127] Figure 5 This is a comparison diagram of the supply guarantee method of the present invention and the conventional method in one embodiment. Detailed Implementation

[0128] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0129] It should be noted that if the embodiments of the present invention involve descriptions such as "first" and "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of those features. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0130] In one embodiment, combined Figure 1 This paper provides an optimization method for emergency resource pre-disaster deployment strategies oriented towards supply guarantee loads. The method includes the following steps:

[0131] Step 1: Considering the requirements of the load for power supply reliability and power quality, construct a node power supply constraint model for the load to be guaranteed, based on the traditional load power supply constraints.

[0132] Step 2: Consider the fuel consumption process of the mobile emergency power supply and establish a deployment, output and fuel consumption model for the mobile emergency power supply.

[0133] Step 3: Consider the support capacity of emergency resources for supply load during a disaster, and establish a pre-disaster deployment model for emergency resources that takes into account both economic and reliability requirements.

[0134] Step 4: Linearize the nonlinear terms in the models of Steps 2 and 3 to transform the original model into a mixed-integer linear programming model.

[0135] Step 5: Solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply guarantee load.

[0136] Furthermore, in one embodiment, step 1 specifically includes:

[0137] Step 1-1: To establish a power demand model for the guaranteed load, it is necessary to first model the power supply constraints of the conventional load, specifically including:

[0138] The power supply constraints of the load nodes are modeled and represented as follows:

[0139]

[0140] In the formula, A 0-1 variable, representing Time-based load nodes Is the device powered on? If yes, the value is 1; otherwise, the value is 0. Represents the set of all load nodes. The total simulation time steps are set;

[0141] The voltage constraints of the load nodes are modeled as follows:

[0142]

[0143] In the formula, for Time-based load nodes The square of the voltage, These represent the upper and lower limits of the square of the voltage at a conventional load node, respectively; here, the formula represents the voltage variation range of a conventional node.

[0144] The power demand constraints of the load nodes are modeled and expressed as follows:

[0145]

[0146]

[0147] In the formula, , They represent Time-based load nodes The size of the active and reactive power demand, Representing load nodes Upper and lower limits of active power under normal operating conditions; Representing load nodes Upper and lower limits of reactive power under normal operating conditions;

[0148] Here, these two formulas represent the load reduction range of the power supply node.

[0149] The active power variation range of the load nodes is modeled and represented as follows:

[0150]

[0151]

[0152] In the formula, The limiting factor representing the fluctuation of node power within one time step; express Time-based load nodes The size of the active power demand;

[0153] Here, these two formulas represent the limits of active power variation at conventional nodes.

[0154] Steps 1-2: Based on the power supply constraints modeling of conventional loads, construct a power supply demand model for guaranteed loads;

[0155] The power supply status of the load to be guaranteed needs to remain stable at all times. Therefore, the specific modeling of the power supply status and load status is as follows:

[0156]

[0157]

[0158] In the formula, Represents the set of load nodes for ensuring power supply;

[0159] Here, the formula indicates that the power supply load in the power grid must always maintain a power supply status and must always be maintained at a normal load level.

[0160] Steps 1-3: To ensure stable power supply to the loads requiring backup power, their voltage should be limited to stricter limits. This ensures more stable power consumption for the loads during disasters, preventing sudden voltage exceedances due to minor disturbances while operating at voltage boundaries. Therefore, the node voltages of the loads requiring backup power during disasters are modeled as follows:

[0161]

[0162] In the formula, These represent the upper and lower limits of the square of the voltage at the load node to ensure power supply.

[0163] Here, the formula indicates that there are stricter voltage limits on the load nodes during disaster relief.

[0164] Furthermore, in one embodiment, step 2 specifically includes:

[0165] Step 2-1: Construct a pre-deployment model for mobile emergency power supplies to determine the number and locations of mobile emergency power supplies to be deployed in the anticipated scenarios.

[0166]

[0167]

[0168]

[0169] In the formula, A 0-1 variable, representing a load node. Whether to deploy a mobile emergency power supply, 1 indicates to deploy, 0 indicates not to deploy; Indicates load node The number of mobile emergency power supplies that need to be deployed; Indicates the number of mobile emergency power supplies that can be dispatched; Indicates a maximum value;

[0170] Here, the first two formulas indicate whether a mobile emergency power supply is deployed at a certain node, and the total number of mobile emergency power supplies cannot exceed the number that can be dispatched; the third formula indicates and The relationship between them.

[0171] Step 2-2, Construct the output model of the mobile emergency power supply:

[0172]

[0173]

[0174]

[0175]

[0176] In the formula, A 0-1 variable, representing a load node. The deployed mobile emergency power supply The indicator of whether or not to exert effort at any given moment: 1 indicates to exert effort, and 0 indicates not to exert effort. , Representing load nodes The deployed mobile emergency power supply The extent of effort exerted, whether or not it yields results; , These represent the upper limits of active and reactive power output of the mobile emergency power supply, respectively.

[0177] Here, the first formula indicates that only when the node restores power can the mobile emergency power supply guarantee power connection to the grid;

[0178] The second formula represents and The relationship between these factors means that a node can only decide whether to provide power to a mobile emergency power source if a mobile emergency power source is deployed.

[0179] The third and fourth formulas indicate that the active and reactive power outputs of mobile emergency power supplies cannot exceed their limits.

[0180] Steps 2-3: Construct a model relating the fuel consumption and output of the mobile emergency power supply.

[0181] In the formula, Indicates that the portable emergency power supply is in Fuel consumption at any given moment; , , , These represent the fuel consumed per unit output of the mobile emergency power supply within different output ranges;

[0182] Steps 2-4: Construct a fuel consumption status model for the mobile emergency power supply at each hourly step:

[0183]

[0184]

[0185]

[0186]

[0187]

[0188] In the formula, Indicates the maximum fuel capacity of the mobile emergency power supply. , These respectively represent the mobile emergency power supply in time, Fuel consumption at any time , These respectively indicate that the mobile emergency power vehicle is in time, Fuel remaining at any given time; This indicates the remaining fuel of the mobile emergency power supply at the first moment.

[0189] Here, the first formula indicates that there is a limit to the fuel of a mobile emergency power source only when a decision is made to deploy it at a certain point; the second formula indicates that fuel consumption only occurs when the mobile emergency power source is outputting power; the third formula indicates that the fuel of the mobile emergency power source must be greater than 0; the fourth formula indicates the amount of mobile emergency power source fuel available at the initial moment of a certain node; and the fifth formula indicates the fuel consumption of the mobile emergency power source.

[0190] Furthermore, in one embodiment, step 3 considers the support capacity of emergency resources for supply load during a disaster, and establishes a pre-disaster deployment model for emergency resources that balances economic and reliability requirements, specifically including:

[0191] Step 3-1: Establish the objective function of the emergency resource pre-disaster deployment model. :

[0192]

[0193] In the formula, Indicates the cost of resource deployment; This indicates the cost of emergency repairs; Indicates the cost of load reduction; Indicates the cost of line movement;

[0194] Here, taking into account both economic efficiency and reliability, the objective function is defined to include four parts: resource deployment cost, emergency repair cost, load shedding cost, and line operation cost. Furthermore, the resource deployment cost includes both local resource deployment cost and the additional resource deployment cost necessary to maintain power supply to the load.

[0195] Preferably, the resource deployment cost for:

[0196]

[0197] In the formula, This indicates the cost of deploying a local mobile emergency power supply. This indicates the cost required to dispatch a mobile emergency power supply from another location; This indicates the number of mobile emergency power supplies that need to be deployed at each node to ensure power supply in the event of a predicted failure. Indicates the number of locally dispatchable mobile emergency power supplies; This represents the set of power grid nodes. The formula represents the total cost of deploying both local and external resources under a specific fault scenario.

[0198] Preferably, to reduce the cost of emergency repairs, the repair team only performs repairs on critical lines during the anticipated disaster. for:

[0199]

[0200] In the formula, This indicates the cost incurred by the emergency repair team in repairing a single line. A variable consisting of 0 and 1, representing the circuit. exist The fault status at any given time; 0 indicates a fault, and 1 indicates otherwise. This represents the set of lines in the power grid. The formula expresses the cost of dispatching a repair team during a disaster in a fault scenario.

[0201] Preferably, to maximize the system's power supply during a disaster, the load reduction cost... for:

[0202]

[0203] In the formula, Indicates load node The cost of load reduction; Indicates load node Load under normal operating conditions; express Time-based load nodes The size of the active power demand, i.e., the load node exist Load under fault scenarios. This formula represents the cost of load reduction under fault scenarios.

[0204] Preferably, in order to ensure the stable state of nodes in the power grid, reduce the number of line switching incidents, and maximize the stability of the grid structure, the line operation cost is... for:

[0205]

[0206] In the formula, This indicates the cost incurred by the line operation; , The variables are 0 and 1, representing respectively... time, Timetable The connection status is 1 if connected, and 0 otherwise. This represents the estimated total duration of the disaster. The formula indicates the total cost of changes to the grid structure under a failure scenario.

[0207] Step 3-2, establish the emergency repair team model, including:

[0208] Because the cost of emergency repairs during extreme disasters far exceeds the cost of post-disaster repairs, the number of repairs should be minimized during a disaster, focusing only on critical power lines, and leaving remaining faults for post-disaster repairs. The specific modeling is as follows:

[0209]

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216] In the formula, The variable is 0-1, representing the emergency repair team. exist Whether the time slot is deployed on the line The value is 1 if deployed, otherwise 0. The variable is 0-1, representing the emergency repair team. Whether it is deployed on the line at the first moment The value is 1 if deployed, otherwise 0. The variable is 0-1, representing the emergency repair team. exist Is the time slot deployed on the line? The value is 1 if deployed, otherwise 0. A 0-1 variable, representing the line exist The fault status at any given time; 0 indicates a fault, and 1 indicates otherwise. A 0-1 variable, representing the line The fault status at the first moment; The variable is 0-1, representing the repair team's... The value is 1 if emergency repairs have started, and 0 otherwise. This indicates the travel time of the route; This represents the set of all routes and outposts; It represents the set of all lines in the system; This represents the set of all faulty lines in the system. This indicates the assembly of all emergency repair teams; Represents the set of all outposts;

[0217] Here, the first formula indicates that a repair team can only be in one location at a time; the second formula indicates the initial location of the repair team; the third formula indicates that each fault point only requires one line to be repaired; the fourth formula indicates that repairs can only begin when the repair team arrives at the fault location; the fifth formula indicates that the repair team can only go to the faulty line; the sixth formula indicates the time limit for the repair team to go to the next node; and the seventh formula indicates the initialization of the faulty line status.

[0218] During a disaster, priority should be given to repairing critical lines that require the shortest repair time to restore more load. Therefore, the model should define the workload required for each line repair and the efficiency of the repair team so that the model can decide on the repair strategy. The modeling is as follows:

[0219]

[0220]

[0221]

[0222]

[0223]

[0224] In the formula, , They represent the lines respectively. exist time, The remaining workload that still needs to be fixed; Indicates the line The remaining workload that still needs to be repaired in the first instance; Indicates the line Total workload required to resolve the fault; The variable is 0-1, representing the repair team's... The value is 1 if emergency repairs have started, and 0 otherwise. To improve the work efficiency of the construction team; 0-1 variables represent Timetable The on / off state is represented by 1 if connected and 0 otherwise. A collection of lines connected to the power supply load; It is the minimum value;

[0225] Here, the first formula represents the workload required to repair the faulty line; the second formula represents the workload required to repair the faulty line at each time step; the third formula represents... and The relationship between these factors is such that when the remaining repair work required for the line is zero, the line returns to normal, and its interruption can be controlled; the fourth formula represents... and The relationship between the two is that when the remaining amount of repair work required for the line is 0, there is no need for the repair team to carry out repairs; the fifth formula indicates that during a disaster, lines near the supply load need to be repaired to ensure that they are no longer isolated nodes.

[0226] Step 3-3: Model the wind-storage power source in the power grid;

[0227] The wind turbine output model is as follows:

[0228]

[0229]

[0230]

[0231] In the formula, express Time-based load nodes The active power output of the wind power at the location; This indicates the upper limit of active power output of wind power; express Time-based load nodes The amount of reactive power output of the wind power at the location; This indicates the upper limit of reactive power output of wind power; , These represent the upper and lower limits of the power factor of the wind power system, respectively. This represents the set of load nodes where the wind power system is located;

[0232] Here, the first two formulas represent the upper and lower limits of wind power output; the third formula represents the limit that the power factor of wind power generation cannot exceed.

[0233] Energy storage systems, as optimized devices to mitigate the volatility of wind power generation, possess bidirectional power throughput capabilities. Furthermore, the spatiotemporal shifting characteristics of energy storage can help ensure stable power supply to loads. The energy storage system model is as follows:

[0234]

[0235]

[0236]

[0237]

[0238]

[0239]

[0240]

[0241] In the formula, , They represent time, Time-based load nodes The amount of active power output of the energy stored at the location; , These represent the upper and lower limits of the active power output of energy storage, respectively. express time The amount of reactive power output from energy storage at the node; , These represent the upper and lower limits of reactive power output from energy storage, respectively. , These represent the upper and lower limits of the power factor of the energy storage system, respectively. A 0-1 variable, representing a load node. Energy storage Whether the device is connected to the power grid at any given time; if connected, the value is 1; otherwise, the value is 0. , The variables are 0-1, representing the load nodes respectively. Energy storage time, Whether the device is currently charging; if it is charging, the value is 1; otherwise, the value is 0. , The variables are 0-1, representing the load nodes respectively. Energy storage time, The value indicates whether the device is in a discharging state at any given time; if it is, the value is 1, otherwise it is 0. , They represent time, The capacity of energy storage at any given time; , This represents the energy storage charging and discharging efficiency parameter; , These represent the upper and lower limits of energy storage capacity, respectively.

[0242] Here, the first and second formulas indicate that the active and reactive power output of energy storage cannot exceed their limits; the third formula indicates that the power factor of energy storage cannot exceed the set limit; the fourth formula indicates that energy storage can only operate in either charging or discharging mode; the fifth formula indicates that the node restores power supply when the wind-storage system is connected to output power; the sixth formula indicates that the energy storage capacity cannot exceed its upper and lower limits; and the seventh formula indicates the capacity of energy storage at each time step.

[0243] Steps 3-4: In the radial distribution network, the linearized Distflow power flow model is used to calculate power balance and whether the power flow exceeds limits. The modeling process is as follows:

[0244]

[0245]

[0246]

[0247]

[0248]

[0249]

[0250] In the formula, , Representing load nodes With load nodes Between lines The magnitude of resistance and reactance; express Timetable The amount of active power flowing through; express Timetable The amount of reactive power flowing through; , They represent Time Node , The amount of active power flowing through; , They represent Timetable , The amount of reactive power flowing through; express Timetable The connection status is indicated by 1 for a connected line and 0 for a disconnected line. These represent the upper and lower limits of the power flowing through the line, respectively. Indicates the line Endpoint set; , Representing load nodes The set of parent and child nodes; Indicates the power provided by the main network. for Time-based load nodes The square of the voltage, This indicates the upper limit of the power provided by the main network.

[0251] Here, for the first formula, it is 0 when non-main grid nodes calculate power balance. The first and second formulas indicate that nodes in the power grid need to satisfy power balance; the third and fourth formulas indicate the calculation of node voltage; the fifth formula indicates that the power flowing through the lines in the power grid needs to meet its constraints; the sixth formula indicates that the power provided by the main grid cannot exceed its upper and lower limits.

[0252] Furthermore, in one embodiment, step 4 specifically includes:

[0253] Step 4-1: Linearize the model from Step 2-3 to obtain:

[0254]

[0255] In the formula, A binary auxiliary variable;

[0256] Step 4-2, after linearization in this invention, can be expressed as the following formula:

[0257]

[0258] In the formula, M0 represents an auxiliary continuous variable; M0 represents the introduced maximum value. Represents the binary variables in a bilinear term; This represents a continuous variable in a bilinear term.

[0259] Linearize the last formula in the energy storage system model in step 3-3, the first two formulas in step 3-4, and the formula in step 4-1 as follows:

[0260]

[0261]

[0262]

[0263]

[0264] In the formula, , , , , , These are the corresponding bilinear terms. , , , , , , , and Auxiliary variables after linearization.

[0265] In one embodiment, an emergency resource pre-disaster deployment strategy optimization system for supply guarantee load is provided, the system comprising:

[0266] The first module is used to implement: considering the needs of the load for power supply reliability and power quality, and based on the traditional load power supply constraints, constructing a node power supply constraint model for the load to be supplied.

[0267] The second module is used to: consider the fuel consumption process of mobile emergency power sources and establish a deployment, output, and fuel consumption model for mobile emergency power sources;

[0268] The third module is used to: consider the support capacity of emergency resources for supply load during disasters, and establish an emergency resource pre-disaster deployment model that takes into account both economic and reliability requirements;

[0269] The fourth module is used to linearize the nonlinear terms in the models of the second and third modules, transforming the original model into a mixed-integer linear programming model.

[0270] The fifth module is used to solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply load.

[0271] Specific limitations regarding the emergency resource pre-disaster deployment strategy optimization system for supply guarantee loads can be found in the limitations of the emergency resource pre-disaster deployment strategy optimization method for supply guarantee loads mentioned above, and will not be repeated here. Each module in the aforementioned emergency resource pre-disaster deployment strategy optimization system for supply guarantee loads can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0272] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements:

[0273] Step 1: Considering the requirements of the load for power supply reliability and power quality, construct a node power supply constraint model for the load to be guaranteed, based on the traditional load power supply constraints.

[0274] Step 2: Consider the fuel consumption process of the mobile emergency power supply and establish a deployment, output and fuel consumption model for the mobile emergency power supply.

[0275] Step 3: Consider the support capacity of emergency resources for supply load during a disaster, and establish a pre-disaster deployment model for emergency resources that takes into account both economic and reliability requirements.

[0276] Step 4: Linearize the nonlinear terms in the models of Steps 2 and 3 to transform the original model into a mixed-integer linear programming model.

[0277] Step 5: Solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply guarantee load.

[0278] For specific limitations on each step, please refer to the limitations on the optimization method of emergency resource pre-disaster deployment strategy for supply guarantee load above, which will not be repeated here.

[0279] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program being implemented when executed by a processor:

[0280] Step 1: Considering the requirements of the load for power supply reliability and power quality, construct a node power supply constraint model for the load to be guaranteed, based on the traditional load power supply constraints.

[0281] Step 2: Consider the fuel consumption process of the mobile emergency power supply and establish a deployment, output and fuel consumption model for the mobile emergency power supply.

[0282] Step 3: Consider the support capacity of emergency resources for supply load during a disaster, and establish a pre-disaster deployment model for emergency resources that takes into account both economic and reliability requirements.

[0283] Step 4: Linearize the nonlinear terms in the models of Steps 2 and 3 to transform the original model into a mixed-integer linear programming model.

[0284] Step 5: Solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply guarantee load.

[0285] For specific limitations on each step, please refer to the limitations on the optimization method of emergency resource pre-disaster deployment strategy for supply guarantee load above, which will not be repeated here.

[0286] As a specific example, the invention will be further described and verified in detail in one embodiment.

[0287] The IEEE 33-bus system was used as the simulation scenario, with guaranteed loads set at 7, 18, 22, and 25, and the remainder as conventional loads. A schematic diagram of the distribution network structure is shown below. Figure 2 As shown, a total of 10 faulty lines are predicted. The system can deploy 2 sets of local emergency power supplies and 2 sets of external emergency power supplies. The total fuel capacity of each mobile emergency power supply is 1200L, and the unit consumption coefficient is... , , , The values ​​are 95, 286, 570, and 950 respectively, and the maximum output of the mobile emergency power supply is 0.32MW. Assuming there are 2 emergency repair teams available, each with a repair efficiency of 2, the number of deployment points is 4. There are 4 wind power storage systems in the power grid, located at nodes 6, 13, 25, and 29 respectively, with a uniform charging and discharging efficiency of 0.8.

[0288] Figure 3 This indicates the pre-deployment locations of the mobile emergency power supply and the repair team, as well as the dispatch status after the anticipated scenario. It can be seen that the mobile emergency power supply was deployed at four designated power supply load locations to ensure that these loads could maintain power supply even in isolated states during a disaster. The repair team worked in conjunction with the mobile emergency power supply to restore grid power. Simultaneously, the repair team repaired critical faults.

[0289] Figure 4 This presents the power restoration results for each node in the power grid under this deployment condition. It can be seen that, using the power consumption of power supply nodes within the same time range under normal conditions as a benchmark, the percentage of power restoration for power supply nodes during the entire disaster is calculated under the deployment conditions of this invention. Under the deployment conditions obtained by the method proposed in this invention, the guaranteed loads are always powered, and there will be no power outages for guaranteed loads during the disaster. Simultaneously, the load restoration of conventional nodes also reaches a high level, and the restored power consumption is also at a high level.

[0290] A conventional pre-disaster deployment model is introduced and compared with the pre-disaster deployment method for supply guarantee established in this invention. The results are as follows: Figure 5As shown, due to the different emergency resource deployment and scheduling schemes, the recovery effect of the supply load in the system differs under the two schemes. Taking the total load of the supply node under normal conditions as the benchmark, in the first 7 time steps, the supply load under the conventional scheduling method never recovered to the maximum load, while the method of this invention can keep the supply load in the optimal power supply state, prevent power outage accidents, and better meet the power supply needs of the supply load during disasters.

[0291] As can be seen from the results of the above embodiments, the emergency resource pre-disaster deployment model for supply-guaranteed loads proposed in this invention can effectively improve the stability and power supply capacity of supply-guaranteed loads during extreme disasters through pre-disaster deployment. During a disaster, the supply-guaranteed loads maintain normal power supply without load reduction. The operational strategy obtained using this invention can fully utilize resources, prevent power outages of supply-guaranteed loads with smaller loads, and improve the operational safety of virtual supply-guaranteed loads.

[0292] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention without departing from its spirit and scope should be included within the protection scope of the present invention.

Claims

1. A method for optimizing pre-disaster deployment strategies of emergency resources for supply guarantee loads, characterized in that, The method includes the following steps: Step 1: Considering the requirements of the load for power supply reliability and power quality, construct a node power supply constraint model for the load to be guaranteed, based on the traditional load power supply constraints. Step 2: Consider the fuel consumption process of the mobile emergency power supply and establish a deployment, output and fuel consumption model for the mobile emergency power supply. Step 3: Consider the support capacity of emergency resources for supply load during a disaster, and establish a pre-disaster deployment model for emergency resources that takes into account both economic and reliability requirements. Step 4: Linearize the nonlinear terms in the models of Steps 2 and 3 to transform the original model into a mixed-integer linear programming model. Step 5: Solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply guarantee load.

2. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 1, characterized in that, Step 1 specifically includes: Step 1-1 involves modeling the power supply constraints for conventional loads, specifically including: The power supply constraints of the load nodes are modeled and represented as follows: In the formula, A 0-1 variable, representing Time-based load nodes Is the device powered on? If yes, the value is 1; otherwise, the value is 0. Represents the set of all load nodes. The total simulation time steps are set; The voltage constraints of the load nodes are modeled as follows: In the formula, for Time-based load nodes The square of the voltage, These represent the upper and lower limits of the square of the voltage at a conventional load node, respectively. The power demand constraints of the load nodes are modeled and expressed as follows: In the formula, , They represent Time-based load nodes The size of the active and reactive power demand, Representing load nodes Upper and lower limits of active power under normal operating conditions; Representing load nodes Upper and lower limits of reactive power under normal operating conditions; The active power variation of the load nodes is modeled and represented as follows: In the formula, The limiting factor representing the fluctuation of node power within one time step; express Time-based load nodes The size of the active power demand; Steps 1-2: Based on the power supply constraint modeling of conventional loads, construct the power supply demand model for guaranteed loads; The specific modeling of power supply status and load status is as follows: In the formula, Represents the set of nodes that guarantee supply load; Steps 1-3: Model the node voltages of the loads required to maintain power supply during a disaster. In the formula, These represent the upper and lower limits of the square of the voltage at the load node to ensure power supply.

3. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 2, characterized in that, Step 2 specifically includes: Step 2-1, Construct a pre-deployment model for mobile emergency power supplies: In the formula, A 0-1 variable, representing a load node. Whether to deploy a mobile emergency power supply, 1 indicates to deploy, 0 indicates not to deploy; Indicates load node The number of mobile emergency power supplies that need to be deployed; Indicates the number of mobile emergency power supplies that can be dispatched; Indicates a maximum value; Step 2-2, Construct the output model of the mobile emergency power supply: In the formula, A 0-1 variable, representing a load node. The deployed mobile emergency power supply The indicator of whether or not to exert effort at any given moment: 1 indicates to exert effort, and 0 indicates not to exert effort. , Representing load nodes The deployed mobile emergency power supply The extent of effort exerted, whether or not it yields results; , These represent the upper limits of active and reactive power output of the mobile emergency power supply, respectively. Steps 2-3: Construct a model relating the fuel consumption and output of the mobile emergency power supply. In the formula, Indicates that the portable emergency power supply is in Fuel consumption at any given moment; , , , These represent the fuel consumed per unit output of the mobile emergency power supply within different output ranges; Steps 2-4: Construct a fuel consumption status model for the mobile emergency power supply at each hourly step: In the formula, Indicates the maximum fuel capacity of the mobile emergency power supply. , These respectively represent the mobile emergency power supply in time, Fuel consumption at any time , These respectively indicate that the mobile emergency power vehicle is in time, Fuel remaining at any given time; This indicates the remaining fuel of the mobile emergency power supply at the first moment.

4. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 3, characterized in that, Step 3 specifically includes: Step 3-1: Establish the objective function of the emergency resource pre-disaster deployment model. : In the formula, Indicates the cost of resource deployment; This indicates the cost of emergency repairs; This indicates the cost of load reduction; Indicates the cost of line movement; Step 3-2, establish the emergency repair team model, including: Only critical power supply lines will be repaired, while remaining faults will be repaired after the disaster to reduce the number of repairs required. The specific modeling is as follows: In the formula, The variable is 0-1, representing the emergency repair team. exist Whether the time slot is deployed on the line The value is 1 if deployed, otherwise it is 0. The variable is 0-1, representing the emergency repair team. Whether it is deployed on the line at the first moment The value is 1 if deployed, otherwise 0. The variable is 0-1, representing the emergency repair team. exist Is the time slot deployed on the line? The value is 1 if deployed, otherwise 0. A 0-1 variable, representing the line exist The fault status at any given time; 0 indicates a fault, and 1 indicates otherwise. A 0-1 variable, representing the line The fault status at the first moment; The variable is 0-1, representing the repair team's... The value is 1 if emergency repairs have started, and 0 otherwise. This indicates the travel time of the route; This represents the set of all routes and outposts; It represents the set of all lines in the system; This represents the set of all faulty lines in the system. This indicates the assembly of all emergency repair teams; Represents the set of all outposts; Define the workload required for each line repair operation and the efficiency of the repair team: In the formula, , They represent the lines respectively. exist time, The remaining workload that still needs to be fixed; Indicates the line The remaining workload that still needs to be repaired in the first instance; Indicates the line Total workload required to resolve the fault; The variable is 0-1, representing the repair team's... The value is 1 if emergency repairs have started, and 0 otherwise. To improve the work efficiency of the construction team; 0-1 variables represent Timetable The on / off state is represented by 1 if connected and 0 otherwise. A collection of lines connected to the power supply load; It is the minimum value; Step 3-3: Model the wind-storage power source in the power grid; The wind turbine output model is as follows: In the formula, express Time-based load nodes The active power output of the wind power at the location; This indicates the upper limit of active power output of wind power; express Time-based load nodes The amount of reactive power output of the wind power at the location; This indicates the upper limit of reactive power output of wind power; , These represent the upper and lower limits of the power factor of the wind power system, respectively. This represents the set of load nodes where the wind power system is located; The energy storage system model is as follows: In the formula, , They represent time, Time-based load nodes The amount of active power output of the energy stored at the location; , These represent the upper and lower limits of the active power output of energy storage, respectively. express time The amount of reactive power output from energy storage at the node; , These represent the upper and lower limits of reactive power output from energy storage, respectively. , These represent the upper and lower limits of the power factor of the energy storage system, respectively. A 0-1 variable, representing a load node. Energy storage Whether the device is connected to the power grid at any given time; if connected, the value is 1; otherwise, the value is 0. , The variables are 0-1, representing the load nodes respectively. Energy storage time, Whether the device is currently charging; if it is charging, the value is 1; otherwise, the value is 0. , The variables are 0-1, representing the load nodes respectively. Energy storage time, The value indicates whether the device is in a discharging state at any given time; if it is, the value is 1, otherwise it is 0. , They represent time, The capacity of energy storage at any given time; , This represents the energy storage charging and discharging efficiency parameter; , These represent the upper and lower limits of energy storage capacity, respectively. Steps 3-4: In the radial distribution network, the linearized Distflow power flow model is used to calculate power balance and whether the power flow exceeds limits. The modeling process is as follows: In the formula, , Representing load nodes With load nodes Between lines The magnitude of resistance and reactance; express Timetable The amount of active power flowing through; express Timetable The amount of reactive power flowing through; , They represent Time Node , The amount of active power flowing through; , They represent Timetable , The amount of reactive power flowing through; express Timetable The connection status is indicated by 1 for a connected line and 0 for a disconnected line. These represent the upper and lower limits of the power flowing through the line, respectively. Indicates the line Endpoint set; , Representing load nodes The set of parent and child nodes; Indicates the power provided by the main network. for Time-based load nodes The square of the voltage, This indicates the upper limit of the power provided by the main network.

5. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 4, characterized in that, The resource deployment cost for: In the formula, This indicates the cost of deploying a local mobile emergency power supply. This indicates the cost required to dispatch a mobile emergency power supply from another location; This indicates the number of mobile emergency power supplies that need to be deployed at each node to ensure power supply in the event of a predicted failure. Indicates the number of locally dispatchable mobile emergency power supplies; This represents the set of power grid nodes.

6. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 4, characterized in that, The cost of emergency repairs for: In the formula, This indicates the cost incurred by the emergency repair team in repairing a single line. A variable consisting of 0 and 1, representing the circuit. exist The fault status at any given time; 0 indicates a fault, and 1 indicates otherwise. It represents the set of lines in the power grid.

7. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 4, characterized in that, The cost of load reduction for: In the formula, Indicates load node The cost of load reduction; Indicates load node Load under normal operating conditions; express Time-based load nodes The size of the active power demand, i.e., the load node exist Load under constant failure scenarios.

8. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 4, characterized in that, The cost of line operation for: In the formula, This indicates the cost incurred by the line operation; , The variables are 0 and 1, representing respectively... time, Timetable The connection status is 1 if connected, and 0 otherwise. The estimated total duration of the disaster.

9. The method for optimizing emergency resource pre-disaster deployment strategies for supply guarantee loads according to claim 4, characterized in that, Step 4 specifically includes: Step 4-1: Linearize the model from Step 2-3 to obtain: In the formula, A binary auxiliary variable; Step 4-2, linearize the last formula in the energy storage system model in Step 3-3, the first two formulas in Step 3-4, and the formula in Step 4-1 as follows: In the formula, , , , , , These are the corresponding bilinear terms. , , , , , , , and Auxiliary variables after linearization.

10. An emergency resource pre-disaster deployment strategy optimization system based on the method of any one of claims 1 to 5, characterized in that, The system includes: The first module is used to implement: considering the needs of the load for power supply reliability and power quality, and based on the traditional load power supply constraints, constructing a node power supply constraint model for the load to be supplied. The second module is used to: consider the fuel consumption process of mobile emergency power sources and establish a deployment, output, and fuel consumption model for mobile emergency power sources; The third module is used to: consider the support capacity of emergency resources for supply load during disasters, and establish an emergency resource pre-disaster deployment model that takes into account both economic and reliability requirements; The fourth module is used to linearize the nonlinear terms in the models of the second and third modules, transforming the original model into a mixed-integer linear programming model. The fifth module is used to solve the mixed-integer linear programming model to obtain a pre-disaster deployment strategy for emergency resources oriented towards supply load.

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